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TitleGroundwater storage variability and annual recharge using well-hydrograph and GRACE satellite data
AuthorHenry, C M; Allen, D M; Huang, J
SourceHydrogeology Journal vol. 19, 2011 p. 741-755,
Alt SeriesEarth Sciences Sector, Contribution Series 20110389
Mediapaper; on-line; digital
File formatpdf
AreaBougouni; Mali
Lat/Long WENS 5.0000 10.0000 15.0000 10.0000
Subjectsgeophysics; hydrogeology; remote sensing; groundwater; groundwater circulation; groundwater discharge; groundwater regimes; satellites; GRACE satellite
Illustrationslocation maps; histograms; graphs; plots; tables; cross-sections
ProgramNational Aquifer Evaluation & Accounting Project, Groundwater Geoscience
AbstractMost studies using GRACE (Gravity Recovery and Climate Experiment) data for examining water storage anomalies have rich hydrogeological databases. Here, GRACE data are analyzed for southern Mali, Africa, a region with sparse hydrogeological data. GRACE data (2002 - 2008) did not overlap with observed groundwaterlevel data (1982 - 2002). Terrestrial water storage from GRACE was corrected for soil moisture using the Global Land Data Assimilation System (GLDAS) model to obtain monthly groundwater storage anomalies and annual net recharge. Historical storage anomalies and net recharge were determined using the water-table fluctuation method for available observation wells. Average annual net recharge averaged 149.1mm (or 16.4% of annual rainfall) and 149.7mm (14.8%) from historical water level and GRACE data, respectively. Monthly storage anomaly lows and peaks were observed in May and September, respectively, but have a shift in peak to November using the corrected GRACE data, suggesting that the GLDAS model may poorly predict the timing of soil-water storage in this region. Notwithstanding problems with the GLDAS model, the soil moisture-corrected GRACE data accurately predict the relative timing and magnitude of groundwater-storage changes, suggesting that GRACE data are valuable for identifying long-term regional changes in groundwater storage in areas with sparse hydrogeological data.