Морской гидрофизический журнал. 2022; 38: 256-275
Ансамбли опасных гидрометеорологических явлений: нормативно-правовые аспекты, терминология и классификация (обзор)
https://doi.org/10.22449/1573-160X-2022-3-237-256Аннотация
Цель. Выполнен анализ современного состояния исследований и достижений в области опасных природных и гидрометеорологических явлений и их ансамблей (мультиопасных явлений) на материале статей, опубликованных в профильных рейтинговых международных и российских научных журналах, и монографий.
Методы и результаты. Рассмотрены нормативно-правовые документы, регламентирующие терминологию в области опасных и мультиопасных природных и гидрометеорологических явлений, различия в принятой терминологии; существующие классификации мультиопасных гидрометеорологических явлений, методы классификаций и возможные перспективы их использования, пороговые значения опасности и методы их расчета; результаты исследований мультиопасных гидрометеорологических явлений на основе данных натурных наблюдений и глобального реанализа. Отдельное внимание уделено современному этапу развития естественных и точных наук в России, вносящих вклад в предотвращение и прогнозирование опасных гидрометеорологических явлений.
Выводы. С увеличением повторяемости опасных природных явлений с начала XXI в. и развитием информационных технологий, таких как создание электронных баз данных, геоинформационных систем, использование спутниковой информации и математического моделирования, появилась возможность анализировать, прогнозировать, оценивать и минимизировать (хоть и в неполной мере) последствия проявлений этих явлений. Показано, что решение проблем прогнозирования, мониторинга и минимизации последствий опасных природных явлений и их сочетаний требует междисциплинарных решений и взаимодействия между всеми заинтересованными сторонами – обществом, властью, наукой, бизнесом. Важно разрабатывать и внедрять планы по интегрированному управлению в регионах, особенно подверженных рискам. Большая проблема, по нашему мнению, заключается в том, что в российской и мировой науке существует большое несоответствие между выводами фундаментальных исследований и решениями, принимаемыми органами власти.
Список литературы
1. Бондур В. Г., Крапивин В. Ф., Савиных В. П. Мониторинг и прогнозирование природных катастроф. М. : Научный мир, 2009. 690 с.
2. Observed variability and trends in extreme climate events: A brief review / D. R. Easterling [et al.] // Bulletin of the American Meteorological Society. 2000. Vol. 81, iss. 3. P. 417–426. https://doi.org/10.1175/1520-0477(2000)0812.3.CO;2
3. Bongaarts J. Human population growth and the demographic transition // Philosophical Transactions of the Royal Society B: Biological Sciences. 2009. Vol. 364, iss. 1532. P. 2985–2990. https://doi.org/10.1098/rstb.2009.0137
4. Middleton N. J., Sternberg T. Climate hazards in drylands: A review // Earth-Science Reviews. 2013. Vol. 126. P. 48–57. https://doi.org/10.1016/j.earscirev.2013.07.008
5. Natural disaster hotspots: A global risk analysis / M. Dilley [et al.]. Washington, DC : World Bank, 2005. 132 p. URL: https://openknowledge.worldbank.org/handle/10986/7376 (date of access: 19.04.2022).
6. A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment / V. Gallina [et al.] // Journal of Environmental Management. 2016. Vol. 168. P. 123–132. https://doi.org/10.1016/j.jenvman.2015.11.011
7. Bell R., Glade T. Multi-hazard analysis in natural risk assessments // Risk Analysis IV / Edited by C. A. Brebbia. WIT Press, 2004. (WIT Transactions on Ecology and the Environment ; vol. 77). doi:10.2495/RISK040181. URL: https://www.witpress.com/elibrary/wit-transactionson-ecology-and-the-environment/77/14298 (date of access: 19.04.2022)
8. Glade T., van Elverfeldt K. MultiRISK: An innovative concept to model natural risks // Landslide risk management : Proceedings of the international conference on landslide risk management, Vancouver, Canada, 31 May-3 June 2005. Leiden, Netherlands : A.A. Balkema, 2005. P. 551–555. https://doi.org/10.1201/9781439833711
9. Understanding risk in an evolving world: Emerging best practices in natural disaster risk assessment / A. L. Simpson [et al.]. Washington, DC : World Bank, 2014. 224 p. URL: https://openknowledge.worldbank.org/handle/10986/20682 (date of access: 19.04.2022).
