1. bookVolume 67 (2019): Issue 1 (March 2019)
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
access type Open Access

Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

Published Online: 07 Nov 2018
Volume & Issue: Volume 67 (2019) - Issue 1 (March 2019)
Page range: 101 - 109
Received: 28 Sep 2017
Accepted: 19 Feb 2018
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
Abstract

This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

Keywords

Blöschl, G., Hall, J., Parajka, J., Perdigão, R.A.P., Merz, B., Arheimer, B., Aronica, G.T., Bilibashi, A., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G.B., Claps, P., Fiala, K., Frolova, N., Gorbachova, L., Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T.R., Kohnová, S., Koskela, J.J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Rogger, M., Salinas, J.L., Sauquet, E., Šraj, M., Szolgay, J., Viglione, A., Volpi, E., Wilson, D., Zaimi, K., Živković, N., 2017. Changing climate shifts timing of European floods. Science, 357, 6351, 588-590. DOI: 10.1126/science.aan2506.10.1126/.aan2506Open DOISearch in Google Scholar

Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A., Wagener, T., 2015. Virtual laboratories: new opportunities for collaborative water science. Hydrol. Earth Syst. Sci., 19, 2101-2117. DOI: 10.5194/hess-19-2101-2015.10.5194/hess-19-2101-2015Open DOISearch in Google Scholar

Chapman, T., 1999. A comparison of algorithms for stream flow recession and baseflow separation. Hydrological Processes, 13, 5, 701-714.10.1002/(SICI)1099-1085(19990415)13:5<701::AID-HYP774>3.0.CO;2-2Open DOISearch in Google Scholar

Clow, D.W., 2010. Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming. Journal of Climate, 23, 2293-2306. https://doi.org/10.1175/2009JCLI2951.1.10.1175/2009JCLI2951.1Open DOISearch in Google Scholar

Collins, D.N., 1998. Rainfall-induced high-magnitude runoff events in highly-glacierized Alpine basins. In: Proceedings of the HeadWater'98 Conference on Hydrology, Water Resources and Ecology in Headwaters (Meran/Merano, Italy, April 1998). IAHS Publ. no. 248, pp. 69-78.Search in Google Scholar

Déry, S.J., Salomonson, V.V., Stieglitz, M., Hall, D.K., Appel, I., 2005. An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modelling in Alaska. Hydrological Processes, 19, 2755-2774. DOI: 10.1002/hyp.5784.10.1002/hyp.5784Open DOISearch in Google Scholar

Dietz, A.J., Wohner, Ch., Kuenzer, C., 2012. European snow cover characteristics between 2000 and 2011 derived from improved MODIS daily snow cover products. Remote Sensing, 4, 8, 2432-2454. DOI: 10.3390/rs4082432.10.3390/rs4082432Open DOISearch in Google Scholar

Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., Sánchez, R., 2015. A snow cover climatology for the Pyrenees from MODIS snow products.10.5194/hessd-11-12531-2014Search in Google Scholar

Hydrol. Earth Syst. Sci., 19, 2337-2351. Hall, D.K., Riggs, G.A., 2016. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: http://dx.doi.org/10.5067/MODIS/MOD10A1.006. [Date Accessed].10.5067/MODIS/MOD10A1.006.[Date]Open DOISearch in Google Scholar

Krajčí, P., Holko, L., Perdigão, R.A.P., Parajka, J., 2014. Estimation of regional snowline elevation (RSLE) from MODIS images for seasonally snow covered mountain basins. Journal of Hydrology, 519, 1769-1778.10.1016/j.jhydrol.2014.08.064Search in Google Scholar

Li, B., Zhu, A.-X., Zhou, C., Zhang, Y., Pei, T., Qin, C., 2008. Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow. Hydrol. Process., 22, 2930-2942. DOI: 10.1002/hyp.6891.10.1002/hyp.6891Open DOISearch in Google Scholar

