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12 results for “Rainfall-Runoff Modeling”
Assessing evapotranspiration realism in rainfall-runoff models using evapotranspiration signatures
<p> </p> <p>This dataset contains simulated actual evapotranspiration (AET) data derived from five conceptual hydrological models and input data applied to 14 catchments in Australia. The data spans the period from 1980 to 2022. The five models included in this dataset are:</p> <ul> <li>SIMHYD</li> <li>IHACRES</li> <li>VIC</li> <li>SACRAMENTO</li> <li>GR4J</li> </ul> <p>These models were implemented in version 2.1 of the MaRRMoT framework.</p> <p>Here, the models were calibrated using two different approaches:</p> <ol> <li>Calibration based on discharge data only. (Folder: ModelCalQ_Data)</li> <li>Calibration using a composite objective function that incorporates both discharge and AET data. (Folder: ModelCalQnAET_Data)</li> </ol> <p>Example script is also included in each model folder, such as ‘<em>Run_Simhyd_MaRRMoT_Cal_Spartan.m’</em>, to facilitate the running of the models and understanding of the calibration process.</p> <p> </p>
Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain - Data
<div> <div>This data and results are for paper: "Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain" by Hosseini et al. 2024 (Preprint - Under review J.Hydro 2024) Available at SSRN: <a href="https://ssrn.com/abstract=4918782" target="_blank" rel="noopener">https://ssrn.com/abstract=4918782</a></div> <div> </div> </div>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Dealing with Sub-pixel Landscape Elements in Distributed Rainfall-Runoff Modelling in Agricultural Catchments
<p>The <strong>Input_Data.zip</strong> file contains the processed data used as input data for the research paper "<strong>Dealing with Sub-pixel Landscape Elements in Distributed Rainfall-Runoff Modelling in Agricultural Catchments</strong>".</p> <ul> <li>The <strong>Digital Elevation Models (DEMs)</strong> are adapted from the 5 m resolution "DEM DHMV II" (available from <a href="https://download.vlaanderen.be/Producten/Detail/938">https://download.vlaanderen.be/Producten/Detail/938</a>). The scaling factors used to calculate per-pixel values of upscaled hydro-physical parameters are derived from the digital elevation model.</li> <li>The raster files representing the <strong>Manning's roughness coefficient</strong> in the watershed for 2006, 2007, 2013, 2016 and 2019 are based on landcover datasets created by combining the Flemish agricultural parcel dataset for each year (available from https://www.vlaanderen.be/datavindplaats/catalogus/landbouwgebruikspercelen-lv-2021) with the road network dataset (available from <a href="https://www.vlaanderen.be/digitaal-vlaanderen/onze-oplossingen/basiskaart-vlaanderen-grb">https://www.vlaanderen.be/digitaal-vlaanderen/onze-oplossingen/basiskaart-vlaanderen-grb</a>) and digitised polygons of forested areas based on areal images (<a href="https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-grootschalig-winteropnamen-kleur-2013-2015-vlaanderen">https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-grootschalig-winteropnamen-kleur-2013-2015-vlaanderen</a>).</li> <li>The <strong>vegetated landscape element (vLE) configurations</strong> were created based on agricultural parcel boundaries (available from <a href="https://www.vlaanderen.be/datavindplaats/catalogus/landbouwgebruikspercelen-lv-2021">https://www.vlaanderen.be/datavindplaats/catalogus/landbouwgebruikspercelen-lv-2021</a>) and rasterising them.</li> <li><strong>Rainfall intensity measurements</strong> were extracted from <a href="http://waterinfo.be">waterinfo.be</a> for the station with name "Niel-bij-St.-Truiden_P", number "01P09_012", and coordinates (LAT/LON) "50.7378581908062/5.14225742349451".</li> <li><strong>Discharge measurements </strong>were extracted from <a href="http://waterinfo.be">waterinfo.be</a> for the discharge station with name "Gingelom/Heulegracht" and number "LS09_15F".</li> </ul> <p>The <strong>scripts.zip</strong> file contains the scripts used to process the data.</p> <p>The Output.zip file contains the output files created by the scripts while making use of the Python-based Landlab model environment (<a href="https://landlab.github.io/#/">https://landlab.github.io/#/</a>)</p> <p> </p> <p>This research was funded by Fonds Wetenschappelijk Onderzoek (FWO), grant number 1SB6821N.</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Rainfall-Runoff Modeling Using Crowdsourced Water Level Data
<p>Input data, geodata, model outputs, and Python scripts used for running and analyzing the hydrological model <a href="https://philippkraft.github.io/cmf/">CMF </a>(parameterized using <a href="https://github.com/thouska/spotpy">SPOTPY</a>) in the frame of the publication "Rainfall-Runoff Modeling Using Crowdsourced Water Level Data" by Weeser et al. (Water Resource Research).</p> <p>The folder WRR_CrowdMod_2019_08_23.zip contains:</p> <ul> <li>Folder <em>input_data</em>: Input data used for the model</li> <li>Folder <em>script_model</em>: The model including the SPOTPY set-up for calibration and validation <ul> <li>Subfolder <em>parameter_validation</em>: Parameter sets used during validation for all analyzed scenarios</li> <li>Subfolder <em>parameter_fluxes</em>: Parameter sets used for the analysis of the fluxes</li> </ul> </li> <li>Folder <em>calibration</em>: Model output during calibration with all 10<sup>6</sup> runs</li> <li>Folder <em>validation</em>: Model outputs generated during validation <ul> <li>Subfolder <em>accepted_simulation_results</em>: modeled discharge by using all accepted parameter sets for each scenario</li> </ul> </li> <li>Folder <em>Fluxes</em>: The fluxes released by the different model components</li> <li>4 Jupyter notebooks: <ul> <li>1_calibration: Script for analyzing the model output generated during calibration. Generates the parameter sets used for validation</li> <li>2_validation: Analyze the results during validation</li> <li>3_fig5_calibration_validation: Script used to generate figure 5</li> <li>4_fig6_fluxes: Script used to generate figure 6 showing the fluxes within the model during validation</li> </ul> </li> </ul> <p>The folder geodata contains two shapefiles representing the spatial data of the catchment.</p>
pywaterinfo dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This forcings dataset is the output of the pywaterinfo (https://fluves.github.io/pywaterinfo/) read in of forcing data (rain and potential evapotranspiration).</p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis</p>
OpenEO dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This dataset is the output of the <a href="https://openeo.org/">OpenEO</a> processing of satellite data (SAR backscatter and LAI). </p> <p>Code related to this dataset can be found <a href="https://github.com/olivierbonte/master_thesis">here</a></p>
Minimal dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>The minimal dataset needed of data which can not be retrieved from the internet by APIs in the preprocessing. Consists of shape, land use and rivers for the Zwalm catchment. </p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis </p>
Preprocessing output for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Outputs of the local preprocessing of the OpenEO data (see <a href="https://doi.org/10.5281/zenodo.7691342">here</a>) and pywaterinfo data (see <a href="https://doi.org/10.5281/zenodo.7689200">here</a>).</p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p>
Inverse observation operator parameters/models for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Both saved models and results of hyperparameter tuning are given. </p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p> <p> </p>
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</p>
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