14 Feb 2023
 | 14 Feb 2023
Status: this preprint is currently under review for the journal HESS.

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

Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli

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.

Dung Trung Vu et al.

Status: open (until 11 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-35', Anonymous Referee #1, 13 Mar 2023 reply
  • RC2: 'Comment on hess-2023-35', Andrea Galletti, 17 Mar 2023 reply

Dung Trung Vu et al.

Dung Trung Vu et al.


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Short summary
The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.