init project

2024-10-23 16:30:58 +08:00 · 2024-10-23 16:30:58 +08:00 · 59af39637a
commit 59af39637a
507 changed files with 219969 additions and 0 deletions
--- a/.RData
+++ b/.RData
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,7 @@
+GO.Rout
+data/*
+confirmData/*
+txt/*
+tmp/*
+zip/*
+
--- a/Deploy/._.DS_Store
+++ b/Deploy/._.DS_Store
--- a/Deploy/shud/._.DS_Store
+++ b/Deploy/shud/._.DS_Store
--- a/Deploy/shud/._GetReady.R
+++ b/Deploy/shud/._GetReady.R
--- a/Deploy/shud/._Step1_RawDataProcessng.R
+++ b/Deploy/shud/._Step1_RawDataProcessng.R
--- a/Deploy/shud/._Step2_DataSubset.R
+++ b/Deploy/shud/._Step2_DataSubset.R
--- a/Deploy/shud/._Step3_BuidModel.R
+++ b/Deploy/shud/._Step3_BuidModel.R
--- a/Deploy/shud/GetReady.R
+++ b/Deploy/shud/GetReady.R
@ -0,0 +1,72 @@
+#' ===============================================================
+#' Author: Lele Shu <shulele@lzb.ac.cn>
+#' Date: 2023.02.03
+#' Function: The AutoSHUD function
+#' 
+#' ===============================================================
+#' 
+wdir = file.path(CV$dirs$deploy)
+source(file.path(CV$dirs$deploy, 'Step1_RawDataProcessng.R'))
+source(file.path(CV$dirs$deploy, 'Step2_DataSubset.R'))
+source(file.path(CV$dirs$deploy, 'Step3_BuidModel.R'))
+source(file.path(CV$dirs$deploy, 'Rfunction/ReadProject.R'))
+
+GetReady.AutoSHUD <- function(CV){
+  xfg <- read.prj(CV)
+  
+  if( !is.null(xfg$fsp.lake) ){
+    LAKEON = TRUE
+  }else{
+    LAKEON = FALSE
+  }
+  
+  pre.sp = list(
+    dem = 'dem.tif',
+    wbd = 'wbd.shp',
+    wbd.buf = 'wbd_buf.shp',
+    stm = 'stm.shp',
+    lake = 'lake.shp',
+    
+    soil.r = 'soil.tif',
+    soil.idx = 'soil_idx.tif',
+    
+    geol.r = 'geol.tif',
+    geol.idx = 'geol_idx.tif',
+    
+    soil.v = 'soil.shp',
+    geol.v = 'geol.shp',
+    
+    lu.r = 'landuse.tif',
+    lu.idx = 'landuse_idx.tif',
+    lu.v = 'landuse.shp',
+    
+    meteo = 'meteo.shp',
+    meteoCov = 'meteoCov.shp')
+  dir.pd.pcs =  file.path(xfg$dir$predata,'PCS')
+  dir.pd.gcs =  file.path(xfg$dir$predata,'GCS')
+  
+  dir.create(dir.pd.pcs, showWarnings = FALSE, recursive = TRUE)
+  dir.create(dir.pd.gcs, showWarnings = FALSE, recursive = TRUE)
+  
+  pd.pcs = lapply(1:length(pre.sp), function(x){ file.path(dir.pd.pcs, pre.sp[[x]])} )
+  names(pd.pcs) = names(pre.sp)
+  
+  pd.gcs = lapply(1:length(pre.sp), function(x){ file.path(dir.pd.gcs, pre.sp[[x]])} )
+  names(pd.gcs) = names(pre.sp)
+  
+  pd.att <- list(
+    geol = file.path(xfg$dir$predata,'GEOL.csv'),
+    soil = file.path(xfg$dir$predata,'SOIL.csv'),
+    landuse = file.path(xfg$dir$predata,'LANDUSE.csv') 
+  )
+  
+  xfg$LAKEON=LAKEON
+  xfg$pd.att=pd.att
+  xfg$pd.pcs=pd.pcs
+  xfg$pd.gcs=pd.gcs
+  
+  cv = CV
+  cv$deploy = utils::modifyList(CV$deploy, xfg) 
+  return(cv)
+}
+
--- a/Deploy/shud/Rfunction/._.DS_Store
+++ b/Deploy/shud/Rfunction/._.DS_Store
--- a/Deploy/shud/Rfunction/._Bakup.readnc.R
+++ b/Deploy/shud/Rfunction/._Bakup.readnc.R
--- a/Deploy/shud/Rfunction/._CMFD_NC2RDS.R
+++ b/Deploy/shud/Rfunction/._CMFD_NC2RDS.R
--- a/Deploy/shud/Rfunction/._CMFD_readnc.R
+++ b/Deploy/shud/Rfunction/._CMFD_readnc.R
--- a/Deploy/shud/Rfunction/._FLDAS_nc2RDS.R
+++ b/Deploy/shud/Rfunction/._FLDAS_nc2RDS.R
--- a/Deploy/shud/Rfunction/._GLDAS_nc2RDS.R
+++ b/Deploy/shud/Rfunction/._GLDAS_nc2RDS.R
--- a/Deploy/shud/Rfunction/._LDAS_UnitConvert.R
+++ b/Deploy/shud/Rfunction/._LDAS_UnitConvert.R
--- a/Deploy/shud/Rfunction/._NLCD.R
+++ b/Deploy/shud/Rfunction/._NLCD.R
--- a/Deploy/shud/Rfunction/._NLCD.colors.R
+++ b/Deploy/shud/Rfunction/._NLCD.colors.R
--- a/Deploy/shud/Rfunction/._NLDAS_RDS2csv.R
+++ b/Deploy/shud/Rfunction/._NLDAS_RDS2csv.R
--- a/Deploy/shud/Rfunction/._ProjectionGrids.R
+++ b/Deploy/shud/Rfunction/._ProjectionGrids.R
--- a/Deploy/shud/Rfunction/._ReadProject.R
+++ b/Deploy/shud/Rfunction/._ReadProject.R
--- a/Deploy/shud/Rfunction/._RivWidth.R
+++ b/Deploy/shud/Rfunction/._RivWidth.R
--- a/Deploy/shud/Rfunction/._SoilGeol.R
+++ b/Deploy/shud/Rfunction/._SoilGeol.R
--- a/Deploy/shud/Rfunction/._fun.LAIRL.R
+++ b/Deploy/shud/Rfunction/._fun.LAIRL.R
--- a/Deploy/shud/Rfunction/._fun.Meteo.R
+++ b/Deploy/shud/Rfunction/._fun.Meteo.R
--- a/Deploy/shud/Rfunction/._fun.SSURGO.R
+++ b/Deploy/shud/Rfunction/._fun.SSURGO.R
--- a/Deploy/shud/Rfunction/._raster2Polygon.R
+++ b/Deploy/shud/Rfunction/._raster2Polygon.R
--- a/Deploy/shud/Rfunction/Bakup.readnc.R
+++ b/Deploy/shud/Rfunction/Bakup.readnc.R
@ -0,0 +1,121 @@
+readnc.time <- function(ncid) {
+  # modified after https://stackoverflow.com/questions/46001573/convert-a-netcdf-time-variable-to-an-r-date-object
+  require(lubridate)
+  ncdims <- names(ncid$dim) #get netcdf dimensions
+  timevar <- ncdims[which(ncdims %in% c("time", "Time", "datetime", "Datetime", "date", "Date"))[1]] #find time variable
+  times <- ncvar_get(ncid, timevar)
+  if (length(timevar)==0) stop("ERROR! Could not identify the correct time variable")
+  timeatt <- ncatt_get(ncid, timevar) #get attributes
+  timedef <- strsplit(timeatt$units, " ")[[1]]
+  timeunit <- timedef[1]
+  if(length(timedef) < 5){
+    # cat("Warning:", tz, "not a valid timezone. Assuming UTC\n")
+    tz <- "UTC"
+  }else{
+    tz <- timedef[5]
+  }
+  timestart <- strsplit(timedef[4], ":")[[1]]
+  if (length(timestart) != 3 || timestart[1] > 24 || timestart[2] > 60 || timestart[3] > 60 || any(timestart < 0)) {
+    cat("Warning:", timestart, "not a valid start time. Assuming 00:00:00\n")
+    warning(paste("Warning:", timestart, "not a valid start time. Assuming 00:00:00\n"))
+    timedef[4] <- "00:00:00"
+  }
+  timestart <- ymd_hms(paste(timedef[3], timedef[4]), tz=tz)
+  f <- switch(tolower(timeunit), #Find the correct lubridate time function based on the unit
+              seconds=seconds, second=seconds, sec=seconds,
+              minutes=minutes, minute=minutes, min=minutes,
+              hours=hours,     hour=hours,     h=hours,
+              days=days,       day=days,       d=days,
+              months=months,   month=months,   m=months,
+              years=years,     year=years,     yr=years,
+              NA
+  )
+  suppressWarnings(if (is.na(f)) stop("Could not understand the time unit format"))
+  timestart + f(times)
+}
+
+
+readnc<-function(ncid= nc_open(fn), 
+                 xyid=c(1,1), vns=NULL){
+
+  if(is.null(vns)){
+    vns = names(ncid$var)
+    vns = vns[!(vns %in% 'time_bnds')] # don't need the time_bnds
+  }
+  
+  nv = length(vns)
+  x.mat = matrix(0, ncol=nv, nrow=ns)
+  
+  for(i in 1:nv){  #reading file
+    vn=vns[i]
+    mat=ncvar_get(ncid, vn)
+    x.v = mat[xyid]
+    x.mat[,i] = x.v
+  }
+  colnames(x.mat) = vns
+  x.mat
+}
+nc.sub <-function(fn, ncid=ncdf4::nc_open(fn),
+                  ext){
+  ncid=nc_open(fns[1])
+  #nc_close(ncid)
+  lon=round(ncvar_get(ncid, 'longitude'), 3)
+  lat=round(ncvar_get(ncid, 'latitude'), 3)
+  nlon=length(lon)
+  nlat = length(lat)
+  #==================
+  loc.confirm <- function(){
+    ext = c(-20, 50, -38, 40)
+    xid=which(lon >= ext[1] & lon <= ext[2])
+    yid=which(lat >= ext[3] & lat <= ext[4])
+    xy.grid = expand.grid(lon[xid], lat[yid])
+    dat = ncvar_get(ncid, 'precip', 
+                    count = c(length(xid), length(yid), 1),
+                    start = c( which(lon == min(lon[xid]) ), which(lat==min(lat[yid]) ), 1) )
+    xyz=cbind(xy.grid, as.numeric(dat) )
+    r=rasterFromXYZ(xyz)
+    png(filename = file.path(odir, paste0('LocationConfirm.png')), height = 11, width = 8, units = 'in', res=100)
+    
+    rplot(r); rplot(wbd, add=T, border=2)
+    grid()
+    dev.off()
+  }
+  nc_close(ncid)
+}
+read.nc2Raster <- function(fn, ncid=ncdf4::nc_open(fn),
+                           plot=TRUE, 
+                           varid=2, xname=NULL, yname=NULL){
+  nv = ncid$nvars
+  if(is.character(varid)){
+    varid = varid
+  }else{
+    vns = names(ncid$var)
+    if(nv>0){
+      varid = vns[varid]
+    }else{
+      varid = vns[1]
+    }
+  }
+  if(is.null(xname) | is.null(yname)){
+    dn = names(ncid$dim)
+    yname = dn[grepl('^lat|^x', tolower(dn) )]
+    xname = dn[grepl('^lon|^y', tolower(dn) )]
+  }
+  x = ncdf4::ncvar_get(ncid, xname)
+  y = ncdf4::ncvar_get(ncid, yname)
+  nx=length(x); ny=length(y);
+  dx=mean(diff(x)); dy=mean(diff(y))
+  r = raster::raster(ncols=nx, nrows=ny)
+  raster::extent(r) = c(min(x), max(x), min(y), max(y)) + c(-dx, dx, -dy, dy)/2
+  val = ncdf4::ncvar_get(ncid, varid)
+  nc_close(ncid)
+  # dim(val)
+  r = raster::setValues(r, t(val[, ny:1 ]) )
+  if(plot){
+    raster::plot(r, main=varid)
+  }
+  r
+}
+
+# vn=vns[4]
+# ncvar_get(ncid, varid = vn, start=)
--- a/Deploy/shud/Rfunction/CMFD_NC2RDS.R
+++ b/Deploy/shud/Rfunction/CMFD_NC2RDS.R
@ -0,0 +1,77 @@
+# read the orginal fldas data and save to .RDS file.
+
+source('AutoSHUD/Rfunction/LDAS_UnitConvert.R')
+source('AutoSHUD/Rfunction/raster2Polygon.R')
+source('AutoSHUD/Rfunction/CMFD_readnc.R')
+
+fun.CMFD.NC2RDS <- function(xfg){
+  varnames = c("Pres", "LRad", "Prec",  "SHum", "SRad", "Temp", "Wind")
+  varnames = c("Prec", "Temp", "SHum", "SRad",  "Wind", "Pres")
+  nv=length(varnames)
+  nyr = length(xfg$years)
+  fn=list.files(xfg$dir.ldas,  pattern = glob2rx(paste0(varnames[1], '*CMFD*.nc') ),
+                recursive = TRUE, ignore.case = TRUE, full.names = TRUE)[1]
+  # cmd = paste('find',xfg$dir.lddas,  ' -name "*CMFD*.nc" > filelist.txt')
+  # system(cmd)
+  # fn=readLines(file.path(xfg$dir.ldas, 'filelist.txt'))[1]
+  
+  xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+  id = xp$id
+  print(length(id))
+  sitenames=xp$sitenames
+  ext=xp$ext
+  nsite = length(id)
+  # print(id)
+  # library(foreach)
+  # library(doMC)
+  # library(doParallel)
+  # registerDoMC(12)
+  # foreach (idd = 1:ndir) %dopar%{
+  msg <-function(i, n, str, ntab=0){
+    message(paste(rep('\t', ntab), collapse = ''),
+            i, '/', n, '\t', str)
+  }
+  for (iyr in 1:nyr) {
+    yr <- xfg$years[iyr]
+    message(iyr, '/', nyr, '\t', yr)
+    fn.rds = file.path(xfg$dir$predata, paste0('CMFD_', yr, '.RDS'))
+    if(file.exists(fn.rds)){
+      next;
+    }
+    arr = array(dim = c(nsite, nv,  366*(24/3)))  #(N_SITE, N_VARNAME, N_TIME)
+    for(ivn in 1:nv){
+      vn = varnames[ivn]
+      message('\t', ivn, '/', nv, '\t', vn)
+      path = file.path(xfg$dir.ldas, vn)
+      fns = list.files(path, pattern = glob2rx(paste0(tolower(vn), '_CMFD_*', yr, '*.nc')), full.names = F)
+      ffns = list.files(path, pattern = glob2rx(paste0(tolower(vn), '_CMFD_*', yr, '*.nc')), full.names = T)
+      nf = length(fns)
+      pnt = 0;
+      for(i in 1:nf){ # vn in the whole year
+        msg(i, nf, fns[i], ntab=3)
+        ncid = nc_open(ffns[i])
+        # debug(readnc)
+        dat=readnc.CMFD(ncid, varid = tolower(vn), ext = ext)
+        nc_close(ncid)
+        nd = dim(dat$arr)
+        if(length(nd)==3){
+          mat = matrix(dat$arr, nrow=nd[1]*nd[2], ncol=nd[3])[id, ]
+        }else{
+          mat= dat$arr
+        }
+        if(i==1){
+          xtime=dat$time
+        }else{
+          xtime = c(xtime, dat$time)
+        }
+        timelen = length(dat$time)
+        arr[ , ivn, 1:timelen + pnt] = mat
+        pnt = pnt+timelen
+      }
+    }
+    arr=arr[,, 1:pnt ]
+    x.t = strftime(xtime, origin=as.POSIXct('1900-01-01'), usetz = FALSE, tz='UTC')
+    dimnames(arr) = list(sitenames, varnames, x.t)
+    saveRDS(arr, file=fn.rds)
+  }
+}
--- a/Deploy/shud/Rfunction/CMFD_RDS2csv.R
+++ b/Deploy/shud/Rfunction/CMFD_RDS2csv.R
@ -0,0 +1,54 @@
+# read the RDS above, to save as .csv file.
+# source('GetReady.R')
+fun.CMFD.RDS2csv <- function(xfg){
+  source('AutoSHUD/Rfunction/LDAS_UnitConvert.R')
+  years = xfg$years
+  fns = file.path(xfg$dir$predata, paste0('CMFD_',years, '.RDS'))
+  fns
+  
+  cns = c("Prec", "Temp", "SHum", "SRad",  "Wind", "Pres")
+  forcnames = c( "Prcp_mm.d", "Temp_C", "RH_%", "Wind_m.s", "RN_w.m2" )
+  
+  nf=length(fns)
+  for(i in 1:nf){
+    x=readRDS(fns[i])  
+    message(i,'/', nf, '\t', basename(fns[i]))
+    y=x[,cns,]
+    if(i==1){
+      dat = y
+    }else{
+      dat=abind::abind(dat, y, along=3)
+    }
+  }
+  dn = dimnames(dat)
+  nd = dim(dat)
+  xl = list()
+  
+  time = as.POSIXct(dimnames(dat)[[3]], tz='UTC')
+  for(i in 1:nd[1]){
+    message(i,'/', nd[1], '\t', dn[[1]][i] )
+    x = t( dat[i,,] )
+    y=unitConvert.CMFD(x)
+    xl[[i]]=as.xts(y, order.by=time)
+  }
+  
+  nx=length(xl)
+  sitename = dn[[1]]
+  sitename
+  fns=paste0(sitename, '.csv')
+  for(i in 1:nx){
+    fn=fns[i]
+    message(i,'/', nx, '\t', fn)
+    write.tsd(xl[[i]], file.path(xfg$dir$forc, fn))
+    if(i==1){
+      xmean = xl[[i]]
+    }else{
+      xmean = xmean + xl[[i]]
+    }
+  }
+  png(fn=file.path(xfg$dir$fig, paste0(xfg$prefix, '_Rawdata','_CMFD_TS.png') ), type='cairo',
+      height=6, width=7, res=200, units = 'in')
+  plot.zoo(xmean/nx, main='CMFD')
+  dev.off()
+  
+}
--- a/Deploy/shud/Rfunction/CMFD_readnc.R
+++ b/Deploy/shud/Rfunction/CMFD_readnc.R
@ -0,0 +1,142 @@
+
+readnc.CMFD<-function(ncid, varid=NULL,  ext = NULL){
+  msg= 'readnc:: '
+  vars = names(ncid$var)
+  nvars = length(vars)
+  if(is.null(varid)){ # read all
+    varid = varid[!(vars %in% 'time_bnds')] # don't need the time_bnds
+  }else if(is.character(varid)){ # read VARID (character) by user
+    if(!all(varid %in% vars)){ # validate the input chars.
+      message(msg, 'ERROR:: some varid is missing in the dataset.\n')
+      print(varid[! (varid %in% vars)])
+      stop('Stop with error.')
+    }
+    varid = varid
+  }else if(is.numeric(varid)) {  # read VARID (index) by user
+    if(max(varid)>nvars || min(varid) < 1){
+      message(msg, 'ERROR:: Wrong value in varid.\n')
+      stop('Stop with error.')
+    }
+    message('Reading VARID = ', vars[varid])
+    varid = vars[varid]
+  }else{  # ERROR
+    message(msg, 'ERROR:: Wrong format of varid.\n')
+    stop('Stop with error.')
+  }
+  
+  ncdims = names(ncid$dim)
+  var.lon <- ncdims[which(grepl('lon', tolower(ncdims)))]
+  var.lat <- ncdims[which(grepl('lat', tolower(ncdims)))]
+  
+  lon <- ncdf4::ncvar_get(ncid, varid = var.lon)
+  lat <- ncdf4::ncvar_get(ncid, varid = var.lat)
+  dx = mean(diff(lon)); dy = mean(diff(lat))
+  xmin = min(lon - dx/2); xmax = max(lon + dx/2)
+  ymin = min(lat - dy/2); ymax = max(lat + dy/2)
+  if(is.null(ext)){
+    ext= c(min(lon), max(lon), min(lat), max(lat))
+  }
+  if(ext[1] < xmin | ext[2] > xmax | ext[3] < ymin | ext[4] > ymax){
+    warning(paste('Extent required is larger than the boundbox of dataset'))
+    message(paste(ext, collaps=','))
+    message(paste(c(xmin,xmax,ymin, ymax), collaps=','))
+  }
+  xid = min(which(abs(lon  - ext[1]) <= dx/2)):max(which(abs(lon  - ext[2]) <= dx/2))
+  yid = min(which(abs(lat  - ext[3]) <= dy/2)):max(which(abs(lat  - ext[4]) <= dy/2))
+  nx = length(xid); ny = length(yid)
+  x.cord = lon[xid]; y.cord = lat[yid]
+  
+  tx = readnc.time(ncid = ncid)
+  ntime = length(tx)
+  arr = array(0, dim=c(nx, ny, ntime), 
+              dimnames= list(x.cord, y.cord, tx))
+  ndims = ncid$ndims
+  start = c(min(xid), min(yid), 1)
+  count = c(nx, ny, ntime)
+    vn=varid[1]
+    arr=ncdf4::ncvar_get(ncid, vn, start = start, count = count)
+  rt = list('x' = x.cord, 'y' = y.cord, 'arr' = arr, 'time' = tx)
+  return(rt)
+}
+# 
+# ncid = nc_open(ffns[i])
+# x0=readnc.CMFD(ncid, varid = tolower(vn))
+# x=readnc.CMFD(ncid, varid = tolower(vn), ext = ext)
+# nc_close(ncid)
+# r0 = xyz2Raster(x0)
+# r=xyz2Raster(x)
+# plot(r0[[1]])
+# plot(r[[1]], add=T)
+# x$arr
+
+
+
+initalGrids <- function(fn, vn, pd.gcs, pd.pcs, sp.ldas=NULL){
+  buf.g = readOGR(pd.gcs$wbd.buf)
+  ext = extent(buf.g)
+  
+  fid = nc_open(fn) 
+  nc.all = rSHUD::readnc(fid, varid = vn)
+  nc.sub = rSHUD::readnc(fid, varid = vn, ext = ext)
+  nc_close(fid)
+  nc.all$x = round(nc.all$x, 3);   nc.all$y = round(nc.all$y, 3)
+  nc.sub$x = round(nc.sub$x, 3);   nc.sub$y = round(nc.sub$y, 3)
+  # undebug(xyz2Raster)
+  r = xyz2Raster(x = nc.all)
+  # debug(xyz2Raster)
+  r.sub = xyz2Raster(x = nc.sub)
+  if(is.null(sp.ldas)){
+    sp.ldas = raster2Polygon(rx = r.sub)
+    # sp.center = gCentroid(sp.ldas, byid=TRUE)
+    
+    # =========PLOT===========================
+    png.control(fn=paste0(prefix, '_LDAS_location.png'), path = xfg$dir$fig, ratio=1)
+    plot(r * 0, col='gray', legend=FALSE)
+    plot(r.sub * 0, col='red', legend=FALSE, add=TRUE)
+    plot(buf.g, add=T)
+    dev.off()
+    
+    # =========Get the data===========================
+    sp0.gcs = spTransform(sp.ldas, xfg$crs.gcs)
+    sp0.pcs = spTransform(sp.ldas, xfg$crs.pcs)
+  }
+  id=which(gIntersects(sp0.gcs, buf.g, byid = T)) 
+  writeshape(sp0.gcs[id, ], file = pd.gcs$meteoCov)
+  writeshape(sp0.pcs[id, ], file = pd.pcs$meteoCov)
+  sitenames = paste0('X', sp0.gcs@data$xcenter, 'Y', sp0.gcs@data$ycenter)
+  sitenames=sitenames[id]
+  
+  # plot(sp0.gcs)
+  # plot(buf.g, add=TRUE, border=2)
+  # plot(sp0.gcs[id, ],add=T, col=3)
+  
+  retval = list(id=id, sitenames=sitenames, ext=ext)
+  return(retval)
+}
+
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+# library(ncdf4)
+# source('Rfunction/nc2fishnet.R')
+# fn = '/Volumes/Forcing/CMFD/Data_forcing_03hr_010deg/Prec/prec_CMFD_V0106_B-01_03hr_010deg_197901.nc'
+# fid = ncdf4::nc_open(fn)
+# x=readnc.CMFD(ncid=fid, varid = 'prec')
+# ncdf4::nc_close(fid)
+# spx = nc.fishnet(x)
+# crs(spx) = sp::CRS('+init=epsg:4326')
+# writeshape(spx, '/Volumes/Forcing/CMFD_fishnet')
+# raster::plot(spx)
+
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+
+# x=dat
+# x$arr=x$arr[,,1]
+# nd=dim(x$arr)
+# for(i in 1:nd[3]){
+#   x$arr[id+nd[1]*nd[2]*(i-1)]=1000
+# }
+# r=xyz2Raster(x)
+# animate(r)
+
+# plot(r.sub);
+# plot(add=T, buf.g)
--- a/Deploy/shud/Rfunction/CMIP6_NCtoRDS.R
+++ b/Deploy/shud/Rfunction/CMIP6_NCtoRDS.R
@ -0,0 +1,90 @@
+# read the orginal fldas data and save to .RDS file.
