Calibrating macro-scale hydrological models in poorly gauged and heavily regulated basins

Abstract

Abstract. The calibration of macro-scale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modelling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macro-scale model setup for the Upper Mekong River Basin) and a hydraulic model used to infer discharge time series from satellite data. Using these two models and Global Sensitivity Analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a co-dependence that emerges for a variety of performance metrics we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this co-dependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remote-sensed observations and is readily transferable to other basins.

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

“…Customising water quality simulation models to the catchment they represent is essential for limiting uncertainty in results and maintaining trust in land use decisions they aim to inform 1 , 2 . Model design and development, referred in here as customisation, is achieved by using observed water quality data from gauging stations for design and verification of models 3 . However, many water catchments globally are ungauged, and a lesser proportion of those have corresponding water quality data to inform model customisation.…”