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<p>Hydrological models are important for assessing and managing water resources amid environmental change, with Land Use and Land Cover (LULC) maps as essential inputs. These maps detail bio-physical land features and resource usage, where any errors can propagate through models, introducing uncertainty. This study evaluates how LULC errors impact hydrological outputs using Google Earth Engine (GEE) and the Soil and Water Assessment Tool (SWAT) for the Mahanadi basin. A base LULC map was created, then altered to introduce classification errors and spatial resolution changes, generating five maps of 60–80% accuracy at 300 m and 3000 m resolutions. Results indicate that, while basin-scale outputs show minimal sensitivity to LULC accuracy, sub-basin outputs—especially evapotranspiration and surface runoff—display notable variations with accuracy and resolution changes. Thus, high-resolution, accurate LULC data help reduce uncertainty locally, although basin-scale outcomes remain largely unaffected by such errors.</p>

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Quantifying LULC Induced Errors in Hydrologic Simulation Using SWAT and GEE

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Nitish Dhakad,
  • Vinod Kumar,
  • Nitesh Patidar,
  • Pratibha Warwade

摘要

Hydrological models are important for assessing and managing water resources amid environmental change, with Land Use and Land Cover (LULC) maps as essential inputs. These maps detail bio-physical land features and resource usage, where any errors can propagate through models, introducing uncertainty. This study evaluates how LULC errors impact hydrological outputs using Google Earth Engine (GEE) and the Soil and Water Assessment Tool (SWAT) for the Mahanadi basin. A base LULC map was created, then altered to introduce classification errors and spatial resolution changes, generating five maps of 60–80% accuracy at 300 m and 3000 m resolutions. Results indicate that, while basin-scale outputs show minimal sensitivity to LULC accuracy, sub-basin outputs—especially evapotranspiration and surface runoff—display notable variations with accuracy and resolution changes. Thus, high-resolution, accurate LULC data help reduce uncertainty locally, although basin-scale outcomes remain largely unaffected by such errors.