Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D.G.; Keller, V.D.J.

Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]

Name: Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]
Published: 2016-11-04
License: https://eidc.ceh.ac.uk/licences/CEH-GEAR-1890-2015/plain

https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca Cite this dataset

THIS DATASET HAS BEEN SUPERSEDED.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]

Data has been updated to include data for 2016 and 2017

If you need access to this 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 2015. 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: 2016-11-04

Format

netCDF

Spatial information

Study area
Spatial representation type: Raster
Spatial reference systems: OSGB 1936 / British National Grid
OSNI 1952 / Irish National Grid
OSNI 1952

Temporal information

Temporal extent: 1890-01-01 to 2015-12-31

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 available) 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 (1890-2014) only by temporal coverage. That is, previous years' data have not been modified.

Citations

Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H. (2015). CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use, Earth Syst. Sci. Data Discuss., 8, 83-112. https://doi.org/10.5194/essdd-8-83-2015

Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H. J., Freer, J., Tanguy, M., … Woods, R. (2018). A rule based quality control method for hourly rainfall data and a 1 km resolution gridded hourly rainfall dataset for Great Britain: CEH-GEAR1hr. Journal of Hydrology, 564, 930–943 https://doi.org/10.1016/j.jhydrol.2018.07.034

Crooks, S. M., & Kay, A. L. (2015). Simulation of river flow in the Thames over 120 years: Evidence of change in rainfall-runoff response? Journal of Hydrology: Regional Studies, 4, 172–195. https://doi.org/10.1016/j.ejrh.2015.05.014

Harrigan, S., Prudhomme, C., Parry, S., Smith, K., & Tanguy, M. (2018). Benchmarking ensemble streamflow prediction skill in the UK. Hydrology and Earth System Sciences, 22(3), 2023–2039. https://doi.org/10.5194/hess-22-2023-2018

Ascott, M. J., Marchant, B. P., Macdonald, D., McKenzie, A. A., & Bloomfield, J. P. (2017). Improved understanding of spatio-temporal controls on regional scale groundwater flooding using hydrograph analysis and impulse response functions. Hydrological Processes, 31(25), 4586–4599. https://doi.org/10.1002/hyp.11380

Bell, V. A., Kay, A. L., Rudd, A. C., & Davies, H. N. (2018). The MaRIUS-G2G datasets: Grid-to-Grid model estimates of flow and soil moisture for Great Britain using observed and climate model driving data. Geoscience Data Journal, 5(2), 63–72. https://doi.org/10.1002/gdj3.55

Nesbitt, A., Dorling, S., & Lovett, A. (2018). A suitability model for viticulture in England and Wales: opportunities for investment, sector growth and increased climate resilience. Journal of Land Use Science, 13(4), 414–438. https://doi.org/10.1080/1747423x.2018.1537312

Simpson, I. R., & McCarthy, M. P. (2018). Structural and sampling uncertainty in observed UK daily precipitation extremes derived from an intercomparison of gridded data sets. International Journal of Climatology, 39(1), 128–142. https://doi.org/10.1002/joc.5789

Parsons, D. J., Rey, D., Tanguy, M., & Holman, I. P. (2019). Regional variations in the link between drought indices and reported agricultural impacts of drought. Agricultural Systems, 173, 119–129. https://doi.org/10.1016/j.agsy.2019.02.015

Coxon, G., Freer, J., Lane, R., Dunne, T., Knoben, W. J. M., Howden, N. J. K., … Woods, R. (2019). DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geoscientific Model Development, 12(6), 2285–2306. https://doi.org/10.5194/gmd-12-2285-2019

Rust, W., Holman, I., Bloomfield, J., Cuthbert, M., & Corstanje, R. (2019). Understanding the potential of climate teleconnections to project future groundwater drought. Hydrology and Earth System Sciences, 23(8), 3233–3245. https://doi.org/10.5194/hess-23-3233-2019

Gnann, S.J., Woods, R. A., & Howden, N.J.K. (2019). Is There a Baseflow Budyko Curve? Water Resources Research, 55(4), 2838–2855. https://doi.org/10.1029/2018wr024464

Afzal, M., & Ragab, R. (2019). Drought Risk under Climate and Land Use Changes: Implication to Water Resource Availability at Catchment Scale. Water, 11(9), 1790. https://doi.org/10.3390/w11091790

Kay, A. L., Bell, V. A., Guillod, B. P., Jones, R. G., & Rudd, A. C. (2018). National-scale analysis of low flow frequency: historical trends and potential future changes. Climatic Change, 147(3-4), 585–599. https://doi.org/10.1007/s10584-018-2145-y

Gnann, S.J., Howden, N.J.K., & Woods, R.A. (2020). Hydrological signatures describing the translation of climate seasonality into streamflow seasonality. Hydrology and Earth System Sciences, 24(2), 561–580. https://doi.org/10.5194/hess-24-561-2020

