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173 results for “CAMELS”
CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg
<p>The CAMELS-LUX dataset encompasses hydro-meteorological time series and catchment attributes for 56 partly nested stream gauges feeding into the Luxembourgish stream network. The data is available at three temporal resolutions: daily, hourly and at a 15-minute resolution and spans the hydrological years from 2004-11-01 to 2021-10-31. The static catchment attributes cover parameters classifying the topography, geology and land use as well as climatic and hydrologic annual statistics of the 17-year time period.</p> <p>While an in depth description of the dataset as well as background information on catchments, the environment and exact calculation methods is provided in the accompanying publication in ESSD, the dataset description below isolates information on the available parameters and data structure contained in the provided files.</p> <p>Please note that the dataset might not include data corrections or validations that are subject to a date later than the date of the retrieval of the data for the processing of this dataset. This dates back to 2022 for most hydrologic time series, and to 2023 for the reanalysis data or the precipitation data. We are aware of duplicate rows in the time series file with a resolution of 15 minutes for catchment 16 as well as time stamp shifts in the precipitation data. We are working on correcting these data to update this dataset.</p> <p><strong>Data structure</strong></p> <p><strong>Time series data</strong></p> <ol> <li>Hydrologic parameters</li> <li>Precipitation parameters</li> <li>Air temperature and potential evapotranspiration parameters</li> <li>Thunderstorm relevant atmospheric parameters</li> <li> Soil Moisture parameters</li> </ol> <p><strong>Static catchment attributes</strong></p> <ol> <li>Basin IDs</li> <li>Meta catchment attributes</li> <li>Climatic catchment attributes</li> <li>Geologic catchment attributes</li> <li>Land use catchment attributes</li> <li>Topographic catchment attributes</li> </ol> <p><strong>Spatial data - shapefiles</strong></p>
Least cost network data for ancient camel transportation in the Eastern desert of Egypt - Desert Networks HiSoMA CNRS
<p>This repository contains the data necessary for the realization of a least cost network for camel transport during antiquity (Ptolemaic and Roman period) in the Egyptian eastern desert. The details of the network construction and data processing can be found in the the associated paper and datapaper.</p> <p>Study paper:<br> Manière, L., Crépy, M., Redon, B. (2020) Building a Model to reconstruct the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries. DOI : <a href="http://doi.org/10.5334/jcaa.67">http://doi.org/10.5334/jcaa.67</a></p> <p>Datapaper:<br> Manière, L., Crépy, M., Redon, B. (2020) Geospatial data from the “Modelling the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries” paper. DOI : <a href="http://doi.org/10.5334/joad.71">http://doi.org/10.5334/joad.71</a></p>
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>
Wflow SBM streamflow estimates for CAMELS data set
<p>The dataset contains 3 simulated timeseries at the basin outlet for the CAMELS dataset created with the wflow_sbm model at various spatial resolutions (3km, 1km, 200m). The data set includes the calculated objective functions NSE, KGE 2009, KGE 2012, and KGE NP.</p>
Virome diversity of Hyalomma dromedarii ticks collected from camels in the United Arab Emirates
<p>Viruses are important components of the microbiome of ticks. Ticks are capable of transmitting several serious viral diseases to humans and animals. Hitherto, the composition of viral communities in <em>Hyalomma dromedarii</em> ticks associated with camels in the United Arab Emirates (UAE) remains unexplored. The purpose of this study was to characterize the RNA virome diversity in male and female <em>H. dromedarii</em> ticks collected from camels in Al Ain, UAE.<strong> </strong>We collected ticks, extracted and sequenced RNA, using Illumina (NovaSeq 6000) and Oxford Nanopore (MinION).<strong> </strong>From the total generated sequencing reads, 180,559 (~0.35 %) and 197,801 (~0.34 %) reads were identified as virus-related reads in male and female tick samples respectively. Taxonomic assignment of the viral sequencing reads was accomplished based on bioinformatic analyses. Further, viral reads were classified into 39 viral families. Poxiviridae, Phycodnaviridae, Phenuiviridae, Mimiviridae, and Polydnaviridae were the most abundant families in the tick viromes. Notably, we assembled the genomes of three RNA viruses, which were placed by phylogenetic analyses in clades that included the Bole tick virus.<strong> </strong>Overall, this study attempts to elucidate the RNA virome of ticks associated with camels in the UAE and the results obtained from this study improve the knowledge of the diversity of viruses in <em>H. dromedarii</em> ticks.</p>
camel
<p>Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study performed before showed the need for a further in-depth analysis in order to identify project clusters. <br> Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. <br> Method: Hierarchical and k-means clustering, as well as Kohonen’s neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. <br> Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B – T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen’s benchmark, which is a substantial finding. <br> Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.</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>
Figure 1 in Effects of commercial oils on the camel tick, Hyalomma dromedarii (Acari: Ixodidae) and their enzyme activities
Figure 1. Mortality percentages of Hyalomma dromedarii semi-engorged females treated with different concentrations of four oils at five successive days after treatment – A. Rosemary; B. Garlic; C. Neem; D. Cyperus. a, b, … etc. indicate significant differences between concentrations (%) of each oil for each day according to Tukey test (P <0.001).
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>
Fig. 3 in Three species of Echinococcus granulosus sensu lato infect camels on the Arabian Peninsula
Fig. 3 Parsimony haplotype network of the 21 camel isolates and 65 worldwide selected sequences. Haplotypes of each region are presented using color coding. White nodes represent hypothetical haplotypes. Sizes of colored nodes are proportional to the number of isolates found per haplotype. The cluster on the lower right side (without KSA isolates) is formed of sequences conforming to the G3 genotype
Fig. 2 in The fossil record of camelids demonstrates a late divergence between Bactrian camel and dromedary
Fig. 2. Time-calibrated equiparsimonious trees. At each node, the probability density computed by diversification is shown (in red, all displaying a left skew). The age of each fossil record (in million years) is shown as a brown bar along each branch, which extends from the oldest to the youngest plausible age for each record. Darker shades represent overlapping possible age ranges, whereas brown dots represent very well-dated fossils. Extant taxa are in bold. A monophyletic Camelus is diagnosed by the loss of p3 and a smaller P3. The Paracamelus clade is diagnosed by a long muzzle. Camelus grattardi lacks derived characters of other representatives of the Camelus clade, the paraglenoid process, a shallower infra-orbital shelf, an oblique ascending ramus of the mandible, a thickened corpus, a broader P4 relative, and long ligament scars on the phalanges. The position of the poorly studied Camelus knoblochi relative to extant forms rests only on the morphology of the choanae.
Fig. 1 in The fossil record of camelids demonstrates a late divergence between Bactrian camel and dromedary
Fig. 1. Probability density histograms of speciation (cladogenesis), extinction and fossilization rates for the three equiparsimonious trees. All rates are in events per lineage and per million years. The height of each box of the plots is proportional to the posterior probability for the corresponding rate to be in the interval delineating its base.
Fig. 1 in Three species of Echinococcus granulosus sensu lato infect camels on the Arabian Peninsula
Fig. 1 Sequence alignment of the 9 haplotypes. Substitutions were indicated with their nucleotide code, deletions were marked by (-), and dots (.) indicate identical nucleotide at the specified position in comparison with the reference sequence AF297617 (Lee et al. 2002). *nucleotides substitution positions, based on the start of the complete cox1 gene, read vertically
Fig. 2 Phylogenetic tree showing the relation between the Saudi Arabian haplotypes with 65 in Three species of Echinococcus granulosus sensu lato infect camels on the Arabian Peninsula
Fig. 2 Phylogenetic tree showing the relation between the Saudi Arabian haplotypes with 65 reference sequences. The Saudi Arabian haplotypes (H01-09) are in bold. The reference sequences along with their accession numbers and origin of isolate were included for each. T. solium was used as an outgroup taxon. The branch to outgroup was shortened by 0.2 substitutions per site
Linked collectors and determiners for: Revision of the camel spider genus Eremocosta Roewer and a description of the female Eremocosta gigas Roewer (Arachnida, Solifugae).
Natural history specimen data linked to collectors and determiners held within, "Revision of the camel spider genus Eremocosta Roewer and a description of the female Eremocosta gigas Roewer (Arachnida, Solifugae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/57f8ff28-59ea-403a-8d53-2f16eef6d0d8">https://bionomia.net/dataset/57f8ff28-59ea-403a-8d53-2f16eef6d0d8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/57f8ff28-59ea-403a-8d53-2f16eef6d0d8">https://gbif.org/dataset/57f8ff28-59ea-403a-8d53-2f16eef6d0d8</a>. Formatted as a Frictionless Data package.
FIG. 7 in Camels from Roman imperial sites in Serbia
FIG. 7. — Cranial view of the 1st posterior phalanges from: A, Viminacium amphitheatre; B, Viminacium-Pirivoj; C, Vranj. Scale in cm.
FIG. 7 in Camels in Romania
FIG. 7. — Comparisons between the biometric data of C. bactrianus (circle), C. dromedarius (triangle) and the Agighiol remains (square) for metatarsus. The measurements follow Angela von den Driesch (1976).
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