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Abstract Understanding the nexus between food, energy, and water systems (FEW) is critical for basins with intensive agricultural water use as they face significant challenges under changing climate and regional development. We investigate the food, energy, and water nexus through a regional hydroeconomic optimization (RHEO) modeling framework. The crop production in RHEO is estimated through a hierarchical regression model developed using a biophysical model, AquaCropOS, forced with daily climatic inputs. Incorporating the hierarchical model within the RHEO also reduces the computation time by enabling parallel programming within the AquaCropOS and facilitates mixed irrigation—rainfed, fully irrigated and deficit irrigation—strategies. To demonstrate the RHEO framework, we considered a groundwater‐dominated basin, South Flint River Basin, Georgia, for developing mixed irrigation strategies over 31 years. Our analyses show that optimal deficit irrigation is economically better than full irrigation, which increases the groundwater pumping cost. Thus, considering deficit irrigation in a groundwater‐dominated basin reduces the water, carbon, and energy footprints, thereby reducing FEW vulnerability. The RHEO also could be employed for analyzing FEW nexus under potential climate change and future regional development scenarios.
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This content will become publicly available on July 1, 2026
Optimal Irrigation Strategies and Adaptive Decision‐Making Under Groundwater Pumping Restrictions and Precipitation Uncertainties
Innovative groundwater management strategies are needed to preserve aquifers for crop irrigation. For sustainability to be lasting, any strategy must balance environmental goals with the economic aims of farmers. These tradeoffs are difficult to manage due to the inherent uncertainty in farming. To address these challenges, we develop a transferable two‐stage stochastic modeling framework to support optimal multi‐year crop and irrigation planning under groundwater pumping restrictions and uncertain precipitation. This modular framework is broadly applicable to regions facing groundwater overuse, helping to balance aquifer sustainability and farmer profitability under uncertainty. We illustrate the model using a case study from western Kansas, USA, where irrigators self‐imposed 5‐year groundwater pumping limits to extend the aquifer's lifespan. While these multi‐year allocation periods offer flexibility, they introduce a temporal dimension to decision‐making beyond typical annual planning. Optimal cropping and irrigation strategies from the stochastic model significantly outperform observed farmer behavior during the first two 5‐year allocation periods (2013–2022), and outperform a deterministic model assuming long‐term average precipitation during dry conditions. We show that optimal crop choices shift from corn to sorghum under more stringent pumping restrictions. Under these constraints, irrigators benefit by conserving water in earlier years and using more in later years, whereas the reverse holds under more lenient restrictions. Extending the allocation window further enhances profitability, though marginal gains diminish beyond 7 years. This modeling framework offers insights for agricultural regions seeking to improve long‐term groundwater management through strategies that support both economic resilience and hydrologic sustainability.
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- Award ID(s):
- 2108196
- PAR ID:
- 10655692
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 61
- Issue:
- 7
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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