Environmental Engineering Reference
In-Depth Information
In addition, requirements of resource management and environmental policy
have strongly favoured the development of IEM (Dolk et al. 1993). Considering
global change, politicians, spatial planers and environmental managers require
predictions describing possible future developments in scenarios, forecasts, and
projections. Information should be regionally differentiated, comprehensible and
immediately available. Environmental management assesses the consequences of
changes, developments or shifts of the systems described. Weighting the rele-
vance of impacts, assessing states, and evaluating ecological functions of entities,
animals, plants, as well as communities, are central objectives (Costanza et al.
1998).
Most dynamic simulation models have limited ability to represent spatial pro-
cesses, as they focus on specific aspects of the processes themselves (see the
chapters on PDE, CA, IBM, and SDM - Chaps. 7, 8, 12, 13 respectively). GIS
offer methods to handle temporal dynamics, analysing the changes of the relevant
parameters across the area. In contrast to many other approaches that represent
space in terms of square boxes or simplified grids, GIS operates with spatial
references in terms of geodetic coordinate systems, e.g. by using geographic or
metric based coordinates. Together, GIS and IEM have the ability to address both
challenges: joining complex data and aggregating them to manageable information.
IEM follows a holistic, area-wide and trans-disciplinary approach. Data or
models describing the status of different subsystems are interacting in a joint
software-framework describing the status of the whole (considered) system, using
adequate indicators and appropriate geo-referenced datasets. IEM simulate the
observed behaviour of an entire system and based on scenarios, intend to provide
projections of future states.
This chapter has the following outline: General technical and conceptual aspects
will be described, which have to be considered when developing and discussing the
results of an IEM. The third section focuses methodically on the stepwise process of
model integration via GIS. The main exemplary GIS-techniques for joining data
layers (and hereby models respectively) and aggregating complex interactions are
described. Section
22.4
describes three examples of IEM coupled by GIS. Finally,
I conclude with general strategies of model coupling with GIS and discuss future
perspectives.
22.2 General Aspects of IEM
Interoperability, dealing with the problems of assembling and interrelating data
and/or models, is one of the central objectives of IEM (Argent 2004; Goodchild
et al. 1997). The technical ability to exchange data and the conceptual capacity to
make use of information intrinsically ties models and GIS to each other (Abel et al.
1994). There are two general options to follow when developing a (integrated)
model:
