GEOSCAN, résultats de la recherche

Menu GEOSCAN


TitreGroundwater storage variability and annual recharge using well-hydrograph and GRACE satellite data
AuteurHenry, C M; Allen, D M; Huang, J
SourceHydrogeology Journal vol. 19, 2011 p. 741-755, https://doi.org/10.1007/s10040-011-0724-3
Année2011
Séries alt.Secteur des sciences de la Terre, Contribution externe 20110389
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.1007/s10040-011-0724-3
Mediapapier; en ligne; numérique
Formatspdf
Lat/Long OENS 5.0000 10.0000 15.0000 10.0000
Sujetstélédétection; eau souterraine; circulation des eaux souterraines; résurgence des eaux souterraines; régimes des eaux souterraines; satellites; géophysique; hydrogéologie
Illustrationslocation maps; histograms; graphs; plots; tables; cross-sections
ProgrammeNational Aquifer Evaluation & Accounting Project, Géoscience des eaux souterraines
Résumé(disponible en anglais seulement)
Most 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.
GEOSCAN ID290076