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201 results for “Streamflow”
Marcell Experimental Forest breakpoint streamflow, 1962 - ongoing
This data publication contains breakpoint streamflow data collected from 1962-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The MEF was formally established in 1962 and contains six watersheds instrumented for hydrologic monitoring, each consisting of an upland portion and a peatland that is the source of a stream leaving the watershed. The watersheds and environmental monitoring at the MEF are part of a long-term research program on the hydrology and biogeochemistry of watersheds with uplands and northern peatlands.
Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow
This dataset includes data inputs from public sources, scripts and outputs to evaluate riparian vegetation reliance on groundwater across California from 2015 to 2020. This dataset accompanies the Rohde et al. paper titled, Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow. The provided scripts process groundwater, vegetation, climate, and streamflow input data from various sources. Further, all output data and statistical analyses are included.
Hydrologic response units (base units for PRMS streamflow model), Andrews Experimental Forest, 1993
Hydrologic Response Units are used as base units for the Precipitation-Runoff Modeling System (PRMS) streamflow model. Created by Alok Sikka as part of landscape runoff modeling.
Shope Fork of Coweeta Creek streamflow at the Coweeta Hydrologic Laboratory from 1934 to 1999
Coweeta Watershed 08 is Shope Fork, one of the four headwater streams forming Coweeta Creek which drains into the Little Tennessee River near Otto (Macon County) North Carolina. WS089 has 759.6 ha, entirely forested except for gravel roads servicing research sites, and ranges in elevation from 701.6 m at the weir to 1600 m at the peak of Albert Mountain. The majority of the watershed has been covered with mixed deciduous forest for the entire period of record except for small clearings associated with raingage and climate station sites and watershed treatments. The two largest treatments were cutting of all forest vegetation on 43.7 ha in 1963 and 59.6 ha in 1977. Natural regeneration revegetated both areas quickly with canopy closure occurring within 10 years or less.
Annual summaries of daily observations from the USGS Streamflow Gauging Station on the Altamaha River near Doctortown, Georgia, for 1932 to 2004
Daily averaged river discharge data were obtained from the United States Geological Survey for streamflow gauging station USGS 02226000 on the Altamaha River near Doctortown, Georgia. Processed tabular data were downloaded from the USGS real-time web server (http://waterdata.usgs.gov/nwis/) by the Georgia Coastal Ecosystems LTER project, documented, and standardized to metric units. Missing values of mean discharge were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated from daily values aggregated by year.
The CH-IRP data set: fortnightly data of δ2H and δ18O in streamflow and precipitation in Switzerland
<p>The data set contains δ<sup>2</sup>H and δ<sup>18</sup>O from fortnightly grab samples in streamflow and corresponding monthly precipitation derived from interpolation for 23 Swiss hydrological catchments.</p> <p>δ<sup>2</sup>H and δ<sup>18</sup>O in streamflow are provided as one ASCII file for each station. Additionally to these time series each of the files contains the Deuterium excess, the streamflow conditions preceding the sampling as well as the z-scores indicating if a sample might be a statistical outlier, assuming the data are normally distributed. All files contain further information for each sample whether double measurement was performed in the lab comments indicating for instance special sampling conditions or storage-related issues that could alter the isotopic composition due to fractionation.</p> <p>For each data file for streamflow data there is a corresponding ASCII file for catchment precipitation. These contain the interpolated δ<sup>2</sup>H and δ<sup>18</sup>O in precipitation for the catchment as well as the source data that were used to derive the interpolated values.</p> <p>Associated data that can be useful for applications are provided. This is mean areal precipitation and temperature (ASCII files) as well as the topographic catchment boundaries (shape files).</p>
The Coastal Streamflow Flux in the Regional Arctic System Model
<p>The Arctic coastal streamflow flux is an important driver of dynamics in the coupled ice-ocean system. We have developed a new streamflow routing model (RVIC), coupled within the Regional Arctic System Model (RASM), to simulate the coastal freshwater flux. RASM is a high-resolution regional Earth system model applied over a Pan-Arctic model domain. This dataset includes distributed daily coastal streamflows between 1979 and 2014 for the RASM domain. In Hamman et al. (2017) we demonstrate the performance of RASM and RVIC-simulated streamflow in fully coupled model simulations and discuss the improvements this derived dataset has, relative to existing distributed datasets in the Arctic.</p> <p>See the following references for further details on this dataset:</p> <p>Hamman, J., B. Nijssen, A. Roberts, A. Craig, W. Maslowski, and R. Osinski, 2017: The Coastal Streamflow Flux in the Regional Arctic System Model. Journal of Geophysical Research: Oceans, doi:10.1002/2016JC012323.</p> <p>Hamman, J., B. Nijssen, M. Brunke, J. Cassano, A. Craig, A. DuVivier, M. Hughes, D.P. Lettenmaier, W. Maslowski, R. Osinski, A. Roberts, and X. Zeng, 2016: Land surface climate in the Regional Arctic System Model. Journal of Climate, doi:10.1175/JCLI-D-15-0415.1.</p>
