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13 results for “Catchment attributes”
CAMELS-ES: Catchment Attributes and Meteorology for Large-Sample Studies – Spain
<p>CAMELS-ES is a hydrometeorological dataset covering 269 catchments in Spain and the time period from 1991 to 2020. It is a contribution to the Caravan initiative, a global community that collects open hydrometeorological data to support global hydrological modelling. As other datasets in Caravan, CAMELS-ES includes both catchment attributes extracted from HydroATLAS and ERA5-Land, meteorological time series from ERA5-Land and discharge records from the Spanish Ministry of the Environment. In addition, CAMELS-ES includes information from the European Flood Awareness System (EFASv5): catchment attributes extracted from the input static maps used in the hydrological model LISFLOOD, and the simulated discharge from EFASv5 long run.</p>
CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany
<h2>Description</h2> <p>CAMELS-DE provides a comprehensive collection of hydro-meteorological timeseries data (e.g. discharge, water level, precipitation, air temperature) and catchment attributes for 1582 streamflow gauges across Germany. The time series data is in daily resolution and spans up to 70 years, from January 1951 to December 2020. The static catchment attributes include information on topography, soils, land cover, hydrogeology and human influences. Additionally, the dataset includes discharge simulations from a regional Long-Short Term Memory (LSTM) network and a conceptual hydrological model (HBV), providing benchmark data for future hydrological modelling studies in Germany.</p> <p>The accompanying data description gives information on data sources, the structure of the data set and contains extensive information on time series and catchment attribute variables. In addition, up-to-date benchmark results of the LSTM and HBV are provided.</p> <blockquote> <p><strong><strong>Important: As CAMELS-DE is continuously developed and updated, please ensure that you cite the correct version of the dataset that you are using.<br><br></strong></strong><strong>The CAMELS-DE data description paper is available here: <a href="https://doi.org/10.5194/essd-16-5625-2024">https://doi.org/10.5194/essd-16-5625-2024</a>.</strong></p> </blockquote> <p>Information about the code and methods for generating CAMELS-DE can be found here: <a title="CAMELS-DE Processing Pipeline" href="https://doi.org/10.5281/zenodo.12760336" target="_blank" rel="noopener">CAMELS-DE Processing Pipeline</a>.</p> <p>CAMELS-DE is also part of the Caravan project, a global hydrological dataset. Due to the use of data products that are available beyond the Germany national boundaries, Caravan-DE includes 305 additional streamflow gauges, resulting in a total of 1887 streamflow gauges: <a href="https://doi.org/10.5281/zenodo.13320514">https://doi.org/10.5281/zenodo.13320514</a>.</p> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <h3>Changelog</h3> <ul> <li><strong>v1.1.0</strong> <ul> <li>LSTM benchmark results are now based on a <strong>LSTM with 10 ensemble members</strong>, changing the median NSE in the testing period from 0.83 to 0.85 <div> <ul> <li>The columns <em>discharge_spec_sim_lstm</em> and <em>discharge_vol_sim_lstm</em> in <em>timeseries_simulated</em> are now based on the median values of the 10 ensemble members.</li> <li>The column <em>NSE_lstm</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> is now calculated from the median simulations of the 10 ensemble members</li> <li><em>model_parameters/LSTM/CAMELS_DE_epochs_training_lstm.zip</em> now contains the epochs of the 10 ensemble members</li> </ul> </div> </li> <li>The columns <em>NSE_lstm</em>, <em>NSE_hbv</em> and <em>training_perc_complete</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> were calculated from 2001 - 2020, now corrected to 2000 - 2020</li> <li>Fixed a bug in the calculation of <em>high_prec_dur</em> and <em>low_prec_dur</em> in <em>CAMELS_DE_climatic_attributes.csv</em> calculation (thank you to Bastian Klein from BfG for reporting this issue)</li> <li>Bayern: removed blank space after gauge and water body name and removed water body name from some gauge names, where it was included as "[gauge_name]_[water_body_name]", e.g. "Würzburg_Main" -> "Würzburg", water body name is now only included in the `water_body_name` column in<code> </code><em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Nordrhein-Westfalen: corrected some wrong river names in <em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Sachsen: added `gauge_elevation_metadata` information to <em>CAMELS_DE_topographic_attributes.csv</em></li> </ul> </li> </ul> <ul> <li><strong>v1.0.0</strong> <ul> <li>CAMELS-DE v1.0.0 is the version of the dataset that is described by the <a href="https://doi.org/10.5194/essd-2024-318">CAMELS-DE data description paper</a>.