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105 results for “river flow”
2021-2022 West False River Emergency Drought Barrier water quality, flow, and fish monitoring
To manage the critically low 2021 water supply for beneficial uses, DWR installed the temporary emergency drought barrier (EDB) on West False River in the Sacramento–San Joaquin Delta (Delta), approximately 5 miles south of Rio Vista, California, in Contra Costa County in June 2021. To monitor the effectiveness and impacts of the EBD, a monitoring program was initiated to track changes in hydrodynamics, water quality, fish, harmful algal blooms, and aquatic weeds in the vicinity of the EDB. The EDB was left in place during the winter of 2021-2022 and removed in fall of 2022. This data set includes all data collected as part of that monitoring program and subsets of ongoing monitoring programs that were used in the final effectiveness report for the EDB.
Water flow velocity data, Shark River Slough (SRS) near Black Hammock island, Everglades National Park (FCE LTER), South Florida from October 2003 to August 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Black Hammock tree island, Everglades National Park using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to March 2021
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Gumbo Limbo Island, Everglades National Park (FCE) from October 2003 - December 2018
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Gumbo Limbo Island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Satinleaf Island, Everglades National Park (FCE LTER) from July 2003 to December 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Satinleaf tree island, Everglades National Park, using Sontek Agronaut water flow sampler.
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
3-hourly water level records (selected high flow events) for the River Garry at Invergarry (Inverness-shire), Scotland
<p>3-hourly records of stage (water level) for the River Garry at Invergarry (Inverness-shire), Gauge A2, for selected high-flow events 1936-1940. Extracts from a record spanning the period 1936-10-01 to 1944-09-30.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records and with the assistance of local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until 2011.</p>
River flows and nutrient discharges to the Atlantic ocean basin
<p>This dataset includes i) River flows and ii) nutrient discharges to the Atlantic ocean basin:</p> <p><strong>River flow</strong> data are a subset of the WaterGAP 2.2d model (Monthly data on a 0.5° x 0.5° grid between 90°N and 60°S. 1901–2016) (Müller Schmied et al. 2020)</p> <p>The original watergap2.2d data is provided in netcdf format. Our postprocessing includes the selection of the coastal cells and the extraction of the monthly values in these cells. The resulting dataset is provided as a shapefile (Global_YearMonthly_River_flow_watermap22_coast.shp), including Date, latitude and longitude of the coastal cell’s centroids and the discharge values (m3s-1).</p> <p>Watergap2.2d outputs offers a very good spatio-temporal extent and resolution, and according to the Müller Schmied et al. 2020, the validation results for streamflow (or discharges values) are reasonably satisfactory, although there is some spatial variability in the performance results. Moreover, the recently published “Global Freshwater Fluxes into the World's Oceans (GRDC, 2021)” product and paper, uses the yearly outcomes of this model, which has also supported our selection.</p> <p><strong>River nutrient discharges</strong> are a subset of observations from the “Global River Water Quality Archive”. (<a href="https://essd.copernicus.org/preprints/essd-2021-51/">Virro et al, 2021</a>), that among the publicly available and downloadable datasets, gathers the highest number of observations as includes data from different international and national databases.</p> <p>The GRQA data is provided as csv files (one file for each nutrient). These files have been processed to subset only observations at stations near the coastline. To do this a intersection between station locations and a buffer of 0.2 degrees around the coastline has been made. For each nutrient, a shapefile with the subsetted observations is provided.</p> <p>All shapefiles provided are accompanied by a “.qmd” file that includes metadata information in QGIS 3 format.</p>
Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This repository corresponds to all the input and output files that were used in the study reported in:</p> <ul> <li>Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., & Blumstein, D. (2024). Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers. Geophysical Research Letters, 51, e2024GL109220. <a href="https://doi.org/10.1029/2024GL109220" target="_blank" rel="noopener">https://doi.org/10.1029/2024GL109220</a>.</li> </ul> <p>When making use of any of the output files of this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Main goals of the publication</strong></p> <p>The corresponding work aims to quantify peak flow event durations at an hourly time scale and their impact on high-frequency river sampling requirements using sampling ratios. The analysis is performed over the Mississippi basin using hourly USGS gages over 2010-2022.</p> <p>The findings derived from these output files have direct implications for future satellite missions concerned with capturing high-frequency dynamics in rivers, including flood events.</p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Daily river flow records for the River Lochy (Mucomir Cut) at Gairlochy, Scotland
