nonCciKeyword

Catchment

8 record(s)

 

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  • The file consists of data sets from Kwale County, Kenya that describe biophysical characteristics of the catchment overlaid as layers. These include Basin, Sub-basins extent, Soil, DEM, Landuse, Slope, Rivers, Outlets and Monitoring Points. The data are in raster, shapefile, polygon, polyline and point format.

  • BGS GeoScour provides river scour susceptibility information for Great Britain using a three-tiered data provision allowing increasing levels of understanding at different resolutions from catchment to local (channel/reach) scales. GeoScour includes 11 GIS layers, providing information on the natural characteristics and properties of catchment and riverine environments for the assessment of river scour in Great Britain. The data product fills a gap in current scour modelling, with the input of geological properties. It provides an improved toolkit to more easily assess and raise the profile of scour risk, now and in the future, to help infrastructure providers and funders prioritise resources, identify remedial works to preclude costly and prevent disruptive failures. The data product has broad applications through its adaptation to suit multiple types of asset likely to be affected by fluvial erosion. The GeoScour Data Product is designed to be used by multiple stakeholders with differing needs and therefore, can be interrogated at a number of levels. Tier 1 data provides a summary overview of the catchment characteristics, typical response type, and evolution. It can be used as a high-level overview for incorporation into catchment management plans, national reviews and catchment comparisons. Tier 2 data are available as smaller catchment areas and focusses on providing data for more detailed catchment management, natural flood management and similar uses. It analyses geological properties such as flood accommodation space, catchment run-off potential, and geomorphology types, as well as additional summary statistics of key environmental parameters such as protected sites and urban coverage. Tier 3 data provide the detailed riverine information that is designed to be incorporated into more complex river scour models. It provides the baseline geological context for river scour development and processes and identifies important factors that should be considered in any scour model. Factors such as material mineralogy, strength and density are key properties that can influence a river’s ability to scour. In addition, an assessment of river fall, sinuosity and flood accommodation space is also provided. This data is of use to all users assessing the propensity for river scour for any given reach of a river across Great Britain. Tier 1 and 2 data are available with an OGL, Tier 3 data is licenced.

  • Hazards data in Sichuan (Dechang, Anning River catchment), China. Data include rainfall, earthquake, river catchment, boundary, geological map, soil map, land-cover map, road-map, DEM.

  • This dataset includes catchment stream inflow and outflow rates, secchi depth, chlorophyll, phytoplankton counts and nutrient concentrations for the lake, inflow, outflow and groundwater spring. The measurements are from a PhD research project at Rostherne Mere in Cheshire. These data were collected to show the relationship between the catchment hydrology and in-lake nutrient loads for assessment of the current catchment nutrient budget. The monitoring study covered a period from January 2016 to January 2017. All data is presented with date, flow rate, nutrient and chlorophyll concentrations and phytoplankton species abundance. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1]. Full details about this dataset can be found at https://doi.org/10.5285/5c6b2bcb-6b10-4c57-a595-ce94a655e709

  • This data set comprises of hourly water quality monitoring and flow data of a site within the River Loddon catchment, UK, from September 2017 to September 2018. Parameters measured were temperature, conductivity, pH, ammonium, turbidity, dissolved oxygen, UV-Vis spectral scan from 197-720nm. Daily samples were also taken at 9am GMT and occasional storm samples were taken hourly and then analysed in the laboratory for pH, conductivity, turbidity, total suspended solids, non-purgeable organic carbon, UV-Vis spectral scan from 200-800nm and 12 pesticide concentrations: 2-4-D, Bentazone, Carbendazim, Carbetamide, Chlorotoluron, Clopyralid, MCPA, Mecoprop, Metaldehyde, Propyzamide, Quinmerac and Metazachlor. This data was created as part of the TWENTY65 project, funded by the Engineering and Physical Sciences Research Council (Grant number: EP/N010124/1) and with some additional funding from Affinity Water and Syngenta. Full details about this dataset can be found at https://doi.org/10.5285/331659d7-da72-48a2-9b52-63c003557990

  • This dataset is model output from the GR4J lumped catchment hydrology model. It provides 500 model realisations of daily river flow, in cubic metres per second (cumecs, m3/s), for 303 UK catchments for the period between 1891-2015. The modelled catchments are part of the National River Flow Archive (NRFA) (https://nrfa.ceh.ac.uk/) and provide good spatial coverage across the UK. These flow reconstructions were produced as part of the Research Councils UK (RCUK) funded Historic Droughts and IMPETUS projects, to provide consistent modelled daily flow data across the UK from 1891-2015, with estimates of uncertainty. This dataset is an outcome of the Historic Droughts Project (grant number: NE/L01016X/1). The data are provided in two formats to help the user account for uncertainty: (1) a 500-member ensemble of daily river flow time series for each catchment, with their corresponding model parameters and evaluation metric scores of model performance. (2) a single river flow time series (one corresponding to the top run of the 500), with the maximum and minimum daily limits of the 500 ensemble members. Full details about this dataset can be found at https://doi.org/10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e

  • This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of daily mean river flow (m3/s) for 260 catchments, for the following time periods: historical baseline (HISTBS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099). It also includes a baseline period (BS: 1975-2005). The catchments correspond to locations of NRFA gauging stations (http://nrfa.ceh.ac.uk/). The data were produced as part of MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity), which was a UK NERC-funded research project (2014-2017) that developed a risk-based approach to drought and water scarcity. Full details about this dataset can be found at https://doi.org/10.5285/f6cac471-7d92-4e6d-be8a-9f7887143058

  • This dataset contains the Standardised Streamflow Index (SSI) data for 303 catchments across the United Kingdom from 1891 to 2015. The SSI is a drought index based on the cumulative probability of a given monthly mean streamflow occurring for a given catchment. Here, the SSI is calculated for the following accumulation periods: 1, 3, 6, 9, 12, 18 and 24 months. Each accumulation period is calculated for calendar end-months. The standard period used to fit the Tweedie distribution is 1961-2010. The SSI was produced by the RCUK-funded Historic Droughts project in order to characterise and explore hydrological drought severity over the period 1891-2015. This dataset is an outcome of the Historic Droughts Project (grant number: NE/L01016X/1). Full details about this dataset can be found at https://doi.org/10.5285/58ef13a9-539f-46e5-88ad-c89274191ff9