Climate-induced storminess forces major increases in future storm surge hazard in the South China Sea region

It is vital to robustly estimate the risks posed by extreme sea levels, especially in tropical regions where cyclones can generate large storm surges and observations are too limited in time and space to deliver reliable analyses. To address this limitation for the South China Sea region, we force a hydrodynamic model with a new synthetic database representing 10,000 years of past/present and future tropical cyclone activity, to investigate climate change impacts on extreme sea levels forced by storm surges (± tides). We show that, as stronger and more numerous tropical cyclones likely pass through this region over the next 30 years, both the spatial extent and severity of storm surge hazard increases. While extreme storm surge events in this location become generally a more frequent occurrence in the future, larger storm surges around Vietnam and China coastlines are projected to regionally amplify this hazard. This threatens low-lying, densely-populated areas such as the Red and Mekong River deltas, while sections of the Cambodian and Thai coastline face previously unseen storm surge hazards. These future hazards strongly signal that coastal flood management and adaptation in these areas should be reviewed for their resilience against future extreme sea levels.

Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model

Storm surges are one of the most devastating coastal disasters. Numerous efforts have continuously been made to achieve better prediction of storm surge variation. In this paper, we propose a parametric cyclone and neural network hybrid model for accurate, long lead-time storm surge prediction. The model was applied to the northeastern coastal region of Taiwan, i.e., Longdong station. A total of 14 historical typhoon events were used for model training and validation, and the results and questions associated with this hybrid approach carefully discussed. Overall, the proposed method reduced the complexity of network structure while retaining the important typhoon indicators. In particular, local pressure and winds estimated from the storm parameters through physically-based parametric cyclone models allow for inferring the possible future influence of a typhoon, unlike the simple collection and direct usage of observation data from local stations in earlier works. Meanwhile, the error-tolerance capability of the neural network alleviated some discrepancy in the model inputs and enabled good surge prediction. Further, the proposed method showed better and faster convergence thanks to the retention of storm information and the reduced dimensions of the search space. The hybrid model presented excellent performance or maintained reasonable capability for short lead-time and long lead-time storm surge prediction. Compared with the pure neural network model under the same network dimensions, the present model demonstrated great improvement in accuracy as the prediction lead time increased to 8 h, e.g., 33–40% (13–21%) and 32–37% (18–29%) RMSE and CE, respectively, in the training/validation phase.

Factors Controlling the Extreme Storm Surge Flooding - Insights From Coastal Sedimentary Record of the Southern Baltic Sea

Climate change and related sea-level rise pose significant threats to sandy lowland coasts, which account for approximately 30% of the global coastline. However, the role of key controlling factors responsible for the frequency and extent of extreme storm surge of inundation regime is not yet fully understood. Here, we present the longest to date, high-resolution sedimentary record of extreme storm surge flooding from the microtidal southern Baltic Sea, spanning two periods: 3.6-2.9 ka BP and 0.7 ka BP until present. Wetland sediments, including sandy event layers, were analyzed by sedimentological (grain size, loss-on-ignition, micromorphology), geochronological (14C, 210Pb, 137Cs), geochemical (XRF), mineralogical (heavy minerals) and micropaleontological (diatoms) methods. Our results revealed that both periods are characterized by high-frequency storm surge flooding in order of 1.3 – 4.2 events per century. They are correlated to widely recognized enhanced storminess periods in NW Europe and took place during both rising and fluctuating sea levels. The presented results show that the storm surge driven coastal inundation frequency and extent largely depend on the development of coastal barriers (e.g., beach ridges). Thus, in the context of the future coastal storm surge hazard, the protection of existing coastal barriers is essential.

Numerical simulation of storm surge associated with severe cyclonic storms in the Bay of Bengal during 2008-11

tc Hkh m".kdfVca/kh; pØokr vkrk gS rc Hkkjr vkSj blds fudVorhZ {ks=ksa esa rwQkuh leqnzh rjaxksa dh vkinkvksa ds dkj.k tku vkSj eky dh Hkkjh gkfu] rVh; <k¡pksa dh {kfr vkSj —f"k dks gkfu igq¡prh gSA uoEcj 1970 esa caxykns’k ¼igys iwohZ ikfdLrku½ esa vk, ,d vR;ar iapaM pØokr dh otg ls yxHkx 3]00]000 yksxksa dh tkus xbZaA uoEcj 1977 esa vkU/kz esa vk, pØokr us Hkkjr ds iwohZ rV dks rgl ugl dj fn;k ftlesa yxHkx 10]000 yksxksa dh tkus xbZaA vDrwcj 1999 esa Hkkjr ds mM+hlk ds rV ij ,d izpaM pØokrh rwQku vk;k ftlls ml {ks= esa laifRr dh vR;kf/kd gkfu gksus ds vfrfjDr 15]000 ls Hkh vf/kd yksxksa dh tkus xbZaA gky gh esa ebZ 2008 esa vk, pØokr uxhZl ls E;kaekj esa yxHk.k 1]40]000 yksxksa dh tkusa xbZa vkSj laifRr dk vR;f/kd ek=k esa uqdlku gqvkA ;s fo’o dh lcls cM+h ekuoh; vkink;sa eq[;r% m".kdfVca/kh; pØokrksa ls lac) gaS o leqnzh rwQkuh rjaxksa ls izR;{k:i ls tqMh gSA vr% ml {ks= esa laf{kIr iwokZuqeku vkSj leqnzh rwQkuh rjaxksa dh iwoZ psrkouh nsus dk izko/kku ml {ks= ds fgr esa gksrk gSA bl 'kks/k i= dk eq[; mÌs’; caxky dh [kkM+h vkSj vjc lkxj esa mBus okyh leqnzh rwQkuh rjaxksa dk iwokZuqeku djus ds fy, gky gh esa fodflr fd, x, ekWMyksa dks izdk’k esa ykuk gSA bl 'kks/k&i= esa o"kZ 2008 ls 2011 ds nkSjku caxky dh [kkM+h esa cus izpaM pØokrksa ls tqM+h leqnzh rjaxksa dk iwokZuqeku [email protected] djus esa fun’kZ ds fu"iknu dk Hkh mYys[k fd;k x;k gSA Storm surge disasters cause heavy loss of life and property, damage to the coastal structures and the losses of agriculture in India and its neighborhood whenever a tropical cyclone approaches. About 3,00,000 lives were lost in one of the most severe cyclone that hit Bangladesh (then East Pakistan) in November 1970. The Andhra Cyclone devastated the eastern coast of India, killing about 10,000 persons in November 1977. Orissa coast of India was struck by a severe cyclonic storm in October 1999, killing more than 15000 people besides enormous loss to the property in the region. More recently the Nargis cyclone of May 2008 killed about 1,40,000 people in Myanmar as well as caused enormous property damage. These and most of the world's greatest human disasters associated with the tropical cyclones have been directly attributed to storm surges. Thus, provision of precise prediction and warning of storm surges is of great interest in the region. The main objective of the present paper is to highlight the recent developments in storm surge prediction model for the Bay of Bengal and the Arabian Sea. Paper also describes the performance of the model in forecasting/simulating the surges associated with severe cyclones formed in the Bay of Bengal during 2008 to 2011.

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.