Reconstructed streamflow for Indian sub-continental river basins, 1951-2021
<p>This is a reconstructed daily and monthly streamflow for 1951-2021 period for the Indian sub-continental river basins. We used a high-resolution vector-based routing model (mizuRoute) to generate streamflow at 9579 stream reaches in the sub-continental river basins. The resulting dataset provides valuable insights into the observed streamflow variations in the Indian subcontinental river basins, aiding hydrological research, water resource management, and understanding the impacts of climate change on river systems. The version 1 (v1) contains only monthly streamflow timeseries. We have added both daily and monthly streamflow timeseries for each river segment in the version 2 (v2) of the current repository.</p>
EStreams: An Integrated Dataset and Catalogue of Streamflow, Hydro-Climatic Variables and Landscape Descriptors for Europe
<p><strong>Check out the paper at: https://doi.org/10.1038/s41597-024-03706-1</strong><strong> (published at Nature Scientific Data).</strong></p> <p>EStreams is an extensive catalog of openly available stream records, along with a dataset of hydro-climatic variables and landscape descriptors for +17,000 European catchments. It spans up to 120 years of records. The catalog provides detailed guidance, enabling users to directly access the sources of streamflow used. Additionally, the dataset comprises catchment-aggregated hydro-climatic indices and signatures, as well as landscape attributes and meteorological records.</p> <p><strong>Updates</strong></p> <ul> <li>30 June 2025: version 1.3<br> <ul> <li>General: <ul> <li>Added the file: "estreams_catchments_hierarchy.csv" file, which defines pairwise hierarchical relationships between sub-catchments and their containing catchments using the "sub_catchment" and "catchment" columns. It can be usefull when making distributed hydrological modelling, and one needs to define the topology of the network. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page was also updated with the new code for computing and saving such information <a href="https://github.com/thiagovmdon/EStreams/blob/main/code/python/E_complementary_extra_codes/estreams_extras_nested_catchments.ipynb" target="_blank" rel="noopener">here</a>. "EStreams/code/python/E_complementary_extra_codes". </li> </ul> </li> <li>In the "streamflow_indices/" folder we: <br> <ul> <li>Updated all indices for all 270 catchments from the Nordrhein-Westfalen region in Germany (basin_id starting with "DENW").<em> </em></li> </ul> </li> <li>In the "hydroclimatic_signatures.csv" file we: <ul> <li>Updated all streamflow signatures (not climatic signatures) for all 270 catchments from the Nordrhein-Westfalen region in Germany (basin_id starting with "DENW"). </li> </ul> </li> </ul> </li> </ul> <ul> <li>11 February 2025: version 1.2<br> <ul> <li>General: <ul> <li>Added the file: "estreams_geologycontinental_attributes.csv", which encompass geological attributes from a European based source: International Hydrogeological Map of Europe (IHME), version 11, downloaded at www.bgr.bund.de, scale: 1:1,500,000. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page was also updated with the new code. </li> </ul> </li> <li>In the "estreams_meteorology_density.csv" file we: <br> <ul> <li>Replaced "NaN" to "0" in the row of catchment CZ000043.</li> </ul> </li> </ul> </li> </ul> <ul> <li>22 October 2024: version 1.1 <br> <ul> <li>In the "estreams_hydrometeo_signatures.csv" file we: <ul> <li>Corrected the field "p_seasonality". The function previously provided in the hydroanalysis python package had a mistake, and it is now corrected. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page and the <a href="https://github.com/dalmo1991/HydroAnalysis" target="_blank" rel="noopener">hydroanalysis GitHub</a> page were now updated with the correct formulation. </li> </ul> </li> </ul> </li> </ul> <ul> <li>07 August 2024: version 1.0 <ul> <li>General: <ul> <li>Version of the official paper release.</li> </ul> </li> <li>In the "estreams_gauging_stations.csv" file we: <ul> <li>Updated the field "nested_catchments" to include also the basin itself, for some cases where the basin was not yet included.