482
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
scales appropriate to their dynamics are deficient. Terrestrial water storage (TWS),
as a fundamental component of the global water cycle, is of great importance for
water resources, climate, agriculture, and ecosystem. TWS controls the partitioning
of precipitation into evaporation and runoff and the partitioning of net radiation into
the sensible and latent heat fluxes. More importantly, TWS change (TWSC) is a basic
quantity in closing terrestrial water budgets (Ngo-Duc et al. 2005; Güntner et al.
2007; Syed et al. 2008; Yeh and Famiglietti 2008).
Among various components of the global water cycle, TWS is one of the most
difficult to estimate. Despite its importance, no extensive networks currently exist for
monitoring large-scale TWS variations and its constitutive components. Reliable datasets of large-scale TWS are extremely scarce. The role of TWS in the global water
cycle has received relatively little attention compared with other hydro-meteorological
processes. As our understanding of interactive Earth system processes grows and the
need for more accurate assessment of world water resources increases, our capability to accurately quantify TWS variations must be greatly expanded. In addition to
contributing to water resources management, better characterization of large-scale
TWSs will improve basin- and regional-scale water balance studies, enable better
parameterizations in land surface models (LSMs), and contribute to improved understanding of land surface–atmosphere interactions.
Historically, global hydrological cycles have been assessed by a synthesis of in situ
observational data, for example, precipitation and streamflow from gauge measurements or air humidity and pressure from atmospheric radiosonde data. Over the last
two decades, global atmospheric reanalysis datasets estimated by the four-dimensional
data assimilation technique have enabled global water balance estimation by using
the atmospheric or combined land–atmosphere water balance computation (Oki et al.
1995; Yeh et al. 1998; Oki 1999; Seneviratne et al. 2004; Hirschi et al. 2006, 2007;
Yeh and Famiglietti 2008). The column-integrated water vapor convergence provides
a global distribution of precipitation minus evapotranspiration if the temporal variation
of precipitable water is considered to be zero. The combined land–atmosphere water
balance computation using atmospheric and river discharge data can be used to estimate the temporal change of spatially averaged TWS over large areas. The accuracy
of atmospheric water balance computations is highly dependent on the size of the area
investigated (>10 6 km 2 as suggested by Rasmusson 1968, 1971 and Yeh et al. 1998).
Another alternative for large-scale TWS estimation is via the large-scale LSMs
used for climatic studies. The state-of-the-art LSMs are constrained by realistic meteorological forcing and ingest satellite- and ground-based observational data using data
assimilation techniques. Although there is still much deficiency in model parameterization and parameter calibration, to date, LSMs remain the only feasible tool to produce
optimal fields of land surface states and fluxes over large areas at any time resolutions.
20.2 GRAVITY RECOVERY AND CLIMATE
EXPERIMENT SATELLITE MISSION
Satellite observations of the earth’s time-variable gravity field from the Gravity
Recovery and Climate Experiment (GRACE) mission (Tapley et al. 2004) have provided another unique opportunity of monitoring TWS variations from space (Rodell
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
scales appropriate to their dynamics are deficient. Terrestrial water storage (TWS),
as a fundamental component of the global water cycle, is of great importance for
water resources, climate, agriculture, and ecosystem. TWS controls the partitioning
of precipitation into evaporation and runoff and the partitioning of net radiation into
the sensible and latent heat fluxes. More importantly, TWS change (TWSC) is a basic
quantity in closing terrestrial water budgets (Ngo-Duc et al. 2005; Güntner et al.
2007; Syed et al. 2008; Yeh and Famiglietti 2008).
Among various components of the global water cycle, TWS is one of the most
difficult to estimate. Despite its importance, no extensive networks currently exist for
monitoring large-scale TWS variations and its constitutive components. Reliable datasets of large-scale TWS are extremely scarce. The role of TWS in the global water
cycle has received relatively little attention compared with other hydro-meteorological
processes. As our understanding of interactive Earth system processes grows and the
need for more accurate assessment of world water resources increases, our capability to accurately quantify TWS variations must be greatly expanded. In addition to
contributing to water resources management, better characterization of large-scale
TWSs will improve basin- and regional-scale water balance studies, enable better
parameterizations in land surface models (LSMs), and contribute to improved understanding of land surface–atmosphere interactions.
Historically, global hydrological cycles have been assessed by a synthesis of in situ
observational data, for example, precipitation and streamflow from gauge measurements or air humidity and pressure from atmospheric radiosonde data. Over the last
two decades, global atmospheric reanalysis datasets estimated by the four-dimensional
data assimilation technique have enabled global water balance estimation by using
the atmospheric or combined land–atmosphere water balance computation (Oki et al.
1995; Yeh et al. 1998; Oki 1999; Seneviratne et al. 2004; Hirschi et al. 2006, 2007;
Yeh and Famiglietti 2008). The column-integrated water vapor convergence provides
a global distribution of precipitation minus evapotranspiration if the temporal variation
of precipitable water is considered to be zero. The combined land–atmosphere water
balance computation using atmospheric and river discharge data can be used to estimate the temporal change of spatially averaged TWS over large areas. The accuracy
of atmospheric water balance computations is highly dependent on the size of the area
investigated (>10 6 km 2 as suggested by Rasmusson 1968, 1971 and Yeh et al. 1998).
Another alternative for large-scale TWS estimation is via the large-scale LSMs
used for climatic studies. The state-of-the-art LSMs are constrained by realistic meteorological forcing and ingest satellite- and ground-based observational data using data
assimilation techniques. Although there is still much deficiency in model parameterization and parameter calibration, to date, LSMs remain the only feasible tool to produce
optimal fields of land surface states and fluxes over large areas at any time resolutions.
20.2 GRAVITY RECOVERY AND CLIMATE
EXPERIMENT SATELLITE MISSION
Satellite observations of the earth’s time-variable gravity field from the Gravity
Recovery and Climate Experiment (GRACE) mission (Tapley et al. 2004) have provided another unique opportunity of monitoring TWS variations from space (Rodell