330 lines
11 KiB
R
330 lines
11 KiB
R
library(rSHUD)
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read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
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x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
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x=round(x, 4)
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x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
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x=round(x, 4)
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x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')[[1]]
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x
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x=round(x, 3)
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plot.zoo(x)
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library(xts)
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plot.zoo(x)
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x=round(x, 3)
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plot.zoo(x[1:200, ])
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/qhh.R", echo=TRUE)
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head(x)
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fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv'
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x=read.tsd(file =fn)[[1]]
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x=round(x, 3)
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write.tsd(x, fn)
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fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
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x=read.tsd(file =fn)[[1]]
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x=round(x, 3)
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plot(x)
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plot.zoo(x)
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x=read.df(/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv)
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fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
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x=read.df(fn)
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x=read.df(fn)[[1]]
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head(x)
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x=readLines(fn)
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fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
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x=readLines(fn)
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tb=read.table(x[-1], header = TRUE)
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head(tb)
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tb=read.table(x[-1], header = TRUE)
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head(tb)
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x=readLines(fn)
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head9x
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head9x
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head(x)
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tb=read.table(x[-1], header = TRUE, sep = '\t')
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head(tb)
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tb=read.table(text=x[-1], header = TRUE, sep = '\t')
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head(tb)
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tb=read.table(text=x[-1], header = TRUE, sep = ' ')
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head(tb)
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tsd = as.xts(tb[, 1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
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head(tsd)
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
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head(tsd)
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plot.zoo(tsd)
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
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head(tsd)
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plot.zoo(tsd)
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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plot(tsd)
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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plot.zoo(tsd)
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*60)
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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plot.zoo(tsd)
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apply.daily(tsd[, 1])
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apply.daily(tsd[, 1], mean)
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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plot.zoo(tsd)
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apply.yearly(apply.daily(tsd[, 1], mean), sum)
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fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
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x=readLines(fn)
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tb=read.table(text=x[-1], header = TRUE, sep = ' ')
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
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write.tsd(tsd, fn)
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#' ===========CACHE CREEK ====================
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fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
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x=readLines(fn)
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tb=read.table(text=x[-1], header = TRUE, sep = ' ')
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tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
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tsd = cbind(tsd[, 1]*1000, tsd[, 2],
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tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
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tt = time(tsd)
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x = tsd[tt >= as.POSIXct('2000-01-01') & tt <= as.POSIXct('2010-01-01')]
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fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
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write.tsd(x, fn)
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x = round(x, 4)
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fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
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write.tsd(x, fn)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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AA=getArea()
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oid = getOutlets()
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qq=readout('rivqdown')
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library(ggplot2)
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autoplot(q[, oid])
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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autoplot(qq[, oid])
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qs = read.tsd('../input/ccw/ccw.tsd.obs')
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qs = read.tsd('../input/ccw/ccw.tsd.obs')[[1]]
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qo = read.tsd('../input/ccw/ccw.tsd.obs')[[1]]
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qq=readout('rivqdown')
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qs=qq[, oid]
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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head(qq)
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qq = cbind(ts2Daily(qs),ts2Daily(qo) )
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autoplot(qq)
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qo = ts2Daily(read.tsd('../input/ccw/ccw.tsd.obs')[[1]])
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qdown = readout('rivqdown')
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qs = ts2Daily(qdown[, oid])
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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xl = loaddata()
