Utilization of satellite observations in the risk assessment methods of meteorological and climatic disasters in urban areas

1Kostyuchenko, Yu.V, 1Bilous, Yu.H, 2Solovyov, DM, 1Kopachevsky, IM
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Science of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
2State institution «Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine», Kyiv; Marine Hydrophysical Institute Ukrainian National Academy of Sciences, Sevastopol
Kosm. nauka tehnol. 2013, 19 ;(6):27-37
https://doi.org/10.15407/knit2013.06.027
Section: Study of the Earth from Space
Publication Language: Ukrainian
Abstract: 

We consider some aspects of the theoretical and methodological basis for utilization of satellite observations of snow cover in the task of climatic, meteorological, hydrological and hydrogeological risk assessment in urban areas. We propose fundamentals of a procedure for the determination of the snow cover characteristics by integrating usage of satellite information (especially, MOD10A1 and SWE products, snow depth normalized index NDSI, local meteorological observations, ground calibration and verification measurements). On the basis of the modified method of Ensemble Transform Kalman Filter (ETKF) and Kernel Principal Component Analysis of data distributions (KPCA), a method of data integration from various sources is proposed. It is shown that in application of urban agglomeration analysis the proposed approach has sufficiently high relative accuracy with respect to existing approaches (separately used MOD10A1 and SWE products). As an example of approach approbation, a case of extreme snowfall in Kyiv during 21–23 of March 2013 is used. A quantitative assessment of risk indicators and parameters of municipal infrastructure vulnerability toward adverse effects of a disaster is presented.

Keywords: municipal infrastructure, satellite data, snow cover
References: 

1. Earth Systems Change over Eastern Europe, Ed.by V.I. Lyalko, 582 p. (Kyiv, 2010) [in Russian].

2. Kostyuchenko Yu.V. Validation data in solving problems of flood risk forecasting. Multispectral remote sensing in nature management, Eds. V.I. Lyalko, M.O.Popov, P.282—283 (Naukova dumka, Kyiv, 2006) [in Ukrainian].

3. Kravchenko A.N. Cascade of hydro-meteorological models for flood forecasting. No.2, 35—42 (2007) [in Russian].

4. Kravchenko O., Kussul N., Lupian E., et al. Water resource quality monitoring using heterogeneous data and high-performance computations. Cybernetics and Systems Analysis,.No.6, 117—126 (2008) [in Russian].
https://doi.org/10.1007/s10559-008-9032-x

5. Petravchuk L. V., Maruhno T. V., Pershyna N. G. et al. Key indicators of health and medical care for citizens of Kyiv [Osnovni pokaznyky zdorov’ja ta medychnoi' dopomogy naselennju m. Kyjeva]. 170 p. (Mis'kyj naukovyj informacijno-analitychnyj centr medychnoi' statystyky, Departament ohorony zdorov’ja Kyi'vs'koi' mis'koi' derzhavnoi' administracii', Kyiv, 2013) [in Ukrainian].

6. Anderson E.A. A point energy and mass balance model of a snow cover. NOAA Technical Report NWS. 19, 150 p. (1976)

7. Christianini N., Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. 212 p.(Univ. Press, Cambridge, 2000).
https://doi.org/10.1017/CBO9780511801389

8. Comprehensive Preparedness guide: Developing and maintaining emergency operations plans, 124 p. (U.S. FEMA, 2011).

9. Cowpertwait P.S.P. A generalized spatial-temporal model of rainfall based on a clustered point process. Proc. Roy. Soc. London A. 450, 163—175 (1995).
https://doi.org/10.1098/rspa.1995.0077

10. Dozier J. Remote sensing of snow in visible and near-infrared wavelengths. Theory and applications of optical remote sensing, Ed. G.Asrar. P. 527—547 (John Wiley & Sons, New York, 1989).

11. Ermoliev Yu., von Winterfeldt D. Risk, security and robust solutions. IIASA Interim Report. IR-10-013, IIASA, 41 p (2010).

