US Corn Belt enhances regional precipitation recycling
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ISSN: 0027-8424
E-ISSN: 1091-6490
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National Academy of Sciences
Abstract
Precipitation recycling, where evapotranspiration (ET) from the land surface contributes to precipitation within the same region, is a critical component of the water cycle. This process is especially important for the US Corn Belt, where extensive cropland expansions and irrigation activities have significantly transformed the landscape and affected the regional climate. Previous studies investigating precipitation recycling typically relied on analytical models with simplifying assumptions, overlooking the complex interactions between groundwater hydrology and agricultural management. In this study, we use high-resolution climate models coupled with an explicit water vapor tracer algorithm to quantify the impacts of shallow groundwater, dynamic crop growth, and irrigation on regional precipitation recycling in the US Corn Belt. We find that these coupled groundwater–crop–irrigation processes reduce surface temperatures and increase the growing season precipitation. The increase in precipitation is attributed to a significant enhancement of the precipitation recycling ratio from 14 to 18%. This enhanced precipitation recycling is stronger in a dry year than normal and wet years, depending on both large-scale moisture transport and local ET. Our study underscores the critical role of groundwater hydrology and agricultural management in altering the regional water cycle, with important implications for regional climate predictions and food and water security.
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Zhang, Z., He, C., Chen, F., Miguez-Macho, G., Liu, C., & Rasmussen, R. (2025). US Corn Belt enhances regional precipitation recycling. Proceedings of the National Academy of Sciences of the United States of America, 122(1). https://doi.org/10.1073/PNAS.2402656121
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https://doi.org/10.1073/pnas.2402656121Sponsors
This material is based upon work supported by the NSF National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Zhe Zhang would like to acknowledge the Advanced Study Program Postdoctoral Fellowship in the NSF National Center for Atmospheric Research. Cenlin He was partially supported by the NSF Convergence Accelerator Program Track J Phase 2 Award #2345039 with Subaward #E2066262. We would like to acknowledge high-performance computing support from Cheyenne provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the NSF.
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Copyright © 2024 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution- NonCommercial-NoDerivatives License 4.0 (CC BY- NC- ND).
Attribution-NonCommercial-NoDerivatives 4.0 International
Attribution-NonCommercial-NoDerivatives 4.0 International








