diff --git a/.buildlibrary b/.buildlibrary index aad36a0..8abc44c 100644 --- a/.buildlibrary +++ b/.buildlibrary @@ -1,7 +1,8 @@ -ValidationKey: '984200' +ValidationKey: '1188540' AutocreateReadme: yes AcceptedWarnings: - 'Warning: package ''.*'' was built under R version' - 'Warning: namespace ''.*'' is not available and has been replaced' AcceptedNotes: 'Unexported object imported by a .:::. call: .GDPuc:::wb_wdi.' allowLinterWarnings: no +enforceVersionUpdate: no diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml index 870f216..46f518a 100644 --- a/.github/workflows/check.yaml +++ b/.github/workflows/check.yaml @@ -49,6 +49,11 @@ jobs: shell: Rscript {0} run: lucode2:::validkey(stopIfInvalid = TRUE) + - name: Verify that lucode2::buildLibrary was successful + if: github.event_name == 'pull_request' + shell: Rscript {0} + run: lucode2:::isVersionUpdated() + - name: Checks shell: Rscript {0} run: | diff --git a/.gitignore b/.gitignore index 5b6a065..cda71e7 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,30 @@ .Rhistory .RData .Ruserdata +.DS_Store +R/convertWEO.R +R/readWEO.R +inst/extdata/.DS_Store +inst/extdata/regional/.DS_Store +inst/extdata/regional/regionmappingEUshares.csv +inst/extdata/regional/regionmappingIEA_ETP-Shares.csv +inst/extdata/regional/regionmappingISO-EDGE_EUR_ETP.csv +inst/extdata/regional/regionmappingWEO.csv +inst/extdata/sectoral/baitregression-files_test.csv +inst/regional/ +inst/sectoral/ +man/aggCells.Rd +man/blend.Rd +man/calcBAIT.Rd +man/calcBAITpars.Rd +man/calcCellHDDCDD.Rd +man/calcHDDCDDFactors.Rd +man/calcStackHDDCDD.Rd +man/cfac.Rd +man/checkDates.Rd +man/convertISIMIPbuildings.Rd +man/convertWEO.Rd +man/prepBaitInput.Rd +man/readISIMIPbuildings.Rd +man/readWEO.Rd +man/smooth.Rd diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2f13466..243f46a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: - id: mixed-line-ending - repo: https://github.com/lorenzwalthert/precommit - rev: v0.3.2.9025 + rev: v0.4.0 hooks: - id: parsable-R - id: deps-in-desc diff --git a/CITATION.cff b/CITATION.cff index 88ce6a5..e25b69d 100644 --- a/CITATION.cff +++ b/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: 'mredgebuildings: Prepare data to be used by the EDGE-Buildings model' -version: 0.5.0 -date-released: '2023-11-23' +version: 0.6.0 +date-released: '2024-03-27' abstract: Prepare data to be used by the EDGE-Buildings model. authors: - family-names: Hasse diff --git a/DESCRIPTION b/DESCRIPTION index d76b7fb..6d20c33 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Type: Package Package: mredgebuildings Title: Prepare data to be used by the EDGE-Buildings model -Version: 0.5.0 -Date: 2023-11-23 +Version: 0.6.0 +Date: 2024-03-27 Authors@R: c( person("Robin", "Hasse", , "robin.hasse@pik-potsdam.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1818-3186")), @@ -23,8 +23,14 @@ Imports: brick (>= 0.1.3), dplyr, GDPuc, + ncdf4, openxlsx, plyr, + pracma, + raster, + rlang, + stringr, + terra, quadprog, quitte, readxl, diff --git a/NAMESPACE b/NAMESPACE index 2fb3c2b..1d8815d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,7 +7,6 @@ export(calcCostDemolition) export(calcCostRenovation) export(calcFEdemandBuildings) export(calcFloorspacePast) -export(calcHDDCDD) export(calcHeatingCapacity) export(calcHeatingSystem) export(calcHouseholdSize) @@ -111,16 +110,16 @@ importFrom(magclass,dimSums) importFrom(magclass,getItems) importFrom(magclass,getNames) importFrom(magclass,getSets) -importFrom(magclass,getYears) -importFrom(magclass,lowpass) importFrom(magclass,mbind) importFrom(magclass,mselect) importFrom(magclass,setNames) -importFrom(magclass,setYears) importFrom(magclass,time_interpolate) +importFrom(ncdf4,nc_open) importFrom(openxlsx,read.xlsx) importFrom(plyr,revalue) +importFrom(pracma,integral2) importFrom(quadprog,solve.QP) +importFrom(quitte,aggregate_map) importFrom(quitte,as.quitte) importFrom(quitte,calc_addVariable) importFrom(quitte,getPeriods) @@ -130,14 +129,32 @@ importFrom(quitte,interpolate_missing_periods) importFrom(quitte,removeColNa) importFrom(quitte,replace_column) importFrom(quitte,revalue.levels) +importFrom(raster,writeRaster) importFrom(readxl,read_xlsx) +importFrom(rlang,.data) importFrom(stats,coef) +importFrom(stats,dnorm) importFrom(stats,lm) importFrom(stats,median) importFrom(stats,na.omit) importFrom(stats,nls) importFrom(stats,predict) importFrom(stats,pweibull) +importFrom(stringr,str_detect) +importFrom(stringr,str_split) +importFrom(stringr,str_sub) +importFrom(terra,aggregate) +importFrom(terra,classify) +importFrom(terra,ext) +importFrom(terra,nlyr) +importFrom(terra,rast) +importFrom(terra,regress) +importFrom(terra,res) +importFrom(terra,setGDALconfig) +importFrom(terra,subset) +importFrom(terra,tapp) +importFrom(terra,writeCDF) +importFrom(tidyr,"%>%") importFrom(tidyr,complete) importFrom(tidyr,gather) importFrom(tidyr,pivot_longer) diff --git a/R/calcBAITpars.R b/R/calcBAITpars.R new file mode 100644 index 0000000..383aaaf --- /dev/null +++ b/R/calcBAITpars.R @@ -0,0 +1,82 @@ +#' calculate regression parameters for BAIT climate variables +#' +#' @description linear regression on historic data to determine regression +#' parameters for surface downdwelling shortwave radiation (rsds), near-surface +#' wind speed (sfcwind) and near-surface specific humidity (huss) with respect +#' to near-surface air temperature (tas). +#' The regression is done with on a simple linear model, where the historical +#' input data covers the years of 2000-2014. For rsds and sfcwind, a simple linear +#' relationship is assumed where for huss an exponential relation is assumed, +#' buildig upon the non-linear relation between water vapor pressure and temperature. +#' +#' @param model specify GCM responsible for data input +#' +#' @return terra SpatRaster covering one regression parameter per layer per cell +#' +#' @author Hagen Tockhorn +#' +#' @importFrom terra regress rast +#' @importFrom madrat toolGetMapping +#' @importFrom magclass as.magpie +#' @importFrom madrat readSource + + +calcBAITpars <- function(model = "GFDL-ESM4") { + + # READ-IN DATA---------------------------------------------------------------- + + files <- toolGetMapping("baitregression-files_test.csv", type = "sectoral") %>% + filter(.data[["gcm"]] == model) + + vars <- unique(files$variable) + + # nolint start + data <- sapply(vars, function(v) { + tmp <- sapply(files[files$variable == v, ]$file, + function(f) { + return(readSource("ISIMIPbuildings", subtype = f)) + }, + USE.NAMES = FALSE) %>% + rast() + + return(tmp) + }, + USE.NAMES = TRUE) + # nolint end + + print("Reading completed") + + + + # PROCESS DATA---------------------------------------------------------------- + + # convert huss into log scale + data$huss <- log(data$huss) + + # convert tas into [C] + data$tas <- data$tas - 273.15 + + + # nolint start + regPars <- sapply(vars[vars != "tas"], function(v) { + x <- data[["tas"]] + y <- data[[v]] + + r <- regress(x = x, y = y, formula = y ~ x) + + names(r) <- c(paste0("a_", v), paste0("b_", v)) + return(r) + }, + USE.NAMES = FALSE) %>% + rast() + # nolint end + + + + # OUTPUT---------------------------------------------------------------------- + + return(list(x = regPars, + class = "SpatRaster", + unit = "(unit)", + description = "Regression parameters for calcHDDCDD")) +} diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 03fb702..df5be0d 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -1,101 +1,866 @@ -#' HDD and CDD scenario and historic data -#' -#' smoothes the HDD and CDD scenario data -#' -#' @param tlimit Temperature threshold for computing HDD and CDD -#' -#' @author Antoine Levesque -#' -#' @importFrom madrat readSource calcOutput -#' @importFrom magclass setNames setYears as.magpie getNames lowpass getYears mbind -#' @importFrom dplyr %>% -#' @importFrom quitte as.quitte -#' @importFrom utils tail -#' @export - -calcHDDCDD <- function(tlimit = 18) { - - stopifnot(as.character(tlimit) %in% as.character(17:25)) - - data <- readSource("HDDCDD", subtype = tlimit) - - # yearsScenario = all years for which history is either NA or 0 - index <- apply(data[, , paste0("history.HDD.history.", tlimit)], c(2, 3), - function(x) { - return(all(is.na(x) | x == 0)) - }) - - yearsScenario <- c(gsub("y", "", attributes(index)$dimnames$period[index]) %>% as.integer(), 2100) - yearsHistory <- setdiff(getYears(data, as.integer = TRUE), yearsScenario) - - convergenceYear <- 2030 - convergenceYears <- c(max(yearsHistory), yearsScenario[yearsScenario <= convergenceYear]) - afterConvergenceYears <- yearsScenario[!yearsScenario %in% convergenceYears] - lambda <- c(rep(1, length(yearsHistory)), - tail(seq(1, 0, length.out = length(convergenceYears)), -1), - rep(0, length(afterConvergenceYears))) - names(lambda) <- c(yearsHistory, yearsScenario) - lambda <- as.magpie(lambda) - - rcps <- grep("history", getNames(data, TRUE)$rcp, value = TRUE, invert = TRUE) - ssps <- grep("history", getNames(data, TRUE)$scenario, value = TRUE, invert = TRUE) - variable <- grep("history", getNames(data, TRUE)$variable, value = TRUE, invert = TRUE) - - data[is.na(data)] <- 0 - data <- mbind(data, (1 / 3) * (setYears(data[, 2099, ], 2100) + - setYears(data[, 2098, ], 2100) + - setYears(data[, 2097, ], 2100))) # give a point for 2100 - - # distribute the history between all scenarios - data <- do.call("mbind", lapply(rcps, function(rcp) { - do.call("mbind", lapply(ssps, function(ssp) { - tmp <- data[, , rcp][, , ssp] - tmp[, yearsHistory, ] <- setNames(data[, yearsHistory, "history"][, , "history"], - getNames(tmp[, yearsHistory, ])) - return(tmp) - })) - })) - - - # Smooth the trajectories - dataSmoothed <- lapply(ssps, function(scenario) { - lapply(rcps, function(rcp) { - thirdDim <- paste(scenario, variable, rcp, tlimit, sep = ".") - out <- lowpass(data[, , thirdDim], i = 100) - return(out) - }) - }) - dataSmoothed <- do.call("mbind", unlist(dataSmoothed, recursive = FALSE)) - - - # Smooth of the transition between historical and scenario values - data <- data * lambda + dataSmoothed * (1 - lambda) - - # replace negative values with 0 (large neg values only for small countries) - data[which(data < 0)] <- 0 - - # Set regions where there is either no value for history or for scenarios to 0 - data["GRL", , ] <- 0 - data[c("ASM", "FSM", "GUM", "MDV", "MHL", "TUV", "TKL", "SJM"), , ] <- 0 - - # Add the no Climate Change scenario - noC <- data[, , "rcp2p6"] - - # Fill with the mean of the five last periods - noC[, yearsScenario, ] <- - dimSums(noC[, c((max(yearsHistory) - 4):max(yearsHistory)), ], dim = 2) / 5 - getNames(noC) <- gsub("rcp2p6", "rcpNoC", getNames(noC)) - data <- mbind(data, noC) - - pop <- calcOutput("Population", aggregate = FALSE, FiveYearSteps = FALSE) - getNames(pop) <- tolower(sub("pop_(....).?$", "\\1", getNames(pop))) - pop <- pop[, , ssps] - y <- intersect(getYears(data), getYears(pop)) - data <- data[, y, ] - pop <- pop[, y, ] - - return(list(x = data, - weight = pop, - unit = "dK/yr", - description = "Heating degree days or cooling degree days")) +#' Calculate HDD and CDD based on outdoor/indoor temperature difference +#' +#' Heating and cooling degree days based on raw outside temperature +#' or bias-adjusted internal temperature (BAIT), driver for space heating and +#' cooling demand in buildings. +#' +#' @param mappingFile file name of sectoral mapping containing input data file names and directories +#' @param bait boolean, use BAIT instead of raw temperature +#' @param multiscen boolean, does \code{mappingFile} cover more than one scenario? +#' @param rasDir absolute path to directory for saving raster files +#' @param cacheDir absolute path to directory for pre-calculated BAIT regression parameters +#' +#' @return magpie object of heating and cooling degree days +#' +#' @author Robin Hasse, Hagen Tockhorn +#' +#' @importFrom madrat toolGetMapping readSource calcOutput toolCountryFill +#' @importFrom tidyr %>% +#' @importFrom dplyr mutate +#' @importFrom rlang .data +#' @importFrom stringr str_sub +#' @importFrom terra setGDALconfig + + +calcHDDCDD <- function(mappingFile, + bait = FALSE, + multiscen = FALSE, + rasDir = NULL, + cacheDir = NULL) { + + # initialize full calculation + makeCalculations <- function(f, m, n, tLim, countries, pop, hddcddFactor, + bait, wBAIT = NULL, params = NULL, suffix = NULL, + rasDir = NULL) { + + if (!is.null(suffix)) { + ftas <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "tas" & f$gcm == m, ][[n, "file"]]) + + if (bait) { + frsds <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "rsds" & f$gcm == m, ][[n, "file"]]) + + fsfc <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "sfcwind" & f$gcm == m, ][[n, "file"]]) + + fhuss <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "huss" & f$gcm == m, ][[n, "file"]]) + } + } else { + ftas <- f[f$variable == "tas" & f$gcm == m, ][[n, "file"]] + frsds <- f[f$variable == "rsds" & f$gcm == m, ][[n, "file"]] + fsfc <- f[f$variable == "sfcwind" & f$gcm == m, ][[n, "file"]] + fhuss <- f[f$variable == "huss" & f$gcm == m, ][[n, "file"]] + } + + print(paste("Processing temperature file:", ftas)) + + if (bait) { + hddcddCell <- compStackHDDCDD(ftas, + tLim, + countries, + pop, + hddcddFactor, + bait, + frsds = frsds, + fsfc = fsfc, + fhuss = fhuss, + wBAIT = wBAIT, + params = params, + rasDir = rasDir) + } else { + hddcddCell <- compStackHDDCDD(ftas, + tLim, + countries, + pop, + hddcddFactor, + bait, + rasDir = rasDir) + } + return(hddcddCell) + } + + + + # PARAMETERS------------------------------------------------------------------ + + setGDALconfig(c("BIGTIFF = YES")) + + + # threshold temperature for heating and cooling [C] + # NOTE: Staffel at. al 2023 gives global average of T_heat = 14, T_cool = 20 + tLim <- list("HDD" = seq(12, 22), "CDD" = seq(20, 26)) + + # standard deviations for temperature distributions + tlimStd <- 5 # threshold + tambStd <- 5 # ambient + + # range of pre-calculated HDD/CDD-values, e.g. [173, 348] K, converted to [C] + tlow <- 173 - 273.15 + tup <- 348 - 273.15 + + + + #--- BAIT parameters + + # The weights (wRSDS, wSFC, wHUSS) for calcBAIT and the smoothing coefficient are assumed to + # be region-independent and equal to the mean of the values given in Staffell + # et al. 2023. + + # The weights below give the respective weight of the difference between the + # climate factor and its counterfactual in calculating BAIT. + wRSDS <- 0.012 + wSFC <- -0.20 + wHUSS <- 0.05 + + # The smoothing coefficient defines the assumed thermal intertia of the building + # and weighs the influence of the internal temperature of the preceding two days. + sig <- 0.50 + + # The blending parameters for the blending of BAIT and raw temperature are like-wise + # taken from the paper. + # At bLower, we consider only BAIT. For higher temperatures, we assume a mix with the ambient + # temperature reaching the highest contribution of bMax at bUpper following a sigmoid function + bLower <- 15 + bUpper <- 23 + bMax <- 0.5 + + # concatenate to vector + wBAIT <- list("wRSDS" = wRSDS, + "wSFC" = wSFC, + "wHUSS" = wHUSS, + "sig" = sig, + "bLower" = bLower, + "bUpper" = bUpper, + "bMax" = bMax) + + # BAIT parameter names + # (note: this is really just a check in case calcBAITpars returns no layer names) + parNames <- c("aRSDS", "bRSDS", "aSFC", "bSFC", "aHUSS", "bHUSS") + + + + # READ-IN DATA---------------------------------------------------------------- + + # list of files that are processed + files <- toolGetMapping(name = mappingFile, + type = "sectoral", + where = "mredgebuildings") %>% + filter(.data[["variable"]] != "") + + # cells -> country mask + fCM <- file.path(files[files$variable == "CountryMask", "file"]) + countries <- readSource("ISIMIPbuildings", subtype = fCM, convert = FALSE) + + # population + fpop <- files %>% filter(.data[["variable"]] == "pop") + pop <- readSource("ISIMIPbuildings", subtype = fpop$file, + convert = FALSE) + + # extract run-specific variables + ssp <- unique(files$ssp[files$variable == "tas"]) + rcp <- unique(files$rcp[files$variable == "tas"]) + model <- unique(files$gcm[files$variable == "tas"]) + + + # bait regression parameters + if (bait) { + baitPars <- calcOutput("BAITpars", + aggregate = FALSE, + model = model, + cacheDir = cacheDir) + + names(baitPars) <- parNames + } + + + + # PROCESS DATA---------------------------------------------------------------- + + # calculate HDD/CDD-factors + hddcddFactor <- compHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) + + + # full calculation of degree days + hddcdd <- do.call( # file iteration + "rbind", + lapply( + seq_len(nrow(filter(files, files$variable == "tas"))), + function(n) { + split <- files[files$variable == "tas" & files$gcm == model, ][[n, "split"]] + + # split large raster files to save memory / speed up processing + if (as.logical(split)) { + hddcddCell <- do.call( + "rbind", + lapply(list("A", "B"), function(suffix) { + tmp <- makeCalculations(f = files, + m = model, + n = n, + tLim = tLim, + countries = countries, + pop = pop, + hddcddFactor = hddcddFactor, + bait = bait, + wBAIT = wBAIT, + params = baitPars, + suffix = suffix, + rasDir = rasDir) + return(tmp) + } + ) + ) + } else { + hddcddCell <- makeCalculations(f = files, + m = model, + n = n, + tLim = tLim, + countries = countries, + pop = pop, + hddcddFactor = hddcddFactor, + bait = bait, + wBAIT = wBAIT, + params = baitPars, + rasDir = rasDir) + } + + hddcddCell <- hddcddCell %>% + mutate("model" = model, + "ssp" = ssp, + "rcp" = rcp) + + gc() + + return(hddcddCell) + } + ) + ) + + rownames(hddcdd) <- c() + + + + # OUTPUT---------------------------------------------------------------------- + + # might be necessary for magpie output + data <- hddcdd %>% + mutate(period = gsub("y", "", .data[["period"]])) %>% + unite("variable", .data[["variable"]], .data[["tlim"]], sep = "_") %>% + unite("scenario", .data[["ssp"]], .data[["rcp"]], sep = "_") + + # prepare output + data <- data %>% + as.quitte() %>% + as.magpie() %>% + toolCountryFill() + + + return(list(x = data)) +} + + +#--- CALCULATE BAIT (building-adjusted internal temperature) + + +#' Check if time period of BAIT input data (rsds, sfc, huss) is congruent with +#' near-surface temperature data (tas). +#' +#' @param baitInput list of raster data encompassing different climate variables +#' @param tasData raster data on near-surface atmosphere temperature +#' +#' @return baitInput with congruent time periods w.r.t. tasData +#' +#' @importFrom terra rast + +checkDates <- function(baitInput, tasData) { + datesT <- names(tasData) + + baitInput <- sapply(names(baitInput), function(var) { #nolint + # fill missing data with means from previous years + # NOTE: "temp" and "baitInput" have the same global temporal lower + # boundary, since "temp" is the constraining dataset, only + # "baitInput" needs to be filled up. + + tmp <- baitInput[[var]] + + datesBait <- names(tmp) + + datesFill <- setdiff(datesT, datesBait) # dates to fill up + daysFill <- unique(substr(datesFill, 6, 11)) + + datesKeep <- intersect(datesBait, datesT) # dates to keep + keep <- length(datesKeep) > 0 + + if (keep) { + tmp <- subset(tmp, datesKeep) + names(tmp) <- datesKeep + } + + if (length(daysFill) > 0) { + baitInputMean <- prepBaitInput(fillWithMean = TRUE, baitInput = baitInput) + + # fill up missing dates with yearly-average value for specific day/cell + baitInputFill <- rast( + lapply( + daysFill, + function(d) { + idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) + r <- rast(replicate(length(idx), baitInputMean[[var]][[d]])) + names(r) <- datesFill[idx] + return(r) + } + ) + ) + + # concatenate data + if (keep) { + tmp <- rast(list(tmp, baitInputFill)) + } else { + tmp <- baitInputFill + } + + # re-order dates + tmp <- rast(tmp[[order(names(tmp))]]) + } + + + if (!identical(names(tmp), names(tasData))) { + warning("Dates of Temperature and BAIT Input Data are not aligned.") + } + return(tmp) + }, + USE.NAMES = TRUE + ) + return(baitInput) +} + + +#' Read in necessary climate data to calculate BAIT or calculate mean values of +#' said climate data to fill missing data in case of temporal mismatch between +#' near-surface atmospherical temperature and other considered climate data. +#' +#' @param frsds file path to raster data on surface downdwelling shortwave radiation +#' @param fsfc file path to raster data on near-surface wind speed +#' @param fhuss file path to raster data on near-surface specific humidity +#' @param baitInput named list of climate data +#' @param fillWithMean boolean, only mean is calculated and returned +#' +#' @return named list with read-in or meaned climate data +#' +#' @importFrom terra tapp +#' @importFrom madrat readSource + +prepBaitInput <- function(frsds = NULL, + fsfc = NULL, + fhuss = NULL, + baitInput = NULL, + fillWithMean = FALSE) { + + if (isTRUE(fillWithMean)) { + # optional: calculate daily means over years to fill missing data + baitInputMean <- sapply( #nolint + names(baitInput), + function(var) { + meanData <- tapp(baitInput[[var]], + unique(substr(names(baitInput[[var]]), 6, 11)), + fun = "mean") + names(meanData) <- gsub("\\.", "-", substr(names(mean), 2, 6)) + return(meanData) + } + ) + return(baitInputMean) + } else { + input <- list( #nolint start + "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), + "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), + "huss" = readSource("ISIMIPbuildings", subtype = fhuss, convert = TRUE)) + return(input) # nolint end + } +} + + +#' Calculate counterfactuals for solar radiation, wind speed, specific humidity +#' and near-surface temperature as function of raster data on near-surface temperature. +#' +#' The expected value of the respective climate variable (except temperature) is +#' calculated from parameters taken from a preceding linear regression done in +#' calcBAITpars where the respective variable is correlated with the near-surface +#' atmospherical temperature. +#' If no cell-resoluted parameters are given, the globally-meaned parameters from +#' Staffel et. all 2023 are taken (see https://doi.org/10.1038/s41560-023-01341-5). +#' +#' @param t raster data on near-surface atmospherical temperature +#' @param type considered climate variable +#' @param params regression parameters as vector or raster object +#' +#' @return counterfactuals for respective climate variable + +cfac <- function(t, type, params = NULL) { + if (is.null(params)) { + params <- switch(type, + s = c(100, 7), + w = c(4.5, -0.025), + h = c(1.1, 0.06), + t = c(16)) + } + + # nolint start + return(switch(type, + s = {params[[1]] + params[[2]] * t}, + w = {params[[1]] + params[[2]] * t}, + h = {exp(params[[1]] + params[[2]] * t)}, + t = {params[[1]]}, + warning("No valid parameter type specified.") + ) + ) + # nolint end +} + + +#' Smooth data over preceding two days +#' +#' @param r raster data to be smoothed +#' @param weight named list with smoothing parameter sig +#' +#' @return smoothed raster data +#' +#' @importFrom terra nlyr + +smooth <- function(r, weight) { + # smooth bait over preceding two days with smoothing parameter sigma + print("smooth") + + # one day indented + r1D <- r[[c(nlyr(r), 1:(nlyr(r) - 1))]] + r1D[[1]] <- 0 + + # two days indented + r2D <- r[[c(nlyr(r) - 1, nlyr(r), 1:(nlyr(r) - 2))]] + r2D[[1:2]] <- 0 + + # smooth + rSmooth <- (r + weight[["sig"]] * r1D + weight[["sig"]]**2 * r2D) / (1 + weight[["sig"]] + weight[["sig"]]**2) + + return(rSmooth) +} + + +#' Weighted blend of BAIT and near-surface atmospherical temperature +#' +#' To adress loss of buildings' internal memory of previous conditions at high +#' outside temperatures due to window opening, etc., BAIT and outside temperature +#' are blended. The blend is active between a lower and upper temperature threshold, +#' \code{bLower} and \code{bUpper}, which are mapped to a range of -5 to +5 of a +#' sigmoid function (corresponding to a 1% and 99% blend). The maximum amount of +#' blending (i.e. the amplitude of the sigmoid function) is given by a parameter \code{bMax}. +#' +#' @param bait raster data on BAIT +#' @param tas raster data on near-surface atmospherical temperature +#' @param weight named list with blending parameters bLower, bUpper, bMax +#' +#' @return blended raster data + +blend <- function(bait, tas, weight) { + print("blend") + + bBar <- (tas - 0.5 * (weight[["bUpper"]] + weight[["bLower"]])) * 10 / (weight[["bUpper"]] - weight[["bLower"]]) + b <- weight[["bMax"]] / (1 + exp(-bBar)) + + blend <- bait * (1 - b) + (tas * b) + return(blend) +} + + +#' Calculate bias-adjusted internal temperature (BAIT) +#' +#' BAIT is calculated from raster data on near-surface atmospherical temperature +#' (tas), surface downdwelling shortwave radiation (rsds), near-surface wind speed +#' (sfcwind) and near-surface specific humidity (huss). The latter three climate +#' parameters are incorporated in the calculation of BAIT as the difference from +#' their real value to the their expected value w.r.t. the near-surface temperature +#' (see \code{\link{cfac}}). These are then incorporated in a weighted sum to +#' account for the respective influence of each climate parameter on BAIT. +#' The raster data containing BAIT is smoothed to account for the +#' buildings' thermal inertia (see \code{\link{smooth}}) and blended with the +#' near-surface temperature (see \code{\link{blend}}). +#' +#' @param baitInput named list containing rsds, sfcwind, huss climate data +#' @param tasData raster data on near-surface atmospherical temperature +#' @param weight named list with weights +#' @param params optional named list with regression parameters from calcBAITpars +#' +#' @return raster object with BAIT values + +compBAIT <- function(baitInput, tasData, weight = NULL, params = NULL) { + if (is.null(weight)) { + warning("Please give appropriate weights for the calculation of BAIT.") + weight <- list("wRSDS" = 0.012, + "wSFC" = -0.20, + "wHUSS" = 0.05, + "sig" = 0.5, + "bLower" = 15, + "bUpper" = 23, + "bMax" = 0.5) + } + + solar <- baitInput$rsds + wind <- baitInput$sfc + hum <- baitInput$huss + + print("calc s") + s <- solar - cfac(tasData, type = "s", params = c(params[["aRSDS"]], params[["bRSDS"]])) + print("calc w") + w <- wind - cfac(tasData, type = "w", params = c(params[["aSFC"]], params[["bSFC"]])) + print("print h") + h <- hum - cfac(tasData, type = "h", params = c(params[["aHUSS"]], params[["bHUSS"]])) + print("calc t") + t <- tasData - cfac(tasData, type = "t", params = NULL) + + # calc bait + print("calc bait") + bait <- tasData + weight[["wRSDS"]] * s + weight[["wSFC"]] * w + weight[["wHUSS"]] * h * t + + # smooth bait + bait <- smooth(bait, weight) + + # # blend bait + bait <- blend(bait, tasData, weight) + + rm(baitInput, solar, wind, hum, s, w, h, t, bait) + + return(bait) +} + + +#--- CALCULATE HDD/CDD + +#' Calculate HDD/CDD values for different ambient/limit temperature combinations +#' +#' HDD/CDD values are pre-calculated for an interval \code{tlow}-\code{tup} and +#' for a set of limit temperatures \code{tlim} with a temperature resolution of +#' 0.1C. +#' +#' The respective heating/cooling degree days are calculated as the difference +#' between the assumed ambient and a limit temperature, aggregated to a full day. +#' The latter defines a threshold above/below which cooling/heating is assumed to +#' be initiated. +#' +#' To account for heterogenity in heating/cooling behavior, the ambient and limit +#' temperature, \code{tamb} and \code{tlim}, are assumed to be normally distributed. +#' This changes the calculation of a degree day to a double integration of +#' \code{tLimit - T_ambient_day} with integration boundaries set at 3 standard +#' deviations, \code{tambStd} and \code{tlimStd}, from \code{tamb} and \code{tlim} +#' respectively. +#' +#' As consequence, the ramp function of \code{HDD_day = max(0, tLimit - T_ambient_day)} +#' changes to a curved function that is above zero even if the mean of \code{T_ambient_day} +#' is above the mean of \code{tLimit}. +#' +#' @param tlow lower temperature boundary +#' @param tup upper temperature boundary +#' @param tlim named list of limit temperature sequences for \code{HDD} and \code{CDD} +#' @param tambStd std of ambient temperature +#' @param tlimStd std of limit temperature +#' +#' @return data frame of HDD/CDD values +#' +#' @importFrom stats dnorm +#' @importFrom pracma integral2 + +compHDDCDDFactors <- function(tlow, tup, tlim, tambStd = 5, tlimStd = 5) { + + # t1 : ambient temperature variable + # t2 : limit temperature variable + + # HDD + heatingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { + h <- dnorm(t2, mean = tlim, sd = tlimStd) * dnorm(t1, mean = tamb, sd = tambStd) * (t2 - t1) + return(h) + } + + # CDD + coolingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { + h <- dnorm(t2, mean = tlim, sd = tlimStd) * dnorm(t1, mean = tamb, sd = tambStd) * (t1 - t2) + return(h) + } + + # check if ambient/limit temperature interval is reasonable + # e.g. tLim = 17C and t_amb = -50C wouldn't give reasonable CDD + checkTDif <- function(tamb, tlim, typeDD, tambStd, tlimStd) { + check <- TRUE + stdDif <- tambStd + tlimStd + if (typeDD == "HDD") { + if (tamb - tlim > stdDif) { + check <- FALSE + } + } else if (typeDD == "CDD") { + if (tlim - tamb > 2 * stdDif) { + check <- FALSE + } + } + return(check) + } + + t <- seq(tlow, tup, .1) + + hddcddFactors <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + do.call( + "rbind", lapply( + t, function(tamb) { + do.call( + "rbind", lapply( + tlim[[typeDD]], function(.tlim) { + if (!checkTDif(tamb, .tlim, typeDD, tambStd, tlimStd)) { + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "tLim" = .tlim, + "factor" = 0, + "factor_err" = 0, + "typeDD" = typeDD) + } else { + + # integration boundaries + x1 <- .tlim - 4 * tlimStd + x2 <- .tlim + 4 * tlimStd + y1 <- min(.tlim - 3 * tlimStd, tamb - 3 * tlimStd) + y2 <- max(.tlim + 3 * tlimStd, tamb + 3 * tlimStd) + + if (typeDD == "HDD") { + f <- integral2(heatingFactor, + xmin = x1, + xmax = x2, + ymin = y1, + ymax = function(x) {x}, #nolint + tamb = tamb, + tambStd = tambStd, + tlim = .tlim, + tlimStd = tlimStd, + reltol = 1e-1) + } else { + f <- integral2(coolingFactor, + xmin = x1, + xmax = x2, + ymin = function(x) {x}, #nolint + ymax = y2, + tamb = tamb, + tambStd = tambStd, + tlim = .tlim, + tlimStd = tlimStd, + reltol = 1e-1) + } + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "tLim" = .tlim, + "factor" = f$Q, + "factor_err" = f$error, + "typeDD" = typeDD) + } + return(tmp) + } + ) + ) + } + ) + ) + } + ) + ) + return(hddcddFactors) +} + + +#' Assign HDD/CDD values for given ambient/limit temperature +#' +#' @param temp raster data containing temperature/BAIT values +#' @param typeDD type of degree day +#' @param tlim limit temperature +#' @param factors data frame with degree day values for \code{temp/tlim} combination +#' +#' @return raster object with HDD/CDD values +#' +#' @importFrom terra classify tapp + +compCellHDDCDD <- function(temp, typeDD, tlim, factors) { + # extract years + dates <- names(temp) + + # add tolerance of 0.04K to avoid machine precision errors + factors <- factors[factors$typeDD == typeDD, ] + + factors <- factors %>% + filter(.data[["tLim"]] == tlim) %>% + dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, + to = .data[["T_amb_K"]] + 0.049, + becomes = .data[["factor"]]) %>% + data.matrix() + + # swap ambient temperature values with corresponding DD values + hddcdd <- classify(temp, factors) + + terra::time(hddcdd) <- as.Date(dates) + + # aggregate to yearly HDD/CDD [K.d/a] + hddcdd <- tapp(hddcdd, "years", fun = sum, na.rm = TRUE) + + names(hddcdd) <- gsub("_", "", names(hddcdd)) + return(hddcdd) +} + + +#' Aggregate cellular HDD/CDD values to country-wide average (population-weighted) +#' +#' @param data raster object containing HDD/CDD values +#' @param weight raster object containing aggregation weights +#' @param mask raster opbject defining (regional) aggregation boundaries +#' +#' @return data frame containing regionally meaned HDD/CDD values +#' +#' @importFrom terra subset + +aggCells <- function(data, weight, mask) { + yearsData <- names(data) + yearsWeight <- names(weight) + + if (!all(yearsData %in% yearsWeight)) { + stop("Time periods of raster file and aggregation weights do not match.") + } + + # loop: years in raster file r + hddcddAgg <- do.call( + "rbind", lapply( + yearsData, function(y) { + # mask data and weights to considered regions + regData <- subset(data, y) * subset(weight, y) * mask + regWeight <- subset(weight, y) * mask + + # aggregate regional data + regDataAgg <- terra::global(regData, "sum", na.rm = TRUE)$sum + regWeightAgg <- terra::global(regWeight, "sum", na.rm = TRUE)$sum + + # calculate weighted sum + weightedAgg <- regDataAgg / regWeightAgg + + tmp <- data.frame("region" = names(mask), + "period" = y, + "value" = round(weightedAgg, 3)) + + rm(regData, regWeight, regDataAgg, regWeightAgg, weightedAgg) + + rownames(tmp) <- c() + return(tmp) + } + ) + ) + return(hddcddAgg) +} + + +#' Calculate country-wise population-weighted HDD/CDD values +#' +#' This function calculates country-wise population-weighted HDD/CDD values for +#' raw ambient temperature or bias-adjusted internal temperature for a given set +#' of limit temperatures from raster data on (various) climate variables. +#' +#' For further processing, raster objects containing degree day data are written +#' for an interval of ten years. +#' +#' @param ftas file name of data on near-surface atmospherical temperature +#' @param tlim named list of limit temperature sequences for \code{HDD} and \code{CDD} +#' @param countries raster opbject defining (regional) aggregation boundaries +#' @param pop raster object containing population data +#' @param factors data frame with degree day values for \code{temp/tlim} combination +#' @param bait boolean, BAIT is used as ambient temperature +#' @param frsds file name of data on surface downdwelling shortwave radiation (optional) +#' @param fsfc file name of data on near-surface wind speed (optional) +#' @param fhuss file name of data on near-surface specific humidity (optional) +#' @param wBAIT named list containing BAIT weights (optional) +#' @param params raster object containing regression parameters from \code{calcBAITpars} (optional) +#' @param rasDir absolute path to directory for saving raster files +#' +#' @importFrom raster writeRaster +#' @importFrom stringr str_split +#' @importFrom terra writeCDF + +compStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, + frsds = NULL, + fsfc = NULL, + fhuss = NULL, + wBAIT = NULL, + params = NULL, + rasDir = NULL) { + + # read cellular temperature + temp <- readSource("ISIMIPbuildings", subtype = ftas, convert = TRUE) + + dates <- names(temp) + + # optional: transform raw temperature into BAIT + if (bait) { + # note: easier to do in [C] + tasData <- temp - 273.15 # [C] + + # read and prepare bait input data + baitInput <- prepBaitInput(frsds, fsfc, fhuss) %>% + checkDates(tasData) + + # calculate bait + temp <- compBAIT(baitInput, tasData, weight = wBAIT, params = params) + + # convert back to [K] + temp <- temp + 273.15 # [K] + } + + # round and assign dates + temp <- terra::round(temp, digits = 1) + names(temp) <- dates + + print("Calculating HDD/CDDs per cell.") + + + fSplit <- str_split(ftas, "_") %>% unlist() + + # loop: typeDD + hddcdd <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + # loop: threshold temperatures + do.call( + "rbind", lapply( + tlim[[typeDD]], function(t) { + hddcddAgg <- compCellHDDCDD(temp, typeDD, t, factors) + + # write raster files + if (!is.null(rasDir)) { + y <- names(hddcddAgg) + + rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + rname <- paste0(rname, if (bait) "_bait" else "", ".nc") + + writeCDF(hddcddAgg, + file.path(rasDir, fSplit[[1]], rname), + overwrite = TRUE) + } + + hddcddAgg <- hddcddAgg %>% + aggCells(pop, countries) %>% + mutate("variable" = typeDD, + "tlim" = t) # [C] + + return(hddcddAgg) + } + ) + ) + } + ) + ) + rm(tasData, baitInput, temp) + + return(hddcdd) } diff --git a/R/convertISIMIPbuildings.R b/R/convertISIMIPbuildings.R new file mode 100644 index 0000000..ac4ba75 --- /dev/null +++ b/R/convertISIMIPbuildings.R @@ -0,0 +1,72 @@ +#' convert ISIMIP data for mredgebuildings +#' +#' @param x MAgPIE object on cellular level +#' @param subtype filename +#' +#' @importFrom stringr str_detect +#' +#' @author Hagen Tockhorn +#' +#' @return rasterBrick object + +convertISIMIPbuildings <- function(x, subtype) { + + # FUNCTIONS------------------------------------------------------------------- + + # fill dates for unnamed data + fillDates <- function(r, filename, pop = FALSE) { + + yStart <- stringr::str_sub(filename, -12, -9) + n <- terra::nlyr(r) + + if (!pop) { + dStart <- as.Date(paste0(yStart, "-1-1")) + dates <- seq.Date(dStart, by = "day", length.out = n) + } else { + print("pop") + dates <- seq(yStart, by = 1, length.out = n) %>% as.character() + } + + # fill dates + names(r) <- dates + return(r) + } + + + # PARAMETERS------------------------------------------------------------------ + + # variable units + unitMapping <- list( + "tas" = "K", + "rsds" = "Wm-2", + "sfcWind" = "ms-1", + "huss" = "gkg-1" + ) + + edgeVars <- c("tas", "rsds", "sfcwind", "huss", "population") + + + # PROCESS DATA---------------------------------------------------------------- + + if (grepl(paste(edgeVars, collapse = "|"), subtype)) { + var <- edgeVars[str_detect(subtype, edgeVars)] + + + if (var == "population") { + x <- fillDates(x, subtype, pop = TRUE) + } else if (!all(nchar(names(x)) == 10)) { + # dates have specific length of n = 10 + x <- fillDates(x, subtype) + } + + if (var == "huss") { + # convert kg/kg -> g/kg + x <- x * 1e3 + } + } + + return(list(x = x, + class = "SpatRaster", + unit = unitMapping[var], + cache = FALSE)) +} diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R new file mode 100644 index 0000000..df08b78 --- /dev/null +++ b/R/readISIMIPbuildings.R @@ -0,0 +1,180 @@ +#' Read relevant ISIMIP data for mredgebuildings +#' +#' Relevant data such as region masks, population and relevant climate data are +#' read in. The relevant file is declared in the subtype with the full file name. +#' +#' If the file name includes a suffix in the form of an integer such as +#' \code{_.filetype}, the file is split into a single year period, e.g. +#' \code{_2001_2010_2.nc} will return data for the second year of the +#' 2001-2010 period, here 2002. +#' +#' @param subtype filename +#' +#' @author Hagen