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1.
This study presents spatio-temporal analysis of droughts in one of the most drought prone region in India–western Rajasthan and develops drought intensity-area-frequency curves for the region. The meteorological drought conditions are analyzed using 6-month standardized precipitation index (SPI-6) estimated at spatial resolution of 0.5° × 0.5°. Spatio-temporal analysis of SPI-6 indicates increase in frequency of droughts at the central part of the region. The non-parametric Mann–Kendall test for seasonal trend analysis showed increase in number of grids under drought during the study period. Further, bivariate frequency analysis of drought characteristics—intensity and areal extent is carried out using copula methods. For modeling joint dependence between drought variables, three copula families namely Gumbel-Hougaard, Frank and Plackett copulas are evaluated. Based on goodness-of-fit as well as upper tail dependence tests, it is found that the Gumbel-Hougaard copula best represents the drought properties. The copula-based joint distribution is used to compute conditional return periods and drought intensity–area–frequency (I–A–F) curves. The I–A–F curves could be helpful in risk evaluation of droughts in the region.  相似文献   

2.
This study presents copula‐based multivariate probabilistic approach to model severity–duration–frequency (S‐D‐F) relationship of drought events in western Rajasthan, India. Drought occurrences are analysed using standardized precipitation index computed on monthly mean areal precipitation, aggregated at a time scale of 6 months. After testing with a series of probability density functions, the drought variable severity is found to be better represented with log‐normal distribution, whereas duration is well fitted with exponential distribution. Four different classes of bivariate copulas – Archimedean, extreme value, Plackett, and elliptical families are evaluated for modelling joint distribution of drought characteristics. It is observed that the extreme value copula – Gumbel–Hougaard copula – performed better as compared with other classes of copulas, based on results of various statistical tests and upper tail dependence coefficient. The joint distribution obtained from best performing copula is then employed to determine conditional return period and to derive drought severity‐duration‐frequency (S‐D‐F) curves for the study region. The results of the study suggests that the copula method can be used effectively to derive the drought S‐D‐F curves, which can be helpful in planning and adopting suitable drought mitigation strategies in drought‐prone areas. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

3.
The paper aims to develop researches on the spatial variability of heavy rainfall events estimation using spatial copula analysis. To demonstrate the methodology, short time resolution rainfall time series from Stuttgart region are analyzed. They are constituted by rainfall observations on continuous 30 min time scale recorded over a network composed by 17 raingages for the period July 1989–July 2004. The analysis is performed aggregating the observations from 30 min up to 24 h. Two parametric bivariate extreme copula models, the Husler–Reiss model and the Gumbel model are investigated. Both involve a single parameter to be estimated. Thus, model fitting is operated for every pair of stations for a giving time resolution. A rainfall threshold value representing a fixed rainfall quantile is adopted for model inference. Generalized maximum pseudo-likelihood estimation is adopted with censoring by analogy with methods of univariate estimation combining historical and paleoflood information with systematic data. Only pairs of observations greater than the threshold are assumed as systematic data. Using the estimated copula parameter, a synthetic copula field is randomly generated and helps evaluating model adequacy which is achieved using Kolmogorov Smirnov distance test. In order to assess dependence or independence in the upper tail, the extremal coefficient which characterises the tail of the joint bivariate distribution is adopted. Hence, the extremal coefficient is reported as a function of the interdistance between stations. If it is less than 1.7, stations are interpreted as dependent in the extremes. The analysis of the fitted extremal coefficients with respect to stations inter distance highlights two regimes with different dependence structures: a short spatial extent regime linked to short duration intervals (from 30 min to 6 h) with an extent of about 8 km and a large spatial extent regime related to longer rainfall intervals (from 12 h to 24 h) with an extent of 34 to 38 km.  相似文献   

4.
The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.  相似文献   

5.
The multisensor precipitation estimates (MPE) data, available in hourly temporal and 4 km × 4 km spatial resolution, are produced by the National Weather Service and mosaicked as a national product known as Stage IV. The MPE products have a significant advantage over rain gauge measurements due to their ability to capture spatial variability of rainfall. However, the advantages are limited by complications related to the indirect nature of remotely sensed precipitation estimates. Previous studies confirm that efforts are required to determine the accuracy of MPE and their associated uncertainties for future use in hydrological and climate studies. So far, various approaches and extensive research have been undertaken to develop an uncertainty model. In this paper, an ensemble generator is presented for MPE products that can be used to evaluate the uncertainty of rainfall estimates. Two different elliptical copula families, namely, Gaussian and t‐copula are used for simulations. The results indicate that using t‐copula may have significant advantages over the well‐known Gaussian copula particularly with respect to extremes. Overall, the model in which t‐copula was used for simulation successfully generated rainfall ensembles with similar characteristics to those of the ground reference measurements. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