Wendt, D.E., Van Loon, A.F., Bloomfield, J.P., & Hannah, D.M. (2020). Asymmetric impact of groundwater use on groundwater droughts. Hydrology and Earth System Sciences, 24(10), 4853–4868. https://doi.org/10.5194/hess-24-4853-2020

Kay, A.L., Davies, H.N., Lane, R. A., Rudd, A.C., & Bell, V.A. (2021). Grid-based simulation of river flows in Northern Ireland: Model performance and future flow changes. In Journal of Hydrology: Regional Studies (Vol. 38, p. 100967). Elsevier BV. https://doi.org/10.1016/j.ejrh.2021.100967

Chengot, R., Knox, J. W., & Holman, I. P. (2021). Evaluating the Feasibility of Water Sharing as a Drought Risk Management Tool for Irrigated Agriculture. Sustainability, 13(3), 1456. https://doi.org/10.3390/su13031456

Stevenson, M., & Bravo, C. (2019). Advanced turbidity prediction for operational water supply planning. Decision Support Systems, 119, 72–84. https://doi.org/10.1016/j.dss.2019.02.009

Roy, B., Singh, M. P., Kaloop, M. R., Kumar, D., Hu, J.-W., Kumar, R., & Hwang, W.-S. (2021). Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network. Applied Sciences, 11(13), 6238. https://doi.org/10.3390/app11136238

Wang, H., & Xuan, Y. (2020). SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies. Environmental Modelling & Software, 124, 104598. https://doi.org/10.1016/j.envsoft.2019.104598

Kim, K. B., Kwon, H.-H., & Han, D. (2022). Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective. Journal of Hydrology, 604, 127261. https://doi.org/10.1016/j.jhydrol.2021.127261

West, H., Quinn, N., & Horswell, M. (2021). Monthly Rainfall Signatures of the North Atlantic Oscillation and East Atlantic Pattern in Great Britain. Atmosphere, 12(11), 1533. https://doi.org/10.3390/atmos12111533

Zhao, B., Mao, J., Dai, Q., Han, D., Dai, H., & Rong, G. (2019). Exploration on hydrological model calibration by considering the hydro-meteorological variability. Hydrology Research, 51(1), 30–46. https://doi.org/10.2166/nh.2019.047

Chen, M., Papadikis, K., & Jun, C. (2021). An investigation on the non-stationarity of flood frequency across the UK. Journal of Hydrology, 597, 126309. https://doi.org/10.1016/j.jhydrol.2021.126309

Zingaro, M., Refice, A., D’Addabbo, A., Hostache, R., Chini, M., & Capolongo, D. (2020). Experimental Application of Sediment Flow Connectivity Index (SCI) in Flood Monitoring. Water, 12(7), 1857. https://doi.org/10.3390/w12071857

Afzal, M., Vavlas, N., & Ragab, R. (2020). Modelling study to quantify the impact of future climate and land use changes on water resources availability at catchment scale. Journal of Water and Climate Change, 12(2), 339–361. https://doi.org/10.2166/wcc.2020.117

Wang, H., & Xuan, Y. (2022). Spatial variation of catchment-oriented extreme rainfall in England and Wales. Atmospheric Research, 266, 105968. https://doi.org/10.1016/j.atmosres.2021.105968

Afzal, M., & Ragab, R. (2020). Assessment of the potential impacts of climate change on the hydrology at catchment scale: modelling approach including prediction of future drought events using drought indices. Applied Water Science, 10(10). https://doi.org/10.1007/s13201-020-01293-1

Ridding, L. E., Newton, A. C., Redhead, J. W., Watson, S. C. L., Rowland, C. S., & Bullock, J. M. (2020). Modelling historical landscape changes. Landscape Ecology, 35(12), 2695–2712. https://doi.org/10.1007/s10980-020-01059-9

Ragab, R., Kaelin, A., Afzal, M., & Panagea, I. (2020). Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows. Hydrological Sciences Journal, 65(11), 1856–1871. https://doi.org/10.1080/02626667.2020.1764961

Roy, B., Singh, M. P., & Singh, A. (2019). A novel approach for rainfall-runoff modelling using a biogeography-based optimization technique. International Journal of River Basin Management, 19(1), 67–80. https://doi.org/10.1080/15715124.2019.1628035

West, H., Quinn, N., & Horswell, M. (2022). Spatio-temporal propagation of North Atlantic Oscillation (NAO) rainfall deviations to streamflow in British catchments. Hydrological Sciences Journal, 67(5), 676–688. https://doi.org/10.1080/02626667.2022.2038791

Faulkner, D., Warren, S., Spencer, P., & Sharkey, P. (2019). Can we still predict the future from the past? Implementing non‐stationary flood frequency analysis in the UK. Journal of Flood Risk Management, 13(1). Portico. https://doi.org/10.1111/jfr3.12582

Kay, A. L., Watts, G., Wells, S. C., & Allen, S. (2019). The impact of climate change on U. K. river flows: A preliminary comparison of two generations of probabilistic climate projections. Hydrological Processes, 34(4), 1081–1088. Portico. https://doi.org/10.1002/hyp.13644

