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| Titel |
Spatial analysis of extreme precipitation deficit as an index for atmospheric drought in Belgium |
| VerfasserIn |
Sepideh Zamani, Hans Van de Vyver, Anne Gobin |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250091601
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| Publikation (Nr.) |
 EGU/EGU2014-5903.pdf |
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| Zusammenfassung |
| The growing concern among the climate scientists is that the frequency of weather extremes
will increase as a result of climate change. European society, for example, is particularly
vulnerable to changes in the frequency and intensity of extreme events such as heat waves,
heavy precipitation, droughts, and wind storms, as seen in recent years [1,2]. A more than
50% of the land is occupied by managed ecosystem (agriculture, forestry) in Belgium.
Moreover, among the many extreme weather conditions, drought counts to have a substantial
impact on the agriculture and ecosystem of the affected region, because its most immediate
consequence is a fall in crop production. Besides the technological advances, a reliable
estimation of weather conditions plays a crucial role in improving the agricultural
productivity. The above mentioned reasons provide a strong motivation for a research on the
drought and its impacts on the economical and agricultural aspects in Belgium. The main
purpose of the presented work is to map atmospheric drought Return-Levels (RL), as first
insight for agricultural drought, employing spatial modelling approaches. The likelihood
of future drought is studied on the basis of precipitation deficit indices for four
vegetation types: water (W), grass (G), deciduous (D) and coniferous forests (C) is
considered.
Extreme Value Theory (EVT) [3,4,5] as a branch of probability and statistics, is dedicated
to characterize the behaviour of extreme observations. The tail behaviour of the EVT
distributions provide important features about return levels. EVT distributions are applicable
in many study areas such as: hydrology, environmental research and meteorology, insurance
and finance. Spatial Generalized Extreme Value (GEV) distributions, as a branch of EVT, are
applied to annual maxima of drought at 13 hydro-meteorological stations across Belgium.
Superiority of the spatial GEV model is that a region can be modelled merging the individual
time series of observations from isolated sites and using a common regression model
based on climatological/geographical covariates. The behaviour of the fitted spatial
GEV-distribution is heavy-tailed with γ - 0.3 over Belgium. A comparison between the
RL-maps using GEV model and the ones obtained from Universal Kriging (UK)
confirms the reliability of the spatial GEV model in explaining atmospheric drought in
Belgium.
References
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Goyette, S., Halsnaes, K., Holt, T., Jylhü, K., Koffi, B., Palutikoff, J., Schöll, R.,
Semmler, T., and Woth, K. (2007), Future extreme events in European climate; an
exploration of Regional Climate Model projections. Climatic Change, 81, 71-95.
[2]Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.
Tignor and H.L. Miller (Eds.)] (2007), king Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.
[3]Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values,
Springer-Verlag Heidelberg, Germany.
[4]Embrechts, P., C. Klüppelberg, and T. Mikosch (1997), Modelling Extremal
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[5]Smith, R., (2004), Statistics of extremes, with application in environment,
insurance and finance, in : Extreme Values in Finance, Telecommunications and
the Environment, edited by: Finkenstadt, B. and Rootzen, H., 373-388, Chapman
and Hall CRC Press, London. |
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