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113 results for “Hydrological Model”
MONITORING AND MODELING OF HYDROLOGICAL PROCESSES IN THE SEMIARID REGION OF BRAZIL: THE CARIRI EXPERIMENTAL BASINS
<p><strong>DATASET DESCRIPTION</strong> - Two experimental basins – the Cariri basins – were installed in a typically semiarid region in the State of Paraíba, Brazil, for obtaining reliable estimates of runoff and soil erosion in different scales to evaluate the influence of the human activities and other factors over the processes of runoff and erosion. In the first basin, located in the municipality of Sumé, the field studies were carried out at three different scales: four micro-basins with an area of around 0.5 ha; nine standard Wischmeier-type erosion plots of 100 m<sup>2</sup> and seven sample plots of 1 m<sup>2</sup>. The experimental units had varied vegetal cover and management and, except the sample plots, were subjected to natural rainfall events only, and were monitored from 1982 to 1991. The total runoff and total sediment yield were determined for each of the events of precipitation. The installations of the second basin, in the near municipality of São João do Cariri, were planned for the continuation of the studies initiated at Sumé, and include erosion plots (100 m<sup>2</sup>), micro-basins, and sub-basins, which are being monitored for runoff and sediment production up to now. Among them, two nested micro-basins were monitored to detect any scale effect at the micro-basin level. Nearly 600 events of natural precipitation, that produced runoff in at least one of the experimental units, have been registered. This bulk of data was utilised to evaluate the influence of various factors, including cultivation practices. The data collected so far has been successfully used to calibrate hydrological models for plots and micro-basins. Parameters have been tested by means of cross validations among micro-basins and sub-basins.</p> <p><strong>FILENAMES </strong>– The data files are divided into three categories: Description of the equipment utilized for collecting data and their locations, the data collected from the monitored experimental basins and another with maps, figures and pictures. The file names are designated with the basin name and the content. The files describing the equipment comprise: BASINNAME_DATADESCRIPTION, where BASINNAME could be EBS or EBSJC. The files with the data collected in the experimental basins are denominated like: BASINNAME_DATANAME. The DATANAME will be one of the three that may be, precipitation, runoff and sediment yield, or climatologic data. The file with maps and other information are identified as: BASINNAME_GEOPHYSICDATANAME, and BASINNAME_PICTURES. The geophysical data refer to topographic data, soil data, land cover and the drainage network. Graphs, pictures, etc., are included in the PICTURES file.</p> <p><strong>DATAFORMAT </strong>– The data file about equipment and localization as well as the data collected in experimental units are of the type “. csv”, the geophysical data are of either “.dwg” or “.shp”. The figures and picture are in the format: .jpeg, .png or .tif.</p> <p><strong>ACKNOWLEDGEMENTS </strong>– SUDENE – the Superintendency for the Development of the Northeast of Brazil with the cooperation of ORSTOM – the French Government Agency for Technical Cooperation Overseas was responsible for implementing the program of Representative and Experimental Basins in the region beginning in the decade of 1970. Pierre Audry, Eric Cadier, Jean Leprun and Michel Molinier, hydrologists and soil scientists from France played key roles in the selection of site, installation of experimental units and beginning the operation of the EBS. Beronildo Freitas was the engineer from SUDENE responsible for technical coordination and administration. The contributions of other researchers and technical people have been listed by Srinivasan and Galvão (2003). The installation of the research catchment at São João de Cariri had the valuable collaboration of GTZ, the cooperation Agency of the Government of Germany. Dr. Ing Ubald Koch was responsible for getting all the equipment, installing them and conducting research work along with the members of the Hydrology Research Group of the Federal Universities of Paraiba and Campina Grande. Late prof. Manoel Gilberto de Barros efficiently coordinated the field work. Eduardo Figueiredo, Celso Santos and Ricardo Aragão have made note worthy contributions. The Ministry of Science and Technology of Brazil has provided the bulk of the financial support needed for the operation of the basins, through its main funding agencies of CNPq (National Council for Development of Science and Technology) and FINEP (Agency for Financing research Studies and Projects).</p>
