Improved understanding of spatio‐temporal controls on regional scale groundwater flooding using hydrograph analysis and impulse response functions

Abstract

Controls on the spatio‐temporal extent of groundwater flooding are poorly understood, despite the long duration of groundwater flood events and distinct social and economic impacts. We developed a novel approach using statistical analysis of groundwater level hydrographs and impulse response functions (IRFs) and applied it to the 2013/2014 Chalk groundwater flooding in the English Lowlands. We proposed a standardized index of groundwater flooding which we calculated for monthly groundwater levels for 26 boreholes in the Chalk. We grouped these standardized series using k‐means cluster analysis and cross‐correlated the cluster centroids with the Standardized Precipitation Index accumulated over time intervals between 1 and 60 months. This analysis reveals 2 spatially coherent groups of standardized hydrographs that responded to precipitation over different timescales. We estimated IRF models of the groundwater level response to effective precipitation for 3 boreholes in each group. The IRF models corroborate the Standardized Precipitation Index analysis showing different response functions between the groups. We applied identical effective precipitation inputs to each of the IRF models and observed differences between the hydrographs from each group. It is suggested this is due to the hydrogeological properties of the Chalk and of overlying relatively low permeability superficial deposits (recent unconsolidated sediments overlying the bedrock, such as clays and tills), which are extensive over 1 of the groups. The overarching controls on groundwater flood response are concluded to be a complex combination of antecedent conditions, rainfall, and catchment hydrogeological properties. These controls should be taken into consideration when anticipating and managing future groundwater flood events. The approach presented is generic and parsimonious and can be easily applied where sufficient groundwater level and rainfall data are available.

In Situ Observations and Lumped Parameter Model Reconstructions Reveal Intra‐Annual to Multidecadal Variability in Groundwater Levels in Sub‐Saharan Africa

“…Conversely, recovery from a longer, multi‐annual extreme event would be anticipated to be slower in group 3 than group 2. These findings are consistent with the analysis of the autocorrelation timescales of standardized groundwater level hydrographs developed by Bloomfield and Marchant (2013) and Ascott et al (2017).…”

“…In the analysis of standardized hydrographs in the United Kingdom, Bloomfield and Marchant (2013) and Ascott et al (2017) showed that differences in temporal variability between observed hydrographs can be characterized by quantifying m max , the timescale over which there is significant autocorrelation in each SGI time series. They showed that variability in m max could be related to attenuation of the recharge signal through the unsaturated zone (as indicated by water table depth) and observed hydrogeological properties (hydraulic diffusivity; the ratio of hydraulic conductivity to storage coefficient) of the aquifer.…”

Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data

“…In this data-driven method, impulse response functions are used to describe how groundwater levels react to different drivers such as precipitation, evaporation, and pumping. The method has been successfully applied to characterize and analyze groundwater systems around the world, for example in Brazil (Manzione et al, 2010), Italy (Fabbri et al, 2011), the United Kingdom (Ascott et al, 2017), and India (van Dijk et al, 2019). The advantages of datadriven models compared to numerical groundwater models are faster model development and a lower number of calibration parameters (e.g., Bakker and Schaars, 2019).…”

Statistical analyses of the effect of a drainage tunnel on landslide hydrogeological characteristics

“…The response characteristics are influenced by many factors such as antecedent rainfall, rainfall intensity, characteristics of rainfall events, permeability, soil depth, and structure of slopes (Appels et al, ; Ascott, Marchant, Macdonald, McKenzie, & Bloomfield, ; Bhaskar et al, ; Hopp & McDonnell, ; Lee et al, ; Major & Iverson, ; Padilla, Onda, Iida, Takahashi, & Uchida, ; Xu et al, ). In this area, most factors did not change.…”