Irrigation by Crop in the Continental United States From 2008 to 2020
Ruess, P. J.; Konar, M.; Wanders, N.; Bierkens, M.
(2023) Water Resources Research, volume 59, issue 2
(Article)
Abstract
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate crop-specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion
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(GWD). To do this, we employ the PCR-GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high-resolution input data on agricultural production and climate within the CONUS to obtain crop-specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km3/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time-period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national-scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
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Keywords: agriculture, crop water use, depletion, groundwater, irrigation, surface water, United States, Water Science and Technology
ISSN: 0043-1397
Publisher: American Geophysical Union
Note: Funding Information: This material is based upon work supported by the National Science Foundation Grant CBET-1844773 (“CAREER: A National Strategy for a Resilient Food Supply Chain”), DEB-1924309 (“CNH2-L: Feedbacks between Urban Food Security and Rural Agricultural Systems”), BCS-2032065 (“RAPID: Spatial Resilience of Food Production, Supply Chains, and Security to COVID-19”), and CBET-2115405 (“SRS RN: Multiscale RECIPES (Resilient, Equitable, and Circular Innovations with Partnership and Education Synergies) for Sustainable Food Systems”). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Department of Agriculture. P. J. Ruess acknowledges the Sloan Minority Ph.D. Program for their financial and structural support, as well as our collaborators and others at Utrecht University who facilitated conducting this research on their national supercomputer Cartesius with the help of SURFsara Amsterdam. All data sources are detailed in Table 1 and are publicly available. We gratefully acknowledge these sources, without which this work would not be possible. We appreciate the constructive feedback from two anonymous reviewers that strengthened this paper. Funding Information: This material is based upon work supported by the National Science Foundation Grant CBET‐1844773 (“CAREER: A National Strategy for a Resilient Food Supply Chain”), DEB‐1924309 (“CNH2‐L: Feedbacks between Urban Food Security and Rural Agricultural Systems”), BCS‐2032065 (“RAPID: Spatial Resilience of Food Production, Supply Chains, and Security to COVID‐19”), and CBET‐2115405 (“SRS RN: Multiscale RECIPES (Resilient, Equitable, and Circular Innovations with Partnership and Education Synergies) for Sustainable Food Systems”). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Department of Agriculture. P. J. Ruess acknowledges the Sloan Minority Ph.D. Program for their financial and structural support, as well as our collaborators and others at Utrecht University who facilitated conducting this research on their national supercomputer Cartesius with the help of SURFsara Amsterdam. All data sources are detailed in Table 1 and are publicly available. We gratefully acknowledge these sources, without which this work would not be possible. We appreciate the constructive feedback from two anonymous reviewers that strengthened this paper. Publisher Copyright: © 2022. The Authors.
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