+# good for PALEOFLOOD project in Arizona. 2021-04-04
+require(ncdf4)
+source('Rfunction/LDAS_UnitConvert.R')
+source('Rfunction/raster2Polygon.R')
+source('Rfunction/CMIP6_readnc.R')
+
+varnames = c('pr','tas', 'huss', 'rsds', 'sfcWind')
+
+nv=length(varnames)
+nyr = length(xfg$years)
+i=1
+ffns = list.files(xfg$dir.ldas, pattern = glob2rx(paste0('*', varnames[i], '*future*.nc')),
+             recursive = TRUE, ignore.case = TRUE, full.names = TRUE)
+# fn ='/Volumes/Download/CMIP6-data/青海湖项目/pr/2015-2051/pr_day_FGOALS-f3-H_highres-future_r1i1p1f1_gr_20510101-20510125.nc'
+# ncid = nc_open(fn)
+# ncid$filename
+# ncvar_get(ncid, varid = cimp.vn[i])
+fn=ffns[1]
+
+xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+id = xp$id
+print(length(id))
+sitenames=xp$sitenames
+ext=xp$ext
+nsite = length(id)
+# print(id)
+# library(foreach)
+# library(doMC)
+# library(doParallel)
+# registerDoMC(12)
+# foreach (idd = 1:ndir) %dopar%{
+msg <-function(i, n, str, ntab=0){
+  message(paste(rep('\t', ntab), collapse = ''),
+          i, '/', n, '\t', str)
+}
+for (iyr in 1:nyr) {
+  yr <- xfg$years[iyr]
+  message(iyr, '/', nyr, '\t', yr)
+  fn.rds = file.path(xfg$dir$predata, paste0('CMIP6_', yr, '.RDS'))
+  if(file.exists(fn.rds)){
+    next;
+  }
+  arr = array(dim = c(nsite, nv,  366*(24/3)))  #(N_SITE, N_VARNAME, N_TIME)
+  for(ivn in 1:nv){
+    vn = varnames[ivn]
+    message('\t', ivn, '/', nv, '\t', vn)
+    path = file.path(xfg$dir.ldas, vn)
+    
+    ffns = list.files(path, pattern = glob2rx(paste0('*', tolower(vn), 
+                                                     '*future*_gr_', yr, '*.nc')),ignore.case = TRUE, 
+                      full.names = T, recursive = TRUE)
+    # ffns
+    fns=basename(ffns)
+    nf = length(fns)
+    pnt = 0;
+    
+    if(('ta'== vn) | ('hur'==vn)){
+      if(nf != 12){
+        message('FILE(s) are missing')
+        print(nf)
+        print(ffns)
+        stop()
+      }
+    }
+    for(i in 1:nf){ # vn in the whole year
+      msg(i, nf, fns[i], ntab=3)
+      ncid = nc_open(ffns[i])
+      # debug(readnc)
+      dat=ReadNC(ncid, varid = vn, ext = ext, t.len=NULL)
+      nc_close(ncid)
+      nd = dim(dat$arr)
+      mat = matrix(dat$arr, nrow=nd[1]*nd[2], ncol=nd[3])[id, ]
+      if(i==1){
+        xtime=dat$time
+      }else{
+        xtime = c(xtime, dat$time)
+      }
+      timelen = length(dat$time)
+      arr[ , ivn, 1:timelen + pnt] = mat
+      pnt = pnt+timelen
+    }
+  }
+  arr=arr[,, 1:pnt ]
+  x.t = strftime(xtime, origin=as.POSIXct('1900-01-01'), usetz = FALSE, tz='UTC')
+  dimnames(arr) = list(sitenames, varnames, x.t)
+  saveRDS(arr, file=fn.rds)
+}
+
+
--- a/Deploy/shud/Rfunction/CMIP6_RDStoCSV.R
+++ b/Deploy/shud/Rfunction/CMIP6_RDStoCSV.R
@ -0,0 +1,68 @@
+# read the RDS above, to save as .csv file.
+source('GetReady.R')
+source('AtuSHUD/Rfunction/LDAS_UnitConvert.R')
+years = xfg$years
+# years=2015:2016
+fns = file.path(xfg$dir$predata, paste0('CMIP6_',years, '.RDS'))
+fns
+
+cns =  c('pr','tas', 'huss', 'rsds', 'sfcWind')
+forcnames = c( "Prcp_mm.d", "Temp_C", "RH_%", "Wind_m.s", "RN_w.m2" )
+
+nf=length(fns)
+for(i in 1:nf){
+  x=readRDS(fns[i])  
+  message(i,'/', nf, '\t', basename(fns[i]))
+  y=x[,cns,]
+  if(i==1){
+    dat = y
+  }else{
+    dat=abind::abind(dat, y, along=3)
+  }
+}
+dn = dimnames(dat)
+nd = dim(dat)
+xl = list()
+
+time = as.POSIXct(dimnames(dat)[[3]], tz='UTC')
+for(i in 1:nd[1]){
+  message(i,'/', nd[1], '\t', dn[[1]][i] )
+  x = t( dat[i,,] )
+  y=unitConvert.CMIP6(x)
+  print(apply(y, 2, range))
+  xl[[i]]=as.xts(y, order.by=time)
+}
+
+nx=length(xl)
+sitename = dn[[1]]
+sitename
+fns=paste0(sitename, '.csv')
+fixdata <- function(x){
+  idx=1:365
+  x[idx + 365] = x[idx]
+  x
+}
+pp=NULL
+for(i in 1:nx){
+  fn=fns[i]
+  message(i,'/', nx, '\t', fn)
+  xl[[i]] = fixdata(xl[[i]])
+  write.tsd(xl[[i]], file.path(xfg$dir$forc, fn))
+  if(i==1){
+    xmean = xl[[i]]
+    pp=xl[[i]]$Precip_mm.d
+  }else{
+    xmean = xmean + xl[[i]]
+    pp=cbind(pp, xl[[i]]$Precip_mm.d)
+  }
+}
+plot(pp)
+xm = xmean/nx
+saveRDS(xm, file.path(xfg$dir$forc, 'forc.mean.RDS'))
+
+png.control(fn=paste0('Rawdata','_CMIP6_TS.png'),
+            path = file.path(xfg$dir$fig), ratio=1)
+plot.zoo(xm, main='CMIP6')
+dev.off()
+plot.zoo(xm, main='CMIP6')
+
--- a/Deploy/shud/Rfunction/CMIP6_readnc.R
+++ b/Deploy/shud/Rfunction/CMIP6_readnc.R
@ -0,0 +1,227 @@
+
+readnc.CMIP6<-function(ncid, varid=NULL,  ext = NULL){
+  msg= 'readnc:: '
+  vars = names(ncid$var)
+  nvars = length(vars)
+  if(is.null(varid)){ # read all
+    varid = varid[!(vars %in% 'time_bnds')] # don't need the time_bnds
+  }else if(is.character(varid)){ # read VARID (character) by user
+    if(!all(varid %in% vars)){ # validate the input chars.
+      message(msg, 'ERROR:: some varid is missing in the dataset.\n')
+      print(varid[! (varid %in% vars)])
+      stop('Stop with error.')
+    }
+    varid = varid
+  }else if(is.numeric(varid)) {  # read VARID (index) by user
+    if(max(varid)>nvars || min(varid) < 1){
+      message(msg, 'ERROR:: Wrong value in varid.\n')
+      stop('Stop with error.')
+    }
+    message('Reading VARID = ', vars[varid])
+    varid = vars[varid]
+  }else{  # ERROR
+    message(msg, 'ERROR:: Wrong format of varid.\n')
+    stop('Stop with error.')
+  }
+  
+  ncdims = names(ncid$dim)
+  var.lon <- ncdims[which(grepl('lon', tolower(ncdims)))]
+  var.lat <- ncdims[which(grepl('lat', tolower(ncdims)))]
+  
+  lon <- ncdf4::ncvar_get(ncid, varid = var.lon)
+  lat <- ncdf4::ncvar_get(ncid, varid = var.lat)
+  dx = mean(diff(lon)); dy = mean(diff(lat))
+  xmin = min(lon - dx/2); xmax = max(lon + dx/2)
+  ymin = min(lat - dy/2); ymax = max(lat + dy/2)
+  if(is.null(ext)){
+    ext= c(min(lon), max(lon), min(lat), max(lat))
+  }
+  if(ext[1] < xmin | ext[2] > xmax | ext[3] < ymin | ext[4] > ymax){
+    warning(paste('Extent required is larger than the boundbox of dataset'))
+    message(paste(ext, collaps=','))
+    message(paste(c(xmin,xmax,ymin, ymax), collaps=','))
+  }
+  xid = min(which(abs(lon  - ext[1]) <= dx/2)):max(which(abs(lon  - ext[2]) <= dx/2))
+  yid = min(which(abs(lat  - ext[3]) <= dy/2)):max(which(abs(lat  - ext[4]) <= dy/2))
+  nx = length(xid); ny = length(yid)
+  x.cord = lon[xid]; y.cord = lat[yid]
+  
+  tx = readnc.time(ncid = ncid)
+  ntime = length(tx)
+  arr = array(0, dim=c(nx, ny, ntime), 
+              dimnames= list(x.cord, y.cord, tx))
+  ndims = ncid$ndims
+  start = c(min(xid), min(yid), 1)
+  count = c(nx, ny, ntime)
+  vn=varid[1]
+  arr=ncdf4::ncvar_get(ncid, vn, start = start, count = count)
+  rt = list('x' = x.cord, 'y' = y.cord, 'arr' = arr, 'time' = tx)
+  return(rt)
+}
+# 
+# ncid = nc_open(ffns[i])
+# x0=readnc.CMFD(ncid, varid = tolower(vn))
+# x=readnc.CMFD(ncid, varid = tolower(vn), ext = ext)
+# nc_close(ncid)
+# r0 = xyz2Raster(x0)
+# r=xyz2Raster(x)
+# plot(r0[[1]])
+# plot(r[[1]], add=T)
+# x$arr
+
+
+
+ReadNC<-function(ncid, varid=NULL,  ext = NULL, t.len=NULL){
+  msg= 'readnc:: '
+  vars = names(ncid$var)
+  nvars = length(vars)
+  if(is.null(varid)){ # read all
+    varid = varid[!(vars %in% 'time_bnds')] # don't need the time_bnds
+  }else if(is.character(varid)){ # read VARID (character) by user
+    if(!all(varid %in% vars)){ # validate the input chars.
+      message(msg, 'ERROR:: some varid is missing in the dataset.\n')
+      print(varid[! (varid %in% vars)])
+      stop('Stop with error.')
+    }
+    varid = varid
+  }else if(is.numeric(varid)) {  # read VARID (index) by user
+    if(max(varid)>nvars || min(varid) < 1){
+      message(msg, 'ERROR:: Wrong value in varid.\n')
+      stop('Stop with error.')
+    }
+    message('Reading VARID = ', vars[varid])
+    varid = vars[varid]
+  }else{  # ERROR
+    message(msg, 'ERROR:: Wrong format of varid.\n')
+    stop('Stop with error.')
+  }
+  
+  ncdims = names(ncid$dim)
+  var.lon <- ncdims[which(grepl('lon', tolower(ncdims)))]
+  var.lat <- ncdims[which(grepl('lat', tolower(ncdims)))]
+  
+  lon <- ncdf4::ncvar_get(ncid, varid = var.lon)
+  lat <- ncdf4::ncvar_get(ncid, varid = var.lat)
+  dx = mean(diff(lon)); dy = mean(diff(lat))
+  xmin = min(lon - dx/2); xmax = max(lon + dx/2)
+  ymin = min(lat - dy/2); ymax = max(lat + dy/2)
+  if(is.null(ext)){
+    ext= c(min(lon), max(lon), min(lat), max(lat))
+  }
+  if(ext[1] < xmin | ext[2] > xmax | ext[3] < ymin | ext[4] > ymax){
+    warning(paste('Extent required is larger than the boundbox of dataset'))
+    message(paste(ext, collaps=','))
+    message(paste(c(xmin,xmax,ymin, ymax), collaps=','))
+  }
+  xid = min(which(abs(lon  - ext[1]) <= dx/2)):max(which(abs(lon  - ext[2]) <= dx/2))
+  yid = min(which(abs(lat  - ext[3]) <= dy/2)):max(which(abs(lat  - ext[4]) <= dy/2))
+  nx = length(xid); ny = length(yid)
+  x.cord = lon[xid]; y.cord = lat[yid]
+  if(is.null(t.len)){
+    t.len = ncid$dim$time$len
+  }
+  
+  arr = array(0, dim=c(nx, ny, t.len ),  
+              dimnames= list(x.cord, y.cord, 1:t.len))
+  ndims = ncid$ndims
+  
+  if(('ta'== varid) | ('hur'== varid)){
+    start = c(min(xid), min(yid), 1, 1)
+    count = c(nx, ny, 8, t.len)  # level 5, from bottom to upper.
+  }else{
+    start = c(min(xid), min(yid), 1)
+    count = c(nx, ny, t.len)
+  }
+  
+  nch = nchar(ncid$filename)
+  tatt = ncatt_get(ncid, 'time')
+  
+  tx = seq.Date(
+    as.Date(substr(ncid$filename, nch-19, nch-12), '%Y%m%d'),
+    as.Date(substr(ncid$filename, nch-10, nch-3), '%Y%m%d'),
+    by='days')
+  arr= ncdf4::ncvar_get(ncid, varid, start = start, count = count)
+  
+  if(('ta'== varid) | ('hur'== varid)){
+    arr=apply(arr, c(1, 2, 4), max, na.rm=TRUE)
+  }else{
+    arr=arr
+  }
+  
+  idx = which(grepl('0229', format(tx, "%m%d")) )
+  if(length(idx)>0){
+    tx = tx[-idx]
+  }
+  rt = list('x' = x.cord, 'y' = y.cord, 'arr' = arr, 'time' = tx)
+  return(rt)
+}
+
+
+initalGrids <- function(fn, vn, pd.gcs, pd.pcs, sp.ldas=NULL){
+  buf.g = readOGR(pd.gcs$wbd.buf)
+  ext = extent(buf.g)
+  
+  fid = nc_open(fn) 
+  nc.all = ReadNC(fid, varid = vn, t.len = 2)
+  nc.sub = ReadNC(fid, varid = vn, ext = ext,  t.len = 2)
+  nc_close(fid)
+  nc.all$x = round(nc.all$x, 3);   nc.all$y = round(nc.all$y, 3)
+  nc.sub$x = round(nc.sub$x, 3);   nc.sub$y = round(nc.sub$y, 3)
+  r = xyz2Raster(x = nc.all)
+  r.sub = xyz2Raster(x = nc.sub)
+  if(is.null(sp.ldas)){
+    sp.ldas = raster2Polygon(rx = r.sub)
+    # sp.center = gCentroid(sp.ldas, byid=TRUE)
+    
+    # =========PLOT===========================
+    png.control(fn=paste0(prefix, '_LDAS_location.png'), path = xfg$dir$fig, ratio=1)
+    plot(r * 0, col='gray', legend=FALSE)
+    plot(r.sub * 0, col='red', legend=FALSE, add=TRUE)
+    plot(buf.g, add=T)
+    dev.off()
+    
+    # =========Get the data===========================
+    sp0.gcs = spTransform(sp.ldas, xfg$crs.gcs)
+    sp0.pcs = spTransform(sp.ldas, xfg$crs.pcs)
+  }
+  id=which(gIntersects(sp0.gcs, buf.g, byid = T)) 
+  writeshape(sp0.gcs[id, ], file = pd.gcs$meteoCov)
+  writeshape(sp0.pcs[id, ], file = pd.pcs$meteoCov)
+  sitenames = paste0('X', sp0.gcs@data$xcenter, 'Y', sp0.gcs@data$ycenter)
+  sitenames=sitenames[id]
+  
+  # plot(sp0.gcs)
+  # plot(buf.g, add=TRUE, border=2)
+  # plot(sp0.gcs[id, ],add=T, col=3)
+  
+  retval = list(id=id, sitenames=sitenames, ext=ext)
+  return(retval)
+}
+
+
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+# library(ncdf4)
+# source('Rfunction/nc2fishnet.R')
+# fn = '/Volumes/Forcing/CMFD/Data_forcing_03hr_010deg/Prec/prec_CMFD_V0106_B-01_03hr_010deg_197901.nc'
+# fid = ncdf4::nc_open(fn)
+# x=readnc.CMFD(ncid=fid, varid = 'prec')
+# ncdf4::nc_close(fid)
+# spx = nc.fishnet(x)
+# crs(spx) = sp::CRS('+init=epsg:4326')
+# writeshape(spx, '/Volumes/Forcing/CMFD_fishnet')
+# raster::plot(spx)
+
+# xp = initalGrids(fn=fn, vn=tolower(varnames[1]), pd.gcs = pd.gcs, pd.pcs = pd.pcs)
+
+# x=dat
+# x$arr=x$arr[,,1]
+# nd=dim(x$arr)
+# for(i in 1:nd[3]){
+#   x$arr[id+nd[1]*nd[2]*(i-1)]=1000
+# }
+# r=xyz2Raster(x)
+# animate(r)
+
+# plot(r.sub);
+# plot(add=T, buf.g)
--- a/Deploy/shud/Rfunction/FLDAS_RDS2csv.R
+++ b/Deploy/shud/Rfunction/FLDAS_RDS2csv.R
@ -0,0 +1,69 @@
+# read the RDS above, to save as .csv file.
+
+unitConvert <- function(x){
+  t0=273.15
+  prcp = x[,'Rainf_f_tavg']
+  temp = x[,'Tair_f_tavg']
+  SH = x[,'Qair_f_tavg']
+  winds = x[,'Wind_f_tavg']
+  solar= x[,'Swnet_tavg']
+  press = x[,'Psurf_f_tavg']
+  
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  forcnames = c( "Precip", "Temp", "RH", "Wind", "RN" )
+  ret = cbind(prcp * 86400 /1000   , #mm/m2/s(FLDAS) to m/day (PIHM)
+              temp - t0   , # C
+              rh/100  ,  # PERCENTAGE
+              abs(winds) * 86400  , #m/s to m/day
+              solar *24 *3600   )
+  colnames(ret) = forcnames
+  ret = round(ret, 4)
+  ret
+}
+
+
+fns = file.path(dir.predata, paste0(prjname,'-', years, '.RDS'))
+
+cns = c('Rainf_f_tavg', 'Tair_f_tavg','Qair_f_tavg',
+        'Wind_f_tavg', 'Swnet_tavg','Lwnet_tavg', 
+        'Psurf_f_tavg')
+forcnames = c( "Prcp_mm.hr", "Temp_C", "RH_%", "Wind_m.s", "RN_w.m2" )
+
+nf=length(fns)
+for(i in 1:nf){
+  x=readRDS(fns[i])  
+  message(i,'/', nf, '\t', basename(fns[i]))
+  y=x[,cns,]
+  if(i==1){
+    dat = y
+  }else{
+    dat=abind::abind(dat, y, along=3)
+  }
+}
+dn = dimnames(dat)
+nd = dim(dat)
+xl = list()
+
+time = as.Date(dimnames(dat)[[3]],'%Y%m%d')
+for(i in 1:nd[1]){
+  message(i,'/', nd[1], '\t', dn[[1]][i] )
+  x = t( dat[i,,] )
+  y=unitConvert(x)
+  xl[[i]]=as.xts(y, order.by=time)
+}
+nx=length(xl)
+sitename = dn[[1]]
+sitename
+fns=paste0(sitename, '.csv')
+for(i in 1:nx){
+  fn=fns[i]
+  write.tsd(xl[[i]], file.path(dir.forc, fn))
+  if(i==1){
+    xmean = xl[[i]]
+  }else{
+    xmean = xmean + xl[[i]]
+  }
+}
+png.control(fn=paste0('Rawdata','_FLDAS_TS.png'), path = file.path(dir.png), ratio=1)
+plot.zoo(xmean/nx, main='FLDAS')
+dev.off()
--- a/Deploy/shud/Rfunction/FLDAS_nc2RDS.R
+++ b/Deploy/shud/Rfunction/FLDAS_nc2RDS.R
@ -0,0 +1,67 @@
+# read the orginal fldas data and save to .RDS file.
+
+#install.packages('ncdf4')
+require(ncdf4)
+source('GetReady.R')
+source('Rfunction/Fun.readnc.R')
+
+fl=readOGR(file.path(dir.predata, 'FLDAS_GCS.shp'))@data
+head(fl)
+# years=2017:2018
+dirs = file.path(dir.fldas, years)
+
+ndir = length(dirs)
+fn=list.files(dir.fldas, pattern=glob2rx('*.nc'), recursive = T, full.names = T)[1]
+
+fid=nc_open(fn)
+xloc = round(fid$dim$X$vals,2)
+yloc = round(fid$dim$Y$vals, 2)
+nx=length(xloc)
+ny = length(yloc)
+
+#===================================================
+xc = fl[,'xcenter']
+yc = fl[,'ycenter']
+
+xid = match(round(xc, 2), xloc)
+yid = match(round(yc,2), yloc)
+xyid=cbind(xid,yid)
+
+sn = paste0('X',xc*100, 'Y', yc*100)
+ns = length(sn)
+
+vns = names(fid$var)
+vns = vns[!(vns %in% 'time_bnds')] # don't need the time_bnds
+
+mat=ncvar_get(fid, vns[2])
+mat=mat*0+1
+png.control(fn=paste0('Rawdata','_FLDAS_location.png'), path = file.path(dir.png), ratio=1)
+image(xloc, yloc, mat, xlab='Lon', ylab='Lat', main='Coverage of FLDAS') ; grid()
+points(xloc[xid], yloc[yid], col=3)
+plot(wbd.gcs, add=T)
+dev.off()
+
+nv=length(vns)
+for(idd  in 1:ndir){ # for each year dir
+# library(foreach)
+# library(doMC)
+# library(doParallel)
+# registerDoMC(4)
+# foreach (idd = 1:ndir) %dopar%{
+  cdir <- dirs[idd]
+  fns = list.files(cdir, pattern=glob2rx('*.nc'), recursive = T, full.names = T)
+  nf = length(fns)
+  x.arr = array(0, dim=c(ns, nv, nf) )
+  x.t= character(nf)
+  for(j  in 1:nf){  # files in each year
+    fn=fns[j]
+    t=substr(basename(fn), 22, 29)
+    message(j, '/', nf, '\t', t)
+    x.mat = readnc(fn, xyid=xyid, vns=vns)
+    x.t[j] = t
+    x.arr[,,j ] = x.mat 
+  }
+  dimnames(x.arr) = list(sn, vns,  x.t)
+  fn.rds = file.path(dir.predata, paste0(prjname,'-', basename(cdir), '.RDS'))
+  saveRDS(x.arr, file=fn.rds)
+}
--- a/Deploy/shud/Rfunction/Fun.Soil_Geol.R
+++ b/Deploy/shud/Rfunction/Fun.Soil_Geol.R
@ -0,0 +1,83 @@
+
+
+fun.Soil_Geol <- function(xfg, TOP=TRUE,  col_ID = 2:5){
+  if(TOP){
+    fn.r = xfg$fn.soil
+    fn.tab = xfg$fa.soil
+    outdir = xfg$dir$predata
+  }else{
+    fn.r = xfg$fn.geol
+    fn.tab = xfg$fa.geol
+    outdir = xfg$dir$predata
+  }
+  
+  msg = 'fun.Soil_Geol:: '
+  if(!file.exists(fn.r)){
+    message(msg, 'Raster file is missing: ', fn.r)
+    stop('STOP WITH ERROR.')
