Hydrometeorological Phenomena and Multi-Hazards: Mathematical Modelling, Decision Support Systems, Geoinformation Systems (Review)

N. A. Yaitskaya1, 2, ✉, A. A. Magaeva2

1 Federal Research Center the Subtropical Scientific Center of the Russian Academy of Sciences, Sochi, Russian Federation

2 Federal Research Center the Southern Scientific Center of the Russian Academy of Sciences, Rostov-on-Don, Russian Federation

✉ e-mail: yaitskayan@gmail.com

Abstract

Purpose. The article represents the analysis of current state of research and achievements in the field of natural hazards (including hydrometeorological ones), and their ensembles (multi-hazards) based on the papers published in the specialized international and Russian scientific journals and monographs.

Methods and Results. Considered are the modern methods for mathematical modeling of hydrometeorological multi-hazards, the methods for assessing the relations between the hazards and multi-hazards, the existing decision support systems, and the methods for assessing the risks of occurrence of hazards and multi-hazards. The ensemble models and the possibilities of cloud computing were reviewed; the experience of integrating the geoinformation systems and the results of the Earth remote sensing in models was studied. Examples of the modeling platforms and the decision support systems (developed in different countries) intended for application in case of the natural hazards, are represented.

Conclusions. It is shown that solution of the problems including forecasting, monitoring and minimizing the consequences of natural hazards 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. Field observations are of primary importance. Within the framework of the country, an integrated modeling system taking into account complex processes such as hazards, should be necessarily developed. Special attention should be paid to the peculiarities of natural disasters occurring in the northern regions of our country, since they are often characterized by extreme background weather conditions, inaccessibility and remoteness, lack of the infrastructure required for saving people and eliminating the consequences.

Keywords

natural hazards, storm, ice, flood, geographic information system, mathematical modeling, reanalysis, decision support system, planning, risk management

Acknowledgements

The study was carried out at the RFBR financial support within the framework of scientific project No. 20-15-50320 (review of the decision support systems and the assessment of a risk of occurrence of natural hazards and multi-hazards), and within the framework of the state assignment of SSC RAS, project No. 122013100131-9 (review of mathematical models of hydrometeorological multi-hazards).

Original russian text

Original Russian Text © N. A. Yaitskaya, A. A. Magaeva, 2022, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 38, Iss. 4, pp. 372-388 (2022)

For citation

Yaitskaya, N.A. and Magaeva, A.A., 2022. Hydrometeorological Phenomena and Multi-Hazards: Mathematical Modelling, Decision Support Systems, Geoinformation Systems (Review). Physical Oceanography, 29(4), pp. 347-362. doi:10.22449/1573-160X-2022-4-347-362

DOI

10.22449/1573-160X-2022-4-347-362

References

  1. Dilley, M., Chen, R.S., Deichmann, U., Lerner-Lam, A.L. and Arnold, M., 2005. Natural Disaster Hotspots: A Global Risk Analysis. Washington, DC: World Bank, 132 p. Available at: https://openknowledge.worldbank.org/handle/10986/7376 [Accessed: 19 April 2022].
  2. Gallina, V., Torresan, S., Critto, A., Sperotto, A., Glade, T. and Marcomini, A., 2016. A Review of Multi-Risk Methodologies for Natural Hazards: Consequences and Challenges for a Climate Change Impact Assessment. Journal of Environmental Management, 168, pp. 123-132. doi:10.1016/j.jenvman.2015.11.011
  3. Curt, C., 2021. Multirisk: What Trends in Recent Works? – A Bibliometric Analysis. Science of the Total Environment, 763, 142951. doi: 10.1016/j.scitotenv.2020.142951
  4. Van Westen, C.J., Montoya, L., Boerboom, L. and Badilla Coto, E., 2002. Multi-Hazard Risk Assessment Using GIS in Urban Areas: A Case Study for the City of Turrialba, Costa Rica. In: ADPC, 2002. Proceedings of the Regional Workshop on Best Practices in Disaster Mitigation. Bali, pp. 120-136.
  5. Marzocchi, W., Garcia-Aristizabal, A., Gasparini, P., Mastellone, M.L. and Di Ruocco, A., 2012. Basic Principles of Multi-Risk Assessment: A Case Study in Italy. Natural Hazards, 62(2), pp. 551-573. doi:10.1007/s11069-012-0092-x
  6. Cohen, R., Hilton, J., Hasan Khan, S., Wang, Y. and Prakash, M., 2015. Swift: A GPU Based Coupled Hydrodynamic/Hydraulic Framework for Urban Flood Prediction. In: CSIRO, 2015. Proceeding of the Eleventh International Conference on CFD in the Minerals and Process Industries. Melbourne, Australia: CSIRO, pp. 1-6.
  7. Lhomme, J., Sayers, P., Gouldby, B., Samuels, P., Wills, M. and Mulet-Marti, J., 2009. Recent Development and Application of a Rapid Flood Spreading Method. In: P. Samuels, S. Huntington, W. Allsop and J. Harrop, eds., 2009. Flood Risk Management: Research and Practice. London: Taylor and Francis Group, pp. 15-24.
  8. Schumann, G., Kirschbaum, D., Anderson, E. and Rashid, K., 2016. Role of Earth Observation Data in Disaster Response and Recovery: From Science to Capacity Building. In: F. Hossain, ed., 2016. Earth Science Satellite Applications. Cham: Springer, pp. 119- 146. doi:10.1007/978-3-319-33438-7_5
  9. Ujjwal, K.C., Garg, S., Hilton, J., Aryal, J. and Forbes-Smith, N., 2019. Cloud Computing in Natural Hazard Modeling Systems: Current Research Trends and Future Directions. International Journal of Disaster Risk Reduction, 38, 101188. doi:10.1016/j.ijdrr.2019.101188
  10. Schneider, P.J. and Schauer, B.A., 2006. HAZUS–Its Development and Its Future. Natural Hazards Review, 7(2), pp. 40-44. doi:10.1061/(ASCE)1527-6988(2006)7:2(40)
  11. Pranantyo, I.R., Fadmastuti, M. and Chandra, F., 2015. InaSAFE Applications in Disaster Preparedness. AIP Conference Proceedings, 1658(1), 060001. doi:10.1063/1.4915053
  12. Newham, L.T.H., Jakeman, A.J. and Letcher, R.A., 2007. Stakeholder Participation in Modelling for Integrated Catchment Assessment and Management: An Australian Case Study. International Journal of River Basin Management, 5(2), pp. 79-91. doi:10.1080/15715124.2007.9635308
  13. Schmidt, J., Matcham, I., Reese, S., King, A., Bell, R., Henderson, R., Smart, G., Cousins, J., Smith, W. and Heron, D., 2011. Quantitative Multi-Risk Analysis for Natural Hazards: A Framework for Multi-Risk Modelling. Natural Hazards, 58(3), pp. 1169-1192. doi:10.1007/s11069-011-9721-z
