Introduction
A water supply forecast is a prediction of streamflow volume that will flow
past a point on a stream during a specified season, typically in the spring and
summer. The NRCS, in cooperation with the National Weather Service, issues water
supply forecasts for over 750 points in the western United States near the first
of the month between January and June each year and at other times as requested.
The basis of water supply forecasting lies in the fact that most of the
annual streamflow in western North America originates as snowfall that has
accumulated in the mountains during the winter and early spring. This snowpack
serves as a natural reservoir, storing water during the winter and releasing it
during the spring and summer snowmelt season. The delay between when the snow
falls and when it melts is what makes it possible for hydrologists to make
predictions of snowmelt runoff.
In some areas, however, snowmelt is not as dominant, and therefore
forecasting is more difficult. For example, on the west side of the Cascade
Mountains, the east slope of the Rocky Mountains, and in parts of the Southwest,
winter, spring, and/or summer rainfall can supply a significant amount of the
streamflow volume. Since the source of this runoff occurs in the future at the
time forecasts are issued, and it cannot easily be predicted, forecast
uncertainty is higher in these areas than in dominantly snowmelt basins.
Models
Most water supply forecasts are made using statistical models. These are
equations that express a mathematical relationship between the predictor
variables (snowpack, precipitation, antecedent streamflow, etc.) and the
seasonal streamflow volume of interest. Statistical models have the advantage
that they are relatively simple and straightforward to calibrate and use, and
they are usually acceptably accurate. The disadvantages are that they require
long historical records (preferably greater than 20 years), and they do not
represent all known physical processes that affect streamflow.
An alternative to statistical models is simulation models. These models
attempt to represent, to a greater or lesser extent, all of the main physical
processes affecting the movement of water within a watershed and the generation
of streamflow. They operate on a continuous basis using a daily or shorter time
step. The main advantage of simulation models is that, by explicitly accounting
for physical processes, they have a more complete description of what is
happening in the watershed and can potentially make more accurate streamflow
predictions, especially under unusual circumstances. Other advantages of
simulation models are that they can be run year-around and can produce other
outputs besides seasonal streamflow volumes, such as full hydrographs and other
hydrograph-based quantities. The disadvantages of simulation models are that
they require significantly more input data than statistical models, are more
difficult and time consuming to calibrate, require more complex output
interpretation, and require more database and software infrastructure. Although
the use of simulation models is limited at present, they nevertheless have much
potential, and their use will increase in the future.
Uncertainty
Regardless of the model used, no forecast is perfect, so each forecast has
uncertainty. The sources of forecast uncertainty are unknown future weather,
model error (due to mathematical form and/or incomplete process descriptions),
and data error (erroneous values or incomplete site coverage).
To express this uncertainty, the forecast is presented not as a single value
but as a range of values, each with a specific probability of occurrence. The
wider the spread among these values, the more uncertain the forecast. As the
season progresses, forecasts generally become more accurate.
See “Interpreting
Water Supply Forecasts” (http://www.wcc.nrcs.usda.gov/factpub/intrpret.html)
for information on understanding forecast uncertainty and the range of forecast
values for each point.
Data
Snowpack measurements from the NRCS’s SNOTEL system and manual snowcourses,
as well as measurements from similar remote data networks in California, British
Columbia, and Alberta, provide the key data source for making snowmelt runoff
predictions. Measurements of other variables are used as well, such as
precipitation, antecedent streamflow, groundwater, temperature, and large-scale
climate indices. These other data are obtained from various federal and state
agencies.
Statistical Model Development
The first step in developing statistical forecasting models is for the
hydrologist to select a set of candidate predictor variables. Data sites are
selected by examining a map and obtaining data from those sites in and near the
basin of interest. The predictor variables considered for each forecast issue
month include current snow water equivalent, precipitation for past months,
streamflow for past months, large-scale climate teleconnection indices (e.g.,
Southern Oscillation Index) that have a proven relationship to the basin, and
other variables that have predictive capabilities, such as fall groundwater
levels or spring temperature.
Once the data are assembled, the hydrologist then does a preliminary
screening, based on simple correlation analysis, to select the variables that
have a potentially useful predictive relationship and to discard those that have
a negligible relationship.
With these candidate variables, the statistical models are then developed.
Generally, separate equations are developed for each forecast issuance date
using only those variables available at that time. Month-to-month consistency in
the forecasts is ensured by maintaining a high degree of similarity in data site
and variable usage throughout the forecasting season.
Most statistical models used by the NRCS have been developed using the
methods described by Garen (1992) (paper available at
http://www.wcc.nrcs.usda.gov/publications/wsfpubs.html). These methods
include principal components regression, jackknife (cross-validation) testing,
and a variable combinations search algorithm.
Forecast Operations
Forecasting hydrologists increasingly rely on data reported by real-time or
near real-time automatic reporting data networks. On the first working day of
the month, enough data are generally available from these networks for the
hydrologist to begin executing the forecast models and reviewing the forecasts.
Some data are less timely than this, particularly precipitation data collected
by cooperative weather observers and some streamflow and reservoir data. It may
be necessary to wait another day or two to compute forecasts for basins that use
these data.
The hydrologist reviews each forecast for reasonableness and for spatial and
temporal consistency, making adjustments as necessary, in consultation with NRCS
Water Supply Specialists. Graphical and GIS-based visualization tools assist in
this process. Once the hydrologist is satisfied with the forecasts, they are
shared with the National Weather Service River Forecast Centers for their
review. Further adjustments are made as necessary during this “coordination”
process to arrive at mutually agreed-upon forecasts.
In some selected basins and in response to special requests, mid-month
forecasts are issued. These are usually generated from the following
first-of-month forecasting models, for which the snow water equivalent and
precipitation variables have been extrapolated from current observations to the
first of the following month, based on selected assumptions for the intervening
period.
Simulation models are also executed for selected basins. Forecasts are
prepared using the Ensemble Streamflow Prediction technique in which multiple
future streamflow scenarios are generated. Each scenario is based on the current
watershed state and a different historically observed climate input series to
represent the (future) forecast period. These streamflow scenarios can be
summarized statistically to obtain forecasts and associated uncertainty of not
only seasonal streamflow volumes but also other hydrologic quantities such as
peak flow, date to recede below a threshold flow, etc. Simulation models are
being used as an alternative procedure for seasonal streamflow volume forecasts
as well as the basis for additional forecast products.
Conclusion
Water supply forecasts in the western United States have been produced since
the relationship between winter snowpack and spring and summer streamflow was
noted a century ago by the “father” of snow surveying and water supply
forecasting, Dr. James Church, who was a professor at the University of Nevada.
The program has evolved over the decades, developing a widespread network of
snow measurement sites and more and more sophisticated measurement equipment,
data analysis techniques, and forecasting models. This evolution and program
development continues, exploiting new capabilities made possible by advancements
in computing power, the Internet, GIS, and hydrologic science.
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