+  }
+  if(!file.exists(fn.tab)){
+    message(msg, 'Attribute file is missing: ', fn.tab)
+    stop('STOP WITH ERROR.')
+  }
+  
+  if(file.exists(fn.r)){
+    r=raster(fn.r)
+  }else{
+    messge(msg, 'File does not exist or empty: ', fn.r)
+    stop(paste(msg, 'Exit with error in '))
+  }
+  # plot(r)
+  # x=foreign::read.dbf(fn.tab)
+  x=read.df(file=fn.tab)[[1]]
+  
+  ur =sort(unique(r))
+  nr=length(ur)
+  r1 = reclassify(r, cbind(ur, 1:nr))
+  idx = which(x[, 1] %in% ur)
+  fx <- function(x){
+    y = matrix(as.numeric( as.matrix(x)), ncol=4)
+    for(i in 1:4){
+      cc = y[, i]
+      if(i==4){
+        cc[cc<1.1] = NA
+      }
+      y[is.na(cc), i] = round(mean(cc, na.rm=TRUE), 3)
+    }
+    return(y)
+  }
+  cn = c('ID', 'SILT', 'CLAY', 'OM', 'BD')
+  texture = fx(x[idx, col_ID]) # ONLY the texture values, FOUR colums.
+  texture = cbind(1:nr, texture)
+  colnames(texture) = cn
+  if(TOP){
+    # write.df(texture, file=file.path(outdir, 'SOIL.csv'))
+    # para = PTF.soil(x=texture, rm.outlier = T)
+    fp = xfg$pd.pcs$soil.r
+    fg = xfg$pd.gcs$soil.r
+    fatt=xfg$pd.att$soil
+  }else{
+    # texture = fx(x[idx, col_ID])
+    write.df(texture, file=file.path(outdir, 'GEOL.csv'))
+    # para = PTF.geol(x=texture, rm.outlier = T)
+    # raster::writeRaster(r1, filename = file.path(outdir, 'GEOL.tif'), overwrite=TRUE)
+    fp = xfg$pd.pcs$geol.r
+    fg = xfg$pd.gcs$geol.r
+    fatt = xfg$pd.att$geol
+  }
+  
+  write.df(texture, file=fatt)
+  r2 = raster::projectRaster(r1, crs=xfg$crs.pcs)
+  raster::writeRaster(r1, filename = fp, overwrite=TRUE)
+  raster::writeRaster(r2, filename = fg, overwrite=TRUE)
+  texture[is.na(texture) | is.nan(texture)] = -9999
+  message(msg, 'Texture: ')
+  print(apply(texture, 2, summary))
+  # message(msg, 'Hydrologic parameters: ')
+  # print(apply(para, 2, summary))
+  
+  rl = list('raster' = r1, 
+            'texture' = texture)
+  return (rl)
+}
+
--- a/Deploy/shud/Rfunction/GLDAS_RDS2csv.R
+++ b/Deploy/shud/Rfunction/GLDAS_RDS2csv.R
@ -0,0 +1,90 @@
+# read the RDS above, to save as .csv file.
+
+source('AutoSHUD/Rfunction/LDAS_UnitConvert.R')
+write.tsd.custom <- function (x, file, append = F, quite = F, header = NULL) 
+{
+  mat = as.matrix(rbind(x))
+  nr = nrow(x)
+  nc = ncol(x)
+  if (!quite) {
+    message("Writing ", file)
+  }
+  tt = stats::time(x)
+  tday = as.numeric(difftime(tt, tt[1], units = "days"))
+  if (is.null(header)) {
+    t0 = format(time(x)[1], "%Y%m%d")
+    header = c(nr, nc + 1, t0)
+  }
+  dd = data.frame(Time_Day = tday, mat)
+  write(header, file = file, ncolumns = length(header), append = append, 
+        sep = "\t")
+  write(colnames(dd), file = file, ncolumns = nc + 1, append = T, 
+        sep = "\t")
+  write(t(dd), file = file, ncolumns = nc + 1, append = T, 
+        sep = "\t")
+}
+
+fun.GLDAS.RDS2csv <- function(xfg){
+  fns = file.path(xfg$dir$predata, paste0(xfg$prjname,'-', xfg$years, '.RDS'))
+  cns = c('Rainf_f_tavg', 'Tair_f_inst','Qair_f_inst',
+          'Wind_f_inst', 'Swnet_tavg','Lwnet_tavg', 
+          'Psurf_f_inst')
+  forcnames = c( "Prcp_mm.d", "Temp_C", "RH_%", "Wind_m.s", "RN_w.m2" )
+  
+  nf=length(fns)
+  for(i in 1:nf){
+    x=readRDS(fns[i])  
+    message(i,'/', nf, '\t', basename(fns[i]))
+    y=x[,cns,]
+    if(i==1){
+      dat = y
+    }else{
+      dat=abind::abind(dat, y, along=3)
+    }
+  }
+  dn = dimnames(dat)
+  nd = dim(dat)
+  xl = list()
+  
+  # TODDO: modify the time to extract correct time
+  time.tag = dimnames(dat)[[3]]
+  idx = which(!grepl(' ', time.tag))
+  time.tag[idx] = paste0(time.tag[idx], '00:00:00')
+  time = lubridate::ymd_hms(time.tag)
+  # time = as.POSIXct(dimnames(dat)[[3]], format= "%Y%m%d%H")
+  diff_seconds = as.numeric(difftime(time[[2]], time[[1]], units="hours")) * 3600
+  diff_seconds
+
+  for(i in 1:nd[1]){
+    message(i,'/', nd[1], '\t', dn[[1]][i] )
+    x = t( dat[i,,] )
+    y=unitConvert.GLDAS(x, diff_seconds)
+    xl[[i]]=as.xts(y, order.by=time)
+  }
+  nx=length(xl)
+  sitename = dn[[1]]
+  sitename
+  fns=paste0(sitename, '.csv')
+  xmean = NULL
+  for(i in 1:nx){
+    fn=fns[i]
+    # write this in correct format
+    # write.tsd.custom(xl[[i]], file.path(xfg$dir$forc, fn))
+    write.tsd(xl[[i]], file.path(xfg$dir$forc, fn))
+    if(all(is.na(xl[[i]]))){
+    }else{
+      if(is.null(xmean)){
+        xmean = xl[[i]]
+      }else{
+        xmean = xmean + xl[[i]]
+      }
+    }
+  }
+  xmean = xmean / nx
+  xmean[is.na(xmean)] <- -999
+  if(all(is.na(xmean))){}else{
+    png(file = file.path(xfg$dir$fig, paste0(xfg$prefix, '_GLDAS_TS.png')), height=7, width=11, res=200, unit='in')
+    plot.zoo(xmean, main='GLDAS')
+    dev.off()
+  }
+}
--- a/Deploy/shud/Rfunction/GLDAS_nc2RDS.R
+++ b/Deploy/shud/Rfunction/GLDAS_nc2RDS.R
@ -0,0 +1,111 @@
+# read the orginal fldas data and save to .RDS file.
+
+#install.packages('ncdf4')
+require(ncdf4)
+# library(rSHUD)
+# source('GetReady.R')
+
+# =========Forcing Coverage===========================
+# sp0 = readOGR(xfg$fsp.forc)
+# ========= Get the GRID===========================
+# spx = readOGR(xfg$pd.gcs$meteoCov)
+# fl=spx@data
+fun.GLDAS.nc2RDS <- function(xfg, res=0.25){
+  buf.g = readOGR(xfg$pd.gcs$wbd.buf)
+  ext = extent(buf.g)
+  dir.years = file.path(xfg$dir.ldas, xfg$years)
+  
+  ndir = length(dir.years)
+  xfg$dir.ldas
+  
+  writelog(msg=paste0('forcing dir:', xfg$dir.ldas), caller = caller)
+  fns=list.files(file.path(xfg$dir.ldas, '2002/100'), pattern=glob2rx('*.nc4'), recursive = T, full.names = T)[1]
+  # cmd = paste('find',xfg$dir.lddas,  ' -name "*.nc4" > filelist.txt')
+  # system(cmd)
+  # fns=readLines(file.path(xfg$dir.ldas, 'filelist.txt'))
+  
+  fid=nc_open(fns[1])  #打开一个NC文件
+  nc.all = rSHUD::readnc(fid, varid = 2)
+  nc.sub = rSHUD::readnc(fid, varid = 2, ext = ext)
+  nc_close(fid)
+  r = xyz2Raster(x = nc.all)
+  r.sub = xyz2Raster(x = nc.sub, res=res)
+  
+  # plot(r.sub); 
+  # plot(add=T, buf.g)
+  
+  vns = names(fid$var)
+  vns = vns[! grepl('time', tolower(vns))] # don't need the time_bnds
+  # =========PLOT===========================
+  go.plot <- function(prefix){
+    png(file.path(xfg$dir$fig, paste0(prefix, '_LDAS_location.png')), height=8, width=15, units = 'in', res=200)
+    plot(r * 0, col='gray', legend=FALSE)
+    plot(r.sub * 0, col='red', legend=FALSE, add=TRUE)
+    plot(buf.g, add=T)
+    grid();
+    mtext(side=3, 'GLDAS coverage (0.25 deg)')
+    dev.off()
+  }; go.plot(xfg$prefix)
+  # =========Get the data===========================
+  xfg$res = 0.25
+  yx = expand.grid(nc.sub$y, nc.sub$x)
+  rn = paste0('X', yx[, 2], 'Y',yx[,1])
+  ext.fn = c(range(yx[, 2]), range(yx[, 1]) ) + c(-1, 1, -1, 1) *0.5 * xfg$res
+  ext.fn
+  sp.forc = fishnet(xx=seq(ext.fn[1], ext.fn[2], xfg$res ), 
+                    yy=seq(ext.fn[3], ext.fn[4], xfg$res), crs=xfg$crs.gcs)
+  go.plot <- function(prefix){
+    png(file.path(xfg$dir$fig, paste0(prefix, '_LDAS_Coverage.png')), height=8, width=8, units = 'in', res=200)
+    plot(sp.forc, axes=TRUE); 
+    plot(buf.g, border='red', add=T); grid()
+    text(yx[, 2], yx[, 1]+xfg$res/5, paste0('X', yx[, 2]))
+    text(yx[, 2], yx[, 1]-xfg$res/5, paste0('Y', yx[, 1]))
+    mtext(side=3, 'GLDAS coverage (0.25 deg)')
+    dev.off()
+  }; go.plot(xfg$prefix)
+  
+  sp0.gcs = spTransform(sp.forc, xfg$crs.gcs)
+  sp0.pcs = spTransform(sp.forc, xfg$crs.pcs)
+  writeshape(sp0.gcs, file = xfg$pd.gcs$meteoCov)
+  writeshape(sp0.pcs, file = xfg$pd.pcs$meteoCov)
+
+  ns = length(rn)
+  nv=length(vns)
+  # library(foreach)
+  # library(doMC)
+  # library(doParallel)
+  # registerDoMC(12)
+  # foreach (idd = 1:ndir) %dopar%{
+  for (idd in 1:ndir) {
+    cdir <- dir.years[idd]
+    message(idd, '/', ndir, '\t', basename(cdir))
+    fn.rds = file.path(xfg$dir$predata, paste0(xfg$prjname,'-', basename(cdir), '.RDS'))
+    if(!file.exists(fn.rds)){
+      fns = list.files(cdir, pattern=glob2rx('*.nc4'), recursive = T, full.names = T)
+      nf = length(fns)
+      x.t= character(nf)
+      for(j  in 1:nf){  # files in each year
+        fn=fns[j]
+        message('\t', j, '/', nf, '\t', basename(fn))
+        ncid = nc_open(fn)
+        # debug(readnc)
+        x.nc = readnc(ncid, varid=vns, ext=ext)
+        nc_close(ncid)
+        if(j == 1){
+          d3 = dim(x.nc$arr)
+          x.arr = array(0, dim=c(d3[1] * d3[2], nv, nf) )
+        }
+        x.t[j] = strftime(x.nc$time, usetz = FALSE, tz='UTC')
+        x.arr[ , , j] = matrix(x.nc$arr, ncol=nv)
+      }
+      dimnames(x.arr) = list(rn, vns,  x.t)
+      saveRDS(x.arr, file=fn.rds)
+    }else{
+    }
+  }
+}
+
+# xfg = CV$xfg
+# xfg$prefix = 'AutoSHUD_S2'
+# fun.GLDAS.nc2RDS(xfg)
+# fun.GLDAS.RDS2csv(xfg)
--- a/Deploy/shud/Rfunction/LDAS_UnitConvert.R
+++ b/Deploy/shud/Rfunction/LDAS_UnitConvert.R
@ -0,0 +1,94 @@
+unitConvert.NLDAS <- function(x, diff_seconds){
+  t0=273.15
+  prcp = x[,'APCP']
+  temp = x[,'TMP']
+  SH = x[,'SPFH']
+  winds = x[,'UGRD']
+  solar= x[,'DSWRF']
+  press = x[,'PRES']
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  forcnames = c( "Precip_mm.d", "Temp_C", "RH_1", "Wind_m.s", "RN_w.m2" )
+  ret = cbind(prcp * 86400 / diff_seconds, # mm/hr(NLDAS) to mm/day (SHUD)
+              temp - t0   , # C
+              rh  ,  # Ratio 0-1
+              abs(winds), #m/s 
+              solar  # w/m2
+  )
+  colnames(ret) = forcnames
+  ret = round(ret, 4)
+  ret
+}
+
+unitConvert.CMIP6 <- function(x, diff_seconds){
+  # "pr"      "tas"      "huss"     "rsds"    "sfcWind"
+  t0=273.15
+  prcp = x[,'pr']
+  temp = x[,'tas']
+  temp[temp < 200] = t0
+  SH = x[,'huss']
+  winds = x[,'sfcWind']
+  solar= x[,'rsds']
+  press = 80000
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  # plot(rh)
+  forcnames = c( "Precip_mm.d", "Temp_C", "RH_1", "Wind_m.s", "RN_w.m2" )
+  ret = cbind(prcp * 86400 ,   # "kg m-2 s-1" (GLDAS )  to "mm  day" (SHUD)
+              temp - t0   , # C
+              rh / 100  ,  # Ratio 0-1
+              abs(winds), #m/s 
+              solar  # w/m2
+  )
+  colnames(ret) = forcnames
+  ret = round(ret, 4)
+  ret
+}
+# tsd = as.xts(x, order.by=as.Date(rownames(x)))
+# plot(tsd[, 1])
+# y=unitConvert.CMIP6(x)
+
+unitConvert.GLDAS <- function(x, diff_seconds){
+  t0=273.15
+  prcp = x[,'Rainf_f_tavg']
+  temp = x[,'Tair_f_inst']
+  SH = x[,'Qair_f_inst']
+  winds = x[,'Wind_f_inst']
+  solar= x[,'Swnet_tavg']
+  press = x[,'Psurf_f_inst']
+  
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  forcnames = c( "Precip_mm.d", "Temp_C", "RH_1", "Wind_m.s", "RN_w.m2" )
+
+  ret = cbind(prcp * 86400 ,   # "kg m-2 s-1" (GLDAS ncid$var$Rainf_tavg$units)  to "mm m-2 day" (SHUD)
+              temp - t0   , # C
+              rh  ,  # Ratio 0-1
+              abs(winds), #m/s 
+              solar  # w/m2
+  )
+  colnames(ret) = forcnames
+  ret = round(ret, 4)
+  ret
+}
+
+unitConvert.CMFD <- function(x){
+  t0=273.15
+  prcp = x[,'Prec']
+  temp = x[,'Temp']
+  SH = x[,'SHum']
+  winds = x[,'Wind']
+  solar= x[,'SRad']
+  press = x[,'Pres']
+  
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  forcnames = c( "Precip_mm.d", "Temp_C", "RH_1", "Wind_m.s", "RN_w.m2" )
+  p_mm.day = prcp * 24 
+  p_mm.day[p_mm.day  < 1e-4 ] = 0.0  # prcp < 0.1 mm/day, No rain then.
+  ret = cbind(prcp*24  , #mm/hr(CMFD) to mm/day (SHUD 2021.12)
+              temp - t0   , # C
+              rh/100  ,  # Ratio 0-1
+              abs(winds)  , #m/s to m/s
+              solar   )  # w/m2
+  ret = round(ret, 4)
+  colnames(ret) = forcnames
+  ret
+}
+
--- a/Deploy/shud/Rfunction/NLCD.R
+++ b/Deploy/shud/Rfunction/NLCD.R
@ -0,0 +1,75 @@
+#' PIHM Analysis project.
+#' Developed by Lele Shu( lele.shu at gmail.com  lzs157 at psu.edu ) 
+#' Created by  Thu Apr 16 10:53:00 EDT 2015
+#'  <- ============================================
+#'  Current version is for PIHM 2.0 and above;
+#' 
+
+NLCD2lc <- function (years=1979:2016){
+    att=readatt()
+    lc= att[,4];
+        message('LC  = ')
+    ulc=sort(unique(att[,4]))
+        print( ulc)
+    if (length( which (ulc <10 | ulc >100) ) ){
+        stop('Current LC code is not NLCD')
+    }
+    # VEGPRMT.TBL
+    veg = fun.vegtable(lc =ulc )
+    if (PIHMVER ==2.4){
+        writeveg(path=inpath,fn=paste0(PRJNAME,'.lc'), x=veg)
+    }else{
+        writeveg(path=dirname(inpath), x=veg)
+    }
+
+    # att file
+    for (i in 1:length(ulc)){
+         key = ulc[i];
+        lc[which(lc == key) ]= i
+    }
+    att[,'LC']= lc
+    #att[,'LAI']=lc
+    writeatt(att)
+    
+    #.lai FILE.
+    lr = fun.lairl(lc=ulc,years=years)
+    writelai(x=lr)
+}
+
+
+
+lc.OID2NLCD <- function(lc){
+    att <- readatt();
+#lc=sort(unique( att[,'LC'] ) );
+
+    oid <- att[,'LC'] ;
+    ids <- numeric(length(lc));
+    len <- length(lc);
+
+    for( i in 1:len) {
+        att[oid==i,'LC']=lc[i];
+    }
+    ret <- att;
+   writeatt(att);
+}
+lc.NLCD2OID <- function(){
+    att <- readatt();
+    lc=sort(unique( att[,'LC'] ) );
+    if (min(lc) < 11 | max(lc) > 95){
+        print(lc)
+        stop('Check the value of LC in att, they may not be the NLCD code')
+    }
+    oid <- att[,'LC'] ;
+    len <- length(lc);
+
+    
+    write.table(file=paste0(PRJNAME,'.NLCD.csv'), 
+                cbind('OID'=1:len, 'NLCD'=lc),
+                quote=FALSE, row.names=FALSE)
+    for( i in 1:len) {
+        att[oid==lc[i],'LC']=i;
+    }
+    ret <- att;
+    
+   writeatt(att);
+}
--- a/Deploy/shud/Rfunction/NLCD.colors.R
+++ b/Deploy/shud/Rfunction/NLCD.colors.R
@ -0,0 +1,96 @@
+#' PIHM Analysis project.
+#' Developed by Lele Shu( lele.shu at gmail.com  lzs157 at psu.edu ) 
+#' Fri Sep  2 15:32:54 EDT 2016
+#' =============================================
+#' @param  x, which is the integer number defined in NLCD 2001,2006, and 2011.  Any value out of (0,100) will be assigned as black.
+#' @keywords NLCD, Landuse, colormap
+#' @return HEX color strings
+#' @examples
+#' lc=c(11,21,22,23,24,31,41,42,43,52,71,81,82,90,95)
+#' nx=length(x)
+#' x = numeric(nx)+1
+#' names(x)= lc
+#' barplot(lc, x, col=NLCD.colors(lc) )
+#' @source See the link of NLCD: \url{http://www.mrlc.gov/nlcd11_leg.php} 
+NLCD.colors <- function(x,def.col = '#EEEEEE'){
+   # 2001, 2006, 2011 version 
+   #reference http://www.mrlc.gov/nlcd11_leg.php
+    
+    lccol = matrix(def.col, nrow=100)
+    lccol[1]=	"#00fa00"
+    lccol[11]=	"#476ba1"
+    lccol[12]=	"#d1defa"
+    lccol[21]=	"#decaca"
+    lccol[22]=	"#d99482"
+    lccol[23]=	"#ee0000"
+    lccol[24]=	"#ab0000"
+    lccol[31]=	"#b3aea3"
+    lccol[32]=	"#fafafa"
+    lccol[41]=	"#68ab63"
+    lccol[42]=	"#1c6330"
+    lccol[43]=	"#b5ca8f"
+    lccol[51]=	"#a68c30"
+    lccol[52]=	"#ccba7d"
+    lccol[71]=	"#e3e3c2"
+    lccol[72]=	"#caca78"
+    lccol[73]=	"#99c247"
+    lccol[74]=	"#78ae94"
+    lccol[81]=	"#dcd93d"
+    lccol[82]=	"#ab7028"
+    lccol[90]=	"#bad9eb"
+    lccol[91]=	"#b5d4e6"
+    lccol[92]=	"#b5d4e6"
+    lccol[93]=	"#b5d4e6"
+    lccol[94]=	"#b5d4e6"
+    lccol[95]=	"#70a3ba"
+
+    x=round(x)
+    #x=sort(round(x), decreasing=FALSE);
+    ret = rep(def.col, length(x));
+    
+    id = which( x %in% 1:100)
+    ret[id] = lccol[x[id]]
+    
+    return(ret)
+}
+
+NLCD.names <- function(x,def.name = 'n/a'){
+   # 2001, 2006, 2011 version 
+   #reference http://www.mrlc.gov/nlcd11_leg.php
+    
+    nlcd.names = matrix(def.name, nrow=100)
+    nlcd.names[1]=	"n/a"
+    nlcd.names[11]=	"Open Water"
+    nlcd.names[12]=	"Perennial Ice/Snow"
+    nlcd.names[21]=	"Developed, Open Space"
+    nlcd.names[22]=	"Developed, Low Intensity"
+    nlcd.names[23]=	"Developed, Medium Intensity"
+    nlcd.names[24]=	"Developed, High Intensity"
+    nlcd.names[31]=	"Barren Land (Rock/Sand/Clay)"
+    nlcd.names[32]=	"n/a"
+    nlcd.names[41]=	"Deciduous Forest"
+    nlcd.names[42]=	"Evergreen Forest"
+    nlcd.names[43]=	"Mixed Forest"
+    nlcd.names[51]=	"Dwarf Scrub"
+    nlcd.names[52]=	"Shrub/Scrub"
+    nlcd.names[71]=	"Grassland/Herbaceous"
+    nlcd.names[72]=	"Sedge/Herbaceous"
+    nlcd.names[73]=	"Lichens"
+    nlcd.names[74]=	"Moss"
+    nlcd.names[81]=	"Pasture/Hay"
+    nlcd.names[82]=	"Cultivated Crops"
+    nlcd.names[90]=	"Woody Wetlands"
+    nlcd.names[91]=	"n/a"
+    nlcd.names[92]=	"n/a"
+    nlcd.names[93]=	"n/a"
+    nlcd.names[94]=	"n/a"
+    nlcd.names[95]=	"Emergent Herbaceous Wetlands"
+
+    x=round(x);
+    ret = rep(def.name, length(x));
+    
+    id = which( x %in% 1:100)
+    ret[id] = nlcd.names[x[id]]
+    
+    return(ret)
+}
--- a/Deploy/shud/Rfunction/NLDAS_RDS2csv.R
+++ b/Deploy/shud/Rfunction/NLDAS_RDS2csv.R
@ -0,0 +1,40 @@
+
+
+unitConvert <- function(x, diff_seconds){
+  t0=273.15
+  prcp = x[,'APCP']
+  temp = x[,'TMP']
+  SH = x[,'SPFH']
+  winds = x[,'UGRD']
+  solar= x[,'DSWRF']
+  press = x[,'PRES']
+  
+  rh = 0.263*press*SH/exp(17.67 * (temp - t0) /(temp - 29.65) ) # specific hum to relative hum
+  forcnames = c( "Precip", "Temp", "RH", "Wind", "RN" )
+  ret = cbind(prcp * 86400 / diff_seconds, # mm/hr(NLDAS) to mm/day (SHUD)
+              temp - t0   , # C
+              rh  ,  # PERCENTAGE
+              abs(winds), #m/s 
+              solar  # w/m2
+  )
+  # ret = cbind(prcp * diff_seconds /1000   , #mm/m2/s(FLDAS) to m/day (SHUD)
+  #             temp - t0   , # C
+  #             rh/100  ,  # PERCENTAGE
+  #             abs(winds) * diff_seconds  , #m/s to m/day
+  #             solar *diff_seconds  )
+  colnames(ret) = forcnames
+  ret
+}
+# 
+for(i in 1:n3){
+  fn.csv = filename[i]
+  message(i, '/', n3, '\t', basename(fn.csv))
+  xt = as.xts(t(aa[, , i]), order.by = ts)
+  yt = unitConvert(xt, 3600)
+  print(mean(apply.yearly(yt$Precip, sum)/24))
+}
+
+# xt = as.xts(t(aa[, , i]), order.by = ts)
+# pd = apply.daily(xt$APCP, sum)
+# apply.yearly(pd, sum)
+
--- a/Deploy/shud/Rfunction/NLDAS_nc2RDS.R
+++ b/Deploy/shud/Rfunction/NLDAS_nc2RDS.R
@ -0,0 +1,136 @@
+# read the orginal fldas data and save to .RDS file.
