Work

Integrating remote sensing data with watershed-scale ecohydrological modeling in diverse forest and agricultural landscapes of the Upper Mississippi River Basin

Year: 2025

Type: article

Abstract: The spatial misalignment between natural watersheds and grid-based remote sensing data presents challenges in ecohydrological modeling. To address this issue, we developed a comprehensive SWAT model for the Upper Mississippi River Basin, utilizing USDA-delineated HUC (Hydrologic Unit Code)-12 subbasins averaging approximately 100 km 2 . This approach facilitated the effective integration of remotely sensed data on evapotranspiration (ET), leaf area index (LAI), and net primary productivity (NPP). To reduce prediction uncertainty, land use types—forests, crops, and grasslands—were clustered based on climatic and geological characteristics. Following calibration and validation from 1990 to 2020, the model exhibited robust performance, achieving R 2 and NSE values greater than 0.75 and maintaining percent bias below 25% for ET, LAI, and NPP across nearly 5,000 subbasins. Additionally, simulated crop yields closely matched USDA observations. These findings highlight the efficacy of a HUC-12-based model in simulating water and carbon fluxes across diverse landscapes with remote sensing data.

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2025.106588

Authors: Avay Risal, Ritesh Karki, Junyu Qi

Institutions: Earth System Science Interdisciplinary Center, University of Maryland, College Park

Language: English

Topic: Hydrology and Watershed Management Studies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action, Clean water and sanitation

Open Access status: hybrid

Estimated APC paid: $3,500