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Paper No. 3
Presentation Time: 2:15 PM
INTEGRATION OF REMOTE SENSING, HYDROLOGIC, AND GEOPHYSICAL DATA TO DETERMINE THE TIME-VARYING BEHAVIOR OF A HYDROGEOLOGIC SYSTEM
In order to evaluate and carefully manage a groundwater resource, we need to understand the time-varying behavior of the interconnected, hydrogeologic system. We describe here a new methodology we will use for the integration of remote sensing, hydrologic and geophysical data into the decision-making framework for water management. We have chosen for our study area the San Luis Basin, which spans the border of Colorado and New Mexico. We use remote sensing and hydrologic data sets to evaluate the dominant temporal signatures of key components of the freshwater cycle in the basin. These components include precipitation, evapotranspiration, stream flow, and the changes in storage of water as atmospheric vapor, soil moisture, groundwater, and snow. All data are processed to derive monthly averages over the entire basin. This analysis allows us to identify the basin-scale, long-term trends in the supply of freshwater. Water management in the basin, however, requires a more detailed understanding of the spatial and temporal patterns of groundwater recharge and withdrawal. The challenge in the San Luis Basin, and a central component of our research, is characterizing the spatially heterogeneous, time-varying behavior of the groundwater aquifers. Our approach involves using a combination of surface and airborne geophysical data and interferometric synthetic aperture radar (InSAR) data. The geophysical data are used to develop an improved model of the subsurface structure and lithology. The interferograms that we have created from the InSAR data provide information about the deformation of the land surface in the time period 1992 to 1999. The use of these forms of data, along with available hydrologic data, allows us to develop a model of the subsurface that captures key information about the structure, connectivity, and movement of groundwater in the aquifer system that could not be obtained with more limited data sets.