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