  14. Hagedorn, R., Doblas-Reyes, F.J. and Palmer, T.N., 2005. The Rationale behind the Success of Multi-Model Ensembles in Seasonal Forecasting – I. Basic Concept. Tellus A: Dynamic Meteorology and Oceanography, 57(3), pp. 219-233. doi:10.3402/tellusa.v57i3.14657
  15. Ahmadalipour, A., Rana, A., Moradkhani, H. and Sharma, A., 2017. Multi-Criteria Evaluation of CMIP5 GCMs for Climate Change Impact Analysis. Theoretical and Applied Climatology, 128(1-2), pp. 71-87. doi:10.1007/s00704-015-1695-4
  16. Raju, K.S. and Kumar, D.N., 2018. Impact of Climate Change on Water Resources with Modeling Techniques and Case Studies. Singapore: Springer, 266 p. doi:10.1007/978-981- 10-6110-3
  17. Shrestha, S., Sharma, S., Gupta, R. and Bhattarai, R., 2019. Impact of Global Climate Change on Stream Low Flows: A Case Study of the Great Miami River Watershed, Ohio, USA. International Journal of Agricultural and Biological Engineering, 12(1), pp. 84-95.
  18. Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y. and Kanani, R., 2011. Impacts of Climate Change on Runoffs in East Azerbaijan, Iran. Global and Planetary Change, 78(3–4), pp. 137-146. doi:10.1016/j.gloplacha.2011.06.003
  19. Shadmehri Toosi, A., Doulabian, S., Ghasemi Tousi, E., Calbimonte, G.H. and Alaghmand, S., 2020. Large-Scale Flood Hazard Assessment under Climate Change: A Case Study. Ecological Engineering, 147, 105765. doi:10.1016/j.ecoleng.2020.105765
  20. Yang, C., Goodchild, M., Huang, Q., Nebert, D., Raskin, R., Xu, Y., Bambacus, M. and Fay, D., 2011. Spatial Cloud Computing: How Can the Geospatial Sciences Use and Help Shape Cloud Computing? International Journal of Digital Earth, 4(4), pp. 305-329. doi: 10.1080/17538947.2011.587547
  21. Huang, Q., Li, J. and Li, Z., 2018. A Geospatial Hybrid Cloud Platform Based on Multi- Sourced Computing and Model Resources for Geosciences. International Journal of Digital Earth, 11(12), pp. 1184-1204. doi:10.1080/17538947.2017.1385652
  22. Wex, F., Schryen, G., Feuerriegel, S. and Neumann, D., 2014. Emergency Response in Natural Disaster Management: Allocation and Scheduling of Rescue Units. European Journal of Operational Research, 235(3), pp. 697-708. doi:10.1016/j.ejor.2013.10.029
  23. Van Westen, C.J., 2000. Remote Sensing for Natural Disaster Management. International Archives of Photogrammetry and Remote Sensing, 33(B7), pp. 1609-1617. Available at: https://www.isprs.org/proceedings/xxxiii/congress/part7/1609_XXXIII-part7.pdf [Accessed: 25 March 2022].
  24. Laituri, M. and Kodrich, K., 2008. On Line Disaster Response Community: People as Sensors of High Magnitude Disasters Using Internet GIS. Sensors, 8(5), pp. 3037-3055. doi:10.3390/s8053037
  25. Jeyaseelan, A.T., 2004. Droughts and Floods Assessment and Monitoring Using Remote Sensing and GIS. In: M. V. K. Sivakumar, P. S. Roy, K. Harmsen, S. K. Saha, Eds., 2004. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. Proceedings of the Training Workshop, 7–11 July, 2003, Dehra Dun, India. Geneva: WMO, pp. 291-313. Available at: http://www.wamis.org/agm/pubs/agm8/Paper-14.pdf [Accessed: 25 March 2022].
  26. Manfré, L.A., Hirata, E., Silva, J.B., Shinohara, E.J., Giannotti, M.A., Larocca, A.P.C., and Quintanilha, J.A., 2012. An Analysis of Geospatial Technologies for Risk and Natural Disaster Management. ISPRS International Journal of Geo-Information, 1(2), pp. 166-185. doi:10.3390/ijgi1020166
