|2009 Portland GSA Annual Meeting (18-21 October 2009)|
|Paper No. 227-1|
|Presentation Time: 8:05 AM-8:20 AM|
INTEGRATION OF GRACE DATA WITH INFERENCES FROM TRADITIONAL DATASETS FOR A BETTER UNDERSTANDING OF THE TIME-DEPENDENT WATER STORAGE VARIABILITY IN LARGE-SCALE AQUIFERS: CASE STUDIES FROM AFRICA
MARSALA, Peter1, EL SAYED, Mohamed2, SULTAN, Mohamed3, WAHR, John4, MILEWSKI, Adam5, BECKER, Richard6, WELTON, Benjamin5, and BALEKAI, Rajesh7, (1) Geosciences, Western Michigan University, 1903 W. Michigan Avenue, Kalamazoo, MI 49008, email@example.com, (2) Geosciences, Western Michigan University, 1903 W. Michigan Avenue, Kalamazoo, 49008, (3) Geosciences, Western Michigan University, 1903 W. Michigan Ave, Kalamazoo, MI 49008-5241, (4) Physics, University of Colorado at Boulder, 2000 Colorado Avenue, Boulder, CO 80309, (5) Geosciences, Western Michigan University, 1903 W. Michigan Avenue, 1187 Rood Hall, Kalamazoo, MI 49008, (6) Environmental Sciences, University of Toledo, 2801 W. Bancroft, Toledo, OH 43606, (7) Computer Science, Western Michigan University, 1903 W. Michigan Avenue, Kalamazoo, MI 49008|
We developed and applied an interdisciplinary system approach involving analyses of GRACE gravity together with relevant remote sensing, topography, and geologic data sets to assess the capabilities of GRACE data for monitoring groundwater recharge, discharge, and flow in large-scale aquifers (e.g., Nubian Aquifer) across the African continent. The Grace data over the African continent were co-registered to other spatial data sets (e.g., geologic maps, Landsat TM, SRTM, MODIS, TRMM) and derived data products (e.g., NDVI, false color satellite images, slope, stream networks, watershed boundaries, distribution of basement uplifts) and analyzed in a web-based GIS environment for a better understanding of the inter-relationships between these spatial data sets. We examined water thickness half degree grids of GRACE data for 63 months between April 2002 and October 2007. The data was processed according to the following steps: (1) the temporal mean was remove, (2) Sean Swenson's destriping method was applied, (3) smoothed using Gaussian smoothing (radius: 250-km), (4) monthly water storage removed using Goddard's GLDAS/NOAH model, (5) the Red Sea signal was fitted and removed. Anthropogenic effects have not been accounted for, as the water storage model was forced with only precipitation. Comparisons between the examined data sets in the web-based GIS show the following general observations across the entire African continent: (a) large positive anomalies (standard deviation > 6 cm) observed on standard deviation images generated over periods of one, two, three, four, five, and six years, were persistent over the same areas and increased with the increase in sample population (number of observations), (b) anomalous areas on standard deviation images correlated spatially with the slopes and foothills of mountains over which the largest cumulative (4/2002 to 10/2007) precipitation (>3000 mm) was observed, (c) time series analyses over the anomalous areas showed persistent seasonal variations that are consistent with seasonal precipitation patterns. From these observations, the possibility arises that we are examining the effects of recharge, and/or surface runoff, and/or ground water flow.
2009 Portland GSA Annual Meeting (18-21 October 2009)
General Information for this Meeting
|Session No. 227|
Groundwater Resources in Developing Countries—The Contributions and Legacy of Robert N. Farvolden: A Memorial Session to Commemorate the 50th Anniversary of the Hydrogeology Division
Oregon Convention Center: D139/140
8:00 AM-12:00 PM, Wednesday, 21 October 2009
Geological Society of America Abstracts with Programs, Vol. 41, No. 7, p. 578
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