The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
Open accessAbstract
Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.
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Cites
High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management
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The CAMELS data set: catchment attributes and meteorology for large-sample studies
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Changing climate shifts timing of European floods
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A. P., Merz, B., Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., et al. (2017) · Science · doi:10.1126/science.aan2506 · Cited by 1,020
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Cited by
Hybrid forecasting: blending climate predictions with AI models
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., et al. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-1865-2023 · Cited by 281
Ghajarnia, N., Kalantari, Z., & Destouni, G. (2021) · Water Resources Research · doi:10.1029/2020wr029377 · Cited by 21
Metadata
- Authors
- 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.
- Venue
- Earth System Science Data
- Publisher
- Copernicus GmbH
- Published
- 2020-10-12
- Volume
- 12
- Issue
- 4
- Pages
- 2459-2483
- ISSN
- 1866-3508 (ISSN-L)
- 1866-3516
- OpenAlex
- W3016438096
- Cited by
- 312
- References
- 70
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. https://app.cochat.ai/papers/10.5194/essd-12-2459-2020
- The paper
- 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. https://doi.org/10.5194/essd-12-2459-2020
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