10. Curt C. Multirisk: What trends in recent works? – A bibliometric analysis // Science of The Total Environment. 2021. Vol. 763. 142951. https://doi.org/10.1016/j.scitotenv.2020.142951
11. Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from civil protection stakeholders / N. Komendantova [et al.] // International Journal of Disaster Risk Reduction. 2014. Vol. 8. P. 50–67. https://doi.org/10.1016/j.ijdrr.2013.12.006
12. Terminology of natural hazards and disasters: A review and the case of Brazil / B. E. Monte [et al.] // International Journal of Disaster Risk Reduction. 2021. Vol. 52. 101970. doi:10.1016/j.ijdrr.2020.101970
13. Liu B., Siu Y. L., Mitchell G. Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment // Natural Hazards and Earth System Sciences. 2016. Vol. 16, iss. 2. P. 629–642. https://doi.org/10.5194/nhess-16-629-2016
14. Elliott M., Trono A., Cutts N. D. Coastal hazards and risk // Coastal zone management / Ed. D. R. Green. Thomas Telford Publishing, 2010. Chapter 17. P. 396–432. https://doi.org/10.1680/czm.35164.0017
15. Jones D. Environmental hazards: The challenge of change: Environmental hazards in the 1990s: problems, paradigms and prospects // Geography. 1993. Vol. 78, no. 2. P. 161–165. URL: https://www.jstor.org/stable/40572498 (date of access: 01.05.2022).
16. Mitchell J. T., Cutter S. L. Global Change and Environmental Hazards: Is the World Becoming More Disastrous? Washington, D. C. : Association of American Geographers, 1997. 216 p.
17. Below R., Wirtz A., Guha-Sapir D. Disaster category classification and peril terminology for operational purposes : working paper. Brussels : Centre for Research on the Epidemiology of Disasters, 2009. 20 p. URL: https://cred.be/downloadFile.php?file=sites/default/files/DisCatClass_264.pdf (date of access: 01.05.2022).
18. Gill J. C., Malamud B. D. Reviewing and visualizing the interactions of natural hazards // Reviews of Geophysics. 2014. Vol. 52, iss. 4. P. 680–722. https://doi.org/10.1002/2013RG000445
19. White G. F., Kates R. W., Burton I. Knowing better and losing even more: the use of knowledge in hazards management // Global Environmental Change Part B: Environmental Hazards. 2001. Vol. 3, iss. 3–4. P. 81–92. https://doi.org/10.1016/S1464-2867(01)00021-3
20. Basic principles of multi-risk assessment: a case study in Italy / W. Marzocchi [et al.] // Natural Hazards. 2012. Vol. 62, iss. 2. P. 551–573. https://doi.org/10.1007/s11069-012-0092-x
21. Wipulanusat W., Nakrod S., Prabnarong P. Multi-hazard risk assessment using GIS and RS applications: a case study of Pak Phanang Basin // Walailak Journal of Science and Technology. 2009. Vol. 6, iss. 1. P. 109–125. URL: https://wjst.wu.ac.th/index.php/wjst/article/view/76 (date of access: 01.05.2022).
22. Loat R. Risk management of natural hazards in Switzerland. Bern, 2010. 15 p. URL: https://www.sistemaprotezionecivile.it/allegati/1149_Svizzera_Risk_Management.pdf (date of access: 01.05.2022).