  27. Montoya, L., 2003. Geo-Data Acquisition through Mobile GIS and Digital Video: An Urban Disaster Management Perspective. Environmental Modelling and Software, 18(10), pp. 869- 876. doi:10.1016/S1364-8152(03)00105-1
  28. Cutter, S.L., 2003. GI Science, Disasters, and Emergency Management. Transactions in GIS, 7(4), pp. 439-446. doi:10.1111/1467-9671.00157
  29. Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K. and Mehl, H., 2007. Satellite Image Analysis for Disaster and Crisis-Management Support. IEEE Transactions on Geoscience and Remote Sensing, 45(6), pp. 1520-1528. doi:10.1109/TGRS.2007.895830
  30. Garg, S., Forbes-Smith, N., Hilton, J. and Prakash, M., 2018. SparkCloud: A Cloud-Based Elastic Bushfire Simulation Service. Remote Sensing, 10(1), 74. doi:10.3390/rs10010074
  31. Li, Z., Yang, C., Huang, Q., Liu, K., Sun, M. and Xia, J., 2017. Building Model as a Service to Support Geosciences. Computers, Environment and Urban Systems, 61(B), pp. 141-152. doi:10.1016/j.compenvurbsys.2014.06.004
  32. Behzad, B., Padmanabhan, A., Liu, Yong, Liu, Yan, and Wang, S., 2011. Integrating CyberGIS Gateway with Windows Azure: a Case Study on MODFLOW Groundwater Simulation. In: ACM, 2011. Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems. Chicago: Association for Computing Machinery, pp. 26-29. doi:10.1145/2070770.2070774
  33. Kalabokidis, K., Athanasis, N., Gagliardi, F., Karayiannis, F., Palaiologou, P., Parastatidis, S. and Vasilakos, C., 2013. Virtual Fire: A Web-Based GIS Platform for Forest Fire Control. Ecological Informatics, 16, pp. 62-69. doi:10.1016/j.ecoinf.2013.04.007
  34. Puthal, D., Nepal, S., Ranjan, R. and Chen, J., 2016. A Secure Big Data Stream Analytics Framework for Disaster Management on the Cloud. In: IEEE, 2016. 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, pp. 1218-1225. doi:10.1109/HPCC- SmartCity-DSS.2016.0170
  35. Al-Fares, M., Loukissas, A. and Vahdat, A., 2008. A Scalable, Commodity Data Center Network Architecture. ACM SIGCOMM Computer Communication Review, 38(4), pp. 63- 74. doi:10.1145/1402946.1402967
  36. Rossi, C., Heyi, M.H. and Scullino, F., 2017. A Service Oriented Cloud‐Based Architecture for Mobile Geolocated Emergency Services. Concurrency and Computation: Practice and Experience, 29(11), e4051. doi:10.1002/cpe.4051
  37. Li, J., Li, Q., Khan, S.U. and Ghani, N., 2011. Community-Based Cloud for Emergency Management. In: IEEE, 2011. 2011 6th International Conference on System of Systems Engineering. IEEE, pp. 55-60. doi:10.1109/SYSOSE.2011.5966573
  38. Sedan, O., Negulescu, C., Terrier, M., Roulle, A., Winter, T. and Bertil, D., 2013. Armagedom – a Tool for Seismic Risk Assessment Illustrated with Applications. Journal of Earthquake Engineering, 17(2), pp. 253-281. doi:10.1080/13632469.2012.726604
  39. Kislov, A.V., Rivin, G.S., Platonov, V.S., Varentsov, M.I., Rozinkina, I.A., Nikitin, M.A. and Chumakov, M.M., 2018. Mesoscale Atmospheric Modeling of Extreme Velocities over the Sea of Okhotsk and Sakhalin. Izvestiya, Atmospheric and Ocean Physics, 54(4), pp. 322- 326. doi:10.1134/S0001433818040242
  40. Diansky, N.A., Panasenkova, I.I. and Fomin, V.V., 2019. Investigation of the Barents Sea Upper Layer Response to the Polar Low in 1975. Physical Oceanography, 26(6), pp. 467- 483. doi:10.22449/1573-160X-2019-6-467-483
