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.     

Spatial Modeling and Analysis for Shoreline Change Detection and Coastal Erosion Monitoring

Coastal erosion presents a serious problem throughout U.S. coastal areas. The Ohio Geological Survey estimates that more than 3,200 acres of Ohio's Lake Erie shore have been lost to erosion since the 1870s, resulting in economic losses exceeding tens of millions of dollars per year. This article presents research results of a project that monitors shoreline erosion using high-resolution imagery and examines erosion causes. Spatial modeling and analysis methods are applied to the project area along the south shore of Lake Erie. The shoreline is represented as a dynamically-segmented linear model that is linked to a large amount of data describing shoreline changes. A new method computes an instantaneous shoreline using a digital water level model, a coastal terrain model, and bathymetric data. This method provides an algorithm for deriving the Mean-Lower Low Water (MLLW) and the Mean High Water (MHW) shorelines that are essential to navigation charts. The results describe a part of our effort towards a coastal spatial information infrastructure to support management and decision-making in the dynamic coastal environment.     

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.     

Land Cover Change and its Environmental Significance in the Herbert River Catchment,North-east Queensland

There has been considerable recent concern over the amount of vegetation clearance in the wet tropics of northern Australia. This paper reports on the results of a case study undertaken in the lower Herbert River catchment in north-east Queensland, which utilised remote sensing and Geographic Information Systems (GIS) to assess both spatial and temporal changes in land cover since European settlement in the mid-nineteenth century. We demonstrate that since European settlement there has been a substantial reduction in the area of Melaleuca, rainforest, and eucalyptus-dominated land-cover patterns. We also provide a range of quantitative measures to show that the landscape diversity, integrity and quality of these ecosystems have also declined between the 1860s and 1996. We conclude that reform is required at policy, planning and enterprise levels if the ecological, economic and social values of these systems are to be maintained in the future.     

Overview of GIS Applications in Estuarine Monitoring and Assessment Research

Geographic information systems (GIS) tools are now considered integral in estuarine monitoring and assessment research. A synopsis is presented of our estuarine applications of GIS in the Northeast region of the U.S. The applications discussed cover sample site selection, support for field sampling activities, quality assurance of data, spatial display of geographic referenced information, quantitative spatial analysis of data, and communication of results.     

Remote Sensing of Mangrove Change Along the Tanzania Coast

This article contributes to the understanding of the changes in distribution and total area of mangrove forests along the mainland Tanzania coast over the past decade. Mangroves are recognized as critical coastal habitat requiring protection and special attention. The Tanzania coastline forms a suitable habitat for establishment of mangrove forests. Mangrove forests are distributed from Tanga in the north to Mtwara in the south covering approximately 109,593 hectares from 1988-1990 and about 108,138 hectares in 2000. The largest continuous mangrove stands are found in the districts of Rufiji, Kilwa, Tanga-Muheza, and Mtwara. Comparison of data between these two time periods shows that the geographic coverage of mangroves has no dramatic change in the past decade. The Tanzania Mangrove Management Project and other closely related programs and efforts pertaining to mangrove conservation contribute to direct restoration and natural regeneration of mangroves. This study documents the changes of mangroves and demonstrates that remote sensing and GIS offer important data and tools in the advancement of coastal resource management and ecosystem monitoring. Application of geographic information technologies is critical for improved coastal resources management and decision making for sustainable development in Tanzania.     

