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160 results for “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
Stormwater Nitrogen in Arizona (SNAZ): runoff and stormwater-mediated export from urbanized catchments within the greater Phoenix metropolitan area, Arizona, USA (2010-2012)
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). Data and expertise garnered by stormwater monitoring near the outflow of the IBW helped pave the way for a more expansive stormwater research effort facilitated by a leveraged grant from the National Science Foundation (DEB-0918457, NSF Eco
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
Model estimates of runoff, dissolved organic carbon, soil temperature and moisture for Elson Lagoon watershed, Alaska, 1981-2020
This dataset contains model estimates of dissolved organic carbon (DOC) yield (mg C/m^2) and runoff (mm), for surface and subsurface flows, soil temperature (degree C), and soil moisture (% of soil volume) for grid cells spanning the Elson Lagoon watershed in northwest Alaska. Daily air temperature, precipitation, and wind speed data from Utqiagvik airport were used for meteorological forcings for the daily simulation by the Permafrost Water Balance Model (PWBM) from 1981 to 2020. The DOC and runoff data files are organized by grid cell and month. The soil temperature and soil moisture files are organized by grid cell and day of year (DOY), and contain values for the first eight model soil layers, with centers of the layers at 1, 3, 8, 13, 23, 33, 45, 55 cm depth. The estimates are most useful for analyses of the dynamics of the watershed’s surface and subsurface runoff and DOC yield. Leachate DOC concentrations can be obtained using the gridded runoff and yield values. A manuscript describing the data and associated analysis has been accepted for publication in Environmental Research Letters (Rawlins et al., 2021).
Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [<em>Balsamo et al.,</em> 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files;</p> <p> ECMWF_Interim_Land_<strong><em>yyyy</em></strong>.tar.gz (Note: <strong><em>yyyy</em></strong> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a></p> <p> </p> <p>References:</p> <p>Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a></p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Particulate organic carbon (POC) concentration in meltwater runoff of Leverett Glacier, Russell Glacier, and Isunnguata Sermia, southwest Greenland (2009-2018)
<p>This dataset describes particulate organic carbon (POC) and particulate carbon (PC) concentrations of suspended sediments in the proglacial rivers of 3 land-terminating glaciers in the Kangerlussuaq area, Southwest Greenland: Leverett Glacier (LG), Leverett River; Russell Glacier (RG), Akuliarusiarsuup Kuua; and Isunnguata Sermia (IS), Isortoq River. Both the Leverett River and Akuliarusiarsuup Kuua are tributaries of the Qinnguata Kuussua (also known as Watson River). The data have already been part of 3 different publications (Lawson et al. 2014, Kohler et al. 2017, and Vrbická et al. 2022) but are archived here for the first time.</p> <p>POC data was collected for LG during the 2009 and 2010 melt seasons (Lawson et al. 2014) as well as 2015 (Kohler et al. 2017). For the 2018 melt season, only total carbon concentrations of suspended sediments (PC) is archived as opposed to POC (see Vrbická et al. 2022).</p>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
Seasonal N dynamics and fluxes of nitrogen in leachate and runoff from experimental rainfalls on fertilized and unfertilized lawns in Baltimore County, Maryland
The aim of this research was to examine the spatial and temporal variation in export control points of nitrogen on residential lawns (locations prone to mobilizing nitrogen during a rain event) and to examine if previously measured hydrobiogeochemical properties were predictive of N mobilization in lawns. This data set contains measurements of saturated infiltration rates, sorptivity, soil moisture, soil organic matter, bulk density, pH, soil nitrate, soil ammonium, N2O, N2 and CO2 fluxes from soil cores, nitrogen mineralization rates and fluxes of N in runoff and leachate from fertilized and unfertilized residential and institutional lawns. Study lawns were located at homes of people who agreed to volunteer their lawn for the study from a door knocking campaign. Four sampling houses were located in an exurban neighborhood in Baisman Run. Five sampling houses were located in a suburban neighborhood in Dead Run. Two sampling locations on institutional lawns were located at University of Maryland Baltimore County. At the exurban study houses and institutional lawns sites, we identified one hillslope to conduct sampling on. At the Dead Run houses we identified one hillslope on the front yard and one in the backyard as there were distinct locations that were not present in the exurban neighborhood. Locations within the yards for sampling were selected based on sampling conducted in October 2017. Locations were grouped into four categories based on have either high or low potential denitrification rates and high or low saturated infiltration rates (n=48). These locations were also distributed across yard types (exurban, suburban or institutional), fertilizer treatments, and hillslope location (top or bottom of hillslope). At each sampling location we ran a Cornell Sprinkle Infiltrometer to generate an experimental rainfall during which we collected runoff and leachate to quantity N flux. We also measure sorptivity and saturated infiltration rates. Volumetric water conten
ASR01 Short-term assessment of effects of burning on infiltration, runoff, and sediment and nutrient loss on Tallgrass Prairie using rainfall simulation, 1989
Rainfall simulation and overland flow experiments were performed on four plots at a single site on Konza from May to August, 1989. Two plots were treated with a late spring burn and two plots were left unburned. Five simulations were performed on burned plots and three simulatons on unburned plots. Each simulation consisted of a “dry run” followed 24 hours later by a 'wet run'. The dry run consisted of rainfall applied at an intesity of approximately 60 mm/hour. The wet run was the same as a dry run, except when the rainfall was complete, overland flow was applied directly at the top of the plots to simulate run off coming from upslope. Measurements taken include overland flow velocity, water application rate, runoff, hydrograph, water flow depth, sediment content, nitrogen and phosphorus content and percent ground cover (See A.B. Duell, Effects of burning on infiltration, overland flow, and sediment loss on tallgrass prairie, M.S. thesis, Kansas State University, 82pp. for further details).
Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0
<p>This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [<em>Rodell et al.,</em> 2004] LSM outputs, available at;</p> <p><a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a></p> <p>The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;</p> <p> GLDAS.2.0_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009)</p> <p> </p> <p>Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.</p> <p> </p> <p>Other necessary links associated with this database:</p> <p>RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a></p> <p> </p> <p>References:</p> <p>David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p> <p>Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a></p>
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre
<p>Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. This dataset is an extension to <em>Caravan</em>, a global community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 2023).</p> <p>The extension includes a subset of those hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy (Attribution 4.0 International; CC BY 4.0). In total, the dataset covers stations from 5356 catchments and 25 countries worldwide with a time series record from 1950 – 2023.</p> <p>GRDC is an international data centre operating under the auspices of the World Meteorological Organization (WMO) at the German Federal Institute of Hydrology (BfG). Established in 1988, it holds the most substantive collection of quality assured river discharge data worldwide. Primary providers of river discharge data and associated metadata are the National Hydrological and Hydro-Meteorological Services of WMO Member States.</p> <p>Reference:</p> <p>Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). <a href="https://doi.org/10.1038/s41597-023-01975-w">https://doi.org/10.1038/s41597-023-01975-w</a></p> <p><strong>Update:</strong></p> <p>With version 0.2 a bug has been fixed that affected the time series of four bands of all GRDC gauges in the GRDC extension. The affected bands were total_precipitation, surface_net_solar_radiation, surface_net_thermal_radiation and potential_evaporation, i.e. all features that are accumulated over the day, as per definition of ERA5-Land.<br>For details look at https://github.com/kratzert/Caravan/issues/26.</p> <p>Version 0.3: Data description file added.<br><br>Version 0.4: Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) in the meteorological forcing data and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").<br><br>Version 0.5: License overview of the respective countries has been added.<br>Dataset description has been modified and improved.<br><br>Version 0.6: The attribute tables are sorted alphabetically. Minor inconsistencies in the data description file have been corrected.</p> <p> </p> <p><strong>Dataset structure:</strong></p> <p>The dataset is provided in the following two file formats:<br>1. caravan-grdc-extension-csv.zip: provides the time series data as comma-separated text files (CSV) (downloadable as 8.8 GB zip archive)<br>2. caravan-grdc-extension-nc.zip: provides the time series data in the Network Common Data Form (NetCDF) (downloadable as 7.6 GB zip archive)</p> <p><strong>The data in the versions 0.1-0.3 are identical. Version 0.4 added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND.</strong></p> <p>Further details of the structure of the dataset are described in the data description file.</p>
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>
Observation based gridded annual runoff estimates over Victoria, Australia
<p>The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012. The methodology extended the R package <em>rtop</em> to allow <em>top-kriging</em> with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf</p> <p> </p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Stormwater runoff pollution of an existing catchment consisting essentially of apartment buildings in Braunschweig/Germany.
<p>This dataset includes stormwater runoff concentrations of pollutants (COD, TP, DP, NO<sub>3</sub>, NH<sub>4</sub>, TSS) taken from a sampling point in Braunschweig, Germany. The total catchment size is 5 ha with approximately 1.8 ha total imperviousness consisting essentially of apartment buildings from the 1970s and roads. This catchment was chosen for comparatively clear delimitation to different land uses considering location-independent results. Samples were taken as part of the research project TransMiT (https://www.transmit-zukunftsstadt.de/). More information including sampling and analytical methods are detailed in the corresponding journal paper "Dynamization of Urban Runoff Pollution and Quantity" and supplementary data, submitted to the MDPI-journal Water.</p> <p>Description of fields:</p> <p>- SamplingPoint:<br> - storm sewer: stormwater runoff sampled during individual rain events in a storm water sewer manhole receiving runoff from the entire catchment<br> - SamplingTime: time of sampling during individual rain event (CET)<br> - COD: measured value of chemical oxygen demand (COD)<br> - TP: measured value of total phosphorus (TP)<br> - DP: measured value of dissovled phosphorus (COD)<br> - NO<sub>3</sub>: measured value of nitrate (NO3)<br> - NH<sub>4</sub>: measured value of ammonium (NH4)<br> - TSS: measured value of total suspended solids (TSS)<br> - N/A: data not available<br> - UnitsAbbreviation: all data given in milligram per litre (mg/L)</p> <p>The data file is provided in comma separated format ("TransMiT.csv") and contains concentrations of all samples.</p> <p> </p>
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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.