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

Abstract

Abstract Most readily available landuse/landcover (LULC) data are developed using growing season remote sensing images often at annual time steps. We used the Dynamic World near real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to growing vs. non-growing season LULC for temperate watersheds of the eastern United States. Non-growing season LULC had more built area and less tree cover than growing season data due to seasonal impacts on classifications rather than actual LULC changes (e.g., quick construction or succession). In mixed-LULC watersheds, seasonal LULC classification inconsistencies could lead to differences in model outputs depending on the LULC season used, such as an increase in watershed nitrogen yields simulated by the Soil and Water Assessment Tool. Within reason, using separate calibration for each season may compensate for these inconsistencies, but lead to different model parameter optimizations. Our findings provide guidelines on the use of near real-time and high temporal resolution LULC in geospatial models.