492
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
the spring and summer of 2005, which can also be clearly seen in the groundwater
storage plot. More recently, from 2007 to 2009, both groundwater and soil moisture
have been on a multiyear steep rising trend related to wetter climatic conditions in
Illinois.
Previous studies on Illinois hydroclimatology (Yeh et al. 1998; Rodell and
Famiglietti 2001; Seneviratne et al. 2004; Yeh and Famiglietti 2008) have concluded
that the changes in groundwater and soil moisture are the largest components of
TWS variations at the monthly time scale. Figure 20.4 indicates that both of them
have comparable amplitude of about 50–100 mm. For the two subsurface storages,
Yeh et al. (1998) showed the similarity in the seasonal cycle, with the peak occurring in March and a trough in August (for soil moisture) or September (for groundwater). Concerning snow, it is relatively insignificant, since Illinois is located at the
upwind direction of Lake Michigan such that the lake-effect snow is irrelevant (Yeh
et al. 1998). A close examination of the Illinois snow data reveals that, after a day
with snow occurrence, the snow accumulation lasted only 1–5 days for most of the
cases. Therefore, the snow storage effect in Illinois is insignificant in monthly water
balance computation. Rodell and Famiglietti (2001) (Figure 20.2) further showed
that monthly changes in snow and reservoir storage are only occasionally significant, with a maximum of ~10 mm/month. In addition, Yeh et al. (1998) and Yeh and
Famiglietti (2008) have found close agreement between regional evaporation estimates from both the terrestrial and atmospheric water balance computations, suggesting that the role of human withdrawal or interference in streamflow may not be
significant in Illinois. Given the limited role of snow and other surface water storages
in this region, they are no considered major TWS components here.
20.4.3 MatSiRo lSM
The hydrologic modeling framework used in this study consists of a LSM-MATSIRO
(Takata et al. 2003; Kim et al. 2009) and a global runoff routing scheme–total runand a global runoff routing scheme–total runand a global runoff routing scheme–total runoff integrated pathway (TRIP) (Oki and Sud 1998). The MATSIRO model has a
single-layer of canopy, three variable snow layers with a subgrid distribution of snow
cover, and five soil layers of 4-m total thickness. Similar to most LSMs, MATSIRO
lacks any explicit representation of water table dynamics, which can have an important contribution to seasonal TWS variations, particularly for humid regions such
as Illinois. TRIP is a global river routing scheme that routes the runoff simulated
by MATSIRO through river networks based on topographic gradient and an effecthrough river networks based on topographic gradient and an effecand an effec- ffective velocity defined as an integrated mean velocity of rainwater traveling from land
surface to river mouth through various paths. Thus, TRIP can effectively simulate
unrepresented fast subsurface processes as a part of its dynamics (Oki 1999).
Therefore, river storage calculated by TRIP is virtually the water storage moving
laterally toward stream outlets, including down-slope surface fl ow, shallow latdown-slope surface fl ow, shallow latdown-slope surface fl ow, shallow latsurface fl ow, shallow latflow, shallow latshallow lat- lateral groundwater movement, and channel flow. The MATSIRO-TRIP modeling
framework was successfully applied to study the terrestrial water cycle (Hirabayashi
et al. 2005; Kim et al. 2009) and the assessment of hydrologic extremes on the global
scale (Hirabayashi et al. 2008).
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
the spring and summer of 2005, which can also be clearly seen in the groundwater
storage plot. More recently, from 2007 to 2009, both groundwater and soil moisture
have been on a multiyear steep rising trend related to wetter climatic conditions in
Illinois.
Previous studies on Illinois hydroclimatology (Yeh et al. 1998; Rodell and
Famiglietti 2001; Seneviratne et al. 2004; Yeh and Famiglietti 2008) have concluded
that the changes in groundwater and soil moisture are the largest components of
TWS variations at the monthly time scale. Figure 20.4 indicates that both of them
have comparable amplitude of about 50–100 mm. For the two subsurface storages,
Yeh et al. (1998) showed the similarity in the seasonal cycle, with the peak occurring in March and a trough in August (for soil moisture) or September (for groundwater). Concerning snow, it is relatively insignificant, since Illinois is located at the
upwind direction of Lake Michigan such that the lake-effect snow is irrelevant (Yeh
et al. 1998). A close examination of the Illinois snow data reveals that, after a day
with snow occurrence, the snow accumulation lasted only 1–5 days for most of the
cases. Therefore, the snow storage effect in Illinois is insignificant in monthly water
balance computation. Rodell and Famiglietti (2001) (Figure 20.2) further showed
that monthly changes in snow and reservoir storage are only occasionally significant, with a maximum of ~10 mm/month. In addition, Yeh et al. (1998) and Yeh and
Famiglietti (2008) have found close agreement between regional evaporation estimates from both the terrestrial and atmospheric water balance computations, suggesting that the role of human withdrawal or interference in streamflow may not be
significant in Illinois. Given the limited role of snow and other surface water storages
in this region, they are no considered major TWS components here.
20.4.3 MatSiRo lSM
The hydrologic modeling framework used in this study consists of a LSM-MATSIRO
(Takata et al. 2003; Kim et al. 2009) and a global runoff routing scheme–total runand a global runoff routing scheme–total runand a global runoff routing scheme–total runoff integrated pathway (TRIP) (Oki and Sud 1998). The MATSIRO model has a
single-layer of canopy, three variable snow layers with a subgrid distribution of snow
cover, and five soil layers of 4-m total thickness. Similar to most LSMs, MATSIRO
lacks any explicit representation of water table dynamics, which can have an important contribution to seasonal TWS variations, particularly for humid regions such
as Illinois. TRIP is a global river routing scheme that routes the runoff simulated
by MATSIRO through river networks based on topographic gradient and an effecthrough river networks based on topographic gradient and an effecand an effec- ffective velocity defined as an integrated mean velocity of rainwater traveling from land
surface to river mouth through various paths. Thus, TRIP can effectively simulate
unrepresented fast subsurface processes as a part of its dynamics (Oki 1999).
Therefore, river storage calculated by TRIP is virtually the water storage moving
laterally toward stream outlets, including down-slope surface fl ow, shallow latdown-slope surface fl ow, shallow latdown-slope surface fl ow, shallow latsurface fl ow, shallow latflow, shallow latshallow lat- lateral groundwater movement, and channel flow. The MATSIRO-TRIP modeling
framework was successfully applied to study the terrestrial water cycle (Hirabayashi
et al. 2005; Kim et al. 2009) and the assessment of hydrologic extremes on the global
scale (Hirabayashi et al. 2008).