+# good for PALEOFLOOD project in Arizona. 2021-04-04
+require(ncdf4)
+source('Rfunction/LDAS_UnitConvert.R')
+
+sp.ldas = readOGR(pd.gcs$meteoCov)
+fl = sp.ldas@data
+
+buf.g = readOGR(pd.gcs$wbd.buf)
+ext = extent(buf.g)
+
+ext.nldas = c(-125, -67, 25, 53)
+res = xfg$res
+xx = seq(ext.nldas[1] + res / 2, ext.nldas[2] - res / 2, by=res)
+yy = seq(ext.nldas[3] + res / 2, ext.nldas[4] - res / 2, by=res)
+sp.fnpt =fishnet(xx = xx, yy=yy, crs =crs(sp.ldas), type='point')
+# writeshape(sp.fnpt, 'fnpt')
+# idx.fn = rgeos::gContains(sp.ldas, sp.fnpt, byid = TRUE)
+# idx.fn = gIntersects(sp.ldas, sp.fnpt, byid = TRUE)
+# idx = unlist(apply(idx.fn, 2, which))
+# pt.xy = sp.fnpt@data
+
+nlon = length(xx)
+nlat = length(yy)
+px = sp.ldas@data$xcenter
+py = sp.ldas@data$ycenter
+idx.cr=cbind( match(px, xx), match(py, yy)) # match column and row.
+
+dir.years = file.path(xfg$dir.ldas, xfg$years)
+
+ndir = length(dir.years)
+fn=list.files(xfg$dir.ldas, pattern=glob2rx('*.RDS'), recursive = T, full.names = T)[1]
+fn
+x=readRDS(fn)
+# vns = dimnames(x$data)[[3]]
+fun.toRaster <- function(x){
+  r = raster(); 
+  extent(r) = ext.nldas; 
+  res(r) = xfg$res
+  r=setValues(r, as.numeric(x))
+  r
+}
+
+r = fun.toRaster( x$data[, nlat:1 , 1, 1] )
+tmp =  x$data[, , 1, 1]*0
+tmp[idx.cr[, 1], idx.cr[, 2]]= 1 #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+r1 = fun.toRaster(x=tmp[, nlat:1])
+
+png.control(fn=paste0(prefix, '_LDAS_location.png'), path = xfg$dir$fig, ratio=1, ht=9, wd=12, res=300)
+# plot(r, col='gray',legend=FALSE)
+plot(r1,legend=FALSE)
+plot(sp.ldas, add=TRUE, border=4)
+plot(buf.g, add=T)
+dev.off()
+# stop()
+
+filename = paste0('X', px, 'Y', py)
+vns = c("TMP", "SPFH", "PRES", "UGRD", "VGRD", "PEVAP", "APCP", "DSWRF" )
+
+i.dir=1
+ns=length(sp.ldas)
+nv=length(vns)
+
+# library(foreach)
+# library(doMC)
+# library(doParallel)
+# registerDoMC(12)
+# foreach (idd = 1:ndir) %dopar%{
+for (i.dir in 1:ndir) {
+  cdir <- dir.years[i.dir]
+  fns = list.files(cdir, pattern=glob2rx('*.RDS'), recursive = T, full.names = T)
+  nf = length(fns)
+  # message('Number of RDS files: ', nf)
+  message(i.dir, '/', ndir, '\t', basename(cdir), '\t', nf)
+  x.arr = array(NA, dim=c(nv, nf*24, ns) )
+  nlen = 0
+  str.time = character(nf*24)
+  fn.rds = file.path(xfg$dir$predata, paste0(xfg$prjname,'-', basename(cdir), '.RDS'))
+  if(!file.exists(fn.rds)){
+    x.year = NULL
+    for(i.file  in 1:nf){  # files in each year
+      fn=fns[i.file]
+      message('\t', i.file, '/', nf, '\t', basename(fn))
+      x=readRDS(fn)
+      x.day = array(dim = c(length(vns), dim(x$data)[4], ns))
+      for( i.site in 1:ns){
+        x.day[, , i.site] = x$data[idx.cr[i.site, 1],idx.cr[i.site, 2], vns , ]
+      }
+      xt.i = dimnames(x$data)[[4]]
+      nt = length(xt.i)  
+      x.arr[, nlen + (1:nt)  , ] = x.day
+      str.time[nlen + (1:nt) ] = xt.i
+      nlen = nlen+nt;
+    }
+    print(nlen)
+    # x.arr = array(NA, dim=c(nv, nf*24, ns) )
+    x.arr = x.arr[1:nv, 1:nlen, 1:ns]
+    str.time=str.time[1:nlen]
+    dns = list(vns, str.time, filename)
+    dimnames(x.arr) = dns
+    saveRDS(x.arr, file=fn.rds)
+    # xt.i = as.POSIXct(dimnames(x.day)[[2]], format='%Y-%m-%d.%H.%M.%S',  usetz = FALSE, tz='UTC')
+  }else{
+    x.arr = readRDS(fn.rds)
+    dns = dimnames(x.arr)
+  }
+  n2=dim(x.arr)[2]
+  if(i.dir == 1){
+    nmove = 0
+    NN2 =  c(nv, 24*366*ndir, ns)
+    aa = array(dim = NN2)
+    dds = rep('NA', NN2[2])
+  }else{
+  }
+  dds[nmove + 1:n2] = dimnames(x.arr)[[2]]
+  aa[ , nmove + 1:n2, ] = x.arr[vns, ,]
+  nmove = nmove + n2
+}
+aa=aa[, 1:nmove, ]
+stime = dds[1:nmove]
+dimnames(aa) = list(dns[[1]], stime, dns[[3]])
+
+n3  = dim(aa)[3]
+ts = as.POSIXct(stime, format='%Y-%m-%d.%H.%M.%S', tz='UTC')
+filename = file.path(xfg$dir$forc, paste0(dimnames(aa)[[3]], '.csv') )
+for(i in 1:n3){
+  fn.csv = filename[i]
+  message(i, '/', n3, '\t', basename(fn.csv))
+  xt = as.xts(t(aa[, , i]), order.by = ts)
+  yt = unitConvert.NLDAS(xt, 3600)
+  # barplot(apply.daily(yt$Precip, max)[1:150])
+  # readline(paste0(i))
+  # print(mean(apply.yearly(yt$Precip, sum)/24))
+  write.tsd(yt, file = fn.csv)
+}
+
--- a/Deploy/shud/Rfunction/ProjectionGrids.R
+++ b/Deploy/shud/Rfunction/ProjectionGrids.R
@ -0,0 +1,10 @@
+
+GaussKruger <- function(){
+  
+  x = fishnet(ext=c(-180, 180, -90, 90), dx = 6, dy=180)
+  plot(x, axes=T)
+  
+  x
+  
+}
+
--- a/Deploy/shud/Rfunction/ReadProject.R
+++ b/Deploy/shud/Rfunction/ReadProject.R
@ -0,0 +1,190 @@
+read.prj <- function(CV){
+  fn.prj = CV$deploy$config 
+  crs.gcs = CV$para$gcs
+  dir.predata = CV$dirs$model
+  
+  message('Reading project file ', fn.prj)
+  getVAL <- function(x, valname, real=FALSE, defVal = NULL){
+    names(x) = toupper(names(x))
+    valname = toupper(valname)
+    y = x[[valname]]
+    if(is.null(y)) {
+      # if(is.null(defVal)){
+      #   message('Error: value ', valname, ' is missing')
+      #   stop('GetVal')
+      # }else{
+      r = NULL
+      # }
+    }else{
+      r = y
+    }
+    if(real){
+      r = as.numeric(r)
+    }
+    return(r)
+  }
+  
+  if(file.exists(fn.prj)){
+    # tmp=read.table(fn.prj, header = F, row.names = 1)
+    t1 = readLines(fn.prj, skipNul = TRUE)
+    idx = which( grepl('^#', t1) | grepl('^ *$', t1) )
+    if(length(idx)>0){
+      t2 = t1[-1 * idx]
+    }else{
+      t2 = t1
+    }
+    tmp=read.table(text=t2, header = F, row.names = 1)
+    xcfg = data.frame(t(tmp), stringsAsFactors = FALSE)
+    # print(xcfg)
+  }else{
+    stop('File missing: ', fn.prj)
+  }
+  
+  isoil = getVAL(xcfg, 'Soil', TRUE)
+  ilanduse = getVAL(xcfg, 'landuse', TRUE)
+  iforcing = getVAL(xcfg, 'forcing', TRUE)
+  if(isoil >= 1){
+    # local soil data.
+    fn.soil = getVAL(xcfg, 'fn.soil')
+    fn.geol = getVAL(xcfg, 'fn.geol')  
+    tab.soil = getVAL(xcfg, 'tab.soil')  
+    tab.geol = getVAL(xcfg, 'tab.geol')  
+  }else{
+    # global soil data.
+    dir.soil = getVAL(xcfg, 'dir.soil')  
+  }
+  
+  dir.ldas = getVAL(xcfg, 'dir.ldas')
+  dir.out = getVAL(xcfg, 'dir.out')
+  dout.forc = getVAL(xcfg, 'dout.forc')
+  
+  prjname=getVAL(xcfg, 'prjname')
+  years=getVAL(xcfg, 'startyear', TRUE): getVAL(xcfg, 'endyear', TRUE)
+  
+  fsp.wbd = getVAL(xcfg, 'fsp.wbd')
+  fsp.stm = getVAL(xcfg, 'fsp.stm')
+  fsp.forc = getVAL(xcfg, 'fsp.forc')
+  fsp.lake = getVAL(xcfg, 'fsp.lake')
+  
+  fr.dem = getVAL(xcfg, 'fr.dem')
+  fn.landuse = getVAL(xcfg, 'fn.landuse')
+  tab.landuse = getVAL(xcfg, 'tab.landuse')
+  MaxArea = getVAL(xcfg, 'MaxArea', TRUE, defVal = 1) * 1e6
+  NumCells = getVAL(xcfg, 'NumCells', TRUE, 1000)
+  AqDepth = getVAL(xcfg, 'AqDepth', TRUE, 10)
+  flowpath = getVAL(xcfg, 'flowpath', TRUE, 0)
+  MinAngle = getVAL(xcfg, 'MinAngle', TRUE, 31)
+  MAX_SOLVER_STEP = getVAL(xcfg, 'MAX_SOLVER_STEP', TRUE, 2)
+  CRYOSPHERE = getVAL(xcfg, 'CRYOSPHERE', TRUE, 2)
+  STARTDAY = getVAL(xcfg, 'STARTDAY', TRUE, 0)
+  ENDDAY = getVAL(xcfg, 'ENDDAY', TRUE, 365)
+  DistBuffer = getVAL(xcfg, 'DistBuffer', TRUE, CV$para$distBuff)
+  
+  if(is.null(CV$para$pcs)){
+    crs.fn <- getVAL(xcfg, 'fsp.crs')
+    if( !is.null(crs.fn)){
+      if(file.exists(crs.fn)){
+        message('CRS file:', crs.fn)
+        crs.pcs <- raster::crs(rgdal::readOGR(crs.fn))
+      }else{
+        message('CRS file is missing. So Albers projection is used')
+        crs.pcs <-  rSHUD::crs.Albers(rgdal::readOGR(fsp.wbd))
+        message(crs.pcs)
+      }
+    }else{
+      message('CRS file is missing. So Albers projection is used')
+      crs.pcs <-  rSHUD::crs.Albers(rgdal::readOGR(fsp.wbd))
+      message(crs.pcs)
+    }
+  }else{
+    crs.pcs = CV$para$pcs
+  }
+  
+  # ===============================
+  tol.wb = getVAL(xcfg, 'tol.wb', TRUE, NULL)
+  tol.rivlen = getVAL(xcfg, 'tol.rivlen', TRUE, NULL)
+  
+  RivWidth = getVAL(xcfg, 'RivWidth', TRUE, NULL)
+  RivDepth = getVAL(xcfg, 'RivDepth', TRUE, NULL)
+  
+  QuickMode = (getVAL(xcfg, 'QuickMode') > 0) 
+  
+  dir.png =file.path(dir.out, 'Figure')
+  dir.modelin <- file.path(dir.out, 'input', prjname)
+  dir.modelout <- file.path(dir.out, 'output', paste0(prjname, '.out') )
+  
+  dir.forc=getVAL(xcfg, 'fc.out',  real=FALSE, file.path(dir.out, 'forcing'))
+  dirlist = list(out=dir.out, 
+                 fig=dir.png, 
+                 predata=dir.predata, 
+                 modelin=dir.modelin, 
+                 modelout=dir.modelout,
+                 forc = dir.forc)
+  if(isoil < 1){
+    dirlist = c(dirlist, soil=dir.soil)
+  }
+  tmp=lapply(dirlist, dir.create, showWarnings=F, recursive=T)
+  
+  # Some Constant values in the working environments.
+
+  
+  cfg = list('prjname' = prjname,
+             'years' = years,
+             'crs.pcs'=crs.pcs,
+             'crs.gcs'=crs.gcs,
+             'fsp.wbd'=fsp.wbd,
+             'fsp.stm'=fsp.stm,
+             'fsp.lake' = fsp.lake,
+             'fr.dem'=fr.dem,
+             
+             'dir'=dirlist,
+             'iforcing' = iforcing,
+             'fsp.forc'=fsp.forc,
+             'dout.forctsd' = dout.forc,
+             
+             
+             'ilanduse' = ilanduse,
+             'fn.landuse'=fn.landuse,
+             'tab.landuse'=tab.landuse,
+             
+             'isoil' = isoil,
+             
+             'para' = list(
+               'NumCells'=NumCells,
+               'AqDepth'=AqDepth,
+               'MaxArea'=MaxArea,
+               'QuickMode'=QuickMode,
+               'tol.wb'=tol.wb,
+               'tol.rivlen'=tol.rivlen,
+               'RivWidth'=RivWidth,
+               'RivDepth'=RivDepth,
+               'DistBuffer'=DistBuffer,
+               'flowpath' = flowpath,
+               'MinAngle'=MinAngle,
+               'MAX_SOLVER_STEP' = MAX_SOLVER_STEP,
+               'CRYOSPHERE'=CRYOSPHERE,
+               'STARTDAY'=STARTDAY,
+               'ENDDAY'=ENDDAY
+             )
+  )
+  if(isoil>=1){
+    cfg = c(cfg, 
+            'isoil' = isoil,
+            'fn.soil'=fn.soil,
+            'fn.geol'=fn.geol,
+            'tab.soil'=tab.soil,
+            'tab.geol'=tab.geol)
+  }else{
+    # void
+  }
+  if(iforcing < 1){
+    # res = getVAL(xcfg, 'res', real = TRUE)
+    cfg = c(cfg,   dir.ldas = dir.ldas)
+  }else{
+    # void
+  }
+  return(cfg)
+}
+
+# 
+# xfg <- read.prj(CV)
--- a/Deploy/shud/Rfunction/RivWidth.R
+++ b/Deploy/shud/Rfunction/RivWidth.R
@ -0,0 +1,27 @@
+AA = seq(1e3, AA1, length.out = 10)[-1]
+AA = 10 ^ (0:6 ) * 1e6
+a = 3.2
+ord=rbind(1:5)
+b= max(ord) * .05
+
+# W = a*log(AA)^(b*ord)
+fx.W <- function(x) {return(  (log(AA) / 1) ^ (1.2^x) )}
+
+fx.W <- function(x, a, b) {return( log10(AA/1000) ^ a /(1 + exp(- b * 2^x)) )}
+fx.D <- function(w){return( log(w) ^ (1+b) ) }
+
+
+# fx.W <- function(x) {return(log(AA) * log(AA) ^  (b * x) )}
+# 
+# fx.W <- function(x) {return( a + b ^ (1/ log(AA) ^  (b * x) ) )}
+w =  round(t(apply(ord, 2, fx.W, a=a, b=b)), 0)
+d = round(fx.D(w), 1)
+
+dim(w)
+par(mfrow=c(2,1))
+matplot(w, type='l', log=''); grid()
+matplot(d, type='l', log=''); grid()
+
+print(round(AA/1e6))
+apply(d, 1, summary)
+apply(w, 1, summary)
--- a/Deploy/shud/Rfunction/SoilGeol.R
+++ b/Deploy/shud/Rfunction/SoilGeol.R
@ -0,0 +1,15 @@
+
+SoilGeol <- function(spm, rdsfile){
+  # rdsfile=file.path(dir.pihmgis, 'Soil_sl1.tif')  
+  xy = coordinates(spm)
+  # plot(xy, asp=1)
+  xl=readRDS(rdsfile)
+  nx=length(xl)
+  mat=matrix(0, nrow=nrow(xy), ncol=4)
+  for(i in 1:nx){
+    r=xl[[i]]
+    mat[,i]=raster::extract(xl[[i]], xy)
+  }
+  colnames(mat) = names(xl)
+  return(mat)
+}
--- a/Deploy/shud/Rfunction/fun.LAIRL.R
+++ b/Deploy/shud/Rfunction/fun.LAIRL.R
@ -0,0 +1,123 @@
+# cfun <- function (x,tab, type=1) {
+#   #Source: http://www.pihm.psu.edu/EstimationofVegitationParameters.htm
+#   dlai=rbind(c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
+#              c(   8.76,  9.16,  9.827,  10.093,  10.36,  10.76,  10.493,  10.227,  10.093,  9.827,  9.16,  8.76),
+#              c(   5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117,  5.117),
+#              c(    8.76,  9.16,  9.827,  10.093,  10.36,  10.76,  10.493,  10.227,  10.093,  9.827,  9.16,  8.76),
+#              c(    0.52,  0.52,  0.867,  2.107,  4.507,  6.773,  7.173,  6.507,  5.04,  2.173,  0.867,  0.52),
+#              c(    4.64,  4.84,  5.347,  6.1,  7.4335,  8.7665,  8.833,  8.367,  7.5665,  6,  5.0135,  4.64),
+#              c(    5.276088,  5.528588,  6.006132,  6.4425972,  7.2448806,  8.3639474,  8.540044,  8.126544,  7.2533006,  6.3291908,  5.6258086,  5.300508),
+#              c(   2.3331824,  2.4821116,  2.7266101,  3.0330155,  3.8849492,  5.5212224,  6.2395131,  5.7733017,  4.1556703,  3.1274641,  2.6180116,  2.4039116 ),
+#              c(   0.580555,  0.6290065,  0.628558,  0.628546,  0.919255,  1.7685454,  2.5506969,  2.5535975,  1.7286418,  0.9703975,  0.726358,  0.6290065 ),
+#              c(    0.3999679,  0.4043968,  0.3138257,  0.2232945,  0.2498679,  0.3300675,  0.4323964,  0.7999234,  1.1668827,  0.7977234,  0.5038257,  0.4043968),
+#              c(    0.782,  0.893,  1.004,  1.116,  1.782,  3.671,  4.782,  4.227,  2.004,  1.227,  1.004,  0.893),
+#              c(    0.782,  0.893,  1.004,  1.116,  1.782,  3.671,  4.782,  4.227,  2.004,  1.227,  1.004,  0.893),
+#              c(   0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001,  0.001 ),
+#              c(    1.2867143,  1.3945997,  1.5506977,  1.7727263,  2.5190228,  4.1367678,  5.0212291,  4.5795799,  2.8484358,  1.8856229,  1.5178736,  1.3656797)
+#   );
+#   drl=rbind(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
+#             c(	1.112, 1.103, 1.088, 1.082, 1.076, 1.068, 1.073, 1.079, 1.082, 1.088, 1.103, 1.112),
+#             c(	2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653, 2.653),
+#             c(	1.112, 1.103, 1.088, 1.082, 1.076, 1.068, 1.073, 1.079, 1.082, 1.088, 1.103, 1.112),
+#             c(	0.52, 0.52, 0.666, 0.91, 1.031, 1.044, 1.042, 1.037, 1.036, 0.917, 0.666, 0.52),
+#             c(	0.816, 0.8115, 0.877, 0.996, 1.0535, 1.056, 1.0575, 1.058, 1.059, 1.0025, 0.8845, 0.816),
+#             c(	0.7602524, 0.7551426, 0.7772204, 0.8250124, 0.846955, 0.8449668, 0.8471342, 0.8496604, 0.8514252, 0.8299022, 0.7857734, 0.7602744),
+#             c(	0.35090494, 0.34920916, 0.36891486, 0.40567288, 0.42336056, 0.42338372, 0.42328378, 0.42485112, 0.42631836, 0.40881268, 0.37218526, 0.35096866),
+#             c(	0.05641527, 0.05645892, 0.05557872, 0.05430207, 0.05425842, 0.05399002, 0.05361482, 0.0572041, 0.05892068, 0.05821407, 0.05709462, 0.05645892),
+#             c(	0.03699235, 0.03699634, 0.03528634, 0.03272533, 0.03272134, 0.03270066, 0.03268178, 0.03907616, 0.04149324, 0.04032533, 0.03823134, 0.03699634),
+#             c(	0.0777, 0.0778, 0.0778, 0.0779, 0.0778, 0.0771, 0.0759, 0.0766, 0.0778, 0.0779, 0.0778, 0.0778),
+#             c(	0.0777, 0.0778, 0.0778, 0.0779, 0.0778, 0.0771, 0.0759, 0.0766, 0.0778, 0.0779, 0.0778, 0.0778),
+#             c(	0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112, 0.0112),
+#             c(	0.1947138, 0.19413424, 0.20831414, 0.23348558, 0.24574614, 0.24605016, 0.24538258, 0.24630454, 0.247455, 0.23527388, 0.20963734, 0.19478494)
+#   );
+#   if(missing('tab') ){     #undefined table, use the default table.
