Annual Report 2008.pdf - SAMSI

the efficient treatment of complex models in computer experiments, and will have wide
applicability throughout the physical and environmental sciences.
The working group on Inference and Uncertainty Analysis of Hydrological Models has
made major progress in two areas. First, the major problem of modeling bias of model
output is the difficulty of predicting bias when the model is used to predict system
behavior for new external driving forces or for predicting future behavior. During this
program, a recently developed technique for the estimation of time-dependent parameters
was combined with the concept of describing bias in model output to a framework for
supporting the identification of causes of bias. The concept was applied to a simple
hydrological model. Such a model-internal bias description can support the identification
of causes of bias and the development of an improved model structure that may provide a
better predictive capability.
Statistical emulators can contribute significantly to making Bayesian computation
techniques applicable to computationally demanding simulation programs. Major deficits
of conventional Gaussian Process emulators were their numerical difficulties for densely
spaced input dimensions (what frequently happens for the time dimension of dynamic
models) and their ignorance of known mechanisms represented by the simulation model.
Many fruitful discussions during the program led to the development of a dynamic
emulator that addresses both of these problems. It is based on a simplified, linear state
space model that represents a simplification of the description of the mechanisms in a
system and Gaussian Processes for the innovation terms of this state space model to
correct for the bias of linearization (and, possibly, dimension reduction). This emulator is
currently being applied to the hydrological model.
The working group on Terrestrial Models is attacking the problem of predicting long
term biodiversity, through use of extensive modeling. Activities have focused on
parameterization and analysis of a large forest simulator. Preliminary results indicate that
the model predictions are quite different from current beliefs in the field. This is a
collaboration of statisticians and environmental scientists. We view this collaboration as
still in the formative stages. The group has developed an emulator for stochastic
simulators, the specific application being the large forest simulator. The group is also
building a model to predict the spatial and temporal evolution of soil moisture under
varying climatic conditions. Soil moisture is an important factor in tree growth and
fecundity, and these predictions are part of the group's larger goal of studying the effect
of climate change on forest dynamics.
b) RISK ANALYSIS, EXTREME EVENTS AND DECISION THEORY
Much of the work of this program will be finalized in the coming months, but research by
the working groups has already produced preliminary results of considerable interest.
The past half decade has seen great interest and also great scientific progress on risk
analysis; both natural and man-caused events have focused on both the science and the
modeling of these events and on the analysis of their associated risks.
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