  41. Diansky, N.A., Stepanov, D.V., Fomin, V.V. and Chumakov, M.M., 2020. Water Circulation off the Northeastern Coast of Sakhalin during the Passage of Three Types of Deep Cyclones over the Sea of Okhotsk. Russian Meteorology and Hydrology, 45(1), pp. 29- 38. doi:10.3103/S1068373920010045
  42. Fleischhauer, M., 2006. Spatial Relevance of Natural and Technological Hazards. In: P. Schmidt-Thomé, ed., 2006. Natural and Technological Hazards and Risks Affecting the Spatial Development of European Regions. Vammalan Kirjapaino Oy: Geological Survey of Finland, pp. 7-16.
  43. Wipulanusat, W., Nakrod, S. and Prabnarong, P., 2009. Multi-Hazard Risk Assessment Using GIS and RS Applications: A Case Study of Pak Phanang Basin. Walailak Journal of Science and Technology, 6(1), pp. 109-125. doi:10.2004/wjst.v6i1.76
  44. O'Keefe, P., Westgate, K. and Wisner, B., 1976. Taking the Naturalness out of Natural Disasters. Nature, 260, pp. 566-567. https://doi.org/10.1038/260566a0
  45. Norris, F.H., Stevens, S.P., Pfefferbaum, B., Wyche, K.F. and Pfefferbaum, R.L., 2008. Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. American Journal of Community Psychology, 41(1-2), pp. 127-150. doi:10.1007/s10464-007-9156-6
  46. Carpignano, A., Golia, E., Di Mauro, C., Bouchon, S. and Nordvik, J.-P., 2009. A Methodological Approach for the Definition of Multi‐Risk Maps at Regional Level: First Application. Journal of Risk Research, 12(3-4), pp. 513-534. doi:10.1080/13669870903050269
  47. Zuccaro, G., Leone, M.F. and Martucci, C., 2020. Future Research and Innovation Priorities in the Field of Natural Hazards, Disaster Risk Reduction, Disaster Risk Management and Climate Change Adaptation: A Shared Vision from the ESPREssO Project. International Journal of Disaster Risk Reduction, 51, 101783. doi:10.1016/j.ijdrr.2020.101783
  48. Loat, R., 2010. Risk Management of Natural Hazards in Switzerland. Bern, 15 p. Available at: https://www.sistemaprotezionecivile.it/allegati/1149_Svizzera_Risk_Management [Accessed: 01 May 2022].
  49. Bell, R. and Glade, T., 2004. Multi-Hazard Analysis in Natural Risk Assessments. In: C. A. Brebbia, ed., 2004. Risk Analysis IV. WIT Transactions on Ecology and the Environment, Vol. 77. Ashurst, Southampton: WIT Press, pp. 197-206. doi:10.2495/RISK040181. Available at: https://www.witpress.com/elibrary/wit-transactions-on-ecology-and-theenvironment/77/14298 [Accessed: 19 April 2022].
  50. Shah, M.A.R., Renaud, F.G., Anderson, C.C., Wild, A., Domeneghetti, A., Polderman, A., Votsis, A., Pulvirenti, B., Basu, B. [et al.], 2020. A Review of Hydro-Meteorological Hazard, Vulnerability, and Risk Assessment Frameworks and Indicators in the Context of Nature-Based Solutions. International Journal of Disaster Risk Reduction, 50, 101728. doi:10.1016/j.ijdrr.2020.101728
  51. Carrick, N.A. and Ostendorf, B., 2007. Development of a Spatial Decision Support System (DSS) for the Spencer Gulf Penaeid Prawn Fishery, South Australia. Environmental Modelling and Software, 22(2), pp. 137-148. doi:10.1016/j.envsoft.2005.07.025
  52. Van Delden, H., Stuczynski, T., Ciaian, P., Paracchini, M.L., Hurkens, J., Lopatka, A., Shi, Y., Prieto, O.G., Calvo, S. [et al.], 2010. Integrated Assessment of Agricultural Policies with Dynamic Land Use Change Modelling. Ecological Modelling, 221(18), pp. 2153-2166. doi:10.1016/j.ecolmodel.2010.03.023