</li> <li>Addition of the federal state of Saarland, resulting in 27 additional catchments and coverage of all federal states except the city states of Berlin, Bremen and Hamburg. This also leads to a change in the title of the dataset from 1555 catchments to 1582 catchments.</li> <li>Addition of HBV model parameters and LSTM model training period epochs.</li> <li>Catchment DE911970: Removal of erroneous zero discharge values at the beginning of the measurement period.</li> <li>Minor fixes such as the elimination of discrepancies between the variable names in the dataset and in the data description.</li> <li>We were able to identify and fix some of these problems based on the feedback from the community, thank you very much!</li> </ul> </li> </ul>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
<p>Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. Since publication in 2021, CAMELS-AUS (Australia) has served as a resource for the study of hydrological change, arid-zone hydrology, and hydrological model improvement. In this update, the dataset has been significantly enhanced both temporally and spatially. The new dataset comprises information for over twice as many catchments (561 compared to 222). The streamflow and climatic information are updated a further eight years (2022 compared to 2014). Lastly, the attribute information is improved, particularly with respect to hydrological statistics (signatures) and uncertainty in streamflow. Together, these updates make CAMELS-AUS Version 2 a more comprehensive and current resource for hydrological research and applications. </p>
CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India
<p>We introduce <strong>CAMELS-IND</strong> (<em><strong>C</strong>atchment <strong>A</strong>ttributes and <strong>ME</strong>teorology for <strong>L</strong>arge-sample <strong>S</strong>tudies – <strong>India</strong></em>), a dataset containing hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India, of which 242 catchments have observed streamflow data available for over 30% of the period between 1980 to 2020. This dataset aims to foster large-sample hydrological studies within India and encourage the inclusion of Indian catchments in global hydrological research.</p> <p>The data set covers <strong>41 years</strong> of data between <em><strong>1st January 1980</strong></em> and <em><strong>31st December 2020</strong></em> for each catchments: daily time series of available streamflow observations, meteorological data such as precipitation, air temperature, solar radiation, relative humidity, wind speed, potential and actual evapotranspiration, and soil moisture. Additionally, CAMELS-IND includes regionally trained LSTM-model predicted streamflow for all 472 catchments. The static catchment attributes includes location and topography, climate, hydrological signatures, land-use, land cover, soil, geology, and anthropogenic influences.</p> <p>The corresponding manuscript is published in the Journal "Earth System Science Data" (ESSD).<br>Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, <a href="https://doi.org/10.5194/essd-17-461-2025" target="_blank" rel="noopener">https://doi.org/10.5194/essd-17-461-2025</a>, 2025.</p> <p>The data description file (<strong><em>CAMELS_IND_Data_Description.pdf</em></strong>) contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources.</p> <p> </p> <h3><strong>### CAMELS-IND attributes/forcings history</strong></h3> <p>--------------------------------------<br><strong>Version 2.2: March 2025</strong><br>--------------------------------------<br><strong>Major changes/additions:</strong><br>- Updated streamflow observations: CAMELS-IND now includes 242 catchments with streamflow observations for more than 30% of the period between 1980 and 2020.<br>- "<em>CAMELS_IND_Catchments_Streamflow_Sufficient.zip</em>" contains a subset of 242 catchments with observed streamflow data available for more than 30% of the duration between 1980 and 2020.</p> <p><strong>Minor changes:</strong><br>- Correction to the forcing column headings for 'evap_canopy (kg/m²/s)' and 'evap_surface (kg/m²/s)': the units have been updated to (mm/day).<br>- Corrections made to the gauge_id mapping for basin codes 12 and 15.</p> <p> </p> <p>--------------------------------------<br><strong>Version 2.1: October 2024</strong><br>--------------------------------------<br>Data described in revised ESSD paper - <br><strong>Major changes/additions:</strong><br>- The dataset name "<em>CAMELS-INDIA</em>" has been changed back to "<em>CAMELS-IND</em>" to align with the naming convention of other CAMELS datasets.<br>- A Python script file “<em>filter_catchment.py</em>” is added to filter out the subset of the dataset based on flow data availability.