Snow cover characteristics for the hydrological Volga sub-basins and their influence on spring floods in the Republic of Bashkortostan

Olga I Khristodulo; A F Atnabaev; J Leitner; I Klein; A J Dietz; A V Vorobev

Computer Science and Information Technologies, Computer Science and Information Technologies 2017

Olga I Khristodulo, A F Atnabaev, J Leitner, I Klein, A J Dietz, A V Vorobev

Last modified: 2018-02-14

Abstract

Knowledge about long term snow cover characteristics within a hydrological basin is essential information for regional and local authorities regarding water availability and flood hazard prediction. Population, economy and environment in the Republic of Bashkortostan (Russia) are strongly affected by regular spring floods. Stronger snowmelt due to steep temperature increase or due to higher snow deposition in the winter period cause local inundations in the Republic of Bashkortostan. In this study we analyze daily snow cover extent data for Volga sub‐catchments on the territory of the Republic of Bashkortostan and the influence of snow cover extent on periodic occurring spring floods at four different hydrostations. The snow cover extents and snow cover characteristics were derived from daily MODIS data with a spatial resolution of 500 meters. Parameters such as early and late season snow cover duration and its deviation from long-term mean were processed and calculated for the study region. Additionally, a binary logistic regression function was performed to reveal the quantitative impact and relation between snow cover extent and local spring floods. The results indicate strong correlation between the rate of snowmelt and occurred spring floods which proof the reliability of moderate spatial resolution remote sensing data towards local-scale flood events. It demonstrates that daily snow cover data with moderate resolution can be used to understand local spring floods.

Keywords

snow characteristics; spring floods;

References

1. Adamowski, J.F., 2008. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol. 353, 247–266. doi:10.1016/j.jhydrol.2008.02.013

2. Amlien, J., 2008. Remote sensing of snow with passive microwave radiometers - A review of current algorithms. Oslo, Norway.

3. Baltes-Götz, B., 2008. Logistische Regressionsanalyse mit SPSS. Univ. Trier 2006, 1–92. doi:10.1524/9783486710625

4. Bathurst, J.C., Iroumé, A., Cisneros, F., Fallas, J., Iturraspe, R., Novillo, M.G., Urciuolo, A., Bièvre, B. de, Borges, V.G., Coello, C., Cisneros, P., Gayoso, J., Miranda, M., Ramírez, M., 2011. Forest impact on floods due to extreme rainfall and snowmelt in four Latin American environments 1: Field data analysis. J. Hydrol. 400, 281–291. doi:10.1016/j.jhydrol.2010.11.044

5. Bewick, V., Cheek, L., Ball, J., 2005. Statistics review 14: Logistic regression. Crit. Care 9, 112–118. doi:10.1186/cc3045

6. Carroll, M., Dimiceli, C., Townshend, J., Noojipady, P., Sohlberg, R., 2008. UMD Global 250 meter Land Water Mask User Guide.

7. Chen, S., Fang, L., Li, H., Chen, W., Huang, W., 2011. Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl River Estuary, China. ISPRS J. Photogramm. Remote Sens. 66, 356–364. doi:10.1016/j.isprsjprs.2011.01.004

8. Dietz, A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sens. 6, 12752–12775. doi:10.3390/rs61212752

9. Dietz, A.J., 2013. Central Asian Snow Cover Characteristics between 1986 and 2012 derived from Time Series of Medium Resolution Remote Sensing Data. PhD Dissertation. University of Wuerzburg.

10. Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sens. Lett. 6, 844–853. doi:10.1080/2150704X.2015.1084551

11. Dietz, A.J., Kuenzer, C., Gessner, U., Dech, S., 2012. Remote sensing of snow – a review of available methods. Int. J. Remote Sens. 33, 4094–4134.

12. Eisfelder, C., Klein, I., Niklaus, M., Kuenzer, C., 2014. Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables. J. Arid Environ. 103, 17–30. doi:10.1016/j.jaridenv.2013.12.005

13. Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D., Dech, S., 2013. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet. Change 110, Part, 74–87.

14. Hall, D.K., Martinec, J., 1985. Remote Sensing of Ice and Snow. Chapman & Hall, London.

15. Hall, D.K., Riggs, G.A., 2007. Accuracy assessment of the MODIS snow products † 1547, 1534–1547. doi:10.1002/hyp

16. Hall, D.K., Riggs, G.A., Salomonson, V. V, Digirolamo, N.E., Bayr, K.J., 2002. MODIS snow-cover products. Remote Sens. Environ. 83, 181–194.

17. Howard, G., Pedley, S., Barrett, M., Nalubega, M., Johal, K., 2003. Risk factors contributing to microbiological contamination of shallow groundwater in Kampala, Uganda. Water Res. 37, 3421–3429. doi:http://dx.doi.org/10.1016/S0043-1354(03)00235-5

18. Huang, X., Liang, T., Zhang, X., Guo, Z., 2011. Validation of MODIS snow cover products using Landsat and ground measurements during the 2001–2005 snow seasons over northern Xinjiang, China. Int. J. Remote Sens. 32, 133–152. doi:10.1080/01431160903439924

19. Immerzeel, W.W., Droogers, P., de Jong, S.M., Bierkens, M.F.P., 2009. Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. Remote Sens. Environ. 113, 40–49. doi:10.1016/j.rse.2008.08.010

20. Jönsson, P., Eklundh, L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833–845. doi:10.1016/j.cageo.2004.05.006

21. Klein, I., Dietz, A., Gessner, U., Dech, S., Kuenzer, C., 2015. Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. Remote Sens. Lett. 6, 78–87. doi:10.1080/2150704X.2014.1002945

22. König, M., Winther, J., Isaksson, E., 2001. Measuring Snow and Glacier Ice Properties from Satellite. Rev. Geophys. 39, 1–27.

