Cite this dataset as
Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., Woods, R. (2020). Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB). NERC Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9
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This dataset is available under the terms of the Open Government Licence 
Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB)
This dataset provides hydro-meteorological timeseries and landscape attributes for 671 catchments across Great Britain. It collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological timeseries and catchment attributes. Daily timeseries for the time period 1st October 1970 to the 30th September 2015 are provided for a range of hydro-meteorological data (including rainfall, potential evapotranspiration, temperature, radiation, humidity and flow). A comprehensive set of catchment attributes are quantified describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils, hydrogeology, human influences and discharge uncertainty.
This dataset is intended for the community as a freely available, easily accessible dataset to use in a wide range of environmental data and modelling analyses. A research paper (Coxon et al, CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain) describing the dataset in detail will be made available in Earth System Science Data (https://www.earth-system-science-data.net/).
This dataset is intended for the community as a freely available, easily accessible dataset to use in a wide range of environmental data and modelling analyses. A research paper (Coxon et al, CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain) describing the dataset in detail will be made available in Earth System Science Data (https://www.earth-system-science-data.net/).
Publication date: 2020-02-20
View record as
Formats
Comma-separated values (CSV)
Shapefile
Spatial information
- Study area
-
- Spatial representation types
-
Tabular (text)
Vector - Spatial reference system
- OSGB 1936 / British National Grid
Temporal information
- Temporal extent
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1970-10-01 to 2015-09-30
Provenance & quality
CAMELS-GB provides hydro-meteorological timeseries and catchment attributes for 671 catchments.
Catchment meteorological timeseries are derived from CEH-GEAR, CHESS-PE and CHESS-MET. Catchment flow timeseries and boundaries are sourced from the UK National River Flow Archive.
Many of the catchment attributes are sourced either from the National River Flow Archive or from the catchment meteorological timeseries produced in CAMELS-GB. Land cover catchment attributes are derived from the GB 1km percentage target class, Land Cover Map 2015 (Rowland et al, 2017; https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556). Soil catchment attributes are derived from the European Soil Database Derived Data product (Hiederer, 2013a, 2013b), and the modelled depth to bedrock global product (Pelletier et al., 2016). Hydrogeology catchment attributes are derived from the British Geological Survey hydrogeology map (BGS, 2019) and superficial deposits map. Discharge uncertainty estimates for each catchment are derived using the method described in Coxon et al (2015). Human influence catchment attributes were derived from datasets describing ground/surface water abstractions and discharges sourced from the Environment Agency and from datasets describing reservoir capacity and use from the UK reservoir inventory (Durant and Counsell, 2018).
Where possible we have provided quantitative estimates of uncertainty, including timeseries for two potential evapo-transpiration products, percentiles of soil catchment attributes and the first set of national discharge uncertainty estimates. For all datasets we have highlighted the key uncertainties and limitations of the underlying source data products and any methods used to derive the catchment attributes. We hope to raise awareness and encourage users of the dataset to consider these uncertainties in their analyses.
See supporting information for more details.
Catchment meteorological timeseries are derived from CEH-GEAR, CHESS-PE and CHESS-MET. Catchment flow timeseries and boundaries are sourced from the UK National River Flow Archive.
Many of the catchment attributes are sourced either from the National River Flow Archive or from the catchment meteorological timeseries produced in CAMELS-GB. Land cover catchment attributes are derived from the GB 1km percentage target class, Land Cover Map 2015 (Rowland et al, 2017; https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556). Soil catchment attributes are derived from the European Soil Database Derived Data product (Hiederer, 2013a, 2013b), and the modelled depth to bedrock global product (Pelletier et al., 2016). Hydrogeology catchment attributes are derived from the British Geological Survey hydrogeology map (BGS, 2019) and superficial deposits map. Discharge uncertainty estimates for each catchment are derived using the method described in Coxon et al (2015). Human influence catchment attributes were derived from datasets describing ground/surface water abstractions and discharges sourced from the Environment Agency and from datasets describing reservoir capacity and use from the UK reservoir inventory (Durant and Counsell, 2018).
Where possible we have provided quantitative estimates of uncertainty, including timeseries for two potential evapo-transpiration products, percentiles of soil catchment attributes and the first set of national discharge uncertainty estimates. For all datasets we have highlighted the key uncertainties and limitations of the underlying source data products and any methods used to derive the catchment attributes. We hope to raise awareness and encourage users of the dataset to consider these uncertainties in their analyses.
See supporting information for more details.
Related
Citations
Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., & Woods, R. (2020). CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth System Science Data, 12(4), 2459–2483. https://doi.org/10.5194/essd-12-2459-2020
Kim, K. B., Kwon, H.-H., & Han, D. (2022). Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective. In Journal of Hydrology (Vol. 604, p. 127261). Elsevier BV. https://doi.org/10.1016/j.jhydrol.2021.127261
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., & Matias, Y. (2023). Caravan - A global community dataset for large-sample hydrology. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-01975-w
Bloomfield, J. P., Gong, M., Marchant, B. P., Coxon, G., & Addor, N. (2021). How is Baseflow Index (BFI) impacted by water resource management practices? Hydrology and Earth System Sciences, 25(10), 5355-5379. https://doi.org/10.5194/hess-25-5355-2021
Zheng, Y., Coxon, G., Woods, R., Li, J., & Feng, P. (2023). A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments. Water Resources Research, 59(1). Portico. https://doi.org/10.1029/2022wr033227
Sawada, Y., Okugawa, S., & Kimizuka, T. (2022). Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins. Hydrological Research Letters, 16(4), 73-79. https://doi.org/10.3178/hrl.16.73
Reaver, N.G.F., Kaplan, D.A., Klammler, H., & Jawitz, J.W. (2022). Theoretical and empirical evidence against the Budyko catchment trajectory conjecture. Hydrology and Earth System Sciences, 26(5), 1507-1525. https://doi.org/10.5194/hess-26-1507-2022
Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., Kumar Sahu, R., Greve, P., Slater, L., & Dadson, S.J. (2022). Hydrological concept formation inside long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 26(12), 3079-3101. https://doi.org/10.5194/hess-26-3079-2022
Kiraz, M., Coxon, G. & Wagener, T. (2023) A Signature‐Based Hydrologic Efficiency Metric for Model Calibration and Evaluation in Gauged and Ungauged Catchments. Water Resources Research 59. https://doi.org/10.1029/2023wr035321
Zheng, Y., Coxon, G., Woods, R., Li, J. & Feng, P. (2023) Controls on the Spatial and Temporal Patterns of Rainfall‐Runoff Event Characteristics—A Large Sample of Catchments Across Great Britain. Water Resources Research 59. https://doi.org/10.1029/2022wr033226
Gauch, M. & Lin, J. (2020) A Data Scientist’s Guide to Streamflow Prediction https://doi.org/10.48550/arxiv.2006.12975
Supplemental information
Correspondence/contact details
Authors
https://orcid.org/0000-0002-8837-460X
Lewis, M.
British Geological Survey
Other contacts
- Custodian
-
NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
-
NERC Environmental Information Data Centreinfo@eidc.ac.uk
Additional metadata
- Topic categories
- inlandWaters
- INSPIRE theme
- Hydrography
- Keywords
- climate , human influence , hydrogeology , hydrology , Hydrology , land cover , soil , topography
- Funding
- Natural Environment Research Council Award: NE/L010399/1
- Last updated
- 18 April 2024 16:39