udpate typo in attributes file, and update code to new soils database

main/implementation/01.1_create-netcdfs.R

@@ -112,15 +112,16 @@ if(today_julian <= 183) {
 
   year(c2[year(c2) == lastYear]) <- 2023
   year(c2[year(c2) == currYear]) <- 2024
-  year(c2[year(c2) == nextYear]) <- 2025
+  year(c2[year(c2) == nextYear]) <- 2025 ##AES change to not be hard coded?? (along w/ other year values))
 
 } else {
 
   year(c1[year(c1) == currYear]) <- 1991
-  year(c1[year(c1) == nextYear]) <- 1992
+  year(c1[year(c1) == nextYear & !is.na(c1)])  <- 1992
+
 
   year(c2[year(c2) == currYear]) <- 2023
-  year(c2[year(c2) == nextYear]) <- 2024
+  year(c2[year(c2) == nextYear & !is.na(c2)]) <- 2024
 
 }
 
@@ -240,15 +241,15 @@ for(nc in 1:106){
   )
 
   ## time bounds --------------------------------
-  time_bounds <- if(nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'P') {
+  time_bounds <- if (nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'P') {
     time_bounds_daily_p
-  } else if(nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'H') {
+  } else if (nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'H') {
     time_bounds_daily_h
-  } else if(nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'RP') {
+  } else if (nc_time$units == "days since 1970-01-01" && attributes$TP[nc] == 'RP') {
     time_bounds_daily_rp
-  } else if(nc_time$units == "days since 1970-01-01"  && attributes$TP[nc] == 'EH') {
+  } else if (nc_time$units == "days since 1970-01-01"  && attributes$TP[nc] == 'EH') {
     time_bounds_annually_h
-  } else if(nc_time$units == "days since 1970-01-01"  && attributes$TP[nc] == 'EP') {
+  } else if (nc_time$units == "days since 1970-01-01"  && attributes$TP[nc] == 'EP') {
     time_bounds_annually_p
   } else if (nc_time$units == 'days since 1970-01-01' && attributes$TP[nc] == 'OC') {
     time_bounds_March
@@ -304,8 +305,8 @@ for(nc in 1:106){
       data = western_region.nc1[["data"]],
       data_str = "xyzt",
       data_dims = data_dims_nc,
-      vertical_values = 1:30, 
-      vertical_attributes = list(units = "simulationNumber", positive = "up"),
+      simAxis_values = 1:30, 
+      simAxis_attributes = list(units = "simulationNumber", positive = "up"),
       var_attributes = nc_vars,
       xy_attributes = nc_att_xy,
       crs_attributes = nc_att_crs,

main/implementation/01.2_input-values-into-ncdfs.R

@@ -54,7 +54,7 @@ for(n in seq_along(netCDFnames)){
     if (valueName_short[n] == "oconnor-stemp_95CI_upper") {
       vals <- c(Oconnor_Stats[TP == "Forecast",]$sTemp_mean) + c(Oconnor_Stats[TP == "Forecast",]$sTemp_CI95)
     }
-  }
+  } 
   if (valueName_short[n] == "shriver_historical") vals <- c(Shriver_Stats[TP == 'Historical', 'Prob'])[["Prob"]]
   if (valueName_short[n] == "shriver_prediction") {
     vals <- c(Shriver_Stats[TP != 'Historical', 'Prob'])$Prob

main/implementation/01_main-simulation-runner.R

@@ -5,9 +5,9 @@ rm(list=ls(all=TRUE))
 #     LDFLAGS=-L/sw/lib LIBS=-lhdf5 --with-mpicc=mpicc --with-mpiexec=mpiexec" \
 # RNetCDF_2.9-1.tar.gz
 
-remotes::install_github("DrylandEcology/rSW2st")
-remotes::install_github("DrylandEcology/rSOILWAT2", build_vignettes = FALSE)
-remotes::install_github("DrylandEcology/rSW2funs")
+#remotes::install_github("DrylandEcology/rSW2st")
+#remotes::install_github("DrylandEcology/rSOILWAT2", build_vignettes = FALSE)
+#remotes::install_github("DrylandEcology/rSW2funs")
 suppressMessages(library(rSOILWAT2, quiet = TRUE))
 
 suppressMessages(library(rSW2data, quiet = TRUE))
@@ -26,7 +26,7 @@ suppressMessages(library(RNetCDF, quiet = TRUE))
 suppressMessages(library(ncdf4, quiet = TRUE))
 
