uncertainty in deriving estimates of aquifer storage changes from GRACE observations would arise mainly from the removal, via land surface modeling, of the
effects of soil moisture changes from the gravity signal. Rodell and Famiglietti [54]
were predicting the total uncertainty to be about 8.7 mm. Comparing the 4-year
groundwater storage change bars with the estimated GRACE uncertainty bars in
Fig. 3, one can see that such estimates would have been useful for tracking
groundwater changes in the High Plains aquifer during most 4-year periods.
Yirdaw et al. [55] investigated the Canadian Prairie drought by employing total
water storage anomalies obtained from the GRACE remote sensing satellite mission. The obtained GRACE-based total water storages were validated using storages estimated from the atmospheric-based water balance P-E (precipitationevaporation) computation in conjunction with measured streamflow records. The
results from their study corroborated the potential of the GRACE-based technique
as a useful tool for the characterization of the 2002/2003 Canadian Prairie droughts.
Li et al. [56] assimilated anomalies of terrestrial water storage (TWS) observed
by the GRACE satellite mission into the NASA Catchment land surface model in
western and central Europe for a 7-year period. GRACE data assimilation led to
improved runoff estimates (in temporal correlation and root mean square error)
even in basins smaller than the effective resolution of GRACE. Signals of drought
in GRACE TWS correlated well with the MODIS NDVI in most areas. Although
they detected the same droughts during warm seasons, drought signatures in
GRACE-derived TWS exhibited greater persistence than those in NDVI throughout
all seasons, in part due to limitations associated with the seasonality of
vegetation [56].
To derive estimates of aquifer storage changes from GRACE observations, one
faces the challenge of removing the effects of soil moisture changes from the
gravity signal by means of land surface modeling. Plans for a follow-on mission
to GRACE may result in groundwater storage estimates that are more accurate and
of greater relevance to typical large aquifer systems [57]. The GRACE follow-on
mission is scheduled for 2017 and would refly the identical GRACE spacecraft and
instruments but supplement the micrometer-level accuracy microwave measurement with a laser interferometer with nanometer-level accuracy.
Models are being developed to identify groundwater potential zones [6–8]. For
example, Asadi et al. [1] have performed a model study for sites in the Hyderabad
and other districts of India, which identifies groundwater potential zones using
IRS-ID PAN and LISS-III satellite geocoded data on a 1:50,000 scale. The information from base maps, drainage maps, watershed maps, geomorphology maps,
groundwater table maps, and groundwater infiltration maps were used as data layers
in a GIS to prepare a database. Then the relationships between the GIS data layers
were analyzed and integrated to prepare the groundwater potential zones map.
Machiwal et al. [58] used ten thematic layers in a GIS, including data obtained
by remote sensing, and multi-criteria decision-making techniques (MCDM) to
delineate groundwater potential zones in the Udaipur district of Rajasthan, India.
The GIS layers included topographic elevation, land slope, geomorphology, geology, soil type, pre- and post-monsoon groundwater water depths, annual net
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
43
effects of soil moisture changes from the gravity signal. Rodell and Famiglietti [54]
were predicting the total uncertainty to be about 8.7 mm. Comparing the 4-year
groundwater storage change bars with the estimated GRACE uncertainty bars in
Fig. 3, one can see that such estimates would have been useful for tracking
groundwater changes in the High Plains aquifer during most 4-year periods.
Yirdaw et al. [55] investigated the Canadian Prairie drought by employing total
water storage anomalies obtained from the GRACE remote sensing satellite mission. The obtained GRACE-based total water storages were validated using storages estimated from the atmospheric-based water balance P-E (precipitationevaporation) computation in conjunction with measured streamflow records. The
results from their study corroborated the potential of the GRACE-based technique
as a useful tool for the characterization of the 2002/2003 Canadian Prairie droughts.
Li et al. [56] assimilated anomalies of terrestrial water storage (TWS) observed
by the GRACE satellite mission into the NASA Catchment land surface model in
western and central Europe for a 7-year period. GRACE data assimilation led to
improved runoff estimates (in temporal correlation and root mean square error)
even in basins smaller than the effective resolution of GRACE. Signals of drought
in GRACE TWS correlated well with the MODIS NDVI in most areas. Although
they detected the same droughts during warm seasons, drought signatures in
GRACE-derived TWS exhibited greater persistence than those in NDVI throughout
all seasons, in part due to limitations associated with the seasonality of
vegetation [56].
To derive estimates of aquifer storage changes from GRACE observations, one
faces the challenge of removing the effects of soil moisture changes from the
gravity signal by means of land surface modeling. Plans for a follow-on mission
to GRACE may result in groundwater storage estimates that are more accurate and
of greater relevance to typical large aquifer systems [57]. The GRACE follow-on
mission is scheduled for 2017 and would refly the identical GRACE spacecraft and
instruments but supplement the micrometer-level accuracy microwave measurement with a laser interferometer with nanometer-level accuracy.
Models are being developed to identify groundwater potential zones [6–8]. For
example, Asadi et al. [1] have performed a model study for sites in the Hyderabad
and other districts of India, which identifies groundwater potential zones using
IRS-ID PAN and LISS-III satellite geocoded data on a 1:50,000 scale. The information from base maps, drainage maps, watershed maps, geomorphology maps,
groundwater table maps, and groundwater infiltration maps were used as data layers
in a GIS to prepare a database. Then the relationships between the GIS data layers
were analyzed and integrated to prepare the groundwater potential zones map.
Machiwal et al. [58] used ten thematic layers in a GIS, including data obtained
by remote sensing, and multi-criteria decision-making techniques (MCDM) to
delineate groundwater potential zones in the Udaipur district of Rajasthan, India.
The GIS layers included topographic elevation, land slope, geomorphology, geology, soil type, pre- and post-monsoon groundwater water depths, annual net
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
43