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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
value; for example, snow-free land (25) and cloud (50) detected by one sensor would
be replaced by snow (200) if observed in the other. This procedure is similar to what
was done by Wang et al. (2009), Parajka and Blöschl (2008b), and Gafurov and
Bardossy (2009) during the combination of the Aqua and Terra MODIS products.
The remaining cloud pixels in the TAC are then replaced by the corresponding pixel
values from the unified AMSR-E, thus generating a new snow cover map with a
500-m resolution from the combination of Aqua, Terra, and AMSR-E (Gao et al.
2010b). The overall accuracy obtained from these blended products was 86% compared with 31%, 45%, and 49% of the Terra, Aqua, and Terra/Aqua-combined snow
cover products, respectively, under all sky conditions.
As noted earlier, SCAs derived from satellite products serve as important data
input for SRM to simulate and forecast runoff, both during the snowmelt period and
for conditions of a changed climate. To produce snow cover and runoff for a changed
climate, SRM uses measured snow cover from satellite monitoring in the present climate (Martinec et al. 2008) and modifies them in accordance with changes to the rate
of degree-day accumulation from warming or cooling and in accordance with changes
to new snow accumulation from precipitation associated with a climate scenario. A
limited number of studies have been published regarding effects of climate scenarios
on snowmelt runoff, yet such studies are important from a planning and adaptation
perspective. The next section describes a case study in which several climate change
scenarios are simulated in SRM, and conclusions are drawn from this work, some of
which are directly relevant to the case study and others are more generalized.
10.4 SNOWMELT RUNOFF MODELING UNDER CLIMATE
CHANGE—SNAKE RIVER HEADWATERS CASE STUDY
10.4.1 Model StRuctuRe of SRM
The SRM used in this chapter and illustrated in Figure 10.3 is a temperature degreeday-based model that calculates daily snowmelt, combines this with daily precipitation, and transforms these quantities into a daily runoff component and adds the
remainder into a runoff recession component according to Equation 10.1 (Martinec
et al. 2005). In practice, if the elevation range of a basin exceeds 500-m, a basin
is subdivided into multiple elevation zones (see Figure 10.3), and the first term on
the right-hand side of Equation 10.1 is applied to each zone. These terms are added
together, and Equation 10.1 is solved to get the total contribution to daily flow from
the different zones.
10.4.2 data SouRceS
Data used in this project were obtained from the Natural Resource Conservation
Service (NRCS) SNOTEL Network, the U.S. Geological Survey (USGS) National
Water Information System (NWIS), the U.S. Bureau of Reclamation (USBR) Pacific
Northwest Hydromet Network, and the NSIDC. The SNOTEL data used in this study
were taken from 11 stations in and around the hydrologic basin simulated in this
study, whose location is shown in Figure 10.4. The stations used were Base Camp,
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
value; for example, snow-free land (25) and cloud (50) detected by one sensor would
be replaced by snow (200) if observed in the other. This procedure is similar to what
was done by Wang et al. (2009), Parajka and Blöschl (2008b), and Gafurov and
Bardossy (2009) during the combination of the Aqua and Terra MODIS products.
The remaining cloud pixels in the TAC are then replaced by the corresponding pixel
values from the unified AMSR-E, thus generating a new snow cover map with a
500-m resolution from the combination of Aqua, Terra, and AMSR-E (Gao et al.
2010b). The overall accuracy obtained from these blended products was 86% compared with 31%, 45%, and 49% of the Terra, Aqua, and Terra/Aqua-combined snow
cover products, respectively, under all sky conditions.
As noted earlier, SCAs derived from satellite products serve as important data
input for SRM to simulate and forecast runoff, both during the snowmelt period and
for conditions of a changed climate. To produce snow cover and runoff for a changed
climate, SRM uses measured snow cover from satellite monitoring in the present climate (Martinec et al. 2008) and modifies them in accordance with changes to the rate
of degree-day accumulation from warming or cooling and in accordance with changes
to new snow accumulation from precipitation associated with a climate scenario. A
limited number of studies have been published regarding effects of climate scenarios
on snowmelt runoff, yet such studies are important from a planning and adaptation
perspective. The next section describes a case study in which several climate change
scenarios are simulated in SRM, and conclusions are drawn from this work, some of
which are directly relevant to the case study and others are more generalized.
10.4 SNOWMELT RUNOFF MODELING UNDER CLIMATE
CHANGE—SNAKE RIVER HEADWATERS CASE STUDY
10.4.1 Model StRuctuRe of SRM
The SRM used in this chapter and illustrated in Figure 10.3 is a temperature degreeday-based model that calculates daily snowmelt, combines this with daily precipitation, and transforms these quantities into a daily runoff component and adds the
remainder into a runoff recession component according to Equation 10.1 (Martinec
et al. 2005). In practice, if the elevation range of a basin exceeds 500-m, a basin
is subdivided into multiple elevation zones (see Figure 10.3), and the first term on
the right-hand side of Equation 10.1 is applied to each zone. These terms are added
together, and Equation 10.1 is solved to get the total contribution to daily flow from
the different zones.
10.4.2 data SouRceS
Data used in this project were obtained from the Natural Resource Conservation
Service (NRCS) SNOTEL Network, the U.S. Geological Survey (USGS) National
Water Information System (NWIS), the U.S. Bureau of Reclamation (USBR) Pacific
Northwest Hydromet Network, and the NSIDC. The SNOTEL data used in this study
were taken from 11 stations in and around the hydrologic basin simulated in this
study, whose location is shown in Figure 10.4. The stations used were Base Camp,