Calibrating macroscale hydrological models in  poorly gauged and heavily regulated basins

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

doi:https://doi.org/10.5194/hess-27-3485-2023

Articles | Volume 27, issue 19

https://doi.org/10.5194/hess-27-3485-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Representation of water infrastructures in large-scale hydrological...

https://doi.org/10.5194/hess-27-3485-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 19

Research article

|

06 Oct 2023

Research article |

| 06 Oct 2023

Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

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

Supplement

https://doi.org/10.5194/hess-27-3485-2023-supplement

Data sets

Codes and data of satellite observations reveal 13 years of reservoir filling strategies, operating rules, and hydrological alterations in the Upper Mekong River Basin D. T. Vu https://doi.org/10.5281/zenodo.6299041

210520 dtvu2205 https://github.com/dtvu2205/210520

Model code and software

VICRes Critical-Infrastructure-Systems-Lab https://github.com/Critical-Infrastructure-Systems-Lab/VICRes

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.