 # variables --------------------------------------------------------------------
-isParallel <- TRUE # set to FALSE if you dont want to use pbdMPI to execute runs in parallel 
+isParallel <- FALSE # set to FALSE if you dont want to use pbdMPI to execute runs in parallel 
 nRuns = 30 #is 30 for point based netCDF, but changed to 5 here for testing purposes (this is the number of simulations for each grid?? I think? )
 
 # Begin ------------------------------------------------------------------------
@@ -65,14 +65,14 @@ Sites <- as.data.frame(data.table::fread("main/Data/WeatherDBSitesTable_WestInde
 sites <- dim(Sites)[1]
 
 # load gridded soils data from Daniel (currently an old version, will be updated w/ SOLUS100 data)
-soils_gridClay <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slclay_fx_SOILWAT2_wUS-gm_gn.nc") 
-soils_gridSand <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slsand_fx_SOILWAT2_wUS-gm_gn.nc") 
-soils_gridSilt <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slsilt_fx_SOILWAT2_wUS-gm_gn.nc") 
-soils_gridDensity <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slbdensity_fx_SOILWAT2_wUS-gm_gn.nc") 
-soils_gridThickness <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slthick_fx_SOILWAT2_wUS-gm_gn.nc") 
-soils_gridCoarse <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slcoarse_fx_SOILWAT2_wUS-gm_gn.nc")
-soilGridLats <- var.get.nc(soils_gridClay, "lat")
-soilGridLons <- var.get.nc(soils_gridClay, "lon")
+soils_gridClay <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/claytotal_PED-CONUS4km_SOLUS100.nc") 
+soils_gridSand <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/sandtotal_PED-CONUS4km_SOLUS100.nc") 
+soils_gridSilt <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/silttotal_PED-CONUS4km_SOLUS100.nc") 
+soils_gridDensity <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/dbovendry_PED-CONUS4km_SOLUS100.nc") 
+soils_gridThickness <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/hzthk_PED-CONUS4km_SOLUS100.nc") 
+soils_gridCoarse <- RNetCDF::open.nc(con = "./main/Data/soilsDB_new/fragvol_PED-CONUS4km_SOLUS100.nc")
+soilGridLats <- var.get.nc(soils_gridClay, "latitude")
+soilGridLons <- var.get.nc(soils_gridClay, "longitude")
 
 if(isParallel) {
   alljid <- get.jid(n = sites, method = "block", all = FALSE) 
@@ -152,23 +152,28 @@ for (j in 1:2){#alljid) { # TO DO: use "while" not "for"
 
   ### get soils data for this gridcell
   # get indices for soil grid Lat and Lon
-  soilLat_i <- which(round(soilGridLats,5)==round(Lat,5))
-  soilLon_i <- which(round(soilGridLons,5)==round(Long,5))
+  # the closest latitude
+  soilLat_i <- which((soilGridLats-Lat) == min(abs(soilGridLats - Lat)))
+
+  # the closest longitude 
+  soilLon_i <- which((soilGridLons-Long) == min(abs(soilGridLons-Long)))
+    #round(soilGridLons,2)==round(Long,2))
   #clay
-  clay_i <- var.get.nc(soils_gridClay, "slclay", start = c(soilLon_i, soilLat_i,1), 
-             count = c(1,1,12))
+  clay_i <- var.get.nc(soils_gridClay, "claytotal", start = c(soilLon_i, soilLat_i,1), 
+             count = c(1,1,dim.inq.nc(soils_gridClay, "vertical")$length))
   #sand
-  sand_i <- var.get.nc(soils_gridSand, "slsand", start = c(soilLon_i, soilLat_i,1), 
-                       count = c(1,1,12))
+  sand_i <- var.get.nc(soils_gridSand, "sandtotal", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,dim.inq.nc(soils_gridSand, "vertical")$length))
   #silt
-  silt_i <- var.get.nc(soils_gridSilt, "slsilt", start = c(soilLon_i, soilLat_i,1), 
-                       count = c(1,1,12))
-  #silt
-  coarse_i <- var.get.nc(soils_gridCoarse, "slcoarse", start = c(soilLon_i, soilLat_i,1), 
-                       count = c(1,1,12))
+  silt_i <- var.get.nc(soils_gridSilt, "silttotal", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,dim.inq.nc(soils_gridSilt, "vertical")$length))
+  #coarse material
+  coarse_i <- var.get.nc(soils_gridCoarse, "fragvol", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,dim.inq.nc(soils_gridCoarse, "vertical")$length))
   #thickness
   thickness_i <- 100*var.get.nc(soils_gridThickness, "slthick", start = c(soilLon_i, soilLat_i,1), 
                        count = c(1,1,12))   # also convert thickness to centimeters from meters
+  # bulk density 
   bulkdensity_i <- var.get.nc(soils_gridDensity, "slbdensity", start = c(soilLon_i, soilLat_i,1), 
                        count = c(1,1,12))
 
