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Estimating groundwater recharge for Great Britain

Wang, Lei; Zheng, Rongmeihui; Lin, Weibin; Mu, Xueliang; Hu, Yuepeng. 2014 Estimating groundwater recharge for Great Britain. [Speech] In: 14th UK CARE Annual General Meeting, UK Chinese Association of Resources and Environmen, Bristol, UK, 29 Nov 2014. (Unpublished)

Abstract
Groundwater plays an important role in supporting public water supplies and sustaining river flows. For example, groundwater supplies up to 80% drinking water in southern England. Groundwater recharge determines how much water is available to groundwater system. It is necessary to develop a groundwater recharge model for Great Britain to support the national water resource management. A national groundwater model was constructed using a SLiM model in this study. SLiM, a distributed rainfall-runoff-recharge model, can objectively estimate groundwater recharge and surface runoff based on climate and catchment characteristics. The datasets of Digital Terrain Model (DTM), daily distributed rainfall, potential evapotranspiration, land-cover and hydrology of soil types were collected in this study. The model was then calibrated using river flow datasets from 102 gauging stations across Great Britain. Nash–Sutcliffe coefficient was used to measure the goodness-of-fit between modelled and observed river flow. After calibration, the Nash–Sutcliffe coefficient values for more than 70% of the gauging stations are larger than 0.5. This means that this model can be used to provide sensible groundwater recharge across Great Britain. The model can produce both long-term-average and daily time variant distributed groundwater recharge in Great Britain. Therefore, it can be used to feed recharge datasets to other groundwater models, such as groundwater flow, groundwater vulnerability assessment, and groundwater pollutant transport models. This can also be a basis to investigate the impacts of climate and land-cover changes on groundwater recharge and hence the groundwater system.
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BGS Programmes 2013 > Environmental Modelling
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