# Solar and wind energy enhances drought resilience and groundwater sustainability

## Abstract

Water scarcity brings tremendous challenges to achieving sustainable development of water resources, food, and energy security, as these sectors are often in competition, especially during drought. Overcoming these challenges requires balancing trade-offs between sectors and improving resilience to drought impacts. An under-appreciated factor in managing the water-food-energy (WFE) nexus is the increased value of solar and wind energy (SWE). Here we develop a trade-off frontier framework to quantify the water sustainability value of SWE through a case study in California. We identify development pathways that optimize the economic value of water in competition for energy and food production while ensuring sustainable use of groundwater. Our results indicate that in the long term, SWE penetration creates beneficial feedback for the WFE nexus: SWE enhances drought resilience and benefits groundwater sustainability, and in turn, maintaining groundwater at a sustainable level increases the added value of SWE to energy and food production.

## Introduction

As the central element of the water–food–energy (WFE) nexus1,2,3, effective management of water resources, especially for regulated river basins, is key to meet societal needs, including irrigation supply for food production and reservoir water release for hydropower generation. However, current water management strategies are often carried out independently for each sector, leading to competition for water resources4,5. This is likely to be exacerbated by the potential for increasing severity of drought under climate change6 and growing demand for limited water resources7,8. For example, globally, 54% of hydropower plants compete with irrigation water use9, and this competition between food production and hydropower generation has been increasing with several hot spots identified around the world. The competition usually happens between upstream and downstream sources. For instance, upstream hydroelectric power plants tend to store more water to increase and maintain the hydraulic head for power generation, even during the dry season. In contrast, downstream users need water released from upstream reservoirs to irrigate crops with a different timing (e.g., during the growing season). In some cases, a lack of available surface water puts a burden on groundwater, which also acts as a buffer to alleviate drought, leading to groundwater depletion10,11,12,13, given the slow process of groundwater recharge to aquifers14. Meanwhile, increased water scarcity15,16 and shifts in the timing of streamflow17,18 could further strain the WFE nexus and exacerbate the conflicts or trade-offs between irrigation and hydropower. For instance, traditional reservoir operation rules without consideration of the non-stationarity19 of hydroclimate may no longer be efficient enough to navigate the trade-offs due to the seasonal imbalance between water supply and demand.

Here, we argue that the water allocation trade-offs between hydropower generation and irrigation use, and their future evolution, can be potentially solved by consideration of integrated management tools and the fast increase of low-carbon energy generation, such as solar and wind energy (SWE). Given the fact that SWE deployment is accelerating and is particularly substitutable for hydropower if they are paired with energy storage facilities (e.g., thermal storage, batteries), energy systems are becoming less reliant on hydropower, as well as fossil fuels, especially for developed regions. Consequently, water used to drive turbines for hydropower generation can be saved for irrigation purposes to ensure food production, whilst reducing groundwater usage thereby increasing groundwater sustainability especially under drought. Here we emphasize the social value of SWE for environmental sustainability, which remains poorly understood in the scientific community and policy circles, through a case study in California. We first examine how water scarcity, as well as SWE, influences decisions surrounding the optimal and sustainable allocation of water for hydropower generation and food production. We then estimate the unrecognized and under-appreciated value of SWE beyond its role in the traditional energy sector and the synergies between SWE and groundwater to enhance drought resilience and environmental sustainability. Our analysis can help develop and integrate impact pathways into policy support for positive practical changes for sustainable water and food security.

## Results

### Hydrological and water resources model

Irrigation water requirement (IWR) is simulated with the Community Water Model (CWatM37), which is a macro-scale hydrological and water resources model developed by the Water Program at the International Institute of Applied Systems Analysis (IIASA). In this study, CWatM was forced by the daily meteorological forcing dataset WFDEI (WATCH Forcing Data methodology applied to ERA-Interim data38) at a 0.5$${}^{\circ }$$ spatial resolution and daily temporal resolution covering the 34-year simulation period (1979–2012) (meteorological forcings and key parameters are described in Supplementary Note 2). CWatM inherits the same irrigation scheme as implemented in PCR-GLOBWB8,39, which can separately estimate IWR for paddy and nonpaddy crops classified from the original 26 crop types in MIRCA2000 (ref. 40). The irrigation scheme dynamically links the daily surface and soil water balance with irrigation water, which is more realistic compared to the existing irrigation schemes used in other large-scale hydrological models8. Details on the calculation of irrigation water for paddy and nonpaddy crops are provided in the supplementary materials (Supplementary Note 3).

We calibrated and validated CWatM against streamflow observations from eight USGS stations in California. Model calibration is performed using an evolutionary computational framework called Distributed Evolutionary Algorithms in Python41 (DEAP). The modified version of the Kling–Gupta Efficiency (KGE, see equations in ref. 42) is used as the objective function to be maximized. We have used a population size of 256 and recombination pool size of 32 with the number of generations set to 30 to calibrate CWatM, which proves to be sufficient to achieve convergence. Specifically, we have calibrated the model focusing on snow, evapotranspiration, soil, groundwater, routing process, lakes and reservoirs. Besides the correlation coefficient (R) and KGE, we also use percent bias (B43) and Nash–Sutcliffe coefficient of efficiency (NSE44) to evaluate the performance of CWatM. Time series of observed and simulated streamflow and the associated performance metrics for the calibration and validation periods can be found in the supplementary materials (Supplementary Figs. 2–17). Results demonstrate that CWatM can well reproduce the streamflow variability and magnitude both at daily and monthly time scale.

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

Hydrological simulations from CWatM are available upon request to X.H. Hydroelectricity production can be obtained from the U.S. Energy Information Agency (https://www.eia.gov/). Revenue of field crops can be obtained from Figure 4 in ref. 45.

## Code availability

CWatM codes can be obtained from IIASA’s Water Program at http://www.iiasa.ac.at/cwatm and https://cwatm.github.io. Pseudocode to calculate the optimal point and expansion path can be found in the supplementary material (Supplementary Notes 4–6). Matlab codes for trade-off analysis and Python plotting scripts are available upon request to X.H.

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## Acknowledgements

X.H. would like to thank Prof. Michael Oppenheimer at Princeton University, Prof. Jim Hall at Oxford University, and Dr. Declan Conway and Dr. Kate Gannon at the London School of Economics for helpful discussions and comments. Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria), with financial support from the USA National Member Organization. This material was also based upon work supported by NOAA grant NA14OAR4310218 and the Science, Technology, and Environmental Policy (STEP) fellowship by the Princeton Environmental Institute at Princeton University. J.S. is supported through the UK Research and Innovation Global Challenges Research Fund projects “Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BRECcIA)”, grant number NE/P021093/1 and “FutureDAMS: Design and Assessment of resilient and sustainable interventions in water-energy-food-environment Mega-Systems”, grant number ES/P011373/1.

## Author information

X.H. and J.S. conceived the research and drafted the manuscript. X.H. and K.F. performed the trade-off analysis. X.H. performed the hydrological simulation with help from P.B. X.H. designed and prepared the figures. X.L., A.B.C., Y.W., P.B. and E.F.W. provided comments.

Correspondence to Xiaogang He or Justin Sheffield.

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