<br>- <strong>"<em>CAMELS_IND_Catchments_Streamflow_Sufficient.zip</em>" contains a subset of 228 catchments with observed streamflow data available for more than 30% of the duration between 1980 and 2020.</strong></p> <p> </p> <p>-----------------------------------<br><strong>Version 2: August 2024</strong><br>-----------------------------------</p> <p><strong>Major changes/additions:</strong><br>- The dataset name "<em>CAMELS-IND</em>" has beed changed to "<em>CAMELS-INDIA</em>".<br>- All forcing time series have been extended from 01/01/1980 to 31/12/2020.<br>- Two new forcings, "<em>pet_gleam</em>" and "<em>aet_gleam</em>", have been added.<br>- Several attributes have been added, including gauge elevation, mean drainage path slopes, precipitation uniformity, asynchronicity, gini coefficient, base flow index, stream elasticity, slope of FDC, water table depth, and anthropogenic influence.<br>- Available observed streamflow time series has been added for all 472 catchments for the period 01/01/1980 to 31/12/2020.<br>- Regionally trained LSTM model-predicted streamflow has been added for all 472 catchments for the period 01/01/1980 to 31/12/2020.</p> <p><strong>Minor changes:</strong><br>- Attribute files have been renamed to "<em>camels_India_XXXX</em>"</p> <p>A short descriptions of all attributes and time series is provided in "<em>camels_India_data_description.pdf"</em>.</p> <p><strong>The following attributes are included in CAMELS-INDIA v2:</strong><br>07 attributes : camels_India_name<br>16 attributes : camels_India_topo (topography and location)<br>42 attributes : camels_India_clim (climate indices)<br>73 attributes : camels_India_hydro (hydrological signatures)<br>13 attributes : camels_India_land (land cover characteristics)<br>28 attributes : camels_India_soil (soil characteristics)<br>07 attributes : camels_India_geol (geological characteristics)<br>25 attributes : camels_India_anth (anthropogenic influences)<br>---------------<br><strong>Total:</strong> 211 attributes, 19 catchment mean forcings, and available observed and LSTM-based predicted streamflow time series.</p> <p> </p> <p>--------------------------------<br><strong>Version 1 : April 2024</strong><br>--------------------------------<br>A short descriptions of all attributes and forcings are described in "<em>camels_ind_attributes.xlsx</em>" and "<em>camels_ind_forcings.xlsx</em>"<br><em>Following attributes were included in CAMELS-IND 1.0:</em><br>06 attributes : camels_ind_name<br>14 attributes : camels_ind_topo (topography and location)<br>36 attributes : camels_ind_clim (climate indices)<br>64 attributes : camels_ind_hydro (hydrological signatures)<br>13 attributes : camels_ind_land (land cover characteristics)<br>27 attributes : camels_ind_soil (soil characteristics)<br>07 attributes : camels_ind_geol (geological characteristics)<br>13 attributes : camels_ind_anth (anthropogenic influences)<br>-------------<br><strong>Total:</strong> 180 attributes & 17 catchment mean forcings. </p> <p> </p> <p>-----------------------------------------------------<br><strong>### CONTRIBUTE TO CAMELS-IND</strong><br>-----------------------------------------------------</p> <p>If you are working with a data set covering Indian catchments and would like to contribute catchment averages to <em>CAMELS-IND</em>, please get in touch.</p> <p>We are committed to identifying and correcting errors. If you encounter any unrealistic or suspicious values, please notify us as soon as possible. Thank you for your assistance.</p> <p><strong>Contacts:</strong><br>- Nikunj K. Mangukiya (<em>nikk.mangukiya@gmail.com</em>)<br>- Ashutosh Sharma (<em>ashutosh.sharma@hy.iitr.ac.in</em>)</p> <p> </p> <p>--------------------------------<br><strong>### Acknowledgments</strong><br>--------------------------------</p> <p>The authors gratefully acknowledge the Central Water Commission (CWC), the National Water Informatics Centre (NWIC), and the Ministry of Jal Shakti (MoJS) for providing the streamflow dataset through the online portal, India – Water Resources Information System (India-WRIS; <a href="https://indiawris.gov.in/wris/#/">https://indiawris.gov.in/wris/#/</a>). The authors also extend their gratitude to the India Meteorological Department (IMD), Ministry of Earth Sciences, Government of India, for providing the gridded rainfall and temperature datasets through their respective websites. Additionally, the authors gratefully acknowledge the National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Government of India, for the Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis. The IMDAA reanalysis was produced under the collaboration between UK Met Office, NCMRWF, and IMD, with financial support from the Ministry of Earth Sciences under the National Monsoon Mission programme. The authors utilized numerous publicly available datasets for compiling catchment attributes and meteorological forcing time series, duly acknowledging and citing them where applicable. The authors extend their gratitude to all the researchers and contributing authors of these open-source datasets.