The discipline of hydrology has long focused on quantifying the water balance, which is frequently used to estimate unknown water fluxes or stores. While technologies for measuring water balance components continue to improve, all components of the balance have substantial uncertainty at the watershed scale. Watershed-scale evapotranspiration, storage, and groundwater import or export are particularly difficult to measure. Given these uncertainties, analyses based on assumed water balance closure are highly sensitive to uncertainty propagation and errors of omission, where unknown components are assumed negligible. This commentary examines how greater insight may be gained in some cases by keeping the water balance open rather than applying methods that impose water balance closure. An open water balance can facilitate identifying where unknowns such as groundwater import/export are affecting watershed-scale streamflow. Strategic improvements in monitoring networks can help reduce uncertainties in observable variables and improve our ability to quantify unknown parts of the water balance. Improvements may include greater spatial overlap between measurements of water balance components through coordination between entities responsible for monitoring precipitation, snow, evapotranspiration, groundwater, and streamflow. Measuring quasi-replicate watersheds can help characterize the range of variability in the water balance, and nested measurements within watersheds can reveal areas of net groundwater import or export. Well-planned monitoring networks can facilitate progress on critical hydrologic questions about how much water becomes evapotranspiration, how groundwater interacts with surface watersheds at varying spatial and temporal scales, how much humans have altered the water cycle, and how streamflow will respond to future climate change. Plain Language SummaryThe water balance is a fundamental concept in hydrology that underlies many tools for predicting streamflow, soil moisture, or groundwater availability. It is often expressed as an equation that relates water inputs, outputs, and storage for a watershed. Inputs can be rainfall, snowmelt, or water imports to the watershed. Outputs include water movement into the atmosphere (evaporation, transpiration, and sublimation), streamflow, and water exports through groundwater or human diversions. Water storage can be in snow or ice, surface water bodies, or underground. Each of these water balance components is difficult to measure, and some are rarely measured. Therefore, researchers often simplify the water balance, assuming that difficult to measure quantities, like groundwater imports/exports or changes in water storage, can be neglected. Such simplifying assumptions lead to missed opportunities for discovering where these unknowns in the water balance are important controls on streamflow. This commentary advocates strategically expanding watershed monitoring networks to coordinate monitoring of different water balance components, monitor multiple similar wat...
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.
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