GIS technology and spatial analysis in the coastalzone management




Dr. Kurt Fedra,
Environmental Software & Services GmbH,
Austria
Email: kurt@ess.co.at, http://www.ess.co.at

The coastal zone is home to a considerable part of global population (>25%, based on a 100 km band, IPPC 2007). Many of the world’s major cities are located in the coastal zone. From a spatial planning point of view, a coastal location has major advantages (primarily related to transportation/shipping) as well as the amenities of beaches for recreation, but also the local economy (tourism, fishing). At the same time, there are major and growing challenges. An obvious constraint for fast growing agglomerations is space: a coastal city, extremely simplified, has only half the space available for growth compared to an inland location. Related is the issue of water resources, as any city not located on a major river has only half the hydrological catchment of an inland location, and with the widespread overexploitation of local groundwater resources, may be subject to saltwater intrusion that may further endanger groundwater resources. Waste management (ocean outfalls) from industry, the energy sector, desalination, and sewers (with and without treatment) pose yet another problem to coastal water quality and thus the sustainable use of marine resources. Another “emerging” challenge is due to the spectre of climate change: sea level rise and the expected increase in the frequency and severity of extreme weather events (coastal storms, and surges) endanger the immediate coastal zone.

In summary, while attractive, the coastal zone poses a range of more or less specific problems that call for careful spatial planning to maintain a sustainable environment for human habitation and economic activities.

GIS and Spatial Analysis

GIS is by now a well established technology with a wide range of commercial and public domain solutions and tools, widely used for spatial data management and analysis, and also the efficient communication of any spatially distributed phenomena. At the same time, the increasing availability of high resolution (in time and space) remote sensing data (satellite and aerial photography, e.g., NCSU (2016), Google Earth: www.gosur.com/google-earth) provide useful data, and GIS on increasingly powerful client hardware from workstations, PCs and laptops to tablets and mobile phones, provides the tool for their processing rendering and display. Increasing standardization (OPEN GIS, OGC, 2016) and thus interoperability greatly enhances the practical usability, when data from various sources need to be integrated, and web based solutions facilitate easy and distributed access. There are more GIS users (in a somewhat limited sense of viewing data onia map) using mobile phones than “classical” PCs. The central paradigm, the map, is very familiar: it and can be found in most every car’s dashboard and linked to every search engine on the web, and every smart phone provides map based data, from navigation to the location of the nearest fast food outlet or coffee shop.

Maps are usually well and intuitively understood, and in their pictorial nature coveys (with a usually very reliable geographical background) “precision and credibility”, even for topical maps that may need a more careful interpretation than a road map.

GIS is a powerful tool for the management and display, and within limitations, the analysis of spatial data. Some basic “static” operations like overlay analysis provide a rich repertoire to help understand spatial pattern, and, within limits, (causal) relationships. What a map can show is co-location, co-incidence, but that is not necessarily based on cause-effect, which is what must be the basis for any management, that aims of modifying causes (the driving forces) to obtain desired effects or mitigate problems.

The primary question a GIS can answer is: WHERE; coastal zone management must also ask: WHY, WHAT-IF, and HOW-TO, which calls for an extension of classical GIS technology.

An often used logical framework for analysis and control is DIPSIR (EEA 2007) its possible spatial extensions and integration with GIS should be obvious, providing the additional spatial dimension beyond the basically lumped, procedure model.

Beyond location: dynamics and cause-effect

Coastal process, physical as well as socio-economic, are by definition dynamic. Their time scales range from geological dimensions (coastal morphology, sea level changes) to very short term (tidal to extreme events like storm surges), and on the socio-economic side again from very short-term (e.g., traffic) to much more long-term patterns (e.g., urbanization, land use change, demographics, economic and technological development, climate change).