12. Ermoliev Yu., von Winterfeldt D. Systemic risk and security management. Managing Safety of Heterogeneous Systems, Lecture Notes in Economics and Mathematical Systems. P.19—49 (Springer-Verlag, Berlin; Heidelberg, 2012).

13. Ermoliev Y.M., Ermolieva T.Y., Amendola A., et al. A system approach to management of catastrophic risks. Eur. J. Operational Res. 122, 452—460 (2000).
https://doi.org/10.1016/S0377-2217(99)00246-5

14. Hall D.K., Salomonson V.V., Riggs G.A. MODIS/Terra snow cover monthly L3 global 0.05deg CMG. Version 5. (National Snow and Ice Data Center, Boulder, Colorado USA, 2006).

15. Hall D.K., Salomonson V.V., Riggs G.A. MODIS/Terra snow cover daily L3 global 500 m grid. Version 5. [indicate subset used]. (National Snow and Ice Data Center, Boulder, Colorado USA, 2006).

16. Kostyuchenko Yu.V., Zlateva P., Stoyka Yu., et al. Role of systemic risk in regional ecological long-term threats analysis. Sustainable Civil Infrastructures — Hazards, Risk, Uncertainty, Ed.by K.K.Phoon, M.Beer, S.T.Quek, S.D.Pang (Proc. of Fifth Asian-Pacific Symposium on Structural Reliability and its Applications (APSSRA), 23—25 May, 2012), P. 551—556 (Research Publishing, Singapore, 2012). ISBN: 978-981-07-2219-7:
doi:10.3850/978-981-07-2219-7 P226.

17. Kussul N., Shelestov A., Skakun S. Grid system for flood extent extraction from satellite images. Earth Sci. Inform. 1(3), 105—117 (2008).
https://doi.org/10.1007/s12145-008-0014-3

18. Lee J.-M., Yoo C.K., Choi S.W., et al. Nonlinear process monitoring using kernel principal component analysis. Chem. Eng. Sci. 59, 223 — 234 (2004).
https://doi.org/10.1016/j.ces.2003.09.012

19. National Disaster recovery framework: strengthening disaster recovery for the nation. 116 p. (U.S.FEMA, 2011).

20. National Infrastructure Protection Plan. IS-860.a. 29 p. (U.S. FEMA, 2009).

21. Ogata Y. Estimation of the parameters in the modified Omori formula for aftershock frequencies by the maximum likelihood procedure. J. Phys. Earth. 31, 115—124 (1983). 22. Ramsay B. The interactive multisensor snow and ice mapping system. Hydrol. Processes. 12, 1537—1546 (1998).

23. Scheolkopf B., Smola A. J., Muller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299—1399 (1998).
https://doi.org/10.1162/089976698300017467

24. Strategic National risk assessment. The strategic national risk assessment in support of PPD 8: A comprehensive risk-based approach toward a secure and resilient nation. 7 p. (U.S.FEMA, 2011)

25. Tedesco M., Kelly R., Foster J. L., Chang A. T. C. AMSR-E/ Aqua 5-day L3 global snow water equivalent EASE-grids. Version 2. (National Snow and Ice Data Center, Boulder, Colorado USA, 2004).

26. Villez K., Ruiz M., Sin G., et al. Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes. Water Sci. and Technology, 57(10), 1659—1666 (2008).
https://doi.org/10.2166/wst.2008.143

27. Wang X., Bishop C.H. A Comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci. 60, 1140—1158 (2003).
https://doi.org/10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2

28. Zhang Y.Z., Jin C., Yan S., Chiu L.S. Seasonal snow monitoring in Northeast China using space-borne sensors: preliminary results. Ann. Geogr. Inf. Sci. 14, 113—119 (2008).

29. Zhang Yu., Yan S., Lu Y. Snow cover monitoring using MODIS data in Liaoning province, Northeastern China. Remote Sens. Environ. 2, 777—793 (2010);
https://doi.org/10.3390/rs2030777