</li> </ul> </li> </ul> </li> </ul> <ul> <li>15 June 2024: version 0.2 <ul> <li>General: <ul> <li>Added 2,114 new basins: 1,813 in France, 111 in Iceland, 13 in Estonia and 18 in Serbia; exclusion of a total of 31 stations from the previous release due to overlap with the new stations (Serbia and Iceland GRDC). </li> <li>Fix the name of the meteorological variable "mean air pressure at sea level" from "sp_min" to "sp_mean" in all the meteorological time-series files. </li> <li>Inclusion of an appendix folder with licenses information and a further description of the classes names included for land cover and lithology.</li> <li>Fix the name of the monthly streamflow indices from "mothnly" to "monthly".</li> <li>Correction of the name of the static topographical attributes from "estreams_terrain_attributes.csv" to "estreams_topography_attributes.csv".</li> </ul> </li> <li>In the "estreams_gauging_stations.csv" file we: <ul> <li>Renamed the fields "area", "area_calc" and "area_perc" to respectivelly "area_official", "area_estreams" and "area_rel" for consistency. </li> <li>Added the fields "nested_catchments", "num_days_reliable", "num_days_noflag", "num_days_suspect" and "gauge_flag" were added.</li> <li>Updated the field "gauges_upstream" to include also the basin itself.</li> </ul> </li> <li>In the "estreams_streamflow_catalogue.csv" file we: <ul> <li>Added the field "download_method".</li> <li>Updated the fields "observations" and "references".</li> </ul> </li> <li>In the "estreams_meteorology_coverage.csv" file we: <ul> <li>Updated the names of the fields to: stations_num_{p_mean, t_mean, t_min, t_max, sp_mean, rh_mean, ws_mean, swr_mean} and stations_dens_{p_mean, t_mean, t_min, t_max, sp_mean, rh_mean, ws_mean, swr_mean}, as it is presented in the manuscript. </li> </ul> </li> </ul> </li> </ul>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Data for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"
<p>Data repository for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"</p>
Code and Data to "Quantile regression for temporal streamflow modeling"
<p>This is the accompanying code to "Quantile regression for temporal streamflow modeling", which is part of the manuscript "The Role of Process Heterogeneity in Statistical Modeling", which was submitted to the Austrian Journal of Statistics. </p> <p> </p> <p>The data used in this publication is fully accessible through the <a href="https://doi.org/10.5194/essd-13-4529-2021" target="_blank" rel="noopener">LamaH-CE</a> dataset. The two scripts "functions_create_data.R" and "create_data.R" will create the final dataset used for modelling. </p> <p>"functions_modelling.R" provide the functions for tuning the XGBoost model and computing the SHAP values. An example script is also attached (calc_predictions_shap.R). "analyzing_results.R" and "error_metrics.R" will produce the final output used in the manuscript. Finally, two plots produced in the script are added as pdf. </p> <p>All data analysis was performed in R, and we want to acknowledge the following packages: <a href="https://dplyr.tidyverse.org/">dplyr</a>, <a href="https://tidyr.tidyverse.org/">tidyr</a>, <a href="https://www.jstatsoft.org/v40/i03/">lubridate</a>, <a href="https://purrr.tidyverse.org/">purrr</a>, <a href="https://doi.org/10.18637/jss.v033.i01">glmnet</a>, <a href="https://cran.r-project.org/web/packages/xgboost/index.html">xgboost</a>, <a href="https://CRAN.R-project.org/package=shapr">shapr</a>, <a href="https://CRAN.R-project.org/package=Metrics">Metrics</a>, <a href="https://CRAN.R-project.org/package=gridExtra" target="_blank" rel="noopener">gridExtra</a>, <a href="https://doi.org/10.18637/jss.v014.i06">zoo</a> and <a href="https://CRAN.R-project.org/package=wesanderson">wesanderson</a>. </p> <p> </p>
Data and codes related to the article: Horner et al. Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations
<p>The data files and R code files are related to the article Horner et al. "Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations" published in Hydrological Processes.</p> <p><strong>Data: </strong></p> <p>1/ Q_Craponne.txt</p> <p>Original streamflow time series of Craponne hydrometric station which was used to generate the synthetic stage time series using the theoretical equations of the 5 fictive hydrometric stations.</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Time (%Y-%m-%d %H:%M)<br> Column 2 ==> Streamflow (in m3/s)</p> <p>2/ h_Mercier.txt</p> <p>Original stage time series for Mercier hydrometric station</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Time (%Y-%m-%d %H:%M)<br> Column 2 ==> Stage(in mm)<br> Missing value code: NA</p> <p>3/ Gaugings_Mercier.txt</p> <p>Gaugings (date, stage, streamflow and associated uncertainty) of the Mercier hydrometric station before and after hydraulic control change which occured in November 2013.