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

Moctar Dembélé; Markus Hrachowitz; Hubert H. G. Savenije; Grégoire Mariéthoz; Bettina Schaefli

doi:10.1029/2019wr026085

journal article Jan 01, 2020

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

Moctar Dembélé

Markus Hrachowitz Hubert H. G. Savenije Grégoire Mariéthoz

Bettina Schaefli

Water Resources Research Vol. 56 No. 1 · American Geophysical Union (AGU)

View at Publisher Save 10.1029/2019wr026085

Abstract

AbstractHydrological 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.

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Details

Published: Jan 01, 2020
Vol/Issue: 56(1)
License: View

Authors

M

Moctar Dembélé

Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland; Faculty of Civil Engineering and Geosciences, Water Resources Section Delft University of Technology Delft Netherlands

M

Markus Hrachowitz

Faculty of Civil Engineering and Geosciences, Water Resources Section Delft University of Technology Delft Netherlands

H

Hubert H. G. Savenije

Faculty of Civil Engineering and Geosciences, Water Resources Section Delft University of Technology Delft Netherlands

G

Grégoire Mariéthoz

Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland

B

Bettina Schaefli

Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland; Now at Institute of Geography (GIUB) University of Bern Bern Switzerland

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung Award: SNF,P1LAP2_178071

Bundesbehörden der Schweizerischen Eidgenossenschaft Award: 2016.0533 / Burkina Faso / OP

Cite This Article

Moctar Dembélé, Markus Hrachowitz, Hubert H. G. Savenije, et al. (2020). Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets. Water Resources Research, 56(1). https://doi.org/10.1029/2019wr026085

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

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