  53. Booty, W.G., Wong, I., Lam, D. and Resler, O., 2009. A Decision Support System for Environmental Effects Monitoring. Environmental Modelling and Software, 24(8), pp. 889- 900. doi:10.1016/j.envsoft.2009.02.001
  54. Wong, I.W., McNicol, D.K., Fong, P., Fillman, D., Neysmith, J. and Russell, R., 2003. The WILDSPACE™ Decision Support System. Environmental Modelling and Software, 18(6), pp. 521-530. doi:10.1016/S1364-8152(03)00027-6
  55. Shi, Y., Zuidgeest, M., Salzberg, A., Sliuzas, R., Huang, Z., Zhang, Q., Quang, N., Hurkens, J., Peng, M. [et al.], 2012. Simulating Urban Development Scenarios for Wuhan. In: IEEE, 2012. 2012 6th International Association for China Planning Conference (IACP). IEEE, pp. 1-13. doi:10.1109/IACP.2012.6342974
  56. Myslenkov, S.A., Platonov, V.S., Toropov, P.A. and Shestakova, A.A., 2015. Simulation of Storm Waves in the Barents Sea. Vestnik Moskovskogo universiteta. Seriya 5, Geografiya, (6), pp. 65-75. Available at: https://vestnik5.geogr.msu.ru/jour/article/view/195?locale=ru_RU [Accessed: 12 December 2020] (in Russian).
  57. Nafziger, J., She, Y. and Hicks, F., 2019. Dynamic River Ice Processes in a River Delta Network. Cold Regions Science and Technology, 158, pp. 275-287. doi:10.1016/j.coldregions.2018.09.005
  58. Casini, M., Mocenni, C., Paoletti, S. and Pranzo, M., 2015. Decision Support System Development for Integrated Management of European Coastal Lagoons. Environmental Modelling and Software, 64, pp. 47-57. doi:10.1016/j.envsoft.2014.11.008
  59. Mysiak, J., Giupponi, C. and Rosato, P., 2005. Towards the Development of a Decision Support System for Water Resource Management. Environmental Modelling and Software, 20(2), pp. 203-214. doi:10.1016/j.envsoft.2003.12.019
  60. Marcomini, A., Suter II, G. W. and Critto, A., eds., 2008. Decision Support Systems for Risk- Based Management of Contaminated Sites. New York: Springer, 427 p. doi:10.1007/978-0- 387-09722-0
  61. Koronkevich, N.N., Barabanova, E.A. and Zaytseva, I.S., 2010. The Most Dangerous Manifestation of Extreme Hydrological Situations in Russia. Izvestiya RAN. Seriya Geograficheskaya, (6), pp. 40-47 (in Russian).
  62. Geertman, S. and Stillwell, J., 2003. Planning Support Systems: An Introduction. In: S. Geertman and J. Stillwell, eds., 2003. Planning Support Systems in Practice. Berlin, Heidelberg: Springer, pp. 3-22. doi:10.1007/978-3-540-24795-1_1
  63. McIntosh, B.S., Seaton, R.A.F. and Jeffrey, P., 2007. Tools to Think with? Towards Understanding the Use of Computer-Based Support Tools in Policy Relevant Research. Environmental Modelling and Software, 22(5), pp. 640-648. doi:10.1016/j.envsoft.2005.12.015
  64. Volk, M., Lautenbach, S., van Delden, H., Newham, L.T.H. and Seppelt, R., 2010. How Can We Make Progress with Decision Support Systems in Landscape and River Basin Management? Lessons Learned from a Comparative Analysis of Four Different Decision Support Systems. Environmental Management, 46(6), pp. 834-849. doi:10.1007/s00267- 009-9417-2
  65. Argent, R.M., 2004. An Overview of Model Integration for Environmental Applications ̶ Components, Frameworks and Semantics. Environmental Modelling and Software, 19(3), pp. 219-234. doi:10.1016/S1364-8152(03)00150-6

Download the article (PDF)