WIP on master: af2fdf0 Merge branch 'master' of https://github.com/pi…

…k-piam/mrmagpie

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '26521208'
+ValidationKey: '26555690'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrmagpie: madrat based MAgPIE Input Data Library'
-version: 1.35.7
-date-released: '2023-07-06'
+version: 1.35.8
+date-released: '2023-07-17'
 abstract: Provides functions for MAgPIE country and cellular input data generation.
 authors:
 - family-names: Karstens

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrmagpie
 Title: madrat based MAgPIE Input Data Library
-Version: 1.35.7
-Date: 2023-07-06
+Version: 1.35.8
+Date: 2023-07-17
 Authors@R: c(
     person("Kristine", "Karstens", , "[email protected]", role = c("aut", "cre")),
     person("Jan Philipp", "Dietrich", , "[email protected]", role = "aut"),

R/calcPastureSuit.R

@@ -1,6 +1,9 @@
 #' @title calcPastureSuit
-#' @description Calculate glassland suitable for pasture management based on population and aridity criteria.
-#' @param subtype Select version, climate model and period.
+#' @description Calculate glassland suitable for pasture management based
+#' on population and aridity criteria.
+#' @param datasource Document
+#' @param climatetype Document
+#' @param lpjml Document
 #' @param smoothPrecipitation Smooth precipitation climate data over time
 #' @param smoothOut smooth the Pasture suitability areas variations over time
 #' @return List of magpie object with results on cluster level
@@ -11,36 +14,54 @@
 #' }
 #' @importFrom raster area rasterFromXYZ
 
-calcPastureSuit <- function(datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:ssp126", smoothPrecipitation = 10, smoothOut = 10) {
+calcPastureSuit <- function(datasource = "ISIMIP3bv2", climatetype = "MRI-ESM2-0:ssp126",
+  lpjml =  "LPJmL4_for_MAgPIE_44ac93de", smoothPrecipitation = 10, smoothOut = 10) {
   x <- toolSplitSubtype(climatetype, list(climatemodel = NULL, scenario = NULL))
-  period = "1850-2100"
   # Drivers of managed pastures
-  subtype <- paste(datasource, x$climatemodel, x$scenario, period, "pr", "annual_mean", sep = ":")
-  precipitation <- calcOutput("GCMClimate", subtype = subtype, smooth = smoothPrecipitation, aggregate = FALSE)
   population <- calcOutput("GridPop", subtype = "all", cellular = TRUE, FiveYear = TRUE,
-                           harmonize_until = 2015, aggregate = FALSE)[,,toupper(substring(x$scenario,first = 0,last = 4))]
-  evapotranspiration <- calcOutput("Evapotranspiration", subtype = "H08:mri-esm2-0", aggregate = FALSE)[,,x$scenario]
-  # Need new function for evapt. Should be something like this. So it gets LPJMl evap for the same climate model and spp as the prec
-  # evapotranspiration <- calcOutput("Evapotranspiration", datasource = "LPJmL"?, climatetype = climatetype, aggregate = FALSE)
+                           harmonize_until = 2015,
+                           aggregate = FALSE)[, , toupper(substring(x$scenario, first = 0, last = 4))]
+
+  cellPrep   <- calcOutput("LPJmLClimateInput", climatetype  = climatetype,
+                             variable     = "precipitation:monthlySum",
+                             stage        = "smoothed",
+                             lpjmlVersion = lpjml,
+                             aggregate    = FALSE)
+
+
+  cellPet    <- calcOutput(type = "LPJmL_new", climatetype = climatetype,
+                             subtype   = "mpet",
+                             stage     = "smoothed",
+                             version   = lpjml,
+                             aggregate = FALSE)
+  cellPrep <- mrwater::toolLPJcell2MAgPIEcell(cellPrep)
+  cellPet <- mrwater::toolLPJcell2MAgPIEcell(cellPet)
+
+  yearsCellPet      <- intersect(getYears(cellPet), findset("time"))
+  yearsCellPrep     <- intersect(findset("time"), getYears(cellPrep))
+  years              <- intersect(yearsCellPet, yearsCellPrep)
+  cellPrep           <- dimSums(cellPrep[, years, ], dim = 3)
+  cellPet            <- dimSums(cellPet[, years, ], dim = 3)
 
