Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018

Weihua Fang; Cunmin Guo; Yinan Han; Rongfa Qing

doi:10.1007/s13753-022-00428-z

Volume 13 Issue 4

Aug. 2022

Turn off MathJax

Article Contents

Article Navigation > International Journal of Disaster Risk Science > 2022 > 13(4): 561-576

Weihua Fang, Cunmin Guo, Yinan Han, Rongfa Qing. Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018[J]. International Journal of Disaster Risk Science, 2022, 13(4): 561-576. doi: 10.1007/s13753-022-00428-z

Citation:

Weihua Fang, Cunmin Guo, Yinan Han, Rongfa Qing. Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018[J]. International Journal of Disaster Risk Science, 2022, 13(4): 561-576. doi: 10.1007/s13753-022-00428-z

Citation:

Weihua Fang, Cunmin Guo, Yinan Han, Rongfa Qing. Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018[J]. International Journal of Disaster Risk Science, 2022, 13(4): 561-576. doi: 10.1007/s13753-022-00428-z

PDF

Impact of Tropical Cyclone Avoidance on Fishing Vessel Activity over Coastal China Based on Automatic Identification System Data during 2013–2018

doi: 10.1007/s13753-022-00428-z

Weihua Fang^{1,2,3,4
,
,},
Cunmin Guo^1,2,4,
Yinan Han^1,2,4,
Rongfa Qing^1,2,4

1. Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing, 100875, China;
2. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China;
3. Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, China;
4. Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing, 100875, China

Funds:

This work was mainly supported by the National Key Research and Development Program of China (Grant Nos. 2018YFC1508803 and 2017YFA0604903) and jointly supported by the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (Grant No. GML2019ZD0601).

Available Online: 2022-09-09

Abstract

Abstract

Tropical cyclones (TCs) may cause severe impacts on the activities of coastal fishing vessels. Due to the unavailability or unacceptability of detailed Automatic Identification System (AIS) data that are capable of differentiating fishing activity from navigation, as well as the lack of detailed models and observation data of TC winds, few studies have provided quantitative and reliable assessment of the impacts of TCs on fishing activities. In this study, we modeled snapshots for the TC winds of 52 TCs over the Northwest Pacific (NWP) basin from 2013 to 2018, as well as daily fishing hours and daily hours of presence (hereafter “vessel hours”) of fishing vessels. Based on these data, the spatiotemporal pattern of fishing vessel activity over offshore China was first analyzed and mapped. Then, a TC wind hazard index and absolute and relative impact indices were proposed to assess the impact of the 52 TCs on fishing and vessel hours. Their relationship was then fitted with the cumulative distribution function (CDF) of the log-normal distribution. The results show that in the 2013-2018 period, the most active fishing areas were located in the South China Sea. In each instance, an increase was first observed in the initial several years; then a decrease followed in the yearly total fishing hours per vessel in the remaining years. The relative impact index was significantly correlated to the TC wind hazard index proposed in this study. Based on the quantitative relationship between the specified TC hazard index and the impact indices, it is possible to implement a pre-cyclone rapid loss assessment due to TC avoidance in the future.
- Fishing hours,
- Fishing moratorium,
- Impact index,
- Tropical cyclone,
- Vessel hours

FullText(HTML)

References(36)

References

Anticamara, J.A., R. Watson, A. Gelchu, and D. Pauly. 2011. Global fishing effort (1950-2010):Trends, gaps, and implications. Fisheries Research 107(1-3):131-136.

China Weather. 2010. Tropical cyclone numbering, locating and warning map. China. http://www.weather.com.cn/typhoon/tfzs/04/397397.shtml. Accessed 1 Jul 2022 (in Chinese).

CMA (China Meteorological Administration). 2007. Measures for issuing and disseminating meteorological disaster early warning signals. Beijing, China:CMA. http://www.cma.gov.cn/2011zwxx/2011zflfg/2011zgfxwj/201208/t20120803_180761.html. Accessed 20 Jul 2020 (in Chinese).

