Mon Oct 13, F. Flechtner
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Mehedi Hasan, Howlader Mohammad, Döll, Petra, Hosseini-Moghari, Seyed-Mohammad, Papa, Fabrice, and Güntner, Andreas, 2025. The Benefits And Trade-Offs Of Multi-Variable Calibration Of The Watergap Global Hydrological Model (Wghm) In The Ganges And Brahmaputra Basins. Hydrology and Earth System Sciences, 29:567–596, doi:10.5194/hess-29-567-2025.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025HESS...29..567M,
author = {{Mehedi Hasan}, Howlader Mohammad and {D{\"o}ll}, Petra and {Hosseini-Moghari}, Seyed-Mohammad and {Papa}, Fabrice and {G{\"u}ntner}, Andreas},
title = "{The Benefits And Trade-Offs Of Multi-Variable Calibration Of The Watergap Global Hydrological Model (Wghm) In The Ganges And Brahmaputra Basins}",
journal = {Hydrology and Earth System Sciences},
year = 2025,
month = jan,
volume = {29},
pages = {567-596},
abstract = "{While global hydrological models (GHMs) are affected by large
uncertainties regarding model structure, forcing and calibration
data, and parameters, observations of model output variables are
rarely used to calibrate the model. Pareto-dominance-based
multi-objective calibration, often referred to as Pareto-optimal
calibration (POC), may serve to estimate model parameter sets
and analyse trade-offs among different objectives during
calibration. Within a POC framework, we determined optimal
parameter sets for the WaterGAP global hydrological model (WGHM)
in the two largest basins of the Indian subcontinent
{\textendash} the Ganges and the Brahmaputra, collectively
supporting nearly 580 million inhabitants. The selected model
parameters, determined through a multi-variable, multi-signature
sensitivity analysis, were estimated using up to four types of
observations: in situ streamflow (Q), GRACE and GRACE Follow-On
terrestrial water storage anomaly (TWSA), LandFlux
evapotranspiration (ET), and surface water storage anomaly
(SWSA) derived from multi-satellite observations. While our
sensitivity analysis ensured that the model parameters that are
most influential for the four variables were identified in a
transparent and comprehensive way, the rather large number of
calibration parameters, 10 for the Ganges and 16 for the
Brahmaputra, had a negative impact on parameter identifiability
during the calibration process. Calibration against observed Q
was crucial for reasonable streamflow simulations, while
additional calibration against TWSA was crucial for the Ganges
basin and helpful for the Brahmaputra basin to obtain a
reasonable simulation of both Q and TWSA. Additionally
calibrating against ET and SWSA enhanced the overall model
performance slightly. We identified several trade-offs among the
calibration objectives, with the nature of these trade-offs
closely tied to the physiographic and hydrologic characteristics
of the study basins. The trade-offs were particularly pronounced
in the Ganges basin, in particular between Q and SWSA, as well
as between Q and ET. When considering the observational
uncertainty of the calibration data, model performance decreases
in most cases. This indicates an overfitting to the singular
observation time series by the calibration algorithm. We
therefore propose a transparent algorithm to identify high-
performing Pareto solutions under consideration of observational
uncertainties of the calibration data.}",
doi = {10.5194/hess-29-567-2025},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025HESS...29..567M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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