Kay, A. L. (2021). Simulation of river flow in Britain under climate change: Baseline performance and future seasonal changes. Hydrological Processes, 35(4). Portico. https://doi.org/10.1002/hyp.14137

Hicks, H., Lambert, J., Pywell, R., Hulmes, L., Hulmes, S., Walker, K., Childs, D. Z., & Freckleton, R. P. (2021). Characterizing the environmental drivers of the abundance and distribution of Alopecurus myosuroides on a national scale. Pest Management Science, 77(6), 2726–2736. Portico. https://doi.org/10.1002/ps.6301

Afzal, M., & Ragab, R. (2020). How do climate and land use changes affect the water cycle? Modelling study including future drought events prediction using reliable drought indices. Irrigation and Drainage, 69(4), 806–825. Portico. https://doi.org/10.1002/ird.2467

Dobson, B., Coxon, G., Freer, J., Gavin, H., Mortazavi‐Naeini, M., & Hall, J. W. (2020). The Spatial Dynamics of Droughts and Water Scarcity in England and Wales. Water Resources Research, 56(9). Portico. https://doi.org/10.1029/2020WR027187

Cheng, S., Cheng, L., Liu, P., Qin, S., Zhang, L., Xu, C., Xiong, L., Liu, L., & Xia, J. (2021). An Analytical Baseflow Coefficient Curve for Depicting the Spatial Variability of Mean Annual Catchment Baseflow. Water Resources Research, 57(8). Portico. https://doi.org/10.1029/2020WR029529

Wang, H., & Xuan, Y. (2021). Spatial Variation of Extreme Rainfall Observed From Two Century‐Long Datasets. Geophysical Research Letters, 48(8). Portico. https://doi.org/10.1029/2020GL091933

Rameshwaran, P., Bell, V. A., Brown, M. J., Davies, H. N., Kay, A. L., Rudd, A. C., & Sefton, C. (2021). Use of Abstraction and Discharge Data to Improve the Performance of a National‐Scale Hydrological Model. Water Resources Research, 58(1). Portico. https://doi.org/10.1029/2021WR029787

Byers, E. A., Coxon, G., Freer, J., & Hall, J. W. (2020). Drought and climate change impacts on cooling water shortages and electricity prices in Great Britain. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-16012-2

West, H., Quinn, N., & Horswell, M. (2019). Regional rainfall response to the North Atlantic Oscillation (NAO) across Great Britain. Hydrology Research, 50(6), 1549–1563. https://doi.org/10.2166/nh.2019.015

Kay, A. L., Rudd, A. C., Fry, M., Nash, G., & Allen, S. (2021). Climate change impacts on peak river flows: Combining national-scale hydrological modelling and probabilistic projections. Climate Risk Management, 31, 100263. https://doi.org/10.1016/j.crm.2020.100263

Rudd, A. C., Bell, V. A., & Kay, A. L. (2017). National-scale analysis of simulated hydrological droughts (1891–2015). Journal of Hydrology, 550, 368–385. https://doi.org/10.1016/j.jhydrol.2017.05.018

Kay, A.L., Rudd, A.C., & Coulson, J. (2023). Spatial downscaling of precipitation for hydrological modelling: Assessing a simple method and its application under climate change in Britain. In Hydrological Processes (Vol. 37, Issue 2). Wiley. https://doi.org/10.1002/hyp.14823

Wang, H., & Xuan, Y. (2022). An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia. In Water (Vol. 15, Issue 1, p. 128). MDPI AG. https://doi.org/10.3390/w15010128

Kay, A.L., Lane, R.A., & Bell, V.A. (2022). Grid-based simulation of soil moisture in the UK: future changes in extremes and wetting and drying dates. Environmental Research Letters, 17(7), 074029. https://doi.org/10.1088/1748-9326/ac7a4e

Wang, H., & Xuan, Y. (2022). A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets. Remote Sensing, 14(15), 3823. https://doi.org/10.3390/rs14153823

Kay, A. (2022). Differences in hydrological impacts using regional climate model and nested convection-permitting model data. Climatic Change, 173(1-2). https://doi.org/10.1007/s10584-022-03405-z

Wang, H., & Xuan, Y. (2023). Deep Learning of Extreme Rainfall Patterns Using Enhanced Spatial Random Sampling With Pattern Recognition. In Advanced Hydroinformatics (pp. 343–356). Wiley. https://doi.org/10.1002/9781119639268.ch12

Faulkner, D.S., Longfield, S., Warren, S., & Tawn, J.A. (2024). Modelling non-stationary flood frequency in England and Wales using physical covariates. In Hydrology Research. IWA Publishing. https://doi.org/10.2166/nh.2024.134

Correspondence/contact details

Dr Maliko Tanguy

UK Centre for Ecology & Hydrology

Maclean Building, Benson Lane, Crowmarsh Gifford
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM

enquiries@ceh.ac.uk