107 lines
2.9 KiB
R
107 lines
2.9 KiB
R
#' ===================================================
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#' Analysis the output results from SHUD model
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#' Author: Lele Shu <shulele@lzb.ac.cn>
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#' Website: www.shud.xyz
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#' Date: 2022-10-10
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#' Copyright: MIT-Licence
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#' ===================================================
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#' Function:
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#'
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#'
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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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library(gridExtra)
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# 配置rSHUD工作环境。
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shud.env(prjname = 'qhh', inpath = '../input/qhh/', outpath = '../output/qhh.out/')
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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/1e6)
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ncell = length(ia)
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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() # 读取河流文件
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?rSHUD::readriv
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View(riv)
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View(riv@river)
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lakid = rSHUD::getLakeEleID() #读：湖泊ID
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# sp.mesh = rSHUD::sp.mesh2Shape() # gis:读取三角形网
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# sp.riv = rSHUD::sp.riv2shp() # gis： 读取河流
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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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# raster::plot(sp.mesh[lakid, ], col='lightblue', add=TRUE)
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# grid()
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# 河川径流
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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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# 地下水
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igw = readout('eleygw')
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dim(igw)
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plot(apply(X = igw, MARGIN = 1, FUN = mean))
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# 潜在蒸散发
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ietp = readout('elevetp') # 读取：潜在蒸散发
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head(ietp[, 1:5]) # 查看数据前5列的头部数据
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dim(ietp) # 数据维度
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etp.daily = apply.daily(ietp, sum) /ncell #时间序列：日均值
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etp.monthly = xts::apply.monthly(ietp, sum) /ncell # 时间序列：月均值
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plot(etp.daily)
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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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gg = gridExtra::arrangeGrob(grobs = list(g1, g2))
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ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',dpi = 600)
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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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pa = cbind(etp.monthly, eta.monthly)
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class(pa)
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colnames(pa) = c('ETP', 'ETA')
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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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# 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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# 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[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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