diff --git a/notebooks/BUILT_compare_buildings_wsf.ipynb b/notebooks/BUILT_compare_buildings_wsf.ipynb new file mode 100644 index 0000000..1f519c0 --- /dev/null +++ b/notebooks/BUILT_compare_buildings_wsf.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "import sys, os\n", + "import rasterio\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "\n", + "from shapely.wkt import loads\n", + "\n", + "sys.path.insert(0, \"../src\")\n", + "\n", + "import GOSTrocks.rasterMisc as rMisc\n", + "import GOSTrocks.dataMisc as dMisc\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\WB411133\\AppData\\Local\\Temp\\ipykernel_25180\\3039392467.py:14: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n", + " world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n" + ] + } + ], + "source": [ + "# Local/input files\n", + "iso3 = 'KHM'\n", + "out_folder = \"c:/WBG/Work/KHM_Energy/data\"\n", + "wsf_file = os.path.join(out_folder, \"WSF\", \"wsf.tif\")\n", + "ghsl_file = os.path.join(out_folder, \"GHSL\", \"ghsl.tif\")\n", + "overture_buildings = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29.csv\")\n", + "overture_raster = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29.tif\")\n", + "overture_raster_points = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29_points.tif\")\n", + "for file in [wsf_file, ghsl_file]:\n", + " if not os.path.exists(os.path.dirname(file)):\n", + " os.makedirs(os.path.dirname(file))\n", + "\n", + "# get country extent from geopandas\n", + "world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n", + "world = gpd.read_file(world_filepath)\n", + "country = world[world.iso_a3 == iso3]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FUBAR\n" + ] + } + ], + "source": [ + "\"\"\" Not working with World Bank Firewall\n", + "# Download the WSF data\n", + "if not os.path.exists(wsf_file):\n", + " print(\"Downloading WSF data\")\n", + " wsf_data, wsf_profile = dMisc.download_WSF(country, out_file = wsf_file)\n", + "\n", + "wsf_r = rasterio.open(wsf_file)\n", + "\"\"\"\n", + "print(\"FUBAR\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#Clip GHSL using local files\n", + "local_version = r\"J:\\Data\\GLOBAL\\GHSL\\Built\\GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0.tif\"\n", + "if not os.path.exists(ghsl_file):\n", + " ghsl_raster = rasterio.open(local_version)\n", + " data, profile = rMisc.clipRaster(ghsl_raster, country)\n", + " with rasterio.open(ghsl_file, 'w', **profile) as dst:\n", + " dst.write(data)\n", + "ghsl_r = rasterio.open(ghsl_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in and process Overture buildings\n", + "ob = pd.read_csv(overture_buildings)\n", + "ob_geoms = ob['wkt'].apply(loads)\n", + "inB = gpd.GeoDataFrame(ob, geometry=ob_geoms, crs=4326)\n", + "inB.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# attempt to rasterrize the buildings as polygons\n", + "if not os.path.exists(overture_raster):\n", + " rasterized_buildings = rMisc.rasterizeDataFrame(inB, templateRaster=ghsl_file, mergeAlg=\"ADD\", re_proj=True, nodata=0.)\n", + " with rasterio.open(overture_raster, 'w', **rasterized_buildings['meta']) as dst:\n", + " dst.write_band(1, rasterized_buildings['vals'])\n", + "overture_r = rasterio.open(overture_raster)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# attempt to rasterrize the buildings as points\n", + "if not os.path.exists(overture_raster_points):\n", + " inB_points = inB.copy()\n", + " inB_points['geometry'] = inB_points['geometry'].centroid\n", + " rasterized_buildings = rMisc.rasterizeDataFrame(inB_points, templateRaster=ghsl_file, mergeAlg=\"ADD\", re_proj=True, nodata=0.)\n", + " with rasterio.open(overture_raster_points, 'w', **rasterized_buildings['meta']) as dst:\n", + " dst.write_band(1, rasterized_buildings['vals'])\n", + "overture_r_points = rasterio.open(overture_raster_points) " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare rasterized buildings with built area dataset\n", + "### Open both datasets and threshold them to get built area data\n", + "o_thresh = 1\n", + "ghsl_thresh = 3000\n", + "\n", + "o_data = overture_r_points.read(1)\n", + "o_data = (o_data > o_thresh).astype('uint8')\n", + "\n", + "ghsl_data = ghsl_r.read(1)\n", + "ghsl_data = (ghsl_data > ghsl_thresh).astype('uint8') * 10\n", + "\n", + "combo_data = o_data + ghsl_data\n", + "\n", + "# Write out the combined data\n", + "out_file = os.path.join(out_folder, \"overture_vs_ghsl.tif\")\n", + "if not os.path.exists(out_file):\n", + " meta = overture_r_points.meta.copy()\n", + " meta.update(dtype=rasterio.uint8, nodata=0)\n", + " with rasterio.open(out_file, 'w', **meta) as out_raster:\n", + " out_raster.write_band(1, combo_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "urban_test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/CREAT_FATHOM_VRTs.ipynb b/notebooks/CREAT_FATHOM_VRTs.ipynb deleted file mode 100644 index 533bf63..0000000 --- a/notebooks/CREAT_FATHOM_VRTs.ipynb +++ /dev/null @@ -1,2140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5a156f61", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import boto3\n", - "\n", - "import pandas as pd\n", - "from osgeo import gdal" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5d327aec", - "metadata": {}, - "outputs": [], - "source": [ - "in_file_list = \"/home/wb411133/temp/fathom_files.csv\"\n", - "\n", - "s3_bucket = \"wbg-geography01\"\n", - "s3_prefix = \"FATHOM/v2023/\"\n", - "out_prefix = \"FATHOM/vrts/\"\n", - "\n", - "in_files = pd.read_csv(in_file_list)\n", - "s3 = boto3.resource(\"s3\")\n", - "my_bucket = s3.Bucket(s3_bucket)\n", - "\n", - "return_period = \"1000\"\n", - "defended = \"DEFENDED\"\n", - "\n", - "coastal_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-COASTAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "fluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-FLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "pluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-PLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "321e542a", - "metadata": {}, - "outputs": [], - "source": [ - "obj_count = 0\n", - "all_vals = []\n", - "\n", - "for obj in my_bucket.objects.filter(Prefix=os.path.join(s3_prefix, pluvial_folder)):\n", - " all_vals.append(os.path.join(\"s3://\", s3_bucket, obj.key))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "af0c5ab2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['n00e135.tif',\n", - " 'n00w065.tif',\n", - " 'n00e007.tif',\n", - " 'n00e006.tif',\n", - " 'n00w082.tif',\n", - " 'n00e008.tif',\n", - " 'n00w078.tif',\n", - " 'n00e117.tif',\n", - " 'n00w074.tif',\n", - " 'n00e112.tif',\n", - " 'n00e111.tif',\n", - " 'n00e110.tif',\n", - " 'n00e012.tif',\n", - " 'n00w080.tif',\n", - " 'n00e123.tif',\n", - " 'n00e124.tif',\n", - " 'n00w071.tif',\n", - " 'n00w092.tif',\n", - " 'n00e009.tif',\n", - " 'n00e011.tif',\n", - " 'n00e010.tif',\n", - " 'n00e013.tif',\n", - " 'n00e014.tif',\n", - 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" 'n07e094.tif',\n", - " 'n06w064.tif',\n", - " 'n07e049.tif',\n", - " 'n07e030.tif',\n", - " 'n06w067.tif',\n", - " 'n06w065.tif',\n", - " 'n06w061.tif',\n", - " 'n07e098.tif',\n", - " 'n06e172.tif',\n", - " 'n07e043.tif',\n", - " 'n07e041.tif',\n", - " 'n07e028.tif',\n", - " 'n07e173.tif',\n", - " 'n06w010.tif',\n", - " 'n06w073.tif',\n", - " 'n07e124.tif',\n", - " 'n06w068.tif',\n", - " 'n06w076.tif',\n", - " 'n06w074.tif',\n", - " 'n06w069.tif',\n", - " 'n07e114.tif',\n", - " 'n06w075.tif',\n", - " 'n06w079.tif',\n", - " 'n07e134.tif',\n", - " 'n06w080.tif',\n", - " 'n07e135.tif',\n", - " 'n06w077.tif',\n", - " 'n06w072.tif',\n", - " 'n07e072.tif',\n", - " 'n07e031.tif',\n", - " 'n06w078.tif',\n", - " 'n07e125.tif',\n", - " 'n06w082.tif',\n", - " 'n07e142.tif',\n", - " 'n06w063.tif',\n", - " 'n07e126.tif',\n", - " 'n06w084.tif',\n", - " 'n07e145.tif',\n", - " 'n06w081.tif',\n", - " 'n07e149.tif',\n", - " 'n07e000.tif',\n", - " 'n07e144.tif',\n", - " 'n07e002.tif',\n", - " 'n07e143.tif',\n", - " 'n06w083.tif',\n", - " 'n07e150.tif',\n", - " 'n07e003.tif',\n", - " 'n07e122.tif',\n", - " 'n07e004.tif',\n", - " 'n07e151.tif',\n", - " 'n06w066.tif',\n", - " 'n06w071.tif',\n", - " 'n07e079.tif',\n", - " 'n07e103.tif',\n", - " 'n07e010.tif',\n", - " 'n07e007.tif',\n", - " 'n07e146.tif',\n", - " 'n07e013.tif',\n", - " 'n07e048.tif',\n", - " 'n07e099.tif',\n", - " 'n07e012.tif',\n", - " 'n07e045.tif',\n", - " 'n07e080.tif',\n", - " 'n07e005.tif',\n", - " 'n07e117.tif',\n", - " 'n07e018.tif',\n", - " 'n07e078.tif',\n", - " 'n07e102.tif',\n", - " 'n07e020.tif',\n", - " 'n07e019.tif',\n", - " 'n07e006.tif',\n", - " 'n07e008.tif',\n", - " 'n07e152.tif',\n", - " 'n07e021.tif',\n", - " 'n07e148.tif',\n", - " 'n07e014.tif',\n", - " 'n07e155.tif',\n", - " 'n07e026.tif',\n", - " 'n07e073.tif',\n", - " 'n07e044.tif',\n", - " 'n07e023.tif',\n", - " 'n07e009.tif',\n", - " 'n07e154.tif',\n", - " 'n07e016.tif',\n", - " 'n07e040.tif',\n", - " 'n07e153.tif',\n", - " 'n07e100.tif',\n", - " 'n07e017.tif',\n", - " 'n07e047.tif',\n", - " 'n07e081.tif',\n", - " 'n07e011.tif',\n", - " 'n07e156.tif',\n", - " 'n07e029.tif',\n", - " 'n07e001.tif',\n", - " 'n07e157.tif',\n", - " 'n07e027.tif',\n", - " 'n07e127.tif',\n", - " ...]