Despite significant progress in understanding hydrology, geomorphology, and biogeochemistry processes over the past 40 years, gaps remain in predicting watershed outcomes and meeting diverse water quality and management goals under dynamic weather conditions. Current models, while improved, still fall short in accurate and scalable representations of complex biophysical processes, especially when considering the temporal and spatial variability of complex dry and wet cycles within watersheds. These shortfalls currently constrain efforts to inform the design and optimal placement of Best Management Practices (BMPs) in agricultural and urban watersheds to meet goals related to water quantity, water quality, and climate resilience at the watershed scale. Additionally, the incorporation of real-time, high-frequency monitoring data into models remains limited, with spatial representativeness often uncertain and compromised.

There is a growing need for next-generation watershed models that integrate hybrid approaches that combine artificial intelligence (AI) and machine learning (ML) approaches, statistical methods, process-based understanding of physical processes, and stakeholder-driven datasets to better inform decision-making, adoption, and implementation of various management practices at different scales. These models must support the interactive effects of hydrology, geomorphology, and biogeochemistry, address “critical source areas,” and improve the scalability and reliability of water quality outcomes. At the same time, limited financial resources demand that BMPs be strategically placed, considering both biophysical and socio-economic factors to maximize their effectiveness and adoption. Through this collaborative multi-state research project our team will address these issues and focus on the following specific objectives

1. Develop Next-Generation Hybrid Watershed Models

2. Advance Sensing and High Spatio-Temporal Resolution Monitoring

3. Optimize BMP Placement for Multiple Outcomes

4. Engage Stakeholders for Participatory Model Development

These challenges underscore the necessity for collaboration between multistate projects and research institutions. By combining regional expertise, diverse datasets, and interdisciplinary approaches, such collaboration can enhance model development, improve the design and implementation of Best Management Practices (BMPs), and facilitate the integration of real-time monitoring data. This cooperative framework will better inform watershed-scale strategies to achieve water quantity, quality, and climate resilience goals, ultimately advancing both scientific understanding and practical decision-making in water resource management.