@@ -363,7 +368,7 @@ for (j in 1:2){#alljid) { # TO DO: use "while" not "for"
   if(!interactive() & isParallel) comm.print('Inserting into netCDFs.', Sys.time())
 
 
-  #TO DO: Make this into a function not a script #AES not working as of 3/4/24
+  #TO DO: Make this into a function not a script 
   source('./main/implementation/01.2_input-values-into-ncdfs.R') 
 
 }

main/implementation/nc_atts-all.csv

@@ -98,7 +98,7 @@ swp-deep_90_diff,NearFut.SWP.Deep.diffs.90,swp-deep_dy_gridSTDF_90pct-diffs_pred
 shriver_historical,,shriver_yr_gridSTDF_historical,probability,probability of sagebrush seeding success,,Historical Probability of Sagebrush Seeding Success ,"Based on Shriver, R.K. et al.  (2018). Adapting management to a changing world: Warm temperatures, dry soil, and inter-annual variability limit restoration success of dominant woody shrub in temperate drylands. Global Change Biology. 24","Historical climate data (1991 - last year) is run through Soilwat. Results are used to generate probabilities of seeding success in previous years, based upon the model presented in Shriver et al. 2018.",time: mean within days time: mean over years,days since 1970-01-01,1,32,EH,timeseries
 shriver_prediction,,shriver_yr_gridSTDF_prediction,probability,probability of sagebrush seeding success,,Future Sagebrush Seeding Success for the current year and next year (for 30 realizations of T&P anomalies),"Based on Shriver, R.K. et al.  (2018). Adapting management to a changing world: Warm temperatures, dry soil, and inter-annual variability limit restoration success of dominant woody shrub in temperate drylands. Global Change Biology. 24","30 realizations of the current year are generated via multivariate sampling based upon by NWS 'long-lead forecasts'  anomalies for temperature and precipitation. These realizations are run through Soilwat.   Results are used to generate probabilities of seeding success in the current and next year, based upon the model presented in Shriver et al. 2018. There are data for two years (the t dimension = 2), and 30 values for each year, which are stored along the ""z"" axis ",time: mean within days time: mean over years,days since 1970-01-01,1,2,EP,timeseries
 GISSM_historical,,GISSM_yr_gridSTDF_historical,probability,probability (frequency) of years when big sagebrush seedlings survive in undisturbed natural vegetation,,Historical Mean Establishment,"Based on Schlaepfer, D. R. et al. (2014). Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecological Modelling. 286 https://doi.org/10.1016/j.ecolmodel.2014.04.021",Historical climate data (1991 - last year) is run through Soilwat. Results are the probability (frequency) of years when big sagebrush seedlings survive in undisturbed natural vegetation,time: mean within days time: mean over years,days since 1970-01-01,1,32,EH,timeseries
-GISSM_prediction,,GISSM_yr_gridSTDF_prediction,probability,probability (frequency) of years when big sagebrush seedlings survive in undisturbed natural vegetation,,Future Establishment Probability for the current year and the next year (for 30 realizations of T&P anomalies),"Based on Schlaepfer, D. R. et al. (2014). Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecological Modelling. 286 https://doi.org/10.1016/j.ecolmodel.2014.04.021","30 realizations of the current year are generated via multivariate sampling based upon by NWS 'long-lead forecasts'  anomalies for temperature and precipitation. These realizations are run through Soilwat. Results represent the probability (frequency) of realization of the current and next year when big sagebrush seedlings survive in undisturbed natural vegetation. ; There are data for two years (the t dimension = 2), and 30 values for each year, which are stored along the ""z"" axis ",time: mean within days time: mean over years,days since 1970-01-01,1,2,OC,timeseries
+GISSM_prediction,,GISSM_yr_gridSTDF_prediction,probability,probability (frequency) of years when big sagebrush seedlings survive in undisturbed natural vegetation,,Future Establishment Probability for the current year and the next year (for 30 realizations of T&P anomalies),"Based on Schlaepfer, D. R. et al. (2014). Modeling regeneration responses of big sagebrush (Artemisia tridentata) to abiotic conditions. Ecological Modelling. 286 https://doi.org/10.1016/j.ecolmodel.2014.04.021","30 realizations of the current year are generated via multivariate sampling based upon by NWS 'long-lead forecasts'  anomalies for temperature and precipitation. These realizations are run through Soilwat. Results represent the probability (frequency) of realization of the current and next year when big sagebrush seedlings survive in undisturbed natural vegetation. ; There are data for two years (the t dimension = 2), and 30 values for each year, which are stored along the ""z"" axis ",time: mean within days time: mean over years,days since 1970-01-01,1,2,EP,timeseries
 oconnor-swp_mean,,oconnor-swp_dy_gridSTDF_mean-prediction,swp,soil_water_potential,MPa,Daily Soil Water Potential in 0 - 5 cm soil depth for the month of March in the upcoming year,Based on O'Connor et al. (2020). Small scale water deficits after wildfires create long-lasting ecological impacts. Environmental Research Letters. 15(4),,time: mean within days time: mean over days,days since 1970-01-01,1,31,OC,timeseries
 oconnor-swp_95CI_lower,,oconnor-swp_dy_gridSTDF_95CI_lower-prediction,swp,soil_water_potential,MPa,Daily Soil Water Potential in 0 - 5 cm soil depth for the month of March in the upcoming year,Based on O'Connor et al. (2020). Small scale water deficits after wildfires create long-lasting ecological impacts. Environmental Research Letters. 15(4),,time: mean within days time: mean over days,days since 1970-01-01,1,31,OC,timeseries
 oconnor-swp_95CI_upper,,oconnor-swp_dy_gridSTDF_95CI_upper-prediction,swp,soil_water_potential,MPa,Daily Soil Water Potential in 0 - 5 cm soil depth for the month of March in the upcoming year,Based on O'Connor et al. (2020). Small scale water deficits after wildfires create long-lasting ecological impacts. Environmental Research Letters. 15(4),,time: mean within days time: mean over days,days since 1970-01-01,1,31,OC,timeseries

