Environmental Engineering Reference
In-Depth Information
internal coupling of elements and of the degree of aggregation of the components
(WGBU 1997). To combine different scales and levels of an ecosystem, models
have to include horizontal as well as vertical interactions, interdependencies and
reciprocities of the examined entities as well as human activities (Schellnhuber
et al. 1999). These relations exceed the boundaries of the subunits of ecosystems
and geographic features. They transcend socio-economic sectors and scientific
disciplines. Consequently, “holistic” models require the expertise (and the models)
of different scientific disciplines. In this context, integration has two main objec-
tives: (1) developing interdisciplinary, networked approaches of
joining
disciplin-
ary data, models and methods - joining understood in the sense of conceptually and
technically combining them, and (2) finding methods to handle the resulting
complexity. These objectives include both technical challenges as well as contex-
tual questions, the latter resulting from new aspects following upon data, models
and methods integration. Integrated Environmental Modelling (IEM) addresses
both objectives: IEM couples models from different disciplines to find answers
for holistic questions and helps to aggregate complex results to make the models
operable and communicable.
Integration is a driver for the development of different tools, concepts and
approaches of modelling. As a result, environmental research, which has concentrated
on specific topics and a centred set of methods for quite some time, is now broadening
its methodological approach. Knowledge that was quite centred at first, may now be
more broadly linked with different models to deal with a particular subject and thus
widen the scope of investigations and enable results that were not achievable before.
Consequently, in the context of this broader consideration IEM is a sophisticated
object under investigation. Based on a combination of models, IEM have the potential
to answer newly arising questions by interlinking particular disciplinary knowledge.
The analysis of complex and abstract phenomena like dynamic interactions of
inhomogeneous socio-ecological systems rely especially on sophisticated methods
that might be offered by IEM. The management of catchments, coastal zones, and
marine landscapes where elements with differing dynamics converge, present many
examples for trans-disciplinary approaches dealing with this complexity.
Geographical Information Systems (GIS) offer two general techniques to sup-
port this kind of integration and therefore are an important addition to IEM. GIS are
able to link data based on their location and to aggregate them. Data linkage can be
performed by intersection of the respective geometries or just by overlaying
different thematic layers in a map. Based on their spatial reference, GIS may
serve as an interface between data, models and paradigms. In this way, GIS has
catalysed integrated modelling and environmental research over the last decades.
The needs of management, policy and planners increase the demand for easily
accessible, manipulable and presentable spatial information. GIS have strongly
supported a spatially-explicit way of looking at natural systems: Location, under-
stood as the habitat of organisms, is increasingly used as a level of integration in
ecosystem research, providing the environment for organisms and defining terri-
tories and expansion of biocoenoses. Any information on a habitat having a spatial
reference can be processed and linked with other spatial data in a GIS.
