From: mcleod@Sdsc.Edu
Subject: SCI: Simulation of water-related phenomena
Originator: hlab@milton.u.washington.edu
Organization: University of Washington
Date: Fri, 21 Aug 1992 21:20:51 GMT


Crossposted from comp.simulation


The following sample issue of our electronic magazine, "E-S3",
covering selected topics about computer modeling and simulation,
is sent to you with the compliments of the publisher of the
technical journal SIMULATION, the Society for Computer
Simulation, and John and Suzette McLeod, the Editors of
Simulation in the Service of Society (S3), a special section of
that journal.
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                  E-S3  Vol. 1, No. 8, Part One
 
                based on the August 1992 issue of
              "Simulation in the Service of Society"
John McLeod, Technical Editor     Suzette McLeod, Managing Editor
          8484 La Jolla Shores Dr., La Jolla, CA 92037.
                   E-mail: mcleod@sdsc.bitnet
                                *
                   S3 is a special section of
                           SIMULATION
                   the monthly journal of the
                 SOCIETY for COMPUTER SIMULATION
             P.O.Box 17900, San Diego, CA 92177-7900
            Phone: (619) 277-3888 FAX: (619) 277-3930
                                *
[Copyright Notice: E-S3 is the electronically delivered version 
of "Simulation in the Service of Society" which is a special 
section of SIMULATION, a monthly technical journal of the  
Society for Computer Simulation International. It may be 
reproduced only for personal use or for the use of students. In 
any case full credit must be given to the original source of 
publication: SIMULATION 59:2, August 1992. 
  All rights  reserved, (c) 1992, Simulation Councils, Inc.]
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Oil on Troubled Waters
 
Herewith we present edited excerpts from an article originally
published in Water Resources Research, v. 26, No. 9, September
1990.
 
     Multidimensional Simulation Applied to Water Resources
                          Management
 
             A.S. Camara, F.C. Ferreira, M.J.Seixas
              Environmental Systems Analysis Group
               New University of Lisbon, Portugal
 
                           D.P. Loucks
          School of Civil and Environmental Engineering
                       Cornell University
                        Ithaca, New York
 
Introduction

Mathematical modeling has been traditionally dominated by quantitative
formulations which can be easily manipulated.  Thus reality, which is
usually described qualitatively by pictures and words, has been mapped
into numerical representations.  In this paper we explore how
spatially related objects can be represented and manipulated visually
and how abstract concepts can be represented using natural language.
 
Today models using linguistic and pictorial entities and operators, in
addition to the traditional numerical formulations, are clearly
lacking.  Modelers tend to limit the use of pictures or graphics to
the input-output stages of the modeling process and use natural
language as a medium of interaction between humans and machines.
Theoretical concepts underlying an integrated decision aiding
simulator (IDEAS), which considers numerical, linguistic, and
pictorial entities and operations, are applied to the impact
assessment of an oil spill in the sea.  This example illustrates the
potential applications of IDEAS for environmental and water resources
management.
 
Application
 
The Valdez accident in Alaska contributed to the increasing worldwide
interest in the environmental, social, and economic impacts of oil
spills.  Although many methods have been developed over the years to
assess those impacts, IDEAS may provide a rapid modeling alternative
using more intuitive representations for simulation model variables
and operations.  A generic oil spill model implemented on a
microcomputer, following the IDEAS methodology, is described in the
following paragraphs.
 
Problem Description
 
The assessment of the fate of an oil spill and its effects is a
complex process.  Typically the oil spilled is dispersed immediately
after discharge because of advection and spreading.  The area and
thickness of the oil is then affected by evaporation, sedimentation,
and decay phenomena.  Advection is influenced by dominant winds and
currents.  Spreading results from a dynamic equilibrium between the
forces of gravity, inertia, friction, viscosity, and surface tension.
 
Evaporation accounts for the loss of one third to two thirds of the
oil mass in a period of a few hours after the spillage.  Calculation
of evaporation rate is difficult because it depends on a number of
factors, all of which may change with time.  However, one may say that
the rate of evaporation from a thick, cold slick under calm conditions
will be orders of magnitude slower than from a thin, warm slick under
stormy conditions.  Oil may be transported to the bottom sediments
through hydrodynamic processes.  Oil consumed by the zooplankton also
reaches the sediments in the form of fecal pellets.
 
