Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Dembele, M. (TU Delft Water Resources; University of Lausanne)
Hrachowitz, M. (TU Delft Water Resources)
Savenije, Hubert (TU Delft Water Resources)
Mariéthoz, Grégoire (University of Lausanne)
Schaefli, Bettina (University of Lausanne; University of Bern)

2020

Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.

distributed hydrological model
multiobjective function
multivariable calibration
parameter transferability across scales
spatial patterns
ungauged basins

http://resolver.tudelft.nl/uuid:06811b82-c85d-41c4-be7d-2941dc507a98

https://doi.org/10.1029/2019WR026085

2020-07-01

0043-1397

Water Resources Research, 56 (1)

Institutional Repository

journal article

© 2020 M. Dembele, M. Hrachowitz, Hubert Savenije, Grégoire Mariéthoz, Bettina Schaefli