Why Web GIS May Not Be Enough: A Case Study with the Virtual Research Vessel

During several decades of investigation, the East Pacific Rise seafloor-spreading center at 9°-10°N has been explored by marine geologists, geophysicists, chemists, and biologists, and has emerged as one of the best studied sections of the global midocean ridge. It is an example of a region for which there is now a great wealth of observational data, results, and data-driven theoretical studies. However, these have yet to be fully utilized, either by research scientists or educators. While the situation is improving, a large amount of data, results, and related theoretical models still exist either in an inert, noninteractive form (e.g., journal publications) or as unlinked and currently incompatible computer data or algorithms. Presented here is the prototype of a computational environment and toolset, called the Virtual Research Vessel, to improve the situation by providing marine scientists and educators with simultaneous access to data, maps, and numerical models. While infrastructure is desired and needed for ready access to data and the resulting maps via web GIS in order to link disparate data sets (data to data), it is argued that data must also be linked to models for better exploration of new relations between observables, refinement of numerical simulations, and the quantitative evaluation of scientific hypotheses. For widespread data access, web GIS is therefore only a preliminary step rather than a final solution, and the ongoing implementation of the Virtual Research Vessel (scheduled for final completion in 2004-2005) is a case study for the midocean ridge community to test the effectiveness of moving beyond the data-to-data mode towards data-to-models and data-to-interpretation.     

Remote Sensing of Mangrove Change Along the Tanzania Coast

This article contributes to the understanding of the changes in distribution and total area of mangrove forests along the mainland Tanzania coast over the past decade. Mangroves are recognized as critical coastal habitat requiring protection and special attention. The Tanzania coastline forms a suitable habitat for establishment of mangrove forests. Mangrove forests are distributed from Tanga in the north to Mtwara in the south covering approximately 109,593 hectares from 1988-1990 and about 108,138 hectares in 2000. The largest continuous mangrove stands are found in the districts of Rufiji, Kilwa, Tanga-Muheza, and Mtwara. Comparison of data between these two time periods shows that the geographic coverage of mangroves has no dramatic change in the past decade. The Tanzania Mangrove Management Project and other closely related programs and efforts pertaining to mangrove conservation contribute to direct restoration and natural regeneration of mangroves. This study documents the changes of mangroves and demonstrates that remote sensing and GIS offer important data and tools in the advancement of coastal resource management and ecosystem monitoring. Application of geographic information technologies is critical for improved coastal resources management and decision making for sustainable development in Tanzania.     

Using a GIS to Examine Changes in the Bathymetry of Borrow Pits and in Lower Bay,New York Harbor,USA

Standard analyses with geographic information systems (GIS) and the publicly available GEODAS database were used to highlight bathymetric changes in the Lower Bay complex of New York Harbor. Dredging operations have deepened much of the Lower Bay complex. Approximately 6,580 hectares, or 20% of the bay bottom surveyed in 1934, was deeper in 1979/1982 than during 1934. Half of this deepening, 3,219 hectares or 10% of the bay bottom surveyed during 1934, was deeper by at least 2 m. Surveys conducted by the U.S. Army Corps of Engineers of three borrow pits in the central part of the Lower Bay complex were used to examine sedimentation over a 16-year period from 1979 to 1995. Results were consistent with studies conducted during the 1970s and 1980s that show the pits function as sediment traps. Between 1979 and 1995, sediment accumulated at rates of 6 to 12 cm per year in many portions of the borrow pits.     

四川1960-2010年雷暴观测事实及灾害防范区域研究

利用1960 -2010年四川雷暴逐日观测资料分析四川雷暴特征及变化规律。结果表明,四川雷暴年平均雷暴日数呈逐年减少趋势,高原雷暴发生的日数明显多于盆地区域,川西高原南部山区偏多于川西高原北部。把研究的防御重点信息数据与地理信息系统（GIS）技术相互结合,将雷暴灾情实况、雷暴多发区域、人口、经济情况的属性信息特点进行地理编码,使其转换为包含经度、纬度、海拔等相关地理信息的地理图层shape文件,实现灾害防御数据信息与地理信息的融合。结果表明：盆地东部的南充、广安和高原的盐边为雷暴重点防御一级区域;巴中、阆中、峨眉、盐源为雷暴二级防御区域;绵阳、南充、泸州、阿坝为雷暴三级防御区域;成都、温江、都江堰、达州、遂宁、雅安、宜宾等为雷暴四级防御区域。