+#     tab=switch(type,'lai'=dlai, 'rl'=drl)
+#   }
+#   ret = tab[x,]
+# }
+# 
+# 
+# rep.row<-function(x,n){
+#   for(i in 1:n){
+#     if(i==1){
+#       ret = x;
+#     }else{
+#       ret=rbind(ret, x)
+#     }
+#   }
+#   return(ret)
+# }
+# 
+# rep.col<-function(x,n){
+#   for(i in 1:n){
+#     if(i==1){
+#       ret = x;
+#     }else{
+#       ret=cbind(ret, x)
+#     }
+#   }
+#   return(ret)
+# }
+
+# GLC.LaiRf <- function(lc,years=2000+1:2, if.daily=FALSE){
+#   #years=2000:(2010+1);
+#   years=sort(c(years,max(years)+1))
+#   yrlim=range(years);
+#   ny = length(years)
+#   t1=as.Date(paste(yrlim[1],'-01-01',sep=''))
+#   t2=as.Date(paste(yrlim[2],'-12-31',sep=''))
+#   tdaily = seq.Date(t1,t2,by=1)
+#   DataDaily=xts::as.xts(numeric(length(tdaily)),order.by=tdaily)
+#   DataMon=xts::apply.monthly(DataDaily,FUN=sum)
+#   tmon =as.Date( format(time(DataMon), "%Y-%m-01"))
+#   #tmon = time(DataMon)- days_in_month(time(DataMon))+1
+#   nlc=length(lc)
+#   l = matrix(0, nrow=12, ncol=nlc)
+#   r = matrix(0, nrow=12, ncol=nlc)
+#   for (i in 1:nlc){
+#     l[,i] = cfun(lc[i], type=1)
+#     r[,i] = cfun(lc[i], type=2)
+#   }
+#   lmat = xts::as.xts(rep.row(l, ny), order.by=tmon)
+#   rmat = xts::as.xts(rep.row(r, ny), order.by=tmon)
+#   colnames(lmat)=lc
+#   colnames(rmat)=lc
+#   ret=list('LAI'=lmat, 'RL'=rmat)
+#   if(if.daily){
+#     ld = NA*rep.col(DataDaily, nlc);
+#     rd = NA*rep.col(DataDaily, nlc);
+#     ld[time(lmat),]=lmat
+#     rd[time(rmat),]=rmat
+#     ld=na.approx(ld)
+#     rd=na.approx(ld)
+#     colnames(ld)=lc
+#     colnames(rd)=lc
+#     ret=list('LAI'=ld, 'RL'=rd)
+#   }
+#   return(ret)
+# }
+
+fun.MeltFactor <- function(years=2000+1:2){
+  mf=c(0.001308019, 0.001633298,  0.002131198, 0.002632776, 0.003031171,  0.003197325, 0.003095839, 0.00274524,     0.002260213, 0.001759481, 0.001373646,  0.001202083);
+  years=sort(c(years,max(years)+1))
+  yrlim=range(years);
+  ny = length(years)
+  t1=as.Date(paste(yrlim[1],'-01-01',sep=''))
+  t2=as.Date(paste(yrlim[2],'-12-31',sep=''))
+  tdaily = seq.Date(t1,t2,by=1)
+  DataDaily=as.xts(numeric(length(tdaily)),order.by=tdaily)
+  DataMon=apply.monthly(DataDaily,FUN=sum)
+  #tmon = time(DataMon)- days_in_month(time(DataMon))+1
+  tmon =as.Date( format(time(DataMon), "%Y-%m-01"))
+  ret = as.xts(rep(mf, ny), order.by=tmon)
+  ret
+}
+
+fun.vegtable <- function (lc, file){
+  x=read.csv(file=file.path('table','lc_table.csv'), header = T)
+  y = x[lc,1:8]
+  nr = nrow(y)
+  write(nr, file=file, append = F)
+  write.table(y, file=file, append=T,  row.names = F, col.names = F, quote = F)
+  y
+}
--- a/Deploy/shud/Rfunction/fun.Meteo.R
+++ b/Deploy/shud/Rfunction/fun.Meteo.R
@ -0,0 +1,4 @@
+
+
+
+# sp.meteoSite=readOGR(fn)
--- a/Deploy/shud/Rfunction/fun.SSURGO.R
+++ b/Deploy/shud/Rfunction/fun.SSURGO.R
@ -0,0 +1,39 @@
+
+#############################################################
+# Notes
+#
+#
+# See Notes for packages needed
+#############################################################
+getSoilData <- function(MUKEY, vars = c('silttotal_r', 'claytotal_r', 'om_r', 'dbthirdbar_r'), 
+                        na.rm=TRUE
+                        # ,fn = 'gSSURGO_texture.csv'
+                        ){
+  # cnames = c('sandtotal_r', 'silttotal_r', 'claytotal_r', 'om_r',  
+  #            "ksat_l","ksat_r", "ksat_h", 
+  #            "awc_l", "awc_r", "awc_h")
+  library('soilDB')
+  in.statement <- format_SQL_in_statement(MUKEY)
+  #in.statement = paste(MUKEY);
+  q <- paste("SELECT component.mukey, ", paste(vars, collapse=',') ,
+             "FROM component ", 
+             "JOIN chorizon " , 
+             "ON component.cokey = chorizon.cokey AND mukey ",
+             "IN ", in.statement, 
+             "ORDER BY mukey ", sep=" ")
+  ret <- SDA_query(q)
+  
+  q <- paste("SELECT component.mukey, ", paste(vars, collapse=',') ,
+             "FROM component ", 
+             "JOIN chorizon " , 
+             "ON component.cokey = chorizon.cokey AND mukey ",
+             "IN ", in.statement, 
+             "ORDER BY mukey ", sep=" ")
+  ret <- SDA_query(q)
+  
+  tmp = apply(ret, 1, mean, na.rm=FALSE)
+  ret = ret[!is.na(tmp),]
+  
+  # write.csv(ret, file=fn)
+  return(ret)
+}
--- a/Deploy/shud/Rfunction/nc2fishnet.R
+++ b/Deploy/shud/Rfunction/nc2fishnet.R
@ -0,0 +1,11 @@
+nc.fishnet <- function(x){
+  # fid = ncdf4::nc_open(fn)
+  # x=readnc.CMFD(ncid=fid, varid = varid)
+  # ncdf4::nc_close(fid)
+  
+  res = mean(round(diff(x$x), 4))
+  xlim = range(x$x) + c(-1, 1)*res*0.5
+  ylim = range(x$y) + c(-1, 1)*res*0.5
+  spx = rSHUD::fishnet(xx=seq(xlim[1], xlim[2], by=res), yy=seq(ylim[1], ylim[2], by=res))
+  return(spx)
+}
--- a/Deploy/shud/Rfunction/raster2Polygon.R
+++ b/Deploy/shud/Rfunction/raster2Polygon.R
@ -0,0 +1,9 @@
+
+raster2Polygon <- function(rx){
+  ext= raster::extent(rx)
+  res =raster::res(rx)
+  xx = seq(ext[1], ext[2], res[1])
+  yy = seq(ext[3], ext[4], res[2])
+  spx = rSHUD::fishnet(xx=xx, yy=yy, crs=raster::crs(rx))
+  return(spx)
+}
--- a/Deploy/shud/Rfunction/reindex.R
+++ b/Deploy/shud/Rfunction/reindex.R
--- a/Deploy/shud/Rfunction/unitConvert.R
+++ b/Deploy/shud/Rfunction/unitConvert.R
--- a/Deploy/shud/Step1_RawDataProcessng.R
+++ b/Deploy/shud/Step1_RawDataProcessng.R
@ -0,0 +1,108 @@
+AutoSHUD_Step1 <- function(CV){
+  caller = as.character( deparse(sys.call())   )
+  writelog(msg=caller, caller = caller)
+  
+  writemessage(paste0('Running the AutoSHUD_Step1(CV) '),  caller = caller, CV$task.log)
+  prefix = 'AutoSHUD_S1'
+  xfg = CV$deploy
+  if(is.null(xfg)){ writelog(msg="Error: xfg is missing in CV", caller = caller) }
+  # ================= Boundary =================
+  wbd0 = readOGR(xfg$fsp.wbd)  # Read data
+  wbd0 = gBuffer(wbd0, width=0) # Remove error from irregular polygon.
+  # ---- disolve ----
+  wbd.dis = removeholes(gUnaryUnion(wbd0))
+  
+  # wbd in pcs
+  wb.p = spTransform(wbd0, xfg$crs.pcs) 
+  writeshape(wb.p, xfg$pd.pcs$wbd)
+  
+  # buffer of wbd in pcs
+  buf.p = gBuffer(wb.p, width = xfg$para$DistBuffer) 
+  writeshape(buf.p, xfg$pd.pcs$wbd.buf)
+  
+  buf.g = spTransform(buf.p, xfg$crs.gcs)
+  writeshape(buf.g, xfg$pd.gcs$wbd.buf)
+  
+  wb.g=spTransform(wb.p, CRSobj = xfg$crs.gcs )
+  writeshape(wb.g, xfg$pd.gcs$wbd)
+  
+  
+  # ================= DEM =================
+  dem0=raster(xfg$fr.dem)
+  # -------CROP DEM -----------------
+  # Crop the dem AND conver the dem to PCS.
+  writelog(msg=paste0('Cuting DEM ...'), caller = caller)
+  fun.gdalwarp(f1=xfg$fr.dem,
+               f2=xfg$pd.pcs$dem,
+               t_srs = xfg$crs.pcs,
+               s_srs = crs(dem0),
+               opt = paste0('-cutline ', xfg$pd.pcs$wbd.buf) )
+  # # Crop the dem, output is in GCS
+  fun.gdalwarp(f1=xfg$fr.dem,
+               f2=xfg$pd.gcs$dem,
+               t_srs = xfg$crs.gcs,
+               s_srs = crs(dem0),
+               opt = paste0('-cutline ', xfg$pd.pcs$wbd.buf) )
+  
+  # =========Stream Network===========================
+  writelog(msg=paste0('Stream network ...'), caller = caller)
+  stm0 = readOGR(xfg$fsp.stm)  # data 0: raw data
+  stm1 = spTransform(stm0, xfg$crs.pcs)  # data 1: PCS
+  fun.simplifyRiver <- function(rmDUP=TRUE){
+    riv.xy = extractCoords(stm1)
+    npoint = nrow(riv.xy)
+    mlen = gLength(stm1) / npoint
+    r.dem = raster(xfg$pd.pcs$dem)
+    dx = mean(res(r.dem))
+    if( mlen < dx){
+      stm1 = gSimplify(stm1, tol = dx)
+    }
+    if(rmDUP){
+      res = rmDuplicatedLines(stm1)
+    }else{
+      res = stm1
+    }
+    res
+  }
+  # debug(sp.RiverDown)
+  if(xfg$para$flowpath){
+    stm1 = fun.simplifyRiver(rmDUP = FALSE)
+    stm.p= sp.RiverPath(stm1, tol.simplify = 30)$sp  # clean data with flowpath.
+    stm.p = stm1
+  }else{
+    stm.p = stm1
+  }
+  
+  writelog(msg=paste0('writing stream out: ', xfg$pd.pcs$stm), caller = caller)
+  writeshape(stm.p, file=xfg$pd.pcs$stm)
+  
+  #' ==========================================
+  if(xfg$LAKEON){
+    writemessage(paste0('Lake module is ENABLED  '),  caller = caller, CV$task.log)
+    writelog(msg=paste0('Lake is ON.'), caller = caller)
+    spl0 = readOGR(xfg$fsp.lake)  # data 0: raw data
+    spl1 = removeholes(spl0)
+    spl.gcs = spTransform(spl1, CRSobj = xfg$crs.gcs)
+    writeshape(spl.gcs, xfg$pd.gcs$lake)
+    
+    spl.pcs = spTransform(spl.gcs, CRSobj = xfg$crs.pcs)  # data 1: PCS
+    writeshape(spl.pcs, xfg$pd.pcs$lake)
+  }
+  
+  #' ==== PLOT FIGURE ================
+  writelog(msg=paste0('Ploting'), caller = caller)
+  dem.p = raster(xfg$pd.pcs$dem)
+  png(file.path(xfg$dir$fig, paste0(prefix, '_Rawdata_Elevation.png')), type='cairo',height=8, width=8, units = 'in', res=200)
+  par(mar=c(2, 2, 1, 1) )
+  plot(dem.p)
+  plot(wb.p, add=T, border=2)
+  plot(stm.p, add=T, col=4);
+  if(xfg$LAKEON){
+    plot(spl.pcs, add=TRUE, border='darkblue', lwd=1.5)
+  }
+  grid()
+  dev.off()
+  
+  writelog(paste0('Finished'), caller=caller)
+}
+# AutoSHUD_Step1(CV)
--- a/Deploy/shud/Step2_DataSubset.R
+++ b/Deploy/shud/Step2_DataSubset.R
@ -0,0 +1,95 @@
+AutoSHUD_Step2 <- function(CV){
+  caller = as.character( deparse(sys.call())   )
+  writelog(msg=caller, caller = caller)
+  
+  writemessage(paste0('Running the AutoSHUD_Step2(CV) '),  caller = caller, CV$task.log)
+  raster.reclass <- function(fn.r, fn.tab, fn.ridx){
+    rx = raster(fn.r)
+    att = read.df(fn.tab)[[1]]
+    rcl = cbind(att[, 1], 1:nrow(att))
+    ry = raster::reclassify(rx, rcl)
+    writeRaster(ry, filename = fn.ridx, overwrite=TRUE)
+    # return(ry)
+  }
+  plotfun <-function(fn.r, key){
+    r = raster(fn.r)
+    png(filename = file.path(xfg$dir$fig, paste0(xfg$prefix,'_', key, '.png')), type='cairo',
+        height = 7, width=7, unit='in', res=300)
+    par(mar=c(2, 2, 1, 1))
+    plot(r)
+    plot(wb.p, add=T, border='red', lwd=1)
+    plot(stm.p, add=T, col='blue', lwd=1)
+    grid();  
+    mtext(side=3, line=-1, paste0(key, '(Class)') )
+    dev.off()
+  }
+  xfg = CV$deploy
+  xfg$prefix = 'AutoSHUD_S2'
+  stm.p = readOGR(xfg$pd.pcs$stm)
+  wb.p = readOGR(xfg$pd.pcs$wbd)
+  
+  if(is.null(xfg)){
+    writelog(msg="Error: xfg is missing in CV", caller = caller)
+  }
+  
+  # =======Soil=================================================================
+  # =======Soil=================================================================
+  # source(file.path(CV$dirs$deploy, 'Rfunction/Fun.Soil_Geol.R') )
+  writemessage(paste0('Processing SOIL/Geol data... '),  caller = caller, CV$task.log)
+  # this script for soil/geol data with SpatialData and Attribute Table.
+  message('Processing SOIL/Geol data...')
+  message('fn.soil: ', xfg$fn.soil)
+  message('fa.soil: ', xfg$fa.soil)
+  message('fn.geol: ', xfg$fn.geol)
+  message('fa.geol: ', xfg$fa.geol)
+  fun.gdalcut(f.in = xfg$fn.soil, f.mask = xfg$pd.pcs$wbd.buf,
+              f.out= xfg$pd.pcs$soil.r, t_srs = xfg$crs.pcs, s_srs = xfg$crs.gcs)
+  fun.gdalcut(f.in = xfg$fn.geol, f.mask = xfg$pd.pcs$wbd.buf,
+              f.out= xfg$pd.pcs$geol.r, t_srs = xfg$crs.pcs, s_srs = xfg$crs.gcs)
+  raster.reclass(fn.r = xfg$pd.pcs$soil.r, fn.tab = xfg$fa.soil, fn.ridx = xfg$pd.pcs$soil.idx)
+  raster.reclass(fn.r = xfg$pd.pcs$geol.r, fn.tab = xfg$fa.geol, fn.ridx = xfg$pd.pcs$geol.idx)
+  dat.soil = fun.Soil_Geol(xfg, TOP = TRUE)
+  dat.geol = fun.Soil_Geol(xfg, TOP = FALSE)
+  plotfun(fn.r = xfg$pd.pcs$geol.idx, key = 'Geol')
+  plotfun(fn.r = xfg$pd.pcs$soil.idx, key = 'Soil')
+  #
+  # =======Land Cover ====================================================
+  # =======Land Cover ====================================================
+  writemessage(paste0('Processing landuse data... '),  caller = caller, CV$task.log)
+  if( xfg$ilanduse >= 1){
+    # local landuse map and attribute table.
+    fun.gdalcut(f.in = xfg$fn.landuse, 
+                f.mask = xfg$pd.pcs$wbd.buf,
+                f.out= xfg$pd.pcs$lu.r, 
+                t_srs = xfg$crs.pcs,
+                s_srs = xfg$crs.gcs)
+    raster.reclass(fn.r = xfg$pd.pcs$lu.r, fn.tab = xfg$tab.landuse, fn.ridx = xfg$pd.pcs$lu.idx)
+    plotfun(fn.r = xfg$pd.pcs$lu.idx, key = 'Landuse')
+  }
+  
+  # # =======Forcing =================================================================
+  # # =======Forcing =================================================================
+  writemessage(paste0('Processing reanalysis coverage data... '),  caller = caller, CV$task.log)
+  if(xfg$iforcing >= 1){ 
+    # local map
+    att=read.df(xfg$fc.att)[[1]]
+    sp.forc = xy2shp(xy = cbind(att$LON, att$LAT), crs=xfg$crs.gcs, shape = 'point', df = att)
+    # plot(sp.forc)
+    writeshape(sp.forc, file = xfg$pd.gcs$meteo)
+    sp.forc.pcs = sp::spTransform(sp.forc, xfg$crs.pcs)
+    writeshape(sp.forc.pcs, file = xfg$pd.pcs$meteo)
+    png(filename = file.path(xfg$dir$fig, paste0(xfg$prefix,'_', 'Metero', '.png')),  type='cairo',height = 7, width=7, unit='in', res=300)
+    par(mar=c(2, 2, 1, 1))
+    plot(sp.forc.pcs, axes=TRUE, col='darkgreen')
+    plot(wb.p, add=T, border='red', lwd=1)
+    plot(stm.p, add=T, col='blue', lwd=1)
+    grid();  
+    mtext(side=3, line=-1, paste0('Meterology sites',' (N=', nrow(sp.forc.pcs), ')') )
+    dev.off()
+  }else{
+    stop(paste('WRONG LDAS CODE: ', xfg$iforcing))
+  }
+  writelog(paste0('Finished'), caller=caller)
+  # return(xfg)
+}
+# AutoSHUD_Step2(CV)
--- a/Deploy/shud/Step3_BuidModel.R
+++ b/Deploy/shud/Step3_BuidModel.R
@ -0,0 +1,287 @@
+AutoSHUD_Step3 <- function(CV){
+  caller = as.character( deparse(sys.call())   )
+  writelog(msg=caller, caller = caller)
+  writemessage(paste0('Running the AutoSHUD_Step3(CV) '),  caller = caller, CV$task.log)
+  prefix = 'AutoSHUD_S3'
+  xfg = CV$deploy
+  if(is.null(xfg)){
+    writelog(msg="Error: xfg is missing in CV", caller = caller)
+  }
+  fin <- shud.filein(xfg$prjname, inpath = xfg$dir$modelin, outpath= xfg$dir$modelout)
+  wbd=readOGR(xfg$pd.pcs$wbd)
+  dem=raster(xfg$pd.pcs$dem)
+  buf.g = readOGR(xfg$pd.pcs$wbd.buf)
+  
+  # ==============================================
+  AA1=gArea(wbd)
+  a.max = min(AA1/xfg$para$NumCells, xfg$para$MaxArea)
+  NCELL.MAX = round(AA1 / a.max * 1.5)
+  q.min = xfg$para$MinAngle
+  tol.wb = xfg$para$tol.wb
+  tol.rivlen = xfg$para$tol.rivlen
+  years = xfg$years
+  if(is.null(tol.wb) | is.infinite(tol.wb)){ tol.wb = min(sqrt(a.max), 3000) }
+  if(is.null(tol.rivlen) | is.infinite(tol.rivlen) ){ tol.rivlen = min(sqrt(a.max), 5000) }
+  # 
+  # tol.wb = min(sqrt(a.max)/2, 300)
+  # tol.rivlen = min(sqrt(a.max)*3, 5000)
+  
+  bm.para = data.frame(a.max/1e6,  tol.wb, tol.rivlen)
+  names(bm.para)=c('MaxArea_km2', 'tol.wb', 'MaxRivLen')
+  print(bm.para)
+  
+  ny=length(years)
+  nday = 365*ny + round(ny/4) - 1
+  
+  writemessage(paste0('Area = ', AA1),  caller = caller, CV$task.log)
+  writemessage(paste0('Max cell area = ', a.max),  caller = caller, CV$task.log)
+  writemessage(paste0('NCELL.MAX = ', NCELL.MAX),  caller = caller, CV$task.log)
+  writemessage(paste0('Minimum Angle = ', q.min),  caller = caller, CV$task.log)
+  writemessage(paste0('Simplity tolerance for boundary = ', tol.wb),  caller = caller, CV$task.log)
+  writemessage(paste0('Simplity tolerance for river length = ', tol.rivlen),  caller = caller, CV$task.log)
+  writemessage(paste0('Years ', min(years), '~', max(years)),  caller = caller, CV$task.log)
+  writemessage(paste0('No of Days = ', nday),  caller = caller, CV$task.log)
+  
+  #' ==============================================
+  #' BUFFER
+  wb.dis = rgeos::gUnionCascaded(wbd)
+  wb.s1 = rgeos::gSimplify(wb.dis, tol=tol.wb, topologyPreserve = T)
+  # wb.s2 = sp.simplifyLen(wb.s1, tol.wb)
+  wb.s2 = gSimplify(wb.s1, tol = tol.wb)
+  wb.simp = wb.s2
+  
+  #' ====================================================
+  #' 
+  if(xfg$LAKEON){
+    source(file.path(CV$dirs$deploy, 'SubScript/Sub3_lake.R')  )
+  }else{
+    sp.lake=NULL
+  }
+  
+  tri = shud.triangle(wb=wb.simp,q=q.min, a=a.max, S=NCELL.MAX)
+  
+  # =========Mesh generation=====================================
+  pm = shud.mesh(tri, dem=dem, AqDepth = xfg$para$AqDepth)
+  spm = sp.mesh2Shape(pm, crs = crs(wbd))
+  writeshape(spm, crs(wbd), file=file.path(fin['inpath'], 'gis', 'domain'))
+  print(nrow(spm@data))
+  ia=getArea(pm)
+  nCells = length(spm)
+  writemessage(paste0('No of tirangular cells = ', nCells),  caller = caller, CV$task.log)
+  writemessage(paste0('Mean area of tirangular cells = ', mean(ia)),  caller = caller, CV$task.log)
+  
+  # ==============================================
+  riv0=readOGR(xfg$pd.pcs$stm)
+  if(xfg$para$flowpath){
+    # debug(sp.RiverPath)
+    riv1=sp.RiverPath(riv0)$sp  #Build the River Path --- Dissolve the lines.
