Forest canopies exert a physical influence on the partitioning of precipitation into runoff versus evapotranspiration through several hydrologic processes. This project seeks to illuminate the ways that forest dynamics and disturbance affect hydrologic processes and availability of water for ecosystems and for people.
We combine data from Forest Inventory & Analysis (FIA) plots with remote sensing data and hydrologic models to assess past disturbance effects, test hypotheses about hydrologic process-level responses to disturbance, and make projections about future water resources based on future forest conditions. We seek to provide managers with projections of how various forest management scenarios, or anticipated future forest dynamics, may affect the timing and runoff supplied by forested watersheds.
Using FIA plot data to improve process-based hydrologic models: This project uses FIA tree and understory vegetation data to partition existing remote sensing Leaf Area Index (LAI) maps into overstory versus understory components, and then develops a spatially applied statistical model to create maps of overstory and understory LAI. Although several state-of-the-art hydrologic models are capable of representing overstory canopies, most applications are constrained by a lack of overstory LAI data. The pilot study area is the South Fork Flathead basin of Montana, and we plan to eventually produce LAI overstory and understory layers for all eight Interior West (IW) states. In the future we will update these layers regularly and make them publicly available.
Using FIA-based disturbance maps to predict post-fire water quality: Disturbance maps based on FIA data are being used in the GeoWEPP (Water Erosion Prediction Product) model to predict post-fire water quality for several years following wildfire. This project is an ongoing collaboration between FIA and Weber State University.