Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2017) [CEH-GEAR]

https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556

THIS DATASET HAS BEEN SUPERSEDED The latest version is Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]

Dataset has been updated for 2018 and 2019

If you need access to the archived version, please contact the EIDC

1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2017. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012.

Publication date: 2019-05-16

14 citations

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More information

View numbers valid from 01 June 2023 (information prior to this was not collected)

Format

NetCDF

Spatial information

Study area

Spatial representation type

Raster

Spatial reference systems

OSGB 1936 / British National Grid
OSNI 1952 / Irish National Grid

Temporal information

Temporal extent

1890-01-01 to 2017-12-31

Provenance & quality

The rainfall estimates are derived from the Met Office national database of observed precipitations. To derive the monthly estimates, monthly and daily (when complete month availabe) precipitation totals from the UK raingauge network are used. An additional quality control step (data already quality controlled by the Met Office) was introduced to check events greater than the 200 year return period. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (SAAR61-90), was used to generate the daily and monthly rainfall grids. The daily grids are adjusted so that the monthly totals calculated from those are in agreement with the monthly grids. As an indicator of the quality of the estimates, the dataset also contains for each grid generated a grid containing the distance to the closest raingauge used to interpolate the grid point. A grid containing the number of missing estimates for each year is also provided. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012.

This version of the CEH-GEAR dataset differs from the previous version (1890-2015) only by temporal coverage. That is, previous years' data have not been modified.

Licensing and constraints

THIS DATASET HAS BEEN SUPERSEDED The latest version is Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]

Dataset has been updated for 2018 and 2019

If you need access to the archived version, please contact the EIDC

This dataset is available under the terms of the Open Government Licence

Cite this dataset as:

Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D.G.; Keller, V.D.J. (2019). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2017) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556

BibTeX RIS

Contains material based on Met Éireann data © Met Éireann and Met Office data © Crown copyright.

Users should be aware that the quality of the rainfall data will decrease when the minimum distance (i.e. distance to the closest gauge) increases. The value of the minimum distance is provided alongside rainfall values within the dataset. For values of minimum distance greater than 100 km, rainfall values are not derived.

Citations

Barnes, A, McCullen, N & Kjeldsen, T. (2019). Atmospheric origins of extreme rainfall in the UK. Paper presented at 4th IMA International Conference on Flood Risk, Swansea, UK United Kingdom, 12/09/19 - 13/09/19 https://researchportal.bath.ac.uk/en/publications/atmospheric-origins-of-extreme-rainfall-in-the-uk

Murgatroyd, A., & Hall, J. W. (2021). Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning. In Water Resources Research (Vol. 57, Issue 5). American Geophysical Union (AGU). https://doi.org/10.1029/2020wr028117

Chen, Y., Paschalis, A., Wang, L.-P., & Onof, C. (2021). Can we estimate flood frequency with point-process spatial-temporal rainfall models? In Journal of Hydrology (Vol. 600, p. 126667). Elsevier BV. https://doi.org/10.1016/j.jhydrol.2021.126667

Chengot, R., Knox, J. W., Coxon, G., Cojocaru, G., & Holman, I.P. (2023). An enhanced version of the D-Risk decision support webtool for multi-scale management of water abstraction and drought risks in irrigated agriculture. In Computers and Electronics in Agriculture (Vol. 204, p. 107516). Elsevier BV. https://doi.org/10.1016/j.compag.2022.107516

Chan, W.C.H., Shepherd, T.G., Facer-Childs, K., Darch, G., & Arnell, N.W. (2022). Storylines of UK drought based on the 2010-2012 event. Hydrology and Earth System Sciences, 26(7), 1755-1777. https://doi.org/10.5194/hess-26-1755-2022

Murgatroyd, A., Gavin, H., Becher, O., Coxon, G., Hunt, D., Fallon, E., Wilson, J., Cuceloglu, G., & Hall, J.W. (2022). Strategic analysis of the drought resilience of water supply systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2238). https://doi.org/10.1098/rsta.2021.0292

Colyer, A., Butler, A., Peach, D., & Hughes, A. (2021). How groundwater time series and aquifer property data explain heterogeneity in the Permo-Triassic sandstone aquifers of the Eden Valley, Cumbria, UK. Hydrogeology Journal, 30(2), 445-462. https://doi.org/10.1007/s10040-021-02437-6

Barnes, A.P., Svensson, C., & Kjeldsen, T.R. (2021). North Atlantic air pressure and temperature conditions associated with heavy rainfall in Great Britain. International Journal of Climatology, 42(5), 3190-3207. https://doi.org/10.1002/joc.7414

Barnes, A.P., McCullen, N., & Kjeldsen, T.R. (2022). Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks. Theoretical and Applied Climatology, 151(1-2), 421-432. https://doi.org/10.1007/s00704-022-04242-x

Collins, S.L., Christelis, V., Jackson, C.R., Mansour, M.M., Macdonald, D.M.J. & Barkwith, A.K.A.P. (2020) Towards integrated flood inundation modelling in groundwater-dominated catchments. Journal of Hydrology 591, 125755. https://doi.org/10.1016/j.jhydrol.2020.125755

Griffin, A., Kay, A.L., Sayers, P., Bell, V., Stewart, E., & Carr, S. (2024). Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections. In Hydrology and Earth System Sciences (Vol. 28, Issue 12, pp. 2635–2650). Copernicus GmbH. https://doi.org/10.5194/hess-28-2635-2024

Martin, G., Ingvorsen, L., Willcocks, J., Wiltshire, J., Bates, J., Jenkins, B., Priestley, T., McKay, H. & Croxton, S. (2020). Perennial energy crops and their potential in Scotland: evidence review. ClimateXChange. https://www.climatexchange.org.uk/projects/perennial-energy-crops-and-their-potential-in-scotland-evidence-review/

Wiltshire, J., Freeman, D., Willcocks, J. & Wood, C. (2021). The potential for leguminous crops in Scotland. ClimateXChange. https://www.climatexchange.org.uk/projects/the-potential-for-leguminous-crops-in-scotland/

Dowson, F., Leake, A., Harpham, L., Willcocks, J., Peters, E., David, T., Bates, J., & Wood, C. (2024). Economic potential of energy crops in Scotland. Ricardo Plc. https://doi.org/10.7488/ERA/5478