23. Chiesa C., Laben C., Cicone R. An Asia Pacific natural hazards and vulnerabilities atlas // 30th International Symposium on Remote Sensing of Environment, November 10-14, 2003, Honolulu, Hawaii : proceedings. International Center for Remote Sensing of Environment, 2003.
24. GIS-based landslide susceptibility mapping using hybrid MCDM models / A. S. Jam [et al.] // Natural Hazards. 2021. Vol. 108, iss. 1. P. 1025–1046. https://doi.org/10.1007/s11069-021- 04718-5
25. Development of a drought vulnerability index using MCDM and GIS: study case in São Paulo and Ceará, Brazil / G. de Azevedo Reis [et al.] // Natural Hazards. 2020. Vol. 104, iss. 2. P. 1781–1799. https://doi.org/10.1007/s11069-020-04247-7
26. GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia / D. Souissi [et al.] // Geocarto International. 2020. Vol. 35, iss. 9. P. 991–1017. https://doi.org/10.1080/10106049.2019.1566405
27. New MCDM methods under uncertainty applied to integrated natural risks management / J. M. Tacnet [et al.] // 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2017. P. 193–198. https://doi.org/10.1109/CIVEMSA.2017.7995325
28. Методы многокритериального анализа решений / М. О. Петросян [и др.] // Решетневские чтения : материалы XX Юбилейной международной научно-практической конференции, посвященной памяти генерального конструктора ракетно-космических систем академика М. Ф. Решетнева (09-12 ноября 2016, г. Красноярск) : в 2 ч. Красноярск : Сибирский гос. аэрокосмический ун-т им. акад. М. Ф. Решетнева, 2016. Ч. 2. С. 76–77. URL: https://disk.sibsau.ru/index.php/s/UGP7UcMvdmtYV7o (дата обращения: 01.05.2022).
29. Identification of natural hazards and classification of urban areas by TOPSIS model (case study: Bandar Abbas city, Iran) / R. M. Najafabadi [et al.] // Geomatics, Natural Hazards and Risk. 2016. Vol. 7, iss. 1. P. 85–100. https://doi.org/10.1080/19475705.2013.871353
30. Nyimbili P. H., Erden T., Karaman H. Integration of GIS, AHP and TOPSIS for earthquake hazard analysis // Natural Hazards. 2018. Vol. 92, iss. 3. P. 1523–1546. https://doi.org/10.1007/s11069-018-3262-7
31. Lee G., Jun K.-S., Chung E.-S. Integrated multi-criteria flood vulnerability approach using fuzzy TOPSIS and Delphi technique // Natural Hazards and Earth System Sciences. 2013. Vol. 13, iss. 5. P. 1293–1312. https://doi.org/10.5194/nhess-13-1293-2013
32. Lozoya J. P., Sarda R., Jiménez J. A. A methodological framework for multi-hazard risk assessment in beaches // Environmental Science & Policy. 2011. Vol. 14, iss. 6. P. 685–696. https://doi.org/10.1016/j.envsci.2011.05.002
33. Safaripour M., Rezapour Andabili N. Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model // Advances in Environmental Technology. 2020. Vol. 6, iss. 3. P. 131–138. https://doi.org/10.22104/AET.2021.4766.1287
34. Appelquist L. R., Halsnæs K. The Coastal Hazard Wheel system for coastal multi-hazard assessment & management in a changing climate // Journal of Coastal Conservation. 2015. Vol. 19, iss. 2. P. 157–179. https://doi.org/10.1007/s11852-015-0379-7
35. Micallef S., Micallef A., Galdies C. Application of the Coastal Hazard Wheel to assess erosion on the Maltese coast // Ocean & Coastal Management. 2018. Vol. 156. P. 209–222. https://doi.org/10.1016/j.ocecoaman.2017.06.005
36. Regional coastal erosion assessment based on global open access data: a case study for Colombia / J. Stronkhorst [et al.] // Journal of Coastal Conservation. 2018. Vol. 22, iss. 4. Р. 787–798. https://doi.org/10.1007/s11852-018-0609-x
37. Evaluation of human thermal comfort using UTCI index: case study Khorasan Razavi, Iran / M. Baaghideh [et al.] // Natural Environment Change. 2016. Vol. 2, no. 2. P. 165–175. URL: https://jnec.ut.ac.ir/article_61007_263c37873764f267f74ad7af06cabf05.pdf (date of access: 30.05.2022).
