Regional analysis of low flow using L-moments for Dongjiang basin, South China

Chen, Yongqin David; Huang, Guoru; Shao, Quanxi; Xu, Chong‐Yu

doi:10.1623/hysj.51.6.1051

Cited by 74 publications

(42 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The regulation of reservoir outflows and impacts of land-use/landcover changes have made many streamflow processes exhibit significant changes, which make the flood frequency analysis more tricky than for stationary cases. If no consideration is given to the nonstationary situations in the flood and lowflow frequency analysis (e.g., Chen et al, 2006), the results may be biased. Techniques of flood frequency analysis for nonstationary situations (see Khaliq et al, 2006) should be considered in future research.…”

Section: Discussionmentioning

confidence: 99%

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Wang

Chen

Shi

et al. 2008

Hydrol. Earth Syst. Sci.

148

View full text Add to dashboard Cite

Abstract. 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 methCorrespondence to: W. Wang (w.wang@126.com) ods, 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Wang

Chen

Shi

et al. 2008

Hydrol. Earth Syst. Sci.

148

View full text Add to dashboard Cite

show abstract

“…Also according to Park et al (2001) the lmom are frequently more tractable than the mle and it also gives reliable estimations. Finally, according to Chen et al (2006), the pwm and lmom gives equivalent results. Thus, by following Park et al (2001), Su et al (2009), Sen & Niedzielski (2010 and Morgan et al (2011) the WAK parameters were estimated by using the lmom.…”

Section: The Wakeby Distributionmentioning

confidence: 99%

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Blain

Meschiatti

2014

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012. As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.Distribuições multi-paramétricas para avaliar a probabilidade de ocorrência de eventos extremos de precipitação pluvial R E S U M O Erosão e saturação hídrica do solo estão frequentemente associadas a eventos extremos de precipitação pluvial, tal como as enchentes. Desta forma, a literatura científica indica a necessidade de se realizar estudos que aprimorem a determinação da probabilidade de ocorrência desses eventos. O objetivo principal do estudo foi comparar o desempenho das distribuições multiparamétricas Wakeby, Kappa e Generalizada dos Valores Extremos na estimativa dos máximos anuais de totais de precipitação (diários, dois e três dias) da localidade Campinas-SP (1890-2012. O objetivo secundário foi avaliar a presença de tendências climáticas e a correlação serial nessas variáveis. A função autocorrelação e o teste de Mann-Kendall não indicaram a presença de significativas tendências climáticas e correlações serias em nenhuma das séries. Com base nos resultados dos testes de aderência concluiu-se que as distribuições Kappa e Generalizada dos Valores Extremos apresentam os melhores desempenhos na descrição probabilística das séries do estudo.

show abstract

“…Burn, 1997;Madsen and Rosbjerg, 1997;Adamowski, 2000;Kjeldsen et al, 2002;Jingyi and Hall, 2004;Solín, 2008), droughts (e.g. Clausen and Pearson, 1995;Chen et al, 2006), extreme sea levels (e.g. van Gelder et al, 2000) and wind speeds (e.g.…”

Section: Introductionmentioning

confidence: 99%

Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic

Gaál

Kyselý

2009

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In this paper, we implement the region-ofinfluence (ROI) approach for modelling probabilities of heavy 1-day and 5-day precipitation amounts in the Czech Republic. The pooling groups are constructed according to (i) the regional homogeneity criterion (assessed by a built-in regional homogeneity test), which requires that in a pooling group the distributions of extremes are identical after scaling by the at-site mean; and (ii) the 5T rule, which sets the minimum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period T . The similarity of sites is evaluated in terms of climatological and geographical site characteristics. We carry out a series of sensitivity analyses by means of Monte Carlo simulations in order to explore the importance of the individual site attributes, including hybrid pooling schemes that combine both types of the site attributes with different relative weights.We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20×20 km), the actual distance between the sites plays the most important role in determining the similarity of probability distributions of heavy precipitation. There are, however, differences between the optimum pooling schemes depending on the duration of the precipitation events. While in the case of 1-day precipitation amounts the pooling scheme based on the geographical proximity of sites outperforms all hybrid schemes, for multi-day amounts the inclusion of climatological site characteristics (although with much lower weights compared to the geographical distance) enhances the performance of the pooling schemes. This finding is in agreement with the climatological expectation since multi-day heavy precipitation events are more closely linked Correspondence to: L. Gaál (ladislav.gaal@gmail.com) to some typical precipitation patterns over central Europe (related e.g. to the varied roles of Atlantic and Mediterranean influences) while the dependence of 1-day extremes on climatological characteristics such as mean annual precipitation is much weaker.The findings of the paper show a promising perspective for an application of the ROI methodology in evaluating outputs of regional climate models with high resolution: the pooling schemes might serve for defining weighting functions, and the large spatial variability in the grid-box estimates of high quantiles of precipitation amounts may efficiently be reduced.

show abstract

Regional analysis of low flow using L-moments for Dongjiang basin, South China

Cited by 74 publications

References 21 publications

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic

Contact Info

Product

Resources

About