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Modeling Snowmelt Runoff under Climate Change Scenarios
sensitive to the estimated albedo values, which might make the approach more suitable for runoff simulations than for real-time runoff forecasts. However, Brubaker et
al. (1996) found out that the addition of the net radiation component to temperature
index-based SRM only improved results in two out of six snowmelt seasons. Gillan
et al. (2010) also employed the combination of the radiation component together with
the temperature index–based snow accumulation model; the result compared well
with ground measurements.
Nevertheless, as observed by Rango and Martinec (1995), temperature index
models produce good results when used in connection with runoff models like SRM.
This is because daily deviations are easily smoothed out by the basin response. In
addition, the temperature index-based SRM can easily be used in evaluating various
climate change scenarios associated with a temperature change, because temperature is one of the key climate variables to be affected by climate change (Kustas et
al. 1994).
10.2.2 SnowMelt Runoff Model—StRuctuRe and detailS
The SRM is classified by the World Meteorological Organization (WMO 1990) as a
deterministic conceptual model using semidistributed or larger subareas (elevation
zones; Van Katwijk et al. 1993). The SRM is designed to simulate and forecast daily
stream flow in mountainous basins where snowmelt is a major runoff component
and has also been applied to evaluate the effect of a changed climate on seasonal
snow cover and runoff (Martinec et al. 2005). The fundamental principle of SRM
is to use a temperature index or degree-day factor in the algorithm to model snowmelt with the aid of the ratios of snow cover at the watershed scale determined from
remote sensing observations, leading to the simulation of stream flow from the basin
(Wang et al. 2010; Day 2009; Brubaker et al. 1996). The model is an improvement on
the traditional degree-day approach, since it makes use of remotely sensed observations of the SCA (Kustas et al. 1994). Among several snowmelt forecasting models,
SRM is the most widely used (Tekeli et al. 2005; Ferguson 1999) and the most successful model for simulating runoffs (Wang and Li 2006). Its ability to make use of
remotely sensed snow cover observations has further increased the applicability of
SRM to larger basins, even though it was developed in small European basins by
Martinec in 1975. To date, the model has been applied to over 100 basins, situated
in 29 different countries as reported by Martinec et al. (2005). Furthermore, SRMs
were used several times on different basins to simulate the effects of climate change
on snowmelt runoff patterns (Martinec and Rango 1989; Hong and Guodong 2003;
Rango and Martinec 1994; Van Katwijk et al. 1993). Such success is largely due to
the simplicity of the model and the readily available input parameters that can be
determined easily from basin characteristics of geography, hydrology, and climate
(Hong and Guodong 2003).
Three basic input variables required by SRM to simulate snowmelt runoff or daily
discharge include precipitation, temperature, and SCA (Martinec and Rango 1989;
Martinec et al. 2008); these three SRM variables are also major variables in scenarios of climate change (Hong and Guodong 2003). While precipitation and temperature are easily obtained from published climate data, the SCA can be obtained
Modeling Snowmelt Runoff under Climate Change Scenarios
sensitive to the estimated albedo values, which might make the approach more suitable for runoff simulations than for real-time runoff forecasts. However, Brubaker et
al. (1996) found out that the addition of the net radiation component to temperature
index-based SRM only improved results in two out of six snowmelt seasons. Gillan
et al. (2010) also employed the combination of the radiation component together with
the temperature index–based snow accumulation model; the result compared well
with ground measurements.
Nevertheless, as observed by Rango and Martinec (1995), temperature index
models produce good results when used in connection with runoff models like SRM.
This is because daily deviations are easily smoothed out by the basin response. In
addition, the temperature index-based SRM can easily be used in evaluating various
climate change scenarios associated with a temperature change, because temperature is one of the key climate variables to be affected by climate change (Kustas et
al. 1994).
10.2.2 SnowMelt Runoff Model—StRuctuRe and detailS
The SRM is classified by the World Meteorological Organization (WMO 1990) as a
deterministic conceptual model using semidistributed or larger subareas (elevation
zones; Van Katwijk et al. 1993). The SRM is designed to simulate and forecast daily
stream flow in mountainous basins where snowmelt is a major runoff component
and has also been applied to evaluate the effect of a changed climate on seasonal
snow cover and runoff (Martinec et al. 2005). The fundamental principle of SRM
is to use a temperature index or degree-day factor in the algorithm to model snowmelt with the aid of the ratios of snow cover at the watershed scale determined from
remote sensing observations, leading to the simulation of stream flow from the basin
(Wang et al. 2010; Day 2009; Brubaker et al. 1996). The model is an improvement on
the traditional degree-day approach, since it makes use of remotely sensed observations of the SCA (Kustas et al. 1994). Among several snowmelt forecasting models,
SRM is the most widely used (Tekeli et al. 2005; Ferguson 1999) and the most successful model for simulating runoffs (Wang and Li 2006). Its ability to make use of
remotely sensed snow cover observations has further increased the applicability of
SRM to larger basins, even though it was developed in small European basins by
Martinec in 1975. To date, the model has been applied to over 100 basins, situated
in 29 different countries as reported by Martinec et al. (2005). Furthermore, SRMs
were used several times on different basins to simulate the effects of climate change
on snowmelt runoff patterns (Martinec and Rango 1989; Hong and Guodong 2003;
Rango and Martinec 1994; Van Katwijk et al. 1993). Such success is largely due to
the simplicity of the model and the readily available input parameters that can be
determined easily from basin characteristics of geography, hydrology, and climate
(Hong and Guodong 2003).
Three basic input variables required by SRM to simulate snowmelt runoff or daily
discharge include precipitation, temperature, and SCA (Martinec and Rango 1989;
Martinec et al. 2008); these three SRM variables are also major variables in scenarios of climate change (Hong and Guodong 2003). While precipitation and temperature are easily obtained from published climate data, the SCA can be obtained