Scarce water resources across the globe have prompted the development of data‐driven water budgets to account for and distribute limited water more effectively across various land uses and purposes. Data‐driven approaches for estimating individual water budget components have been extensively developed and subsequently reviewed (e.g., evapotranspiration, precipitation, groundwater, surface water, runoff), but the state of the art of data‐driven approaches for estimating and integrating complete water budgets has not been the subject of a review paper to our knowledge. In this review, we fill this void by reviewing 81 systematically identified publications from the last decade (2012–2022) on data‐driven water budget approaches. We describe the current state of measurements and data products for data‐driven water budgets for various spatiotemporal scales. Our analysis suggests that spatiotemporal parameters drive the approach for data‐driven water budgets, with larger spatiotemporal scales relying more on satellite remote sensing data products and smaller spatiotemporal scales relying more on ground‐based monitoring. The incorporation of satellite remote sensing data products and ground‐based monitoring was common across various spatiotemporal scales and enabled the estimation of complete water budgets in areas of limited data availability. We conclude that improved reporting of simplifying assumptions, uncertainty analysis methods, and data sources are required for the alignment of water budget estimations between resource managers at varied spatiotemporal scales. Our review calls for the standardization of data‐driven water budget reporting protocols to improve the interpretability of data‐driven water budgets across decision‐makers working at various spatiotemporal scales.

This paper explores the possibility and plausibility of developing a hybrid simulation method combining agent-based (AB) and system dynamics (SD) modeling to address the case study of produced water management (PWM). In southeastern New Mexico, the oil and gas industry generates large volumes of produced water, while at the same time, freshwater resources are scarce. Single-method models are unable to capture the dynamic impacts of PWM on the water budget at both the local and regional levels, hence the need for a more complex hybrid approach. We used the literature, information characterizing produced water in New Mexico, and our preliminary interviews with subject matter experts to develop this framework. We then conducted a systematic literature review to summarize state-of-the-art of hybrid modeling methodologies and techniques. Our research revealed that there is a small but growing volume of hybrid modeling research that could provide some foundational support for modelers interested in hybrid modeling approaches for complex natural resource management issues. We categorized these efforts into four classes based on their approaches to hybrid modeling. It appears that, among these classes, PWM requires the most sophisticated approach, indicating that PWM modelers will need to face serious challenges and break new ground in this realm.

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R² of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.

This study created a framework for assessing the spatial and temporal distribution of the supply and demand of four potential produced water (PW) reuse options: agriculture, dust suppression, power generation, and river flow augmentation using Eddy and Lea counties in the southeastern New Mexico Permian Basin as a case study. Improving the PW management in the oil and gas industry is important in areas with limited water resources and increasing restrictions on PW disposal. One option in the PW management portfolio is fit-for-purpose reuse, but a lack of adequate information on PW quality, volumes, and the spatiotemporal distribution of PW supply and demand precludes its reuse. Using the framework, we determined that a 1.1-mile grid cell for data aggregation is a sufficient spatial scale for capturing the granular data needed for PW management decisions. The annual available PW supply for the two counties was estimated to be 45,460,875 m³ (36,870 acre-feet). The annual cumulative estimated demand was 647,656,261 m³ (525,064 acre-feet) for the four potential use cases—far exceeding PW supply. The maps generated using the framework illustrated that much of the supply and demand are spatially dispersed. The spatiotemporal analysis framework provides a generic methodology that can be used for PW management in other basins or for assessing alternative waters at the local and regional scales where management occurs.

Remote sensing evapotranspiration (ET) models have the potential to be powerful tools for water planning and management, particularly for agriculture. OpenET is an emerging web-based tool that uses satellite imagery and climate data for calculating six distinct ET models, and an ensemble model of the six models, to provide estimates of actual ET (ET_a) which is useful for field-scale irrigation management decisions. Previous studies examining the performance of individual models included in the OpenET platform showed some models used in OpenET consistently predicted lower values of ET_a in dryland regions relative to in-situ measurements. The OpenET research team has made modifications to address these isues. There are few studies examining if the modified models included in OpenET sill produce lower values of ET_a compared to field values in dryland environments. This study compared satellite-based OpenET estimates of ET_a from three alfalfa fields in the Mesilla Valley, New Mexico, USA–one field with measurements of ET_a from an eddy covariance tower and two fields with estimated crop evapotranspiration (ET_c)–during the 2017 growing season to investigate if OpenET ET_a estimates demonstrate an underestimation bias. OpenET ET_a estimates were tested against in-situ ET_a measurements and ET_c estimates using two sample t-tests and Mann-Whitey U tests to determine if there were any significant differences in means between the two groups. Model seasonal percent mean bias error ranged from −33.99 to +11.37%. eeMETRIC and SIMS seasonal estimates were within ±15% of in-situ measurements at any of the three sites and within ±10% of in-situ measurements on average. SSEBop and DisALEXI produced significantly different monthly ET_a estimates (p-values < 0.05) when data were extracted using the OpenET field polygons. The results of the small sample of fields suggest the OpenET models may estimate lower values of ET_a relative to the field data. Future research should improve the methodology for assessing accuracy of OpenET in small agricultural fields in the western United States.

Alfalfa has been vilified as one of the thirstiest crops in the semi-arid southwestern USA and partially responsible for decreased water security in the Colorado, Rio Grande and San Joaquin River basins. We demonstrate that adoption of summer deficit irrigation in alfalfa production can yield large water savings and flexibility to adapt to changing water supplies with minimal on-field economic impact. We estimate that deficit irrigation of alfalfa can save 1333 to 4157 million cubic meters of water annually (16-50 % of total alfalfa water use) over 926,000 ha in the southwestern USA if summer deficit irrigation (suspended after July 1) were implemented. Cash-flow analyses, assuming a producer sells conserved irrigation water and forfeits revenue from alfalfa sales beyond August 1, indicate a possible win-win net benefit at a price point of greater than $71 (Tulare, CA) or $44 (Imperial, CA) per 1000 m³ of irrigation water. We document the benefits of summer deficit irrigation for water conservation and farm and regional agricultural economic viability in rural communities. We acknowledge its practical challenges and risks as it will require infrastructure for water trading, support systems and mechanisms for producer decision-making, and improvements to field-level water accounting. These findings underscore the need to reassess the role of alfalfa in water management, not just as a water-intensive crop but also as a flexible one that may be a key instrument in achieving agricultural resilience against the backdrop of climate variability and recurring droughts.