Articles | Volume 22, issue 2
https://doi.org/10.5194/hess-22-1299-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-1299-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Department of Civil Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey
Juliane Mai
Department Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada
Gorka Mendiguren
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Julian Koch
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Luis Samaniego
Department Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
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- Temporal and spatial evolutionary trends of regional extreme precipitation under different emission scenarios: Case study of the Jialing River Basin, China S. Zhang et al. 10.1016/j.jhydrol.2023.129156
- Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making M. Daoud et al. 10.1016/j.jhydrol.2024.131411
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- Effect of data length, spin-up period and spatial model resolution on fully distributed hydrological model calibration in the Moselle basin Ö. Ekmekcioğlu et al. 10.1080/02626667.2022.2046754
- Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM A. Tanim et al. 10.1016/j.envsoft.2024.106122
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Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Satellite data offer great opportunities to improve spatial model predictions by means of...