+    riv1=riv0
+    riv2=rmDuplicatedLines(riv1)
+  }else{
+    riv1 = riv0
+    riv2=riv1
+  }
+  lens=gLength(riv2, byid=TRUE)
+  summary(lens)
+  spr = sp.CutSptialLines(sl=riv2, tol=tol.rivlen)
+  # writeshape(spr, file=file.path(xfg$dir$predata, 'spr'))
+  go.png <- function(){
+    png(file = file.path(xfg$dir$fig, paste0(prefix, '.1_datain.png')), type='cairo', height=7, width=7, res=300, unit='in')
+    par(mar=c(2, 2, 1, 1) )
+    plot(dem);  
+    plot(wbd, add=T, border='red', lwd=1); plot(riv2, add=T, lwd=1, col='blue');
+    mtext(line=-1,side=3, cex=1,'DEM-WBD-Riv');     grid()
+    dev.off()
+  };   go.png()
+  # ======FORCING FILE======================
+  sp.meteoSite = rgdal::readOGR(xfg$pd.pcs$meteo)
+  sp.meteoSite@data$ID = paste0(CV$json$meteorological_data, '_',  sp.meteoSite@data$FILENAME)
+  sp.forc = rSHUD::ForcingCoverage(sp.meteoSite = sp.meteoSite, 
+                                   pcs=xfg$crs.pcs, gcs=xfg$crs.gcs, 
+                                   dem=dem, wbd=wbd)
+  # head(sp.forc@data)
+  write.forc(sp.forc@data, path = '../ETV/TSD',
+             startdate = paste0(min(years), '0101'), 
+             file=fin['md.forc'])
+  
+  
+  go.png <-function(){
+    png(file.path(xfg$dir$fig, paste0(prefix, '.2_buf_riv_forc.png')), type='cairo', height=7, width=7, units = 'in', res=300)
+    par(mar=c(2, 2, 1, 1) )
+    plot(dem);grid()
+    plot(buf.g, add=T, axes=T, lwd=1)
+    plot(wbd, add=T, border='red', lwd=1)
+    plot(riv2, add=T, col='blue', lwd=1)
+    plot(sp.meteoSite, add=T, col='darkgreen', cex=0.3)
+    plot(sp.forc, add=T, lwd=0.5, lty=2)
+    grid();     mtext(line=-1,side=3, cex=1, 'DEM-WBD-Riv-Meteo');     dev.off()
+  }; go.png();
+  
+  # xfg$dir$fig = file.path(xfg$dir$modelin, 'fig')
+  gisout = file.path(xfg$dir$modelin, 'gis')
+  dir.create(xfg$dir$modelin, showWarnings = F, recursive = T)
+  dir.create(xfg$dir$fig, showWarnings = F, recursive = T)
+  dir.create(gisout, showWarnings = F, recursive = T)
+  
+  go.png <-function(){
+    png(file.path(xfg$dir$fig, paste0(prefix, '.3_domain.png')), type='cairo', height=7, width=7, units = 'in', res=300)
+    par(mar=c(2, 2, 1, 1) )
+    plot_sp(spm, 'Zmax', axes=TRUE, lwd=0.3, border='gray')
+    plot(spr, add=T, col=rgb(0.8, 0.8, 0.8, alpha = 0.5), lwd=2)
+    plot(spr, add=T, col='blue', lwd=1)
+    mtext(line=-1,side=3, cex=1, paste0('SHUD triangular mesh (Ncell = ', nCells, ')' ))
+    grid();     title('');     dev.off()
+  }; go.png()
+  
+  # ======LANDUSE======================
+  rlc.idx = raster(xfg$pd.pcs$lu.idx)
+  para.lc = read.df(xfg$tab.landuse)[[1]]
+  para.lc = para.lc[, -1*ncol(para.lc)]
+  para.lc[, 1] = 1:nrow(para.lc)
+  
+  # ====== generate  .att ======================
+  r.soil.idx = raster(xfg$pd.pcs$soil.idx)
+  r.geol.idx = raster(xfg$pd.pcs$geol.idx)
+  pa=shud.att(tri,  
+              r.soil = r.soil.idx, r.geol = r.geol.idx, r.lc = rlc.idx, 
+              r.forc = sp.forc, r.BC = 0, sp.lake = sp.lake)
+  
+  fx <- function(x){ x[is.na(x)] = median(x, na.rm = TRUE); return(x) }
+  pa = apply(pa, 2, fx)
+  
+  spm@data = cbind(spm@data, pa)
+  writeshape(spm, crs(wbd), file=file.path(fin['inpath'], 'gis', 'domain'))
+  
+  # ====== generate  .riv ======================
+  pr=shud.river(spr, dem)
+  pr@rivertype$Width= pr@rivertype$Width * xfg$para$RivWidth
+  pr@rivertype$Depth= xfg$para$RivDepth + (1:nrow(pr@rivertype) - 1 )*0.5
+  pr@rivertype$BankSlope = 1
+  spr@data = data.frame(pr@river, pr@rivertype[pr@river$Type,])
+  writeshape(spr, crs(wbd), file=file.path(gisout, 'river'))
+  
+  # Cut the rivers with triangles
+  sp.seg=sp.RiverSeg(spm, spr)
+  writeshape(sp.seg, crs(wbd), file=file.path(gisout, 'seg'))
+  
+  # Generate the River segments table
+  prs = shud.rivseg(sp.seg)
+  
+  # Generate initial condition
+  # debug(shud.ic)
+  if(xfg$LAKEON){
+    lakestage = 30;
+  }else{
+    lakestage = NULL
+  }
+  pic = shud.ic(ncell = nrow(pm@mesh), nriv = nrow(pr@river), lakestage=lakestage,
+                AqD = xfg$para$AqDepth)
+  
+  # Generate shapefile of mesh domain
+  cfg.para = shud.para(nday = nday)
+  cfg.para['INIT_MODE']=3
+  # calibration
+  cfg.calib = shud.calib()
+  
+  #soil/geol/landcover
+  if(xfg$para$QuickMode){
+    message('\n !!! QUICK MODE in SOIL/GEOL parameters!!!\n')
+    para.soil = PTF.soil()
+    para.geol = PTF.geol()
+  }else{
+    # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    # !!!!! the column of soil/geol file must be 5 colume: ID   SILT CLAY     OM    BD
+    # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    asoil = as.matrix(read.df(xfg$pd.att$soil)[[1]])[, -1]
+    ageol = as.matrix(read.df(xfg$pd.att$geol)[[1]])[, -1]
+    asoil=rbind(asoil)
+    ageol=rbind(ageol)
+    asoil[asoil[, 3] > 5, 3] = NA  # Organic Matter must less 5.
+    ageol[ageol[, 3] > 5, 3] = NA
+    fx <- function(x){ x[is.na(x) | is.nan(x)] = mean(x, na.rm=TRUE); return(x) }
+    asoil = rbind(apply(asoil, 2, fx))
+    ageol = rbind(apply(ageol, 2, fx))
+    para.soil = data.frame(PTF.soil(asoil))
+    para.geol = data.frame(PTF.geol(ageol))
+    # plot(ageol[, 3], para.geol[, 2], xlab='OM', ylab='KsatV(m_d)', log='x')
+    fun.plot <- function(dat, ylab=''){
+      nd = length(dat)
+      x = (1:nd)/nd
+      y=sort(dat)
+      plot(x, y, xlab='', ylab='', pch=20, col='blue',type='b');
+      mtext(side = 1, line=2, 'Exceedance'); 
+      mtext(side = 2, line=2, ylab);
+      grid()
+    }
+    go.png <-function(){
+      png(file.path(xfg$dir$fig, paste0(prefix, '.4_soilgeol_para.png')), type='cairo', height=7, width=7, units = 'in', res=300)
+      par(mar=c(3, 3, 1, 1), mfrow=c(2,2) )
+      fun.plot(para.soil$KsatV.m_d., ylab='Soil KsatV (m/day)')
+      fun.plot(para.soil$Beta, ylab='Soil Beta (-)')
+      fun.plot(para.geol$KsatV.m_d., ylab='Geol KsatV (m/day)')
+      fun.plot(para.geol$ThetaS.m3_m3., ylab='Geol Porosity (m3/m3)')
+      dev.off()
+    }; go.png()
+  }
+  
+  # ====== LAI ======================
+  ts.lai = read.tsd(CV$etv$landuse.lai)[[1]]
+  write.tsd(ts.lai, file = fin['md.lai'])
+  
+  #MeltFactor
+  # mf =read.tsd(CV$etv$meltfactor)
+  mf = MeltFactor(years = years)
+  write.tsd(mf, file=fin['md.mf'])
+  
+  # write SHUD input files.
+  write.mesh( pm, file = fin['md.mesh'])
+  write.riv(pr, file=fin['md.riv'])
+  write.ic(pic, file=fin['md.ic'])
+  
+  write.df(pa, file=fin['md.att'])
+  write.df(prs, file=fin['md.rivseg'])
+  write.df(para.lc, file=fin['md.lc'])
+  write.df(para.soil, file=fin['md.soil'])
+  write.df(para.geol, file=fin['md.geol'])
+  
+  cfg.para$START=xfg$para$STARTDAY
+  cfg.para$END=xfg$para$ENDDAY
+  cfg.para$CRYOSPHERE = xfg$para$CRYOSPHERE
+  cfg.para$MAX_SOLVER_STEP = xfg$para$MAX_SOLVER_STEP
+  
+  write.config(cfg.para, fin['md.para'])
+  write.config(cfg.calib, fin['md.calib'])
+  
+  if( any( is.na(pm@mesh) ) |  any( is.na(pm@point) ) ){
+    message('NA in .SP.MESH file')
+  }
+  if(any( is.na(pr@river)) ){
+    message('NA in .SP.RIV file')
+  }
+  if(any( is.na(pa)) ){
+    message('NA in .SP.ATT file')
+  }
+  pp = shud.env(prjname = xfg$prjname, inpath = xfg$dir$modelin, outpath = xfg$dir$modelout)
+  ia= getArea()
+  ma = MeshAtt()
+  go.plot <- function(){
+    png(file.path(xfg$dir$fig, paste0(prefix, '.5_HistArea.png')), type='cairo', height=7, width=7, units = 'in', res=300)
+    par(mfrow=c(2, 1))
+    hist(ia/1e6, xlab='', ylab='', main='Histgram of triangle area', freq=TRUE); 
+    mtext(side=1, line=2,'Area (km2)')
+    mtext(side=2, line=2,'Count')
+    hist(sqrt(ia)/1e3, xlab='', ylab='', main='Histgram of equivalent resolution', freq=TRUE); 
+    mtext(side=1, line=2,'Length (km)')
+    mtext(side=2, line=2,'Count')
+    par(mfrow=c(1, 1))
+    dev.off()
+  }; go.plot()
+  
+  # ModelInfo()
+  message('Ncell = ', nrow(pm@mesh))
+  message('Nriv = ', nrow(pr@river))
+  
+  writelog(paste0('Finished'), caller=caller)
+  return(nCells)
+}
+# AutoSHUD_Step3(CV)
--- a/Deploy/shud/SubScript/._Sub2.1_Soil_ISRIC_SoilGrids.R
+++ b/Deploy/shud/SubScript/._Sub2.1_Soil_ISRIC_SoilGrids.R
--- a/Deploy/shud/SubScript/._Sub2.2_Landcover_GLC.R
+++ b/Deploy/shud/SubScript/._Sub2.2_Landcover_GLC.R
--- a/Deploy/shud/SubScript/._Sub2.3_Forcing_0.4NLDAS.R
+++ b/Deploy/shud/SubScript/._Sub2.3_Forcing_0.4NLDAS.R
--- a/Deploy/shud/SubScript/._Sub2.3_Forcing_LDAS.R
+++ b/Deploy/shud/SubScript/._Sub2.3_Forcing_LDAS.R
--- a/Deploy/shud/SubScript/._Sub2_iForcing_1.1.R
+++ b/Deploy/shud/SubScript/._Sub2_iForcing_1.1.R
--- a/Deploy/shud/SubScript/._Sub_iSoil_0.1.R
+++ b/Deploy/shud/SubScript/._Sub_iSoil_0.1.R
--- a/Deploy/shud/SubScript/._Sub_iSoil_0.2.R
+++ b/Deploy/shud/SubScript/._Sub_iSoil_0.2.R
--- a/Deploy/shud/SubScript/._Sub_iSoil_1.1.R
+++ b/Deploy/shud/SubScript/._Sub_iSoil_1.1.R
--- a/Deploy/shud/SubScript/Sub2.1_Soil_ISRIC_SoilGrids.R
+++ b/Deploy/shud/SubScript/Sub2.1_Soil_ISRIC_SoilGrids.R
@ -0,0 +1,78 @@
+# THIS IS FOR ISRIC_SoilGrids SOIL DATA ONLY
+
+soil.str = c('CLYPPT_M_sl',
+             'SNDPPT_M_sl',
+             'SLTPPT_M_sl',
+             'ORCDRC_M_sl',
+             'BLDFIE_M_sl')
+tmp=expand.grid(soil.str, c(1,7) )
+fns=paste0(tmp[,1], tmp[,2], "_250m.tif")
+fns1=paste0(tmp[,1], tmp[,2], "_250m_PCS.tif")
+
+d0 = dir.soil
+d1 = file.path(dir.predata, 'soil')
+dir.create(d1, showWarnings = F, recursive = T)
+cmds=paste('gdalwarp -overwrite -q -cutline',
+           file.path(dir.predata, 'wbd_buf.shp'), '-crop_to_cutline -of GTiff ',
+           file.path(d0, fns), file.path(d1, fns) )
+cmds1=paste('gdalwarp -overwrite -dstnodata -9 -q',
+            '-s_srs', paste0("'", crs.gcs, "'"),
+            '-t_srs', paste0("'", crs(wbd), "'"),
+            file.path(d1, fns), file.path(d1, fns1) )
+cmds
+nc=length(cmds)
+for(i in 1:nc){ # gdalwarp cut the data with wbd_buf
+  cm=cmds[i]
+  message(i, '/', nc, '\t', fns[i])
+  system(cm)
+  system(cmds1[i])
+}
+# stop()
+ffns = file.path(d1, fns1)
+nf=length(ffns)
+for(i in 1:nf){
+  cm=cmds[i]
+  message(i, '/', nc, '\t', fns1[i])
+  r=raster(ffns[i])
+  png.control(fn=paste0(fns1[i],'.png'), path = file.path(dir.png,'Rawdata_Soil'), ratio=1)
+  plot(r)
+  plot(wbd.buf, add=T, axes=T, lwd=2)
+  plot(wbd, add=T, border=3, lwd=2)
+  plot(stm, add=T, col=2, lwd=2)
+  title(fns[i])
+  dev.off()
+}
+
+
+# =========Save to RDS =============
+do.SoilGeology <- function(lyr, indir, vns= c('SLT', 'CLY', 'ORCDRC', 'BLD')){
+  # lyr='sl1'
+  # vns= c('SLT', 'CLY', 'ORCDRC', 'BLD')
+  # lyr = 'sl1'
+  # nv = dim(rl)[3]
+  nv=length(vns)
+  rl=list()
+  for(i in 1:nv){
+    message(i,'/', nv, '\t', vns[i])
+    pattern=glob2rx(paste0(vns[i], '*', lyr, '*PCS.tif'))
+    r = raster(list.files(indir, pattern = pattern,full.names = TRUE) )
+    if(i==1){
+      rc = r
+      res(rc)=res(r)*5
+      extent(rc)=extent(r)
+      rc[]=1
+    }
+    if(i==3){#Pribyl, D. W. (2010). A critical review of the conventional SOC to SOM conversion factor. GCSderma, 156(3–4), 75–83. https://doi.org/10.1016/j.GCSderma.2010.02.003
+      r = r /10/0.58;
+    }
+    if(i==4){ # kg/m3 = g/cm3
+      r = r / 1000
+    }
+    rr=resample(r, rc)
+    rl[[i]] =rr
+  }
+  names(rl) = vns
+  saveRDS(rl, file.path(dir.predata, paste0('Soil_', lyr, '.RDS')))
+}
+do.SoilGeology(lyr='sl1', indir=file.path(dir.predata, 'soil'))
+do.SoilGeology(lyr='sl7', indir=file.path(dir.predata, 'soil'))
--- a/Deploy/shud/SubScript/Sub2.1_Soil_SSURGO.R
+++ b/Deploy/shud/SubScript/Sub2.1_Soil_SSURGO.R
@ -0,0 +1,53 @@
+#' 
+#' THIS IS FOR US gSSURGO SOIL DATA ONLY
+#' 
+source('Rfunction/fun.SSURGO.R')
+#' ==========Soil Raster =============
+fn0.soil = file.path(xfg$dir$soil, 'gSSURGO-mukey.tif')
+r0 = raster(fn0.soil)
+tmpsoil = file.path(dir.pd.pcs, 'soil_mukey.tif')
+fun.gdalcut( f.in = fn0.soil, 
+             f.mask = pd.pcs$wbd.buf, 
+             f.out = tmpsoil, 
+             s_srs = crs(r0), t_srs = xfg$crs.pcs)
+r1 = raster(tmpsoil)
+uk = sort(unique(r1))
+
+rcl = cbind(uk, 1:length(uk))
+r.cl = reclassify(r1, rcl=rcl)
+plot(r.cl)
+writeRaster(r.cl, filename = pd.pcs$soil.r, overwrite=TRUE)
+
+#' ========Soil texture ==============
+# cnames = c('sandtotal_r', 'silttotal_r', 'claytotal_r', 'om_r',  'dbthirdbar_r',
+#            "ksat_l","ksat_r", "ksat_h", 
+#            "awc_l", "awc_r", "awc_h")
+vars =  c('sandtotal_r', 
+          'silttotal_r', 
+          'claytotal_r',
+          'om_r',  
+          'dbthirdbar_r',
+          "ksat_r", "awc_r")
+
+df0 = getSoilData(MUKEY = uk, vars = vars, na.rm=TRUE)
+
+nu = length(uk)
+att = matrix(0, nrow=nu, ncol=4)
+i=1
+cnames = c('silttotal_r', 
+           'claytotal_r',
+           'om_r',  
+           'dbthirdbar_r')
+print(cnames)
+for(i in 1:nu){
+  ikey = uk[i]
+  message(i, '/', nu, '\t', ikey)
+  idx = which(df0$mukey %in% ikey)
+  x=apply(df0[idx, cnames], 2, mean, na.rm=TRUE)
+  att[i, ] = x
+}
+plot(r.cl)
+colnames(att) = cnames
+write.df(att, file=pd.att$geol)
+write.df(att, file=pd.att$soil)
+
--- a/Deploy/shud/SubScript/Sub2.2_Landcover_GLC.R
+++ b/Deploy/shud/SubScript/Sub2.2_Landcover_GLC.R
@ -0,0 +1,97 @@
+fun.lc.GLC <- function(xfg){
+  fun.gdalcut(f.in = xfg$fn.landuse, 
+              f.mask = xfg$pd.pcs$wbd.buf,
+              f.out= xfg$pd.pcs$lu.r, 
+              t_srs = xfg$crs.pcs,
+              s_srs = xfg$crs.gcs)
+  
+  r.lu = raster(xfg$pd.pcs$lu.r)
+  
+  wb.p = readOGR(xfg$pd.pcs$wbd)
+  # stm.p = readOGR(xfg$pd.pcs$stm)
+  
+  go.plot <- function(prefix){
+    tab = read.df('AutoSHUD/Table/USGS_GLC.csv', sep='\t')[[1]]
+    clr = read.table('AutoSHUD/Table/LCType_color.clr', sep='\t')
+    tocol = function(x){rgb(x[, 1], x[, 2],x[, 3], min(1, x[, 4]) )
+    }
+    col =  tocol(clr[, 2:5]/255)
+    ulc = cellStats(r.lu, unique, na.rm=TRUE)
+    
+    brk = 0:17
+    txt = rep('', 17); txt[ulc] = '(x)'
+    labs = paste0(tab$remark, txt)
+    png(filename = file.path(xfg$dir$fig, paste0(prefix,'_Landuse.png')), height = 7, width=9, unit='in', res=200)
+    par(mar=c(3, 3, 3, 15))
+    plot(r.lu, legend=FALSE, col=col, breaks=brk)
+    # plot(r.lu, legend.only=TRUE, col=col, breaks=brks, label=labs)
+    plot(r.lu, legend.only=TRUE, breaks=brk, col=col, 
+         smallplot=c(0.67,0.70, 0.1,0.9),
+         legend.width=2, legend.shrink=.5, cex=2, horizontal=FALSE,
+         axis.args=list(at=0:16+.5, labels=labs, cex.axis=.75),
+         legend.args=list(text='',side=3, font=2, cex=0.8))
+    
+    plot(wb.p, add=T, border='red', lwd=2)
+    # plot(stm.p, add=T, col='blue', lwd=1)
+    grid()
+    title('Landuse: USGS Global Land Cover')
+    dev.off()
+  };
+  go.plot(xfg$prefix)
+  
+  
+  # lc1 = 0:16 # GLCC classifications
+  # lc2 = 0:12 # LSM classifications
+  # rcl = rbind(c(0,0),
+  #             c(1,1),
+  #             c(2,2),
+  #             c(3,3),
+  #             c(4,4),
+  #             c(5,5),
+  #             c(6,6),
+  #             c(7,7),
+  #             c(8,8),
+  #             c(9,9),
+  #             c(10,10),
+  #             c(11,0),
+  #             c(12,11),
+  #             c(13,13),
+  #             c(14,11),
+  #             c(15,0),
+  #             c(16,12))
+  # rcl[,2]=rcl[,2]+1 #classes start from 1, instead of 0;
+  # 
+  # r.lsm = raster::reclassify(r.lu, rcl)
+  # ulc = unique(r.lsm)
+  # 
+  # writeRaster(r.lsm,  filename = file.path(xfg$dir$predata, 'pcs', 'Landuse_idx.tif'), overwrite=T)
+  # # saveraster(r.lsm, fn=file.path(outpath, 'Landuse_PCS') )
+  # 
+  # # ===========================
+  # cn=c('INDEX','LAIMAX','RS','RGL','ALBMAX','SHDFAC','ROUGH','DROOT','SoilDgrd','ImpArea')
+  # # vtab = t(matrix(c(1,0.00000,100.00000,30.00000,0.13500,    0.00000,0.10000,0.60000,0.00000,0,
+  # #                   2,10.76000,125.00000,30.00000,0.18200,    0.80000,0.32000,0.60000,0.00000,0,
+  # #                   3,5.11700,150.00000,96.07728,0.21300,    0.90000,0.32000,0.60000,0.00000,0,
+  # #                   4,10.76000,150.00000,30.00000,0.18200,    0.80000,0.36000,0.60000,0.00000,0,
+  # #                   5,7.17300,100.00000,72.00234,0.23600,    0.80000,0.36000,0.60000,0.00000,0,
+  # #                   6,8.83300,125.00000,52.56440,0.20250,    0.79500,0.40000,0.60000,0.00000,0,
+  # #                   7,8.54004,173.51021,55.99480,0.21129,    0.79986,0.35000,0.60000,0.00000,0,
+  # #                   8,3.66000,300.00000,82.93310,0.25245,    0.80184,0.40000,0.40000,0.00000,0,
+  # #                   9,3.66000,300.00000,126.09371,0.24959,    0.62501,0.30000,0.40000,0.00000,0,
+  # #                   10,2.60000,170.00000,142.33158,0.26652,    0.21818,0.25000,0.40000,0.00000,0,
+  # #                   11,2.90000,40.00000,100.00000,0.28802,    0.72552,0.10000,0.40000,0.05000,0,
+  # #                   12,4.78200,40.00000,100.00000,0.24992,    0.83537,0.20000,0.40000,0.50000,0,
+  # #                   13,0.00100,174.99974,155.98361,0.38000,    0.07489,0.02000,0.05000,0.30000,0,
+  # #                   14,0.00100,200.00000,97.19872,0.24650,    0.10000,0.02000,0.05000,0.90000,0.5 
+  # # ), nrow=length(cn)))
+  # # 
+  # # colnames(vtab)=cn
+  # # vtab[, 'ROUGH']=(vtab[, 'ROUGH']/10+0.08)
+  # # debug(read.df)
+  # vtab =  read.df('Table/USGS_GLC.csv')
+  # 
+  # write.df(vtab, file=file.path(xfg$dir$predata,'LANDUSE.csv') )
+  # write.table(vtab, file.path(xfg$dir$predata,'LanduseTable.csv'), quote=F, 
+  #             col.names = T, row.names = F)
+  return(xfg) 
+}
--- a/Deploy/shud/SubScript/Sub2.2_Landcover_nlcd.R
+++ b/Deploy/shud/SubScript/Sub2.2_Landcover_nlcd.R
@ -0,0 +1,25 @@
+fun.lc.NLCD <- function(xfg){
+  r = raster::raster(xfg$fn.landuse)
+  fun.gdalcut(f.in = xfg$fn.landuse, 
+              f.mask = pd.pcs$wbd.buf, 
+              f.out = pd.pcs$lu.r, 
+              s_srs = crs(r), 
+              t_srs = xfg$crs.pcs
+  )
+  r1 = raster(pd.pcs$lu.r)
+  plot(r1)
+  
+  alc = sort(unique(r1))
+  
+  att=read.table('AutoSHUD/Table/nlcd.csv', header = TRUE)
+  att = att[att$INDEX %in% alc, ] # find the value only exist in the nlcd file.
