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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2018-182
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2018-182
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Sep 2018

Research article | 06 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Probabilistic Risk Assessment of Livestock Snow Disasters in the Qinghai-Tibetan Plateau

Tao Ye1,2, Weihang Liu1,3, Peijun Shi1,2, Yijia Li1,2, Jidong Wu1,2, and Qiang Zhang1,2 Tao Ye et al.
  • 1Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 2Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs and Ministry of Education, Beijing 100875, China
  • 3School of Geographic Science, East China Normal University, Shanghai 200241, China

Abstract. Understanding risk using a quantitative risk assessment offers critical information for risk-informed reduction actions, investing in building resilience, and planning for adaptation. This study develops an event-based probabilistic risk assessment model for livestock snow disasters in the Qinghai-Tibetan Plateau (QTP) region and derives risk assessment results based on historical climate conditions (1980–2015) and present-day prevention capacity. In the model a hazard module was developed to identify/simulate individual snow disaster events based on boosted regression trees. Together with a fitted quantitative vulnerability function, and exposure derived from vegetation type and grassland carrying capacity, risk metrics based on livestock mortality and mortality rate were estimated. In our results, high risk regions include the Nyainqêntanglha Range, Tanggula Range, Bayankhar Mountains and the region between the Kailas Range and neighboring Himalayas. In these regions, annual livestock mortality rates were estimated as >2% and mortality was estimated as >2 sheep unit/km2 at a return period of 1/20a. Prefectures identified with extremely high risk included Yushu in Qinghai Province and Naqu, Shigatse, Linzhi, and Nagri in the Tibet Autonomous Region. In these prefectures, a snow disaster event with return period of 1/20a or higher can easily claim a total loss of more than 200000 sheep units. Our results provide a quantitative reference for preparedness and insurance solutions in reducing mortality risk. The methodology developed here can be further adapted to future climate change risk analyses and provide important information for planning climate change adaption in the QTP region.

Tao Ye et al.
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Status: final response (author comments only)
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