</p> <p>Column separator: semi-colon (;)<br> Column 1 ==> Date (%Y-%m-%d)<br> Column 2 ==> Stage_m (in m)<br> Column 3 ==> Streamflow_m3_per_s (in m3/s)<br> Column 3 ==> Uncertainty (unitless)</p> <p><strong>R codes:</strong></p> <p>0/ other ressources:<br> All the ressources related to the bayesian estimation of the rating curve can be found on github: <a href="https://github.com/BaM-tools">https://github.com/BaM-tools</a><br> Time aggregation of time series was done using the tAgg R package available on github: <a href="https://github.com/IvanHeriver/tAgg">https://github.com/IvanHeriver/tAgg</a></p> <p>1/ functions.R<br> This files contains several functions. Comments within the file explains each of the function:</p> <ul> <li>hydraulicEquations(): returns a list of the theoretical equations for the rating curves of the five fictive cases</li> <li>hydraulicEquationsInverter(): inverts of a theoretical rating curve (given a function Q=f(h), it returns a function h=f(Q))</li> <li>computeAM30(): computes the AM30</li> <li>generate_nonsyst_errors(): generates a matrix of non systematic errors</li> <li>generate_syst_errors(): generates a matrix of systematic errors</li> <li>get_resampling_indices_from_periodicity(): returns the indices of the resampling time steps used to generate systematic errors given a time vector and a periodicity</li> </ul> <p>Some of the code require the following packages: dplyr and RcppRoll</p> <p>2/ examples.R<br> This file contains some code illustrating the usage of the functions in the "functions.R" file.</p> <p> </p>
The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components
<p>The excel file <em>Attributes_manuscript.csv</em> contains the mean annual variables and the catchments' attributes used in the manuscript: "<strong>The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components</strong>". Details of each attributes are indicated in the .txt file <em>Attributes_description.txt</em>.</p>
08NL007 daily streamflow dataset from Water Survey of Canada
<p>Sample daily streamflow data used in Confluence article on circular statistics in hydrology.</p>
WaterBench-Iowa: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting
<p>WaterBench-Iowa is a comprehensive benchmark dataset for streamflow forecasting. It follows FAIR data principles that are prepared with a focus on convenience for utilizing in data-driven and machine learning studies and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies. This dataset has a high temporal and spatial resolution with rich metadata and relational information, which can be used for varieties of deep learning and machine learning research. To some extent, WaterBench makes up for the lack of a unified benchmark in earth science research. We highly encourage researchers to use the WaterBench for deep learning research in hydrology.</p>
Total water storage anomalies (TWSA) and streamflow in the Mississippi River Basin for 01/2003 to 12/2016
<p>This dataset includes observations and simulations of total water storage anomalies (TWSA) as well as streamflow that were used in the paper Schulze et al. (2024). The data covers the time period from 01/2003 to 12/2016. The TWSA [in millimeters] are basin averages, while the streamflow data [in cubic meters per second] refer to 68 gauge stations distributed over the Mississippi River Basin. </p> <p>The TWSA observations are derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The streamflow observations are provided by the Global Runoff Database (GRDC).</p> <p>The simulations are uncalibrated model runs of the WaterGAP Global Hydrology Model (WGHM; based on WaterGAP version 2.2d) as well as an Open Loop Simulation and simulations by assimilating GRACE-derived TWSA and/or streamflow observations. The TWSA observations are assimilated on a 4°x4° grid. The streamflow observations are assimilated at (1) all gauge stations with mean observed streamflow above 500 cubic meters per second, (2) all gauge stations with mean observed streamflow between 10 and 100 cubic meters per second, and (3) at randomly selected gauge stations.</p> <p>All files are packaged into a single ZIP archive for easy access and distribution.</p> <p>More details about the dataset and the assimilation are provided in:<br>Schulze, K., Kusche, J., Gerdener, H., Döll, P., Müller Schmied, H. (2024): Benefits and Pitfalls of GRACE and Streamflow Assimilation for Improving the Streamflow Simulations of the WaterGAP Global Hydrology Model. Submitted to Journal of Advances in Modeling Earth Systems (JAMES).</p>
Identification and Regionalization of Streamflow Routing Parameters for the HLM Hydrological Model in Iowa
<p>The tables contained the metrics and the peak flows estimated using HLM under three different parameterizations. </p>
Daily streamflow time series in the Tirgua River Basin (Paso Viboral), Venezuela
<p>Daily streamflow time series derived from the raw data provided by the National Institute of Meteorology and Hydrology (INAMEH) for the period from January 1963 to December 1996 (34 years). Site: Paso Viboral, San Carlos, Cojedes [-68,605964° W; 9,719648° N]. Data frame with year, month, day and flow in m<sup>3</sup>/s. </p>
Training Deep Learning Models to Estimate SWAT Parameters using Streamflow Observations
<p>This folder provides the simulation and observational data</p> <p>Simulation data using SWAT (1000 realz)<br> Train, Val, and Test splits (80/10/10)<br> Observational data for ARW (WY2000-2016)</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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