Predicting mean annual and mean monthly streamflow in Colorado ungauged basins. (17th February 2021)

• Record Type: Journal Article
• Authors: Eurich, Abby; Kampf, Stephanie K.; Hammond, John C.; Ross, Matt; Willi, Katie; Vorster, Anthony G.; Pulver, Bryce
• Is Part Of: River research and applications. Volume 37:Number 4(2021)
• Page Start: 569
• Page End: 578
• Publication Date: 2021-02-17
• Subjects: Colorado -- mean annual streamflow -- mean monthly streamflow -- regional regression -- snow persistence -- StreamStats -- ungauged basins; Rivers -- Regulation -- Periodicals; Rivers -- Periodicals; 551.483
• DOI: 10.1002/rra.3778
• Languages: English
• ISSNs: 1535-1459

Abstract

Abstract: River managers often need estimates of streamflow for ungauged streams. These estimates can be used in water rights acquisitions, in‐stream flow management, habitat assessment, water quality planning, and stream hazard identification. This publication describes new regression models for predicting mean annual and mean monthly streamflow in Colorado. Unlike previous regional regression studies, the new models incorporate snow persistence (SP), the fraction of time a watershed remains snow covered. Models were developed using streamflow data from 131 watersheds with drainage areas <1, 500 km 2, no transbasin diversions, and <10% urban area. In addition to SP, topographic, climate, geologic, and hydrologic region variables were used in model predictions. All new models had very good performance, with <6% absolute bias and stronger performance compared to current regional regression models in StreamStats. The mean annual model had the strongest performance, with Nash‐Sutcliffe efficiency coefficient (NSE) of 0.93 and <2% absolute bias. Mean monthly models had best performance during snowmelt runoff months (May‐Jul; NSE ≥0.79; absolute % bias ≤ 4) and weaker performance during low flow months (Aug‐Apr; NSE ≥ 0.59; % bias ≤ 5). Tests of the mean annual model using decadal average streamflow from 1910s to 2000s show very good performance (NSE > 0.75), but predictions were biased low by 14–28% in wetter decades. All equations and coefficients needed to run the models are presented in the publication appendix, and the associated data release includes the spatial data and model code, which can be applied using R or within an R‐based Shiny web app.