Scholarly record
IDENTIFICATION OF CATCHMENTS WITH SIMILAR FLOOD CHARACTERISTICS IN THE ALPINE-CARPATHIAN BASINS
Abstract
This study uses a pooling approach to identify catchments with similar flood characteristics in the Alpine-Carpathian region. The identification of similar regions is performed using the nonhierarchical k-means clustering algorithm and different sets of catchments characteristics (catchment area, forest cover, mean catchment elevation) and flood seasonality characteristic (concentration of flood around the mean date of flood occurrence). The optimal number of cluster is determined using the silhouette method. The analysis is conducted in the period in the period 1960-2010 using 555 gauging stations located in Slovakia (59) and Austria (496), with the length of observation ranging 30 to 51 years. The analysis is performed for two different combinations of catchments characteristics and flood seasonality characteristic. The first combination includes the mean catchment elevation, forest cover and concentration of flood around the mean date of flood occurrence (r). The second combination is the same as above and also includes the catchment area. Based on silhouette method, 2 clusters can be considered the optimal number of clusters for both combinations. Not including the catchment area into the analysis (first combination) resulted in both, a higher global average silhouette width and higher average silhouette with over each individual clusters. These larger silhouette width indicates higher confidence in the two clusters obtained. Cluster 1 is located in the lowland areas and is characterized by wide temporal spread of the flood timing (February to July). Cluster 2 is located in mountain areas and is characterized by summer floods (June and July), which have a high temporal concentration (strong seasonality)
Publication Impact Profile
Publication details
References12
Hall, J., Arheimer, B., Aronica, G.T., Bilibashi, A., Boháč, M., Bonacci, O. Borga, M., Burlando, P., Castellarin, A., Chirico, G.B., Claps, P., Fiala, K., Gaál, L., Gorbachova, L., Gül, A., Hannaford, J., Kiss, A., Kjeldsen, T., Kohnová, S., Koskela, J.J., Macdonald, N., Mavrova-Guirguinova, M., Ledvinka, O., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Osuch, M., Parajka, J., Perdigão, R.A.P., Radevski, I., Renard, B., Rogger, M., Salinas, J.L., Sauquet, E., Šraj, M., Szolgay, J., Viglione, A., Volpi, E., Wilson, D., Zaimi, K., Blöschl, G., A European Flood Database: facilitating comprehensive flood research beyond andministrative boundaries, Proceedings of the International Association of Hydrological Sciences,vol. 370, pp 89-95, 2015
Hundecha, Y., Parajka, J., Viglione, A., Flood type classification and assessment of their past changes across Europe, Hydrol. Earth Syst. Sci. Discuss., Manuscipt under review for Journal Hydrol. Earth Syst. Sci. Discuss.
Karahoca, A., Karahoca, D., Şanver, M. (2012). Survey of Data Mining and Applications (Review from 1996 to Now), Data Mining Applications in Engineering and Medicine, Associate Prof. Adem Karahoca (Ed.), InTech, DOI: 10.5772/48803. Available from: https://www.intechopen.com/books/data-mining-applications-in-engineering-and-medicine/survey-of-data-mining-and-applications-review-from-1996-to-now-
Kassambara, A., Mundt, F. (2017). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.5. https://CRAN.R project.org/package=factoextra
Kaufman. L., Rousseeuw, P.J., Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, 1990.
Kuentz, A., Arheimer, B., Hundecha, Y., Wagener, T., Understanding hydrologic variability across Europe throught catchment classification, Hydrol. Earth Syst. Sci., vol. 21/ issue 6, pp 2863-2879, 2017.
MacQueen, J.B., Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. Univesity of California Press., 1967, pp. 281-297.
Mediero, L., Kjeldsen, T.R., Macdonald, N., Kohnová, S., Merz, B., Vorogushyn, S., Wilson, D., Alburquerque, T., Blöschl, G., Bogdanowicz, E., Castellarin, A., Hall, J., Kobold, M., Kriauciuniene, J., Lang, M., Madsen, H., Onuşluel Gül, G., Perdigão, R.A.P., Roald, L.A., Salinas, J.L., Toumazis, A.D., Veijalainen, N., Þórarinsson, Óðinn, Identification of coherent flood regions across Europe by using the longest streamflow records, Journal of Hydrology, vol. 528, pp 341-360, 2015.
Merz, B., Nguyen, V.D., Vorogushyn, S., Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist ? Journal of Hydrology, vol. 541, pp 824-836, 2016.
Parajka, J., Kohnová, S., Bálint, G., Barbuc, M., Borga, M., Claps, P., Cheval, S., Dumitrescu, A., Gaume, E., Hlavčová, K., Merz, R., Pfaundler, M., Stancalie, G., Szolgay, J., Blöschl, G., Seasonal characteristics of flood regimes across the Alpine-Capathian range, Journal of Hydrology, vol. 394/issue 1-2, pp. 78-89, 2010.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. url http://www.R-project.org/.
Solín, Ľ., Floods: size estimates for small basins of Slovakia by the method of regional frequency analysis, Geografický časopis, vol. 57/issue 4, pp. 287-307,2005.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
