Trends in Groundwater Levels in Alluvial Aquifers of the Murray–Darling Basin and Their Attributions

Abstract

Groundwater levels represent the aggregation of different hydrological processes acting at multiple spatial and temporal scales within aquifer systems. Analyzing trends in groundwater levels is therefore essential to quantify available groundwater resources for beneficial use, and to devise plans/policies to better manage these resources. In this work, three trend analysis methods are employed to detect long-term (1971–2021) trends in annual mean/minimum/maximum depth to water table (DTW) at 910 bores. This analysis is performed in eight main alluvial systems in the Murray–Darling Basin (MDB), Australia, which concentrate nearly 75% of groundwater use. The results show: (a) an overall increasing trend in DTW across alluvial aquifers attributable to changes in recharge from rainfall and groundwater extraction; (b) the analysis methods employed show similar statistical significances and magnitudes, but differences exist; (c) the annual minimum DTW has a smaller trend magnitude than annual mean DTW, and the annual maximum DTW has a larger trend magnitude than mean DTW; (d) trends in annual rainfall and potential evaporation, and cumulative number of production bores, are consistent with the groundwater trends; (e) irrigation is responsible for some of the decreasing trend in groundwater level. These results could be used to target further research and monitoring programs, and inform groundwater resource management decisions in the MDB.

Long-Term Trends and Variability of Hydroclimate Variables and Their Linkages with Climate Indices in the Songhua River

“…Three trend analysis methods [21] were used in this study to explore the trends of precipitation, temperature, and streamflow. These methods were chosen because they are the most three popular trend analysis methods in the literature with different theories and assumptions.…”

“…It basically builds a linear regression model between a time series of hydroclimate variables (precipitation, temperature, and streamflow in this study) with the time [21]. The significance level α = 0.05 (which corresponds to a p < 0.025 with a two-sided test) is used in this study to detect whether a linear regression slope is statistically significant.…”

“…The two-period method simply compares the mean values of hydroclimate variables in two time periods. To further explore the trends at different quantiles of hydroclimate variables, equal lengths for these two periods are required by using the ITA method [21,26].…”

Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

“…The study area is located in southeast Australia in the state of New South Wales (NSW, Figure 1), where a severe and prolonged dry period occurred in 1997-2009, which is generally referred to as the Millennium Drought [19]. This drought event, together with increasing groundwater extraction, has contributed to and added to the significant groundwater depletion observed in the last 50 years [20]. Ground deformation that occurs as a result of irreversible, inelastic compaction that follows dewatering is of concern for the NSW state government.…”

“…Critical head analysis was performed in 416 bores (a subset of the 975 bores with sufficient historical records). Groundwater levels, which represent the aggregation of different hydrogeological processes, including hydroclimate conditions and groundwater extraction [20], were correlated in this study with deformation in order to determine where in space and at which aquifer depths groundwater oscillations could lead to elastic compaction and expansion observed at the ground surface by InSAR.…”