Rfmerge Error

[Ahmadffrahmand]

Respected Sir,

I created a zoo object from my precipitation file of 11 station and projected as utm zone 42, but following the instruction when i want to create a spatial object of the precipitation data i got the NA projection.

all other layers have the same utm zone 42 projection. but after running the Rfmerge i am getting this error. setwd("D:\Time Series\chirps\station data and precipitation data") ValparaisoPPts<-read.csv(file="data_ALLLL_copy.csv",header=TRUE, stringsAsFactors = FALSE)

dd<-seq(as.Date("2008-05-01"), as.Date("2019-12-31"), by = "day")

#################################################################### ldates <- hydroTSM::dip("2008-05-01", "2019-12-31") ldates <- seq(from=as.Date("2008-05-01"), to=as.Date("2019-12-31"), by="day")

class(ValparaisoPPts) names(CHIRPS5km) <- ldates names(PERSIANNcdr5km) <- ldates head(ValparaisoPPgis) main <- paste("Daily precipitation for the station", ValparaisoPPgis$Code[1]) ylab <- "Precipitation [mm]" x.ts <- ValparaisoPPts[,1] hydroTSM::hydroplot(x.ts, pfreq="o", main=main, ylab= ylab) plot(x.ts, main=main, ylab= ylab, col="blue") grid() chirps.total <- sum(CHIRPS5km, na.rm= FALSE) persiann.total <- sum(PERSIANNcdr5km, na.rm= FALSE) plot(chirps.total, main = "CHIRPSv2 [May - Aug] ", xlab = "Longitude", ylab = "Latitude") plot(ValparaisoSHP[1], add=TRUE, col="transparent") plot(persiann.total, main = "PERSIANN-CDR [May - Dec]", xlab = "Longitude", ylab = "Latitude") plot(ValparaisoSHP[1], add=TRUE, col="transparent") #################reading as zoo object K101111<- zoo(x=ValparaisoPPts$K101111, order.by=dd) K101112<- zoo(x=ValparaisoPPts$K101112, order.by=dd) K101113<- zoo(x=ValparaisoPPts$K101113, order.by=dd) K101114<- zoo(x=ValparaisoPPts$K101114, order.by=dd) K101115<- zoo(x=ValparaisoPPts$K101115, order.by=dd) K101116<- zoo(x=ValparaisoPPts$K101116, order.by=dd) K101117<- zoo(x=ValparaisoPPts$K101117, order.by=dd) K101118<- zoo(x=ValparaisoPPts$K101118, order.by=dd) K101119<- zoo(x=ValparaisoPPts$K101119, order.by=dd) K1011110<- zoo(x=ValparaisoPPts$K1011110, order.by=dd) K1011111<- zoo(x=ValparaisoPPts$K1011111, order.by=dd) ValparaisoPPts = cbind(K101111, K101112, K101113, K101114, K101115, K101116, K101117, K101118, K101119, K1011110, K1011111)

######################### Station csv which is Station location in studyarea ### ValparaisoPPgis<-read.csv(file="kunduz_ST4.csv",header=TRUE, stringsAsFactors = FALSE) names(ValparaisoPPgis)[1]<-c("Code") str(ValparaisoPPgis) class(ValparaisoPPgis) ValparaisoSHP <- sf::st_transform(ValparaisoPPgis, crs=32642) stations <- ValparaisoPPgis ( stations <- st_as_sf(stations, coords = c('lon', 'lat'), crs = 32642) ) stations.utm <- sf::st_transform(stations, crs=32642) st.coords <- st_coordinates(stations.utm) lon <- st.coords[, "X"] lat <- st.coords[, "Y"]