   # Cell area calculation
   landcoords <- as.data.frame(toolGetMapping("magpie_coord.rda", type = "cell", where = "mappingfolder"))
   landcoords <- cbind(landcoords, rep(1, nrow(landcoords)))
   landcoords <- raster::rasterFromXYZ(landcoords)
-  crs(landcoords) <- "+proj=longlat" # outputs cell are in km2
+  raster::crs(landcoords) <- "+proj=longlat" # outputs cell are in km2
   cellSize <- raster::area(landcoords)
   cellSize <- cellSize * landcoords
   cellSize <- as.magpie(cellSize)
   cellSize <- toolOrderCells(collapseDim(addLocation(cellSize), dim = c("x", "y")))
 
   # population density
+  population <- population[getCells(cellPet), , ] # fixing the order of the population cells (should not be necessary!)
   popDensity <- (population * 1e6) / cellSize # population density in number of people per km2
   popDensity[is.infinite(popDensity)] <- 0
   popDensity[is.nan(popDensity)] <- 0
 
-  yearsCom <- intersect(getYears(popDensity), getYears(precipitation))
+  years <- intersect(getYears(popDensity), getYears(cellPrep))
 
-  aridity <- precipitation[, yearsCom, ] / evapotranspiration[, yearsCom, ]
+  aridity <- cellPrep[, years, ] / cellPet[, years, ]
   aridity[is.infinite(aridity) | is.nan(aridity)] <- 0
   # 0.5 aridity threshold for managed pastures. Same from HYDE 3.2.
   aridity[aridity < 0.5] <- 0
@@ -71,7 +92,8 @@ calcPastureSuit <- function(datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:
   calibReg <- histPastrReg[, pastLy, ] / pastureSuitAreaReg[, pastLy, ]
   calibReg[is.infinite(calibReg)] <- 1
   calibReg[is.nan(calibReg)] <- 0
-  pastureSuitArea[, future, ] <- toolAggregate(calibReg, rel = map, from = "iso", to = "celliso") * pastureSuitArea[, future, ]
+  pastureSuitArea[, future, ] <- toolAggregate(calibReg, rel = map,
+                                              from = "iso", to = "celliso") * pastureSuitArea[, future, ]
 
   pastureSuitArea[is.infinite(pastureSuitArea) | is.nan(pastureSuitArea) | is.na(pastureSuitArea)] <- 0
   pastureSuitArea[pastureSuitArea < 0] <- 0
@@ -85,7 +107,7 @@ calcPastureSuit <- function(datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:
   pastureSuitArea <- toolHoldConstant(pastureSuitArea, findset("time"))
   pastureSuitArea <- collapseNames(pastureSuitArea)
   pastureSuitArea[, pastAll, ] <- histPastr[, pastAll, ]
-
+  pastureSuitArea <- setNames(pastureSuitArea, "yields")
   return(list(
     x = pastureSuitArea,
     weight = NULL,
@@ -94,12 +116,3 @@ calcPastureSuit <- function(datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:
     isocountries = FALSE
   ))
 }
-
-
-    pet    <- calcOutput("LPJmL_new", version = "LPJmL4_for_MAgPIE_44ac93de",
-                         climatetype = "MRI-ESM2-0:ssp370", subtype = "mpet",
-                         stage = "smoothed", aggregate = FALSE)
-    prec   <- calcOutput("LPJmLClimateInput", lpjmlVersion = "LPJmL4_for_MAgPIE_44ac93de",
-                         climatetype  = "GSWP3-W5E5:historical",
-                         variable = "precipitation:monthlySum",
-                         stage = "smoothed", aggregate = FALSE)

