- 1Universidad Católica San Antonio de Murcia, Civil and Environmental Engineering, Spain (sasadi@ucam.edu)
- 2Centro de Investigaciones sobre Desertificación (CIDE), CSIC-UV-GVA, Carretera CV 315, km 10,3, Valencia, Moncada, 46113, Spain
Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. This study focuses on the Tagus Headwaters River Basin (THRB) in Spain, a key hydrological basin providing essential water for urban, industrial, and irrigation purposes. Additionally, a significant portion of its water resources is transferred to the Segura River Basin through the Tagus-Segura water transfer, Spain’s most extensive hydraulic infrastructure. Given that nearly all available water in the THRB is allocated for these demands, precise streamflow forecasting is vital. For streamflow estimation in this basin, we evaluated the Soil and Water Assessment Tool (SWAT+), a physically-based model, and three AI-based models: support vector regression (SVR), feed-forward neural network (FFNN), and long short-term memory (LSTM) models, across four gauging stations within the THRB. For the AI-based models, rainfall and time-lagged runoff data were used as input data. Additionally, an ensemble machine learning technique was evaluated, using the outputs of both physically-based and AI-based individual models as inputs for the ensemble model. The results show that the AI-based models and the ensemble machine learning technique significantly outperformed the SWAT+ model. While the precision of the AI-based models was considerably higher than that of the SWAT+ model, the application of the ensemble technique enhanced the precision of the AI-based models by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period. Furthermore, the Shapley Additive Explanations (SHAP) methodology was used to explore how each model contributes to the predictions in the ensemble technique. This work was supported by the Spanish Ministry of Science and Innovation, under grants PID2021-128126OA-I00.
How to cite: Asadi, S., Jimeno-Sáez, P., López-Ballesteros, A., and Senent-Aparicio, J.: In the application of physically-based and interpretable AI-based models for streamflow simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18102, https://doi.org/10.5194/egusphere-egu25-18102, 2025.