A TOPSIS-Based Multicriteria Assessment of Hydrologic Model Calibration Using Satellite-Derived Evapotranspiration and Streamflow Data

Abstract

Hydrological models are often calibrated using in situ streamflow observations that include sufficiently long and continuous records. However, this process becomes challenging in poorly gauged or ungauged basins where such data is scarce. Even in gauged basins, relying solely on single-objective calibration using observed streamflow does not ensure reliable forecasts since optimising model parameters based only on streamflow does not guarantee that the model correctly represents all key processes. The calibration of hydrological models that integrate Earth observations with in situ measurements presents a promising approach to address the shortcomings of conventional streamflow-only calibration. This study introduces a novel technique for order of preference by similarity to ideal solution (TOPSIS)-based multivariate calibration framework that integrates satellite-derived evapotranspiration (ET) data from the Global Land Evaporation Amsterdam Model (GLEAM) with streamflow observations to enhance variable infiltration capacity (VIC) model performance in the Wardha River Basin, India. Four calibration strategies were evaluated: streamflow-only (S1), spatial ET constraints (S2), temporal ET constraints (S3) and spatiotemporal ET constraints (S4). The TOPSIS multiple-criteria decision analysis ranked calibration effectiveness across six gauging stations, with five used for independent validation. Results demonstrate that temporal ET calibration (S3) achieved a 10%–15% improvement in streamflow simulation efficiency (KGE) over streamflow-only calibration, with TOPSIS scores ranging from 0.65 to 0.90 across validation stations. S3 reduced peak flow overestimation by 20%–25% during monsoon periods, while spatiotemporal calibration (S4) improved soil moisture correlation with ESA-CCI observations by 22%.