R/fullCELLULARMAGPIE.R

@@ -206,17 +206,13 @@ fullCELLULARMAGPIE <- function(rev = 0.1, dev = "",
              subtype = "/co2/Nreturn0p5", # nolint
              lsu_levels = c(seq(0, 2.2, 0.2), 2.5), past_mngmt = "mdef",
              file = paste0("f31_grassl_yld.mz"), years = magYears, aggregate = FALSE)
-#  calcOutput("PastureSuit",  subtype = paste("ISIMIP3bv2", "MRI-ESM2-0", "1850_2100", sep = ":"),
-#             file = paste0("f31_pastr_suitability_", ctype, ".mz"), years = magYears, aggregate = "cluster")
-#  calcOutput("PastureSuit",  subtype = paste("ISIMIP3bv2", "MRI-ESM2-0", "1850_2100", sep = ":"),
-#             file = "f31_pastr_suitability.mz", years = magYears, aggregate = FALSE)
-
-  calcOutput("PastureSuit", datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:ssp126", 
+  calcOutput("PastureSuit", datasource = "ISIMIP3bv2", climatetype = "MRI-ESM2-0:ssp126",
+             lpjml =  "LPJmL4_for_MAgPIE_44ac93de",
              file = paste0("f31_pastr_suitability_", ctype, ".mz"), years = magYears, aggregate = "cluster")
-  calcOutput("PastureSuit", datasource = "ISIMIP3bv2",climatetype = "MRI-ESM2-0:ssp126", 
+  calcOutput("PastureSuit", datasource = "ISIMIP3bv2", climatetype = "MRI-ESM2-0:ssp126",
+             lpjml =  "LPJmL4_for_MAgPIE_44ac93de",
              file = "f31_pastr_suitability.mz", years = magYears, aggregate = FALSE)
 
-
   if (grepl("+PastrMngtLevels", dev)) {
     calcOutput("PastrMngtLevels", climatetype = paste0("MRI-ESM2-0", ":", climatescen),
                options = c("brazil_1", "brazil_2", "brazil_4"), cost_level = c(1, 2, 3),

README.md

@@ -1,6 +1,6 @@
 # madrat based MAgPIE Input Data Library
 
-R package **mrmagpie**, version **1.35.7**
+R package **mrmagpie**, version **1.35.8**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrmagpie)](https://cran.r-project.org/package=mrmagpie) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4319612.svg)](https://doi.org/10.5281/zenodo.4319612) [![R build status](https://github.com/pik-piam/mrmagpie/workflows/check/badge.svg)](https://github.com/pik-piam/mrmagpie/actions) [![codecov](https://codecov.io/gh/pik-piam/mrmagpie/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrmagpie) [![r-universe](https://pik-piam.r-universe.dev/badges/mrmagpie)](https://pik-piam.r-universe.dev/builds)
 
@@ -39,7 +39,7 @@ In case of questions / problems please contact Kristine Karstens <karstens@pik-p
 
 To cite package **mrmagpie** in publications use:
 
-Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F, Führlich P (2023). _mrmagpie: madrat based MAgPIE Input Data Library_. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 1.35.7, <URL: https://github.com/pik-piam/mrmagpie>.
+Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F, Führlich P (2023). _mrmagpie: madrat based MAgPIE Input Data Library_. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 1.35.8, <URL: https://github.com/pik-piam/mrmagpie>.
 
 A BibTeX entry for LaTeX users is
 
@@ -48,7 +48,7 @@ A BibTeX entry for LaTeX users is
   title = {mrmagpie: madrat based MAgPIE Input Data Library},
   author = {Kristine Karstens and Jan Philipp Dietrich and David Chen and Michael Windisch and Marcos Alves and Felicitas Beier and Patrick {v. Jeetze} and Abhijeet Mishra and Florian Humpenoeder and Pascal Führlich},
   year = {2023},
-  note = {R package version 1.35.7},
+  note = {R package version 1.35.8},
   doi = {10.5281/zenodo.4319612},
   url = {https://github.com/pik-piam/mrmagpie},
 }