Decision support experiments and evaluations using seasonal to ...

losses of snowpack and the tendencies for more
precipitation to fall as rain rather than snow in
a warmer world reduce overall forecast skill,
shrinking the areas where snowpack contributes
strong predictability and also making antecedent
runoff a less reliable predictor. Thus, many
areas where warm-season runoff volumes are
accurately predicted historically are likely to
lose some forecast skill along with their snowpack.
Overall, the average skill declines by
about 2 percent (out of a historical average of
35 percent) for the January to March volumes
and by about 4 percent (out of a historical
average of 53 percent) for April to July. More
importantly, though, are the declines in skill
at grid cells where historical skills are greatest,
nearly halving the occurrence of high-end
(>0.8) January-to-March skills and reducing
high-end April-to-July skills by about 15 percent
(Figure 2.15).
This enhanced loss among the most skillful grid
cells reflects the strong reliance of those grid
cells on historical snowpacks for the greater
part of their skill, snowpacks which decline
under the imposed 2ºC warmer conditions.
Overall, skills associated with antecedent runoff
are more strongly reduced for the April-to-
July runoff volumes, with reductions from an
average contribution of 24 percent of variance
predicted (by antecedent runoff) historically
to 21 under the 2ºC warm conditions; for the
January to March volumes, skill contributed by
antecedent runoff only declines from 18.6 percent
to 18.2 percent under the imposed warmer
conditions. The relative declines in
the contributions from snowpack
and antecedent runoff make antecedent
runoff (or, more directly,
soil moisture, for which antecedent
runoff is serving as a proxy here) a
more important predictor to monitor
in the future (for a more detailed
discussion, see Section 2.4.2).
It is worth noting that the changes
in skill contributions illustrated in
Figure 2.14 are best-case scenarios.
The skills shown are skills that
would be provided by a complete
recalibration of forecast equations
to the new (imposed) warmer conditions,
based on 50 years of runoff
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
history. In reality, the runoff and forecast conditions
are projected to gradually and continually
trend towards increasingly warm conditions,
and fitting new, appropriate forecast equations
(and models) will always be limited by having
only a brief reservoir of experience with each
new degree of warming. Consequently, we must
expect that regression-based forecast equations
will tend to be increasingly and perennially out
of date in a world with strong warming trends.
This problem with the statistics of forecast
skill in a changing world suggests development
and deployment of more physically based, less
statistically based forecast models should be a
priority in the foreseeable future (Herrmann,
1999; Gleick, 2000; Milly et al., 2008).
2.2.3.3 Skill oF climate ForecaSt-driven
hydrologic ForecaStS
The extent to which the ability to forecast
U.S. precipitation and temperature seasons
in advance can be translated into long-lead
hydrologic forecasting has been evaluated by
Wood et al. (2005). That evaluation compared
hydrologic variables in the major river basins
of the western conterminous United States as
simulated by the VIC hydrologic model (Liang
et al., 1994), forced by two different sources of
temperature and precipitation data: (1) observed
historical meteorology (1979 to 1999); and (2)
by hindcast climate-model-derived six-monthlead
climate forecasts.
The Wood et al. (2005) assessment quantified
and reinforced an important aspect of the hydro-
Figure 2.15 Distributions of overall fractions of variance predicted, in Figure 2.13, of January
to March (curves) and April to July (histograms) runoff volumes under historical (black)
and +2°C warmer conditions (Dettinger, 2007).
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