23. Kuchment, L.S., Gelfan, A.N., 1996. The determination of the snowmelt rate and the meltwater outflow from a snowpack for modelling river runoff generation. J. Hydrol. 179, 23–36. doi:10.1016/0022-1694(95)02878-1

24. Kuenzer, C., Klein, I., Ullmann, T., Georgiou, E.F., Baumhauer, R., Dech, S., 2015. Remote sensing of river delta inundation: Exploiting the potential of coarse spatial resolution, temporally-dense MODIS time series. Remote Sens. 7, 8516–8542. doi:10.3390/rs70708516

25. Leinenkugel, P., Kuenzer, C., Oppelt, N., Dech, S., 2013. Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas — A novel product for the Mekong Basin. Remote Sens. Environ. 136, 180–198. doi:10.1016/j.rse.2013.05.004

26. Luce, C.H., Tarboton, D.G., Cooley, K.R., 1998. The infuence of the spatial distribution of snow on basin-averaged snowmelt\r. Hydrol. Process. 12, 1671–1683. doi:10.1002/(SICI)1099-1085(199808/09)12:10/11<1671::AID-HYP688>3.0.CO;2-N

27. Mair, A., El-Kadi, A.I., 2013. Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. J. Contam. Hydrol. 153, 1–23. doi:10.1016/j.jconhyd.2013.07.004

28. Martinec, J., Rango, a, Roberts, R., 2008. Snowmelt runoff model (SRM) user’s manual. Agric. Exp. Stn. Spec. Rep. 100. doi:10.3882/j.issn.1674-2370.2010.03.003

29. Maurer, E.P., Rhoads, J.D., Dubayah, R.O., Lettenmaier, D.P., 2003. Evaluation of the snow-covered area data product from MODIS. Hydrol. Process. 17, 59–71. doi:10.1002/hyp.1193

30. Merkuryeva, G., Merkuryev, Y., Sokolov, B. V., Potryasaev, S., Zelentsov, V.A., Lektauers, A., 2015. Advanced river flood monitoring, modelling and forecasting. J. Comput. Sci. 10, 77–85. doi:10.1016/j.jocs.2014.10.004

31. Mood, C., 2010. Logistic regression: Why we cannot do what We think we can do, and what we can do about it. Eur. Sociol. Rev. 26, 67–82. doi:10.1093/esr/jcp006

32. Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., Mcintyre, A., Tan, P., Curnow, S., Ip, A., 2016. Remote Sensing of Environment Water observations from space : Mapping surface water from 25 years of Landsat imagery across Australia 174, 341–352. doi:10.1016/j.rse.2015.11.003

33. NAGLER, T., ROTT, H., MALCHER, P., MULLER, F., 2008. Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting. Remote Sens. Environ. 112, 1408–1420. doi:10.1016/j.rse.2007.07.006

34. Nolan, B.T., Hitt, K.J., Ruddy, B.C., 2002. Probability of nitrate contamination of recently recharged groundwaters in the conterminous United States. Environ. Sci. Technol. 36, 2138–45. doi:10.1021/es0113854

35. NSIDC, 2013. NSIDC website [WWW Document]. Cold L. Process. F. Exp.

36. Ozdemir, A., 2011. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J. Hydrol. 405, 123–136. doi:10.1016/j.jhydrol.2011.05.015

37. Parajka, J., Blöschl, G., 2006. Validation of MODIS snow cover images over Austria. Hydrol. Earth Syst. Sci. 10, 679–689.

38. Riggs, G., Hall, D.K., 2004. Snow Mapping with the MODIS Aqua Instrument, in: 61st Eastern Snow Conference. Portland, Maine, USA, pp. 81–84.

39. Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C., 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sens. Environ. 114, 35–49. doi:10.1016/j.rse.2009.08.011

40. Schreider, S.Y., Whetton, P.H., Jakeman, A.J., Pittock, A.B., 1997. Runoff modelling for snow-affected catchments in the Australian alpine region, eastern Victoria. J. Hydrol. 200, 1. doi:10.1016/S0022-1694(97)00006-1

41. Simic, A., Fernandes, R., Brown, R., Romanov, P., Park, W., 2004. Validation of VEGETATION, MODIS, and GOES+ SSM/I snow-cover products over Canada based on surface snow depth observations. Hydrol. Process. 18, 1089–1104. doi:10.1002/hyp.5509

42. Singh, P., Spitzbart, G., Hübl, H., Weinmeister, H.W., 1997. Hydrological response of snowpack under 202, 1–20.

43. Solberg, R., Koren, H., Amlien, J., 2006. A review of optical snow cover algorithms. Nor. Regnesentral Nor. Comput. Cent.

44. Sui, J., Koehler, G., 2001. Rain-on-snow induced flood events in southern Germany. J. Hydrol. 252, 205–220. doi:10.1016/S0022-1694(01)00460-7

45. Tekeli, a. E., Akyürek, Z., Şorman, a. A., Şensoy, A., Şorman, a. Ü., 2005. Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sens. Environ. 97, 216–230. doi:10.1016/j.rse.2005.03.013

46. UBA, 2014. Environmental monitoring using satellite remote sensing in Bashkortostan [WWW Document]. URL https://www.umweltbundesamt.de/en/topics/environmental-monitoring-using-satellite-remote

47. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114, 106–115. doi:10.1016/j.rse.2009.08.014

48. Weather and Climate [WWW Document], 2014. URL http://www.pogodaiklimat.ru/monitor.php?id=27612

49. Yang, D., 2003. Streamflow response to seasonal snow cover extent changes in large Siberian watersheds. J. Geophys. Res. 108, 4578. doi:10.1029/2002JD003149

Full Text: PDF (English)