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J Hydrol (Amst). 2017 Feb;545:410-423. doi: 10.1016/j.jhydrol.2016.12.028. Epub 2016 Dec 21.

Comparing potential recharge estimates from three Land Surface Models across the Western US.

Author information

1
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona.
2
Department of Environmental Sciences, University of California Riverside, Riverside.
3
Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland.
4
NCAR HR Regional Modelling, Boulder, Colorado.

Abstract

Groundwater is a major source of water in the western US. However, there are limited recharge estimates available in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US. Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01-15% for Mosaic, 3.2-42% for Noah, and 6.7-31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge rates in data limited regions.

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