Soil Moisture Based Calibration of a Hybrid Hydrological-Neural Network Model in Data Scarce Basins

Reliable river discharge simulation generally relies on observed streamflow data for model calibration; however, such observations are often uncertain or unavailable in data-scarce regions, limiting the applicability of conventional hydrological models. This study presents a hybrid modeling framework that uses soil moisture as an alternative calibration variable to improve discharge simulations in the absence of reliable streamflow observations. The framework couples a two-layer version of the daily lumped MISDc (Modello Idrologico Semi-Distribuito in continuo) hydrological model with a Feedforward Neural Network (FFNN), which is employed to enhance parameter calibration by exploiting soil moisture dynamics. The proposed approach is evaluated across three contrasting basins: Tahanaout in semi-arid Morocco, and Colorso (Italy) and Bibeschbach (Luxembourg) in temperate climates. Both in situ and ERA5-Land soil moisture datasets are used as calibration inputs. Model performance is assessed using multiple hydrological metrics, including Mean Absolute Error (MAE), Kling-Gupta Efficiency (KGE), and the correlation coefficient (R). Results show that the hybrid MISDc-FFNN framework substantially improves river discharge simulations compared to the traditional model. Across all basins, MAE is reduced by up to 61%, KGE increases by more than 200%, and R improves by up to 87%, with consistent performance gains observed for both observed and reanalysis-based soil moisture. These findings demonstrate the potential of soil moisture driven calibration strategies to enhance hydrological modeling in data-scarce environments, offering a viable pathway for improved water resources assessment and flood risk management where discharge observations are limited or unreliable.

 

Keywords: Soil moisture; river discharge simulation; hydrological modeling; machine learning; ERA5-Land; data-scarce regions; feedforward neural network