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1b91c1bf", - "metadata": {}, - "outputs": [], - "source": [ - "# out_vrt = os.path.join('s3://', s3_bucket, out_prefix, f'{pluvial_folder}.vrt')\n", - "out_vrt = f\"{pluvial_folder}.vrt\"\n", - "vrt_options = gdal.BuildVRTOptions(resampleAlg=\"cubic\", addAlpha=True)\n", - "my_vrt = gdal.BuildVRT(out_vrt, all_vals, options=vrt_options)\n", - "my_vrt = None" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e2acf1d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['s3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e006.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e007.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e008.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e009.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e010.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e011.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e012.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e013.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n00e014.tif',\n", - 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" 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e113.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e114.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e115.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e116.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e117.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e118.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e119.tif',\n", - " 's3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n07e120.tif',\n", - 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"output_type": "execute_result" - } - ], - "source": [ - "all_vals" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42816118", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Earth Engine", - "language": "python", - "name": "ee" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/FATHOM/CLIP_Flood_data_iso3.ipynb b/notebooks/FATHOM/CLIP_Flood_data_iso3.ipynb new file mode 100644 index 0000000..ffc3d00 --- /dev/null +++ b/notebooks/FATHOM/CLIP_Flood_data_iso3.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19a7c712", + "metadata": {}, + "source": [ + "# Extracting Data for Country by ISO3\n", + "\n", + "The Fathom flood data are stored as individual tiles, organized into folders per model on the GOST AWS bucket. We have generated virtual rasters (.vrt) for each model, making reading and extracting easier." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "021f6828", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import boto3\n", + "import rasterio\n", + "\n", + "import geopandas as gpd\n", + "\n", + "sys.path.insert(0, \"../../src\")\n", + "\n", + "import GOSTrocks.dataMisc as dMisc\n", + "import GOSTrocks.rasterMisc as rMisc\n", + "from GOSTrocks.misc import tPrint" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e567134e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'c:\\\\WBG\\\\Work\\\\Code\\\\GOSTrocks\\\\src'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.path.abspath(\"../../src\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "87475280", + "metadata": {}, + "outputs": [], + "source": [ + "iso3 = \"KEN\"\n", + "out_folder = f\"/home/wb411133/temp/FATHOM/{iso3}\"\n", + "if not os.path.exists(out_folder):\n", + " os.makedirs(out_folder)\n", + " \n", + "# This demo uses the default national boundaries included with GeoPandas, but this can be changed here\n", + "world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n", + "world = gpd.read_file(world_filepath)\n", + "inB = world.loc[world['iso_a3'] == iso3].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af38e413", + "metadata": {}, + "outputs": [], + "source": [ + "# Select layer to downlaod\n", + "flood_type = [\"COASTAL\",\"FLUVIAL\",\"PLUVIAL\"]\n", + "defence = [\"DEFENDED\"]\n", + "return_period = ['1in5','1in10','1in50']\n", + "climate_model = [\"PERCENTILE50\"]\n", + "year = [\"2020\"]\n", + "\n", + "# all_vrts is a pandas dataframe with all the vrt paths to the global datasets, with columns defining\n", + "# the various models' defining attributes\n", + "all_vrts = dMisc.get_fathom_vrts(True)\n", + "sel_images = all_vrts.loc[(all_vrts['FLOOD_TYPE'].isin(flood_type)) & (all_vrts['DEFENCE'].isin(defence)) & \n", + " (all_vrts['RETURN'].isin(return_period)) & (all_vrts['CLIMATE_MODEL'].isin(climate_model))]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fd239144", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RETURNFLOOD_TYPEDEFENCEDEPTHYEARCLIMATE_MODELPATH
01in10COASTALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
11in10COASTALDEFENDEDDEPTH2030SSP1_2.6s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
21in10COASTALDEFENDEDDEPTH2030SSP2_4.5s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
31in10COASTALDEFENDEDDEPTH2030SSP3_7.0s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
41in10COASTALDEFENDEDDEPTH2030SSP5_8.5s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
\n", + "
" + ], + "text/plain": [ + " RETURN FLOOD_TYPE DEFENCE DEPTH YEAR CLIMATE_MODEL \\\n", + "0 1in10 COASTAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "1 1in10 COASTAL DEFENDED DEPTH 2030 SSP1_2.6 \n", + "2 1in10 COASTAL DEFENDED DEPTH 2030 SSP2_4.5 \n", + "3 1in10 COASTAL DEFENDED DEPTH 2030 SSP3_7.0 \n", + "4 1in10 COASTAL DEFENDED DEPTH 2030 SSP5_8.5 \n", + "\n", + " PATH \n", + "0 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "1 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "2 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "3 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "4 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_vrts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ae96ba27", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RETURNFLOOD_TYPEDEFENCEDEPTHYEARCLIMATE_MODELPATH
01in10COASTALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
261in10FLUVIALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
521in10PLUVIALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
3251in5COASTALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
3511in5FLUVIALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
\n", + "
" + ], + "text/plain": [ + " RETURN FLOOD_TYPE DEFENCE DEPTH YEAR CLIMATE_MODEL \\\n", + "0 1in10 COASTAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "26 1in10 FLUVIAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "52 1in10 PLUVIAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "325 1in5 COASTAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "351 1in5 FLUVIAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "\n", + " PATH \n", + "0 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "26 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "52 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "325 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "351 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sel_images.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bf49fd1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:31:04\tGLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n" + ] + } + ], + "source": [ + "# For each image in the selected images dataframe, we clip out the area of interest\n", + "# which is defined by the ioso3 code, but could be any GeoDataFrame\n", + "\n", + "for idx, row in sel_images.iterrows(): \n", + " out_file = os.path.join(out_folder, os.path.basename(row['PATH']))\n", + " if not os.path.exists(out_file):\n", + " cur_r = rasterio.open(row['PATH'])\n", + " rMisc.clipRaster(cur_r, inB, out_file)\n", + " tPrint(os.path.basename(row['PATH']))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "urban_test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/FATHOM/Extract_flood_tiles.ipynb b/notebooks/FATHOM/Extract_flood_tiles.ipynb deleted file mode 100644 index d48be76..0000000 --- a/notebooks/FATHOM/Extract_flood_tiles.ipynb +++ /dev/null @@ -1,242 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "29e399a7", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/wb411133/.conda/envs/ee/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "import boto3\n", - "\n", - "import geopandas as gpd\n", - "\n", - "\n", - "sys.path.insert(0, \"/home/wb411133/Code/gostrocks/src\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "efa57703", - "metadata": {}, - "outputs": [], - "source": [ - "s3_bucket = \"wbg-geography01\"\n", - "s3_prefix = \"FATHOM/v2023/\"\n", - "\n", - "s3 = boto3.resource(\"s3\")\n", - "s3_downloader = boto3.client(\"s3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "05d3ba17", - "metadata": {}, - "outputs": [], - "source": [ - "fathom_tile_extents = (\n", - " \"s3://wbg-geography01/FATHOM/v2023_support_files/fathom_tile_extents.geojson\"\n", - ")\n", - "fathom_extents = gpd.read_file(fathom_tile_extents)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "7ba85b89", - "metadata": {}, - "outputs": [], - "source": [ - "# define extent by iso3 code\n", - "sel_iso3 = \"MWI\"\n", - "world_filepath = gpd.datasets.get_path(\"naturalearth_lowres\")\n", - "world = gpd.read_file(world_filepath)\n", - "sel_bounds = world.loc[world[\"iso_a3\"] == sel_iso3].unary_union" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "72e10e0a", - "metadata": {}, - "outputs": [], - "source": [ - "# Define extent by bounding box [minx, miny, maxx, maxy]\n", - "# bbox = [176.058746,-10.791870,179.871353,-5.642500]\n", - "# sel_bounds = box(*bbox)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "457f0702", - "metadata": {}, - "outputs": [], - "source": [ - "# select tiles that intersect the sel_bounds\n", - "sel_tiles = fathom_extents.loc[fathom_extents.intersects(sel_bounds)]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "2e75e2f2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sel_tiles.unary_union" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "1a75fa42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(21, 4)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sel_tiles.shape" - ] - }, - { - "cell_type": "markdown", - "id": "87d26b8e", - "metadata": {}, - "source": [ - "# Select flooding layer" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "64821e28", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error downloading s10e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s10e032.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s10e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s11e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s11e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s12e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s12e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s13e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s13e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s13e032.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s14e032.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s14e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s14e035.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s14e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s15e035.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s15e033.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s15e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s16e035.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s16e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s17e035.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n", - "Error downloading s17e034.tif for GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\n" - ] - } - ], - "source": [ - "### TODO - this section add projections options\n", - "return_period = \"1000\"\n", - "defended = \"DEFENDED\"\n", - "\n", - "coastal_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-COASTAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "fluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-FLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "pluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-PLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "\n", - "download_folder = \"/home/wb411133/temp/FATHOM/TESTING_Download_Malawi\"\n", - "for c_folder in [coastal_folder, fluvial_folder, pluvial_folder]:\n", - " out_folder = os.path.join(download_folder, c_folder)\n", - " try:\n", - " os.makedirs(out_folder)\n", - " except:\n", - " pass\n", - "\n", - "for idx, row in sel_tiles.iterrows():\n", - " tiff = f'{row[\"ID\"]}.tif'\n", - " for c_folder in [coastal_folder, fluvial_folder, pluvial_folder]:\n", - " try:\n", - " s3_downloader.download_file(\n", - " s3_bucket,\n", - " os.path.join(s3_prefix, c_folder, tiff),\n", - " os.path.join(download_folder, c_folder, tiff),\n", - " )\n", - " except:\n", - " print(f\"Error downloading {tiff} for {c_folder}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ca04504", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Earth Engine", - "language": "python", - "name": "ee" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/FATHOM/Generate_footprints.ipynb b/notebooks/FATHOM/PROCESSING_NOTEBOOKS/Generate_footprints.ipynb similarity index 100% rename from notebooks/FATHOM/Generate_footprints.ipynb rename to notebooks/FATHOM/PROCESSING_NOTEBOOKS/Generate_footprints.ipynb diff --git a/notebooks/FATHOM/PROCESSING_NOTEBOOKS/Transfer_Data_AWS.ipynb b/notebooks/FATHOM/PROCESSING_NOTEBOOKS/Transfer_Data_AWS.ipynb new file mode 100644 index 0000000..224d7e0 --- /dev/null +++ b/notebooks/FATHOM/PROCESSING_NOTEBOOKS/Transfer_Data_AWS.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "import boto3\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/wb411133/temp/World_Bank_Global_3_Complete.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ms3_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's3://'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms3_bucket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms3_prefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0min_files\u001b[0m \u001b[0;34m=\u001b[0m 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true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m 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handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1361\u001b[0m \"\"\"\n\u001b[0;32m-> 1362\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1364\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/wb411133/temp/World_Bank_Global_3_Complete.csv'" + ] + } + ], + "source": [ + "in_file_list = '/home/wb411133/temp/World_Bank_Global_3_Complete.csv'\n", + "out_folder = os.path.join(os.path.dirname(in_file_list), \"FATHOM\")\n", + "s3_bucket = 'wbg-geography01'\n", + "s3_prefix = 'FATHOM/v2023/'\n", + "s3_out = os.path.join('s3://',s3_bucket, s3_prefix)\n", + "\n", + "in_files = pd.read_csv(in_file_list)\n", + "s3 = boto3.resource('s3')\n", + "my_bucket = s3.Bucket(s3_bucket)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find all files already copied\n", + "all_folders = []\n", + "for obj in my_bucket.objects.filter(Prefix=s3_prefix):\n", + " all_folders.append(obj.key.split(\"/\")[-2])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "processed_folders = list(set(all_folders))\n", + "delivered_folders = in_files['Layer'].values\n", + "sel_folders = [x for x in delivered_folders if not x in processed_folders]\n", + "sel_files = in_files.loc[in_files['Layer'].isin(sel_folders)].copy()\n", + "sel_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "with open(os.path.join(out_folder, \"aaa_download_upload_2.sh\"), 'w') as out_file:\n", + " out_file.write('#!/bin/bash\\n')\n", + " for idx, row in sel_files.iterrows():\n", + " fathom_path = row['AWS_Path']\n", + " local_folder = os.path.join(out_folder, row['Layer'])\n", + " gost_folder = os.path.join(s3_out, row['Layer'])\n", + " if not os.path.exists(local_folder):\n", + " os.makedirs(local_folder)\n", + " \n", + " cur_out_folder = os.path.join(s3_prefix, row['Layer'])\n", + " obj_count = 0\n", + " for obj in my_bucket.objects.filter(Prefix=cur_out_folder):\n", + " obj_count += 1\n", + " print(f\"{row['Layer']}: {obj_count}\")\n", + " if obj_count == 0:\n", + " download_command = f'aws s3 sync --profile fathom {fathom_path} {local_folder}'\n", + " upload_command = f'aws s3 sync {local_folder} {gost_folder}'\n", + " remove_command = f'rm -R {local_folder}'\n", + "\n", + " out_file.write(download_command)\n", + " out_file.write('\\n')\n", + " out_file.write(upload_command)\n", + " out_file.write('\\n')\n", + " out_file.write(remove_command)\n", + " out_file.write('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_vals = []\n", + "for idx, row in in_files.iterrows():\n", + " all_vals.append(row['Layer'].split('-'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xx = pd.DataFrame(all_vals, columns=['GLOBAL', \"Size\",'Offset','return','type','defense','depth','year','projection','v1','v2'])\n", + "xx.