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wb = wb.all(xl=xl)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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shud.env(prjname = 'ccw', inpath = '../input/ccw/', outpath = '../output/ccw.out/')
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AA = getArea()
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oid = getOutlets()
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qo = ts2Daily(read.tsd('../input/ccw/ccw.tsd.obs')[[1]])
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qdown = readout('rivqdown')
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qs = ts2Daily(qdown[, oid])
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ts = time(qs)
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to = time(qo)
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ct= ts[to %in% ts]
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qq = cbind('SIM' = qs, 'OBS' = qo/86400)[ts,]
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autoplot(qq)
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autoplot(qq[,1])
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xl = loaddata()
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wb = wb.all(xl=xl)
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autoplot(qq[,])
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qq = cbind('SIM' = qs, 'OBS' = qo)[ts,]
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autoplot(qq[,])
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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autoplot(qq[,])
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source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
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rm(list=ls())
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library(rSHUD)
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library(xts)
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library(raster)
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library(rgdal)
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library(sp)
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library(ggplot2)
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shud.env(prjname = 'qhh', inpath = '../input/qhh/', outpath = '../output/qhh.out/')
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AA = getArea()
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hist(AA)
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ia = getArea()
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AA = sum(ia)
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hist(ia)
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fdc(ia)
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fdc(ia/1e6)
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hist(ia)
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oid = getOutlets()
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oid = getOutlets() # 获得河流出口的序号(index)
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oid
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riv = rSHUD::readriv()
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riv = rSHUD::readriv()
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View(riv)
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?rSHUD
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?rSHUD::readriv
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?rSHUD::readriv
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obs=rnorm(100)
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sim=obs+rnorm(100)/2
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QvsO(cbind(obs,sim))
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View(riv)
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View(riv@river)
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sp.riv = rSHUD::sp.riv2shp()
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plot(sp.riv)
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plot(sp.riv, axes=TRUE)
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raster::plot(sp.riv, axes=TRUE)
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grid()
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sp.mesh = rSHUD::sp.mesh2Shape()
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raster::plot(sp.mesh, axes=TRUE) #绘图：三角形网
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raster::plot(sp.mesh, axes=TRUE, lwd=0.2) #绘图：三角形网
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raster::plot(sp.mesh, axes=TRUE, lwd=0.2, border='green') #绘图：三角形网
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raster::plot(sp.mesh, axes=TRUE, lwd=0.2, border='gray70') #绘图：三角形网
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raster::plot(sp.riv, add=TRUE, col='blue')
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lakid = rSHUD::getLakeEleID()
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raster::plot(sp.mesh[lakid, ], col='lightblue', add=TRUE)
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grid()
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qdown = rSHUD::readout('rivqdown')
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dim(qdown)
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dim(riv@river)
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View(riv@river)
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plot(qdown[, 1:100])
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xts::plot.xts(qdown[, oid])
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xts::plot.xts(qdown[, oid[1]])
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xts::plot.xts(qdown[, oid])
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# 地下水
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igw = readout('eleygw')
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dim(igw)
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?apply
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plot(apply(X = igw, MARGIN = 1, FUN = mean))
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# 蒸散发
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ietp = readout('elevetp')
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dim(ietp)
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plot(apply(X = ietp, MARGIN = 1, FUN = mean))
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head(ietp[, 1:10])
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head(ietp[, 1:5])
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?apply.daily(ietp, )
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dim(ietp)
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ncol(ietp)
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ncell = length(ia)
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ncell = length(ia)
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ncell
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apply.daily(ietp, sum) /ncell
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etp.daily = apply.daily(ietp, sum) /ncell
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plot(etp.daily)
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ggplot2::autoplot(etp.daily)
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ggplot2::autoplot(etp.daily*1000) +xlab('Time') +ylab('ETP (mm/day)')
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ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
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ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
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ggplot2::autoplot(etp.montly*1000) +
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xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
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etp.monthly = xts::apply.monthly(ietp, sum) /ncell # 时间序列：月均值
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ggplot2::autoplot(etp.monthly*1000) +
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xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
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# 实际蒸散发
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iaet = readout('elevaet') # 读取：实际蒸散发
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aet.daily = apply.daily(iaet, sum) /ncell #时间序列：日均值
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# 实际蒸散发
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ieta = readout('eleveta') # 读取：实际蒸散发
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eta.daily = apply.daily(ieta, sum) /ncell #时间序列：日均值
