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《水文科学杂志》2013,58(4)
Abstract Abstract The Gumbel distribution has been the prevailing model for quantifying risk associated with extreme rainfall. Several arguments including theoretical reasoning and empirical evidence are supposed to support the appropriateness of the Gumbel distribution. These arguments are examined thoroughly in this work and are put into question. Specifically, theoretical analyses show that the Gumbel distribution is quite unlikely to apply to hydrological extremes and its application may misjudge the risk, as it underestimates seriously the largest extreme rainfall amounts. Besides, it is shown that hydrological records of typical length (some decades) may display a distorted picture of the actual distribution, suggesting that the Gumbel distribution is an appropriate model for rainfall extremes while it is not. In addition, it is shown that the extreme value distribution of type II (EV2) is a more consistent alternative. Based on the theoretical analysis, in the second part of this study an extensive empirical investigation is performed using a collection of 169 of the longest available rainfall records worldwide, each having 100–154 years of data. This verifies the inappropriateness of the Gumbel distribution and the appropriateness of EV2 distribution for rainfall extremes. 相似文献
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Most natural disasters are caused by water‐/climate‐related hazards, such as floods, droughts, typhoons, and landslides. In the last few years, great attention has been paid to climate change, and especially the impact of climate change on water resources and the natural disasters that have been an important issue in many countries. As climate change increases the frequency and intensity of extreme rainfall, the number of water‐related disasters is expected to rise. In this regard, this study intends to analyse the changes in extreme weather events and the associated flow regime in both the past and the future. Given trend analysis, spatially coherent and statistically significant changes in the extreme events of temperature and rainfall were identified. A weather generator based on the non‐stationary Markov chain model was applied to produce a daily climate change scenario for the Han River basin for a period of 2001–2090. The weather generator mainly utilizes the climate change SRES A2 scenario driven by input from the regional climate model. Following this, the SLURP model, which is a semi‐distributed hydrological model, was applied to produce a long‐term daily runoff ensemble series. Finally, the indicator of hydrologic alteration was applied to carry out a quantitative analysis and assessment of the impact of climate change on runoff, the river flow regime, and the aquatic ecosystem. It was found that the runoff is expected to decrease in May and July, while no significant changes occur in June. In comparison with historical evidence, the runoff is expected to increase from August to April. A remarkable increase, which is about 40%, in runoff was identified in September. The amount of the minimum discharge over various durations tended to increase when compared to the present hydrological condition. A detailed comparison for discharge and its associated characteristics was discussed. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation 
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下载免费PDF全文 A new method of parameter estimation in data scarce regions is valuable for bivariate hydrological extreme frequency analysis. This paper proposes a new method of parameter estimation (maximum entropy estimation, MEE) for both Gumbel and Gumbel–Hougaard copula in situations when insufficient data are available. MEE requires only the lower and upper bounds of two hydrological variables. To test our new method, two experiments to model the joint distribution of the maximum daily precipitation at two pairs of stations on the tributaries of Heihe and Jinghe River, respectively, were performed and compared with the method of moments, correlation index estimation, and maximum likelihood estimation, which require a large amount of data. Both experiments show that for the Ye Niugou and Qilian stations, the performance of MEE is nearly identical to those of the conventional methods. For the Xifeng and Huanxian stations, MEE can capture information indicating that the maximum daily precipitation at the Xifeng and Huanxian stations has an upper tail dependence, whereas the results generated by correlation index estimation and maximum likelihood estimation are unreasonable. Moreover, MEE is proved to be generally reliable and robust by many simulations under three different situations. The Gumbel–Hougaard copula with MEE can also be applied to the bivariate frequency analysis of other extreme events in data‐scarce regions. 相似文献
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Investigation on the properties of the relationship between rare and extreme rainfall and flood volumes, under some distributional restrictions 总被引：1，自引：1，他引：0
Mauro Naghettini Nebai Tavares Gontijo Maria Manuela Portela 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(6):859-872
