Effect of catchment properties and flood generation regime on copula selection for bivariate flood frequency analysis

Filipova, V.; Lawrence, D.; Klempe, H.

doi:10.1007/s11600-018-0113-6

Artykuł - szczegóły

Tytuł artykułu

Effect of catchment properties and flood generation regime on copula selection for bivariate flood frequency analysis

Autorzy

Filipova V. , Lawrence D. , Klempe H.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-018-0113-6

Warianty tytułu

Języki publikacji

Abstrakty

Applying copula-based bivariate flood frequency analysis is advantageous because the results provide information on both the flood peak and volume. More data are, however, required for such an analysis, and it is often the case that only data series with a limited record length are available. To overcome this issue of limited record length, data regarding climatic and geomorphological properties can be used to complement statistical methods. In this paper, we present a study of 27 catchments located throughout Norway, in which we assess whether catchment properties, flood generation processes and flood regime have an effect on the correlation between flood peak and volume and, in turn, on the selection of copulas. To achieve this, the annual maximum flood events were first classified into events generated primarily by rainfall, snowmelt or a combination of these. The catchments were then classified into flood regime, depending on the predominant flood generation process producing the annual maximum flood events. A contingency table and Fisher’s exact test were used to determine the factors that affect the selection of copulas in the study area. The results show that the two-parameter copulas BB1 and BB7 are more commonly selected in catchments with high steepness, high mean annual runoff and rainfall flood regime. These findings suggest that in these types of catchments, the dependence structure between flood peak and volume is more complex and cannot be modeled effectively using a one-parameter copula. The results illustrate that by relating copula types to flood regime and catchment properties, additional information can be supplied for selecting copulas in catchments with limited data.

Słowa kluczowe

bivariate frequency analysis Archimedean copula flood regime

analiza częstotliwościowa dwuwymiarowa kopuła archimedesowska

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2018

Tom

Vol. 66, no. 4

Strony

791--806

Opis fizyczny

Bibliogr. 32 poz.

Twórcy

autor

Filipova V.

valeriya.filipova@hit.no

University College of Southeast Norway, INHM, Gullbringvegen 36, 3800 Bø, Norway

autor

Lawrence D.

dela@nve.no

Norwegian Water Resources and Energy Directorate, P. O. Box 5091 Maj., 0301 Oslo, Norway

autor

Klempe H.

