Attributing variations of temporal and spatial groundwater recharge: a statistical analysis of climatic and non-climatic factors

This paper demonstrated the benefits of statistical methods when investigating the climatic and non-climatic drivers responsible for variations in groundwater recharge with a series of up to 43 years of annual recharge for 426 bores in South-East South Australia. We identified the factors influencing groundwater recharge based on 71 climatic metrics and 13 non-climatic metrics (including groundwater abstraction). The results showed: 1) Rainfall during April to October was the most important variable influencing recharge temporal variation, with its decline identified as the most significant factor related to recharge reduction; 2) In contrast, a negative correlation between rainfall during December to February (DJF) and annual groundwater recharge was found. This suggests that a seasonal shift in rainfall (such as decreasing rainfall during April to October and an increase during DJF) can result in a decline in recharge even when the annual rainfall remains unchanged; 3) The length of wet spells (consecutive rain days) and increasing PET were additional significant predictors for recharge temporal variation. It demonstrated that a simple empirical relationship (such as recharge as a fixed percentage of rainfall) is not a reliable estimation of renewable groundwater resources under changing climatic conditions; 4) There is a statistically significant spatial correlation between mean groundwater depth and recharge, and this implies that a reduction in rainfall can lead to a positive feedback loop of declining recharge and water level; 5) Spatially the most statistically significant factors influencing groundwater recharge were soil types and land attributes. The findings of this study can identify which stressors should be included when investigating the impact of climate change on groundwater recharge

Trends and variability of water balance components over a tropical savanna and Eucalyptus forest in Australia

Abstract In this paper, the long-term dynamics of water balance components in two different contrasting ecosystems in Australia were simulated with an ecohydrological model (WAter Vegetation Energy and Solute modelling (WAVES)) over the period 1950–2015. The selected two ecosystems are woodland savanna in Daly River and eucalyptus forest in Tumbarumba. The WAVES model was first manually calibrated and validated against soil water content measured by cosmic-ray probe and evapotranspiration measured with eddy flux techniques. The calibrated model was then used to simulate long-term water balance components with observed climate data at two sites. Analyzing the trends and variabilities of potential evapotranspiration and precipitation is used to interpret the climate change impacts on ecosystem water balance. The results showed that the WAVES model can accurately simulate soil water content and evapotranspiration at two study sites. Over the period of 1950–2015, annual evapotranspiration at both sites showed decreasing trends (−1.988 mm year−1 in Daly and −0.381 mm year−1 in Tumbarumba), whereas annual runoff in Daly increased significantly (5.870 mm year−1) and decreased in Tumbarumba (–0.886 mm year−1). It can be concluded that the annual runoff trends are consistent with the rainfall trends, whereas trends in annual evapotranspiration are influenced by both rainfall and potential evapotranspiration. The results can provide evidence for controlling the impacting factors for different ecosystems under climate change.</jats:p