Articles | Volume 24, issue 3
https://doi.org/10.5194/hess-24-1081-2020
© Author(s) 2020. 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-24-1081-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using hydrological and climatic catchment clusters to explore drivers of catchment behavior
Florian U. Jehn
CORRESPONDING AUTHOR
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Konrad Bestian
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Lutz Breuer
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstraße 3, 35392 Giessen, Germany
Philipp Kraft
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Tobias Houska
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
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- Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al. 10.1016/j.jhydrol.2022.128555
- Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions A. Hövel et al. 10.1016/j.jhydrol.2023.130585
- Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region L. Johnston et al. 10.1061/(ASCE)HE.1943-5584.0002200
- A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
- On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. 10.5194/hess-25-5805-2021
- Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos 10.1007/s11269-024-03881-1
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- A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli 10.1029/2021WR030263
- Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
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- Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
- Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. 10.1007/s40808-021-01298-y
Latest update: 23 Nov 2024
Short summary
We grouped 643 rivers from the United States into 10 behavioral groups based on their hydrological behavior (e.g., how much water they transport overall). Those groups are aligned with the ecoregions in the United States. Depending on the groups’ location and other characteristics, either snow, aridity or seasonality is most important for the behavior of the rivers in a group. We also find that very similar river behavior can be found in rivers far apart and with different characteristics.
We grouped 643 rivers from the United States into 10 behavioral groups based on their...