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eta.monthly = xts::apply.monthly(ieta, sum) /ncell # 时间序列：月均值
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plot(etp.daily)
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ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
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ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ATP (mm/day)') + theme_bw()
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ggplot2::autoplot(etp.monthly*1000) +
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xlab('Time') +ylab('ATP (mm/month)') + theme_bw()
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g1 = ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
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g2 = ggplot2::autoplot(etp.monthly*1000) +
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xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
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?GGally
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library(gridExtra)
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gridExtra::arrangeGrob(list(g1, g2))
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g1 = ggplot2::autoplot(etp.daily*1000) +
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xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
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g2 = ggplot2::autoplot(etp.monthly*1000) +
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xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
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gridExtra::arrangeGrob(grobs = list(g1, g2))
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gg = gridExtra::arrangeGrob(grobs = list(g1, g2))
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gg
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plot(gg)
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ggsave(filename = 'etp.png', plot=gg)
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ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
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dpi = 200)
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ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
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dpi = 100)
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ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
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dpi = 600)
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pa = cbind(etp.monthly, eta.monthly)
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class(pa)
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autoplot(pa)
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autoplot(pa, facets = NULL)
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colnames(pa) = c('ETP', 'ETA')
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autoplot(pa, facets = NULL)
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autoplot(pa, facets = NULL)+
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xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
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autoplot(pa, facets = NULL)+
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xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
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autoplot(pa*1000, facets = NULL)+
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xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
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et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
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apply.monthly(ieta[, lakid], sum)/nlake )
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# 对比陆地与湖泊实际蒸散发量，月均值
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nlake =length(lakeid)
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et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
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apply.monthly(ieta[, lakid], sum)/nlake )
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colnames(et.vs) = c('Land', 'Lake')
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autoplot(et.vs*1000, facets = NULL)+
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xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
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lakid = rSHUD::getLakeEleID() #读：湖泊ID
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# 对比陆地与湖泊实际蒸散发量，月均值
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nlake =length(lakeid)
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et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
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apply.monthly(ieta[, lakid], sum)/nlake )
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colnames(et.vs) = c('Land', 'Lake')
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autoplot(et.vs*1000, facets = NULL)+
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xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
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# 对比陆地与湖泊实际蒸散发量，月均值
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nlake =length(lakid)
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et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
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apply.monthly(ieta[, lakid], sum)/nlake )
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colnames(et.vs) = c('Land', 'Lake')
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autoplot(et.vs*1000, facets = NULL)+
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xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
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qdown = rSHUD::readout('rivqdown')
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dim(qdown)
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xts::plot.xts(qdown[, oid])
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q = apply.daily(qdown[, oid], sum)
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prcp = rSHUD::readout('elevprcp')
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# Hydrograph
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qdown = rSHUD::readout('rivqdown')
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prcp = rSHUD::readout('elevprcp')
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q = apply.daily(qdown[, oid], sum)
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p = apply.daily(prcp, sum)
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# Hydrograph
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qdown = rSHUD::readout('rivqdown')
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prcp = rSHUD::readout('elevprcp')
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q = apply.daily(qdown[, oid], sum)
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p = apply.daily(prcp, sum)
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pq = cbind(p, q)
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colnames(pq) = c('Precipitation', 'Discharge')
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rSHUD::hydrograph(pq)
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rSHUD::hydrograph(pq[1:(730) + (7*365), ])
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rSHUD::hydrograph(pq[1:(730) + (6*365), ])
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# 水量平衡
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xl = loaddata()
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wb = wb.all(xl = xl)
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library(raster)
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xy=list(cbind(c(0, 2, 1), c(0, 0, 2)), cbind(c(0, 2, 1), c(0, 0, 2))+2)
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sp1 = xy2shp(xy=xy, shape = 'polygon')
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raster::plot(sp1, axes=TRUE, col='gray')
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sp2 = xy2shp(xy=xy, shape = 'lines')
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raster::plot(sp2, add=TRUE, lty=2, lwd=3,col='red')
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sp3 = xy2shp(xy=xy, shape = 'POINTS')
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raster::plot(sp3, add=TRUE, pch=1, cex=2)
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