Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “water runoff”
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Rainfall runoff water chemistry from 2 x 2 meter plots in grassland and creosotebush communities at the Jornada Basin LTER, 1988-1990
This data package contains data from natural rainfall-runoff plots installed on lands managed by the Chihuahuan Desert Rangeland Research Center (CDRRC) and the Jornada Experimental Range (JER) during the early years of Jornada Basin LTER (LTER-I and II). The purpose of this study was to analyze dissolved chemicals in surface runoff after precipitation events from creosotebush and grassland areas. In addition, some plots were treated with chlordane to remove termites. Nine hydrology runoff plots in the creosotebush sites (Larrea tridentata) were established in 1983 and 12 black grama (Bouteloua eriopoda) grassland runoff plots were established in 1989. Chemical analyses began in 1988 (1989 for grassland plots). Each plot was 2 × 2 square meters and surrounded on 3 sides by a metal frame. At the lower slope of the plot is a trough that collects the runoff for analysis. The data set includes the date of sampling, plot IDs, volume (L) of surface water runoff collected, and concentrations of F, Cl, NO3, SO4, NH4, Ca, Mg, Na, K, total N, and total P in mg/L. This study was completed in fall 1990.
Tracing the imprint of river runoff on Arctic water mass transformation [dataset]
<p>This dataset contains the underlying data for the manuscript Lambert et al., Tracing the imprint of river runoff<br> variability on Arctic water mass transformation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Both files contain variables with the general notation:<br> S..., which are the cumulative salt fluxes;<br> S..2, which are the salinity-transformation fluxes;<br> T..., which are the cumulative heat fluxes; and<br> T..2, which are the temperature-transformation fluxes.</p> <p>-----------------------------------<br> In the file crfdata.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> emp: evaporation-precipitation, small en neglected in the manuscript<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> fsiso/ftiso: isopycnal diffusion of salt/heat<br> fsdia/ftdia: diapycnal diffusion of salt/heat<br> sec: advection across the collective Arctic gateways</p> <p>Each variable is of size [4,12,nS] or [4,12,nT] where nS is the number of salinity bins, equal to the length of variable S<br> and nT is the number of temperature bins, equal to the length of variable T</p> <p>The first dimension is ordered as follows:<br> 0: delta_s, the equilibrium response to a 30% increase in total Arctic river runoff<br> 1: tau_s, the e-folding time scale of this response in months<br> 2: std, the standard deviation of the control value<br> 3: ctrl, the average control value</p> <p>The second dimension indicates the calendar month</p> <p>-----------------------------------------<br> In the file pp2.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> adv: advection across the collective Arctic gateways<br> dif: total isopycnal + diapyncal diffusion</p> <p>Each variable is of size [2,nS] or [2,nT]</p> <p>The first dimension is:<br> 0: explained model variance between 0 and 1<br> 1: explained model variance where correlations with p>.05 equal NaN</p>
Supplementary data to: Soil Moisture to Runoff (SM2R) A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics
<p>This data archive includes simulated monthly runoff anomaly using the Soil Moisture to Runoff model during 1981‒2020 over the representative drainage basin in each water tower. Please see Readme for more data information. For calculation details please see the publication.</p>
Stormwater filter water quality and quantity data - treatment of road runoff in the city of Vantaa, Finland
<p>The water quantity and quality dataset is from road runoff filters in the city of Vantaa, Finland. Data are from 6 campaigns in 2017 and 2019. The data is documented in: Koivusalo, H., Dubovik, M., Wendling, L., Assmuth, E., Sillanpää, N., Kokkonen, T. 2023. Performance of sand and mixed sand-biochar filters for treatment of road runoff quantity and quality. Accepted to Water. </p> <p> </p>
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</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>
Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks
<p>Abstract</p> <p>Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users.</p> <p>A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale.</p> <p>The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network.</p> <p> </p> <p>Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China)</p> <p> </p> <p>Citations</p> <p>If you find our code to be useful, please cite the following papers:</p> <p>Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020).</p> <p>Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021).</p> <p>Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).</p>
A soil moisture-dependent model to simulate water table depth and proportions of surface and subsurface runoff and its validation at basin scale
<p>The data is the simulations of the SMD-model, Sy-mdoel, adn Noah-MP in three basins in China and the USA. The vaiables are monthly water table depth, soil moisture, subsurface and total runoff. </p>
ScienceDex guides
Understand access before you commit
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.