Reconstruction of multi‐decadal groundwater level time‐series using a lumped conceptual model

Abstract

Multi-decadal groundwater level records, which provide information about long-term variability and trends, are relatively rare. Whilst a number of studies have sought to reconstruct river flow records, there have been few attempts to reconstruct groundwater level time-series over a number of decades. Using long rainfall and temperature records, we developed and applied a methodology to do this using a lumped conceptual model. We applied the model to six sites in the UK, in four different aquifers: Chalk, limestone, sandstone and Greensand. Acceptable models of observed monthly groundwater levels were generated at four of the sites, with maximum Nash -Sutcliffe Efficiency scores of between 0.84 and 0.93 over the calibration and evaluation periods, respectively. These four models were then used to reconstruct the monthly groundwater level time-series over approximately 60 years back to 1910. Uncertainty in the simulated levels associated with model parameters was assessed using the Generalized Likelihood Uncertainty Estimation method. Known historical droughts and wet period in the UK are clearly identifiable in the reconstructed levels, which were compared using the Standardized Groundwater Level Index. Such reconstructed records provide additional information with which to improve estimates of the frequency, severity and duration of groundwater level extremes and their spatial coherence, which for example is important for the assessment of the yield of boreholes during drought periods.

Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance

“…Differing from the studies of Hassan et al [27] and Jackson et al [32], this study's GLUE results with a NSE of 0.6 (>0.5 in Jackson et al [32]; >0.65 in Ritter and Muñoz-Carpena [42]) indicated that most of the top 100 realizations based on the above simulation approach, which were selected by spatial relations among the hydraulic conductivities of aquifers, were behavioral parameter sets. The results highlight the importance of the spatial relations among the hydraulic conductivities of aquifers in the simulated hydraulic conductivities ln(K).…”

“…Therefore, we prefer the NSE values to be larger than 0.0 and approaching 1.0. In their ground water studies, Jackson et al [32] and Marchant et al [33] selected an NSE value of 0.5 to represent the threshold of determination of non-behavioral simulations and behavioral simulations. Ritter and Muñoz-Carpena [42] determined an NSE threshold for performance evaluation of hydrological models at 0.65.…”

“…When the values of the NSE increased from 0.6 to 0.7, the number of non-behavioral parameter sets increased slightly, except for Aquifer 3 wherein the semivariogram presented a pure nugget which is evidence of a highly heterogeneous physical formation at Aquifer 3. The use of GLUE with our proposed simulation approach in assessing and conditioning groundwater modeling results and uncertainties is suitable, however, GLUE approaches have been criticized and evaluated for not being formally Bayesian [27,[31][32][33]46].…”

“…This determination refers to "equifinality" for model uncertainty analysis [30]. It is noted that most relevant hydrological and groundwater studies have tended to use GLUE for their uncertainty analysis, such as Chu et al [29] and Mirzaei et al [31] in hydrological modeling; Hassan et al [27], Jackson et al [32], and Marchant et al [33] in groundwater modeling; and Wang et al [34] and Huang et al [28] in wetland modeling.…”

Spatio-temporal modelling of the status of groundwater droughts

“…Site specific, lumped parameter models are more flexible and typically easier to update and are increasingly being developed to provide a range of groundwater hydrological services, including GWL status assessments. For example, AquiMod, a lumped parameter model (Mackay et al, 2014;Jackson et al, 2016;Marchant et al, 2016) driven by rainfall data, is used to produce 1-3 month outlooks for GWL for the UK (Hydrological Outlooks, 2017;Prudhomme et al, 2017). Recently, Marchant et al (2016) incorporated AquiMod as a fixed effect into a mixed model for simulation of GWLs and used it to quantify uncertainties in GWL simulations (GWL reconstructions) using formal likelihood methods.…”