Landscapes of Globalisation in SE Asia

As economies continue to expand in Southeast Asia, urban and rural landscapes are undergoing industrial-scale change at a staggering pace. A number of growing industries are responsible for these changes, from soil and biodiversity loss caused by palm-oil deforestation to rainforest flooded in the interest of “climate neutral” hydropower. To best understand the wide-reaching effects of these transformations, a radically interdisciplinary approach is needed to unravel the intersection between environmental degradation, economics and culture. Is the quest for biofuels and carbon-neutral energy to support burgeoning largely urban populations, sometimes in other nations, effectively shifting the environmental costs to rural communities? What are the trade-offs between economic development in rural communities vs. loss of habitat and traditions, as well as clean water and air? By exploring the complex intersections from a liberal arts and sciences perspective that attempts to view these challenges though interdisciplinary lenses, can we come to solutions to limit the damage before losses are irreversible? While the last may be an overly lofty goal, it is critical to have this approach as part of the conversation, as the siloed problem-solving methodology only gets us so far

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at line uri \u3ehttps://doi.org/10.17026/dans-x6c-y7x2\u3e (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

The large tsunami of 26 December 2004: Field observations and eyewitnesses accounts from Sri Lanka, Maldives Is. and Thailand

Abstract Post-event field surveys were conducted and measurements were taken in Sri Lanka and Maldives about two weeks after the catastrophic Indian Ocean tsunami of 26 December 2004. The measurements taken were cross-checked after interviewing with local people. In the southwest, south and east coastal zones of Sri Lanka maximum water levels ranging from h = 3 m to h = 11 m a.m.s.l. were estimated. The highest values observed were in the south of the island: Galle h ∼ 10 m, Hambantota h ∼ 11m. Maximum inundation of d ∼ 2 km was observed in Hambantota. The heavy destruction and thousands of victims caused in coastal communities, buildings and infrastructure, like railways and bridges, is attributed not only to physical parameters, like the strength of the tsunami hydrodynamic flow, coastal geomorphology and the wave erosional action in soil, but also to anthropogenic factors including the increased vulnerability of the non-RC buildings and the high population density. Local people usually described the tsunami as a series of three main waves. The leading wave phase was only a silent sea level rise of h ≤ 1.5 m and d ≤ 150 m, while the second wave was the strongest one. The first two waves occurred between 09:00 and 09:30 local time, depending on the locality. It is well documented that near Galle, southern part, the strong wave arrived at 09:25:30. In the west coast the third wave was a late arrival which possibly represents reflection phases. In Maldives, three waves were also reported to arrive between 09:00 and 09:30 local time. Maximum water level was only h ∼ 3 m in Laamu Atoll, which is interpreted by the wave amplitude damping by the coral reef to the east of the island complex as well as to that the tsunami did not arrived at high tide time. Damage was observed in several islands of Maldives but this was minimal as compared to the heavy destruction observed in Sri Lanka. About 25 Greek eyewitnesses, who happened to experience the tsunami attack in Padong and Blue Lagoon Port of Phuket island as well as in Maya Bay, Phi-Phi islands, Thailand, were interviewed on the basis of a standard questionnaire. The first sea motion was a retreat of at least 100 m. Then, two main waves arrived, the first being the strong one occurring at about 09:55–10:05 local time, with h ∼ 6m in Padong causing significant destruction and human victims. The collected information clearly indicates that the tsunami propagated as the leading crest wave to the west side, e.g. in Sri Lanka and Maldives, and as the leading trough wave to the east, e.g. in Thailand

Pathways to Coastal Resiliency: The Adaptive Gradients Framework

Current and future climate-related coastal impacts such as catastrophic and repetitive flooding, hurricane intensity, and sea level rise necessitate a new approach to developing and managing coastal infrastructure. Traditional “hard” or “grey” engineering solutions are proving both expensive and inflexible in the face of a rapidly changing coastal environment. Hybrid solutions that incorporate natural, nature-based, structural, and non-structural features may better achieve a broad set of goals such as ecological enhancement, long-term adaptation, and social benefits, but broad consideration and uptake of these approaches has been slow. One barrier to the widespread implementation of hybrid solutions is the lack of a relatively quick but holistic evaluation framework that places these broader environmental and societal goals on equal footing with the more traditional goal of exposure reduction. To respond to this need, the Adaptive Gradients Framework was developed and pilot-tested as a qualitative, flexible, and collaborative process guide for organizations to understand, evaluate, and potentially select more diverse kinds of infrastructural responses. These responses would ideally include natural, nature-based, and regulatory/cultural approaches, as well as hybrid designs combining multiple approaches. It enables rapid expert review of project designs based on eight metrics called “gradients”, which include exposure reduction, cost efficiency, institutional capacity, ecological enhancement, adaptation over time, greenhouse gas reduction, participatory process, and social benefits. The framework was conceptualized and developed in three phases: relevant factors and barriers were collected from practitioners and experts by survey; these factors were ranked by importance and used to develop the initial framework; several case studies were iteratively evaluated using this technique; and the framework was finalized for implementation. The article presents the framework and a pilot test of its application, along with resources that would enable wider application of the framework by practitioners and theorists

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies

Maldives field survey after the December 2004 Indian Ocean tsunami

Summarization: The tsunami of 26 December 2004 severely affected the Maldives at a distance of 2,500 km from the epicenter of the magnitude 9.0 earthquake. The Maldives provide an opportunity to assess the impact of a tsunami on coral atolls. Two international tsunami survey teams (ITSTs) surveyed a total of 13 heavily damaged islands. The islands were visited by seaplane on 14–15 and 18–19 January 2005. We recorded tsunami heights of up to 4 m on Vilufushi on the basis of the location of debris in trees and watermarks on buildings. Each watermark was localized by means of a global positioning system (GPS) and was photographed. Numerous eyewitness interviews were recorded on video. The significantly lower tsunami impact on the Maldives as compared with Sri Lanka is largely due to the topography and bathymetry of the atoll chain.Παρουσιάστηκε στο: Earthquake Spectr