</p> <p> </p> <p>--------------------------------<br><strong>### Disclaimer</strong><br>--------------------------------</p> <p>The CAMELS-IND dataset provided on this webpage is openly accessible for academic and research purposes. While efforts have been made to ensure data accuracy, the authors do not take any responsibility for errors, omissions, or misuse of the data. Users must cite the following paper when utilizing the dataset and acknowledge that all interpretations and conclusions drawn from the data are their own. We encourage users to cite/acknowledge the original data sources wherever required based on the source data usage policies. The dataset is provided "as is" without any warranties, and users are advised to check for updates. It is strongly recommended that users exercise caution and verify the data before using it for any purpose. The authors assume no responsibility for any consequences arising from the use or misuse of this dataset.</p> <p><strong>How to cite:</strong> Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, <a href="https://doi.org/10.5194/essd-17-461-2025" target="_blank" rel="noopener">https://doi.org/10.5194/essd-17-461-2025</a>, 2025.</p>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil - link to files.
<blockquote> <h3><strong>Version 1.2 (March 2025): </strong>Now with longer time series, expanded stream gauge coverage, meteorological data from additional sources, soil moisture time series, and observed rainfall time series from 11,853 rain gauges.</h3> </blockquote> <p> </p> <p>This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies – Brazil) accompanying the paper: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, <a href="https://doi.org/10.5194/essd-12-2075-2020" target="_blank" rel="noopener">https://doi.org/10.5194/essd-12-2075-2020</a>, 2020.</p> <p>CAMELS-BR provides daily observed streamflow time series for 4,025 stream gauges, daily observed rainfall for 11,853 rain gauges, daily meteorological time series and 65 attributes for 897 catchments in Brazil.</p> <p>The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from five products, (iii) actual evapotranspiration extracted from three products, (iv) potential evapotranspiration extracted from two products, (v) reference evapotranspiration extracted from one product, (vi) minimum, mean, and maximum temperature extracted from three products, and (vii) soil moisture extracted from two products.</p> <p>The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.</p> <p>The data follow the same standards as other CAMELS datasets such as for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).</p> <p><strong>How to cite:</strong> Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.</p> <p> </p> <h3><strong>Changes in CAMELS-BR version 1.2:</strong></h3> <p><strong>Major changes</strong></p> <ul> <li>Updated streamflow time series up to February 2025 (where available), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Some historical records have changed slightly due to ANA's quality control procedures. For eight gauges (see the readme.txt file), data are merged from 2025 and 2019 records (i.e. from CAMELS-BR version 1.1).</li> <li>Increased the stream gauge coverage to 4025 stream gauges (including both quality-controlled and non-quality-controlled series), up from 3679 in version 1.1.</li> <li>Added daily observed rainfall time series for 11853 rain gauges (not catchment averages), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Data include quality flags but are mostly not quality-controlled.</li> <li>Added a GeoPackage file with coordinates for 11853 rain gauges.</li> <li>Updated precipitation time series (catchment averages) up to October 2024 (where available). Now derived from: CHIRPS v2.0; CPC; ERA5-Land; MSWEP v2.8; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil – 864 catchments).</li> <li>Updated actual evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from: GLEAM v4.2a; ERA5-Land; and MGB-SA.</li> <li>Updated potential evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from GLEAM v4.2a and ERA5-Land.</li> <li>Added reference evapotranspiration time series (catchment averages). Derived from BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Updated daily maximum, mean, and minimum temperature time series (catchment averages) up to October 2024 (where available). Now derived from: CPC; ERA5-Land; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Added daily soil moisture time series (catchment averages) up to December 2024 (where available). Computed from GLEAM v4.2a and ERA5-Land.</li> <li>Improved meteorological data processing. Catchment averages now account for pixel fraction coverage.