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| Titel |
Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China |
| VerfasserIn |
W. Wang, X. Chen, P. Shi, P. H. A. J. M. Gelder |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 12, no. 1 ; Nr. 12, no. 1 (2008-02-01), S.207-221 |
| Datensatznummer |
250010469
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| Publikation (Nr.) |
 copernicus.org/hess-12-207-2008.pdf |
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| Zusammenfassung |
| Extreme hydro-meteorological events have become the focus of more and more
studies in the last decade. Due to the complexity of the spatial pattern of
changes in precipitation processes, it is still hard to establish a clear
view of how precipitation has changed and how it will change in the future.
In the present study, changes in extreme precipitation and streamflow
processes in the Dongjiang River Basin in southern China are investigated
with several nonparametric methods, including one method (Mann-Kendall test)
for detecting trend, and three methods (Kolmogorov–Smirnov test, Levene's
test and quantile test) for detecting changes in probability distribution.
It was shown that little change is observed in annual extreme precipitation
in terms of various indices, but some significant changes are found in the
precipitation processes on a monthly basis, which indicates that when
detecting climate changes, besides annual indices, seasonal variations in
extreme events should be considered as well. Despite of little change in
annual extreme precipitation series, significant changes are detected in
several annual extreme flood flow and low-flow series, mainly at the
stations along the main channel of Dongjiang River, which are affected
significantly by the operation of several major reservoirs. To assess the
reliability of the results, the power of three non-parametric methods are
assessed by Monte Carlo simulation. The simulation results show that, while
all three methods work well for detecting changes in two groups of data with
large sample size (e.g., over 200 points in each group) and large
differences in distribution parameters (e.g., over 100% increase of scale
parameter in Gamma distribution), none of them are powerful enough for small
data sets (e.g., less than 100 points) and small distribution parameter
difference (e.g., 50% increase of scale parameter in Gamma distribution).
The result of the present study raises the concern of the robustness of
statistical change-detection methods, shows the necessity of combined use of
different methods including both exploratory and quantitative statistical
methods, and emphasizes the need of physically sound explanation when
applying statistical test methods for detecting changes. |
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