38. Esmaili R., Montazeri M. The determine of the Mashad bioclimatic condition base on hourly data // Geography and Environmental Planning. 2013. Vol. 24, no. 1. Р. 215–230. URL: https://gep.ui.ac.ir/article_18590.html?lang=en (date of access: 25.05.2022).
39. Yaglou C. P., Minard D. Control of heat casualties at military training centers // A.M.A. Archives of Industrial Health. 1957. Vol. 16, no. 4. Р. 302–316. URL: https://archive.org/details/sim_a-m-a-archives-of-industrial-health_1957-10_16_4/page/316/mode/2up (date of access: 25.05.2022)
40. Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index / P. Peduzzi [et al.] // Natural Hazards and Earth System Sciences. 2009. Vol. 9, iss. 4. P. 1149– 1159. https://doi.org/10.5194/nhess-9-1149-2009
41. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness / F. H. Norris [et al.] // American Journal of Community Psychology. 2008. Vol. 41, iss. 1–2. P. 127–150. https://doi.org/10.1007/s10464-007-9156-6
42. Greiving S., Fleischhauer M., Lückenkötter J. A methodology for an integrated risk assessment of spatially relevant hazards // Journal of Environmental Planning and Management. 2006. Vol. 49, iss. 1. P. 1–19. https://doi.org/10.1080/09640560500372800
43. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology / L. Gaál [et al.] // Water Resources Research. 2012. Vol. 48, iss. 4. W04511. doi:10.1029/2011WR011509
44. Satellite image analysis for disaster and crisis-management support / S. Voigt [et al.] // IEEE Transactions on Geoscience and Remote Sensing. 2007. Vol. 45, no. 6. P. 1520–1528. https://doi.org/10.1109/TGRS.2007.895830
45. Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data / K. E. Joyce [et al.] // Natural Hazards. 2014. Vol. 73, iss. 2. P. 137–163. doi:10.1007/s11069-014-1122-7
46. Kerle N., Oppenheimer C. Satellite remote sensing as a tool in lahar disaster management // Disasters. 2002. Vol. 26, iss. 2. P. 140–160. https://doi.org/10.1111/1467-7717.00197
47. Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards / D. M. Tralli [et al.] // ISPRS Journal of Photogrammetry and Remote Sensing. 2005. Vol. 59, iss. 4. Р. 185–198. https://doi.org/10.1016/j.isprsjprs.2005.02.002
48. An analysis of geospatial technologies for risk and natural disaster management / L. A. Manfré [et al.] // ISPRS International Journal of Geo-Information. 2012. Vol. 1, iss. 2. P. 166–185. doi:10.3390/ijgi1020166
49. Montoya L. Geo-data acquisition through mobile GIS and digital video: an urban disaster management perspective // Environmental Modelling & Software. 2003. Vol. 18, iss. 10. P. 869– 876. doi:10.1016/S1364-8152(03)00105-1
50. Zou Q. Research on cloud computing for disaster monitoring using massive remote sensing data // 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2017. P. 29–33. doi:10.1109/ICCCBDA.2017.7951879
51. Böhm K., Mehler-Bicher A., Fenchel D. GeoVisualAnalytics in the public health sector // Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. IEEE, 2011. P. 291–294. doi:10.1109/ICSDM.2011.5969049
52. Climate engine: Cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding / J. L. Huntington [et al.] // Bulletin of the American Meteorological Society. 2017. Vol. 98, iss. 11. P. 2397–2410. doi:10.1175/BAMS-D-15-00324.1
Morskoy Gidrofizicheskiy Zhurnal. 2022; 38: 256-275
Ensembles of Hazardous Hydrometeorological Phenomena: Legal and Regulatory Aspects, Terminology and Classification (Review)
https://doi.org/10.22449/1573-160X-2022-3-237-256Abstract
Purpose. The article represents the analysis of current state of research and achievements in the field of hazardous natural and hydrometeorological phenomena, and their ensembles (multi-hazardous phenomena) based on the papers published in the specialized international and Russian scientific journals and monographs.