+  
+  natt = nrow(att)
+  rcl = cbind(att[, 1], 1:natt)
+  lc.att = data.frame('ID'=1:natt, att[, -1])
+  write.df(lc.att,  file = pd.att$landuse)
+  
+  r2 = raster::reclassify(r1, rcl)
+  writeRaster(r2, filename = pd.pcs$lu.idx, overwrite=TRUE)
+  
+}
--- a/Deploy/shud/SubScript/Sub2.3_Forcing_0.4NLDAS.R
+++ b/Deploy/shud/SubScript/Sub2.3_Forcing_0.4NLDAS.R
@ -0,0 +1,55 @@
+buf.g = readOGR(pd.gcs$wbd.buf)
+wb.g = readOGR(pd.gcs$wbd)
+
+if(xfg$iforcing == 0.3){ res = 0.25 }
+if(xfg$iforcing == 0.4){ res = 0.125 }
+ext = extent(buf.g)
+
+ext.fn = c(floor(ext[1]), ceiling(ext[2]), floor(ext[3]), ceiling(ext[4]) )
+
+sp.fn =fishnet(xx = seq(ext.fn[1], ext.fn[2], by=res),
+               yy = seq(ext.fn[3], ext.fn[4], by=res),
+               crs =crs(buf.g), type='polygon')
+id=which(gIntersects(sp.fn, buf.g, byid = T))
+sp.ldas = sp.fn[id,]
+plot(sp.ldas); plot(add=T, buf.g, border=3); plot(add=T, wb.g, border=2)
+# writeshape(sp.ldas, file=file.path(dir.predata, 'LDAS_GCS'))
+writeshape(sp.ldas, file=pd.gcs$meteoCov)
+
+sp.ldas.pcs = spTransform(sp.ldas, xfg$crs.pcs)
+writeshape(sp.ldas.pcs, file=pd.pcs$meteoCov)
+# writeshape(sp.ldas.pcs, file=file.path(dir.predata, 'LDAS'))
+
+png.control(fn=paste0(prefix, '_LDAS.png'), path = xfg$dir$fig, ratio=1)
+plot(sp.fn, axes=T); grid()
+plot(sp.ldas, add=T, col=3)
+plot(wb.g, add=T, border=2)
+plot(buf.g, add=T, border=4)
+title('LDAS')
+dev.off()
+
+
+
+# Forcing: 
+# 0 LDAS mode: 0.1 CLDAS, 0.2 FLDAS, 0.3 GLDAS 0.4 NLDAS
+# 1 Local data: 1.1 Points of metereo-station 1.2 Polygon of coverage
+
+if ( xfg$iforcing == 0.1 ) {  # FLDAS
+  message('USING FLDAS FORCING DATA')
+  source('Rfunction/CLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/CLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if ( xfg$iforcing == 0.2 ) {  # FLDAS
+  message('USING FLDAS FORCING DATA')
+  source('Rfunction/FLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/FLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if( xfg$iforcing == 0.3  ){ # GLDAS
+  message('USING GLDA FORCING DATA')
+  source('Rfunction/GLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/GLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if( xfg$iforcing == 0.4 ){  # nldas
+  message('USING NLDAS FORCING DATA')
+  source('Rfunction/NLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/NLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else{
+  stop(paste('WRONG LDAS CODE: ', xfg$iforcing))
+}
--- a/Deploy/shud/SubScript/Sub2.3_Forcing_LDAS.R
+++ b/Deploy/shud/SubScript/Sub2.3_Forcing_LDAS.R
@ -0,0 +1,69 @@
+#' INPUT: 
+#'       pd.gcs
+#'       xfg
+#'       
+#' OUTPUT:
+#'       sp.ldas, write file: DataPre/pcs/meteoCov.shp
+#'       
+#'       
+#'       
+#'       
+buf.g = readOGR(pd.gcs$wbd.buf)
+wb.g = readOGR(pd.gcs$wbd)
+
+if(xfg$iforcing == 0.3){ res = 0.25 } #GLDAS
+if(xfg$iforcing == 0.4){ res = 0.125 } #NLDAS
+
+ext = extent(buf.g)
+ext.fn = c(floor(ext[1]), ceiling(ext[2]), floor(ext[3]), ceiling(ext[4]) )
+sp.fn =fishnet(xx = seq(ext.fn[1], ext.fn[2], by=res),
+               yy = seq(ext.fn[3], ext.fn[4], by=res),
+               crs =crs(buf.g), type='polygon')
+
+id=which(gIntersects(sp.fn, buf.g, byid = T))
+sp.ldas = sp.fn[id,]
+plot(sp.ldas); plot(add=T, buf.g, border=3); plot(add=T, wb.g, border=2)
+# writeshape(sp.ldas, file=file.path(dir.predata, 'LDAS_GCS'))
+writeshape(sp.ldas, file=pd.gcs$meteoCov)
+
+sp.ldas.pcs = spTransform(sp.ldas, xfg$crs.pcs)
+writeshape(sp.ldas.pcs, file=pd.pcs$meteoCov)
+# writeshape(sp.ldas.pcs, file=file.path(dir.predata, 'LDAS'))
+
+png.control(fn=paste0(prefix, '_LDAS.png'), path = xfg$dir$fig, ratio=1)
+plot(sp.fn, axes=T); grid()
+plot(sp.ldas, add=T, col=3)
+plot(wb.g, add=T, border=2)
+plot(buf.g, add=T, border=4)
+title('LDAS')
+dev.off()
+
+
+
+# Forcing: 
+# 0 LDAS mode: 0.1 CLDAS, 0.2 FLDAS, 0.3 GLDAS 0.4 NLDAS
+# 1 Local data: 1.1 Points of metereo-station 1.2 Polygon of coverage
+
+if ( xfg$iforcing == 0.1 ) {  
+  # FLDAS
+  message('USING FLDAS FORCING DATA')
+  source('Rfunction/CLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/CLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if ( xfg$iforcing == 0.2 ) {  
+  # FLDAS
+  message('USING FLDAS FORCING DATA')
+  source('Rfunction/FLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/FLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if( xfg$iforcing == 0.3  ){ 
+  # GLDAS
+  message('USING GLDA FORCING DATA')
+  source('Rfunction/GLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/GLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else if( xfg$iforcing == 0.4 ){  
+  # NLDAS
+  message('USING NLDAS FORCING DATA')
+  source('Rfunction/NLDAS_nc2RDS.R') # read the orginal fldas data and save to .RDS file.
+  source('Rfunction/NLDAS_RDS2csv.R') # read the RDS above, to save as .csv file.
+}else{
+  stop(paste('WRONG LDAS CODE: ', xfg$iforcing))
+}
--- a/Deploy/shud/SubScript/Sub2_iForcing_1.1.R
+++ b/Deploy/shud/SubScript/Sub2_iForcing_1.1.R
@ -0,0 +1,19 @@
+# library(rgdal)
+# library(raster)
+# library(rgeos)
+# library(rSHUD)
+x=readOGR(xfg$fsp.forc)
+# y=readOGR(
+
+ysp=spTransform(y, crs(x))
+e1 = extent(ysp)
+e2 = extent(x)
+rw = c(min(e1[1], e2[1]), 
+       max(e1[2], e2[2]),
+       min(e1[3], e2[3]),
+       max(e1[4], e2[4]) ) + c(-1, 1, -1, 1)
+vx=voronoipolygons(x, rw=rw, crs=crs(x))
+plot(vx); plot(add=T, x); plot(add=T, ysp)
+
+vx@data=data.frame(vx@data, 'ID'=x@data$ID)
+writeshape(vx, '/Users/leleshu/Dropbox/Project/2020_Heihe/shud/Data/Forcing')
--- a/Deploy/shud/SubScript/Sub3_lake.R
+++ b/Deploy/shud/SubScript/Sub3_lake.R
@ -0,0 +1,3 @@
+spl0 = readOGR(pd.pcs$lake)
+sp.lake = gSimplify(spl0, bm.para$tol.wb)
+
--- a/Deploy/shud/SubScript/Sub_iSoil_0.1.R
+++ b/Deploy/shud/SubScript/Sub_iSoil_0.1.R
@ -0,0 +1,65 @@
+# this script for HWSD data only.
+
+cut_HWSD <- function(fn.r, 
+                     fn.buf,
+                     fn.dbf,
+                     fout.mask,
+                     fa.soil, 
+                     fa.geol,
+                     crs.out){
+  crs.gcs = sp::CRS('+init=epsg:4326')
+  # cmd=paste('gdalwarp -overwrite -cutline', 
+  #           fn.buf, 
+  #           '-dstnodata -9999',
+  #           '-s_srs', paste0("'",  as.character(crs.gcs), "'"),
+  #           '-t_srs', paste0("'",  as.character(crs.out), "'"),
+  #           '-crop_to_cutline -of GTiff ',
+  #           fn.r, fout.mask)
+  # message(cmd)
+  # system(cmd)
+  fun.gdalcut(f.in = fn.r, f.mask = fn.buf, f.out = fout.mask,
+              s_srs = crs.gcs, t_srs = crs.out)
+  r = raster::raster(fout.mask)
+  ur=sort(raster::unique(r))
+  x = foreign::read.dbf(fn.dbf)
+  
+  cn =  c('SILT', 'CLAY', 'OC', 'BULK_DEN')
+  cn.t = paste0('T_', cn )
+  cn.s=paste0('S_', cn )
+  idx = x[, 1] %in% ur
+  y.soil = x[idx,  c('ID', cn.t)]
+  y.geol = x[idx,   c('ID', cn.s)]
+  fn1 = fn2 = fout.mask
+  raster::extension(fn1) ='.Soil.csv'
+  raster::extension(fn2) ='.Geol.csv'
+  write.table(y.soil, file = fn1, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  write.table(y.geol, file = fn2, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  # foreign::write.dbf(y.geol, file = fn2)
+  # write.table(y, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  return(list('Soil'=y.soil, 'Geol'=y.geol))
+}
+
+indir = xfg$dir$soil
+outdir = xfg$dir$predata
+fn.buf = pd.gcs$wbd.buf
+
+fn.r = file.path(indir, 'hwsd.bil')
+fn.dbf = file.path(indir, 'hwsd.dbf')
+
+fout.mask = file.path(xfg$dir$predata, 'hwsd.tif')
+fa.soil = file.path(xfg$dir$predata, 'hwsd.Soil.csv')
+fa.geol = file.path(xfg$dir$predata, 'hwsd.Geol.csv')
+
+
+tmp = cut_HWSD(fn.r, fn.buf, fn.dbf,
+               fout.mask,  fa.soil, fa.geol, 
+               crs.out = xfg$crs.pcs)
+
+xfg = c(xfg, 
+        list(fn.soil = fout.mask,
+             fn.geol = fout.mask,
+             fa.soil = fa.soil,
+             fa.geol = fa.geol) )
+
+dat.soil = fun.Soil_Geol(xfg$fn.soil, xfg$fa.soil, outdir = xfg$dir$predata, TOP = TRUE)
+dat.geol = fun.Soil_Geol(xfg$fn.geol, xfg$fa.geol, outdir = xfg$dir$predata, TOP = FALSE)
--- a/Deploy/shud/SubScript/Sub_iSoil_0.2.R
+++ b/Deploy/shud/SubScript/Sub_iSoil_0.2.R
@ -0,0 +1,55 @@
+# this script for HWSD data only.
+
+cut_HWSD <- function(fn.r, 
+                     fn.buf,
+                     fn.dbf,
+                     fout.mask,
+                     fa.soil, 
+                     fa.geol,
+                     crs.out, 
+                     crs.gcs){
+  fun.gdalcut(f.in = fn.r, f.mask = fn.buf, f.out = fout.mask, s_srs = crs.gcs, t_srs = crs.out)
+  r = raster::raster(fout.mask)
+  ur = sort(raster::unique(r))
+  x = foreign::read.dbf(fn.dbf)
+  
+  cn =  c('SILT', 'CLAY', 'OC', 'BULK_DEN')
+  cn.t = paste0('T_', cn )
+  cn.s=paste0('S_', cn )
+  idx = x[, 1] %in% ur
+  y.soil = x[idx,  c('ID', cn.t)]
+  y.geol = x[idx,   c('ID', cn.s)]
+  fn1 = fn2 = fout.mask
+  raster::extension(fn1) ='.Soil.csv'
+  raster::extension(fn2) ='.Geol.csv'
+  write.table(y.soil, file = fn1, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  write.table(y.geol, file = fn2, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  # foreign::write.dbf(y.geol, file = fn2)
+  # write.table(y, quote = FALSE, row.names = FALSE, col.names = TRUE)
+  return(list('Soil'=y.soil, 'Geol'=y.geol))
+}
+
+fun.soil.hwsd <-function(xfg){
+  indir = xfg$dir$soil
+  outdir = xfg$dir$predata
+  fn.buf = xfg$pd.gcs$wbd.buf
+  
+  fn.r = file.path(indir, 'hwsd.bil')
+  fn.dbf = file.path(indir, 'hwsd.dbf')
+  
+  fout.mask = file.path(xfg$dir$predata, 'hwsd.tif')
+  fa.soil = file.path(xfg$dir$predata, 'hwsd.Soil.csv')
+  fa.geol = file.path(xfg$dir$predata, 'hwsd.Geol.csv')
+  
+  tmp = cut_HWSD(fn.r, fn.buf, fn.dbf, fout.mask,  fa.soil, fa.geol,  crs.out = xfg$crs.pcs, crs.gcs = xfg$crs.gcs)
+  
+  xfg = c(xfg, 
+          list(fn.soil = fout.mask,
+               fn.geol = fout.mask,
+               fa.soil = fa.soil,
+               fa.geol = fa.geol) )
+  dat.soil = fun.Soil_Geol(xfg=xfg, TOP = TRUE)
+  dat.geol = fun.Soil_Geol(xfg=xfg, TOP = FALSE)
+  return(xfg)
+}
+
--- a/Deploy/shud/SubScript/Sub_iSoil_1.1.R
+++ b/Deploy/shud/SubScript/Sub_iSoil_1.1.R
@ -0,0 +1 @@
+
--- a/Deploy/shud/Table/.LCType_color.clr.swp
+++ b/Deploy/shud/Table/.LCType_color.clr.swp
--- a/Deploy/shud/Table/._.DS_Store
+++ b/Deploy/shud/Table/._.DS_Store
--- a/Deploy/shud/Table/._nlcd.csv
+++ b/Deploy/shud/Table/._nlcd.csv
--- a/Deploy/shud/Table/LCType_color.clr
+++ b/Deploy/shud/Table/LCType_color.clr
@ -0,0 +1,18 @@
+0	186	248	254	255	 Water
+1	20	103	12	256	 Evergreen Needle leaf Forest
+2	85	168	89	257	 Evergreen Broadleaf Forest
+3	127	208	31	258	 Deciduous Needle leaf Forest
+4	115	234	105	259	 Deciduous Broadleaf Forest
+5	79	205	136	260	 Mixed Forests
+6	211	115	121	261	 Closed Shrublands
+7	250	236	165	262	 Open Shrublands
+8	184	234	142	263	 Woody Savannas
+9	248	234	44	264	 Savannas
+10	245	194	108	265	 Grasslands
+11	66	135	206	266	 Permanent Wetland
+12	251	255	45	267	 Croplands
+13	240	0	26	268	 Urban and Built-Up
+14	142	144	23	269	 Cropland/Natural Vegetation Mosaic
+15	251	220	212	270	 Snow and Ice
+16	189	189	189	271	 Barren or Sparsely Vegetated
+
--- a/Deploy/shud/Table/USGS_GLC.csv
+++ b/Deploy/shud/Table/USGS_GLC.csv
@ -0,0 +1,19 @@
+17	6.000						
+INDEX	ALBEDO	VEGFRAC	ROUGH	RZD	SOILDGRD	IMPAF	remark
+0	0.080	0.000	0.020	0.000	0.000	0.000	Water
+1	0.140	0.800	0.070	0.600	0.000	0.000	Evergreen Needle leaf Forest
+2	0.100	0.900	0.070	0.600	0.000	0.000	Evergreen Broadleaf Forest
+3	0.140	0.800	0.070	0.600	0.000	0.000	Deciduous Needle leaf Forest
+4	0.120	0.800	0.070	0.600	0.000	0.000	Deciduous Broadleaf Forest
+5	0.110	0.700	0.060	0.600	0.000	0.000	Mixed Forests
+6	0.120	0.700	0.060	0.000	0.000	0.000	Closed Shrublands
+7	0.180	0.500	0.050	0.400	0.000	0.000	Open Shrublands
+8	0.100	0.625	0.045	0.400	0.000	0.000	Woody Savannas
+9	0.150	0.218	0.045	0.400	0.000	0.000	Savannas
+10	0.150	0.726	0.040	0.400	0.000	0.000	Grasslands
+11	0.100	0.200	0.035	0.000	0.000	0.000	Permanent Wetland
+12	0.250	0.835	0.040	0.400	0.500	0.000	Croplands
+13	0.246	0.200	0.010	0.050	0.900	0.900	Urban and Built-Up
+14	0.200	0.835	0.040	0.400	0.500	0.000	Cropland/Natural Vegetation Mosaic
+15	0.650	0.000	0.020	0.000	0.000	0.500	Snow and Ice
+16	0.300	0.010	0.035	0.050	0.600	0.000	Barren or Sparsely Vegetated
--- a/Deploy/shud/Table/nlcd.csv
+++ b/Deploy/shud/Table/nlcd.csv
@ -0,0 +1,21 @@
+INDEX	LAIMAX	RMIN	RSREF	ALBEDO	VEGFRAC	ROUGH	RZD	SOILDGRD	IMPAF
+11	0.8540044	0.002011668	7861966	0.1617001	0.0799863	0.037	0	0	0
+12	3.416018	0.002023736	12613991	0.1426286	0.0799863	0.045	0	0	0
+21	4.997306	0.001886202	8555289	0.2610298	0.741792	0.012	0.19	0.58	0.54
+22	4.925537	0.001803889	8422173	0.2506489	0.741792	0.022	0.085	0.58	0.72
+23	4.997306	0.001886202	8555289	0.2610298	0.741792	0.012	0.19	0.58	0.54
+24	4.925537	0.001803889	8422173	0.2506489	0.741792	0.022	0.085	0.58	0.72
+31	0.1175883	0.002061292	12710286	0.2441793	0.0892185	0.036	0.19	0.36	0
+41	6.2166	0.002013886	6462902	0.241202	0.792552	0.058	0.58	0.04	0
+42	5.0835	0.001874998	8521377	0.239257	0.882552	0.068	0.52	0.04	0
+43	7.2126	0.002013886	4951408	0.211052	0.788052	0.052	0.58	0.04	0
+51	1.528394	0.001941826	9920107	0.2740451	0.2689095	0.045	0.4	0.04	0
+52	3.444959	0.002013886	10669047	0.2534286	0.6350565	0.043	0.4	0.04	0
+71	4.3039	0.002002312	11784039	0.2684654	0.660457	0.040	0.26	0.3	0
+72	2.8696	0.001921294	9123698	0.282433	0.660457	0.038	0.365	0.3	0
+73	4.3039	0.002002312	11784039	0.2684654	0.660457	0.040	0.26	0.3	0
+74	2.8696	0.001921294	9123698	0.282433	0.660457	0.038	0.365	0.3	0
+81	4.782	0.00179886	8640000	0.2632525	0.8243818	0.040	0.4	0.34	0
+82	2.8696	0.001422424	9123698	0.2481395	0.7593188	0.038	0.365	0.54	0
+90	2.298238	0.00202546	12573771	0.2094803	0.5625045	0.044	0.24	0	0
+95	2.8692	0.001921294	9123800	0.272718	0.652968	0.038	0.36	0.06	0
--- a/GO.R
+++ b/GO.R
@ -0,0 +1,142 @@
+#' ===============================================================
+#' Author: Lele Shu <shulele@lzb.ac.cn>
+#' Date: 2023.05.05
+#' Function: The core function of the GHDC
+#' ===============================================================
+#' 
+rm(list=ls())
+source('getReady.R')
+debug=FALSE
+# debug=TRUE
+
+CV=list()
+# system('cp -r bak/* data/')
+CV$serv = load.conf(file = file.path('script', paste0('service_', Sys.info()['nodename'], '.cfg.txt') ) )
+CV$para = list(projname = '', Area = -999, distBuff = -999, NumCellMin = -999,
+               dataonly = FALSE, # prepare data only. 
+               gcs = sp::CRS('+init=epsg:4326'), pcs='')
+CV$figtype='cairo'
+caller = 'GO.R'
+
+dir.out <- file.path(CV$serv$DIR.WORKING, 'rProcessing')
+dir.log <- file.path(CV$serv$DIR.WORKING, 'rlog')
+
+tmp = lapply(list(dir.out, dir.log), dir.create,  showWarnings = FALSE, recursive = TRUE)
+
+# LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d%H%M%S' ), '.log'))
+LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d' ), '.log'))
+assign('LOGFILE',LOGFILE, envir = .GlobalEnv)
+
+if(!file.exists(LOGFILE)){
+  write('', file=LOGFILE)
+}
+writelog(LOGFILE, caller = caller)
+writelog("======== RUNING ==========", caller = caller)
+
+
+task.fns = list.files(path = CV$serv$TARGET.DIR, pattern = glob2rx('*.txt'),  full.names = TRUE)
+task.N = length(task.fns)
+if(task.N <= 0){
+  writelog(paste('Empty dirs. Nothing to do. ( path = ', normalizePath(CV$serv$TARGET.DIR), ')'), caller = caller)
+  stop()
+}
+
+writelog(paste(task.N, 'task(s) in', CV$serv$TARGET.DIR), caller = caller)
+
+itask = 1
+# for(itask in 1:task.N){
+for(itask in 1:task.N){
+  task.fn = task.fns[itask]
+  task.backup(task.fn, CV)
+  
+  LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d' ), '.log'))
+  assign('LOGFILE',LOGFILE, envir = .GlobalEnv)
+  writelog(paste('===================================='),  caller = caller)
+  writelog(paste('\t', itask, '/', task.N, 'task \t', task.fn), caller = caller)
+  
+  theTask = readLines(task.fn)
+  theTask = gsub('/home/wwwroot/r', '.', theTask)
+  writelog(paste('\t', itask, '/', task.N, 'task \t', theTask), caller = caller)
+  writelog(paste('\t++++++START++++++:', theTask), caller = caller)
+  
+  theTask = normalizePath(theTask)
+  CV=configure(CV, theTask)
+  # BACKUP the input.zip, input.json to UserData.
+  fn.input = list.files(path=theTask, pattern = glob2rx('input*'), full.names = TRUE, recursive = TRUE)
+  file.copy(from = fn.input, to = CV$dirs$userdata,  overwrite = TRUE, recursive = TRUE)
+  file.copy(from = task.fn, to = file.path(CV$dirs$prj, basename(task.fn)), overwrite = TRUE, recursive = TRUE)
+  
+  #————— Clean the Task file. —————————————————————————————————————
+  writelog(paste('REMOVING task file:', task.fn), caller = caller)
+  if(!debug){
+    unlink(task.fn, recursive = TRUE, force = TRUE, expand = TRUE)
+  }
+  #——————————————————————————————————————————
+ 
+  ##### 1. Task.Ready.....
+  writemessage(paste0('Trying to run task.ready(CV)'),  caller = caller, CV$task.log)
+  iflag=try(task.ready(CV),TRUE)
+  if(  class(iflag)=="try-error"  )   { 
+    writemessage(paste0('ERROR: something wrong in task.ready(CV). '),  caller = caller, CV$task.log)
+    warning('Task stop at task.ready(CV).')
+    print(iflag)
+    task.clean(task.fn);    next 
+  }  else { 
+    CV = iflag  
+    writemessage(paste0('Finish the task.ready(CV). '),  caller = caller, CV$task.log)
+    message('Step of task.ready finished. \n\n')
+  } 
+  
+  ###### 2. Extract ETV
+  writemessage(paste0('Trying to run ETV.Delineation(CV)'),  caller = caller, CV$task.log)
+  iflag=try(ETV.Delineation(CV),TRUE)
+  if(  class(iflag)=="try-error"  )   { 
+    writemessage(paste0('ERROR: something wrong in ETV.Delineation(CV). '),  caller = caller, CV$task.log)
+    warning('Task stop at ETV.Delineation(CV).')
