Analysis of groundwater drought building on the standardised precipitation index approach

A new index for standardising groundwater level

time series and characterising groundwater droughts, the

Standardised Groundwater level Index (SGI), is described.

The SGI builds on the Standardised Precipitation Index (SPI)

to account for differences in the form and characteristics of

groundwater level and precipitation time series. The SGI is

estimated using a non-parametric normal scores transform

of groundwater level data for each calendar month. These

monthly estimates are then merged to form a continuous index.

The SGI has been calculated for 14 relatively long, up

to 103 yr, groundwater level hydrographs from a variety of

aquifers and compared with SPI for the same sites. The relationship

between SGI and SPI is site specific and the SPI

accumulation period which leads to the strongest correlation

between SGI and SPI, qmax, varies between sites. However,

there is a consistent positive linear correlation between

a measure of the range of significant autocorrelation in the

SGI series, mmax, and qmax across all sites. Given this correlation

between SGI mmax and SPI qmax, and given that periods

of low values of SGI can be shown to coincide with previously

independently documented droughts, SGI is taken to

be a robust and meaningful index of groundwater drought.

The maximum length of groundwater droughts defined by

SGI is an increasing function of mmax, meaning that relatively

long groundwater droughts are generally more prevalent

at sites where SGI has a relatively long autocorrelation

range. Based on correlations between mmax, average unsaturated

zone thickness and aquifer hydraulic diffusivity, the

source of autocorrelation in SGI is inferred to be dependent

on dominant aquifer flow and storage characteristics.

For fractured aquifers, such as the Cretaceous Chalk, autocorrelation

in SGI is inferred to be primarily related to

autocorrelation in the recharge time series, while in granular

aquifers, such as the Permo–Triassic sandstones, autocorrelation

in SGI is inferred to be primarily a function of intrinsic

saturated flow and storage properties of aquifer. These results

highlight the need to take into account the hydrogeological

context of groundwater monitoring sites when designing

and interpreting data from groundwater drought monitoring

networks.