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xx['projection'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xx.loc[xx['projection'] == 'SSP1_2.6']['year'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download and upload specific scenarios" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aws s3 sync --profile fathom s3://fathom-products-global/fathom-global/v3/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0 /home/wb411133/temp/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0\n", + "aws s3 sync /home/wb411133/temp/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0 s3://wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0\n", + "rm -R /home/wb411133/temp/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0\n" + ] + } + ], + "source": [ + "sel_scenario = 'GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0'\n", + "bucket = \"fathom-products-global\"\n", + "prefix = f\"fathom-global/v3/{sel_scenario}\"\n", + "\n", + "local_folder = os.path.join(\"/home/wb411133/temp/FATHOM\", sel_scenario)\n", + "fathom_path = f's3://{bucket}/{prefix}'\n", + "gost_folder = f\"s3://wbg-geography01/FATHOM/v2023/{sel_scenario}\"\n", + "\n", + "if not os.path.exists(local_folder):\n", + " os.makedirs(local_folder)\n", + "\n", + "download_command = f'aws s3 sync --profile fathom {fathom_path} {local_folder}'\n", + "upload_command = f'aws s3 sync {local_folder} {gost_folder}'\n", + "remove_command = f'rm -R {local_folder}'\n", + "\n", + "print(download_command)\n", + "print(upload_command)\n", + "print(remove_command)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Earth Engine", + "language": "python", + "name": "ee" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/FATHOM/PROCESSING_NOTEBOOKS/generate_fathom_vrts.ipynb b/notebooks/FATHOM/PROCESSING_NOTEBOOKS/generate_fathom_vrts.ipynb new file mode 100644 index 0000000..60811d6 --- /dev/null +++ b/notebooks/FATHOM/PROCESSING_NOTEBOOKS/generate_fathom_vrts.ipynb @@ -0,0 +1,1454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "import sys, os, boto3, json\n", + "import rasterio\n", + "\n", + "import xml.etree.ElementTree as ET\n", + "import pandas as pd\n", + "\n", + "sys.path.insert(0, \"../../src\")\n", + "import GOSTrocks.dataMisc as dMisc\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s3_bucket = 'wbg-geography01'\n", + "s3_prefix = 'FATHOM/v2023/'\n", + "s3_out = os.path.join('s3://',s3_bucket, s3_prefix)\n", + "\n", + "s3 = boto3.resource('s3')\n", + "my_bucket = s3.Bucket(s3_bucket)\n", + "\n", + "# Find all files already copied\n", + "all_folders = []\n", + "for obj in my_bucket.objects.filter(Prefix=s3_prefix):\n", + " all_folders.append(obj.key.split(\"/\")[-2])\n", + "all_folders\n", + "all_folders = list(set(all_folders))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# build list of rasters for generating VRT\n", + "local_path = os.path.join(\"v2023\",\"GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\")\n", + "in_folder = os.path.join(template_folder, local_path)\n", + "all_tiffs = [f\"{local_path}/{x}\" for x in os.listdir(in_folder)]\n", + "\n", + "with open(os.path.join(template_folder, \"s3_tiffs.txt\"), 'w') as out:\n", + " for p in all_tiffs:\n", + " out.write(f'{p}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "template_folder = '/home/wb411133/temp'\n", + "coastal_template = os.path.join(template_folder, \"GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.0.vrt\")\n", + "other_template = os.path.join(template_folder, \"GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\")\n", + "\n", + "class generate_vrt_from_template():\n", + " def __init__(self, template_vrt, new_dataset):\n", + " self.template_vrt = template_vrt\n", + " self.new_dataset = new_dataset\n", + " self.old_dataset = os.path.basename(template_vrt)[:-4]\n", + " self.new_vrt = self.template_vrt.replace(self.old_dataset, self.new_dataset)\n", + "\n", + " def update_vrt(self):\n", + " tree = ET.parse(self.template_vrt)\n", + " root = tree.getroot()\n", + "\n", + " for child in root.iter(\"SourceFilename\"):\n", + " child.text = child.text.replace(self.old_dataset, self.new_dataset)\n", + "\n", + " tree.write(self.new_vrt, xml_declaration=False)\n", + "\n", + "'''\n", + "new_ds = \"GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0\"\n", + "xx = generate_vrt_from_template(template_vrt, new_ds)\n", + "xx.update_vrt()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create template vrts\n", + "for new_ds in all_folders:\n", + " flood_type = new_ds.split(\"-\")[4]\n", + " vrt_template = other_template\n", + " if flood_type == \"COASTAL\":\n", + " vrt_template = coastal_template\n", + " xx = generate_vrt_from_template(vrt_template, new_ds)\n", + " xx.update_vrt()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# aws s3 cp . s3://wbg-geography01/FATHOM/ --exclude \"*\" --include \"*.vrt\" --recursive" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inspect copied VRTs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dMisc.get_fathom_vrts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s3_bucket = 'wbg-geography01'\n", + "s3_prefix = 'FATHOM/'\n", + "\n", + "s3 = boto3.resource('s3')\n", + "my_bucket = s3.Bucket(s3_bucket)\n", + "\n", + "all_vrts = []\n", + "for o in my_bucket.objects.filter(Prefix=s3_prefix):\n", + " if o.key.endswith(\".vrt\"):\n", + " print(o.key)\n", + " full_vrt_path = f's3://{s3_bucket}/{o.key}'\n", + " all_vrts.append(full_vrt_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RETURNFLOOD_TYPEDEFENCEDEPTHYEARCLIMATE_MODELPATH
01in10COASTALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
11in10COASTALDEFENDEDDEPTH2030SSP1_2.6s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
21in10COASTALDEFENDEDDEPTH2030SSP2_4.5s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
31in10COASTALDEFENDEDDEPTH2030SSP3_7.0s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
41in10COASTALDEFENDEDDEPTH2030SSP5_8.5s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
\n", + "
" + ], + "text/plain": [ + " RETURN FLOOD_TYPE DEFENCE DEPTH YEAR CLIMATE_MODEL \\\n", + "0 1in10 COASTAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "1 1in10 COASTAL DEFENDED DEPTH 2030 SSP1_2.6 \n", + "2 1in10 COASTAL DEFENDED DEPTH 2030 SSP2_4.5 \n", + "3 1in10 COASTAL DEFENDED DEPTH 2030 SSP3_7.0 \n", + "4 1in10 COASTAL DEFENDED DEPTH 2030 SSP5_8.5 \n", + "\n", + " PATH \n", + "0 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "1 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "2 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "3 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "4 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_vrts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "COASTAL 208\n", + "FLUVIAL 208\n", + "PLUVIAL 104\n", + "Name: FLOOD_TYPE, dtype: int64" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_vrts['FLOOD_TYPE'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "all_vrts = dMisc.get_fathom_vrts(True)\n", + "all_res = {}\n", + "for idx, row in all_vrts.iterrows():\n", + " #vrt_path = row['PATH']\n", + " #xx = rasterio.open(vrt_path)\n", + " filename = os.path.basename(row['PATH'])\n", + " year = row['YEAR']\n", + " climate_model = row[\"CLIMATE_MODEL\"]\n", + " if climate_model == \"PERCENTILE50\":\n", + " climate_model = \"CURRENT\"\n", + " flood_type = row['FLOOD_TYPE'].lower()\n", + " defence = row['DEFENCE'].lower()\n", + " ret = row['RETURN']\n", + " label = \"_\".join([flood_type, defence, ret, climate_model, year])\n", + " ret=ret.replace(\"in\", \" in \")\n", + " if year == '2020':\n", + " description = f\"Global {defence} {flood_type} flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a {ret} year return period.\"\n", + " else:\n", + " description = f\"Global {defence} {flood_type} flood model based on {climate_model} climate model for year {year}. Flood depth is measured in cm expected flood depth, based on a {ret} year return period.\"\n", + " all_res[label] = {\"description\":description,'filename':row['PATH']}" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'coastal_defended_1in10_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in10_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in10_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in10_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in10_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in10_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 10 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in100_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in100_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in100_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in100_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in100_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 100 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in1000_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in1000_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in1000_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in1000_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in1000_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 1000 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in20_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in20_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in20_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in20_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in20_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 20 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in20-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in200_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in200_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in200_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in200_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in200_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 