Optimal parameters of GR4J and HBV hydrological models used in the OpenForecast v2 system
<p>The database contains optimal parameters of GR4J and HBV hydrological models, which are operationally used in the second version of the OpenForecast system (openforecast.github.io).</p>
Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management: Model output
<p>This archive includes the output from the General Ocean Turbulence Model for a series of simulations that used differing model drivers (inflow discharge, Q, and air temperature, T). The Experiment_output.zip contains text files generated from the GOTM workflow containing modelled water temperatures and the Mod_z.txt are the corresponding depths for these temperatures. </p><p>The file name corresponds to the change made to the driving data from baseline (unchanged conditions), a combination of air temperature <i>increase </i>and flow percentage change e.g. Mod_temp_T_2_Q_1.5 refers to an air temperature increase of 2 degrees Celsius and a flow increase of 50% and a Mod_temp_T_3.5_Q_0.7 refers to an air temperature increase of 3.5 degrees Celsius and a flow decrease of 30%.</p>
Value of water level class observations for parameter set selection in hydrological modelling
<p>Data corresponding to the study "<span>Value of water level class observations for parameter set selection in hydrological modelling</span>", published in the Hydrological Sciences Journal.</p>
Evaluating country-scale irrigation demand through parsimonious agro-hydrological modeling
<p>WaterCROPv2 is an advanced agro-hydrological model designed to estimate irrigation<br>water demand at a national scale by effectively balancing hydrological accuracy with manageable data<br>requirements. Building on the original WaterCROPv1 model, WaterCROPv2 incorporates several<br>significant enhancements, including hourly computation, rainwater canopy interception, soil-dependent5<br>leakage dynamics, and daily evapotranspiration trends based on localized meteorological data.</p> <p>WaterCROPv2 was used to assess mean irrigation water demand for maize from 2005<br>to 2015, illustrating its potential as a decision-support tool for policymakers. </p>
Are deep learning models in hydrology entity aware?
<p>This is the dataset and code accompanying the publication "Are deep learning models in hydrology entity aware?" of Benedikt Heudorfer, Hoshin V. Gupta, and Ralf Loritz to allow reproducible results. </p>
CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE)
<p>CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE). Data are ready for publication on MyDewetra platform</p>
A dataset of modeled hydrologic alteration and ecological consequences in stream reaches of the conterminous United States
<p>A dataset of modeled anthropogenically induced hydrologic alteration values for 43 hydrologic indices and modeled losses in native fish biodiversity as a result of streamflow modification, both mapped at the NHDPlus V1 and V2 stream-reach resolution.</p>
Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system
<p>Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model. </p> <p>Manuscript Citation:</p> <p>Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage<sup>,</sup> and R. Dunlap,<strong> </strong>2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,<em>Water Resour. Res.,</em></p>
Code of "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation"
<p>Code used to prepare a paper entitled "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation" which was submitted to Hydrology and Earth System Sciences.</p>
Integrated hydrological model results for Lower Triangle Region in East River Watershed, Colorado, WYs 2016 and 2017
<p><strong>Summary</strong></p> <p>This data package contains numerical simulation results of integrated hydrology in Lower Triangle Region in East River Watershed, Colorado. The system is forced with <a href="https://daymet.ornl.gov/">DAYMET</a> precipitation and climate data of the region for the water years 2016 and 2017. The results are computed on triangular multi-resolution meshes with resolutions ranging from 10 meter to 80 meter. The purpose of the data is to assess the influence of surface-subsurface exchange on distributed and aggregated hydrological response.</p> <p><strong>Material and Methods</strong></p> <p>Data has been generated by the <a href="https://amanzi.github.io/">Advanced Terrestrial Simulator (ATS)</a> v1.0. The output format of ATS for spatially distributed data is <a href="https://www.hdfgroup.org/solutions/hdf5">HDF5</a> and can be viewed, for example, using <a href="https://hpc.llnl.gov/software/visualization-software/visit">VisIt</a> or <a href="https://www.paraview.org/">ParaView</a>. The format of point data is plain text.</p> <p><strong>README content</strong></p> <p>The uploaded files have been created using the unix split(1) command to limit the size of each individual package. Files can be merged under a unix system through:</p> <p><code>$ cat OZGEN_ETAL_2022.zip.partaa OZGEN_ETAL_2022.zip.partab OZGEN_ETAL_2022.zip.partac > output.zip</code></p> <p>Unzip via</p> <p><code>$ unzip output.zip</code></p> <p>or using a graphical environment.</p>
