Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making.Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making. Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making.

We conduct statewide and regional geospatial analyses using Earth observation data, remote sensing, and AI-based modeling frameworks. This research supports large-scale assessment of hydrologic and environmental conditions, including flood susceptibility, land-use impacts, and spatial variability in water resources. By integrating satellite data with modeling and machine learning, we enable: - Scalable environmental monitoring - Rapid assessment across large spatial domains - Data-driven insights to support policy and planningOur lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making. Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making.

We design and evaluate decision support systems (DSS) that help guide grazing and land-management decisions under hydrologic and environmental constraints. These tools integrate modeling, monitoring data, and scenario analysis to support adaptive management strategies that balance productivity with water-quality protection. Our work emphasizes: - Field-scale decision relevance - User-friendly interfaces for non-experts - Co-design with stakeholders, ranchers, and agencies These systems aim to translate complex science into practical, actionable guidance.Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making. Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making.

We develop and apply physically based hydrologic and water quality models to quantify water flow, storage, and nutrient dynamics across agricultural landscapes, rangelands, wetlands, and watersheds. Our work focuses on linking field-scale processes to watershed-scale outcomes, with particular attention to nutrient transport, runoff generation, and hydrologic connectivity. These models support evaluation of best management practices (BMPs), assessment of environmental risk, and scenario-based analysis under changing climate, land use, and management conditions.Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making. Our lab advances the use of artificial intelligence and machine learning to improve environmental prediction, mapping, and process understanding. We integrate AI methods with traditional hydrologic knowledge to enhance model performance, interpretability, and scalability. Applications include: * Pattern recognition from large geospatial datasets * Hybrid modeling frameworks combining physics and data-driven approaches * Flood and environmental risk mapping A strong emphasis is placed on explainable and responsible AI, ensuring transparency and usability for scientific and applied decision-making.