Author level two table-of-contents and contracts: April 10th, 2000
Draft of chapter: May 31st, 2000
Peer Reviews Complete: June 30th, 2000
Camera-ready Deadline: July 15th, 2000
Book to appear: September 1st, 2000 (available at meeting)

Chapter 8: Modeling Physical Systems

Helena Mitasova, Geographic Modeling Systems Laboratory, 
Department of Geography, University of Illinois at Urbana-Champaign, Urbana
Lubos Mitas, National Center for Supercomputing Applications,
University of Illinois at Urbana-Champaign, Urbana, USA

Summary

  How this chapter fits into the book
    Explains and illustrates how principles and concepts 
    described in previous chapters are applied to modeling of Physical systems
  What is unique to this chapter
    The chapter describes the modeling of specific
    terrestrial physical processes :  movement and distribution of
    water, sediment and pollutants and how they influence and shape 
    the landscapes. The chapter also demonstrates the importance
    of physical systems as components of integrated modeling 
    because both humans and ecosystems depend on underlying physical 
    systems, such as water and soil.
  Name any overall approaches
    Lumped and distributed models. Empirical and process-based 
    models and their combination. Steady state and dynamic models.
    Numerical solutions by finite difference, finite element 
    and Green' function Monte Carlo(path simulations).
    Continuous time and space concepts. 
    Role of GIS for storing, managing,analysis and visualization
    of data, modeling tools in GIS suitable for models of physical systems.
    Issues of calibration, validation and sensitivity analysis.
  Itemize a list of models referred to
    We will include refrences to a wide range of models with a selected
    few state of the art models described in more detail. Examples of models:
    GMS/SMS/WMS (groundwater, surface water and water modeling systems)
    HEC models, IDOR2D,3D, BASIN, ANSWERS, AGNPS, SWAT, WEPP, EPIC, GLEAMS, 
    NAPRA, LHIA, CASC2D, USLE, USPED, SIMWE, SIBERIA, Topmodel, 
    TopoG (online), CHILD(MIT), MIKE-BASIN, (Danish hydrologic Institute), EUROSEM

Body of the chapter text

1. Introduction

Models of physical systems in the context of environmental modeling,
their role as core modules in integrated systems. Purpose of modeling for
analysis and understanding of observed phenomena, testing of hypotheses 
and theories, prediction of spatio-temporal systems behavior under 
various conditions and scenarios  and new discoveries of functioning 
of physical geospatial phenomena enabled by unique capabilities of
computer experiments 
Role of GIS on the development and applications of  physical systems models.

2. Physical landscape processes and their models

2.1 Specific physical processes.

Surface water overland/channels/lakes,
subsurface water and groundwater, soil erosion, sediment transport
and deposition, landscape evolution (geomorphology), chemicals - nutrients
and pollutants in soil and water. 

2.2 Digital representation of physical systems: 

a) spatially agregated (lumped):
homogeneous units - watersheds, hydrologic units, hillslope segments, 
polygons (soil type, land cover)
b) spatially variable (distributed): multivariate functions(fields) represented
by regular or irregular grids (meshes).

2.3 Modeling approaches.

Empirical, deterministic, combination, with examples.
Complexity of physical processes:  non-linear behavior, stochastic components and
feedback loops over spatial and temporal scales, therefore models 
can represent the processes only at a certain level of simplification. 
Empirical models are based on statistical analysis of observed data, 
and they are usually applicable only to the same conditions under which 
the observations were made. Process based models are based on understanding 
of physical processes and their mathematical description.
Models of complex physical systems often use combination of empirical 
and process based approaches

3. Distributed process-based (deterministic) models

Area of the most active research and development.
Processes are described by differential equations, with a unique input leading to
unique output for well-defined linear models and with multiple outputs 
possible for non-linear models.

3.1 Numerical approaches to solution of the underlying equations 

(after discretization: modification to run on a
grid or a mesh, and parametrization: seting parameters to account 
for subgrid processes): 
          finite difference 
               principle (Press et al 1992) 
               example (Saghafian 1996 in Goodchild et al 1996: CASC2d) 
          finite element 
               principle, meshes (Burnett 1987) 
               example (Vieux 1996 in Goodchild et al 1996: r.water.fea) 
          path simulation 
               principle: based on random walker representation,
               note: not to be confused with stochastic simulations 
               example: path simulation solution of sediment flow 
               continuity equation (Mitas and Mitasova 1998). 

