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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
generate new S(t) curves automatically for the climate change scenarios from the
curves generated for the historical period based on degree-days of melt as described
by Martinec et al. (2005).
Finally, we employed SRM both in a historical simulation mode and in a climate
scenario mode. For the climate simulations, we used results from three different,
internationally recognized GCMs (see Table 10.3) generated for the IPCC using
the A1B carbon scenario. The temperature and precipitation output corresponded
to monthly averages over two 20-year modeling periods centered at the years 2030
and 2080, relative to the base period 1980–1999, providing six different climate scenarios to simulate in SRM (see Table 10.4).
Annual stream flow hydrographs from the SRM simulations associated with the
historical period and the six climate scenarios are shown in Figures 10.13 and 10.14.
The historical mode shows that the observed stream flow was accurately simulated by
SRM using the SCA method that we developed. For the climate change simulations,
four of the simulations produced an increase in the annual volume of streamflow,
and two produced a reduction; in all cases, this was due to corresponding changes
in annual precipitation. In addition, all climate scenarios advanced the spring runoff
earlier in time, both in terms of the occurrence of the peak runoff and in terms of the
time at which 50% of the total water-year flow occurred. This occurred because all
of the climate scenarios showed temperature warming through every month of the
year, albeit in different amounts in different months.
There are several significant conclusions that may be drawn from these results.
One relates to the variation of impact of climate change, that is, whether a climate change scenario, particularly related to precipitation, exacerbates or dampens the effect of the precipitation changes on annual stream flow runoff volume.
When simulating the effects of climate change on snowmelt runoff, it is important
to differentiate when and how perturbations to temperature and precipitation vary
throughout the year. This ought to be done at least on a seasonal basis, but preferably at a monthly time step. In our case study, the effect of an annual increase in
precipitation became more pronounced with respect to annual streamflow, whereas
the effect of a decrease in annual precipitation was slightly dampened owing to when
these changes occurred throughout the year. Secondly, there are differing degrees of
uncertainty between output variables from climate models. Precipitation is among
the less certain. Therefore, it is important to simulate a range of values of these
variables in order to gain a perspective on the breadth of possible impacts of climate
scenarios. Thirdly, under a global warming scenario, runoff will move earlier into
the springtime. This may have important implications for retention, storage, and
distribution of the water resources.
Snowmelt runoff has tremendous importance for much of the world. This importance spans a wide geographic as well as sectoral range. Consequently, there are
many uses for simulations of climate change impacts on snowmelt runoff, especially
for developing adaptation plans for a wide range of impacts. Remote sensing provides useful input for snowmelt runoff modeling and will continue to increase in
importance as new methods and instruments can be developed and as existing challenges to its use are overcome.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
generate new S(t) curves automatically for the climate change scenarios from the
curves generated for the historical period based on degree-days of melt as described
by Martinec et al. (2005).
Finally, we employed SRM both in a historical simulation mode and in a climate
scenario mode. For the climate simulations, we used results from three different,
internationally recognized GCMs (see Table 10.3) generated for the IPCC using
the A1B carbon scenario. The temperature and precipitation output corresponded
to monthly averages over two 20-year modeling periods centered at the years 2030
and 2080, relative to the base period 1980–1999, providing six different climate scenarios to simulate in SRM (see Table 10.4).
Annual stream flow hydrographs from the SRM simulations associated with the
historical period and the six climate scenarios are shown in Figures 10.13 and 10.14.
The historical mode shows that the observed stream flow was accurately simulated by
SRM using the SCA method that we developed. For the climate change simulations,
four of the simulations produced an increase in the annual volume of streamflow,
and two produced a reduction; in all cases, this was due to corresponding changes
in annual precipitation. In addition, all climate scenarios advanced the spring runoff
earlier in time, both in terms of the occurrence of the peak runoff and in terms of the
time at which 50% of the total water-year flow occurred. This occurred because all
of the climate scenarios showed temperature warming through every month of the
year, albeit in different amounts in different months.
There are several significant conclusions that may be drawn from these results.
One relates to the variation of impact of climate change, that is, whether a climate change scenario, particularly related to precipitation, exacerbates or dampens the effect of the precipitation changes on annual stream flow runoff volume.
When simulating the effects of climate change on snowmelt runoff, it is important
to differentiate when and how perturbations to temperature and precipitation vary
throughout the year. This ought to be done at least on a seasonal basis, but preferably at a monthly time step. In our case study, the effect of an annual increase in
precipitation became more pronounced with respect to annual streamflow, whereas
the effect of a decrease in annual precipitation was slightly dampened owing to when
these changes occurred throughout the year. Secondly, there are differing degrees of
uncertainty between output variables from climate models. Precipitation is among
the less certain. Therefore, it is important to simulate a range of values of these
variables in order to gain a perspective on the breadth of possible impacts of climate
scenarios. Thirdly, under a global warming scenario, runoff will move earlier into
the springtime. This may have important implications for retention, storage, and
distribution of the water resources.
Snowmelt runoff has tremendous importance for much of the world. This importance spans a wide geographic as well as sectoral range. Consequently, there are
many uses for simulations of climate change impacts on snowmelt runoff, especially
for developing adaptation plans for a wide range of impacts. Remote sensing provides useful input for snowmelt runoff modeling and will continue to increase in
importance as new methods and instruments can be developed and as existing challenges to its use are overcome.