Mangini, W., Viglione, A., Hall, J., Hundecha, Y., Ceola, S., Montanari, A., Rogger, M., Salinas, J.L., Borzì, I., Parajka, J., 2018. Detection of trends in magnitude and frequency of flood peaks across Europe, Hydrological Science Journal, https://doi.org/10.1080/02626667.2018.1444766. (In press).10.1080/02626667.2018.1444766.()Open DOISearch in Google Scholar

Merz, R., Blöschl, G., 2003. A process typology of regional floods. Water Resources Research, 39, 12, 39, 1340. DOI: 10.1029/2002WR001952, 12.10.1029/2002WR00195212Open DOISearch in Google Scholar

Mioduszewski, J.R., Rennermalm, A.K., Robinson, D.A., Mote, T.L., 2014. Attribution of snowmelt onset in Northern Canada.10.1002/2013JD021024Search in Google Scholar

J. Geophys. Res. Atmos., 119, 9638-9653. DOI: 10.1002/ 2013JD021024. Parajka, J., 2017. Catalogue of identified flood peaks from GRDC dataset (FLOOD TYPE experiment). DOI: 10.5281/zenodo.581436.10.1002/2013JD021024.Parajka,J.,2017.peaksfromGRDCdataset().DOI:10.5281/zenodo.581436Open DOISearch in Google Scholar

Parajka, J., Blöschl, G., 2006. Validation of MODIS snow cover images over Austria. Hydrology and Earth System Sciences, 10, 679-689.10.5194/hess-10-679-2006Search in Google Scholar

Parajka, J., Blöschl, G., 2012. MODIS-based snow cover products, validation, and hydrologic applications. In: Chang, N.B., Hong, Y. (Eds.): Multiscale Hydrologic Remote Sensing: Perspectives and Applications. CRC Press, Taylor & Francis Group, Boca Raton, Florida, USA, pp. 185-212.10.1201/b11279-9Search in Google Scholar

Parajka, J., Holko, L., Kostka, Z., Blöschl, G., 2012. MODIS snow cover mapping accuracy in a small mountain catchment - comparison between open and forest sites. Hydrology and Earth System Sciences, 16, 2365-2377.10.5194/hess-16-2365-2012Open DOISearch in Google Scholar

Paudel, K.P., Andersen, P., 2011. Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology. Remote Sens. Environ., 115, 5, 1234-1246.10.1016/j.rse.2011.01.006Search in Google Scholar

Riboust, P., Thirel, G., Le Moine, N., Ribstein, P., 2019. Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology and Hydromechanics, 67, 70-81.10.2478/johh-2018-0004Search in Google Scholar

Thomas, B.F., Vogel, R.M., Kroll, C.N., Famiglietti, J.S., 2013. Estimation of the base flow recession constant under human interference. Water Resources Research, 49, 7366-7379. DOI: 10.1002/wrcr.20532.10.1002/wrcr.20532Open DOISearch in Google Scholar

Vogel, R.M., Kroll, C.N., 1996. Estimation of baseflow recession constants. Water Resources Management, 10, 303-320.10.1007/BF00508898Search in Google Scholar

Wang, X., Xie, H., Liang, T., Huang, X., 2009. Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China. Hydrol. Process., 23, 3, 419-429.10.1002/hyp.7151Search in Google Scholar

Wang, W., Huang, X., Deng, J., Xie, H., Liang, T., 2015. Spatio-temporal change of snow cover and its response to climate over the Tibetan plateau based on an improved daily cloud-free snow cover product. Remote Sens., 7, 1, 169-194.10.3390/rs70100169Search in Google Scholar

Xinghua, L, Wenxuan, F., Huanfeng, S., Chunlin, H., Liangpei, Z., 2017. Monitoring snow cover variability (2000-2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method. Journal of Hydrology, 551, 314-327.10.1016/j.jhydrol.2017.05.049Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo