Can the dataset of field based recharge estimates in Australia be used to predict recharge in data-poor areas?

Abstract

Effective management of water resources requires that all elements of the water balance be estimated. Groundwater recharge measurements are difficult, time consuming and expensive. In some cases a field study cannot be justified and simple empirical relationships are used to estimate recharge, and often the value chosen is simply a percentage of rainfall. This paper aims to use a database of 4386 field based estimates of recharge from 172 studies in Australia to produce simple empirical relationships that relate recharge to nationally available datasets and hence can be used to estimate recharge in data-poor areas in a scientifically defensible way.<br> <br> It was found that the vegetation and soil type were critical determinants in forming relationships between average annual rainfall and average annual recharge. Climate zones and surface geology (lithology) were not found to be significant determinants in the relationship between rainfall and recharge. The method used to estimate recharge had an impact upon the magnitude of the recharge estimates due to the spatial and temporal scales over which the different methods estimate recharge.<br> <br> Relationships have been developed here between average annual rainfall and average annual recharge for combinations of soil and vegetation type that can be used with only nationally available datasets to provide a recharge estimate. These relationships can explain 60% of the variance in recharge measurements across Australia. The uncertainty in the recharge estimated using these relationships is generally greater than an order of magnitude. This means that if these relationships are used to help determine water allocations, then the precautionary principle should limit allocations to less than about 5% of the estimated recharge. If allocations are greater than this, a more detailed site specific study is warranted

Deep drainage estimates using multiple linear regression with percent clay content and rainfall

“…In order to improve the statistical correlation between deep drainage, clay content and rainfall, as developed in this paper; the database of Crosbie et al (2010a) was filtered to include only those recharge and deep drainage estimates that reported rainfall amount and clay content of the soil. Less than 5 % (202 recharge/deep drainage estimates) of the original database (Crosbie et al, 2010a) fitted these criteria ( Fig.…”

“…The soil type in these relationships were classified into five discrete groups, however the soil properties relevant to deep drainage are known to exist as a continuum rather than discrete classes (Kennett-Smith et al, 1994). This paper tests the finding of SKM (2002) that the average clay content of the top 2 m of the soil profile is the best proxy for estimating deep drainage by examining a more substantial database (Crosbie et al, 2010a) in terms of the data quantity and spatial variability (including soil and rainfall variability) to provide the best unsaturated zone proxy for estimating deep drainage.…”

“…Crosbie et al (2010a) recently compiled and reviewed a database of 4386 recharge and deep drainage estimates over 172 studies across dry land areas of Australia. This included a review of several previous Australian recharge review papers (Kennett-Smith et al, 1994;Petheram et al, 2000Petheram et al, , 2002Cook et al, 2001;Tolmie and Silburn, 2003;Silberstein, 2010).…”

“…This included a review of several previous Australian recharge review papers (Kennett-Smith et al, 1994;Petheram et al, 2000Petheram et al, , 2002Cook et al, 2001;Tolmie and Silburn, 2003;Silberstein, 2010). Crosbie et al (2010b) then used this database to develop a tool for estimating the relationships between recharge and rainfall, soil type and land use in data poor areas. The soil type in these relationships were classified into five discrete groups, however the soil properties relevant to deep drainage are known to exist as a continuum rather than discrete classes (Kennett-Smith et al, 1994).…”

Water Balance to Recharge Calculation: Implications for Watershed Management Using Systems Dynamics Approach

“…Information is generally limited because of complex hydrogeology, difficulties in and costs of data collection, and the need for frequent monitoring. For example, recharge is considered to be one of the most difficult components of a water-balance to quantify accurately as it varies significantly in both temporal and spatial dimensions [12][13][14][15][16][17][18][19]. Effective management of groundwater aquifers is heavily influenced by the accurate estimation of recharge.…”