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70 results for “river catchment”
LTER-Italy site Saldur River Catchment figure
<p>Geographical representation of the LTER-Italy site Saldur River Catchment (LTER_EU_IT_099) - DEIMS-ID <a href="https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1">https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1</a></p>
Mask of large scale river catchments
<p>The catchment mask provides information about the location of large scale river catchments on a global grid. Its purpose is the provision of a common reference for the computation of area averages, especially for the analysis of Earth System Model output.</p>
PIE LTER time series of nutrient grab samples from Ipswich River and Parker River watershed catchments, Masachusetts, with frequency ranging from weekly to monthly between 2001 and 2019.
Time series of nutrient grab samples collected by hand (i.e. not with the Sigma autosampler) with frequency ranging from weekly to monthly between 2001 and 2016. Sites include three headwater catchments of contrasting land use (forest, urban, wetland), and the mouth of the two main watersheds draining to the Plum Island Estuary (Ipswich and Parker R.). An additional time series was collected for a site in the Upper Ipswich at North Reading and at Fish Br. in Boxford – this sampling was ended in 2002. All Samples were analysed for NO3, NO2, NH4, PO4, TDN and DOC. Si was analyzed until 2002. Particulates, anions and TSS have been analyzed since 2006.
SBC LTER: Nutrient concentrations and algae cover in the Ventura River catchment, California, 2008
Results from these data were reported in: Klose, K., Cooper, S. D., Leydecker, A. D. and Kreitler, J. 2012. Relationships among catchment land use and concentrations of nutrients, algae, and dissolved oxygen in a southern California river. Freshwater Science, 2012, 31(3):908-927 doi: 10.1899/11-155.1 Data not reported here: Chlorophyll-a, physicochemical and land use parameters (e.g., land-use type, water depth, substratum size, % open canopy, and water velocity) were used in the paper's analysis and so were also collected, but are not reported here. Nutrient diffusing substrata (NDS) were deployed at 12 sites to assess the nutrient(s) limiting algal growth; these data are also not reported here. Macroalgal cover and stream nutrients are reported during spring and summer 2008 at 15 stream and estuarine sites in the Ventura River catchment in southern California, USA. Data were collected within a mosaic of undeveloped, agricultural, and urban areas to examine relationships among land use, nutrients, algae, and dissolved oxygen (see paper, linked below). This dataset reports major dissolved nutrients (phosphate, nitrate, ammonium) and total dissolved nitrogen and phosphorus, from May to September 2008, and percent cover of macroalgae (benthic and floating) at the same sites at the beginning and end of this period.
Geochemical sediment fingerprinting dataset from Oroua river catchment, New Zealand
<p>Geochemical dataset collected to determine key source contributions to overbank sediment deposition for specific particle size fractions as described in "Vale, S., Smith, H., Matthews, A., & Boyte, S. (2020). Determining sediment source contributions to overbank deposits within stopbanks in the Oroua River, New Zealand, using sediment fingerprinting. <i>Journal of Hydrology (New Zealand)</i>, <i>59</i>(2), 147-172." </p>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</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>
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>
Limitations of Catchment Area Estimates and River Basin Boundaries from CWC and WRIS
<p>The following files are included:<br> [Item 1 'share_stations.txt'] list of 100 stations used in this study and their attributes, pipe(|) delimited<br> [Item 2 'share_hydrosheds_rivers'] one shapefile of HydroSHEDS river network up to Farakka<br> [Item 3 'share_merit_rivers'] one shapefile of MERIT river network up to Farakka<br> [Item 4 'share_hydrosheds_reloc'] one shapefile of relocated stations, HydroSHEDS network<br> [Item 5 'share_merit_reloc'] one shapefile of relocated stations, MERIT network<br> [Item 6 folder 'share_bounds'] shapefiles of delineated catchment boundaries associated with each station (100 total)<br> [Item 7 'share_station_maps.pdf'] PDF file of maps showing location of each station and associated catchment boundary (100 pages)<br> </p>
Fig. 3 in The herpetofauna of the Cubango, Cuito, and lower Cuando river catchments of south-eastern Angola
Fig. 3. Map of the study area in south-eastern Angola, indicating surveyed sites for May‒June 2015 survey.
