Prediction of Khash city due to hurricane crisis by Gamble method and partial series

Document Type : Research Article

Authors

1 Regional planning student, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran.

2 university of sistan and blochestsn

3 MA in regional planning - Faculty of Architecture and Urban Planning - Shahid Beheshti University - Iran.

4 Postdoctoral in Urban Planning, Faculty of Geography, University of Tehran, Tehran, Iran.

10.22067/geoeh.2023.81449.1337

Abstract

Storm is one of the natural and climatic hazards that has a direct and indirect effect on urban and rural settlements. This article has been prepared with the aim of predicting the vulnerability of Shahrkhash due to the storm crisis using the Gamble method and partial series for the necessity of paying attention to urban crisis management. The research method is descriptive-analytical and applied. The research data were extracted from Khash synoptic station monthly.Taking into account the threshold speed of the storm of 15 meters per second, the monthly, seasonal and annual mud storms of Khash city were drawn with WRPLOT 8.0.2 software. Also, by Gamble method and partial series, the forecasting of storm return periods of 1 to 100 years for monthly, seasonal and annual time frame was done based on the latest recorded data (1365 to 1397). The results of the research showed that in the monthly period of July and June, the most storms came from the south and south-west, and the months of February and January had the least frequency and intensity of storms from the north-west direction in the studied area. Also, the seasons of winter and spring had the highest frequency and intensity of storms, while autumn was the calmest season. Analytical results with the above two models showed that storm return periods are predicted with higher intensity by partial series method and with lower intensity by Gumbel method. The partial series method is a more suitable method than the Gamble distribution for predicting severe storms due to the higher limit predictions, which are important in the safety of structures and reducing human and financial losses. It seems to be the best way to manage the crisis.

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Articles in Press, Accepted Manuscript
Available Online from 13 June 2023
  • Receive Date: 03 March 2023
  • Revise Date: 23 April 2023
  • Accept Date: 03 June 2023