6.
ABSTRACT

This study provides a spatio-temporal analysis of the great floods that occurred in South America in 1983 using hydrometeorological data and outputs from a continental-scale hydrological-hydrodynamic model. In the extreme year 1983, there were three main flooding periods (February, June and July) in many South American river basins, such as the Araguaia, Tocantins, São Francisco, Uruguay, La Plata and its tributaries, resulting in high discharge of the Paraguay River for many months. Depth–area–duration curves show that 3-day precipitation events in northern regions of South America were among the largest 15 events in the period 1980–2015 but only for specific locations, whereas in southern areas, the most extreme events in the same period were for larger durations (≥7-day precipitation). Modelled total export of water volume to the oceans indicates that rivers draining to the South Atlantic reached an anomaly of 3.7 during 1983, followed by 1998 (1.9) and 1992 (1.1), all of them corresponding to El Niño years.  相似文献   

7.
Analyses of the spatio-temporal variability of precipitation extremes defined by eleven extreme precipitation indices in Shandong were conducted by utilizing the methods of linear regression, ensemble empirical mode decomposition (EEMD) and Mann–Kendall test. The results revealed that statistically significant decreasing trends existed for almost all extreme precipitation indices except for the consecutive dry days (CDD) and simple daily intensity index. A periodicity of 10–15 years for precipitation extremes is detected by EEMD analysis. Greatest 5-day total rainfall (RX5day), very wet days (R95p) and annual total wet-day precipitation (PRCPTOT) experienced decreasing trends in the region stretching from the southeast coast to the west, while the spatial distribution of the decreasing trends for other indices was more complicated. Moreover, the frequency of occurrence in precipitation extremes at Changdao station, surrounded by the sea in the northeast region, increased in contrast to surrounding stations. This may suggest a possible effect from the local marine environment on extreme precipitation. In addition, the stations with statistically significant positive trends for CDD were mainly located in mid-west Shandong and along the southeast coast, where the extreme precipitation and total rainfall were, on the contrary, characterized by decreasing trends. These results indicate that drought or severe drought events have become more frequent in those regions. Analysis of large-scale atmospheric circulation changes indicates that a strengthening anticyclonic circulation and increasing geopotential height as well as decreasing strength of monsoonal flow in recent decades may have contributed to the variations in extreme precipitation in Shandong.  相似文献   

8.
Extreme rainfall events are of particular importance due to their severe impacts on the economy, the environment and the society. Characterization and quantification of extremes and their spatial dependence structure may lead to a better understanding of extreme events. An important concept in statistical modeling is the tail dependence coefficient (TDC) that describes the degree of association between concurrent rainfall extremes at different locations. Accurate knowledge of the spatial characteristics of the TDC can help improve on the existing models of the occurrence probability of extreme storms. In this study, efficient estimation of the TDC in rainfall is investigated using a dense network of rain gauges located in south Louisiana, USA. The inter-gauge distances in this network range from about 1 km to 9 km. Four different nonparametric TDC estimators are implemented on samples of the rain gauge data and their advantages and disadvantages are discussed. Three averaging time-scales are considered: 1 h, 2 h and 3 h. The results indicate that a significant tail dependency may exist that cannot be ignored for realistic modeling of multivariate rainfall fields. Presence of a strong dependence among extremes contradicts with the assumption of joint normality, commonly used in hydrologic applications.  相似文献   

9.
Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation, and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature. For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2 and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation process over the Dongjiang River basin. In pre‐flood seasons (April to June), the mixing of the dry and cold air originated from northern China and the moist warm air releases excessive rainstorms to this basin, while in post‐flood seasons (July to October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

