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Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia

Author

Listed:
  • Guobin Fu

    (CSIRO Environment, Floreat, WA 6014, Australia)

  • Stephanie R. Clark

    (CSIRO Environment, Eveleigh, NSW 2015, Australia)

  • Dennis Gonzalez

    (CSIRO Environment, Adelaide, SA 5000, Australia)

  • Rodrigo Rojas

    (CSIRO Environment, Dutton Park, Brisbane, QLD 4102, Australia
    Current address: SLR Consulting Australia Pty Ltd., Brisbane, QLD 4000, Australia.)

  • Sreekanth Janardhanan

    (CSIRO Environment, Dutton Park, Brisbane, QLD 4102, Australia)

Abstract

Understanding the temporal patterns in groundwater levels and their spatial distributions is essential for quantifying the natural and anthropogenic impacts on groundwater resources for better management and planning decisions. The two most popular clustering analysis methods in the literature, hierarchical clustering analysis and self-organizing maps, were used in this study to investigate the temporal patterns of groundwater levels from a dataset with 910 observation bores in the largest river system in Australia. Results showed the following: (1) Six dominant cluster patterns were found that could explain the temporal groundwater trends in the Murray–Darling Basin. Interpretation of each of these patterns indicated how groundwater in each cluster behaved before, during, and after the Millennium Drought. (2) The two methods produced similar results, indicating the robustness of the six dominant patterns that were identified. (3) The Millennium Drought, from 1997 to 2009, had a clear impact on groundwater level temporal variability and trends. An example causal attribution analysis based on the clustering results (using a neural network model to represent groundwater level dynamics) is introduced and will be expanded in future work to identify drivers of temporal and spatial changes in groundwater level for each of the dominant patterns, leading to possibilities for better water resource understanding and management.

Suggested Citation

  • Guobin Fu & Stephanie R. Clark & Dennis Gonzalez & Rodrigo Rojas & Sreekanth Janardhanan, 2023. "Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16295-:d:1287409
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    References listed on IDEAS

    as
    1. Stephanie R. Clark & Dan Pagendam & Louise Ryan, 2022. "Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks," IJERPH, MDPI, vol. 19(9), pages 1-31, April.
    2. Inge E. M. Graaf & Tom Gleeson & L. P. H. (Rens) van Beek & Edwin H. Sutanudjaja & Marc F. P. Bierkens, 2019. "Environmental flow limits to global groundwater pumping," Nature, Nature, vol. 574(7776), pages 90-94, October.
    3. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
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