Tropical cyclone-generated storm surges are among the world's most deadly and destructive natural hazards. This paper provides the first comprehensive global review of tropical storm surge data sources, observations, and impacts while archiving data in SURGEDAT, a global database. Available literature has provided data for more than 700 surge events since 1880, the majority of which are found in the western North Atlantic (WNA), followed by Australia/Oceania, the western North Pacific (WNP), and the northern Indian Ocean (NIO). The Bay of Bengal (BOB) in the NIO consistently observes the world's highest surges, as this subbasin averages five surges ≥5 m per decade and has observed credible storm tide levels reaching 13.7 m. The WNP observes the highest rate of low-magnitude surges, as the coast of China averages 54 surges ≥1 m per decade, and rates are likely higher in the Philippines. The U.S. Gulf Coast observes the second highest frequency of both high-magnitude (≥5 m) and low-magnitude (≥1 m) surges. The BOB observes the most catastrophic surge impacts, as 59% of global tropical cyclones that have killed at least 5000 people occurred in this basin. The six deadliest cyclones in this region have each killed at least 140,000 people, and two events have killed 300,000. Storm surge impacts transportation, agriculture, and energy sectors in the WNA. Oceania experiences long-term impacts, including contamination of fresh water and loss of food supplies, although the highest surges in this region are lower than most other basins.

The problem of clustering climate data observation sites and grouping them by their climate types is considered. Machine learning–based clustering algorithms are used in analyzing climate data time series from more than 3,000 climate observation sites in the United States, with the objective of classifying climate type for regions across the United States. Understanding the climate type of a region has applications in public health, environment, actuarial science, insurance, agriculture, and engineering. In this study, daily climate data measurements for temperature and precipitation from the time period 1946–2015 have been used. The daily data observations were grouped into three derived data sets: a monthly data set (daily data aggregated by month), an annual data set (daily data aggregated by year), and a threshold exceeding frequency data set (threshold exceeding frequency provides the frequency of occurrence of certain climate extremes). Three existing clustering algorithms from the literature, namely, k‐means, density‐based spatial clustering of applications with noise, and balanced iterative reducing and clustering using hierarchies, were each applied to cluster each of the data sets, and the resulting clusters were assessed using standardized clustering indices. The results from these unsupervised learning techniques revealed the suitability and applicability of these algorithms in the climate domain. The clusters identified by these techniques were also compared with existing climate classification types such as the Köppen classification system. Additionally, the work also developed an interactive web and map‐based data visualization system that uses efficient big data management techniques to provide clustering solutions in real time and to display the results of the clustering analysis.

Tropical cyclone–generated storm surges are among the world's most deadly and costly natural disasters. The destructive nature of this hazard was clearly seen last fall, as Hurricane Sandy generated a devastating storm surge along the mid‐Atlantic coast. The storm killed 147 people and caused approximately $50 billion in economic losses [Blake et al., 2012].

We applied the newly developed WRF-Hydro model to investigate the hydroclimatic trend encompassing the three basins in Southwest Louisiana as well as their connection with large-scale atmospheric drivers. Using the North American Land Data Assimilation System Phase 2 (NLDAS-2), we performed a multi-decadal model hindcast covering the period of 1979-2014. After validating the model's performance against available observations, trend and wavelet analysis were applied on the time series of hydroclimatic variables from NLDAS-2 (temperature and precipitation) and model results (evapotranspiration, soil moisture, water surplus, and streamflow). Trend analysis of model-simulated monthly and annual time series indicates that the regional climate is warming and drying over the past decades, specifically during spring and summer (growing season). Wavelet analysis reveals that, since the late 1990s, the anomaly of evapotranspiration, soil moisture, and streamflow exhibits high coherency with that of precipitation. Pettitt's test detects a possible change-point around the year 2004, after which the monthly precipitation decreased from 140 to 120 mm, evapotranspiration slightly increased from 80 to 83 mm, and water surplus decreased from 60 to 38 mm. Changes in regional climate conditions are closely correlated with large-scale climate dynamics such as the Atlantic Multidecadal Oscillation (AMO) and El Niño Southern Oscillation (ENSO).

No abstract