Probabilistic Assessment of Storm Surge Hazard along Coastal Regions of Sindh Province, Pakistan

A phenomenon that brings about abnormal rise in the sea level is referred as storm surge generated as a result of tropical cyclones. Tropical cyclone are low pressure systems which are often very powerful and intense causing serious damages by producing destructive winds, surges and heavy rainfall. Over the past decades, North Indian Ocean has been subjected to the catastrophe of tropical cyclones leading to massive economic and human losses. The coastal zones of Pakistan, India, Bangladesh, Myanmar, Sri Lanka and Oman are consistently affected by storm surges. Although Arabian Sea is potential region for cyclones, but the frequency of cyclones is occasional and mostly during the monsoon and post monsoon season. A number of tropical cyclones dating back to 100 years have struck Pakistan’s coastal areas in the years 1895, 1902, 1907, 1948, 1964, 1985, 1999, 2001, 2007, and 2010. In view of rapid recurrences of cyclones in past few years this study aims to carry out probabilistic assessment of tropical cyclone hazard to estimate the return periods for surge heights for the province of Sindh. The CAPRA ERN-Hurricane model simulate surge heights along the shoreline for provided historic cyclone tracks from IBTrACS v04r00 and employing topography, bathymetry, wind exposure and soil roughness as other input parameters for the model. Furthermore, the frequency analysis by Gumbel method has been adopted in the study to estimate surge heights for 25, 100 and 500 years return periods. ERN Hurricane model estimates highest surge value of 0.6385 m for tropical cyclone 02A 1999. Second highest surge value in historic cyclone is 0.6067 m, which corresponds to tropical cyclone Phet 2010. While frequency analysis estimates maximum surge value of 0.5905 m, 0.7352 m, 0.9041 m for 25, 100 and 500 year return periods respectively. Storm surge hazard data is utilized in making hazard maps illustrating the intensities and inundation of cyclone over return periods. The data and maps from this study can contribute in planning and developing suitable strategies to mitigate loss for areas at potential risk.