Methods and Results. Normative legal documents regulating terminology in the field of hazardous and multi-hazardous natural and hydrometeorological phenomena, differences in the adopted terminology; existing classification of the multi-hazardous hydrometeorological phenomena, classification methods and possible prospects for their application, hazard threshold values and the methods for their calculation; results of the studies of multi-hazardous hydrometeorological phenomena based on the field observations data and global reanalysis are considered. Special attention is paid to the current stage of development of natural and exact sciences in Russia which contribute to preventing and forecasting of hazardous hydrometeorological phenomena.
Conclusions. With increase in the recurrence of hazardous natural phenomena since the beginning of the 21st century and the development of information technologies, such as creation of electronic databases, geoinformation systems, application of satellite information and mathematical modeling, it became possible to analyze, forecast, evaluate and minimize (albeit incompletely) the consequences of such phenomena. It is shown that solution of the problems including forecasting, monitoring and minimizing the consequences of hazardous natural phenomena and their combinations requires interdisciplinary solutions, on the one hand, and interaction between all the stakeholders – society, government, science and business, on the other. It is important to develop and implement an integrated management in the regions that are particularly at risk. A significant problem, in our opinion, consists in the fact that both in Russian and world science there is a large gap between the fundamental research and the decisions taken by the authorities.
References
1. Bondur V. G., Krapivin V. F., Savinykh V. P. Monitoring i prognozirovanie prirodnykh katastrof. M. : Nauchnyi mir, 2009. 690 s.
2. Observed variability and trends in extreme climate events: A brief review / D. R. Easterling [et al.] // Bulletin of the American Meteorological Society. 2000. Vol. 81, iss. 3. P. 417–426. https://doi.org/10.1175/1520-0477(2000)0812.3.CO;2
3. Bongaarts J. Human population growth and the demographic transition // Philosophical Transactions of the Royal Society B: Biological Sciences. 2009. Vol. 364, iss. 1532. P. 2985–2990. https://doi.org/10.1098/rstb.2009.0137
4. Middleton N. J., Sternberg T. Climate hazards in drylands: A review // Earth-Science Reviews. 2013. Vol. 126. P. 48–57. https://doi.org/10.1016/j.earscirev.2013.07.008
5. Natural disaster hotspots: A global risk analysis / M. Dilley [et al.]. Washington, DC : World Bank, 2005. 132 p. URL: https://openknowledge.worldbank.org/handle/10986/7376 (date of access: 19.04.2022).
6. A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment / V. Gallina [et al.] // Journal of Environmental Management. 2016. Vol. 168. P. 123–132. https://doi.org/10.1016/j.jenvman.2015.11.011
7. Bell R., Glade T. Multi-hazard analysis in natural risk assessments // Risk Analysis IV / Edited by C. A. Brebbia. WIT Press, 2004. (WIT Transactions on Ecology and the Environment ; vol. 77). doi:10.2495/RISK040181. URL: https://www.witpress.com/elibrary/wit-transactionson-ecology-and-the-environment/77/14298 (date of access: 19.04.2022)
8. Glade T., van Elverfeldt K. MultiRISK: An innovative concept to model natural risks // Landslide risk management : Proceedings of the international conference on landslide risk management, Vancouver, Canada, 31 May-3 June 2005. Leiden, Netherlands : A.A. Balkema, 2005. P. 551–555. https://doi.org/10.1201/9781439833711
9. Understanding risk in an evolving world: Emerging best practices in natural disaster risk assessment / A. L. Simpson [et al.]. Washington, DC : World Bank, 2014. 224 p. URL: https://openknowledge.worldbank.org/handle/10986/20682 (date of access: 19.04.2022).
