The HWRM group is conducting research is several topics that allow us to integrate and understand socio-environmental systems holistically. These research topics include

1. Modeling and assessment of Integrated socio-environmental systems,
2. Sustainability of Ag Production (Crop growth and yield modeling, rangeland productivity)
3. Water resources management (SEBF, consumptive water use, evapotranspiration, agricultural, water modeling and measurement, hydrologic systems modeling)
4. Drought monitoring and impact assessment,
5. Land use land cover change assessment,
6. Artificial intelligence and dryland ecosystems resilience,
7. Enhancing interdisciplinary research in STEM.

Modeling and Assessment of Integrated Socio-environmental Systems.

New Mexico Food-Energy-Water Systems: This project focuses on modeling and visualization New Mexico’s (NM’s) food-energy-water systems (FEWS). The interconnections, interdependencies, and dynamic response of NM’s FEWS to environmental and socio-economic stress are insufficiently understood. The overarching goal is to develop innovative modeling tools to describe these interconnections and dynamic response due to these stresses. This goal can be achieved by developing a systems dynamic model to simulate and predict the behavior of FEWS and its impacts on human well-being to develop a more resilient integrated socio-environmental systems analyze that can withstand threats such as frequent and prolonged drought events and fluctuating prices of crude oil and natural gas.

Sustainability of Ag Production.

Crop Yield Prediction: To assess the sustainability of crop production in New Mexico and climate change impacts on crop yield, this project was aimed at predicting crop yield of major commodities in the state. This project evaluates crop yield prediction models for four major crops in New Mexico that include corn, sorghum, alfalfa, and wheat using statistical approaches. Plans are underway to use crop growth models.

Rangeland and Livestock Production: The research focuses on providing an improved understanding of climate change impacts on New Mexico’s rangeland livestock production systems. The research evaluates long-term changes in beef cattle production and prices as well as rangeland productivity in response to climate change impacts such as increased temperature and precipitation variability.

Water resources management

Modeling coupled surface and groundwater hydrologic systems: Regions that depend on conjunctive water use rely on surface water and groundwater supplies to meet water demands. the complexity of interconnected hydrological surface and groundwater systems and human activities and interactions (e.g., agriculture and domestic uses) with these systems continuously adapt to variable surface water supply and groundwater recharge due climate change impacts. The HWRM group utilizes physical-based hydrological models to evaluate how agricultural practices affect the sustainability of such coupled systems. The group also utilizes remote sensing data and tools along with these coupled systems for enhanced representation, monitoring, and assessment. For example, the group is assessing the effects of climate change impacts on the sustainability of the conjunctive water use with the Rio Grande project, New Mexico.

Modeling evapotranspiration and surface energy balance fluxes: Evapotranspiration (ET) and surface energy balance fluxes (SEBF) are key variables that can be used to evaluate vegetation growth (managed and natural ecosystems) and including water use and response to environmental stresses. The HWRM group developed a couple surface energy and water balance approach for improved estimates of ET. The group also utilizes some of the existing modeling tools that provide estimates of ET at local and regional scales that use remote sensing-based data. for ground truthing of these estimates, the group uses the state-of-the-art flux measurement and measurement instruments including optical and microwave scintillometer and eddy covariance systems.

Drought Monitoring and Impacts Assessment.

This project was aimed at exploring the use of drought monitoring tools/indicators (e.g., PDSI, SPI, SPEI, DSCI etc.) along with impacts data (e.g., drought impact reporter) to develop a more holistic drought climatology for New Mexico, predict drought impact, and help in developing drought adaptation and mitigation practices.

Land use land cover change assessment.

Natural ecosystems play an essential role in providing goods and services for human’s societal and economic developments. Anthropogenic activities have changed these systems at an unprecedented rate. (e.g., drivers of change in dryland ecosystems, have caused land degradation). The HWRM group uses Earth Observations (e.g., Landsat, MODIS) to feasibly monitor long and short-term spatial and temporal changes of natural ecosystems. Coupled with drought monitoring tools (e.g., PDSI), remote sensing-based products (e.g., NDVI, Rangeland Productivity Product (RPM) the research use EO to evaluate these changes using a number of change detection tools such as the Break for Additive Season and Trends (BFAST), Residual Trend Analysis (RESTREND), the Time Series Segmented Residual Trend Analysis (TSS-RESTREND), and the Continuous Change Detection and Classification (CCDC).

Artificial intelligence and dryland ecosystems resilience

The HWRM group is conducting research that integrates artificial intelligence tools with dryland ecosystems monitoring and modeling approaches for improved understanding and enhance their resilience. One of the topics includes is related to understanding drought impacts on ecosystems and how to disseminate related information to stakeholders using AI. What we learn from recent drought experiences can help reduce the social and environmental effects of future droughts. One of the challenges is in connecting scientific research with ranchers and other land managers whose day-to-day decisions determine how the next drought will affect not just their own herds, but the complex ecosystems that support cattle and many other species. The research will incorporate data and observations from on-the-ground decision-makers, using state-of-the art technology, including remote sensing and machine learning, to boil down vast amounts of data for easier use. The team is developing a framework for delivering information that will be rooted in the needs and preferences of users with boots on the ground, allowing them to both provide information and to access streams of clearly condensed, relevant data.

Enhancing interdisciplinary research and training in STEM.

The HWRM group is collaborating with researchers from agricultural and computer sciences to develop an interdisciplinary convergence research graduate training program that is inclusive and responsive to the pressing needs of innovation at the intersection between Artificial Intelligence (AI) and arid land agriculture. This program will provide rigorous training for cohorts of graduate students from different disciplines. Such training is necessary to equip the future generations of scientists and practitioners with unique skills, knowledge, and abilities to allow them to create innovative AI and transformative solutions to the arid land agricultural challenges.