Identification of sub-trends from hydro-meteorological series
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Date
2016
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Publisher
Stochastic environmental research and risk assessment
Abstract
In hydro-meteorological trend analysis, an alteration
in the given variable is detected by considering the
long-term series as a whole. Whereas the long-term trend
may be absent, the significance of hidden (short-durational)
sub-trends in the series may be important for environmental
management practices. In this paper, a graphical
approach of identifying trend or sub-trends using nonparametric
cumulative rank difference (CRD) was proposed.
To confirm the significance of the visualized trend,
the CRD was translated from the graphical to a statistical
metric. To assess its capability, the performance of the
CRD method was compared with that of the well-known
Mann–Kendall (MK) test. The graphical and statistical
CRD techniques were applied to detect trends and subtrends
in the annual rainfall of 10 River Nile riparian
countries (RNRCs). The co-occurrence of the trend evolutions
in the rainfall with those of the large-scale ocean–
atmosphere interactions was analyzed. The power of the
CRD method was shown to closely agree with that of the
MK test under the various circumstances of sample sizes,
variations, linear trend slopes, and serial correlations. At
the level of significance a = 5 %, the long-term trends
were found present in 30 % of the RNRCs. However at
a = 5 %, the main downward (upward) sub-trends were
found significant in 30 (60 %) of the RNRCs. Generally at
a = 1 %, linkages of the trend evolutions in the rainfall of
the RNRCs were found to those of the influences from the
Atlantic and Indian Oceans. At a = 5 %, influences from
the Pacific Ocean on the rainfall trends of some countries
were also evident.
Description
Keywords
Trend analysis, Trend evolution, Sub-trend identification, Nonparametric cumulative rank deviation (CRD) method, Hydro-meteorology, River Nile riparian countries
Citation
Onyutha, C. (2016). Identification of sub-trends from hydro-meteorological series. Stochastic environmental research and risk assessment, 30(1), 189-205. DOI 10.1007/s00477-015-1070-0