Articles | Volume 13, issue 9
https://doi.org/10.5194/essd-13-4529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe
Christoph Klingler
CORRESPONDING AUTHOR
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
Mathew Herrnegger
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
Viewed
Total article views: 5,483 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Mar 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
3,761 | 1,636 | 86 | 5,483 | 81 | 70 |
- HTML: 3,761
- PDF: 1,636
- XML: 86
- Total: 5,483
- BibTeX: 81
- EndNote: 70
Total article views: 3,862 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Sep 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,991 | 812 | 59 | 3,862 | 70 | 58 |
- HTML: 2,991
- PDF: 812
- XML: 59
- Total: 3,862
- BibTeX: 70
- EndNote: 58
Total article views: 1,621 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Mar 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
770 | 824 | 27 | 1,621 | 11 | 12 |
- HTML: 770
- PDF: 824
- XML: 27
- Total: 1,621
- BibTeX: 11
- EndNote: 12
Viewed (geographical distribution)
Total article views: 5,483 (including HTML, PDF, and XML)
Thereof 5,016 with geography defined
and 467 with unknown origin.
Total article views: 3,862 (including HTML, PDF, and XML)
Thereof 3,626 with geography defined
and 236 with unknown origin.
Total article views: 1,621 (including HTML, PDF, and XML)
Thereof 1,390 with geography defined
and 231 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
27 citations as recorded by crossref.
- Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa P. Omonge et al. 10.1016/j.ejrh.2021.100983
- A Weak-Coupling Flow-Power Forecasting Method for Small Hydropower Station Group B. Chen et al. 10.1155/2023/1214269
- Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: Benefits of a 2D groundwater representation T. Rummler et al. 10.1002/hyp.14510
- On the Evaluation of Both Spatial and Temporal Performance of Distributed Hydrological Models Using Remote Sensing Products T. Nguyen et al. 10.3390/rs14091959
- Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning C. Klingler et al. 10.1007/s00506-022-00891-4
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al. 10.1016/j.jhydrol.2023.129248
- Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface H. McMillan et al. 10.1002/hyp.14481
- A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
- Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
- AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
- A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
- Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
- Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
- Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
- Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
- Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
- A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea J. Jeong et al. 10.1016/j.scitotenv.2022.157699
- Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
- Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl 10.1088/1748-9326/acb8ca
- Changes in the water retention of mountainous landscapes since the 1820s in the Austrian Alps G. Stecher et al. 10.3389/fenvs.2023.1219030
- Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
- Potenzial von Machine Learning bei der kurzfristigen Leistungsprognose innerhalb einer Laufkraftwerkskette C. Klingler et al. 10.1007/s00506-022-00849-6
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
- Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
- A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. 10.3390/hydrology8040144
- LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
24 citations as recorded by crossref.
- Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa P. Omonge et al. 10.1016/j.ejrh.2021.100983
- A Weak-Coupling Flow-Power Forecasting Method for Small Hydropower Station Group B. Chen et al. 10.1155/2023/1214269
- Lateral terrestrial water fluxes in the LSM of WRF‐Hydro: Benefits of a 2D groundwater representation T. Rummler et al. 10.1002/hyp.14510
- On the Evaluation of Both Spatial and Temporal Performance of Distributed Hydrological Models Using Remote Sensing Products T. Nguyen et al. 10.3390/rs14091959
- Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning C. Klingler et al. 10.1007/s00506-022-00891-4
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al. 10.1016/j.jhydrol.2023.129248
- Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface H. McMillan et al. 10.1002/hyp.14481
- A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
- Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
- AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
- A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
- Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
- Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
- Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
- Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
- Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
- A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea J. Jeong et al. 10.1016/j.scitotenv.2022.157699
- Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
- Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl 10.1088/1748-9326/acb8ca
- Changes in the water retention of mountainous landscapes since the 1820s in the Austrian Alps G. Stecher et al. 10.3389/fenvs.2023.1219030
- Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
- Potenzial von Machine Learning bei der kurzfristigen Leistungsprognose innerhalb einer Laufkraftwerkskette C. Klingler et al. 10.1007/s00506-022-00849-6
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
3 citations as recorded by crossref.
- Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
- A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. 10.3390/hydrology8040144
- LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
Discussed (final revised paper)
Discussed (preprint)
Latest update: 09 Dec 2023
Short summary
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains...
Altmetrics
Final-revised paper
Preprint