Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Providing new homes for climate change exiles

Abstract

Given a certain pre-existing commitment to sea-level rise due to the long thermal lags of the ocean system, several million people living in coastal areas and small islands will inevitably be displaced by the middle of the century. These climate exiles will have nowhere to go. Rather than deal with this in an ad hoc manner as the problem arises, the authors propose a mechanism by which these exiles would be given immigration benefits by countries through a formula that ties numbers of immigrants to a country's historical greenhouse gas emissions. Such a compensatory mechanism appears to be a fair way of addressing the problems faced by climate exiles.     

Cyclone life cycle characteristics over the Northern Hemisphere in coupled GCMs

Cyclone activity and life cycle are analysed in the coupled GCMs ECHAM5/OM and ECHAM4/OPYC3. First, the results for the present climate (1978–1999) are compared with ERA-40 and NCEP/NCAR reanalyses, showing a drastic improvement in the representation of cyclone activity in ECHAM5/OM compared to ECHAM4/OPYC3. The total number of cyclones, cyclone intensity, propagation velocity and deepening rates are found to be much more realistic in ECHAM5/OM relative to ECHAM4/OPYC3. Then, changes in extra tropical cyclone characteristics are compared between present day climate and future climate under the emission-scenario A1B using ECHAM5/OM. This comparison is performed using the 20-year time slices 1978–1999, 2070–2090 and 2170–2190, which were considered to be representative for the various climate conditions. The total number of cyclones does not undergo significant changes in a warmer climate. However, regional changes in cyclone numbers and frequencies are evident. One example is the Mediterranean region where the number of cyclones in summer increases almost by factor 2. Some noticeable changes are also found in cyclone life cycle characteristics (deepening rate and propagation velocity). Cyclones in the future climate scenario tend to move slower and their deepening rate becomes stronger, while cyclone intensity does not undergo significant change in a warmer climate. Generally, our results do not support the hypothesis of enhanced storminess under future climate conditions.