Future implications of enhanced hydroclimate variability and reduced snowpack on California's water resources
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Abstract
The Sierra Nevada snowpack, which supplies sixty percent of California’s consumptive water use, is under threat due to anthropogenic climate change. While previous studies have examined the impacts of climate change on mountain snowpack in the Sierra Nevada and across the Western US, few have quantified the risks to monthly irrigation water resources posed by shifting hydroclimate patterns and declining snowmelt runoff. Because they use coarse-resolution models, existing global-scale studies lack regional specificity, while existing regional studies rely on statistical or dynamical ‘downscaling’ of coarse-resolution global models. We use a new simulation of the variable resolution Community Earth System Model 2, which provides high spatiotemporal resolution estimates (14 km horizontal grid spacing, daily-to-hourly outputs) of California’s historical and future hydroclimate. We leverage the US Geological Survey’s recent irrigation water use reanalysis to evaluate basin-scale irrigation water consumption across the Sacramento, San Joaquin, and Tulare basins. Our study provides a comprehensive assessment of the water cycle, examining shifts in precipitation regimes, snowpack dynamics, and the timing and magnitude of runoff under warming scenarios of +1.5 °C, +2.0 °C, and +3.0 °C, based on the 1985–2005 reference period. Additionally, we evaluated the potential of rainfall- and snowmelt-derived runoff to meet monthly basin-scale irrigation water consumption and quantified the resulting water gaps under both modeled historical conditions and the +3°C climate scenario. The Sierra Nevada region is projected to shift from a snow-dominated to a rain-dominated hydrology as the climate warms. In the +3 °C warming scenario, the fraction of precipitation that falls as snow decreases from 51% to 24% in the Northern Sierra Nevada and from approximately 64% to 40% in both the Central and Southern Sierra Nevada. This results in a decline and earlier peak in snow water equivalent, as well as an earlier onset and shorter duration of snowmelt runoff. We find that changes in runoff timing and magnitude under a +3.0 °C scenario will amplify water gaps during the summer months and introduce a new water gap as early as May in the Tulare basin. Under this scenario, the Tulare basin is projected to exhibit the largest yearly water gap (5.8 km3), followed by the San Joaquin basin (4 km3), and the Sacramento basin (3.1 km3). Our findings highlight the vulnerability of California’s agricultural water security to warming-driven shifts in hydroclimate patterns and snowpack loss.
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© 2025 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
Dr Areidy Beltran-Peña was funded by the Stanford Doerr School of Sustainability Dean’s Postdoctoral Fellowship from Stanford University. In addition, this material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program. The SCGSR program is administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by ORAU Under Contract Number DE‐SC0014664. All opinions expressed in this paper are the authors’ and do not necessarily reflect the policies and views of DOE, ORAU, or ORISE. Co-author Dr Alan M Rhoades was funded by the Director, Office of Science, Office of Biological and Environmental Research of the U.S. Department of Energy Regional and Global Model Analysis (RGMA) program through the Calibrated and Systematic Characterization, Attribution and Detection of Extremes (CASCADE) Science Focus Area (Award No. DE-AC02-05CH11231), and the ‘An Integrated Evaluation of the Simulated Hydroclimate System of the Continental US’ (HyperFACETS) project (Award No. DE-SC0016605). Co-author Dr Elizabeth Burakowski was funded through NSF grant (NSF EPSCoR Track 4 Award # 1832959) to perform the VR-CESM2 simulations used in this study. Paolo D’Odorico was funded by the USDA Hatch Multistate Project #W4190 capacity fund. Noah Diffenbaugh acknowledges support from Stanford University. We would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. In addition, this research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. We would like to thank Dr Mark Risser for his technical guidance on executing the logistic regression analysis. Additionally, we extend our heartfelt appreciation to the dedicated team of scientists, software engineers, and administrators who have made significant contributions to the development of the Community Earth System Model used in this study. Their hard work and dedication have been instrumental in the advancement of our research.
Data Availability
The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.25740/tf620qc3346.
Additional Information
Model simulations were performed on the Cheyenne supercomputer at the NCAR-Wyoming Computing Center. Data analysis was performed at the National Energy Research Scientific Computing Center (NERSC). ERA5 is publicly available at the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at https://cds.climate.copernicus.eu/#!/search?text=ERA5. Berkeley Earth data was acquired from http://berkeleyearth.org/data/. NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp) observational data was taken from www.ncei.noaa.gov/products/land-based-station/noaa-global-temp. USGS National Elevation Dataset was accessed from the USGS ScienceBase-Catalog www.sciencebase.gov/catalog/item/542aebf9e4b057766eed286a. The basin boundaries were taken from the U.S. Geological Survey (USGS) Watershed Boundary Dataset: www.usgs.gov/national-hydrography/watershed-boundary-dataset. The VR-CESM2 simulation data used for this study are provided via the following NERSC Gateways—https://portal.nersc.gov/archive/home/a/arhoades/Shared/www/atm/CONUS30x8—and—https://portal.nersc.gov/archive/home/a/arhoades/Shared/www/lnd/CONUS30x8. Modeling data and scripts produced through this study are available in the Stanford Digital Repository (Beltran-Peña et al 2024).
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Funding
- Stanford University
- United States Department of Energy
- DE‐SC0014664
- United States Department of Energy
- DE-AC02-05CH11231
- United States Department of Energy
- DE-SC0016605
- National Science Foundation
- 1832959
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- Division of Geological and Planetary Sciences (GPS)
- Publication Status
- Published