10. Curt C. Multirisk: What trends in recent works? – A bibliometric analysis // Science of The Total Environment. 2021. Vol. 763. 142951. https://doi.org/10.1016/j.scitotenv.2020.142951
11. Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from civil protection stakeholders / N. Komendantova [et al.] // International Journal of Disaster Risk Reduction. 2014. Vol. 8. P. 50–67. https://doi.org/10.1016/j.ijdrr.2013.12.006
12. Terminology of natural hazards and disasters: A review and the case of Brazil / B. E. Monte [et al.] // International Journal of Disaster Risk Reduction. 2021. Vol. 52. 101970. doi:10.1016/j.ijdrr.2020.101970
13. Liu B., Siu Y. L., Mitchell G. Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment // Natural Hazards and Earth System Sciences. 2016. Vol. 16, iss. 2. P. 629–642. https://doi.org/10.5194/nhess-16-629-2016
14. Elliott M., Trono A., Cutts N. D. Coastal hazards and risk // Coastal zone management / Ed. D. R. Green. Thomas Telford Publishing, 2010. Chapter 17. P. 396–432. https://doi.org/10.1680/czm.35164.0017
15. Jones D. Environmental hazards: The challenge of change: Environmental hazards in the 1990s: problems, paradigms and prospects // Geography. 1993. Vol. 78, no. 2. P. 161–165. URL: https://www.jstor.org/stable/40572498 (date of access: 01.05.2022).
16. Mitchell J. T., Cutter S. L. Global Change and Environmental Hazards: Is the World Becoming More Disastrous? Washington, D. C. : Association of American Geographers, 1997. 216 p.
17. Below R., Wirtz A., Guha-Sapir D. Disaster category classification and peril terminology for operational purposes : working paper. Brussels : Centre for Research on the Epidemiology of Disasters, 2009. 20 p. URL: https://cred.be/downloadFile.php?file=sites/default/files/DisCatClass_264.pdf (date of access: 01.05.2022).
18. Gill J. C., Malamud B. D. Reviewing and visualizing the interactions of natural hazards // Reviews of Geophysics. 2014. Vol. 52, iss. 4. P. 680–722. https://doi.org/10.1002/2013RG000445
19. White G. F., Kates R. W., Burton I. Knowing better and losing even more: the use of knowledge in hazards management // Global Environmental Change Part B: Environmental Hazards. 2001. Vol. 3, iss. 3–4. P. 81–92. https://doi.org/10.1016/S1464-2867(01)00021-3
20. Basic principles of multi-risk assessment: a case study in Italy / W. Marzocchi [et al.] // Natural Hazards. 2012. Vol. 62, iss. 2. P. 551–573. https://doi.org/10.1007/s11069-012-0092-x
21. Wipulanusat W., Nakrod S., Prabnarong P. Multi-hazard risk assessment using GIS and RS applications: a case study of Pak Phanang Basin // Walailak Journal of Science and Technology. 2009. Vol. 6, iss. 1. P. 109–125. URL: https://wjst.wu.ac.th/index.php/wjst/article/view/76 (date of access: 01.05.2022).
22. Loat R. Risk management of natural hazards in Switzerland. Bern, 2010. 15 p. URL: https://www.sistemaprotezionecivile.it/allegati/1149_Svizzera_Risk_Management.pdf (date of access: 01.05.2022).
23. Chiesa C., Laben C., Cicone R. An Asia Pacific natural hazards and vulnerabilities atlas // 30th International Symposium on Remote Sensing of Environment, November 10-14, 2003, Honolulu, Hawaii : proceedings. International Center for Remote Sensing of Environment, 2003.