Modeling the hydrological cycle in the atmosphere of Mars: Influence of a bimodal size distribution of aerosol nucleation particles
<p>Data from figures.</p>
Data for "Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model"
<p>The data files arranged here correspond to the data used in the paper: “Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model”, submitted to <em>Water</em>.</p> <p>The data organized as follows:</p> <ul> <li>Data_type_20150912_time_steps.mat: the rainfall data for 3 different products of the X-band radar (FIR filter, a=200, b=1.6; FIR filter, a=150, b=1.3; simple filter, a=150, b=1.3) for the event of 12-13 September 2015, over an area of 64 km x 64 km.</li> <li>Data_type_Event_time_steps.mat: X-band radar data (FIR filter, a=150, b=1.3) for the events of 16 September 2015 and 5-6 October 2015, over an area of 64 km x 64 km.</li> <li>Sub-catchment_name_Data_type_Event.txt: the rainfall series for each of 26 sub-catchments of the model, for 3 different types of rainfall data (C-band, X-band and rain gauges) for the events of 12-13 September 2015, 16 September 2015 and 5-6 October 2015.</li> <li>X-band_Pixels_Event.txt: the rainfall series for all 6 X-band radar pixels corresponding to the 6 rain gauges for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015).</li> <li>X-Band_Optim 20150916_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with X-band data for the 16 September 2015 event, with the implementation of the tool mimicking the regulation optimization.</li> <li>Data_type_Event_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with 3 different types of rainfall data (C-band, X-band and rain gauges) for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015), without the implementation of the tool mimicking the regulation optimization.</li> </ul> <p>The original C-band radar data remains property of Météo-France and was provided to the authors for this research study, without any possibility of data disclosure.</p> <p>The details on how the rainfall series were generated over each sub-catchment could be found in the paper.</p> <p>The authors greatly acknowledge partial financial supports of the Chair “Hydrology for resilient cities” endowed by Veolia, and of the Department of Science and Technology of the Brazilian Army. The authors are thankful to M Bernard Urban (Météo-France) for providing access to the C-band radar data and documentation in the framework of the INTERREG NWE RainGain project.</p>
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Mapping shallow groundwater solute footprints in arid regions using a hydrologically enhanced species distribution model
<p>The topography-only SDM of shallow groundwater and deep groundwater, the final models-SDM maps of shallow groundwater, their improvements, the original dataset of water chemistry, and the related R script in the study are available here</p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling
<p>This dataset and the scripts accompany the manuscript "<strong>Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling</strong>". The manuscript is published in the Journal Climatic Change.</p>
A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers
<p>KazmierczakGregov24_data.zip contains the Matlab scripts and data necessary to reproduce the results and figures of the article "A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers" by Kazmierczak, Gregov, Coulon, and Pattyn. For more details, please, open the README.txt file or contact elise (dot) kazmierczak (at) ulb (dot) be or thomas (dot) gregov (at) uliege (dot) be.</p>
Measured and modeled data of the thermo-radiative and hydrological exhanges of a lawn in a residential area (FluxSAP 2012 campaign)
<p>The dataset includes wind, soil water content and soil temperature data measured in a private home garden collected during the FluxSAP campaign in Nantes in 2012. It also includes equivalent data modeled by the surface model for natural soils and vegetation called ISBA for four sensitivity experiments corresponding to different model configurations.</p>
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.