Seasonal variation in landcover estimates reveals sensitivities and opportunities for environmental models

Abstract

Most readily available landuse/landcover (LULC) data are developed using growing season remote sensing images and/or annual time steps. We used new Dynamic World near real-time global LULC to compare how geospatial environmental models of water quality and hydrology respond to growing vs. non-growing season LULC data. Non-growing season LULC had more built area and less tree cover than growing season data due to seasonal impacts on classifications. We evaluated the impacts of these seasonal LULC estimate differences on water quality and quantity models that span a range of complexity, including the Soil and Water Assessment Tool (SWAT). We found that in mixed-LULC watersheds, seasonal LULC classification differences could cause large differences in model outputs depending on the LULC season used. Within reason, model parameter optimization may compensate for these differences using separate calibration for each season. These findings provide opportunities for further investigations with hydrologic, climate, biogeochemical, and ecological models.