ValparaisoPPgis.utm <- data.frame(Id=stations.utm[["Code"]], lon=lon, lat=lat) ValparaisoPPgis<-ValparaisoPPgis.utm crs(ValparaisoPPgis) class(ValparaisoPPgis) ValparaisoPPgis <- st_set_crs(ValparaisoPPgis, crs = +proj=longlat +datum=WGS84 +no_defs ) ################################################################# ##################################Study area Border############# setwd("D:\Time Series\chirps\studyarea") ValparaisoSHP = st_read(dsn = "kunduz_WGS.shp",layer="kunduz_WGS") ValparaisoSHP = st_read(dsn = ("kunduz_WGS.shp")) ValparaisoSHP <- sf::st_transform(ValparaisoSHP, crs=32642) ValparaisoSHP.utm <- sf::st_transform(ValparaisoSHP, crs=32642) crs(ValparaisoSHP)
ValparaisoSHP<-ValparaisoSHP.utm plot(ValparaisoSHP) #######################################Chirps_v2_monthly data setwd("D:\Time Series\chirps\chirsps2") chirps<-list.files(path=getwd(), pattern="tif$") # 140 files ch<-raster(chirps[1]) #crs(ch)<- +proj=utm +zone=42 +datum=WGS84 +units=m +no_defs ch <- st_set_crs(ch, "+proj=utm +zone=42 +datum=WGS84 +units=m +no_defs") crs(ch) <- CRS('+init=EPSG:32642') crs(ch) crs(CHIRPS5km)<- CRS('+init=EPSG:32642') crs(CHIRPS5km) ###################################################### ############### ersion Data set ############# setwd("D:\Time Series\chirps\cdr")
prsnncdr.fname<-list.files(path=getwd(), pattern=".tif$") # 140 files cdr<-raster(prsnncdr.fname[1]) crs(cdr) <- CRS('+init=EPSG:32642') crs(cdr)<- CRS('+init=EPSG:32642') crs(cdr) crs(PERSIANNcdr5km)<- CRS('+init=EPSG:32642') crs(PERSIANNcdr5km) ######################################################### ############# Dem of study area #################### setwd("D:\Time Series\chirps\dem") dem.fname<-raster("Kunduz_Dem.tif") dem<-raster(dem.fname) crs(dem) <- CRS('+init=EPSG:32642') crs(dem) crs(ValparaisoDEM5km)<- CRS('+init=EPSG:32642') crs(ValparaisoDEM5km) plot(ValparaisoDEM5km, main="SRTM-v4", xlab="Longitude", ylab="Latitude", col=terrain.colors(255)) plot(ValparaisoSHP[1], add=TRUE, col="transparent") plot(stations[1], add=TRUE, pch = 16, col="black")

#########################################################3 CHIRPS5km <- brick(ch) PERSIANNcdr5km <- brick(cdr) ValparaisoDEM5km <- raster(dem) ######################### resampling of all same spatial resolution ### rsch<-resample(PERSIANNcdr5km,CHIRPS5km, method='bilinear') rsdem<-resample(ValparaisoDEM5km ,CHIRPS5km,method='bilinear')

CHIRPS5km <- brick(CHIRPS5km) ValparaisoDEM5km <- raster(rsdem) PERSIANNcdr5km<-brick(rsch) ############################################################3

CHIRPS5km <- brick(CHIRPS5km) PERSIANNcdr5km <- brick(rsch) ValparaisoDEM5km <- raster(rsdem) #####################################################projections#### ################################################################ extent(ValparaisoDEM5km) #crs(cdr)<- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +to wgs84=0,0,0" extent(rsch) ##########################################################

covariates <- list(chirps=CHIRPS5km, persianncdr=PERSIANNcdr5km, dem=ValparaisoDEM5km) ################################################################ onWin <- ( (R.version$os=="mingw32") | (R.version$os=="mingw64") ) ifelse(onWin, parallel <- "parallelWin", parallel <- "parallel") par.nnodes <- min(parallel::detectCores()-1,5) ###################### RfMerge ###############################

rfmep <- RFmerge(x=ValparaisoPPts, metadata=ValparaisoPPgis, cov=covariates, id="Id", lon="lon", lat = "lat", mask=ValparaisoSHP, training=0.8, parallel=parallel, par.nnodes=par.nnodes)

Error in sapply(x, fromDisk) & sapply(x, inMemory) : operations are possible only for numeric, logical or complex types In addition: Warning message: In RFmerge.zoo(x = ValparaisoPPts, metadata = ValparaisoPPgis, cov = covariates, : Invalid argument: 'cov' and 'mask' have different CRS. Please ensure they actually have the same CRS !

Also when i run this part of the code i get this error.

names(CHIRPS5km) <- ldates Error in names<-(*tmp*, value = c(14000, 14001, 14002, 14003, 14004, : incorrect number of layer names i have daily precipitation data and monthly chirps data. could you please instruct me how to resolve this part. the code i run is attached .

Regards