Articles | Volume 21, issue 3
https://doi.org/10.5194/hess-21-1769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-1769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
Rohini Kumar
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
Matthias Cuntz
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
INRA, Université de Lorraine, UMR1137 Ecologie et Ecophysiologie
Forestières, Champenoux, France
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
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- Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
- Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery F. Hermanns et al. 10.3390/rs13101885
- High-resolution impact-based early warning system for riverine flooding H. Najafi et al. 10.1038/s41467-024-48065-y
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- The effect of soil moisture anomalies on maize yield in Germany M. Peichl et al. 10.5194/nhess-18-889-2018
Latest update: 14 Dec 2024
Short summary
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of hydro-meteorological variables for Germany. Here we describe the derivation of national-scale parameter sets and analyze the uncertainty of the estimated hydrologic variables (focusing on the parametric uncertainty). Our study highlights the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of...