The fact that rainfall data are usually more abundant and more readily regionalized than streamflow data has motivated hydrologists to conceive methods that incorporate the hydrometeorologial information into flood frequency analyses. Some of them, particularly those derived from the French GRADEX method, involve assumptions concerning the relationship between extreme rainfall and flood volumes, under some distributional restrictions. In particular, for rainfall probability distributions exhibiting exponential-like upper tails, it is possible to derive the shape and scale of the probability distribution of flood volumes by hypothesizing the basic properties of such a relationship, under rare and/or extreme conditions. This paper focuses on a parametric mathematical model for the relationship between rare and extreme rainfall and flood volumes under exponentially-tailed distributions. The model is analyzed and fitted to rare and extreme events derived from hydrological simulation of long stochastically-generated synthetic series of rainfall and evaporation for the Indaiá River basin, located in south-central Brazil. The paper also provides a sensitivity analysis of the model parameters in order to better understand flood events under rare and extreme conditions. By working with hydrologically plausible hypothetical events, the modeling approach proved to be a useful way to explore extraordinary rainfall and flood events. The results from this exploratory analysis provide grounds to derive some conclusions regarding the relative positions of the upper tails of the probability distributions of rainfall and flood volumes. 相似文献
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J. DA COSTA 《水文科学杂志》2013,58(4):487-488
Abstract The drought event which reached severe levels in 1972 and 1973 caused a major disaster in the Sahalian and sub-Sahalian zones in Africa. This disaster has drawn attention to the need for data surveys and detailed studies for meaningful long-term measures to combat the effects of future droughts. The study reported in this paper is an attempt to assess the hydrological aspects of the drought event in Nigeria in 1972 and 1973. There exist relatively long and reliable records of rainfall within the drought zone, while records for runoff, water level and groundwater are few and far between. Data available are analysed to determine evidence of trend and persistence (short and long-term). An examination of the rainfall records showed that extreme dry years at all the stations tended to recur at about the same time. The time interval between these extreme dry years was about 30 years. It was also observed from the spectral analysis of the records that most of the spectra for all the stations showed a generally high level of variance at low frequency. The limited information on runoff and groundwater precludes a detailed statistical analysis from being carried out on the annual series of runoff. However, the runoff data at some stations snowed that the magnitude of runoff in the drought year 1972/1973 was about 22–72 per cent of the average value for the length of record available (about eight years). Moreover, the long-term water-level record of Lake Chad revealed a similar trend for the occurrence of extreme dry years to that observed in the rainfall record. 相似文献
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《水文科学杂志》2013,58(4)
Abstract Abstract In the first part of this study, theoretical analyses showed that the Gumbel distribution is quite unlikely to apply to hydrological extremes and that the extreme value distribution of type II (EV2) is a more consistent choice. Based on these theoretical analyses, an extensive empirical investigation is performed using a collection of 169 of the longest available rainfall records worldwide, each having 100–154 years of data. This verifies the theoretical results. In addition, it shows that the shape parameter of the EV2 distribution is constant for all examined geographical zones (Europe and North America), with value κ = 0.15. This simplifies the fitting and the general mathematical handling of the distribution, which become as simple as those of the Gumbel distribution. 相似文献
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Yue-Ping Xu Martijn J. Booij Yang-Bin Tong 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):567-578
With the increase of both magnitude and frequency of hydrological extreme events such as drought and flooding, the significance
of adequately modeling hydrological extreme events is fully recognized. Estimation of extreme rainfall/flood for various return
periods is of prime importance for hydrological design or risk assessment. However, due to knowledge and data limitation,
uncertainty involved in extrapolating beyond available data is huge. In this paper, different sources of uncertainty in statistical
modeling of extreme hydrological events are studied in a systematic way. This is done by focusing on several key uncertainty
sources using three different case studies. The chosen case studies highlight a number of projects where there have been questions
regarding the uncertainty in extreme rainfall/flood estimation. The results show that the uncertainty originated from the
methodology is the largest and could be >40% for a return period of 200 years, while the uncertainty caused by ignoring the
dependence among multiple hydrological variables seems the smallest. In the end, it is highly recommended that uncertainty
in modeling extreme hydrological events be fully recognized and incorporated into a formal hydrological extreme analysis. 相似文献