Decision support experiments and evaluations using seasonal to ...

2.4.2.2 improvementS in hydrologic
modeling techniqueS
Efforts to improve hydrologic simulation techniques
have been pursued in many areas since
the inception of hydrologic modeling in the
1960s and 1970s when the Stanford Watershed
Model (Crawford and Linsley, 1966), the Sacramento
Model (Burnash et al., 1973) and others
were created. More recently, physically-based,
distributed and semi-distributed hydrologic
models have been developed, both at the watershed
scale (e.g., Wigmosta et al., 1994; Boyle
et al., 2000) to account for terrain and climate
inhomogeneity, and at the regional scale (Liang
et al., 1994 among others). Macroscale models
(like the Sacramento Model and the Stanford
Watershed Model) were partly motivated by
the need to improve land surface representation
in climate system modeling approaches
(Mitchell et al., 2004), but these models have
also been found useful for hydrologic applications
related to water management (e.g.,
Hamlet and Lettenmaier, 1999; Maurer and
Lettenmaier, 2004; Wood and Lettenmaier,
2006). The NOAA North American Land Data
Assimilation Project (Mitchell et al., 2004) and
NASA Land Information System (Kumar et al.,
2006) projects are leading agency-sponsored
research efforts that are focused on advancing
the development and operational deployments
of the regional/physically based models. These
efforts include research to improve the estimation
of observed parameters (e.g., use of satellite
remote sensing for vegetation properties and
distribution), the accuracy of meteorological
forcings, model algorithms and computational
approaches. Progress in these areas has the
potential to improve the ability of hydrologic
models to characterize land surface conditions
for forecast initialization, and to translate future
meteorology and climate into future hydrologic
response.
Aside from improving hydrologic models and
inputs, strategies for hydrologic model implementation
are also important. Model calibration—,
the identification of optimal parameter
sets for simulating particular types of hydrologic
output (single or multiple)—has arguably
been the most extensive area of research toward
improving hydrologic modeling techniques
(e.g., Wagener and Gupta, 2005, among others).
This body of work has yielded advances in the
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
understanding of the model calibration problem
from both practical and theoretical perspectives.
The work has been conducted using models at
the watershed scale to a greater extent than the
regional scale, and the potential for applying
these techniques to the regional scale models
has not been explored in depth.
Data assimilation is another area of active research
(e.g., Andreadis and Lettenmaier 2006;
Reichle et al., 2002; Vrugt et al., 2005; Seo et
al., 2006). It is a process in which verifying
observations of model state or output variables
are used to adjust the model variables as the
model is running, thereby correcting simulation
errors on the fly. The primary types of
observations that can be assimilated include
snow water equivalent and snow covered area,
land surface skin temperature, remotely sensed
or in situ soil moisture, and streamflow. NWS-
RFS has the capability to do objective data
assimilation. In practice, NWS (and other agencies)
perform a qualitative data assimilation,
in which forecaster judgment is used to adjust
model states and inputs to reproduce variables
such as streamflow, snow line elevation and
snow water equivalent prior to initializing an
ensemble forecast.
2.4.3 Calibration of
Hydrologic Model Forecasts
Even the best real-world hydrologic models have
biases and errors when applied to specific gages
or locations. Statistical models often are tuned
well enough so that their biases are relatively
small, but physically-based models often exhibit
significant biases. In either case, further
improvements in forecast skill can be obtained,
in principle, by post-processing model forecasts
to remove or reduce any remaining systematic
errors, as detected in the performance of the
models in hindcasts. Very little research has
been performed on the best methods for such
post-processing (Schaake et al., 2007), which
is closely related to the calibration corrections
regularly made to weather forecasts. Seo et al.
(2006), however, describe an effort being undertaken
by the National Weather Service for
short lead hydrologic forecasts, a practice that
is more common than for longer lead hydrologic
forecasts. Other examples include work
by Hashino et al. (2007) and Krzysztofowicz
(1999). At least one example of an application
Efforts to improve
hydrologic simulation
techniques have been
pursued in many areas
since the inception of
hydrologic modeling in
the 1960s and 1970s.
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