main/test_code/TestGISSMOutput.R

@@ -0,0 +1,22 @@
+# determining the accuracy of GISSM output
+# Alice Stears 
+# 27 August 2024
+
+
+# load packages -----------------------------------------------------------
+
+library(tidyverse)
+library(RNetCDF)
+library(terra)
+
+# Load data ---------------------------------------------------------------
+
+gissm_hist <- terra::rast(x = "./projects/07_TestOutputForFRESC/trimmed_netCDFs/GISSM_yr_gridSTDF_historical_042024.nc")
+gissm_pred <- terra::rast(x = "./outputs/20240827/GISSM_yr_gridSTDF_prediction_082024.nc", drivers="NETCDF")
+
+# get the prediction data as a netCDF
+gissm_pred_nc <- open.nc("./outputs/20240827/GISSM_yr_gridSTDF_prediction_082024.nc")
+# get the data in the netcdf
+test <- var.get.nc(gissm_pred_nc, variable = "probability")#, start = c(194, 435, 1, 1), count = c(10, 1, 30, 2))
+
+RNetCDF::print.nc(gissm_pred_nc)

projects/07_TestOutputForFRESC/GenerateOutputForFRESC.R

@@ -76,6 +76,16 @@ Sites <- whichSites
 
 sites <- dim(Sites)[1]
 
