Abstract:
Data assimilation offers an attractive way
to merge and interpret the increasing
amount of hydrologic information
provided by remote sensing and
ground-based data sources. But a
number of formidable conceptual and
operational challenges need to be
confronted before the potential of
hydrologic data assimilation can be
realized. These include 1) development of
assimilation methods which are
computationally feasible while making best
use of all available information, 2)
identification of realistic error models, 3)
development of methods for identifying
systematic model and measurement
biases, 4) development of robust
estimation algorithms that are not overly
sensitive to outliers or erroneous model
inputs, 5) identification of rigorous
methods for quantifying the accuracy of
data assimilation products. Two of the
most promising options for large-scale
hydrologic data assimilation are dynamic
variational methods (4DVAR) and
ensemble Kalman filtering (EnKF). This
talk examines the distinctive features of
each, compares their advantages and
disadvantages, and discusses the
prospects for dealing with the challenges
listed above.