CME (Chicago Mercantile Exchange) Group. 2009. CME Hurricane index futures and options. USA. https://www.cmegroup.com/trading/weather/files/WT106_NEWHurricaneFC.pdf. Accessed 1 Jul 2022.

Elsberry, R.L. 1987. A global view of tropical cyclones. Monterey, CA:Office of Naval Research.

Emanuel, K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436(7051):686-688.

Fang, W., and X. Shi. 2012. A review of stochastic modeling of tropical cyclone track and intensity for disaster risk assessment. Advances in Earth Science 27(8):866-875 (in Chinese).

Gelchu, A., and D. Pauly. 2007. Growth and distribution of port-based fishing effort within countries' EEZ from 1970 to 1995. Fisheries Centre Research Reports 15(4). https://s3-us-west-2.amazonaws.com/legacy.seaaroundus/doc/Researcher+Publications/dpauly/PDF/2007/Books%26Chapters/GrowthDistributionPartBasedFishingEffort.pdf. Accessed 12 Dec 2020.

Georgiou, P.N., A.G. Davenport, and P.J. Vickery. 1983. Design wind speeds in regions dominated by tropical cyclones. Journal of Wind Engineering and Industrial Aerodynamics 13(1-3):139-152.

Guiet, J., E. Galbraith, D. Kroodsma, and B. Worm. 2019. Seasonal variability in global industrial fishing effort. PLoS ONE 14(5):Article e0216819.

Han, X., Y. Li, and Z. Yue. 2016. On risk evaluation and zoning of typhoon disaster to fishery in China. Ocean Development and Management 33(5):64-69 (in Chinese).

IMO (International Maritime Organization). 2004. International convention for the safety of life at sea (SOLAS). https://newsmagnify.it/wp-content/uploads/2017/08/SOLAS-International-Convention-for-the-safety-of-life-at-sea-2004.pdf. Accessed 20 Oct 2020.

Jovel, J.R., and M.S. Mudahar. 2010. Damage, loss, and needs assessment guidance notes (Vol. 2):Conducting damage and loss assessments after disasters (Chinese). Washington, DC:World Bank Group. http://documents.worldbank.org/curated/en/837041468336025149/Conducting-damage-and-loss-assessments-after-disasters. Accessed 20 Oct 2020.

Knapp, K.R., M.C. Kruk, D.H. Levinson, H.J. Diamond, and C.J. Neumann. 2010. The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data. Bulletin of the American Meteorological Society 91(3):363-376.

Kroodsma, D.A., J. Mayorga, T. Hochberg, N.A. Miller, K. Boerder, F. Ferretti, B. Worm, and B. Bergman et al. 2018. Tracking the global footprint of fisheries. Science 359(6378):904-907.

Li, Y., and W. Fang. 2012. A rapid loss index for tropical cyclone disasters in China. In Proceedings of the 2012 Fifth International Joint Conference on Computational Sciences and Optimization, 23-26 June 2012, Harbin, China, 747-750.

Lin, W., and W. Fang. 2013. Regional characteristics of Holland B parameter in typhoon wind field model for Northwest Pacific. Tropical Geography 33(2):124-132 (in Chinese).

MARA (Ministry of Agriculture and Rural Affairs of the People's Republic of China). 2017. China fishery statistical yearbook 2016. Beijing, China:MARA (in Chinese).

MARA (Ministry of Agriculture and Rural Affairs of the People's Republic of China). 2018a. China fishery statistical yearbook 2017. Beijing, China:MARA (in Chinese).

MARA (Ministry of Agriculture and Rural Affairs of the People's Republic of China). 2018b. Notice on the adjustment of the marine fishing moratorium system. Beijing, China:MARA. http://www.moa.gov.cn/gk/zcfg/nybgz/201802/t20180209_6136812.htm. Accessed 1 May 2022 (in Chinese).

Meng, Y., M. Matsui, and K. Hibi. 1997. A numerical study of the wind field in a typhoon boundary layer. Journal of Wind Engineering and Industrial Aerodynamics 67-68:437-448.

