Evaluating the added value of multi-variable calibration of SWAT with remotely sensed evapotranspiration data for improving hydrological modeling

Hydrological processes in a watershed consist of multiple sub-processes (such as plant growth, evapotranspiration, water yield, and soil-water balance) that have complex interactions. The common practice of calibrating hydrological models against only a single variable (e.g., streamflow) can lead to parameter uncertainty (also known as equifinality), resulting in significant uncertainties in the representation and simulation of sub-processes. As multi-variable calibration can be a potential solution to this issue, we tested the integration of spatially and temporally gridded remotely sensed evapotranspiration (RS-ET) data with the Soil and Water Assessment Tool (SWAT) hydrological model. This approach was intended to reduce equifinality by enhancing related hydrological sub-processes in both space and time rather than improving the evaluation metrics at the streamflow outlet. We further introduced the principle of repeated measure design in the calibration process, where the SWAT was calibrated under two different schemes: Scheme1 (using only streamflow data) and Scheme2 (using both RS-ET and streamflow data). The model's performance was evaluated using the concept of stability at multiple spatial scales (basin outlet, sub-basins, and hydrological response units) and aspects (different model outputs and most sensitive calibrated parameters). The significance of the difference between the stabilities produced by the two schemes was estimated using the Mann-Whitney U test. Testing this approach in Meichuan Basin (China) showed that Scheme2 substantially reduced equifinality for calibrated parameters and model outputs compared to Scheme1. In addition, the model solutions and outputs for Scheme2 were significantly different from Scheme1. Our results demonstrate the added value of using increasingly available open-access RS-ET data for improving hydrological model calibration.

Automated summary

The study tested the integration of remotely sensed evapotranspiration data with a hydrological model, demonstrating that multi-variable calibration significantly reduces parameter equifinality and improves the representation of hydrological sub-processes. Evaluation of model performance at various spatial scales showed that this approach outperforms traditional calibration methods using only streamflow data.