1. Assessing surface-groundwater interactions for sustaining spring wetlands of the Great Artesian Basin, Australia
- Author
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Pankaj R. Kaushik, Christopher E. Ndehedehe, Ryan M. Burrows, Mark R. Noll, and Mark J. Kennard
- Subjects
Spring wetlands ,Surface-groundwater interaction ,Great Artesian Basin ,Partial Least Square Regression ,Gravity Recovery and Climate Experiment ,Ecology ,QH540-549.5 - Abstract
The Great Artesian Basin (GAB) is one of the world’s largest actively recharging aquifers. Groundwater discharges from the GAB sustain numerous spring wetlands, which have great ecological, scientific, and socio-economic significance. However, groundwater extraction and variation over time have had an impact on the quantity and area of spring wetlands with a 38% decline in extent since 1900. A major barrier to understanding variability in surface–groundwater interactions in spring wetlands of the GAB is the lack of observational data across critical spatial and temporal scales. Satellite observations have the ability to overcome this barrier and allow the evaluation of spring wetland responses to groundwater storage (GWS) variation. We investigated how GWS, and its associated drivers such as evapotranspiration (ET), soil moisture storage (SMS), and rainfall, in the GAB, influence the extent of surface water at five spring supergroups (Eulo, Barcaldine, Flinders, Springsure, and Springvale). We used satellite observations (2002–2017) to assess ET, SMS, rainfall, the normalized difference vegetation index (NDVI) and the modified normalized difference water index (mNDWI) for observing surface–groundwater interactions. NDVI responded positively to the GWS variation over the GAB and varies from sub-basin to sub- basin, with higher correlations in the Carpentaria sub-basin and some parts of Central and Western Eromanga. GWS variations was correlated with ET, SMS, rainfall, NDVI, mNDWI and surface water level (SWL). After a strong La Niña began in 2010, we uncovered relatively higher linear relationships between different components (ET, SMS, rainfall, NDVI, mNDWI, and SWL) and GWS variation (R2 > 0.50) than before the La Niña (R2
- Published
- 2023
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