24. GIS-based landslide susceptibility mapping using hybrid MCDM models / A. S. Jam [et al.] // Natural Hazards. 2021. Vol. 108, iss. 1. P. 1025–1046. https://doi.org/10.1007/s11069-021- 04718-5
25. Development of a drought vulnerability index using MCDM and GIS: study case in São Paulo and Ceará, Brazil / G. de Azevedo Reis [et al.] // Natural Hazards. 2020. Vol. 104, iss. 2. P. 1781–1799. https://doi.org/10.1007/s11069-020-04247-7
26. GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia / D. Souissi [et al.] // Geocarto International. 2020. Vol. 35, iss. 9. P. 991–1017. https://doi.org/10.1080/10106049.2019.1566405
27. New MCDM methods under uncertainty applied to integrated natural risks management / J. M. Tacnet [et al.] // 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2017. P. 193–198. https://doi.org/10.1109/CIVEMSA.2017.7995325
28. Metody mnogokriterial'nogo analiza reshenii / M. O. Petrosyan [i dr.] // Reshetnevskie chteniya : materialy XX Yubileinoi mezhdunarodnoi nauchno-prakticheskoi konferentsii, posvyashchennoi pamyati general'nogo konstruktora raketno-kosmicheskikh sistem akademika M. F. Reshetneva (09-12 noyabrya 2016, g. Krasnoyarsk) : v 2 ch. Krasnoyarsk : Sibirskii gos. aerokosmicheskii un-t im. akad. M. F. Reshetneva, 2016. Ch. 2. S. 76–77. URL: https://disk.sibsau.ru/index.php/s/UGP7UcMvdmtYV7o (data obrashcheniya: 01.05.2022).
29. Identification of natural hazards and classification of urban areas by TOPSIS model (case study: Bandar Abbas city, Iran) / R. M. Najafabadi [et al.] // Geomatics, Natural Hazards and Risk. 2016. Vol. 7, iss. 1. P. 85–100. https://doi.org/10.1080/19475705.2013.871353
30. Nyimbili P. H., Erden T., Karaman H. Integration of GIS, AHP and TOPSIS for earthquake hazard analysis // Natural Hazards. 2018. Vol. 92, iss. 3. P. 1523–1546. https://doi.org/10.1007/s11069-018-3262-7
31. Lee G., Jun K.-S., Chung E.-S. Integrated multi-criteria flood vulnerability approach using fuzzy TOPSIS and Delphi technique // Natural Hazards and Earth System Sciences. 2013. Vol. 13, iss. 5. P. 1293–1312. https://doi.org/10.5194/nhess-13-1293-2013
32. Lozoya J. P., Sarda R., Jiménez J. A. A methodological framework for multi-hazard risk assessment in beaches // Environmental Science & Policy. 2011. Vol. 14, iss. 6. P. 685–696. https://doi.org/10.1016/j.envsci.2011.05.002
33. Safaripour M., Rezapour Andabili N. Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model // Advances in Environmental Technology. 2020. Vol. 6, iss. 3. P. 131–138. https://doi.org/10.22104/AET.2021.4766.1287
34. Appelquist L. R., Halsnæs K. The Coastal Hazard Wheel system for coastal multi-hazard assessment & management in a changing climate // Journal of Coastal Conservation. 2015. Vol. 19, iss. 2. P. 157–179. https://doi.org/10.1007/s11852-015-0379-7
35. Micallef S., Micallef A., Galdies C. Application of the Coastal Hazard Wheel to assess erosion on the Maltese coast // Ocean & Coastal Management. 2018. Vol. 156. P. 209–222. https://doi.org/10.1016/j.ocecoaman.2017.06.005
36. Regional coastal erosion assessment based on global open access data: a case study for Colombia / J. Stronkhorst [et al.] // Journal of Coastal Conservation. 2018. Vol. 22, iss. 4. R. 787–798. https://doi.org/10.1007/s11852-018-0609-x
37. Evaluation of human thermal comfort using UTCI index: case study Khorasan Razavi, Iran / M. Baaghideh [et al.] // Natural Environment Change. 2016. Vol. 2, no. 2. P. 165–175. URL: https://jnec.ut.ac.ir/article_61007_263c37873764f267f74ad7af06cabf05.pdf (date of access: 30.05.2022).
