f710bed1-e564-47bf-b82c-4c2a2fe2810e
English
ISO/IEC 8859-1 (also known as Latin 1)
dataset
dataset
NERC EDS Environmental Information Data Centre
Lancaster Environment Centre, Library Avenue, Bailrigg
Lancaster
LA1 4AP
UK
info@eidc.ac.uk
https://eidc.ac.uk/
EIDC website
The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences.
information
pointOfContact
2024-02-27T16:17:33
UK GEMINI
2.3
OSGB 1936 / British National Grid
Historic reconstructions of daily river flow for 303 UK catchments (1891-2015)
2018-03-12
publication
2018-02-23
creation
https://catalogue.ceh.ac.uk/id/f710bed1-e564-47bf-b82c-4c2a2fe2810e
10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e
doi:
Smith, K.A., Tanguy, M., Hannaford, J., Prudhomme, C. (2018). Historic reconstructions of daily river flow for 303 UK catchments (1891-2015). NERC Environmental Information Data Centre 10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e
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
Dr. Katie Smith
UK Centre for Ecology & Hydrology
enquiries@ceh.ac.uk
pointOfContact
Smith, K.A.
Centre for Ecology & Hydrology
enquiries@ceh.ac.uk
https://orcid.org/0000-0003-1060-9103
ORCID record
ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers.
information
author
Tanguy, M.
Centre for Ecology & Hydrology
enquiries@ceh.ac.uk
https://orcid.org/0000-0002-1516-6834
ORCID record
ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers.
information
author
Hannaford, J.
Centre for Ecology & Hydrology
enquiries@ceh.ac.uk
https://orcid.org/0000-0002-5256-3310
ORCID record
ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers.
information
author
Prudhomme, C.
European Centre for Medium Range Weather Forecasts
enquiries@ceh.ac.uk
https://orcid.org/0000-0003-1722-2497
ORCID record
ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers.
information
author
NERC EDS Environmental Information Data Centre
enquiries@ceh.ac.uk
custodian
NERC Environmental Information Data Centre
enquiries@ceh.ac.uk
publisher
UK Centre for Ecology & Hydrology
enquiries@ceh.ac.uk
owner
notPlanned
Hydrography
theme
GEMET - INSPIRE themes, version 1.0
2008-06-01
publication
otherRestrictions
no limitations
otherRestrictions
This resource is made available under the terms of the Open Government Licence
otherRestrictions
© UK Centre for Ecology & Hydrology
otherRestrictions
If you reuse this data, you should cite: Smith, K.A., Tanguy, M., Hannaford, J., Prudhomme, C. (2018). Historic reconstructions of daily river flow for 303 UK catchments (1891-2015). NERC Environmental Information Data Centre https://doi.org/10.5285/f710bed1-e564-47bf-b82c-4c2a2fe2810e
textTable
100
English
utf8
inlandWaters
1891-01-01
2015-11-30
-8.648
1.768
49.864
60.861
Comma-separated values (CSV)
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
distributor
https://data-package.ceh.ac.uk/sd/f710bed1-e564-47bf-b82c-4c2a2fe2810e.zip
Supporting information
Supporting information available to assist in re-use of this dataset
information
https://catalogue.ceh.ac.uk/datastore/eidchub/f710bed1-e564-47bf-b82c-4c2a2fe2810e
Download the data
Download a copy of this data
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dataset
dataset
Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
2010-12-08
The GR4J model (v 1.0.2) was run over the calibration period (1982-2014) using 500,000 Latin Hypercube Sampled model parameter sets. These model parameters were assessed against observations from the National River Flow Archive (NRFA). For two catchments (the Thames at Kingston, and the Lea at Feildes Weir) the model was also calibrated against naturalised flows. The model was calibrated using a multi-objective approach comprising of 6 evaluation metrics: Nash Sutcliffe Efficiency (NSE), NSE on log flows (log NSE), Mean Absolute Percent Error (MAPE), Absolute Percent Bias (PBIAS), Absolute Percent Error in Mean Annual Minimum flows over a 30 day accumulation period (MAM30), and Absolute Percent Error in the flow exceeded 95% of the time (Q95). The 500,000 model runs were then ranked by each evaluation metric, the ranks were summed, and the runs were reordered according to this final rank. Finally, in order to prevent uneven trade-offs between metrics, the runs were re-ordered according to thresholds of acceptability. Reconstructed flow timeseries were then run for the top 500 ranking model parameter sets, using PET (Potential Evapotranspiration) (Tanguy et al., 2017: doi https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c), and reconstructed daily rainfall data, provided by the UK Met Office. The modelled data, and the supporting metadata files, were exported from the R software programme as comma separated value files (.csv), and ingested into the EIDC in this format.