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Abstract This study investigates residential indoor water consumption variability across 39 US cities using data from 26,441 single‐family smart water meters. Employing functional data analysis and mixed‐effects random forest, we identified distinct usage patterns across city clusters, with 13 high and 6 low water‐using cities (all in coastal California) differing significantly from 20 medium water‐using cities. Shower and toilet use were primary drivers of indoor use differences between clusters, influenced by both behavioral and fixture efficiency factors. The presence of appliances, certain household features, and weather also affect indoor water use, with varying influence on indoor water use across clusters. Our findings highlight the effectiveness of state‐level water efficiency interventions and emphasize the importance of considering both behavioral factors and appliance efficiency in conservation strategies, providing valuable insights for targeted water demand management in urban areas. 

Abstract Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real‐time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision‐making to effectively reduce Scope 2 water usage and emissions. 

Abstract Civil infrastructure underpins urban receipts of food, energy, and water (FEW) produced in distant watersheds. In this study, we map flows of FEW goods from watersheds of the contiguous United States to major population centers and highlight the critical infrastructure that supports FEW flows. To do this, we draw upon detailed records of agriculture, electricity, and public water supply production and couple them with commodity flow and infrastructure information. We also compare the flows of virtual water embedded in food and energy commodity flows with physical water flows in inter‐basin water transfer projects around the country. We found that the virtual blue water transfers through crops and electricity to major US cities was 53 billion and 8 billion m³in 2017, respectively, while physical interbasin water transfers for crops, electricity, and public supply water averaged 20.8 billion m³. Highways are the primary infrastructure used to import virtual water associated with food and fuel into cities, although waterways and railways are most utilized for long‐distance transport. All of the 204 watersheds in the contiguous US support the food, energy, and/or water supplies of major US cities, with dependencies stretching far beyond each city's borders. Still, most cities source the majority of their FEW and embedded water resources from nearby watersheds. Infrastructure such as water supply dams and inland ports serve as important buffers for both local and supply‐chain sourced water stress. These findings can inform efforts to reduce water resources and infrastructure risks in domestic supply chains. 

Understanding inequality in groundwater access and cropland ownership is critical for assessing the sustainability and equity of agricultural systems, especially in regions facing climatic and socioeconomic patterns such as drought and cropland consolidation. These two forms of access are deeply interconnected: for instance, cropland ownership often determines who can access and control groundwater. Due to data challenges, however, few studies have quantified groundwater access inequality in the same ways that land ownership has been quantified. Similarly, the regional scale of most analyses to date limits our understanding of factors that shape and modify these interconnections. Our study aims to address this gap by constructing a novel geospatial dataset by matching groundwater wells with cropland parcels across California’s Central Valley. We quantify the magnitude and spatial patterns of groundwater and cropland inequality and examine how it scales with land ownership, crop types, and surface water access. Our results indicate substantial inequality in both groundwater access and land ownership, with the top decile of well owners possessing 46.4% of the region’s total well capacity. These well owners are more likely to allocate groundwater to high-revenue, water-intensive perennials such as almonds and walnuts. Furthermore, large landholders tend to have far more wells, deeper and higher-capacity wells, and greater access to surface water resources. However, we observe consistently wider inequality in land ownership than water access, and larger landowners possess less well depth and capacity per hectare. We discuss the implications of these findings in the context of California’s historical lack of regulation on groundwater, particularly with respect to inequality in open access vs private property resources. We also consider possible lessons for future groundwater regulation and distribution mechanisms for groundwater rights under California’s Sustainable Groundwater Management Act. 

Growing societal water demands and decreasing water supplies are straining the water available for communities in many basins. Once water supplies have been fully allocated and developing new water supplies is infeasible, the best option to meet growing water demands is often to reallocate water from rural agricultural water uses. Yet, the dynamics and implications of these rural‐to‐urban water transfers are poorly understood. Here, we integrate an agent‐based model with an input‐output model to capture the behavior of individual irrigators and examine how their water transfer decisions propagate through the broader rural economy and shape social dynamics. As a demonstration of our model, the rural community represents Alamosa County while the city represents the city of Denver, both located in Colorado, Unites States. We find that the greatest long‐term decline in crop water use corresponds with higher city growth rates while the greatest short‐term decline corresponds with larger farmer discount rates. As farmers sell their water rights to the City, economic activity from the crop production sector declines, causing unemployment in the crop production sector to increase and demand from the service sectors to decrease, which results in output declining in these economic sectors as well. Thus, a negative impact on the agricultural sector will cause some negative impact on other economic sectors, such as professional, health care, and recreational services. This research brings new insights that can be used to evaluate the socio‐economic impacts of water transfers and shape policy to minimize potential negative externalities associated with water transfers. 

Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water consumption are insufficient in providing the spatial granularity and temporal depth required for comprehensive long‐term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption ‐ distinguishing between rainwater, surface water, and groundwater ‐ of the 30 most widely irrigated crops in the US from 1981 to 2019 at 2.5 arc min. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km³, 70% of which was from irrigation. Corn and alfalfa accounted for approximately 16.7 and 24.8 km³of average annual blue crop water consumption, respectively, which is nearly two‐fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the US Geological Survey and US Department of Agriculture. Our data set and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability. 

The United States is a major producer and exporter of agricultural goods, fulfilling global demands for food, fiber, and fuel while generating substantial economic benefits. Agriculture in the U.S. not only dominates land use but also ranks as the largest water-consuming sector. High-resolution cropland mapping and insights into cultivation trends are essential to enhance sustainable management of land and water resources. Existing data sources present a trade-off between temporal breadth and spatial resolution, leading to gaps in detailed geographic crop distribution. To bridge this gap, we adopted a data-fusion methodology that leverages the advantages of various data sources, including county-level data from the U.S. Department of Agriculture, along with several gridded land use datasets. This approach enabled us to create annual maps, termed HarvestGRID, of irrigated and harvested areas for 30 key crops across the U.S. from 1981 to 2019 at a resolution of 2.5 arc minutes. Over the past four decades, irrigated harvested area has remained relatively stable nationally; however, several western states exhibit a declining trend, while some eastern states show an upward trend. Notably, more than 50% of the irrigated land in the U.S. lies above three major aquifers: the High Plains, Central Valley, and Mississippi Embayment Aquifers. We assessed the accuracy of HarvestGRID by comparing it with other large-scale gridded cropland databases, identifying both consistencies and discrepancies across different years, regions, and crops. This dataset is pivotal for analyzing long-term cropland use patterns and supports the advancement of more sustainable agricultural practices. 

Irrigation reduces crop vulnerability to drought and heat stress and thus is a promising climate change adaptation strategy. However, irrigation also produces greenhouse gas emissions through pump energy use. To assess potential conflicts between adaptive irrigation expansion and agricultural emissions mitigation efforts, we calculated county-level emissions from irrigation energy use in the US using fuel expenditures, prices, and emissions factors. Irrigation pump energy use produced 12.6 million metric tonnes CO2-e in the US in 2018 (90% CI: 10.4, 15.0), predominantly attributable to groundwater pumping. Groundwater reliance, irrigated area extent, water demand, fuel choice, and electrical grid emissions intensity drove spatial heterogeneity in emissions. Due to heavy reliance on electrical pumps, projected reductions in electrical grid emissions intensity are estimated to reduce pumping emissions by 46% by 2050, with further reductions possible through pump electrification. Quantification of irrigation-related emissions will enable targeted emissions reduction efforts and climate-smart irrigation expansion.