This dataset is a model output created using the BGS AquiMod model. It provides monthly groundwater level relative to the Ordnance Datum (maOD) from 1891 to 2015, reconstructed for 54 observation boreholes across the UK. Based on the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, 90th percentile and 10th percentile confidence bounds have been estimated and are given for each of reconstructed groundwater level time series.
Publication date: 2018-03-09
This dataset is part of the following
Groundwater levels have been reconstructed using BGS’ AquiMod model. Monte Carlo (MC) simulations were undertaken to calibrate the models. These MC parameters were randomly sampled within a finite parameter range to produce one million parameter sets. The upper and lower bounds of the range for each of parameter were defined based on published data on aquifer properties or expert judgment. The quantification of uncertainty is based on the Generalised Likelihood Uncertainty Estimation (GLUE). Parameter sets are considered to be behavioural if their Nash Sutcliffe Efficiency score exceeds 0.5. The 10th and 90th percentiles of the reconstructed GWL values on each date are the values which are exceeded by 90 and 10 % of the reconstructions using the behavioural parameter vectors, respectively.
Source data for the AquiMod models consisted of the following: groundwater level (GWL) data, precipitation, and temperature data. The monthly groundwater level data are been taken from the National Groundwater Level Archive, NGLA (http://www.bgs.ac.uk/research/groundwater/datainfo/levels/ngla.html). Groundwater level records in the NGLA are irregular and typically at a frequency of a month or less. Consequently, raw groundwater level data was linearly interpolated to a monthly time step prior to use in AquiMod. Precipitation and temperature data were taken from an updated / extended version of UKCP09 produced by the Historic Droughts project. Potential evapotranspiration is estimated from the temperature data (Tanguy et al., 2017: doi https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c).