200 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in200-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in5_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in5_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in5_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in5_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in5_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 5 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in5-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in50_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in50_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in50_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in50_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in50_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 50 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_CURRENT_2020': {'description': 'Global defended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP1_2.6_2030': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP2_4.5_2030': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP3_7.0_2030': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP5_8.5_2030': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP1_2.6_2050': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP2_4.5_2050': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP3_7.0_2050': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP5_8.5_2050': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP1_2.6_2080': {'description': 'Global defended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP2_4.5_2080': {'description': 'Global defended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP3_7.0_2080': {'description': 'Global defended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_defended_1in500_SSP5_8.5_2080': {'description': 'Global defended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_CURRENT_2020': {'description': 'Global undefended coastal flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP1_2.6_2030': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP2_4.5_2030': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP3_7.0_2030': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP5_8.5_2030': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP1_2.6_2050': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP2_4.5_2050': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP3_7.0_2050': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP5_8.5_2050': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP1_2.6_2080': {'description': 'Global undefended coastal flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP2_4.5_2080': {'description': 'Global undefended coastal flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP3_7.0_2080': {'description': 'Global undefended coastal flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'coastal_undefended_1in500_SSP5_8.5_2080': {'description': 'Global undefended coastal flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-COASTAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_CURRENT_2020': {'description': 'Global defended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP1_2.6_2030': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP2_4.5_2030': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP3_7.0_2030': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP5_8.5_2030': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP1_2.6_2050': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP2_4.5_2050': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP3_7.0_2050': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP5_8.5_2050': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP1_2.6_2080': {'description': 'Global defended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP2_4.5_2080': {'description': 'Global defended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP3_7.0_2080': {'description': 'Global defended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_defended_1in500_SSP5_8.5_2080': {'description': 'Global defended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_CURRENT_2020': {'description': 'Global undefended fluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP1_2.6_2030': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP2_4.5_2030': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP3_7.0_2030': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP5_8.5_2030': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP1_2.6_2050': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP2_4.5_2050': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP3_7.0_2050': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP5_8.5_2050': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP1_2.6_2080': {'description': 'Global undefended fluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP2_4.5_2080': {'description': 'Global undefended fluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP3_7.0_2080': {'description': 'Global undefended fluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'fluvial_undefended_1in500_SSP5_8.5_2080': {'description': 'Global undefended fluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-FLUVIAL-UNDEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_CURRENT_2020': {'description': 'Global defended pluvial flood model based on current climate. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP1_2.6_2030': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2030-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP2_4.5_2030': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2030-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP3_7.0_2030': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2030-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP5_8.5_2030': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2030. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2030-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP1_2.6_2050': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2050-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP2_4.5_2050': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2050-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP3_7.0_2050': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2050-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP5_8.5_2050': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2050. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2050-SSP5_8.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP1_2.6_2080': {'description': 'Global defended pluvial flood model based on SSP1_2.6 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2080-SSP1_2.6-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP2_4.5_2080': {'description': 'Global defended pluvial flood model based on SSP2_4.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2080-SSP2_4.5-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP3_7.0_2080': {'description': 'Global defended pluvial flood model based on SSP3_7.0 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2080-SSP3_7.0-PERCENTILE50-v3.0.vrt'},\n", + " 'pluvial_defended_1in500_SSP5_8.5_2080': {'description': 'Global defended pluvial flood model based on SSP5_8.5 climate model for year 2080. Flood depth is measured in cm expected flood depth, based on a 1 in 500 year return period.',\n", + " 'filename': 's3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2080-SSP5_8.5-PERCENTILE50-v3.0.vrt'}}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_res" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"fathom_file_descriptions.json\", 'w') as out_f:\n", + " json.dump(all_res, out_f)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "json.dump?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DEBURGGING" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a list of files for gdalbuildvrt\n", + "folder = \"/home/wb411133/temp/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0\"\n", + "all_files = [f'v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in500-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/{x}' for x in os.listdir(folder)]\n", + "all_files\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/FATHOM/Transfer_Data_AWS.ipynb b/notebooks/FATHOM/Transfer_Data_AWS.ipynb deleted file mode 100644 index da9fa48..0000000 --- a/notebooks/FATHOM/Transfer_Data_AWS.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import boto3\n", - "\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "in_file_list = \"/home/wb411133/temp/World_Bank_Global_3_Complete.csv\"\n", - "out_folder = os.path.join(os.path.dirname(in_file_list), \"FATHOM\")\n", - "s3_bucket = \"wbg-geography01\"\n", - "s3_prefix = \"FATHOM/v2023/\"\n", - "s3_out = os.path.join(\"s3://\", s3_bucket, s3_prefix)\n", - "\n", - "in_files = pd.read_csv(in_file_list)\n", - "s3 = boto3.resource(\"s3\")\n", - "my_bucket = s3.Bucket(s3_bucket)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" local_folder = os.path.join(out_folder, row[\"Layer\"])\n", - " gost_folder = os.path.join(s3_out, row[\"Layer\"])\n", - " if not os.path.exists(local_folder):\n", - " os.makedirs(local_folder)\n", - "\n", - " cur_out_folder = os.path.join(s3_prefix, row[\"Layer\"])\n", - " obj_count = 0\n", - " for obj in my_bucket.objects.filter(Prefix=cur_out_folder):\n", - " obj_count += 1\n", - " print(f\"{row['Layer']}: {obj_count}\")\n", - " if obj_count == 0:\n", - " download_command = (\n", - " f\"aws s3 sync --profile fathom {fathom_path} {local_folder}\"\n", - " )\n", - " upload_command = f\"aws s3 sync {local_folder} {gost_folder}\"\n", - " remove_command = f\"rm -R {local_folder}\"\n", - "\n", - " out_file.write(download_command)\n", - " out_file.write(\"\\n\")\n", - " out_file.write(upload_command)\n", - " out_file.write(\"\\n\")\n", - " out_file.write(remove_command)\n", - " out_file.write(\"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "all_vals = []\n", - "for idx, row in in_files.iterrows():\n", - " all_vals.append(row[\"Layer\"].split(\"-\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GLOBALSizeOffsetreturntypedefensedepthyearprojectionv1v2
