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<article language="en">
	<journal>
		<journal_title>Hydrology and Earth System Sciences</journal_title>
		<journal_url>www.hydrol-earth-syst-sci.net</journal_url>
		<issn>1027-5606</issn>
		<eissn>1607-7938</eissn>
		<volume_number>14</volume_number>
		<issue_number>3</issue_number>
		<publication_year>2010</publication_year>
	</journal>
	<doi>10.5194/hess-14-407-2010</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/14/407/2010/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/14/407/2010/hess-14-407-2010.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/14/407/2010/hess-14-407-2010.pdf</fulltext_pdf>
	<start_page>407</start_page>
	<end_page>418</end_page>
	<publication_date>2010-03-05</publication_date>
	<article_title content_type="html">Flood trends and variability in the Mekong river</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>J. M. Delgado</name>
			<email>jdelgado@gfz-potsdam.de</email>
		</author>
		<author numeration="2" affiliations="1">
			<name>H. Apel</name>
		</author>
		<author numeration="3" affiliations="1">
			<name>B. Merz</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">German Research Centre for Geosciences, Section 5.4, Hydrology, Potsdam, Germany</affiliation>
	</affiliations>
	<abstract content_type="html">Annual maximum discharge is analyzed in the Mekong river in Southeast Asia
with regard to trends in average flood and trends in variability during the
20th century. Data from four gauging stations downstream of Vientiane, Laos,
were used, covering two distinct hydrological regions within the Mekong
basin. These time series span through over 70 years and are the longest daily
discharge time series available in the region. The methods used, Mann Kendal
test (MK), ordinary least squares with resampling (OLS) and non-stationary
generalized extreme value function (NSGEV), are first tested in a Monte Carlo
experiment, in order to evaluate their detection power in presence of
changing variance in the time series. The time series are generated using the
generalized extreme value function with varying scale and location parameter.
NSGEV outperforms MK and OLS, both because it resulted in less type II
errors, but also because it allows for a more complete description of the
trends, allowing to separate trends in average and in variability.&lt;br&gt;
&lt;br&gt;
Results from MK, OLS and NSGEV agreed on trends in average flood behaviour.
However, the introduction of a time-varying scale parameter in the NSGEV
allowed to isolate flood variability from the trend in average flood and to
have a more complete view of the changes. Overall, results showed an
increasing likelihood of extreme floods during the last half of the century,
although the probability of an average flood decreased during the same
period. A period of enhanced variance in the last quarter of the 20th
century, estimated with the wavelet power spectrum as a function of time, was
identified, which confirmed the results of the NSGEV.&lt;br&gt;
&lt;br&gt;
We conclude that the absence of detected positive trends in the hydrological
time series was a methodological misconception due to over-simplistic models.</abstract>
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