Alexander G. Fernald’s research while affiliated with New Mexico State University and other places

Climate change presents serious threats to the sustainability of coupled Water–Agriculture–Community Systems (WACSs) in New Mexico’s Lower Rio Grande (LRG) region. Enhancing the resilience of WACSs is essential for ensuring the system’s long-term adaptability and sustainability. Although the importance of system feedback and dynamic behavior is increasingly acknowledged in resilience studies, many existing assessments fail to account for the complex interconnections and self-organizing nature of these systems. This study utilizes a System Dynamics (SD) simulation modeling and a function-based resilience framework to assess WACSs’ responses to climate change, specifically investigating whether improvements in Water Conveyance Efficiency (WCE) can enhance system resilience in the LRG. The analysis centers on the interaction of socioeconomic and hydrological dynamics, incorporating future climate projections derived from three models: UKMO, GFDL, and NCAR. Findings reveal that under the UKMO scenario, enhanced WCE leads to improved resilience in the groundwater system; however, agricultural-community resilience declines under both the UKMO and GFDL scenarios. While hydrological resilience shows improvement—particularly with increased WCE—the agriculture–community system consistently exhibits limited capacity to adapt or reorganize. The differing outcomes across climate models underscore the sensitivity of WACS resilience to varying climatic conditions.

Drylands are susceptible to erosion by wind and water. Thus, the reestablishment of vegetation is a crucial focus of watershed restoration in order to stabilize soils and improve water infiltration. Currently, limited research exists quantifying vegetation responses to rock detention structures (RDS) in the Chihuahuan Desert, and no studies have quantified the effects of RDS paired with seed introduction. Our study area in the Rincon Arroyo Watershed, in southern New Mexico, tested the hypotheses that two types of RDS, micro‐catchments and stone‐lines paired with seeding, support increases in seedling recruitment and vegetation cover compared to untreated controls. This study also tested whether the application of seed to RDS alters plant communities in comparison to controls. One season after seeded RDS were applied, seedling recruitment, cover, and plant community responses were evaluated at treatment and control areas. Seeded stone‐lines showed significantly greater seedling recruitment (2×) and cover (0.3×) than controls ( p < 0.05), whereas seeded micro‐catchments showed significantly greater cover (0.3×) than controls ( p < 0.05) but did not exhibit significant differences in seedling recruitment compared to controls ( p > 0.05). The lack of a significant difference between plant communities at treatment and control areas ( p > 0.05) suggests that the addition of seed had little to no effect on plant communities or positive responses in seedling recruitment and cover. These results suggest that stone‐lines and micro‐catchments, alone, can support vegetation establishment in degraded dryland watersheds without the addition of seed.

Sustainable water management practices are critical in water-scarce arid regions. Farming heavily depends on irrigation and constitutes a significant portion of water diversions in these areas. Despite previous research on alternative strategies to improve water availability, water management in arid regions remains a complex and multifaceted issue that requires innovative solutions that balance agricultural productivity, water conservation, and environmental sustainability. To address this need, our team utilizes a holistic approach by integrating various factors such as agricultural practices, climate variability, and socioeconomic dynamics to develop effective strategies to mitigate water demand for farming. We will use system dynamics models to understand water budget effects of alternative agricultural strategies on the complex dynamics of arid regions. Our research will be organized in two parts, first, we will conduct water budget field measurements to study how low-water use on traditional crops adapted to this region (e.g., herb, medicinal, and aromatic crop varieties) can grow in water-constrained environments and measure their water consumption. Second, we will compare field measurements and published information on the water consumption of commercial crops within the system dynamics model through scenario analysis and sensitivity testing to identify key leverage points and evaluate the effects of different crop scenarios on water supply for communities in arid lands. Our expected field study results will identify the performance of different crops in terms of water use efficiency, yield stability, and specific crop varieties that can thrive in arid environments, thereby providing alternative agricultural options for farmers. Our expected system dynamics results will illustrate the potential effects of a range of different regional crop scenarios and additional water-saving strategies and interventions that have the potential to reduce water demand and enhance water resilience in arid farming regions. The research findings will provide valuable insights and practical guidance to farmers in arid regions, enabling them to make informed decisions about crop selection and water management practices to optimize agricultural productivity while conserving water resources. This knowledge will also help stakeholders in developing evidence-based policies and regulations aimed at promoting sustainable water management practices, enhancing agricultural resilience, and safeguarding water resources for future generations.

