Articles | Volume 10, issue 2
Earth Syst. Sci. Data, 10, 951–968, 2018
https://doi.org/10.5194/essd-10-951-2018
Earth Syst. Sci. Data, 10, 951–968, 2018
https://doi.org/10.5194/essd-10-951-2018
Review article
01 Jun 2018
Review article | 01 Jun 2018

Historical gridded reconstruction of potential evapotranspiration for the UK

Maliko Tanguy et al.

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Cited articles

Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Fao irrigation and drainage paper 56 – crop evapotranspiration – guidelines for computing crop water requirements, Rome, 1998. 
Aràndiga, F., Donat, R., and Santágueda, M.: The PCHIP subdivision scheme, Appl. Math. Comput., 272, 28–40, https://doi.org/10.1016/j.amc.2015.07.071, 2016. 
Bai, P., Liu, X., Yang, T., Li, F., Liang, K., Hu, S., and Liu, C.: Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions, J. Hydrometeorol., 17, 2259–2274, doi10.1175/JHM-D-15-0202.1, 2016. 
Balkovič, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov, N., Obersteiner, M., Stürmer, B., and Xiong, W.: Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation, Agr. Syst., 120, 61–75, https://doi.org/10.1016/j.agsy.2013.05.008, 2013. 
Barik, M. G.: Remote Sensing-based Estimates of Potential Evapotranspiration for Hydrologic Modeling in the Upper Colorado River Basin Region, PhD, Civil Engineering 0300 UCLA, University of California, Los Angeles, 146 pp., 2014. 
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Short summary
Potential evapotranspiration (PET) is necessary input data for most hydrological models, used to simulate river flows. To reconstruct PET prior to the 1960s, simplified methods are needed because of lack of climate data required for complex methods. We found that the McGuinness–Bordne PET equation, which only needs temperature as input data, works best for the UK provided it is calibrated for local conditions. This method was used to produce a 5 km gridded PET dataset for the UK for 1891–2015.