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Data-driven and Physically-based Models for
Characterization of Processes in Hydrology, Hydraulics,
Oceanography and Climate Change
(6 - 28 Jan 2008)
... Jointly organized with Pacific
Institute for Mathematical Sciences, UBC
Organizing Committee
· Confirmed Visitors
· Overview
· Activities · Membership
Application
Co-chairs
- Sylvia Esterby (University of British Columbia)
- Hans-Rudolf Künsch (ETH Zurich)
- Shie-Yui Liong (National University of Singapore)
Members
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Vladan Babovic (National University of Singapore)
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Wolfgang Kinzelbach (ETH Zurich)
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Pavel Tkalich (National University of Singapore)
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Jim Zidek (University of British Columbia)
Special thanks to Singapore -
Delft Water Alliance for sponsoring a number of visitors.
The 3-week program will consist of a full
week of seminars/lectures, and two weeks of workshops and
research discussions aimed at developing research
collaboration. Three main topics are covered in the program.
They are:
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“Development of a fully integrated
data driven and physical-based models for water
resources management”
-
“Dynamic and Statistical Downscaling
on Climate Change Study”
-
“Nonlinear Wave Dynamics and Tsunami
Modeling”
Physically based modeling maps
natural phenomena to a computer simulation program. There are two basic processes
in this mapping: mathematical modeling and numerical
solution. The mathematical modeling concerns the description
of the natural phenomena by mathematical equations. The
numerical solution involves computing an efficient and
accurate solution of the mathematical equations. Models are
essential tools for synthesizing observations, theory, and
experimental results in order to investigate the physical
phenomena which govern the behavior of water in the system
under study, and to understand how the system is affected by
human activities. Models can be used in both a retrospective
sense, to test the accuracy of modeled changes in the system
by comparing model results with observations of past change,
and in a prognostic sense, for calculating the response of
the system to projected future changes.
This part of the program will focus on improvements of
description of physical, environmental and water quality
processes through hydrodynamics, morphology, hydrology,
water quality, ecology as well as numerical methods and
techniques such as finite difference methods, finite element
methods and boundary element methods, with applications to
physically based modeling of lakes and reservoirs,
prediction of runoff in poorly gauged catchments using
physically based models, and flood modeling.
Data driven modeling and computational intelligence: In
situations when knowledge about the processes to be modelled
is limited, physically based model cannot be built, or they
are inadequate. There are situations, however, when at least
some of the variables characterising a particular process
have been measured, and there is enough data to represent
the input-output relationships associated with the process.
In such cases data-driven models (DDM) can be built that
make it possible to model and forecast some output
variables. An example is the modelling of a rainfall-runoff
relationship using statistical models or artificial neural
networks. Typically, in order to build a DDM, methods of
computational intelligence would be used. Research here is
concentrated on testing various methods and their
combinations in different types of modelling problems, and,
developing new modelling methods.
Often, physically based models do not exhibit the needed
accuracy, or are inadequate to model particular situations,
e.g. those of very high flows for the purpose of flood
forecasting. On the other hand, there may not be enough data
to train data driven models alone. In this case combinations
of models of different types (i.e., hybrid models) could be
a solution. Research in hybrid modeling is aimed at
developing algorithms to ensure optimal combinations of
physically based and data-driven models, and testing the
resulting models in various situations. This paradigm will
explore a number of approaches and techniques, such as data
assimilation based on Kalman filtering, model-error
characterization and its correction; data-model integration
techniques, data-driven knowledge discovery and finally
adaptive and learning modeling environments under which
models adapt their internal structure on the basis of
observed data.
The program will also consider recent development in
statistics relevant to the topical areas described in the
following subsections. Considerable efforts have been made
to assess uncertainty by comparing and combining different
physical models (especially in weather prediction and
climate modeling) and on calibrating complex computer models
with observations, taking non-identifiability and structural
model deficits into account. It should be noted that these
topics are currently the object of a program at SAMSI
(Statistical and Applied Mathematical Sciences Institute).