38. Esmaili R., Montazeri M. The determine of the Mashad bioclimatic condition base on hourly data // Geography and Environmental Planning. 2013. Vol. 24, no. 1. R. 215–230. URL: https://gep.ui.ac.ir/article_18590.html?lang=en (date of access: 25.05.2022).
39. Yaglou C. P., Minard D. Control of heat casualties at military training centers // A.M.A. Archives of Industrial Health. 1957. Vol. 16, no. 4. R. 302–316. URL: https://archive.org/details/sim_a-m-a-archives-of-industrial-health_1957-10_16_4/page/316/mode/2up (date of access: 25.05.2022)
40. Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index / P. Peduzzi [et al.] // Natural Hazards and Earth System Sciences. 2009. Vol. 9, iss. 4. P. 1149– 1159. https://doi.org/10.5194/nhess-9-1149-2009
41. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness / F. H. Norris [et al.] // American Journal of Community Psychology. 2008. Vol. 41, iss. 1–2. P. 127–150. https://doi.org/10.1007/s10464-007-9156-6
42. Greiving S., Fleischhauer M., Lückenkötter J. A methodology for an integrated risk assessment of spatially relevant hazards // Journal of Environmental Planning and Management. 2006. Vol. 49, iss. 1. P. 1–19. https://doi.org/10.1080/09640560500372800
43. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology / L. Gaál [et al.] // Water Resources Research. 2012. Vol. 48, iss. 4. W04511. doi:10.1029/2011WR011509
44. Satellite image analysis for disaster and crisis-management support / S. Voigt [et al.] // IEEE Transactions on Geoscience and Remote Sensing. 2007. Vol. 45, no. 6. P. 1520–1528. https://doi.org/10.1109/TGRS.2007.895830
45. Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data / K. E. Joyce [et al.] // Natural Hazards. 2014. Vol. 73, iss. 2. P. 137–163. doi:10.1007/s11069-014-1122-7
46. Kerle N., Oppenheimer C. Satellite remote sensing as a tool in lahar disaster management // Disasters. 2002. Vol. 26, iss. 2. P. 140–160. https://doi.org/10.1111/1467-7717.00197
47. Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards / D. M. Tralli [et al.] // ISPRS Journal of Photogrammetry and Remote Sensing. 2005. Vol. 59, iss. 4. R. 185–198. https://doi.org/10.1016/j.isprsjprs.2005.02.002
48. An analysis of geospatial technologies for risk and natural disaster management / L. A. Manfré [et al.] // ISPRS International Journal of Geo-Information. 2012. Vol. 1, iss. 2. P. 166–185. doi:10.3390/ijgi1020166
49. Montoya L. Geo-data acquisition through mobile GIS and digital video: an urban disaster management perspective // Environmental Modelling & Software. 2003. Vol. 18, iss. 10. P. 869– 876. doi:10.1016/S1364-8152(03)00105-1
50. Zou Q. Research on cloud computing for disaster monitoring using massive remote sensing data // 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2017. P. 29–33. doi:10.1109/ICCCBDA.2017.7951879
51. Böhm K., Mehler-Bicher A., Fenchel D. GeoVisualAnalytics in the public health sector // Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. IEEE, 2011. P. 291–294. doi:10.1109/ICSDM.2011.5969049
52. Climate engine: Cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding / J. L. Huntington [et al.] // Bulletin of the American Meteorological Society. 2017. Vol. 98, iss. 11. P. 2397–2410. doi:10.1175/BAMS-D-15-00324.1
