Autocalibration of a physically-based hydrological model: does it produce physically realistic parameters?

Abstract. Hydrological models are essential tools when predicting water availability, floods, and droughts. Physically-based models are capable of representing sophisticated degrees of realism compared to conceptual or data-driven models as they explicitly solve equations based on well-established physical laws that are directly related to catchment processes. However, they can require extensive calibration, which can be computationally demanding. This study develops and applies an autocalibration method for SHETRAN, a physically-based model, to improve its performance across 698 catchments in the UK. This paper discusses the process of model calibration, the benefits and caveats of the approach and discuss the extent to which physical realism of the parameters are preserved through the autocalibration.

Results show that the autocalibration process significantly improves SHETRAN’s performance, raising the median NSE value for the 698 catchments from 0.69 to 0.82. After calibration, 85 % of catchments achieve NSE values of ≥0.7, demonstrating a substantial enhancement in accuracy of simulations across a range of catchments with different climatic, hydrological, topographical, and geological characteristics. The greatest improvements were observed in groundwater-dominated catchments, where uncalibrated simulations struggled. Additionally, simulated transmissivity values align well with measured data, providing confidence in the model’s ability to produce parameters that mirror physical realism.

This study highlights the feasibility of applying physically-based models at a national scale when combined with effective autocalibration techniques. Autocalibrated-SHETRAN-UK performs comparably to conceptual and data-driven models, whilst offering improved transparency of hydrological processes. Future work will focus on integrating groundwater levels into the calibration process of SHETRAN and refining the model by introducing more spatial complexity in soil and aquifer representation within the model to better reflect real-world variability. These advancements will further enhance our capability to simulate hydrological responses under changing climatic and land-use conditions using SHETRAN.