Photochemical oxidation and microbial action are the two most
important decay processes, depending on the amount of light and
temperature in the area, respectively.  Cold water with reduced light,
as in the Valdez case, can slow decay.  Warm water with plenty of
light can accelerate it.
 
Figure 1 shows the causal diagram extracted from this problem
description, while Table 1 lists the entities included in the model.
The dictionaries for the pictorial and linguistic entities are shown
in Figure 2 and Table 2 respectively.  Note that current and wind
direction are treated as linguistic entities with north, south, west,
and east directions.
 
Model Results
 
The oil spill model was applied to a generic case, and a sample of the
model outputs using a monochromatic monitor is presented in Figure 3.
This figure illustrates the wealth and complementarity of information
provided by models using numerical, linguistic, and pictorial
entities.  The latter include plant and profile views of the oil spill
and iconic representations of the sea (fish) and shoreline (salt
marshes) wildlife.  In this example the oil spread is starting to
reach the shoreline, damaging the wildlife.  After 18 days, two thirds
of the total amount of oil spilled still remains.  Note that the oil
spread contour seems to have been randomly drawn because of the wind
direction and speed changes.
 
Future Developments
 
Further development of IDEAS will be directed toward the improvement
of pictorial entities representations and the numerical, linguistic,
and pictorial modeling methods.  For example, other pictorial
variables such as orientation, texture, and value will be considered.
Efforts will be made to handle additions or deletions of entities and
relationships during the modeling process.  Modeling of a contaminated
estuary changing from aerobic to anaerobic conditions and back to
aerobic is an example of the need to develop such rules.
 
IDEAS may be useful in combination with a geographic information
system (GIS).  Pictorial and linguistic methods included in the
proposed approach may be applied to solve problems requiring
geographical information.  The environmental impact analysis of water
resource infrastructures is a potential application of an IDEAS-GIS
system.
 
More substantial modeling developments are tied to the adoption of
parallel processing.  The application of cellular automata
formulations, which are ideally implemented on parallel computers,
instead of differential equations, is one of the topics to be studied
in this context.
 
In brief, a cellular automaton considers a discrete lattice of sites,
evolution in discrete time steps, each site taking a finite set of
possible values, the value of each site evolving according to
deterministic or nondeterministic rules, and the rules for the
evolution of a site depending only on a local neighborhood of sites
around it.  This alternative numerical modeling approach may consider
millions of cells.  Cellular automata applied to water quality
modeling thus may enable particle tracking (each cell with a nonzero
value could represent a particle.)  This feature may be relevant if
there is a need to obtain spatial patterns of the pollutants.
 
Parallel processing will certainly benefit the use of linguistic and
pictorial models.  These models divide the continuum into a number of
discrete categories (i.e., high, medium, and low for a linguistic
variable; black, grey, and white for the color of a pictorial object)
and the operations are also scaled on a discrete basis (i.e.,
absorption may be none, half, or total).
 
It is obvious that an aggregate discretization of a continuum, which
is the feasible option for standard computers, could lead to
significant error.  However, disaggregate discretization, which is
feasible with parallel processing, can certainly minimize the errors
of the discrete linguistic and pictorial models, making them
comparable to approximate numerical models, with the advantage of
being more intuitive.
 
Another significant development relates to the creation of pictorial
explanation tools which allow one to determine pictorially the control
options required to obtain a frame B from a frame A.  The tuning of a
television image is a quite similar problem.  In this case there are
few control variables (color, sound, brightness, contrast) and they
are independent.  The search for the perfect image is thus relatively
simple.  Similarly, if one represents control variables as pictures
with color, size, shape, and position, to solve a simple problem one
just changes those properties until one goes from frame A to frame B.
Backtracking those changes allows one to define the required strategy
for turning A into B.  However, if there are many control variables
and their pictorial properties are not independent, the problem may
involve combinatorial optimization.
  
[The original article contained 36 references, 11 figures, and 5
tables, and notes that the work resulted in part from a Visiting
Fulbright Scholarship and a NATO Fellowship granted to the first
author for research on this topic at Cornell University.  Note that
this e-mail version of S3 cannot duplicate figures and tables as they
appear in the version published in the journal SIMULATION. JM]
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