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Our research is at the nexus of climate, hydrology, and communities.
The availability and quality of water resources, essential for ecosystem health, are deeply intertwined with the complex interactions among climate, hydrology, ecosystems, and communities. This raises a critical question:
How will climate change impact water quality and quantity, and what will these changes mean for local communities?
Mechanistic and AI/ML Models
Our research uses a multifaceted approach that integrates high-resolution mechanistic and AI/ML models to address pressing, socially relevant hydrology questions. Through the utilization of transdisciplinary methodologies, we investigate the underlying mechanisms shaping our water resources.
Remote Sensing
We leverage cutting-edge remote sensing technologies to complement our modeling efforts, enhancing the data used within the models. By harnessing satellite imagery, we gain valuable insights into the dynamics of the Earth's surface and its impacts on hydrological processes. By connecting models and remote sensing, we develop a comprehensive understanding of hydrology, which can address real-world challenges.
Community Collaboration
At the heart of our scientific approach lies a commitment to convergent research that is co-created with local communities. Convergent research embodies the collaborative integration of diverse perspectives, methodologies, and knowledge sources to address complex challenges ensuring actionable outcomes with local communities.
The Integrated Systems Hydrology Lab conducts research in the following areas
Watershed Hydrology – Understanding how water flows through landscapes, including the effects of climate, land use, and vegetation.
Hydrological Modeling – Using computer models to predict floods, droughts, and water availability.
Ecohydrology – Investigating how water supports ecosystems and how changes in hydrology impact biodiversity.
Water Quality – Determining the drivers of changing nutrients and contaminates in rivers, lakes, and estuaries.
Surface and Groundwater Interactions – Studying how water moves between rivers, lakes, and underground aquifers.
Water Resources Management – Developing strategies for sustainable water use, conservation, and policy.
Climate Change Impacts – Assessing how global changes affect water cycles and availability.
Urban Hydrology – Studying stormwater management, flooding, and infrastructure in cities.
The the lab takes a holistic approach, combining community knowledge, fieldwork, remote sensing, data science, and modeling to address complex water-related challenges.
Current Projects
National Water Quality Modeling
Our lab is developing the first national water quality model, which will simulate multiple water quality constituents for every major river, lake, and estuary in the contiguous U.S. This model will be applied to both current climate conditions and end-of-century climate scenarios. In the next phase, we plan to enhance the model to provide short-term forecasts at a national scale of the water quality index. Given the established correlation between historically excluded communities and poor air quality, our research will explore whether similar disparities exist in water quality. To do this, we will assess water quality across the country by socio-economic and racial demographics, identifying areas with the most significant water quality issues.
Stream Restoration BMPs
This study focuses on protecting stream water quality and restoring impaired waters in alignment with the Clean Water Act, which aims to “restore and maintain the chemical, physical, and biological integrity of the Nation’s waters.” We will conduct an optimization on the 12 most common best management practices (BMPs) for stream water protection, using three watersheds to evaluate potential trade-offs in design and stream water quality. This analysis will consider five water quality indicators: dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), total suspended solids (TSS), and biochemical oxygen demand (BOD).
River Ice Modeling
River ice plays a crucial role in hydrology, ecology, and infrastructure resilience in Alaska. Accurate monitoring of river ice thickness and phenology (timing of freeze-up and break-up) is essential for understanding climate impacts, ensuring safe winter travel, and managing flood risks. Traditional field measurements of ice thickness are spatially limited, making remote sensing, machine learning, and statistical modeling critical tools for large-scale ice monitoring. We use Landsat optical imagery to detect ice extent augmented by Synthetic Aperture Radar (SAR) due to its ability to penetrate clouds and operate in darkness, which is essential during the Arctic winter. Machine learning models integrate remote sensing data with environmental variables (e.g., air temperature, river discharge, and snow cover) to estimate ice thickness and predict freeze/thaw transitions.
Past Projects
This project was guided by an Indigenous Advisory Council comprised of members representing Indigenous communities in Alaska and Yukon, Canada. Through knowledge co-production, community-based monitoring, and modeling, our research advanced our understanding of climate change impacts river discharge, temperature, and ice.
Sediment diversions are essential for rebuilding Louisiana’s disappearing coastline, but their operation can have unintended consequences. Our research examined how opening a new diversion impacts water levels and flow in Grand Bayou, a vulnerable Indigenous community 17 miles downstream. Using hydrodynamic modeling, we explored how adjusting the diversion’s capacity in its first year can help mitigate flooding and erosion risks in this fragile ecosystem.
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