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

Authors: MYERS, DANIEL - Collaborator; JONES, DAVID - National Park Service; OVIEDO-VARGAS, DIANA - Collaborator; SCHMIT, HOHN - National Park Service; FICKLIN, DARREN - Indiana University; Zhang, Xuesong

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2024
Publication Date: 12/6/2024
Citation: Myers, D., Jones, D., Oviedo-Vargas, D., Schmit, H., Ficklin, D., Zhang, X. 2024. Seasonal variation in landcover estimates reveals sensitivities and opportunities for environmental models. Hydrology and Earth System Sciences. 28:5295-5310. https://doi.org/10.5194/hess-28-5295-2024.
DOI: https://doi.org/10.5194/hess-28-5295-2024

Interpretive Summary: Water managers often use numerical models driven by remote sensing based landuse/landcover (LULC) maps to assess effectiveness of diverse agricultural and urban measures and support sustainable land and water management. Traditional LULC maps are mainly derived from remote sensing images acquired during the growing season. Here, using the Soil and Water Assessment Tool (SWAT) model we examined the differences in hydrologic and water quality modeling results with growing season and non-growing season LULC maps. Our results clearly show the substantial deviation between model nitrate loading between those two types of LULC maps. This finding highlights the need to consider seasonal variations in LULC in future water quality modeling and management.

Technical Abstract: Traditional 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 such as vegetation cycles. We evaluated the impacts of these seasonal LULC estimate differences on models over a range of complexity, including the Soil and Water Assessment Tool (SWAT). Dynamic World can robustly be used to simulate water quality between seasons, but 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, biogeochemistry, and ecology models