article · Ecological Informatics
Calibrating hydrological models using only catchment outlet streamflow data introduces substantial uncertainty into predictions. Incorporating remote sensing-based actual evapotranspiration data alongside streamflow can mitigate these uncertainties and improve model accuracy. However, choosing the most effective evapotranspiration dataset remains challenging because available multi-source products vary in methodologies, parameters, and spatiotemporal resolutions. A comparative evaluation using the Soil and Water Assessment Tool tested eight distinct actual evapotranspiration datasets in single- and multi-variable calibration scenarios. The results confirm that integrating remotely sensed evapotranspiration data significantly enhances the reliability and performance of hydrological model outputs. Furthermore, calibrating the model using evapotranspiration data alone produces reasonable streamflow estimates, demonstrating high value for predicting water flow in ungauged river basins.
Reliable hydrological models are critical for effective water resource management. Traditional calibration depends heavily on physical streamflow gauges, which are absent in many catchments. Demonstrating that satellite-based evapotranspiration data can successfully calibrate models, even without ground streamflow measurements, allows water managers to generate accurate flow predictions in unmonitored river basins and better prepare for water-related risks.
This applied research provides an operational calibration methodology that environmental consultancies, catchment authorities, and water resource managers can integrate into watershed modelling workflows. By enabling streamflow prediction in ungauged catchments using satellite datasets, the approach reduces the need for expensive physical monitoring infrastructure. As a tested framework using established hydrological software, it appears ready for practical application in regional water management.
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The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.
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DOI: 10.1016/j.ecoinf.2023.102352
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