drought

Help for package drought const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {drought} Contents drought-package ACCU BiEmp ESPPred JDSI MSDI PropagationMCC RunDS SDI UMFreq UniEmp Type: Package Title: Statistical Modeling and Assessment of Drought Version: 1.2 Date: 2024-03-19 Author: Zengchao Hao Maintainer: Zengchao Hao <z.hao4univ@gmail.com> Description: Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) < doi:10.1016/j.envsoft.2017.02.008 >. Depends: R (≥ 3.5.0) Imports: stats,copula,corrplot Suggests: MASS License: GPL-3 Repository: CRAN RoxygenNote: 7.1.2 Encoding: UTF-8 NeedsCompilation: no Packaged: 2024-03-19 11:11:03 UTC; HAO Date/Publication: 2024-03-19 12:40:02 UTC Statistical Modeling and Assessment of Drought Description Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>. Details Package: drought Type: Package Version: 1.1 License: GPL-3 References Hao, Z. et al. (2017), An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment, Environ Modell Softw, 91, 199-209. Hao, Z., and V. P. Singh (2015), Drought characterization from a multivariate perspective: A review J. Hydrol., 527 Hao and AghaKouchak (2013) Multivariate Standardized Drought Index: A parametric multi-index model, Advances in Water Resources 57, 12-18. Kao, S. C. and R. S. Govindaraju (2010). A copula-based joint deficit index for droughts. Journal of Hydrology, 380(1-2): 121-134. Hao, Z. et al. (2014). Global integrated drought monitoring and prediction system. Scientific Data, 1 Examples #' X=runif(120, min = 0, max = 100) # 10-year monthly data #' Yc<-ACCU(X,ts=6) # Compute the 6 month accumulated series #' fit1<-SDI(X,ts=6) # Get the standardized drought index (or SPI) #' z=matrix(t(fit1$SDI),ncol=1) #' Res <- RunDS(z, -1)# Get drought duration and severity based on threshold SPI=-1 #' Y=runif(120, min = 0, max = 100) # 10-year monthly data #' fit2<-MSDI(X,Y,ts=6) # Compute the 6 month Multivariate Standardized Drought Index (MSDI) #' fit2$MSDI #Get the empirical MSDI #' PropagationMCC(X, Y, 12, c(-1,1)) # Plot drought propagation Obtain the accumulation of monthly hydro-climatic variables Description Obtain the accumulation of monthly hydro-climatic variables Usage ACCU(X, ts = 6) Arguments X The vector of monthly hydro-climatic variables of n years. ts is the accumulated time scale. ts The accumulated time scale Examples X=runif(120, min = 0, max = 100) # 10-year monthly data Y<-ACCU(X,ts=3) # Compute the 3 month accumulated series Compute the bivariate empirical joint probability Description Compute the bivariate empirical joint probability Usage BiEmp(X, Y) Arguments X The vector of a monthly hydro-climatic variable of n years(e.g., August). Y The vector of a monthly hydro-climatic variable of n years(e.g., August). Value The empirical joint probability of X and Y for a specific month (Gringorten plotting position) Examples X=runif(20, min = 0, max = 100) # 20 monthly values (e.g., August) Y=runif(20, min = 0, max = 100) fit<-BiEmp(X,Y) Drought prediction with ESP method Description Drought prediction with ESP method Usage ESPPred(X, Y, L = 1, m = 7, ts = 6) Arguments X is the monthly variables. Y is the monthly variables. L is the lead time. m is the start time of prediction (or ending of observations) ts is the time scale of monthly variables. Value The prediction of univariate and multivariate drought index based on ESP Examples X=runif(120, min = 0, max = 100) # 10-year monthly data Y=runif(120, min = 0, max = 100) ESPPred(X,Y,L=1,m=7,ts=6) Compute Joint Drought Severity Index with joint distribution Description The JDSI can be computed based on joint distribution or kendall distribution Usage JDSI(X, Y, ts = 6, type = 1) Arguments X is the vector of a monthly hydro-climatic variable of n years. Y is the vector of a monthly hydro-climatic variable of n years. ts is the accumulated time scale. type is the method used to compute the JDSI (1 is Joint distribution and 2 is the Kendall function). Value The multivariate drought index based on the joint distribution or Kendall distribution References Hao, Z. et al. (2017) An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment, Environ Modell Softw, 91, 199-209. Examples X=runif(120, min = 0, max = 100) # 10-year monthly data Y=runif(120, min = 0, max = 100) # 10-year monthly data fit<-JDSI(X,Y,ts=6) z=matrix(t(fit$JDSI),ncol=1) plot(z, type="l", col=1, lwd=2, lty=1, xlim=c(0,120),xlab="Time",ylab="JDSI") Compute the Multivariate Standardized Drought Index (MSDI) Description Based on a pair of monthly hydro-climatic variable (or corresponding marginals), the MSDI is computed using the joint distribution (parametric or nonparametric forms). The current version is based on the Gringorten plotting position. It can be extended to higher dimensions, such as trivariate case including meteorological, agricultural, and hydrological droughts. For the high dimension case, the copula or vine copula method can be employed Usage MSDI(X, Y, ts = 6) Arguments X is the vector of a monthly hydro-climatic variable of n years. Y is the vector of a monthly hydro-climatic variable of n years. ts is the accumulated time scale. Value The monthly MSDI series of different time scales (based on Gringorten plotting position) References Hao and AghaKouchak (2013) Multivariate Standardized Drought Index: A parametric multi-index model, Advances in Water Resources 57, 12-18. Examples X=runif(120, min = 0, max = 100) # 10-year monthly data Y=runif(120, min = 0, max = 100) # 10-year monthly data fit<-MSDI(X,Y,ts=6) # Compute the 6 month drought index fit$ProbEmp2 #Get the empirical drought index (e.g.,Gringorten plotting position ) Compute drought propagation based on maximum correlation Description Compute the pearson correlation between multi-time scale SPI and 1-month SRI to reflect the most possible propagation time (PT) from meteorological drought to hydrological drought. Note here the propagation of meteorological to hydrological drought is used as an example. The propagation of other types of drought can also be computed. Usage PropagationMCC(X, Y, acc = 12, lim = c(-1, 1), color = NA) Arguments X The vector of monthly meteorological variable (e.g., precipitation) Y The vector of monthly hydrological variables (e.g., runoff) acc Maximum of propagation time (or accumulation periods) lim The limits interval for color color Color vector in plot Value Plot of correlation matrix References Xu, Y. et al (2019). Propagation from meteorological drought to hydrological drought under the impact of human activities: A case study in northern China. J. Hydrol. 579, 124147. Zhang Y., Hao Z., Feng S., et al. (2021). Agricultural drought prediction in China based on drought propagation and large-scale drivers. Agr. Water Manage., 255: 107028. Examples X=runif(120, min = 0, max = 100) # 10-year monthly data Y=runif(120, min = 0, max = 100) # 10-year monthly data acc <- 12 lim <- c(-1,1) PropagationMCC(X, Y, acc, lim) Compute drought duration and severity based on run theory Description The input data is monthly drought indices. Duration is defined as the length of consecutive time series when drought index is below the threshold value (e.g., -1). Severity is defined a