3.2 Temporal scales?

Models describe processes at various levels of temporal variation: 
a) steady state, with no temporal variations, often used for 
   diagnostic applications 
b) time series of steady state events, computed by running 
   a steady state model with time series of input parameters, 
   this approach is commonly used for estimation of long term average
   spatial distributions of modeled phenomena (continuous time simulations)
c)dynamic, describing the spatio-temporal variations during 
  a modeled event, used for prognostic applications and forcasting

3.3 Models and the reality

Calibration: the role of parameters and their limits are evaluated 
by parameter scans (Clarke 1996 in Goodchild et al 1997 CDROM, Mitas et al. 1997),
   model results are compared with experiments and parameters are set
   to values which ensure the best reproduction of the experimental data 
Sensitivity analysis, error propagation and uncertainty is performed 
to estimate impact of errors in input data on the model results (2.10: u096) 
Inconsistency between models and reality (Steyaert 1993 in Goodchild et al 1993) 
          only limited number of interacting processes can be treated, 
          process may not be well understood or is treated inadequately, 
          resolution and/or scale may be inadequate, 
          numerical solution can be too sensitive to initial conditions, 
          model can be incorrectly applied to conditions when its 
          assumptions are not valid, 
          errors in input data 

3.3 Distributed Models of physical processes and GIS

Simple modeling is supported by most commercial GIS, 
especially within the raster subsystems 
(ARCGRID, ArcView Spatial Analyst, Intergraph ERMA, IDRISI, GRASS, ERDAS) 
Full integration of complex models may require extensions 
of standard GIS functions such as support
for temporal and 3D/4D data and meshes for finite element methods 
Open data formats and incorporation of customization and application 
development tools stimulate coupling of commercial GIS and modeling 
Use of object oriented technology facilitates more efficient GIS 
implementation and merges the different levels of coupling.

Full integration (embeded coupling), 
integration under a common interface (tight coupling), 
loose coupling, reference to modeling environmnts.

Trend towards making GIS part of the modeling system rather than integrating
models within GIS.


4. Case studies

4.1 Overview of some common models and their applications

examples: bivariate water
          sediment and net erosion/depiosition
          landscape evolution (Bill mame MIT data/obrazky/movie?)
          pollutants (nitrate, ???)
          rivers - SMW, WMS - Doug, Bill Mississipi
          groundwater (rakusania?)
          examples of online models (NAPRA, SWAT, LHSIA)

4.2 Distributed  erosion/deposition modeling for assessment of 
erosion and deposition patterns under various conservation strategies
for community based watershed planning 

4.3 Mulsticsale and High resolution hydrologic and erosion modeling
water flow in areas with depressions and channels, erosion
prevention measures (negative and positive impacts) 

4.4 Surfcae water with SMS - Mississipi

examples: bivariate water
          sediment and net erosion/depiosition
          landscape evolution (Bill mame MIT data/obrazky/movie?)
          pollutants (nitrate, ???)
          rivers - SMW, WMS - Doug, Bill Mississipi
          groundwater (rakusania?)

5. Conclusion and lessons learned

impact of physical systems on human and other biological systems
(ecosystems) - land use, agriculture, urbanization, natural
resources (forestry, mines, groundwater...)

ocurrent trend is that GIS as a single big general system 
is disappearing and GIS is melting into the general
computing infrastructure . We can see assimilations of GIS into 
end-user application rather than integrating
applications into the  GIS which means that GIS will be integral 
part of Water resource modeling and DSS systems

poor support for FP data
lack of finite element tools
spatiotemporal/dynamic data/volume data : tools are in some
systems, use not wide enough
nonGIS adding GIS capabilities (visualization IDL, interpolation
SURFER, ....)

real-time simulations 
     distributed on-line modeling 
     complex systems: integrated models of interacting processes 
     dynamic systems in 3D space 
     object oriented reusable model development environments 

References
Information Resources (e.g. web sites, collections, series etc)
Short bio of the author(s) and photograph

  Case studies in Physical. e.g. Hydrological models, geomorphology,
hazards, air circulation

 Modeling physical systems uses multivariate fields , less often
    networks, transition from lumped (widely available REF, online,
    well established, include wide range of processes
     but they are limited  for solving spatial problems
    new generation fo distributed models, enabled by GIS
    distributed mdoels based on finite element - meshes - problems
    with linking with GIS