At NMSU, there are two main projects that I intend to be launching in the short-term. Prospective PhD students are welcome to lead any of these projects.
A Virtual Watershed Management Laboratory for Sustainable Water Use: The Rio Grande is a critical source of water for agriculture, communities, and ecosystems across the water-limited Southwest. Projected hotter and drier conditions, declining water availability, and more frequent extreme events are expected to further strain the basin’s water resources. Thus, sustaining agricultural production and water supplies will require more adaptive and efficient watershed management strategies.
This project aims to develop a Digital Twin architecture for the Rio Grande watershed by integrating Earth observations, stakeholder-informed land management, process-based watershed modeling, and AI-enhanced post-processing. The resulting framework will serve as a science-based watershed management tool, enabling farmers and ranchers to prepare for weather-related challenges and optimize management decisions in near real time, while allowing watershed managers to anticipate downstream impacts such as sediment exports to water supply reservoirs. This watershed decision support system will help to address regionally important challenges, including woody plant encroachment, which is increasing groundwater and surface water consumption in this already water-limited region. We also intend to employ this framework to assess the effectiveness of nature-based solutions (e.g., riparian restoration with native species), precision rangeland management (e.g., prescribed fire and virtual fencing), agricultural best management practices (e.g., crop switching, deficit irrigation, brackish groundwater desalination), and low-tech process-based restoration (e.g., Beaver Dam Analogs) as adaptation and mitigation strategies to protect water resources across the Rio Grande watershed.
This project aims to develop a Digital Twin architecture for the Rio Grande watershed by integrating Earth observations, stakeholder-informed land management, process-based watershed modeling, and AI-enhanced post-processing. The resulting framework will serve as a science-based watershed management tool, enabling farmers and ranchers to prepare for weather-related challenges and optimize management decisions in near real time, while allowing watershed managers to anticipate downstream impacts such as sediment exports to water supply reservoirs. This watershed decision support system will help to address regionally important challenges, including woody plant encroachment, which is increasing groundwater and surface water consumption in this already water-limited region. We also intend to employ this framework to assess the effectiveness of nature-based solutions (e.g., riparian restoration with native species), precision rangeland management (e.g., prescribed fire and virtual fencing), agricultural best management practices (e.g., crop switching, deficit irrigation, brackish groundwater desalination), and low-tech process-based restoration (e.g., Beaver Dam Analogs) as adaptation and mitigation strategies to protect water resources across the Rio Grande watershed.
Hydrological and Water Quality Responses to Compound and Cascading Climate Events: Many extreme events, such as heatwaves and droughts or droughts followed by intense rainfall, occur concurrently or successively. Although weather-related disasters often result from such events, their hydrological consequences remain underexplored, hindering the development of adaptation strategies. For example, wildfires triggered by hot-dry conditions not only disrupt vegetation cover but also alter soil infiltration capacity and runoff, potentially leading to flash floods, and increasing sediment and nutrient deliveries to streams, lakes, and reservoirs. This project will combine historical hydroclimatic data and Earth observations to detect compound events and understand how they have shaped anomalies in flow regimes, sediment and nutrient exports to water bodies across the CONUS. The lessons gained from this effort will enable us to use climate projections and modeling approaches to assess how compound events might reshape water systems and ecosystem health in the future. This project aims to bridge the gap between cascading climate hazards and hydrological changes and help inform disaster response measures for extreme events.