hzambran / rfmerge Goto Github PK

See

• https://www.r-spatial.org/r/2020/03/17/wkt.html
• https://win-builder.r-project.org/incoming_pretest/sf_0.9-0_20200319_213604/reverseDependencies/summary.txt

somewhere the package makes an assumption of the legacy crs structure, which is going to change. Please modify and submit to CRAN.

See: https://r-spatial.org/r/2022/04/12/evolution.html. sp on CRAN now offers control of the environment variable, so the attached log is from _SP_EVOLUTION_STATUS_=2 _R_CHECK_FORCE_SUGGESTS_=FALSE R CMD check RFmerge_0.1-10.tar.gz:
00check.log

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

I followed the tutorial and tried to run with my own data. Everything seems to run well but the RFmerge function gave this error.
Please i need help to fix this error.

Error in if (!sf::st_is(mask, c("POLYGON", "MULTIPOLYGON"))) { :
the condition has length > 1

station data and precipitation data.zip
Respected sir
i run the code and i got this error.
every thing is projected as utm.

data(ValparaisoPPts)
data(ValparaisoPPgis)
data(ValparaisoSHP)

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

Error in RFmerge.zoo(x = pcp, metadata = ValparaisoPPgis, cov = covariates, :
Invalid argument: 'metadata' must have 'id' (identifier), 'lon' (longitude) and 'lat' (latitude) fields !
metadata' must have 'id' (identifier), 'lon' (longitude) and 'lat' (latitude) fields !
x=pcp is the xts object of rainfaill from stations

Excuse me sir, I am interested in using this rfmerge package, if there is missing data (no observations) in the gauge data, how can I keep run this rfmerge library ? Thank you, please give some advice

Hi, while running the RFmerge function I am getting this error quoted below.
"Error in obs.values[i] <- train.ts[day, grep(metadata[, id], names(train.ts))] : replacement has length zero".
Any help would be much appreciated. Also, I would like to have the output as NetCDF, so bring an option to specify output file format. Thanks in advance.

Hi,
I am trying to run the RF-Merge using the trial data provided along with the package, by following the steps in the tutorial (https://cran.r-project.org/web/packages/RFmerge/vignettes/RFmerge-RainfallExample-minimal.pdf). However, when I am in the verification of covariates (section 6.2), the extent and resolution of the CHIRPS data do not match with DEM.

> nlayers(CHIRPS5km)
[1] 243
> ( nlayers(CHIRPS5km) == nlayers(PERSIANNcdr5km) )
[1] TRUE
> ( nlayers(CHIRPS5km) == nrow(ValparaisoPPts) )
[1] TRUE
> extent(CHIRPS5km)
class      : Extent 
xmin       : -71.85 
xmax       : -69.95 
ymin       : -34 
ymax       : -32 
> ( extent(CHIRPS5km) == extent(PERSIANNcdr5km) )
[1] TRUE
> ( extent(CHIRPS5km) == extent(ValparaisoDEM5km) )
[1] FALSE
> res(CHIRPS5km)
[1] 0.05 0.05
> ( res(CHIRPS5km) == res(PERSIANNcdr5km) )
[1] TRUE TRUE
> ( res(CHIRPS5km) == res(ValparaisoDEM5km) )
[1] FALSE FALSE

I am following the exact steps mentioned in the example tutorial. What is the solution for this?