Decision support experiments and evaluations using seasonal to ...

climate variations. The rationale for an attribution
effort is that forecasts have greater value if
we know why the forecasted event happened,
either before or after the event, and why a forecast
succeeded or failed, after the event. The
need to distinguish natural from human-caused
trends, and trends from fluctuations, is likely
to become more and more important as climate
change progresses. SI forecasts are likely to fail
from time to time or to realize less probable
ranges of probabilistic forecasts. Knowing that
forecasters understand the failures (in hindsight)
and have learned from them will help
to build increasing confidence through time
among users. Attempts to attribute causes to
important climate events began as long ago as
the requests from Congress to explain the 1930s
Dust Bowl. Recently NOAA has initiated a Climate
Attribution Service (see: ) that will combine historical records,
climatic observations, and many climate
model simulations to infer the principal causes
of important climate events of the past and present.
Forecasters can benefit from knowledge of
causes and effects of specific climatic events as
well as improved feedbacks as to what parts of
their forecasts succeed or fail. Users will also
benefit from knowing the reasons for prediction
successes and failures.
2.4.2 Improving Initial Hydrologic
Conditions for Hydrologic and
Water Resource Forecasts
Operational hydrologic and water resource
forecasts at SI time scales derive much of
their skill from hydrologic initial conditions,
with the particular sources of skill depending
on seasons and locations. Better estimation
of hydrologic initial conditions will, in some
seasons, lead to improvements in SI hydrologic
and consequently, water resources forecast skill.
The four main avenues for progress in this area
are: (1) augmentation of climate and hydrologic
observing networks; (2) improvements in hydrologic
models (i.e., physics and resolution); (3)
improvements in hydrologic model calibration
approaches; and (4) data assimilation.
2.4.2.1 hydrologic obServing
networkS
As discussed previously (in Section 2.2),
hydrologic and hydroclimatic monitoring networks
provide crucial inputs to hydrologic and
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
water resource forecasting models at SI time
scales. Continuous or regular measurements
of streamflow, precipitation and snow water
contents provide important indications of the
amount of water that entered and left river
basins prior to the forecasts and thus directly
or indirectly provide the initial conditions for
model forecasts.
Observed snow water contents are particularly
important sources of predictability in most of
the western half of the United States, and have
been measured regularly at networks of snow
courses since the 1920s and continually at
SNOTELs (automated and telemetered snow
instrumentation sites) since the 1950s. Snow
measurements can contribute as much as threefourths
of the skill achieved by warm-season
water supply forecasts in the West (Dettinger,
2007). However, recent studies have shown that
measurements made at most SNOTELs are not
representative of overall basin water budgets,
so that their value is primarily as indices of
water availability rather than as true monitors
of the overall water budgets (Molotch and
Bales, 2005). The discrepancy arises because
most SNOTELs are located in clearings, on flat
terrain, and at moderate altitudes, rather than
the more representative snow courses that historically
sampled snow conditions throughout
the complex terrains and micrometeorological
conditions found in most river basins. The
discrepancies limit some of the usefulness of
SNOTEL measurements as the field of hydrologic
forecasting moves more and more towards
physically-based, rather than empirical-statisti-
The need to
distinguish natural
from human-caused
trends, and trends
from fluctuations,
is likely to become
more and more
important as climate
change progresses.
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