0GLOBAL1ARCSECNW_OFFSET1in1000COASTALDEFENDEDDEPTH2020PERCENTILE50v3.0/None
1GLOBAL1ARCSECNW_OFFSET1in1000COASTALUNDEFENDEDDEPTH2020PERCENTILE50v3.0/None
2GLOBAL1ARCSECNW_OFFSET1in1000FLUVIALDEFENDEDDEPTH2020PERCENTILE50v3.0/None
3GLOBAL1ARCSECNW_OFFSET1in1000FLUVIALUNDEFENDEDDEPTH2020PERCENTILE50v3.0/None
4GLOBAL1ARCSECNW_OFFSET1in1000PLUVIALDEFENDEDDEPTH2020PERCENTILE50v3.0/None
\n", - "
" - ], - "text/plain": [ - " GLOBAL Size Offset return type defense depth year \\\n", - "0 GLOBAL 1ARCSEC NW_OFFSET 1in1000 COASTAL DEFENDED DEPTH 2020 \n", - "1 GLOBAL 1ARCSEC NW_OFFSET 1in1000 COASTAL UNDEFENDED DEPTH 2020 \n", - "2 GLOBAL 1ARCSEC NW_OFFSET 1in1000 FLUVIAL DEFENDED DEPTH 2020 \n", - "3 GLOBAL 1ARCSEC NW_OFFSET 1in1000 FLUVIAL UNDEFENDED DEPTH 2020 \n", - "4 GLOBAL 1ARCSEC NW_OFFSET 1in1000 PLUVIAL DEFENDED DEPTH 2020 \n", - "\n", - " projection v1 v2 \n", - "0 PERCENTILE50 v3.0/ None \n", - "1 PERCENTILE50 v3.0/ None \n", - "2 PERCENTILE50 v3.0/ None \n", - "3 PERCENTILE50 v3.0/ None \n", - "4 PERCENTILE50 v3.0/ None " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx = pd.DataFrame(\n", - " all_vals,\n", - " columns=[\n", - " \"GLOBAL\",\n", - " \"Size\",\n", - " \"Offset\",\n", - " \"return\",\n", - " \"type\",\n", - " \"defense\",\n", - " \"depth\",\n", - " \"year\",\n", - " \"projection\",\n", - " \"v1\",\n", - " \"v2\",\n", - " ],\n", - ")\n", - "xx.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SSP1_2.6 80\n", - "SSP2_4.5 80\n", - "SSP5_8.5 80\n", - "PERCENTILE50 40\n", - "Name: projection, dtype: int64" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx[\"projection\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2030 40\n", - "2050 40\n", - "Name: year, dtype: int64" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx.loc[xx[\"projection\"] == \"SSP1_2.6\"][\"year\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "RasterioIOError", - "evalue": "Range downloading not supported by this server!", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCPLE_AppDefinedError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mrasterio/_shim.pyx\u001b[0m in \u001b[0;36mrasterio._shim.open_dataset\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mrasterio/_err.pyx\u001b[0m in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mCPLE_AppDefinedError\u001b[0m: Range downloading not supported by this server!", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mRasterioIOError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://data.worldpop.org/GIS/Population/Global_2000_2020_Constrained/2020/BSGM/DZA/dza_ppp_2020_constrained.tif'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/rasterio/env.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0menv_ctor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/rasterio/__init__.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;31m# None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdriver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msharing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"r+\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m s = get_writer_for_path(path, driver=driver)(\n", - "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mRasterioIOError\u001b[0m: Range downloading not supported by this server!" - ] - } - ], - "source": [ - "xx = rasterio.open(\n", - " \"https://data.worldpop.org/GIS/Population/Global_2000_2020_Constrained/2020/BSGM/DZA/dza_ppp_2020_constrained.tif\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.776613235473633" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "5008642 / 1024 / 1024" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Earth Engine", - "language": "python", - "name": "ee" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/FATHOM/Vizualize_Flood_types.ipynb b/notebooks/FATHOM/Vizualize_Flood_types.ipynb index 699b01e..07d1f94 100644 --- a/notebooks/FATHOM/Vizualize_Flood_types.ipynb +++ b/notebooks/FATHOM/Vizualize_Flood_types.ipynb @@ -1,20 +1,21 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "6142d832", + "metadata": {}, + "source": [ + "# Mapping Fathom flood data\n", + "\n", + "The Fathom flood data are stored on an AWS bucket as tiles in virtual rasters. This code extracts values for specific scenarios, for a specific country, and plots them." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 22, "id": "53f91b00", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/wb411133/.conda/envs/ee/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -22,121 +23,257 @@ "import rasterio\n", "\n", "import pandas as pd\n", + "import geopandas as gpd\n", "\n", + "sys.path.insert(0, \"../../src\")\n", "\n", - "sys.path.insert(0, \"/home/wb411133/Code/gostrocks/src\")\n", - "\n", - "import GOSTRocks.mapMisc as mapMisc" + "import GOSTrocks.mapMisc as mapMisc\n", + "import GOSTrocks.dataMisc as dMisc\n", + "import GOSTrocks.rasterMisc as rMisc\n", + "from GOSTrocks.misc import tPrint" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "e25b9b4d", "metadata": {}, "outputs": [], "source": [ - "in_file_list = \"/home/wb411133/temp/fathom_files.csv\"\n", - "\n", - "s3_bucket = \"wbg-geography01\"\n", - "s3_prefix = \"FATHOM/v2023/\"\n", - "\n", - "in_files = pd.read_csv(in_file_list)\n", - "s3 = boto3.resource(\"s3\")\n", - "my_bucket = s3.Bucket(s3_bucket)" + "iso3 = \"KEN\"\n", + "out_folder = f\"/home/wb411133/temp/FATHOM/{iso3}\"\n", + "if not os.path.exists(out_folder):\n", + " os.makedirs(out_folder)\n", + " \n", + "# This demo uses the default national boundaries included with GeoPandas, but this can be changed here\n", + "world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n", + "world = gpd.read_file(world_filepath)\n", + "inB = world.loc[world['iso_a3'] == iso3].copy()" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "id": "f0947351", "metadata": {}, "outputs": [], "source": [ - "tile = \"n01w028.tif\"\n", - "return_period = \"1000\"\n", - "defended = \"DEFENDED\"\n", + "# Select layer to downlaod\n", + "flood_type = [\"COASTAL\",\"FLUVIAL\",\"PLUVIAL\"]\n", + "defence = [\"DEFENDED\"]\n", + "return_period = ['1in50']\n", + "climate_model = [\"PERCENTILE50\"]\n", + "year = [\"2020\"]\n", "\n", - "coastal_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-COASTAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "fluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-FLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "pluvial_folder = f\"GLOBAL-1ARCSEC-NW_OFFSET-1in{return_period}-PLUVIAL-{defended}-DEPTH-2020-PERCENTILE50-v3.0\"\n", - "\n", - "coastal_file = os.path.join(\"s3://\", s3_bucket, s3_prefix, coastal_folder, tile)\n", - "fluvial_file = os.path.join(\"s3://\", s3_bucket, s3_prefix, fluvial_folder, tile)\n", - "pluvial_file = os.path.join(\"s3://\", s3_bucket, s3_prefix, pluvial_folder, tile)" + "# all_vrts is a pandas dataframe with all the vrt paths to the global datasets, with columns defining\n", + "# the various models' defining attributes\n", + "all_vrts = dMisc.get_fathom_vrts(True)\n", + "sel_images = all_vrts.loc[(all_vrts['FLOOD_TYPE'].isin(flood_type)) & (all_vrts['DEFENCE'].isin(defence)) & \n", + " (all_vrts['RETURN'].isin(return_period)) & (all_vrts['CLIMATE_MODEL'].isin(climate_model))]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "35fe61a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RETURNFLOOD_TYPEDEFENCEDEPTHYEARCLIMATE_MODELPATH
3901in50COASTALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
4161in50FLUVIALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
4421in50PLUVIALDEFENDEDDEPTH2020PERCENTILE50s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_...