<p>Daily river flows of the River Lochy (Mucomir Cut) at Gairlochy. Approx grid reference NN183840</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records.</p> <p>Record spans the period 1935-10-01 to 1944-09-30 with no gaps.</p> <p>Units cubic feet per second. Based on a calibration developed from curret meter measurements applied to stage measurements taken once per day.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>The catchment is in many respects natural, but Loch Lochy has the Caledonian Canal running through it, completed in 1822.</p> <p>Subsequent to McClean's colletion of these records, the flow of the River Lochy via the Mucomir Cut was harnessed by the North of Scotland Hydro-Electric Board by the construction of the Mucomir Power Station, commissioned in 1962.</p> <p>At the time of writing (2024), the Scottish Environment Protection Agency (SEPA) operate gauges in the Lochy system at Gairlochy and Camisky.</p>
Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations
<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network’s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station’s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station’s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>
River flow and catchment rainfall records for the River Garry at Invergarry (Inverness-shire), Scotland, 1913-1915
<p>Daily mean flows for the River Garry at Invergarry (Inverness-shire), Gauge A1, spanning the period 1913-01-01 to 1915-12-31.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records, assisted by local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until May 2011.</p>
ADCP data of ice-covered and open-channel (macro-turbulent) flow, Pulmanki River, 2016-2020
<p>README of ADCP_data_Lotsari_et_al_Water_opened.zip</p> <p><br> The ADCP data was the basis of the following paper:<br> Macro-turbulent flow and its impacts on sediment transport potential of a subarctic river during ice-covered and open-channel conditions <br> Eliisa Lotsari (1, 2), Michael Dietze (3), Maria Kämäri (4), Petteri Alho (2,5), Elina Kasvi (6,2)</p> <p>1 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, FI-80101, Joensuu, Finland. eliisa.lotsari@uef.fi<br> 2 Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Turku, Finland.<br> 3 Section 4.6 Geomorphology, German Research Centre for Geosciences GFZ Potsdam, D-14473 Potsdam, Germany. mdietze@gfz-potsdam.de<br> 4 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland. maria.kamari@ymparisto.fi<br> 5 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430, Masala, Finland. mipeal@utu.fi<br> 6 Turku University of Applied Sciences, Joukahaisenkatu 3, FI-20520, Turku, Finland. elina.kasvi@turkuamk.fi</p> <p>(Note: During the time of data gathering, Maria Kämäri worked at the University of Eastern Finland, and Elina Kasvi at the University of Turku)</p> <p>The data set has been measured with Sontek M9 or S5 sensors, depending on the time step (Table 1).</p> <p>Table 1. The measurement times, their acronyms (applied in the above mentioned publication)<br> and applied sensors. In the acronyms of the measurement times W=winter low flow period, S=spring<br> (snow-melt flood period), A=autumn low flow period. These information are presented also<br> in the Table 1 of the above-mentioned publication.<br> Date Acronym Sensor<br> 17.2.2016 W2016 M9<br> 25.5.2016 S2016 S5 <br> 10.9.2016 A2016 M9<br> 16.2.2017 W2017 M9 <br> 31.5.2017 S2017 M9 <br> 9.9.2017 A2017 M9 <br> 9.2.2018 W2018 M9 <br> 23.5.2018 S2018 S5 <br> 8.9.2018 A2018 S5 <br> 8.2.2019 W2019 M9 <br> 21.5.2019 S2019 M9 <br> 6.2.2020 W2020 M9 </p> <p>RiverSurveyor Live software, and its most recent version, was used each time.<br> The measurements have been done at Pulmanki River (69°55'59.09" N; 28° 2'34.32" E), Northern Finland, during 2016 - 2020. <br> The data is in directories of corresponding measurement times. The measurement<br> locations cs1, cs2, cs3, cs4, csA, csB and csC can be found in the paper (Figs. 1 and 2). <br> The data is in raw Matlab file format, as exported from the RiverSurveyor Live software.<br> The coordinate system is ENU.</p> <p>When used, the referencing to the paper and DOI ( 10.5281/zenodo.3855035 ) are required.</p>
Morphodynamic stability of river and tidal bifurcations around bars tested in the Fast Flow Facility
<p>Multithread rivers such as the Jamuna and Mekong have networks of channels and bars that change with every flood. Tidal systems such as the Scheldt, Humber and Columbia estuaries and short tidal basins in the Wadden Sea and in Florida, have perpetually changing and interacting channels and shoals formed by ebb and flood currents. Current models fail to forecast these natural dynamics, yet main channels are economically important shipping fairways, whilst shoal areas that emerge and submerge daily are ecologically valuable habitats. Human interference, changing river discharge and sealevel rise threaten all functions. Furthermore, there are strong indications that fairway deepening leads to reduced urban safety due to enhanced flow resistance by groynes in rivers and enhanced tidal range in estuaries (e.g. Bolla Pittaluga et al. 2015 in AWR, Seminara et al., in EH 2011). This enhances dike failure risk during low water level and flooding during high water level. We urgently need dynamic forecasting models to optimise management strategies for these multiple functions (Wang et al. 2012 in Ocean Coastal Manage., Coco et al. 2013 in Mar. Geol.).