Resumen
Forest management depends on forest condition data and the ability to quantify the impacts of management activities to make informed decisions. Spatially quantifying water yield (WY) from forests across large landscapes enables managers to consider potential WY changes when designing forest management plans. Current forest water yield datasets are either spatially coarse or too restricted to specific sites with in situ monitoring to support some project-level forest management decisions. In this study, we spatially apply a stand-level southern pine WY model over a forested landscape in the Florida panhandle. We informed the WY model with pine leaf area index inputs created from lidar remote sensing and field data, a spatial and temporal aridity index from PRISM and MODIS data, and a custom depth to groundwater dataset. Baseline WY conditions for the study area were created using the Esri and Python tools we developed to automate the WY workflow. Several timber thinning scenarios were then used to quantify water yield increases from forest management activities. The results of this methodology are detailed (10 m spatial resolution) forest WY raster datasets that are currently being integrated with other spatial datasets to inform forest management decisions.