R/getWaterUse.R

In lerban/CityWaterBalance: Track Flows of Water Through an Urban System

#' Gather time series of water use data
#' 
#' This function gathers and summarizes water use data from USGS NWIS by county,  
#' year, source (surface or groundwater) and quality (fresh or saline) 
#'
#' @param states list of state abbreviations
#' @param counties list of county name lists for each state
#' @param years list of years of available data
#' @importFrom dataRetrieval readNWISuse
#' @return list of dataframes of water withdrawals (MGD) by use category, 
#' one for each water source and quality:
#'  \item{swf}{surface water, fresh}
#'  \item{gwf}{groundwater, fresh}
#'  \item{sws}{surface water, saline}
#'  \item{gws}{groundwater, saline}  
#' @examples
#' wu <- getWaterUse(c('IL'), list(c('Cook', 'DeKalb')))
#' @export 

getWaterUse <- function(states, counties, years = "ALL") {
    
    # --- load data ----
    a <- list()
    b <- list()
    c <- list()
    d <- list()
    e <- list()
    
    for (i in 1:length(states)) {
        
        # get NWIS water use data
        w <- dataRetrieval::readNWISuse(stateCd = states[i], 
                                        countyCd = counties[[i]], years = years)
        
        # exclude double-counting and totals
        w <- w[, -grep(".population.|.deliveries.|Fossil.|Geothermal", 
                       colnames(w))]
        w <- w[, -grep("Nuclear.|Hydroelectric.|.cooling.|.Animal", 
                       colnames(w))]
        w <- w[, -grep(".Stock.|..Crop.|.Golf.|.acres|.person", colnames(w))]
        w <- w[, -grep(".facilities|.hours", colnames(w))]
        
        # info
        info <- w[, grep("state|county|year", colnames(w))]
        colnames(info) <- c("statecode", "state", "countycode", "county", 
                            "year")
        
        # replace missing data with NA but keep numeric
        w <- data.frame(suppressWarnings(lapply(w, as.numeric)))
        
        # --- summarize water use data ----
        
        # rename categories
        categorize <- function(data) {
            colnames(data)[grep("Public.", colnames(data))] = "Public"
            colnames(data)[grep("Domestic.", colnames(data))] = "Domestic"
            colnames(data)[grep("Commercial.", colnames(data))] = "Commercial"
            colnames(data)[grep("Industrial.", colnames(data))] = "Industrial"
            colnames(data)[grep("Irrigation.", colnames(data))] = "Irrigation"
            colnames(data)[grep("Livestock.", colnames(data))] = "Livestock"
            colnames(data)[grep("Aquaculture.", colnames(data))] = "Aquaculture"
            colnames(data)[grep("Mining.", colnames(data))] = "Mining"
            colnames(data)[grep("Thermoelectric.", colnames(data))] = 
              "Thermoelectric"
            return(data)
        }
        
        # fresh surface water withdrawals
        swf <- w[, grep(".surface.water.withdrawals..fresh..", colnames(w))]
        swf <- categorize(swf)
        swf <- cbind(info, swf)
        a[[i]] <- swf
        
        # fresh groundwater withdrawals
        gwf <- w[, grep(".groundwater.withdrawals..fresh..", colnames(w))]
        gwf <- categorize(gwf)
        gwf <- cbind(info, gwf)
        b[[i]] <- gwf
        
        # saline surface water withdrawals
        sws <- w[, grep(".surface.water.withdrawals..saline..", colnames(w))]
        sws <- categorize(sws)
        sws <- cbind(info, sws)
        c[[i]] <- sws
        
        # saline ground water withdrawals
        gws <- w[, grep(".groundwater.withdrawals..saline..", colnames(w))]
        gws <- categorize(gws)
        gws <- cbind(info, gws)
        d[[i]] <- gws
        
    }
    
    swf <- do.call("rbind", a)
    gwf <- do.call("rbind", b)
    sws <- do.call("rbind", c)
    gws <- do.call("rbind", d)
    info <- do.call("rbind", e)
    
    return(list(swf = swf, gwf = gwf, sws = sws, gws = gws))
    
}