GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 37-8
Presentation Time: 3:45 PM

QUANTIFYING DIFFUSE RECHARGE TO THE EDWARDS AQUIFER USING MICRO-METEOROLOGY AND REMOTE SENSING


CALDWELL, Todd G.1, BONGIOVANNI, Tara1, YOUNG, Michael H.1 and GARY, Marcus O.2, (1)Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, University Station, Box X, Austin, TX 78713, (2)Edwards Aquifer Authority, 1615 N. St. Mary's St, San Antonio, TX 78215, todd.caldwell@beg.utexas.edu

Climate, vegetation, soils, and geology govern groundwater recharge at varying scales. In karst systems, recharge often is quantified as a discrete process from losses along stream reaches. Interfluve or diffuse recharge, studied here, is commonly estimated as a fraction of annual precipitation, and together these estimates are needed for water resource management. However, diffuse recharge can be a significant and consistent contributor to groundwater. Here, we used field measurements of evapotranspiration (ET) and remote sensing to quantify upland drainage area contribution to diffuse deep percolation across 11,400 ha of the Edwards-Trinity Aquifer recharge zones near San Antonio, Texas. Three eddy covariance systems were used to measure energy and CO2 gas fluxes over a grassland, oak savanna, and cedar woodland ecosystem, the primary vegetation cover in central Texas. After despiking and gap filling, energy balance closure from 30-minute mean fluxes were 86-88%. Monthly Landsat surface reflectance-derived spectral indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Modified SAVI (MSAVI), were obtained over each eddy covariance footprint and used to model the measured monthly ET flux using nonlinear regression. Correlation coefficients of 0.53, 0.86, 0.90, and 0.89 were obtained for each index, respectively. We scaled this relationship monthly across all upland Landsat pixels to produce monthly ET maps at 30 m resolution. We derived a maximum monthly potential recharge by subtracting ET from gage-corrected, Nexrad precipitation. Preliminary results over 13 months indicate a mean maximum monthly recharge potential of 24 mm ranging from -64 to 147 mm (negative recharge implying some carryover in water from the prior month). Using cumulative water balances, mean maximum monthly recharge potential was 35 ± 11% of precipitation. These results indicate that diffuse recharge may be significantly higher than commonly assumed, given that much of this water has likely made it beyond the root zone. We plan to refine this model as our data set grows and use geophysics and modeling to assess the role of storage dynamics in soils and epikarst.