Tockhorn +#' +#' @importFrom stringr str_split +#' @importFrom terra rast subset aggregate ext res +#' @importFrom ncdf4 nc_open +#' +#' @note +#' folder structure in inputdata/sources/ISIMIPbuildings is expected to be: +#' country masks : var/ +#' population : var/scenario +#' other : var/scenario/model +#' +#' @note currently, this function only reads data from ISIMIP3b + + +readISIMIPbuildings <- function(subtype) { + + # PARAMETERS------------------------------------------------------------------ + + baitVars <- c("tas", "sfcwind", "rsds", "huss") + + firstHistYear <- 1960 + + + # FUNCTIONS------------------------------------------------------------------- + + splitSubtype <- function(subtype) { + vars <- list() + + # nolint start + if (grepl("countrymask", subtype)) { + vars[["variable"]] <- "countrymask" + } + + else if (grepl("population", subtype)) { + subSplit <- str_split(subtype, "_") %>% unlist() + + vars[["variable"]] <- subSplit[[1]] + vars[["scenario"]] <- subSplit[[2]] + } + + else if (any(sapply(baitVars, grepl, x = subtype))) { + subSplit <- str_split(subtype, "_") %>% unlist() + + vars[["variable"]] <- subSplit[[5]] + vars[["scenario"]] <- subSplit[[4]] + vars[["model"]] <- subSplit[[1]] + + # raster data will be split into individual years + if (length(subSplit) > 9) { + # split index defines the year + vars[["idx"]] <- gsub(".nc", "", tail(subSplit, 1)) %>% + as.numeric() + + # temporal range of data + vars[["yStart"]] <- subSplit[[8]] + vars[["yEnd"]] <- subSplit[[9]] + + # year of interest + vars[["year"]] <- seq(vars[["yStart"]] %>% + as.numeric(), + vars[["yEnd"]] %>% + as.numeric())[[vars[["idx"]]]] %>% + as.character() + + vars[["subtype"]] <- sub("_(1[0-9]|\\d)\\.nc$", ".nc", subtype) + } + } else { + stop("Invalid subtype given.") + } + # nolint end + + return(vars) + } + + + # determine period range of subset + getRanges <- function(vars) { + # total temporal range + dRange <- seq.Date(from = as.Date(paste0(vars[["yStart"]], "-01-01")), + to = as.Date(paste0(vars[["yEnd"]], "-12-31")), + by = "day") + + # temporal range of interest + yRange <- seq.Date(from = as.Date(paste0(vars[["year"]], "-01-01")), + to = as.Date(paste0(vars[["year"]], "-12-31")), + by = "day") + + # indices of range of interest + idxRange <- match(yRange, dRange) + + return(list("yRange" = yRange, + "idxRange" = idxRange)) + } + + + # PROCESS DATA---------------------------------------------------------------- + vars <- splitSubtype(subtype) + + # region mask + if (vars[["variable"]] == "countrymask") { + fpath <- file.path("countrymasks", subtype) + varNames <- names(nc_open(fpath)[["var"]]) + countries <- list() + + for (var in varNames) { + countries[[var]] <- suppressWarnings(rast(fpath, subds = var)) + } + + r <- rast(countries) + names(r) <- gsub("m_", "", varNames) + + x <- list(x = r, class = "SpatRaster") + } + + + # population + else if (vars[["variable"]] == "population") { #nolint + fpath <- file.path(vars[["variable"]], vars[["scenario"]], subtype) + + if (vars[["scenario"]] == "picontrol") { + r <- suppressWarnings(rast(fpath)) + } else { + r <- suppressWarnings(rast(fpath, subds = "total-population")) + } + + subtype <- gsub(".nc", "", subtype) + + # rename years + years <- tail(strsplit(subtype, "_")[[1]], 2) + names(r) <- years[1]:years[2] + + # filter relevant years + r <- subset(r, as.numeric(names(r)) >= firstHistYear) + + # aggregate to common resolution of 0.5 deg + if (any(res(r) != 0.5)) { + r <- aggregate(r, fun = "sum", fact = round(0.5 / res(r), 3)) + } + + x <- list(x = r, class = "SpatRaster", cache = FALSE) + } + + + # climate data + else if (any(vars[["variable"]] %in% baitVars)) { #nolint + # slice single years + if (!is.null(vars[["yStart"]])) { + fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) + ranges <- getRanges(vars) + + r <- suppressWarnings(rast(fpath, lyrs = ranges[["idxRange"]])) + names(r) <- ranges[["yRange"]] + } + + # full data set + else { #nolint + fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], subtype) + r <- suppressWarnings(rast(fpath)) + } + + x <- list(x = r, class = "SpatRaster", cache = FALSE) + } + + else {stop("Subtype was incorrectly split or invalid subtype given.")} #nolint + + return(x) +} diff --git a/README.md b/README.md index cec8b70..f705081 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # Prepare data to be used by the EDGE-Buildings model -R package **mredgebuildings**, version **0.5.0** +R package **mredgebuildings**, version **0.6.0** -[![CRAN status](https://www.r-pkg.org/badges/version/mredgebuildings)](https://cran.r-project.org/package=mredgebuildings) [![R build status](https://github.com/pik-piam/mredgebuildings/workflows/check/badge.svg)](https://github.com/pik-piam/mredgebuildings/actions) [![codecov](https://codecov.io/gh/pik-piam/mredgebuildings/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mredgebuildings) [![r-universe](https://pik-piam.r-universe.dev/badges/mredgebuildings)](https://pik-piam.r-universe.dev/builds) +[![CRAN status](https://www.r-pkg.org/badges/version/mredgebuildings)](https://cran.r-project.org/package=mredgebuildings) [![R build status](https://github.com/pik-piam/mredgebuildings/workflows/check/badge.svg)](https://github.com/pik-piam/mredgebuildings/actions) [![codecov](https://codecov.io/gh/pik-piam/mredgebuildings/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mredgebuildings) ## Purpose and Functionality @@ -38,7 +38,7 @@ In case of questions / problems please contact Robin Hasse . +Hasse R, Führlich P, Levesque A, Tockhorn H (2024). _mredgebuildings: Prepare data to be used by the EDGE-Buildings model_. R package version 0.6.0, . A BibTeX entry for LaTeX users is @@ -46,8 +46,8 @@ A BibTeX entry for LaTeX users is @Manual{, title = {mredgebuildings: Prepare data to be used by the EDGE-Buildings model}, author = {Robin Hasse and Pascal Führlich and Antoine Levesque and Hagen Tockhorn}, - year = {2023}, - note = {R package version 0.5.0}, + year = {2024}, + note = {R package version 0.6.0}, url = {https://github.com/pik-piam/mredgebuildings}, } ``` diff --git a/inst/extdata/regional/regionmappingIEA_ETP.csv b/inst/extdata/regional/regionmappingIEA_ETP.csv new file mode 100644 index 0000000..7331c04 --- /dev/null +++ b/inst/extdata/regional/regionmappingIEA_ETP.csv @@ -0,0 +1,250 @@ +X;CountryCode;EEAReg;OECD +Afghanistan;AFG;rest;Non-OECD +Aland Islands;ALA;EUR;Non-OECD +Albania;ALB;rest;Non-OECD +Algeria;DZA;rest;Non-OECD +American Samoa;ASM;rest;Non-OECD +Andorra;AND;rest;Non-OECD +Angola;AGO;rest;Non-OECD +Anguilla;AIA;rest;Non-OECD +Antarctica;ATA;rest;Non-OECD +Antigua and Barbuda;ATG;rest;Non-OECD +Argentina;ARG;rest;Non-OECD +Armenia;ARM;rest;Non-OECD +Aruba;ABW;rest;Non-OECD +Australia;AUS;rest;OECD +Austria;AUT;EUR;OECD +Azerbaijan;AZE;rest;Non-OECD +Bahamas;BHS;rest;Non-OECD +Bahrain;BHR;rest;Non-OECD +Bangladesh;BGD;rest;Non-OECD +Barbados;BRB;rest;Non-OECD +Belarus;BLR;rest;Non-OECD +Belgium;BEL;EUR;OECD +Belize;BLZ;rest;Non-OECD +Benin;BEN;rest;Non-OECD +Bermuda;BMU;rest;Non-OECD +Bhutan;BTN;rest;Non-OECD +Bolivia, Plurinational State of;BOL;rest;Non-OECD +Bonaire, Sint Eustatius and Saba;BES;rest;Non-OECD +Bosnia and Herzegovina;BIH;rest;Non-OECD +Botswana;BWA;rest;Non-OECD +Bouvet Island;BVT;rest;Non-OECD +Brazil;BRA;BRA;Non-OECD +British Indian Ocean Territory;IOT;rest;Non-OECD +Brunei Darussalam;BRN;ASEAN;Non-OECD +Bulgaria;BGR;EUR;Non-OECD +Burkina Faso;BFA;rest;Non-OECD +Burundi;BDI;rest;Non-OECD +Cambodia;KHM;ASEAN;Non-OECD +Cameroon;CMR;rest;Non-OECD +Canada;CAN;rest;OECD +Cape Verde;CPV;rest;Non-OECD +Cayman Islands;CYM;rest;Non-OECD +Central African Republic;CAF;rest;Non-OECD +Chad;TCD;rest;Non-OECD +Chile;CHL;rest;OECD +China;CHN;CHN;Non-OECD +Christmas Island;CXR;rest;Non-OECD +Cocos (Keeling) Islands;CCK;rest;Non-OECD +Colombia;COL;rest;OECD +Comoros;COM;rest;Non-OECD +Congo;COG;rest;Non-OECD +Congo, the Democratic Republic of the;COD;rest;Non-OECD +Cook Islands;COK;rest;Non-OECD +Costa Rica;CRI;rest;Non-OECD +Cote d Ivoire;CIV;rest;Non-OECD +Croatia;HRV;EUR;Non-OECD +Cuba;CUB;rest;Non-OECD +Curacao;CUW;rest;Non-OECD +Cyprus;CYP;EUR;Non-OECD +Czech Republic;CZE;EUR;OECD +Denmark;DNK;EUR;OECD +Djibouti;DJI;rest;Non-OECD +Dominica;DMA;rest;Non-OECD +Dominican Republic;DOM;rest;Non-OECD +Ecuador;ECU;rest;Non-OECD +Egypt;EGY;rest;Non-OECD +El Salvador;SLV;rest;Non-OECD +Equatorial Guinea;GNQ;rest;Non-OECD +Eritrea;ERI;rest;Non-OECD +Estonia;EST;EUR;OECD +Ethiopia;ETH;rest;Non-OECD +Falkland Islands (Malvinas);FLK;rest;Non-OECD +Faroe Islands;FRO;EUR;Non-OECD +Fiji;FJI;rest;Non-OECD +Finland;FIN;EUR;OECD +France;FRA;EUR;OECD +French 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+Portugal;PRT;EUR;OECD +Puerto Rico;PRI;rest;Non-OECD +Qatar;QAT;rest;Non-OECD +Reunion;REU;rest;Non-OECD +Romania;ROU;EUR;Non-OECD +Russian Federation;RUS;RUS;Non-OECD +Rwanda;RWA;rest;Non-OECD +Saint Barthelemy;BLM;rest;Non-OECD +Saint Helena, Ascension and Tristan da Cunha;SHN;rest;Non-OECD +Saint Kitts and Nevis;KNA;rest;Non-OECD +Saint Lucia;LCA;rest;Non-OECD +Saint Martin (French part);MAF;rest;Non-OECD +Saint Pierre and Miquelon;SPM;rest;Non-OECD +Saint Vincent and the Grenadines;VCT;rest;Non-OECD +Samoa;WSM;rest;Non-OECD +San Marino;SMR;rest;Non-OECD +Sao Tome and Principe;STP;rest;Non-OECD +Saudi Arabia;SAU;rest;Non-OECD +Senegal;SEN;rest;Non-OECD +Serbia;SRB;rest;Non-OECD +Seychelles;SYC;rest;Non-OECD +Sierra Leone;SLE;rest;Non-OECD +Singapore;SGP;ASEAN;Non-OECD +Sint Maarten (Dutch part);SXM;rest;Non-OECD +Slovakia;SVK;EUR;OECD +Slovenia;SVN;EUR;OECD +Solomon Islands;SLB;rest;Non-OECD +Somalia;SOM;rest;Non-OECD +South Africa;ZAF;ZAF;Non-OECD +South Georgia and the South Sandwich Islands;SGS;rest;Non-OECD +South Sudan;SSD;rest;Non-OECD +Spain;ESP;EUR;OECD +Sri Lanka;LKA;rest;Non-OECD +Sudan;SDN;rest;Non-OECD +Suriname;SUR;rest;Non-OECD +Svalbard and Jan Mayen;SJM;rest;Non-OECD +Swaziland;SWZ;rest;Non-OECD +Sweden;SWE;EUR;OECD +Switzerland;CHE;rest;OECD +Syrian Arab Republic;SYR;rest;Non-OECD +Taiwan, Province of China;TWN;rest;Non-OECD +Tajikistan;TJK;rest;Non-OECD +Tanzania, United Republic of;TZA;rest;Non-OECD +Thailand;THA;ASEAN;Non-OECD +Timor-Leste;TLS;rest;Non-OECD +Togo;TGO;rest;Non-OECD +Tokelau;TKL;rest;Non-OECD +Tonga;TON;rest;Non-OECD +Trinidad and Tobago;TTO;rest;Non-OECD +Tunisia;TUN;rest;Non-OECD +Turkey;TUR;rest;OECD +Turkmenistan;TKM;rest;Non-OECD +Turks and Caicos Islands;TCA;rest;Non-OECD +Tuvalu;TUV;rest;Non-OECD +Uganda;UGA;rest;Non-OECD +Ukraine;UKR;rest;Non-OECD +United Arab Emirates;ARE;rest;Non-OECD +United Kingdom;GBR;EUR;OECD +United States;USA;USA;OECD +United States Minor Outlying Islands;UMI;rest;Non-OECD +Uruguay;URY;rest;Non-OECD +Uzbekistan;UZB;rest;Non-OECD +Vanuatu;VUT;rest;Non-OECD +Venezuela, Bolivarian Republic of;VEN;rest;Non-OECD +Viet Nam;VNM;ASEAN;Non-OECD +Virgin Islands, British;VGB;rest;Non-OECD +Virgin Islands, U.S.;VIR;rest;Non-OECD +Wallis and Futuna;WLF;rest;Non-OECD +Western Sahara;ESH;rest;Non-OECD +Yemen;YEM;rest;Non-OECD +Zambia;ZMB;rest;Non-OECD +Zimbabwe;ZWE;rest;Non-OECD diff --git a/inst/extdata/sectoral/baitregression-files.csv b/inst/extdata/sectoral/baitregression-files.csv new file mode 100644 index 0000000..23f1693 --- /dev/null +++ b/inst/extdata/sectoral/baitregression-files.csv @@ -0,0 +1,45 @@ +gcm,rcp,ssp,variable,start,end,folder,file +GFDL-ESM4,historical,historical,tas,2001,2010,/p/projects/isimip/isimip/ISIMIP3b/InputData/climate/atmosphere/bias-adjusted/global/daily/historical/GFDL-ESM4,gfdl-esm4_r1i1p1f1_w5e5_historical_tas_global_daily_2001_2010.nc 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--- a/man/calcHDDCDD.Rd +++ b/man/calcHDDCDD.Rd @@ -2,16 +2,35 @@ % Please edit documentation in R/calcHDDCDD.R \name{calcHDDCDD} \alias{calcHDDCDD} -\title{HDD and CDD scenario and historic data} +\title{Calculate HDD and CDD based on outdoor/indoor temperature difference} \usage{ -calcHDDCDD(tlimit = 18) +calcHDDCDD( + mappingFile, + bait = FALSE, + multiscen = FALSE, + rasDir = NULL, + cacheDir = NULL +) } \arguments{ -\item{tlimit}{Temperature threshold for computing HDD and CDD} +\item{mappingFile}{file name of sectoral mapping containing input data file names and directories} + +\item{bait}{boolean, use BAIT instead of raw temperature} + +\item{multiscen}{boolean, does \code{mappingFile} cover more than one scenario?} + +\item{rasDir}{absolute path to directory for saving raster files} + +\item{cacheDir}{absolute path to directory for pre-calculated BAIT regression parameters} +} +\value{ +magpie object of heating and cooling degree days } \description{ -smoothes the HDD and CDD scenario data +heating and cooling degree days based on raw outside temperature +or bias-adjusted internal temperature (BAIT), driver for space heating and +cooling demand in buildings } \author{ -Antoine Levesque +Robin Hasse, Hagen Tockhorn }