</li> <li>Reformatted meteorological time series files. Files now includes data from different products, with columns renamed for clarity.</li> <li>Hydrological and climatic indices were not updated, despite the new streamflow and meteorological data.</li> </ul> <p><strong>Minor changes</strong></p> <ul> <li>Updated stream gauge coordinates based on ANA's website on 27 February 2025. Coordinates were updated for 73 gauges in the 897 selected catchments and for 298 gauges across all catchments.</li> <li>Streamflow time series now include quality flag values from 0 to 7 (see the readme.txt file), previously from 0 to 4 in CAMELS-BR version 1.1. Flags from 5 to 7 may be present only in the last few years of data.</li> <li>Streamflow time series files for the 897 selected gauges now include values in both millimeters per day and cubic meters per second.</li> <li>Removed streamflow time series with fewer than 180 days of measurement.</li> <li>Converted gauge and catchment spatial data from Shapefile (.shp) to GeoPackage (.gpkg).</li> <li>Catchment areas computed by GSIM (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") flagged as "caution" for quality were set to "nan" due to low reliability.</li> <li>Updated catchment areas computed by ANA (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") to reflect the newest ANA's data from 27 February 2025. Streamflow values in millimeters per day remain unchanged because unit conversions rely on GSIM areas.</li> <li>Set catchment areas with zero squared kilometers, as computed by ANA, to "nan".</li> <li>Removed CPC daily mean temperature time series (catchment averages) because they were a simple average of minimum and maximum temperatures. For daily mean temperatures, refer to ERA5-Land data (now included) as they are computed from hourly data.</li> </ul> <p> </p>
Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
<p><strong>Citation</strong></p> <p>Zhang, Alice Tianbo, and Vincent Xinyi Gu. 2023. “Global Dam Tracker: A Database of More than 35,000 Dams with Location, Catchment, and Attribute Information.” <em>Scientific Data</em> 10 (1): 111.</p> <p><a href="https://www.nature.com/articles/s41597-023-02008-2">https://www.nature.com/articles/s41597-023-02008-2</a></p> <p> </p> <p><strong>Description</strong></p> <p>We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.</p>
HydroPenIndia: A catalogue of streamflow metrics, meteorological time series and catchment attributes of Peninsular India
<p><em>HydroPenIndia, </em>a catalogue of streamflow metrics, hydro-meteorological time series and landscape attributes of 204 catchments of Peninsular India is introduced. This catalogue consists of daily hydro-meteorological time series (rainfall, soil moisture, potential evapotranspiration, actual evapotranspiration, maximum temperature, minimum temperature, longwave radiation, shortwave radiation, wind speed and humidity) for a period of 36 years from 1980-2015. The time series of 26 streamflow metrics, 13 topographic metrics, 12 climate indices, 15 hydrologic signatures, 8 land cover descriptors, 6 geologic characteristics, 6 soil characteristics (see table 8) and 13 human intervention indices are also included in this dataset. <em>HydroPenIndia</em> is an initiative to encourage hydrologists to advance knowledge of hydrological processes by contributing to fundamental research questions on Indian catchments. Free availability of the dataset will provide access to global users to represent India in large-sample hydrology studies. <em>HydroPenIndia</em> is derived from multiple databases to help researchers start their research without wasting time on collecting and processing datasets. This catalogue will motivate researchers to solve pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unravelling regional scale hydrologic functioning and climate change impact assessment over Peninsular India.</p>
DAM-IN: Comprehensive Dam Catchment Attributes in India
<p>We present one of the most comprehensive datasets of dams in India, “DAM-IN”. The dam attributes for India (DAM-IN) consist of six important catchment characteristics, including topography, geology and groundwater, soil, Land Use and Land Cover (LULC), climate, and human-induced activities. These six catchment characteristics were developed using various reanalysis, observed, and remote-sensing data for more than 5700 dams in India. Moreover, a large number of attributes were estimated for each catchment characteristic of the dam. Overall, the comprehensive DAM-IN dataset consists of high-quality catchment attributes for each dam in India for multi-temporal and spatial resolution. The DAM-IN data is useful for various stakeholders to conduct scientific research, dam safety, and impact analysis in India. Moreover, the DAM-IN data can be used for reservoir management, various water quantity and quality studies, environmental risk assessment, and climate change risk analysis. The catchment and meteorological attributes can be used as input for hydrological, hydraulic, and machine-learning modeling. Using the developed attributes, the effective planning and management of sustainable river infrastructure and water resource allocation can be performed.</p>