+    print(iflag)
+    task.clean(task.fn);    next 
+  }  else {
+    writemessage(paste0('Finish the ETV.Delineation(CV). '),  caller = caller, CV$task.log)
+    message('Step of ETV.Delineation finished. \n\n')} 
+  
+  writemessage(paste0('Trying to run ExtractETV(CV)'),  caller = caller, CV$task.log)
+  iflag=try(ExtractETV(CV),TRUE)
+  if(  class(iflag)=="try-error"  )   { 
+    writemessage(paste0('ERROR: something wrong in ExtractETV(CV). '),  caller = caller, CV$task.log)
+    warning('Task stop at ExtractETV(CV).')
+    print(iflag)
+    task.clean(task.fn);    next 
+  }  else {
+    writemessage(paste0('Finish the ExtractETV(CV). '),  caller = caller, CV$task.log)
+    message('Step of ExtractETV finished. \n\n')} 
+  
+  ###### 3. Model Deployment 
+  iflag=try(model.Deploy(CV),TRUE)
+  writemessage(paste0('Trying to run model.Deploy(CV)'),  caller = caller, CV$task.log)
+  if(  class(iflag)=="try-error"  )   { 
+    writemessage(paste0('ERROR: something wrong in model.Deploy(CV). '),  caller = caller, CV$task.log)
+    warning('Task stop at model.Deploy(CV).')
+    print(iflag)
+    task.clean(task.fn);    next 
+  }  else {
+    writemessage(paste0('Finish the model.Deploy(CV). '),  caller = caller, CV$task.log)
+    message('Step of model.Deploy finished. \n\n')  } 
+  
+  ###### 4. Task done. Data packaging. update info.json/citys.js =====
+  iflag=try(task.done(CV),TRUE)
+  writemessage(paste0('Trying to run task.done(CV)'),  caller = caller, CV$task.log)
+  if(  class(iflag)=="try-error"  )   { 
+    writemessage(paste0('ERROR: something wrong in task.done(CV). '),  caller = caller, CV$task.log)
+    warning('Task stop at task.done(CV)')
+    task.clean(task.fn);  
+    # next 
+  }  else {
+    writemessage(paste0('Finish the task.done(CV). '),  caller = caller, CV$task.log)
+    message('Step of task.done finished. \n\n')
+  } 
+  writemessage(paste0('======== END ========'),  caller = caller, CV$task.log)
+  writelog(paste('\n'),  caller = caller)
+}
+
+
+
--- a/GO.R.backup
+++ b/GO.R.backup
@ -0,0 +1,123 @@
+#' ===============================================================
+#' Author: Lele Shu <shulele@lzb.ac.cn>
+#' Date: 2023.05.05
+#' Function: The core function of the GHDC
+#' ===============================================================
+#' 
+rm(list=ls())
+source('getReady.R')
+debug=FALSE
+debug=TRUE
+
+CV=list()
+# system('cp -r bak/* data/')
+CV$serv = load.conf(file = file.path('script', paste0('service_', Sys.info()['nodename'], '.cfg.txt') ) )
+CV$para = list(projname = '', Area = -999, distBuff = -999, NumCellMin = -999,
+               dataonly = FALSE, # prepare data only. 
+               gcs = sp::CRS('+init=epsg:4326'), pcs='')
+CV$figtype='cairo'
+caller = 'GO.R'
+
+dir.out <- file.path(CV$serv$DIR.WORKING, 'rProcessing')
+dir.log <- file.path(CV$serv$DIR.WORKING, 'rlog')
+
+tmp = lapply(list(dir.out, dir.log), dir.create,  showWarnings = FALSE, recursive = TRUE)
+
+# LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d%H%M%S' ), '.log'))
+LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d' ), '.log'))
+assign('LOGFILE',LOGFILE, envir = .GlobalEnv)
+
+if(!file.exists(LOGFILE)){
+  write('', file=LOGFILE)
+}
+writelog(LOGFILE, caller = caller)
+writelog("======== RUNING ==========", caller = caller)
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+  writelog('Empty args. Nothing to do. ', caller = caller)
+  stop()
+}
+
+task.fn <- args[1]
+
+writelog(paste('starting task: ', task.fn), caller = caller)
+
+task.backup(task.fn, CV)
+
+LOGFILE = file.path(dir.log, paste0(format(Sys.time(), '%Y%m%d' ), '.log'))
+assign('LOGFILE',LOGFILE, envir = .GlobalEnv)
+writelog(paste('===================================='),  caller = caller)
+
+theTask = gsub('/home/wwwroot/r', '.', task.fn)
+writelog(paste('\t++++++START++++++:', theTask), caller = caller)
+
+theTask = normalizePath(theTask)
+CV=configure(CV, theTask)
+# BACKUP the input.zip, input.json to UserData.
+fn.input = list.files(path=theTask, pattern = glob2rx('input*'), full.names = TRUE, recursive = TRUE)
+file.copy(from = fn.input, to = CV$dirs$userdata,  overwrite = TRUE, recursive = TRUE)
+
+
+##### 1. Task.Ready.....
+writemessage(paste0('Trying to run task.ready(CV)'),  caller = caller, CV$task.log)
+iflag=try(task.ready(CV),TRUE)
+if(  class(iflag)=="try-error"  )   { 
+  writemessage(paste0('ERROR: something wrong in task.ready(CV). '),  caller = caller, CV$task.log)
+  warning('Task stop at task.ready(CV).')
+  print(iflag)
+}  else { 
+  CV = iflag  
+  writemessage(paste0('Finish the task.ready(CV). '),  caller = caller, CV$task.log)
+  message('Step of task.ready finished. \n\n')
+} 
+
+###### 2. Extract ETV
+writemessage(paste0('Trying to run ETV.Delineation(CV)'),  caller = caller, CV$task.log)
+iflag=try(ETV.Delineation(CV),TRUE)
+if(  class(iflag)=="try-error"  )   { 
+  writemessage(paste0('ERROR: something wrong in ETV.Delineation(CV). '),  caller = caller, CV$task.log)
+  warning('Task stop at ETV.Delineation(CV).')
+  print(iflag)
+}  else {
+  writemessage(paste0('Finish the ETV.Delineation(CV). '),  caller = caller, CV$task.log)
+  message('Step of ETV.Delineation finished. \n\n')} 
+
+writemessage(paste0('Trying to run ExtractETV(CV)'),  caller = caller, CV$task.log)
+iflag=try(ExtractETV(CV),TRUE)
+if(  class(iflag)=="try-error"  )   { 
+  writemessage(paste0('ERROR: something wrong in ExtractETV(CV). '),  caller = caller, CV$task.log)
+  warning('Task stop at ExtractETV(CV).')
+  print(iflag)
+}  else {
+  writemessage(paste0('Finish the ExtractETV(CV). '),  caller = caller, CV$task.log)
+  message('Step of ExtractETV finished. \n\n')} 
+
+###### 3. Model Deployment 
+iflag=try(model.Deploy(CV),TRUE)
+writemessage(paste0('Trying to run model.Deploy(CV)'),  caller = caller, CV$task.log)
+if(  class(iflag)=="try-error"  )   { 
+  writemessage(paste0('ERROR: something wrong in model.Deploy(CV). '),  caller = caller, CV$task.log)
+  warning('Task stop at model.Deploy(CV).')
+  print(iflag)
+}  else {
+  writemessage(paste0('Finish the model.Deploy(CV). '),  caller = caller, CV$task.log)
+  message('Step of model.Deploy finished. \n\n')  } 
+
+###### 4. Task done. Data packaging. update info.json/citys.js =====
+iflag=try(task.done(CV),TRUE)
+writemessage(paste0('Trying to run task.done(CV)'),  caller = caller, CV$task.log)
+if(  class(iflag)=="try-error"  )   { 
+  writemessage(paste0('ERROR: something wrong in task.done(CV). '),  caller = caller, CV$task.log)
+  warning('Task stop at task.done(CV)')
+  # next 
+}  else {
+  writemessage(paste0('Finish the task.done(CV). '),  caller = caller, CV$task.log)
+  message('Step of task.done finished. \n\n')
+} 
+writemessage(paste0('======== END ========'),  caller = caller, CV$task.log)
+writelog(paste('\n'),  caller = caller)
+
+
+
--- a/21
+++ b/21
@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Simulator of Hydrological Unstructured Domains
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/Markdown/Aster_GDEM.png
+++ b/Markdown/Aster_GDEM.png
--- a/Markdown/Figure/shudxyz.jpeg
+++ b/Markdown/Figure/shudxyz.jpeg
--- a/Markdown/Figure/weixin_search.png
+++ b/Markdown/Figure/weixin_search.png
--- a/Markdown/ReadMe_cn.Rmd
+++ b/Markdown/ReadMe_cn.Rmd
@ -0,0 +1,91 @@
+---
+title: "全球水文数据云"
+author: 舒乐乐 [shulele@lzb.ac.cn](mailto:shulele@lzb.ac.cn)
+date: 2022/10/05
+output:
+  html_document:
+    toc: true
+    theme: united
+    df_print: paged
+---
+
+
+![](../static/res/logo_cn.png)
+
+## 数据平台介绍
+
+全球水文数据云(Global Hydrologic Data Cloud, GHDC)，是一个快速获取水文建模基础数据，实现快速模型部署的云平台，可实现全球任意流域、任意大陆、任意国家范围快速水文模型部署。用户仅需提供欲建模的流域边界，GHDC即可自动化完成数据前处理，不仅生成建模所需要的基础数据（高程、土地利用、土壤质地、水系等），并能够制备SHUD(Simulator of Hydrologic Unstructured Domains)模型所需要的输入数据。用户可根据需要进行水文模拟、空间分析、数据挖掘等工作。
+
+![GHDC的工作流程](../static/res/GHDC_flow.png)
+
+
+### 版权和权益
+GHDC并不生成数据，仅提供提供数据处理服务。数据版权和权益全部归属于原始数据的作者们。当前平台处理的数据全部为开放版权(Public Domains)的数据产品——允许数据处理、修改和重发布，放弃数据版权权益。本平台不提供未使用开放版权的数据产品的数据处理。例如GLDAS, NLDAS和FLDAS数据都使用了开放版权，本平台可提供数据处理服务；但是CMFD，CLDAS等再分析数据资料未使用开放版权，因此本平台无法提供数据处理服务。 若用户需要CMFD等未开放版权的数据集的处理任务，请联系网站作者[(shulele@lzb.ac.cn)](mailto:shulele@lzb.ac.cn)商讨。
+
+### 原始数据源
+当前可用数据见表：
+
+| 数据分类 | 数据名称 | 数据属性 | 数据版权 | 数据源 | 
+|:---------|:---------|:---------|:---------|:---------|
+| 高程 | ASTER GDEM |  全球 30m| Public Domain |  |
+| 土地利用 | USGS Land Cover  |  全球 1km| Public Domain |  |
+| 土壤质地 |  HWSD v1.0 |   全球 1km | Public Domain |  |
+| 气象再分析资料 | GLDAS |  全球 0.25度，1950至今| Public Domain |  |
+| 气象再分析资料 | FLDAS |  全部部分区域 0.1度，1979至今| Public Domain |  |
+| 气象再分析资料 | NLDAS |  美国 0.125度，1979至今 | Public Domain |  |
+| 气象再分析资料 | CLDAS |  中国 0.125度 | **版权私有** |  |
+| 气象再分析资料 | CMFD |  中国 0.1度 | **版权私有** |  |
+| | | | | |
+
+![ASTER Global DEM](../static/res/Aster_GDEM.png)
+![GLDAS, FLDAS, NLDAS, NCA-LDAS覆盖范围](../static/res/ldas-domain.jpg)
+
+
+
+## 如何获取数据
+
+1. 提供流域边界，文件格式为ESRI Shapefile。请将Shapefile包含的四个基本子文件(.shp, .shx, .dbf, .shx)打包为.zip文件，并上传。
+2. 提供模拟所需要的参数，包含项目名称、模拟年份、最小单元数，最大单元面积，含水层厚度等。
+3. 填写邮箱地址，并提交任务。 请查看您的邮箱，GHDC会发送一封邮件；请点击邮箱内的确认链接。点击确认链接之后，GHDC才会启动数据处理任务。
+4. GHDC数据处理完成后，会向您的邮箱再发送一封邮件，包含数据下载链接。
+5. 请通过数据下载链接下载处理好的数据。
+6. 模拟愉快~~~
+
+## 数据引用
+
+每个使用本平台的数据服务的用户都需要给GHDC平台建设者、程序开发者、SHUD模型作者和原始数据作者足够的学术肯定，因此**强烈建议您使用以下引用**。
+
+- 数据服务和SHUD模型
+    - Shu, L., Ullrich, P. A., & Duffy, C. J. (2020). Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method. Geoscientific Model Development, 13(6), 2743–2762. https://doi.org/10.5194/gmd-13-2743-2020
+    - Shu L.，Chang Y.，Wang J.，et al. A brief review of numerical distributed hydrological model SHUD［J］. Advances in Earth Science，2022，37（7）：680-691. DOI：10.11867/j.issn.1001-8166.2022.025.
+
+- NLDAS 
+    - Cosgrove, B. A. (2003). Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research, 108(D22), 8842. https://doi.org/10.1029/2002JD003118
+
+- Landuse, USGS 0.5 km MODIS-based Global Land Cover Climatology
+    - Broxton, P.D., Zeng, X., Sulla-Menashe, D., Troch, P.A., 2014a: A Global Land Cover Climatology Using MODIS Data. J. Appl. Meteor. Climatol., 53, 1593-1605. doi: http://dx.doi.org/10.1175/JAMC-D-13-0270.1
+
+## 致谢
+本平台的开发受到多个机构的研究经费支持。
+
+- 中国科学院率先行动“BR计划”：数值方法水文模型 
+- 中国科学院寒旱区陆面过程与气候变化重点实验室：CLM-SHUD耦合模型研发 
+- 青海省防灾减灾重点实验室开放基金项目：布哈河流域径流变化及水循环机理研究 
+- 国家冰川冻土沙漠科学数据中心：水文模型自动化建模系统研发 
+- 美国能源部(DOE)： Model Integration through Knowledge-Rich Data and Process Composition 
+- 美国自然科学基金(NSE)：Knowledge-Guided Machine Learning: A Framework to Accelerate Scientific Discovery 
+
+
+
+## 微信群
+
+公众号二维码
+![公众号](Figure/weixin_search.png)
+
+
+
+使用微信扫描二维码添加管理员，可以加入微信群。
+
+![拉群微信](Figure/shudxyz.jpeg)
+
+
--- a/Markdown/ReadMe_cn.html
+++ b/Markdown/ReadMe_cn.html
--- a/Markdown/ReadMe_en.Rmd
+++ b/Markdown/ReadMe_en.Rmd
@ -0,0 +1,85 @@
+---
+title: "The Global Hydrologic Data Cloud (GHDC)"
+author: Lele Shu [shulele@lzb.ac.cn](mailto:shulele@lzb.ac.cn)
+date: 2022/10/05
+output:
+  html_document:
+    toc: true
+    theme: united
+    df_print: paged
+---
+
+![](../static/res/logo_en.png)
+
+## What is the GHDC
+
+The Global Hydrologic Data Cloud (GHDC)is to provide a rapid and reproducible hydrological model development in any watershed, any continent, or any country. The users provide the watershed boundary, GHDC will do the data retrieve, subset clipping, reprojection, spatial-temporal data pre-processing, and so forth. Then, the user will receive a download link for the watershed.
+
+### License
+
+We do not produce data, but process data for modelers. So the license and right belong to original data creators. The current datasets support Public Domain, that allows reprocess and redistribute the data.
+
+### Data Source
+
+| Category                | Data name       | Feature                   | License       | Data source |
+|:------------------------|:----------------|:--------------------------|:--------------|:------------|
+| Elevation               | ASTER GDEM      | Global 30m                | Public Domain |             |
+| Landuse                 | USGS Land Cover | Global 1km                | Public Domain |             |
+| Soil                    | HWSD v1.0       | Global 1km                | Public Domain |             |
+| Reanalysis forcing data | GLDAS           | Global 0.25deg, 1950-     | Public Domain |             |
+| Reanalysis forcing data | FLDAS           | Continental 0.1deg, 1979- | Public Domain |             |
+| Reanalysis forcing data | NLDAS           | US 0.125deg, 1979-        | Public Domain |             |
+| Reanalysis forcing data | CLDAS           | China 0.125deg            | **private**   |             |
+| Reanalysis forcing data | CMFD            | China 0.1deg              | **private**   |             |
+|                         |                 |                           |               |             |
+
+![ASTER Global DEM](../static/res/Aster_GDEM.png)
+
+![GLDAS, FLDAS, NLDAS, NCA-LDAS覆盖范围](../static/res/ldas-domain.jpg)
+
+
+## How to require a data service
+
+1.  Provide the watershed boundary. The file is in ESRI Shapefile format. The shapefile (including the .shp, .dbf, .shx, .prj files) must be ziped in a .zip file, and uploaded to GHDC.
+2.  Fill the form of parameters.
+3.  Fill your email address, and submit your request.
+4.  Check you email and click the confirm link in the email from GHDC. After your confirmation, the GHDC starts data processing.
+5.  After GHDC data processing, you will received the second email, which includes a download link. Please download the data within 14 days.
+6.  Enjoy your data and modeling journey.
+
+## References/citations
+
+For giving right credits to the platform developers, model developers and data creators, we suggest our users strongly to add following citations into your future work.
+
+-   SHUD:
+
+    -   Shu, L., Ullrich, P. A., & Duffy, C. J. (2020). Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method. Geoscientific Model Development, 13(6), 2743--2762. <https://doi.org/10.5194/gmd-13-2743-2020>
+    -   Shu L.,Chang Y.,Wang J.,et al. A brief review of numerical distributed hydrological model SHUD［J］. Advances in Earth Science,2022,37（7）: 680-691. DOI: 10.11867/j.issn.1001-8166.2022.025.
+
+-   NLDAS
+
+    -   Cosgrove, B. A. (2003). Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research, 108(D22), 8842. <https://doi.org/10.1029/2002JD003118>
+
+-   Landuse, USGS 0.5 km MODIS-based Global Land Cover Climatology
+
+    -   Broxton, P.D., Zeng, X., Sulla-Menashe, D., Troch, P.A., 2014a: A Global Land Cover Climatology Using MODIS Data. J. Appl. Meteor. Climatol., 53, 1593-1605. doi: <http://dx.doi.org/10.1175/JAMC-D-13-0270.1>
+
+## Acknowledge
+
+Thanks to the funding supporters.
+
+-   Chinese Academy of Sciences, BR program: Numerical hydrological model development
+-   Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences: Coupled CLM-SHUD model development
+-   Qinghai Key Laboratory of Disaster Prevention: Streamflow change and hydrological mechanism in Buha River (Grant No. QFZ-2021-Z02).
+-   National Cryosphere Desert Data Center (NCDC) : Automatic hydrological model deployment system.
+-   Department of Energy (USA), Knowledge-Guided Machine Learning: A Framework to Accelerate Scientific Discovery
+-   National Science Fundation (USA), Model Integration through Knowledge-Rich Data and Process Composition
+
+## Wechat Group
+
+Scanning the QR code follows our Wechat official page.
+![公众号](Figure/weixin_search.png)
+
+
+You may scan the following QR code to add the admin, then join the Wechat group.
+![拉群微信](Figure/shudxyz.jpeg)
--- a/Markdown/ReadMe_en.html
+++ b/Markdown/ReadMe_en.html
--- a/Markdown/emailerror.md
+++ b/Markdown/emailerror.md
@ -0,0 +1,27 @@
+尊敬的SHUD用户[emailaddress]：
+
+您好,感谢您使用水文云服务。 
+
+您的数据请求失败。具体失败原因请看本邮件末尾的说明。请您排除故障后，重新提交数据请求。
+
+如果您还有更多疑问，请邮件联系( shulele@lzb.ac.cn )。
+
+SHUD研究组( https://www.shud.xyz )
+
+
+----------------------------------------------------
+
+
+Dear SHUD-user[emailaddress],
+
+Thanks for using Global Hydrologic Data Cloud (https://www.ghdc.ac.cn or https://shud.ncdc.ac.cn ).
+
+You data request failed, unfortunately. The error information is following this email. Please resubmit your data request after issue-fixing.
+
+If you have more questions or suggestions, please contact ( shulele@lzb.ac.cn ).
+
+Yours sincerely,
+SHUD group ( https://www.shud.xyz )
+
+ 
+=================== Error information ===================
--- a/Markdown/emailout.md
+++ b/Markdown/emailout.md
@ -0,0 +1,29 @@
+尊敬的SHUD用户[emailaddress]：
+
+您好,感谢您使用水文云服务。
+
+您从全球水文数据云(https://www.ghdc.ac.cn , 备用地址：https://shud.ncdc.ac.cn )请求的数据已经处理完毕。您可以通过链接(dataurl)下载数据，该数据临时存放时间为14天，14天后数据链接将失效。
+
+水文云服务并不生产数据，只通过公开数据集进行数据处理；感谢原数据的分享。现能够提供的数据都使用了开放版权(Public Domain)——不保留版权权益；版权开放给公共用途；允许对数据进行处理、分享、再发布。
+
+**强烈建议所有数据用户遵守引用规范，给予数据生产者和处理者充分的学术肯定各数据和程序的引用请参考数据包内文件：Citation.bib**。
+
+如果您对下载的数据有疑问，或者对shuddata服务有建议，请邮件联系( shulele@lzb.ac.cn )。
+
+SHUD研究组( https://www.shud.xyz )
+
+
+----------------------------------------------------
+
+Dear SHUD-user[emailaddress],
+
+Thanks for using Global Hydrologic Data Cloud (https://www.ghdc.ac.cn or https://shud.ncdc.ac.cn ).
+
+The data you required via HCS is ready for download. Please download the data through (dataurl), **in 14 days**. After 14 days, the link will be invalid.
+
+Our HCS does not produce any data but process and redistribute the public-accessible data. Original data producers share the raw data with Public Domain licenses, allowing editing, modification, and redistribution of the data. But, we suggest our data users give appropriate references/citations for each dataset. **The citation information of the data is saved in the file Citation.bib in the data folder downloaded.**
+
+If you have more questions or suggestions, please contact ( shulele@lzb.ac.cn ).
+
+Yours sincerely,
+SHUD group( https://www.shud.xyz )
--- a/Markdown/errorMSG/._e_input_openwbdFail.txt
+++ b/Markdown/errorMSG/._e_input_openwbdFail.txt
--- a/Markdown/errorMSG/._e_input_spPolygon.txt
+++ b/Markdown/errorMSG/._e_input_spPolygon.txt
--- a/Markdown/errorMSG/e_input_openwbdFail.txt
+++ b/Markdown/errorMSG/e_input_openwbdFail.txt
@ -0,0 +1,11 @@
+当前上传的边界文件无法使用GDAL打开。
+可能原因是1.非英文方式的文件名或文件夹名称；2. 文件格式与GDAL不兼容；3. 投影信息(.prj文件)缺失
+流域边界必须为多边形的shapefile，无法处理线条、点、或者栅格文件定义的流域边界。
+并且，边界文件的投影信息(.prj文件)是合理的。
+
+The uploaded boundary file cannot be open with GDAL.
+Possible reasons are 1. non-English name of folder/files; 2. incompatibility 
+between input file and GDAL format; 3. Projection infomation (.prj file) is missing.
+The watershed boundary file must be SpatialPolygon/SpatialPolygonDataFrame 
+(in shapefile format) with a valid projection. The PolyLine/Points shapefiles 
+or tiff format are not acceptable. 
--- a/Markdown/errorMSG/e_input_spPolygon.txt
+++ b/Markdown/errorMSG/e_input_spPolygon.txt
@ -0,0 +1,6 @@
+流域边界必须为多边形的shapefile，无法处理线条、点、或者栅格文件定义的流域边界。
+并且，边界文件的投影信息(.prj文件)是合理的。
+
+The watershed boundary file must be SpatialPolygon/SpatialPolygonDataFrame 
+(in shapefile format) with a valid projection. The PolyLine/Points shapefiles 
+or tiff format are not acceptable. 
--- a/Markdown/ldas-domain.jpg
+++ b/Markdown/ldas-domain.jpg
--- a/Show More
+++ b/Show More