Fig. 9 in The herpetofauna of the Cubango, Cuito, and lower Cuando river catchments of south-eastern Angola
Fig. 9. Selective amphibians and reptiles from south-eastern Angola. A. Ptychadena taeniocelis; B. Xenopus petersii; C. Ptychadena cf. grandisonae; D. Ichnotropis spp; E. Causus cf. rasmusseni; F. Causus cf. rasmusseni; G. Boaedon cf. angolensis; H. Trachylepis cf. spilogaster.
Fig. 2 in The herpetofauna of the Cubango, Cuito, and lower Cuando river catchments of south-eastern Angola
Fig. 2. Map of the study area in south-eastern Angola, indicating surveyed sites for April 2013 survey.
Fig. 5 in The herpetofauna of the Cubango, Cuito, and lower Cuando river catchments of south-eastern Angola
Fig. 5. Cuito River Basin: A. Cuito River at Cuito-Cuanavale; B. Cuito River south of Menongue; C. lower Cuito River near village Rito; D. floodplain just north of Menongue; bottom - Source of the Cuito River surround by dry grassland (and base camp), wetlands around the source lake, and miombo woodlands on higher ground.
Figure 5 in Biodiversity, DNA barcoding data and ecological traits of caddisflies (Insecta, Trichoptera) in the catchment area of the Mediterranean karst River Cetina (Croatia)
Figure 5. Maximum likelihood phylogram based on a fragment of COI (DNA barcode region) showing the related relationships of the genus Glossosoma. The bootstrap values (BS) are marked on the branches in the order NJ/ML. BS values less than 80 are not shown. The groups delineated by ABGD approach are shown on the right side of the tree. Specimens which genomic DNA was extracted in this study are written in bold letter.
Figure 1 in Biodiversity, DNA barcoding data and ecological traits of caddisflies (Insecta, Trichoptera) in the catchment area of the Mediterranean karst River Cetina (Croatia)
Figure 1. Map of the study area with sampling stations. Names and corresponding abbreviations of the stations are listed in Table 1.
Figure 3 in Biodiversity, DNA barcoding data and ecological traits of caddisflies (Insecta, Trichoptera) in the catchment area of the Mediterranean karst River Cetina (Croatia)
Figure 3. MDS analysis of caddisfly fauna similarity at stations on the Cetina, the Ruda, the Grab and the Rumin rivers.
Figure 2 in Biodiversity, DNA barcoding data and ecological traits of caddisflies (Insecta, Trichoptera) in the catchment area of the Mediterranean karst River Cetina (Croatia)
Figure 2. Cluster analysis of caddisfly fauna similarity at stations on the rivers Cetina, Ruda, Grab and Rumin.
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
FIGURE 13 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment
FIGURE 13 Male and female thoracopods VIII of the four genera described for WA. (A, B) male ThVIII of Pilbaranella ethelensis; (C, D) male ThVIII of Fortescuenella serenitatis; (E, F) male ThVIII of Anguillanella callawaensis; (G, H) male ThVIII of Muccanella cundalinensis; (I) female ThVIII of Pilbaranella ethelensis; (J) female ThVIII of Fortescuenella serenitatis; (K) female ThVIII of Anguillanella callawaensis; (L) female ThVIII of Muccanella cundalinensis. Scale bar in mm
FIGURE 10 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment
FIGURE 10 Muccanella cundalinensis gen. et sp. nov., male holotype (A, B, D, F, H, I); female allotype (E, G); male paratype (C). (A) Antennula (dorsal view); (B) antenna (dorsal view); (C) Paragnath male WAMC57343; (D) labrum; (E) labrum female WAMC57341; (F) palp and mandible male holotype; (G) palp and mandible female allotype; (H) maxillule; (I) maxilla. Scale bar in mm.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.