</p> <p>Here we target firstly river bifurcations and secondly the mutually evasive ebb- or flood-dominated channels that form around bars and are found in all sandy tidal systems in the world (van Veen 1950/2002 in J. R. Dutch Geograph. Soc.). The cause for the mutual evasion is still incompletely understood despite the fact that they also appear in our numerical model results and experiments (Canestrelli et al., in JGR 2010; Kleinhans et al. 2015 in JGR). The nodes where ebb and flood channels connect can be seen as asymmetric bifurcations where one channel is preferred during ebb and the other during flood. Such bifurcations are critical elements that partition flow and sediment through the channel network, govern bar merging and splitting and are locations where bed steps form in shipping lanes, as in river bifurcations. Stability and equilibrium configurations are mostly unknown for tidal bifurcations except for one recent theory (Wang et al in prep.). In particular, we have a fair understanding of the tidal dynamics, but this is incomplete for the morphodynamics, especially related to understanding the sediment division at the bifurcation.</p> <p>We take advantage of the better but yet incomplete understanding of river bifurcations. The stability of river bifurcations has been studied for two decades in fieldwork, experimentation, linear stability theory and numerical modelling (e.g. Wang et al. 1995, JHR, see review in Kleinhans et al. 2013, ESPL) and our recent theory (Bolla Pittaluga et al. 2015 in GRL) synthesises many of the earlier results as follows: In bedload-dominated rivers, symmetrical bifurcations are unstable and develop towards a highly asymmetrical division of discharge and sediment. The same is the case for suspended sediment-dominated rivers, but the theory predicts stable bifurcations for intermediate sediment mobility. However, there is very little data for conditions intermediate between low and high mobility rivers. Moreover, we have no idea whether bifurcations in reversing tidal flow are unstable for similar configurations and conditions as in rivers. Here we mean configurations that are entirely free of topographic forcings on the flow: straight channels split into two channels over some length and depth.</p> <p>Our objective was therefore to experimentally investigate bifurcation stability in a range of sediment mobilities in unidirectional flow and reversing tidal flow ceteris paribus.</p>
Climate impact_River flow_Sweden
<p>Climate-impact ensemble of River flow (m<sup>3</sup> yr<sup>-1</sup>) for 12 selected hydropower plants in Swedish rivers, as well as the total river discharge to the Swedish coast. The dataset include modelled time-series from 18 ensemble members with daily values from 1981-2100, calculated with the HYPE model code (HYPE_version_4_8_0) in the S-HYPE model set-up (s-hype2012_version_2_0_0). Model code can be downloaded from: http://hypecode.smhi.se/ and climate projections from ESGF at https://www.cordex.org/. The data is in Excel format with one sheet per climate projection.</p>
hydropower impact_river flow_Sweden
<p>River flow (m<sup>3</sup> yr<sup>-1</sup>) for 12 selected hydropower plants in Swedish rivers as well as the total river discharge to the Swedish coast. The dataset include both Naturalised River Flow and Regulated River Flow, in daily time-series from 1981-2010, calculated with the HYPE model code (HYPE_version_4_3_1) in S-HYPE model set-ups (QR: s-hype2012_version_1_2_1 resp. QN: s-hype2012QN_version_1_2_1). Model code can be downloaded from: http://hypecode.smhi.se/ and model results with high spatial resolution can be downloaded from http://vattenwebb.smhi.se/. The results for the whole country of Sweden have been published in: Arheimer, B. and Lindström, G. 2014. Electricity vs Ecosystems – understanding and predicting hydropower impact on Swedish river flow. Evolving Water Resources Systems: Understanding, Predicting and Managing Water–Society Interactions. Proceedings of ICWRS2014, Bologna, Italy, June 2014; IAHS Publ. 364:313-319.</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data published in manuscript "Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4" by Castro-Morales et al.
<p>This data is published in the manuscript<strong>:</strong></p> <p>Castro-Morales, K., Canning, A., Körtzinger, A., Göckede, M., Küsel, K., et al. (2022). Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO<sub>2</sub> and CH<sub>4</sub>. <em>Journal of Geophysical Research: Biogeosciences</em>, 127, e2021JG006485. <a href="https://doi.org/10.1029/2021JG006485">https://doi.org/10.1029/2021JG006485</a>.</p> <p>The data contains the water properties and gases data measured at a site in Ambolikha River, meteorological data measured at an eddy covariance tower located in the neighbor floodplain, and data from the analysis of dissolved organic matter in river water samples. The data was collected between 26 June, 2019 and 02 August, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_Ambolikha_River.txt" should be read before accessing the data. The authors recommend downloading Version 2.0 because it is the most up to date data.</p> <p>For questions contact the main and corresponding author Dr. Karel Castro-Morales at: karel.castro.morales@uni-jena.de</p>
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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)
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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.