The goal of this study is to investigate the usefulness of the relatively new 30 m spatial and <5.7-day temporal resolution Harmonized Landsat Sentinel-2 (HLS) dataset for calculating vegetation index-based crop coefficients (KcVI) for estimating field scale crop evapotranspiration (ETc). Increased spatial and temporal resolution ETc estimates are needed for improving irrigation scheduling, monitoring impacts of water conservation programs, and improving crop yield. The crop coefficient (Kc) method is widely used for estimating ETc. Remote sensing vegetation indices (VI) are highly correlated to Kc and allow the creation of a KcVI but the approach is limited by the availability of high temporal and spatial resolutions. We selected and calculated sixteen commonly used VIs using HLS data and regressed them against field-measured ET for alfalfa in the Mesilla Valley, New Mexico to create linear KcVI models. All models showed good agreement with Kc (r² > 0.67 and RMSE < 0.15). ETc prediction resulted in an MAE ranging between 0.35- and 0.64-mm day⁻¹, an MSE ranging between 0.20- and 0.75-mm day⁻¹ and an MAPD ranging between 10.0 and 16.5%. The largest differences in predicted ETc occurred early in the growing season and during cutting periods when the spectral signal could be influenced by soil background or irrigation events. The results suggest that applying the KcVI approach to the HLS dataset can help fill in the data gap in remote sensing ET tools. Future work should focus on assessing additional crops and integration into other tools such as the emerging OpenET platform.

Prolonged drought exacerbated by climate change in the Mesilla Valley, one of the major agricultural areas of New Mexico, USA, is causing a shortage of surface water from the Rio Grande for irrigation. Farmers in the Valley are using groundwater for irrigation and complementing it with limited surface water from the river (Rio Grande). Managing irrigation water better is vital to sustaining agriculture in the Valley. Remote sensing (RS)-based crop evapotranspiration (ETa) models offer significant advantages over traditional methods. The ET maps generated by these RS models provide valuable information that can be used to manage irrigation water and crops in water-scarce areas. This study used METRIC and SSEBop RS models to map the ET of alfalfa on a private farm that is managed as commonly practiced in the Valley. The integrated ET values of the two models are compared to those of the ETa measured using the eddy covariance method. The comparison showed that 91.55% of the variability in SSEBop ETa estimates can be explained by the variability in the METRIC ETa estimates, and the variability in eddy covariance ETa can explain 93.07% of the variability in METRIC ETa and 86.01% in the SSEBop Eta estimates. Both METRIC and SSEBop reflected the ETa of alfalfa during full growth and harvesting periods. However, the absolute percent mean relative difference (MRD) of ET was higher for two out of three cuttings by SSEBop (>32%) compared to those for METRIC and eddy covariance. The spatiotemporal variabilities in crop ET estimates using METRIC and SSEBop showed a need to improve on-farm irrigation conveyance and on-the-field irrigation efficiency. Overall, RS models can provide spatiotemporal maps of ET that can be used for decision-making to manage irrigation water better and improve crop yield on a field, farm, and regional scale.

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 (ETa) 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 ETa 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 ETa compared to field values in dryland environments. This study compared satellite-based OpenET estimates of ETa from three alfalfa fields in the Mesilla Valley, New Mexico, USA–one field with measurements of ETa from an eddy covariance tower and two fields with estimated crop evapotranspiration (ETc)–during the 2017 growing season to investigate if OpenET ETa estimates demonstrate an underestimation bias. OpenET ETa estimates were tested against in-situ ETa measurements and ETc 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 ETa 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 ETa 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.

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.