Annual Estimates of Recharge,Quick‐Flow Runoff,and Evapotranspiration for the Contiguous U.S. Using Empirical Regression Equations |
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| Authors: | M Reitz WE Sanford GB Senay J Cazenas |
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| Affiliation: | 1. National Research Program‐Eastern Branch, U.S. Geological Survey, Reston, Virginia;2. Earth Resources Observation and Science Center, North Central Climate Science Center, U.S. Geological Survey, Fort Collins, Colorado |
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| Abstract: | This study presents new data‐driven, annual estimates of the division of precipitation into the recharge, quick‐flow runoff, and evapotranspiration (ET) water budget components for 2000‐2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick‐flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick‐flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick‐flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first‐order estimates of these quantities across the CONUS, even where field measurements are sparse. |
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| Keywords: | recharge runoff evapotranspiration hydrologic cycle groundwater management |
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