\n", + "
" + ], + "text/plain": [ + " RETURN FLOOD_TYPE DEFENCE DEPTH YEAR CLIMATE_MODEL \\\n", + "390 1in50 COASTAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "416 1in50 FLUVIAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "442 1in50 PLUVIAL DEFENDED DEPTH 2020 PERCENTILE50 \n", + "\n", + " PATH \n", + "390 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "416 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... \n", + "442 s3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_... " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sel_images" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 23, + "id": "c388810f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "08:44:55\ts3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-COASTAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:46:01\ts3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n", + "08:47:12\ts3://wbg-geography01/FATHOM/GLOBAL-1ARCSEC-NW_OFFSET-1in50-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0.vrt\n" + ] + } + ], + "source": [ + "flood_data = {}\n", + "for idx, row in sel_images.iterrows():\n", + " fluvialR = rasterio.open(row['PATH'])\n", + " rData, profile = rMisc.clipRaster(fluvialR, inB)\n", + " flood_data[row['FLOOD_TYPE']] = [rData, profile]\n", + " tPrint(row['PATH'])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, "id": "11401dea", "metadata": { "scrolled": true }, "outputs": [ { - "ename": "RasterioIOError", - "evalue": "'/vsis3/wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n01w028.tif' does not exist in the file system, and is not recognized as a supported dataset name.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCPLE_OpenFailedError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mrasterio/_shim.pyx\u001b[0m in \u001b[0;36mrasterio._shim.open_dataset\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mrasterio/_err.pyx\u001b[0m in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: '/vsis3/wbg-geography01/FATHOM/v2023/GLOBAL-1ARCSEC-NW_OFFSET-1in1000-FLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v3.0/n01w028.tif' does not exist in the file system, and is not recognized as a supported dataset name.", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mRasterioIOError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfluvialR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfluvial_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmapMisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatic_map_raster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfluvialR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Blues'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/rasterio/env.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0menv_ctor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.conda/envs/ee/lib/python3.9/site-packages/rasterio/__init__.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;31m# None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdriver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msharing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"r+\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m s = get_writer_for_path(path, driver=driver)(\n", - "\u001b[0;32mrasterio/_base.pyx\u001b[0m in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mRasterioIOError\u001b[0m: 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "fluvialR = rasterio.open(fluvial_file)\n", - "mapMisc.static_map_raster(\n", - " fluvialR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", - ")" + "rData, profile = flood_data['FLUVIAL']\n", + "with rMisc.create_rasterio_inmemory(profile, rData) as floodR:\n", + " mapMisc.static_map_raster(\n", + " floodR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", + " )" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "7d0e285f", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "pluvialR = rasterio.open(pluvial_file)\n", - "mapMisc.static_map_raster(\n", - " pluvialR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", - ")" + "rData, profile = flood_data['PLUVIAL']\n", + "with rMisc.create_rasterio_inmemory(profile, rData) as floodR:\n", + " mapMisc.static_map_raster(\n", + " floodR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", + " )" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "1a2fbfc7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "coastalR = rasterio.open(coastal_file)\n", - "mapMisc.static_map_raster(\n", - " coastalR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", - ")" + "rData, profile = flood_data['COASTAL']\n", + "with rMisc.create_rasterio_inmemory(profile, rData) as floodR:\n", + " mapMisc.static_map_raster(\n", + " floodR, colormap=\"Blues\", thresh=[0, 0.01, 0.1, 0.2, 0.5, 1, 5]\n", + " )" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fcc9f41d", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/Overture_Maps_Data_Access.ipynb b/notebooks/Overture_Maps_Data_Access.ipynb new file mode 100644 index 0000000..47a94bc --- /dev/null +++ b/notebooks/Overture_Maps_Data_Access.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Downloading data from Overture Maps on Aamzon S3\n", + "### Relying on https://pypi.org/project/overturemapsdownloader/" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "import duckdb\n", + "from typing import Any\n", + "import json\n", + "import geopandas as gpd\n", + "\n", + "from osgeo import ogr\n", + "from shapely import wkb\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(42,)]\n" + ] + } + ], + "source": [ + "con = duckdb.connect()\n", + "print(con.execute('SELECT 42').fetchall())\n", + "# To install an extension (this is usually done only once)\n", + "try:\n", + " con.install_extension('httpfs')\n", + " con.install_extension('spatial')\n", + " con.load_extension('httpfs')\n", + " con.load_extension('spatial')\n", + " con.execute(\"SET s3_region='us-west-2'\")\n", + "except Exception as e:\n", + " print(f\"Failed to install extension: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the query to read from S3 and filter the data\n", + "query = '''\n", + "SELECT\n", + " type,\n", + " subType,\n", + " localityType,\n", + " adminLevel,\n", + " isoCountryCodeAlpha2,\n", + " JSON(names) AS names,\n", + " JSON(sources) AS sources,\n", + " ST_GeomFromWkb(geometry) AS geometry\n", + "FROM read_parquet(\n", + " 's3://overturemaps-us-west-2/release/2023-07-26-alpha.0/theme=admins/type=*/*',\n", + " filename=true,\n", + " hive_partitioning=1\n", + ")\n", + "WHERE adminLevel = 2\n", + " AND ST_GeometryType(ST_GeomFromWkb(geometry)) IN ('POLYGON','MULTIPOLYGON')\n", + "LIMIT 5\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57c0effa632a4cc5b3c187fb3535e945", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "IOException", + "evalue": "IO Error: Connection error for HTTP HEAD to 'https://overturemaps-us-west-2.s3.amazonaws.com/release/2023-07-26-alpha.0/theme%3Dadmins/type%3Dlocality/20230725_211237_00132_5p54t_e608b636-1b9e-4e3c-ad00-d93fb09ed323'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIOException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[36], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Execute the query and store the result\u001b[39;00m\n\u001b[0;32m 2\u001b[0m df \u001b[38;5;241m=\u001b[39m con\u001b[38;5;241m.\u001b[39mexecute(query)\u001b[38;5;241m.\u001b[39mfetchdf()\n\u001b[1;32m----> 3\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mcon\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfetchall()\n", + "\u001b[1;31mIOException\u001b[0m: IO Error: Connection error for HTTP HEAD to 'https://overturemaps-us-west-2.s3.amazonaws.com/release/2023-07-26-alpha.0/theme%3Dadmins/type%3Dlocality/20230725_211237_00132_5p54t_e608b636-1b9e-4e3c-ad00-d93fb09ed323'" + ] + } + ], + "source": [ + "# Execute the query and store the result\n", + "df = con.execute(query).fetchdf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to convert it to a Shapely geometry object\n", + "def try_wkb_loads(data):\n", + " try:\n", + " return wkb.loads(data)\n", + " except Exception as e:\n", + " print(f\"Failed to convert geometry: {e}\")\n", + " return None\n", + " \n", + "df['geometry'] = df['geometry'].apply(try_wkb_loads)\n", + "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs=4326)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import dask.dataframe as dd\n", + "import geopandas as gpd\n", + "import dask_geopandas as dgpd \n", + "\n", + "from shapely.geometry import box" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "iso3 = 'KHM'" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "df = dd.read_parquet(\n", + " 's3://overturemaps-us-west-2/release/2023-07-26-alpha.0/theme=places/type=place/*',\n", + " columns=['bbox', 'geometry'],\n", + " engine='pyarrow',\n", + " index='id',\n", + " dtype_backend='pyarrow',\n", + " storage_options={\"anon\": True},\n", + " parquet_file_extensions=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'POLYGON ((107.61454796756243 10.48654368737523, 107.61454796756243 14.570583807834282, 102.34809939983302 14.570583807834282, 102.34809939983302 10.48654368737523, 107.61454796756243 10.48654368737523))'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get extent of selected country as a bounding box polygon\n", + "world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n", + "world = gpd.read_file(world_filepath)\n", + "\n", + "sel_country = world[world['iso_a3'] == iso3]\n", + "country_box = box(*sel_country.total_bounds)\n", + "str(country_box)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Series' object has no attribute 'within'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\wbg\\Anaconda3\\envs\\omaps\\Lib\\site-packages\\dask_expr\\_core.py:457\u001b[0m, in \u001b[0;36mExpr.