Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States

Abstract

Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus. This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability. Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data. Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually. Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate.