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The Ba River catchment and delta on the island of Viti Levu, Fiji, supports a wealth of livelihoods and is populated by diverse communities who are living with an increased frequency and intensity of hydro-meteorological hazards (floods, cyclones and droughts). Participatory mapping as part of focus group discussions is a tool that can be used to elucidate communities’ understanding of the differing impacts of multiple hazards, as well as the strategies used to prepare and respond to different hazards. In this chapter, the authors present the results of qualitative research undertaken with members of three communities along the Ba River, from the Nausori highlands to the coastal mangroves, with a particular focus on recent floods (2009, 2012) and Tropical Cyclone Winston (2016). The communities draw on a wide range of livelihood strategies from fishing and agriculture to tourism and outside work. Natural hazard events vary in their impact on these livelihood strategies across the landscape and seascape, so that community members can adjust their activities accordingly. The temporal ‘signatures’ of ongoing impacts are also variable across communities and resources. The results suggest that taking a broad, landscape (and seascape) approach to understanding how communities draw livelihoods is valuable in informing effective and inclusive adaptation strategies for environmental change. Furthermore, documenting how the landscape is used in a mapped output may be a valuable tool for future social impact assessment for resource extraction activities.

This is a case of a crisis project management which showcases the unpredictable nature of the project and the role of management in handling the crisis. It is the case of a very severe cyclonic storm hitting the city of Visakhapatnam plant during October, 2014. The whole city was devastated and so was the situation in the Steel plant as it was under zero power conditions for around 10 days. This case gives need for managing an integrated steel plant in case of very severe cyclonic storm and documents the sequence of events and managing unforeseen uncertainty using NTCP concepts.

To forecast the path of tropic cyclones by using a non‐linear statistical forecasting technique – the method of successive analogy.

Non‐linear statistical forecasting models can describe the non‐linear relationship between the factors and the forecasting objects and the real atmospheric movement more accurately, so they usually have stronger forecasting capability. In practice, however, it is shown that the relationships between predictors and predictands sometimes are so complex that it is very difficult or even impossible to establish the kind of non‐linear mathematical model. Therefore, it is an important topic for atmospheric science to solve non‐linear prediction problem of atmospheric systems by using the non‐function model approach.

The objective and quantitative prediction of tropical cyclone moving path can be given by using the method of successive analogy, a non‐linear forecasting technique, and calculating the similarity parameters between the grayscale field and the height field.

Further experiments are needed to verify this technique.

A very useful technique for solving non‐linear problem.

Illustrates the new technique of solving non‐linear statistic problem and its application.

Tropical storms Urduja and Vinta battered the Philippines in December 2017. Despite advances in disaster risk reduction efforts of the country, the twin December storms caused numerous deaths in the Visayas and Mindanao regions. Analysis of these events shows that alerts raised during the Pre-Disaster Risk Assessment (PDRA) for both storms were largely ineffective because they were too broad and general calling for forced evacuations in too many provinces. Repeated multiple and general warnings that usually do not end up in floods or landslides, desensitize people and result in the cry-wolf effect where communities do not respond with urgency when needed. It was unlike the previous execution of PDRA from 2014 to early 2017 by the National Disaster Risk Reduction and Management Council (NDRRMC), which averted mass loss of lives in many severely impacted areas because of hazard-specific, area-focused and time-bound warnings. PDRA must reinstate specific calls, where mayors of communities are informed by phone hours in advance of imminent danger to prompt and ensure immediate action. Mainstreaming Climate Change Adaptation and Disaster Risk Reduction information using probabilistic (multi-scenario) hazard maps is also necessary for an effective early warning system to elicit appropriate response from the community. The paper aims to discuss these issues.

Methods of early warning through the PDRA of the National Disaster Mitigation and Management Council (NDRRMC) of the Philippines during tropical storm Urduja and Typhoon Vinta were assessed in this study and compared to the previous PDRA system from 2014 to early 2017.

It was found out that the numerous casualties were due to inadequate warning issued during the approach of the tropical cyclones. During an impending hazard, warnings must be accurate, reliable, understandable and timely. Despite the availability of maps that identified safe zones for different communities, warnings raised during the PDRA for both tropical cyclones were deemed too general calling for evacuations of whole provinces. As such, not all communities were evacuated in a timely manner because of failure in the key elements of an effective early warning system.

To avoid future disasters from happening, it is recommended that the PDRA reinstate its hazards-specific, area-focused and time-bound warnings. Similarly, to increase the resilience of communities, more work on mainstreaming of Climate Change Adaptation and Disaster Risk and Vulnerability Reduction systems for communities must be done as well. Learning from the lessons of these previous disasters will enable communities, their leaders and every stakeholder, not to repeat the same mistakes in the future.

The coastal zone of Bangladesh that is in the front line of the battle against climate change faced over 200 natural disasters in the past 40 years, and most of the disasters were cyclones. The inevitable cyclone shelter (CS), the backbone of disaster management (DM), provides short-term safety for the disaster victims in Bangladesh. This study aims to explore the community-based limitations and sustainable development features of CSs including the gender issues.

A questionnaire survey was carried out among 230 community people to identify the requirements and sustainable development features of CSs. A field visit was carried out in 23 CSs to capture its existing facilities. Key informant interviews were conducted in the office of Upazila Engineers to strengthen survey data.

This research found that the plan of CSs, quality of construction, capacity, facilities, entrance and exit, space allocation, management and policy were not capable enough to fulfill the needs and requirements of the community people. Due to lack of separate facilities, women and girls avoided shelters for fear of sexual and mental harassment in CSs, as they had experiences in the earlier events of cyclones. Insufficient facilities discourage community from using the shelters.

Women and girls were shy to share their experience in CSs. The historical data were limited in the study area. To the best of the authors’ knowledge, this research presents the actual community-based outcome. During CCRIP training program, the authors met 3,625 community people, and participatory discussions were made to explore the participants’ experiences and perceptions about the sustainable development of CSs.

South-Asian coastal zones are prone to natural, quasi-natural hazard and disasters, where shelters are required for protecting lives of community people during such disasters such as cyclones, storm surges, and floods. Therefore, this study can help in making sustainable development decisions in terms of constructing shelters in disaster-prone countries like Bangladesh.

The outcomes of this investigation are useful for uplifting psychosocial status to protect lives during disasters such as cyclones, storm surges and floods and increase accessibility to shelters, and users will consider CSs as a social asset. In turn, the acceptability of CSs into community level are expected to be increased for combating against cyclones, storm surges, and floods.

This study introduces the bottom-up approach that refers to the community-based decision-making to identify the limitations and sustainable improvement of CSs. This research contributes to bridging the gaps between decision-makers and users of CSs. From the authors’ field experience, it can be said that this is the first fieldwork regarding the objectives.

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

Mangrove has the potential to adapt climate change threats like sea level rise, extreme high water events, and coastal erosions. The large stretched root systems of the mangrove acts as a natural barrier to catch hold of the run off soil, leading to accretion of coastal areas. Due to human and other natural activities, mangroves in different parts of the world are being degraded. Citing examples from India, this chapter provides ways of unique mangrove comanagement system with the involvement of local communities, NGOs, and local governments.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year.

Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed.

Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking.

This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck.

For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of.

Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.