Of course, a time series of maps can depict changes over time, and eventually animations. However, to analyse, predict and ultimately “control” (in some usually limited sense) change, we need different tools, like spatially distributed models (Fedra, 2004, 2006;, Figure 1,2)

These can be topological networks (traffic, water resources networks) or various forms of discretisation, from simple regular grid to more complex, 3D and adaptive, curvilinear forms. Again the interface with the GIS world (providing the spatial input data, initial and boundary conditions), as well as the visualization of model results as “topical maps”, is obvious, convenient, and useful, and has been around for some time (Fedra, 1994).

Scenario analysis and optimization

The first level of decision relevant information for the management of the coastal zone is WHERE and WHY, that already illustrates the progression from description to analysis. Integrated Coastal Zone Management by definition (EC, 2015) involves several criteria and objectives, constraints, including their spatial distribution at various levels of aggregation, geographically or sectoral, functional, administrative related to the structure of the applicable regulatory frameworks. The next step is scenario analysis, asking WHAT IF. What will happen when some of the driving forces (decision variables) are modified to improve the coastal zone’s “performance”, expressed in a number of or criteria (performance indicators) objectives

(as an example, think of “increased net benefit” in an inclusive sense that combines market economic and socio-economic criteria). This is the domain of spatially distributed models. Given the complexity of the coastal zone, the number of possible combinations of decision variables is near endless. This leads to a more systematic exploration of the “decision space” to find the best (in some sense) strategies (efficient, reliable, sustainable) for coastal zone development (Fedra, 2008). In very simple terms, if we can define a “target” state of the coastal zone, we can “ask the computer” to find feasible strategies to get us as close as possible to this target.

Uncertainty, robustness, resilience, sustainability

Whatever data we use, and whatever model formulations we employ, there is always uncertainty involved. Even a supposedly precise background map is only a historical snapshot, that may no longer be correct, up to date. And once we try to anticipate changes, future developments, the uncertainty around our assumptions increases dramatically.

Just think about climate change, the where and how much of temperature increase, sea level rise, changes in precipitation patterns, and the frequency and severity of extreme weather events. One possibility to address uncertainty explicitly, is to include measure of “reliability” and their minimization explicitly in the analysis.

Summary

GIS is a powerful tool for coastal zone analysis, supporting management. Increasingly powerful and mobile client hardware, and the availability of fast growing spatial data resources enable ever more powerful applications. To extend the applicability of

GIS toward a more complex management decision support however, requires the integration with various (spatially distributed) models to address scenario analysis and optimization for policy and management decision support.

References

  • EC (2015) Integrated Coastal Zone Management. ec.europa.eu/environment/iczm/home.htm (accessed 20160324)
  • EEA (2007) The DIPSIR framework as used by the EEA ia2dec.pbe.eea.europa.eu/knowledge_base/Frameworks/doc101182 (accessed 20160324)
  • Fedra, K. (2008) Coastal zone resource management: Tools for a participatory planning and decision making process. 673-686., In: R. Krishnamurthy et. al., [eds]: Integrated Coastal Zone Management - The Global Challenge. Research Publishing Services, Singapore.
  • Fedra, K. (2006) Beyond GIS: Integrating dynamic simulation models and GIS for natural resources and environmental management. 10 pp., In: Map Middle East 2006, Proceedings, CD edition.
  • Fedra, K. and Abdel-Rehim, A. (2004) Spatial Analysis for Coastal Zone Management: Beyond GIS. Proceedings of CoastGIS'03; http://www. gisig.it/coastgis/papers/fedra.htm
  • Fedra, K. (1994) GIS and environmental modeling. In: M.F. Goodchild, B.O. Parks and L.T. Steyaert [eds.] Environmental Modeling with GIS. 35-50, Oxford University Press.
  • IPPC (2007) IPPC Fourth Assessment Report, 6.7 Conclusions: implications for sustainable development. http://www.ipcc.ch/publications_ and_data/ar4/wg2/en/ch6s6-7.html (accessed 20160324)
  • OGC (2016) Open Geospatial Consortium, www.opengeospatial.org (accessed 20160324)
  • NCSU (2016) Coastal GIS Data. NCSU Libraries, North Carolina State University, https://www.lib.ncsu.edu/gis/coastal.html (accessed 20160324)
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