Morph_CNeT: A new GIS tool to extract morphometric attributes characterising channel network topology of Indian catchments
<p><span>Morph_CNeT” (Morphometric Channel Network Extraction Tool) can facilitate extraction of the topology based new morphometric attributes by processing DEM datasets within GIS framework. Morph_CNeT tool is used to create a repository named as Morph_CNeT-India of topological catchment attributes for 1749 gauging stations maintained by the Central Water Commission (CWC) across 22 River basin Systems of India.</span></p>
URA dataset - 40 Basque Country catchments hourly hydro-meteorological data + Catchments Attributes
<p>Basque Country is located in north of Spain on the Atlantic coast. This region, characterized by its humid climatology, has abundant water resources. The catchments, noted for their flashy and humid characteristics, span an area of 4494 Km<sup>2</sup> and include a diverse range of basin sizes from 4 to 1000 Km<sup>2</sup>.</p> <p>The Basque Water Agency (URA), a regional governmental entity in the Basque Country, is responsible for managing water policies and resources in this territory. To support water resources planning and management, URA has compiled a high-quality dataset suitable for data mining using deep learning models. This dataset includes hourly hydro-meteorological timeseries, reflecting the region’s steep, flashy, and humid hydrological dynamics.</p> <p>URA catchments are situated between the Cantabrian Mountains (reaching up to 1300 meters in elevation) in the northwest and the Atlantic Ocean to the north. The region is predominantly covered by grasslands and evergreen forests and benefits from the warming effects of the Gulf Stream. The climate is humid and temperate, with mean annual temperatures ranging from 9°<sup>C</sup> in the mountains to 15°<sup>C</sup> in lower regions. Annual rainfall varies between 1200 and 1600 millimeters, primarily due to the advection of North Atlantic fronts.</p> <p>URA has collected hourly hydro-meteorological timeseries from approximately 100 stations distributed across the region, including rain gauges and water level measurement sites. Accurate rainfall-runoff modeling is critical in this region due to its susceptibility to flash floods. This dataset presents 21 years of hourly timeseries from 40 catchments within the region. This extensive temporal coverage and high-resolution data offer valuable insights into the region’s hydrological dynamics.</p> <p>This Dataset is published with the papers:</p> <p>1- <span>Hosseini, F.; Prieto, C.; Álvarez, C. (2025). </span><span><strong>Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling</strong>—Case Study: Basque Country, Spain. </span><span>J. Hydrol. 132269. doi: </span><span><a href="https://doi.org/10.1016/j.jhydrol.2024.132269"><span>10.1016/j.jhydrol.2024.132269</span></a></span></p> <p>2- <span>Hosseini, F., Prieto, C., & Álvarez, C. (2024a). <strong>Hyperparameter optimization of regional hydrological LSTMs by random search</strong>: A case study from Basque Country, Spain. Journal of Hydrology, 132003. doi: </span><span><a href="https://doi.org/10.1016/j.jhydrol.2024.132003"><span>10.1016/j.jhydrol.2024.132003</span></a></span></p> <p> </p>
Victorian Water and Climate dataset: long-term streamflow, climate, and vegetation observation records and catchment attributes
<p>This dataset contains streamflow, climate, and vegetation data for 155 minimally impaired catchments in Victoria, Australia. </p> <p>What's included:</p> <p>- Streamflow long-term observation records in daily, monthly, and hydroannual (based on March to February water year) resolution</p> <p>- Catchment climate characteristics in daily, monthly, and hydroannual resolution</p> <p>- Catchment vegetation characteristics in monthly resolution and actual evapotranspiration estimate in monthly and hydroannual resolution</p> <p>- Catchment boundaries</p> <p>- Catchment attributes (topographic, geological, soil, groundwater, vegetation, and human impacts characteristics)</p> <p>- Hydroclimatic characteristics for 3 different periods.</p> <p>Precipitation (p), streamflow (q), and PET (pet) data and their derivatives are in mm (i.e. normalised by catchment area). </p> <p>A paper containing a detailed description of the data including units, data sources, and processing details is submitted, please contact Margarita Saft for a private copy of the draft.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.