Mapping potential groundwater-dependent ecosystems for sustainable management

Area of Science:

• Ecology
• Hydrology
• Remote Sensing

Background:

• Groundwater-dependent ecosystems (GDEs) are vital but often lack comprehensive location inventories for sustainable aquifer management.
• Identifying GDEs is crucial for understanding ecosystem health and water resource management.

Purpose of the Study:

• To develop and apply a two-step algorithm for identifying potential GDEs at state/province and aquifer/basin scales.
• To create a GDE index using climate, topography, hydrology, and ecology data for Texas.
• To map detailed GDEs in the Edwards Aquifer region using remote sensing data.

Main Methods:

• Constructed a GIS database for Texas incorporating climate, topography, hydrology, and ecology data.
• Calculated a GDE index combining vegetative and hydrological indicators at the state/province scale.
• Developed a remote sensing algorithm using Landsat ETM+ and MODIS imagery to analyze NDVI dynamics for GDE identification at the aquifer/basin scale.

Main Results:

• Central Texas, particularly the Edwards Aquifer region, showed the highest potential for GDEs.
• The remote sensing algorithm identified 8% of natural vegetation as likely GDEs in the Edwards Aquifer region.
• Identified GDEs were predominantly found on shallow soils (average 45 cm depth) and included species like live oak, ashe juniper, and mesquite.

Conclusions:

• The developed two-step algorithm effectively identifies potential GDEs at multiple scales.
• The study provides crucial data for sustainable management of groundwater resources and GDEs in Texas.
• Remote sensing offers a viable method for detailed GDE mapping, especially in data-scarce regions.