The program will concentrate on bridging the gap and
establishing the bridges between the two approaches (and two
scientific communities) by addressing several specific
topical areas: water resources management, down-scaling in
climate change and non-linear wave and tsunami modeling.
Development of fully integrated data driven and
physical-based models for water resources management
Developing an effective and efficient computational tool for
water resources management of water-scarce regions or
countries like Singapore is of utmost importance. The
program will cover the chain from real time monitoring of
storms, reservoir and sea levels via forecasting of runoff
and flooding to decision making on reservoir operation. Both
water quantity and water quality should be considered in the
study and the applications of smart sensing technologies
should simultaneously be explored.
This topic will concentrate on forecasting storms, surface
runoff and downstream tidal levels in advance of their
actual arrival in a holistic and integrated manner. First
various deterministic models including atmospheric,
rainfall-runoff, reservoir, and coastal hydrodynamics will
be fully integrated. After calibrating and validating the
system, a database containing simulated relevant data
resulting from representative scenarios will be set-up. This
database will be used to train some data driven models which
are known to be computationally of many orders of magnitude
faster than their deterministic counterparts.
Dynamic and Statistical Downscaling in Climate Change
Study
There is an emerging scientific consensus that human action,
especially the release of man-made greenhouse gases, is
leading to global climate change. Some of the most current
research activities are the study of dynamic and statistical
downscaling of climate parameters (e.g. rainfall, sea level)
and extreme weather and climate events. Their impacts
particularly on small islands such as Singapore are of grave
concerns.
This topic will in particular focus on dynamic and
statistical downscaling issues of climate parameters (e.g.
rainfall, sea level). Analytical results from different
General Circulation Models are known to differ
significantly. Taking the more conservative results would
result in prohibitively high cost in adaptation measures
while the other extreme will certainly be catastrophic for
small island states like Singapore. This topic will
critically assess the existing dynamic and statistical
downscaling methods.
Nonlinear wave dynamics and tsunami modeling
Nonlinear waves are observed in all branches of science and
engineering, and are present in different aspects of our
daily life. Physics and biology, road traffic control and
structure of the universe, electronic and communication
systems are affected by the same phenomenon at different
spatial and temporal scales, namely nonlinear wave dynamics.
Nonlinear waves can be significant in an act of creation or
destruction, and be simultaneously fascinating and tragic.
Indian Ocean (2004) Tsunami is a pure example of a series of
events dominated by nonlinear wave dynamics, starting from
tectonic movements and up to tsunami run-up on shore. The
focus of the discussions would be placed on the application
of the theories to nonlinear wave dynamics in the ocean.
The period of the program is from 6 to
28 Jan 2008. The first week will be dedicated totally on
seminars/lectures on three topics described above. Each of
the following 2 weeks will start with two days of
presentations, by a number of invited speakers, focusing on
the topics described above. The remaining three days of each
of these two weeks will be reserved for work in smaller
multi-disciplinary groups. The groups will address a number
of concrete challenges associated with the three topical
areas. The general idea is to arrive at possible research
collaboration in the immediate future; and to draft
scientific publications by the end of the workshop.
Please click here for a copy of the IMS workshop report (MsWord | PDF)
IMS Membership is not required for
participation in above activities. For attendance at these
activities, please complete the online registration form.
If you are an IMS member or are applying for IMS
membership, you do not need to register for these activities.
The Institute for Mathematical Sciences
invites applications for membership for participation in the
above program. Limited funds to cover travel and living
expenses are available to young scientists. Applications
should be received at least three (3) months before the
commencement of membership. Application form is available in
(MSWord|PDF|PS) format for download.
- More information is available by writing to:
- Secretary
Institute for Mathematical Sciences
National University of Singapore
3 Prince George's Park
Singapore 118402
Republic of Singapore
- or email to imssec@nus.edu.sg.
For enquiries on scientific aspects of the program, please
email Yui LIONG at
tmslsy@nus.edu.sg.
Organizing Committee
· Confirmed Visitors
· Overview
· Activities · Membership
Application
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