University College of Southeast Norway, INHM, Gullbringvegen 36, 3800 Bø, Norway

Bibliografia

1. Agresti A (2013) Categorical data analysis, 2nd edn. Wiley, New York, pp 90–96
2. Aksoy H, Wittenberg H (2011) Nonlinear baseflow recession analysis in watersheds with intermittent streamflow. Hydrol Sci J 56(2):226–237. https://doi.org/10.1080/02626667.2011.553614
3. Arciniega A, Breña-Naranjo J, Pedrozo-Acuña A, Appendini C (2016) HYDRORECESSION: a Matlab toolbox for streamflow recession analysis. Comput Geosci 98(87):92. https://doi.org/10.1016/j.cageo.2016.10.005
4. Ben Aissia MA, Chebana F, Ouarda T, Bruneau P, Barbet M (2015) Bivariate index-flood model: case study in Québec, Canada. Hydrol Sci J 60:247–268. https://doi.org/10.1080/02626667.2013.875177
5. Brunner M, Viviroli D, Sikorska A, Vannier A, Favre A-C, Seibert J (2017) Flood type specific construction of synthetic design hydrographs. Water Resour Res 53:1390–1406. https://doi.org/10.1002/2016wr019535
6. Callau Poduje AC, Belli A, Haberlandt U (2014) Dam risk assessment based on univariate versus bivariate statistical approaches: a case study for Argentina. Hydrol Sci J 59(12):2216–2232. https://doi.org/10.1080/02626667.2013.871014
7. Chebana F, Ouarda T (2009) Index flood-based multivariate regional frequency analysis. Water Resour Res. https://doi.org/10.1029/2008wr007490
8. Fischer S, Schumann A, Schulte M (2016) Characterisation of seasonal flood types according to timescales in mixed probability distributions. J Hydrol 539:38–56
9. Fleig A (2013) Norwegian hydrological reference dataset for climate change studies. Technical Report 02. Norwegian water resources and energy directorate. Retrieved from http://webby.nve.no/publikasjoner/rapport/2013/rapport201302.pdf
10. Gaál L, Szolgay J, Kohnová S, Hlavčová K, Parajka J, Viglione A, Merz R, Blöschl G (2015) Dependence between flood peaks and volumes: a case study on climate and hydrological controls. Hydrol Sci J 60(6):968–984. https://doi.org/10.1080/02626667.2014.951361
11. Genest C, Favre A (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12(4):347–368. https://doi.org/10.1061/(asce)1084-0699(2007)12:4(347)
12. Genest C, Rémillard B, Beaudoin D (2009) Goodness-of-fit tests for copulas: a review and a power study. Insur Math Econ 44(2):199–213. https://doi.org/10.1016/j.insmatheco.2007.10.005
13. Gottschalk L, Jensen Jørgen L, Lundquist D, Solantie R, Tollan A (1979) Hydrologic regions in the Nordic countries. Nord Hydrol 10:273–286
14. Grimaldi S, Serinaldi F (2006) Asymmetric copula in multivariate flood frequency analysis. Adv Water Resour 29(8):1155–1167. https://doi.org/10.1016/j.advwatres.2005.09.005
15. Grimaldi S, Petroselli G, Salvadori C, De Michele (2016) Catchment compatibility via copulas: A non-parametric study of the dependence structures of hydrological responses. Adv Water Resour 90:116–133
16. Hirschboeck K, Ely L, Maddox R (2000) Hydroclimatology of meteorologic floods. In: Wohl E (ed) Inland flood hazards: human, riparian and aquatic communities. Cambridge University Press, New York, pp 39–72. https://doi.org/10.5194/nhess-14-295-2014
17. Kojadinovic I, Yan J (2010) Modeling multivariate distributions with continuous margins using the copula R package. J Stat Softw 34(9):1–20
18. Mohr M (2008) New routines for gridding of temperature and precipitation observations for “seNorge.no”. Note no. 08/2008. Met.no, Oslo
19. Poulin A, Huard D, Favre A, Pugin S (2007) Importance of tail dependence in bivariate frequency analysis. J Hydrol Eng 12(4):394–403. https://doi.org/10.1061/(asce)1084-0699(2007)12:4(394)
20. Rémillard B, Scaillet O (2009) Testing for equality between two copulas. J Multivariate Analysis 100(3):377–386
21. Requena AI, Mediero L, Garrote L (2013) A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation. Hydrol Earth Syst Sci 17:3023–3038. https://doi.org/10.5194/hess-17-3023-2013
22. Requena AI, Chebana F, Mediero L (2016) A complete procedure for multivariate index-flood model application. J Hydrol 535:559–580. https://doi.org/10.1016/j.jhydrol.2016.02.004
23. Saloranta T (2014) New version (v.1.1.1) of the seNorge snow model and snow maps for Norway. Report no. 6 – 2014. NVE, Oslo
24. Salvadori G, De Michele C (2004) Frequency analysis via copulas: theoretical aspects and applications to hydrological events. Water Resour Res 40(12):WR003133. https://doi.org/10.1029/2004wr003133
25. Salvadori G, De Michele C, Kottegoda N, Rosso R (2007) Extremes in nature: an approach using copulas. Springer, Dordrecht
26. Sraj M, Bezak N, Brilly M (2015) Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River. Hydrol Process 29(2):225–238. https://doi.org/10.1002/hyp.10145
27. Szolgay J, Gaál L, Kohnová S, Hlavčová K, Výleta R, Bacigál T, Blöschl G (2015) A process-based analysis of the suitability of copula types for peak–volume flood relationships. Proc IAHS 370:183–188. https://doi.org/10.5194/piahs-370-183-2015
28. Tong X, Wang D, Singh V, Wu J, Chen X, Chen Y (2015) Impact of data length on the uncertainty of hydrological Copula modeling. J Hydrol Eng 20(4):05014019. https://doi.org/10.1061/(asce)he.1943-5584.0001039
29. Vandenberghe S, Verhoest N, De Baets B (2010) Fitting bivariate copulas to the dependence structure between storm characteristics: a detailed analysis based on 105 years 10 min rainfall. Water Resour Res 46:W01512. https://doi.org/10.1029/2009wr007857
30. Vormoor K, Lawrence D, Heistermann M, Bronstert A (2014) Climate change impacts on the seasonality and generation processes of floods in catchments with mixed snowmelt/rainfall regimes: projections and uncertainties. Hydrol Earth Syst Sci 19(6):6273–6309. https://doi.org/10.5194/hess-19-913-2015
31. Vormoor K, Lawrence D, Schlichting L, Wilson D, Wong W (2016) Evidence for changes in the magnitude and frequency of observed rainfall vs. snowmelt driven floods in Norway. J Hydrol 538:33–48. https://doi.org/10.1016/j.jhydrol.2016.03.066
32. Yue S, Rasmussen P (2002) Bivariate frequency analysis: discussion of some useful concepts in hydrological application. Hydrol Process 16(14):2881–2898. https://doi.org/10.1002/hyp.1185

Typ dokumentu

Bibliografia

Identyfikator YADDA

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