+# load gridded soils data from Daniel (currently an old version, will be updated w/ SOLUS100 data)
+soils_gridClay <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slclay_fx_SOILWAT2_wUS-gm_gn.nc") 
+soils_gridSand <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slsand_fx_SOILWAT2_wUS-gm_gn.nc") 
+soils_gridSilt <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slsilt_fx_SOILWAT2_wUS-gm_gn.nc") 
+soils_gridDensity <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slbdensity_fx_SOILWAT2_wUS-gm_gn.nc") 
+soils_gridThickness <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slthick_fx_SOILWAT2_wUS-gm_gn.nc") 
+soils_gridCoarse <- RNetCDF::open.nc(con = "./main/Data/soilsDB/slcoarse_fx_SOILWAT2_wUS-gm_gn.nc")
+soilGridLats <- var.get.nc(soils_gridClay, "lat")
+soilGridLons <- var.get.nc(soils_gridClay, "lon")
+
   alljid <- sites
 
 
@@ -111,7 +121,7 @@ suppressWarnings(source('./main/implementation/01.1_create-netcdfs.R')) # TO DO:
 ################### ------------------------------------------------------------
 # Run simulation --------------------------------------------------------------
 
-for (j in 1:alljid){#1:alljid) { # TO DO: use "while" not "for"
+for (j in 1:10){#alljid){#1:alljid) { # TO DO: use "while" not "for"
   i <- j
 
   ################### ------------------------------------------------------------
@@ -152,16 +162,40 @@ for (j in 1:alljid){#1:alljid) { # TO DO: use "while" not "for"
         wdata[[names(wdata)[k]]]@data[wdata[[names(wdata)[k]]]@data[, "Tmax_C"] < wdata[[names(wdata)[k]]]@data[, "Tmin_C"], "Tmax_C"]
     }
   }
+
+  ### get soils data for this gridcell
+  # get indices for soil grid Lat and Lon
+  soilLat_i <- which(round(soilGridLats,5)==round(Lat,5))
+  soilLon_i <- which(round(soilGridLons,5)==round(Long,5))
+  #clay
+  clay_i <- var.get.nc(soils_gridClay, "slclay", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,12))
+  #sand
+  sand_i <- var.get.nc(soils_gridSand, "slsand", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,12))
+  #silt
+  silt_i <- var.get.nc(soils_gridSilt, "slsilt", start = c(soilLon_i, soilLat_i,1), 
+                       count = c(1,1,12))
+  #silt
+  coarse_i <- var.get.nc(soils_gridCoarse, "slcoarse", start = c(soilLon_i, soilLat_i,1), 
+                         count = c(1,1,12))
+  #thickness
+  thickness_i <- 100*var.get.nc(soils_gridThickness, "slthick", start = c(soilLon_i, soilLat_i,1), 
+                                count = c(1,1,12))   # also convert thickness to centimeters from meters
+  bulkdensity_i <- var.get.nc(soils_gridDensity, "slbdensity", start = c(soilLon_i, soilLat_i,1), 
+                              count = c(1,1,12))
+
   ################### ----------------------------------------------------------
   # Part 2 - Sets SW parameters besides weather
   ################### ----------------------------------------------------------
   #setting soilWat specific options
   #sw_in <- new("swInputData") # baseline data # new() creates an empty object of this class, but everything needs to be addedd manually... which may be cumbersome
   sw_in <- swInputData()
   sw_in <- setVeg(sw_in, AllProdInfo, i)
-  sw_in <- setSW(sw_in, Lat, Long, clim)
+  sw_in <- setSW(sw_in, Lat, Long, clim, 
+                 clay_i, sand_i, silt_i, coarse_i, thickness_i, bulkdensity_i)
   #sw_in <- set_soils(sw_in, 2, 35, 35) #AES is done elsewhere in the setSW() function
-  sw_in@site@SoilTemperatureFlag <- TRUE # turns off the soil temp option #AES follow-up with Caitlin why it was turned off? 
+  sw_in@site@SoilTemperatureFlag <- TRUE # turns on the soil temperature 
   swCarbon_Use_Bio(sw_in) <- FALSE # turns off carbon #turns off CO2 fertilization effects... something we could potentially change
   swCarbon_Use_WUE(sw_in) <- FALSE # turns off Water use efficiency 
   swYears_EndYear(sw_in) <- currYear - 1 # the setting for the historical simulation