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 458\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Projection' object has no attribute 'within'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\wbg\\Anaconda3\\envs\\omaps\\Lib\\site-packages\\dask_expr\\_collection.py:517\u001b[0m, in \u001b[0;36mFrameBase.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 514\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 515\u001b[0m \u001b[38;5;66;03m# Fall back to `expr` API\u001b[39;00m\n\u001b[0;32m 516\u001b[0m \u001b[38;5;66;03m# (Making sure to convert to/from Expr)\u001b[39;00m\n\u001b[1;32m--> 517\u001b[0m val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexpr, key)\n\u001b[0;32m 518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(val):\n", + "File \u001b[1;32mc:\\wbg\\Anaconda3\\envs\\omaps\\Lib\\site-packages\\dask_expr\\_core.py:478\u001b[0m, in \u001b[0;36mExpr.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 477\u001b[0m link \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://github.com/dask-contrib/dask-expr/blob/main/README.md#api-coverage\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 478\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m 479\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 480\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis often means that you are attempting to use an unsupported \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 481\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAPI function. Current API coverage is documented here: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlink\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 482\u001b[0m )\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Projection' object has no attribute 'within'\n\nThis often means that you are attempting to use an unsupported API function. Current API coverage is documented here: https://github.com/dask-contrib/dask-expr/blob/main/README.md#api-coverage.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[25], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m geometry \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmap_partitions(gpd\u001b[38;5;241m.\u001b[39mGeoSeries\u001b[38;5;241m.\u001b[39mfrom_wkt, meta\u001b[38;5;241m=\u001b[39mgpd\u001b[38;5;241m.\u001b[39mGeoSeries(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m\"\u001b[39m)) \u001b[38;5;66;03m#.set_crs(4326)\u001b[39;00m\n\u001b[0;32m 2\u001b[0m gdf \u001b[38;5;241m=\u001b[39m dgpd\u001b[38;5;241m.\u001b[39mfrom_dask_dataframe(df, geometry\u001b[38;5;241m=\u001b[39mgeometry)\n\u001b[1;32m----> 4\u001b[0m clipped_gdf \u001b[38;5;241m=\u001b[39m gdf[\u001b[43mgdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwithin\u001b[49m(country_box)]\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(clipped_gdf\u001b[38;5;241m.\u001b[39mhead())\n", + "File \u001b[1;32mc:\\wbg\\Anaconda3\\envs\\omaps\\Lib\\site-packages\\dask_expr\\_collection.py:523\u001b[0m, in \u001b[0;36mFrameBase.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m val\n\u001b[0;32m 521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m 522\u001b[0m \u001b[38;5;66;03m# Raise original error\u001b[39;00m\n\u001b[1;32m--> 523\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n", + "File \u001b[1;32mc:\\wbg\\Anaconda3\\envs\\omaps\\Lib\\site-packages\\dask_expr\\_collection.py:512\u001b[0m, in \u001b[0;36mFrameBase.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 509\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m 510\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 511\u001b[0m \u001b[38;5;66;03m# Prioritize `FrameBase` attributes\u001b[39;00m\n\u001b[1;32m--> 512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 514\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 515\u001b[0m \u001b[38;5;66;03m# Fall back to `expr` API\u001b[39;00m\n\u001b[0;32m 516\u001b[0m \u001b[38;5;66;03m# (Making sure to convert to/from Expr)\u001b[39;00m\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'within'" + ] + } + ], + "source": [ + "geometry = df[\"geometry\"].map_partitions(gpd.GeoSeries.from_wkt, meta=gpd.GeoSeries(name=\"geometry\")) #.set_crs(4326)\n", + "gdf = dgpd.from_dask_dataframe(df, geometry=geometry)\n", + "\n", + "clipped_gdf = gdf[gdf.geometry.within(country_box)]\n", + "print(clipped_gdf.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dask_expr._collection.Series" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geometry.__class__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "urban_test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/GOSTrocks/dataMisc.py b/src/GOSTrocks/dataMisc.py index 4d86e23..ed6aec2 100644 --- a/src/GOSTrocks/dataMisc.py +++ b/src/GOSTrocks/dataMisc.py @@ -1,12 +1,37 @@ +import sys, os import json import urllib import boto3 +import rasterio +import pandas as pd import geopandas as gpd from botocore.config import Config from botocore import UNSIGNED +from . import rasterMisc as rMisc + + +def download_WSF(extent, wsf_url="https://download.geoservice.dlr.de/WSF2019/files/WSF2019_cog.tif", + out_file=""): + """_summary_ + + Parameters + ---------- + extent : _type_ + _description_ + wsf_url : str, optional + _description_, by default "https://download.geoservice.dlr.de/WSF2019/files/WSF2019_cog.tif" + """ + + # Open the WSF COG + wsf_raster = rasterio.open(wsf_url) + data, profile = rMisc.clipRaster(raster=wsf_raster, bounds=extent) + if out_file != "": + with rasterio.open(out_file, 'w', **profile) as dst: + dst.write(data) + return(data, profile) def aws_search_ntl( bucket="globalnightlight", @@ -86,3 +111,21 @@ def get_geoboundaries( f"Cannot find admin dataset {cur_url}. Check out {all_url} for details on what is available" ) ) + +def get_fathom_vrts(return_df = False): + """ Get a list of VRT files of Fathom data from the GOST S3 bucket. Note that the + VRT files are not searched dynamically but are stored in a text file in the same + folder as the function. + + return_df: if True, return a pandas dataframe with the VRT files and their components, defaults to False which returns just the list of VRT files + """ + vrt_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fathom_vrts.txt") + all_vrts = [] + with open(vrt_file, "r") as f: + for line in f: + all_vrts.append(line.strip()) + if return_df: + vrt_pd = pd.DataFrame([x.split("-")[4:10] for x in all_vrts], columns=['RETURN', 'FLOOD_TYPE', 'DEFENCE', 'DEPTH', 'YEAR', 'CLIMATE_MODEL']) + vrt_pd['PATH'] = all_vrts + return vrt_pd + return all_vrts \ No newline at end of file diff --git a/src/GOSTrocks/fathom_vrts.txt b/src/GOSTrocks/fathom_vrts.txt new file mode 100644 index 0000000..6429632 --- /dev/null +++ b/src/GOSTrocks/fathom_vrts.txt @@ -0,0 +1,520 @@ 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a/src/GOSTrocks/ntlMisc.py b/src/GOSTrocks/ntlMisc.py index 71963b8..5aebcff 100644 --- a/src/GOSTrocks/ntlMisc.py +++ b/src/GOSTrocks/ntlMisc.py @@ -14,8 +14,8 @@ if curPath not in sys.path: sys.path.append(curPath) -from dataMisc import aws_search_ntl -from misc import tPrint +from .dataMisc import aws_search_ntl +from .misc import tPrint import rasterMisc as rMisc diff --git a/src/GOSTrocks/rasterMisc.py b/src/GOSTrocks/rasterMisc.py index b57e14b..819441c 100644 --- a/src/GOSTrocks/rasterMisc.py +++ b/src/GOSTrocks/rasterMisc.py @@ -169,6 +169,10 @@ def clipRaster(inR, inD, outFile=None, crop=True): :return: array of [numpy array of data, and rasterio metadata] :rtype: array """ + if isinstance(inR, str): + inR = rasterio.open(inR) + if isinstance(inD, str): + inD = gpd.read_file(inD) if inD.crs != inR.crs: inD = inD.to_crs(inR.crs) inD = inD.buffer(0)