Spatio-temporal modelling of the status of groundwater droughts

Abstract

An empirical (geo)statistical modelling scheme is developed to address the challenges of modelling the severity and distribution of groundwater droughts given their spatially and temporally heterogeneous nature and given typically highly irregular groundwater level observations in space and time. The scheme is tested using GWL measurements from 948 observation boreholes across the Chalk aquifer (UK) to estimate monthly groundwater drought status from 1960 to 2013. For each borehole, monthly GWLs are simulated using empirical mixed models where the fixed effects are based on applying an impulse response function to the local monthly precipitation. Modelled GWLs are standardised using the Standardised Groundwater Index (SGI) and the monthly SGI values interpolated across the aquifer to produce spatially distributed monthly maps of SGI drought status for 54 years for the Chalk. The mixed models include fewer parameters than comparable lumped parameter groundwater models while explaining a similar proportion (more than 65%) of GWL variation. In addition, the empirical modelling approach enables confidence bounds on the predicted GWLs and SGI values to be estimated without the need for prior information about catchment or aquifer parameters. The results of the modelling scheme are illustrated for three major episodes of multi-annual drought (1975-1976; 1988-1992; 2011-2012). They agree with previous documented analyses of the groundwater droughts while providing for the first time a systematic, spatially coherent characterisation of the events. The scheme is amenable to use in near real time to provide short term forecasts of groundwater drought status if suitable forecasts of the driving meteorology are available.

Asymmetric impact of groundwater use on groundwater droughts

“…Eight clusters are identified, of which five clusters are located in the Chalk (C1-5) and three in the Permo-Triassic Sandstone (S1-3) ( Figure 1). The spatial distribution of Chalk clusters (C1, C3, C4) is consistent with clusters previously identified by Marchant and Bloomfield (2018). A separate cluster is identified in East Anglia for 5 reference wells (C2).…”

Changes in groundwater drought associated with anthropogenic warming

“…A number of studies have described major episodes of hydrological drought, including groundwater drought, in the UK since the 19th century (Marsh et al, 2007;Lloyd-Hughes et al, 2010;Bloomfield and Marchant, 2013;Folland et al, 2015;Marchant and Bloomfield, 2018) and the societal impacts of those droughts (Taylor et al, 2009;Lange et al, 2017). Marsh et al (2007) identified seven episodes of major hydrological droughts in England and Wales between 1890 and 2007 using ranked rainfall deficiency time series and analysis of long river flow and groundwater level time series (Marsh et al, 2007, Table 2) as follows: 1890-1910 (known as the "Long Drought"), 1921-1922, 1933-1934, 1959, 1976, 1990-1992and 1995-1997 noted that of these major droughts all but one, the drought of 1959, had sustained and/or severe impacts on groundwater levels.…”

“…All the major droughts typically had large geographical footprints extending over much of England and Wales as well as over parts of north-western Europe (Lloyd-Hughes and Saunders, 2002;Lloyd-Hughes et al, 2010;Fleig et al, 2011;Hannaford et al, 2011). However, regional variations in drought intensities were present within and between the major drought events as a function of spatial differences in driving meteorology and catchment and aquifer properties (Marsh et al, 2007;Bloomfield and Marchant, 2013;Bloomfield et al, 2015;Marchant and Bloomfield, 2018).…”

Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging

“…Groundwater monitoring is essential to groundwater management and provides fundamental information regarding the long-term sustainability and status of an aquifer (Reghunath et al, 2005;Taylor & Alley, 2001). However, groundwater-level measurements are typically irregularly collected and have many spatial and temporal gaps in the record which presents challenges in understanding spatial and temporal changes and stresses to the system (Marchant & Bloomfield, 2018;Oikonomou et al, 2018;Varouchakis & Hristopulos, 2013). In some cases, spatial and temporal data gaps can lead to limited analysis.…”

“…Oikonomou et al (2018) use an exogenous seasonal autoregressive integrated moving average stochastic model and ensemble smoother for predicting water table levels and filling in data gaps. Others studies model groundwater levels and deal with missing data by using higher spatial and/or temporally more frequent data sets, such as remotely sensed data from the GRACE satellites (Mukherjee & Ramachandran, 2018;Sun, 2013), impulse response functions to relate precipitation to groundwater levels (Marchant & Bloomfield, 2018;von Asmuth et al, 2002), machine learning using artificial neural networks (Daliakopoulos et al, 2005;Sahoo et al, 2017), and interpolation methods that use secondary variables to improve estimation in sparsely sampled areas (Desbarats et al, 2002;Passarella et al, 2017;Peterson et al, 2011). Interpolation techniques are commonly used to transform point measurements at groundwater wells to groundwater surfaces across an aquifer.…”