MNR (Ministry of Natural Resources of the People's Republic of China). 2019. National marine economic statistics bulletin 2018. Beijing, China:MNR. http://gi.mnr.gov.cn/201904/t20190411_2404774.html. Accessed 20 July 2020 (in Chinese).

Mo, W., and W. Fang. 2016. Empirical vulnerability functions of building contents to flood based on post-typhoon (Fitow, 201323) questionnaire survey in Yuyao, Zhejiang. Tropical Geography 36(4):633-641 (in Chinese).

MOF (Ministry of Finance of the People's Republic of China). 2015. Notice on the adjustment of domestic fishing and aquaculture oil price subsidy policies to promote the sustained and healthy development of fisheries. Beijing, China:MOF. http://www.mof.gov.cn/gp/xxgkml/jjjss/201507/t20150709_2512172.html. Accessed 10 Oct 2020.

Monteclaro, H., G. Quinitio, M.A. Dino, R. Napata, L. Espectato, K. Anraku, K. Watanabe, and S. Ishikawa. 2018. Impacts of Typhoon Haiyan on Philippine capture fisheries and implications to fisheries management. Ocean & Coastal Management 158:128-133.

Owens, B., and G. Holland. 2010. The Willis Hurricane Index. In Proceedings of 29th Conference on Hurricanes and Tropical Meteorology, 10-14 May 2010, Tucson, AZ, USA. https://ams.confex.com/ams/29Hurricanes/webprogram/29HURRICANES.html. Accessed 1 Jul 2022.

Porter, K. 2021. A beginner's guide to fragility, vulnerability, and risk. In Encyclopedia of earthquake engineering, ed. M. Beer, I.A. Kougioumtzoglou, E. Patelli, and S.K. Au, 4-10. Heidelberg:Springer.

Ren, F., Y. Wang, X. Wang, and W. Li. 2007. Estimating tropical cyclone precipitation from station observations. Advances in Atmospheric Sciences 24(4):700-711.

Ren, L. 2009. Assessment for the loss of income of fishermen caused by storm surge disaster. Master's thesis. Qingdao, China:Ocean University of China (in Chinese).

Tan, C., and W. Fang. 2018. Mapping the wind hazard of global tropical cyclones with parametric wind field models by considering the effects of local factors. International Journal of Disaster Risk Science 9(1):86-99.

Vickery, P.J., and D. Wadhera. 2008. Statistical models of Holland pressure profile parameter and radius to maximum winds of hurricanes from flight-level pressure and H*Wind data. Journal of Applied Meteorology and Climatology 47(10):2497-2517.

Watson, R.A., W.L. Cheung, J.A. Anticamara, R.U. Sumaila, D. Zeller, and D. Pauly. 2013. Global marine yield halved as fishing intensity redoubles. Fish and Fisheries 14(4):493-503.

Xu, M., Q. Yang, and M. Ying. 2005. Impacts of tropical cyclones on Chinese lowland agriculture and coastal fisheries. In Natural disasters and extreme events in agriculture:Impacts and mitigation, ed. M.V.K. Sivakumar, R.P. Motha, and H.P. Das, 137-144. Heidelberg:Springer.

Ying, M., W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen. 2014. An overview of the China Meteorological Administration tropical cyclone database. Journal of Atmospheric and Oceanic Technology 31(2):287-301.

Yu, J., D. Tang, G. Chen, Y. Li, Z. Huang, and S. Wang. 2014. The positive effects of typhoons on the fish CPUE in the South China Sea. Continental Shelf Research 84:1-12.

Zheng, Q., W. Fan, X. Wang, W. Li, Q. Zhang, and S. Zhang. 2016. Typhoon status of offshore fishing grounds in China and its impact analysis on fishing boats. Marine Science Bulletin 35(2):225-234 (in Chinese).

Relative Articles

Supplements(0)

Cited By

Proportional views