10.
Statistics of extremes in hydrology   总被引：4，自引：0，他引：4  
The statistics of extremes have played an important role in engineering practice for water resources design and management. How recent developments in the statistical theory of extreme values can be applied to improve the rigor of hydrologic applications and to make such analyses more physically meaningful is the central theme of this paper. Such methodological developments primarily relate to maximum likelihood estimation in the presence of covariates, in combination with either the block maxima or peaks over threshold approaches. Topics that are treated include trends in hydrologic extremes, with the anticipated intensification of the hydrologic cycle as part of global climate change. In an attempt to link downscaling (i.e., relating large-scale atmosphere–ocean circulation to smaller-scale hydrologic variables) with the statistics of extremes, statistical downscaling of hydrologic extremes is considered. Future challenges are reviewed, such as the development of more rigorous statistical methodology for regional analysis of extremes, as well as the extension of Bayesian methods to more fully quantify uncertainty in extremal estimation. Examples include precipitation and streamflow extremes, as well as economic damage associated with such extreme events, with consideration of trends and dependence on patterns in atmosphere–ocean circulation (e.g., El Niño phenomenon).  相似文献   

11.
Adequately analyzing and modeling the extreme rainfall events is of great importance because of the effects that their magnitude and frequency can have on human life, agricultural productivity and economic aspects, among others. A single extreme event may affect several locations, and their spatial dependence has to be appropriately taken into account. Classical geostatistics is a well-developed field for dealing with location referenced data, but it is largely based on Gaussian processes and distributions, that are not appropriate for extremes. In this paper, an exploratory study of the annual maximum of monthly precipitation recorded in the northern area of Portugal from 1941 to 2006 at 32 locations is performed. The aim of this paper is to apply max-stable processes, a natural extension of multivariate extremes to the spatial set-up, to briefly describe the models considered and to estimate the required parameters to simulate prediction maps.  相似文献   

12.
This study used gridded daily maximum temperature data (1°?×?1°) for 1951–2014 period to analyze the trend in monthly extreme warm days (ExWD) and changes in its probability distribution in each grid. It also analyzed the trend in spatial spread of annual ExWD over the study period at four exceedance levels and further related the number of ExWDs with cereal crop productivity of India. Extreme warm days have increased throughout India but were statistically significant in 42% grids. The increase was consistent over all the months in north-eastern region, southern plateau and both the coastal plains. It also increased significantly over north-western and central India during April to June summer period. The probability distribution of ExWD also changed significantly in many grids, especially in southern plateau and both the coastal plains. The changes indicated increased frequency in the existing levels of extremes and new occurrences of higher frequency of extremes. The analysis of land area affected by different levels of extremes indicated significant increase, with the rate being highest for higher extremes. In terms of extreme warm day temperatures, the study identified southern plateau, east and west coast plains, and north-eastern India as highly vulnerable. Using copula probability model, study showed that increase in ExWD from 20 to 60% may increase the probability of 5% or more yield loss from 17 to 53% for Kharif cereals, 11 to 43% for Rabi cereals and 19 to 63% for wheat crop. The results may be used for devising zone specific adaptation strategies.  相似文献   

13.
Wavelet and cross-wavelet analysis are used to identify and describe spatial and temporal variability in Canadian seasonal precipitation, and to gain further insights into the dynamical relationship between the seasonal precipitation and the dominant modes of climate variability in the Northern Hemisphere. Results from applying continuous wavelet transform to seasonal precipitation series from 201 stations selected from Environment Canada Meteorological Network reveal striking climate-related features before and after the 1940s. The span of available observations, 1900–2000, allows for depicting variance and covariance for periods up to 12 years. Scale-averaged wavelet power spectra are used to simultaneously assess the temporal and spatial variability in each set of 201 seasonal precipitation time series. The most striking feature, in the 2–3-year period and in the 3–6-year period—the 6–12-year period is dominated by white noise and is not considered further—is a net distinction between the timing and intensity of the temporal variability in autumn, winter and spring–summer precipitation. It is found that the autumn season exhibits the most intense activity (or variance) in both the 2–3 year and the 3–6 year periods. The winter season corresponds to the least intense activity for the 2–3 year period, but it exhibits more activity than the spring–summer for the 3–6 year period.Cross-wavelet analysis is provided between the seasonal precipitation and four selected climatic indices: the Pacific North America (PNA), the North Atlantic Oscillation (NAO), the Northern Hemisphere Annular Mode (NAM) originally called the Arctic Oscillation, and the sea surface temperature series over the Niño-3 region (ENSO). The wavelet cross-spectra revealed coherent space–time variability of the climate–precipitation relationship throughout Canada. It is shown that strong climate/precipitation activity (or covariance) in the 2–6 year period starts after 1940 whatever the climatic index and the season. Prior to year 1940, only local and weaker 2–6 year activity is revealed in western Canada essentially in winter and autumn, but overall a non-significant precipitation/climate relationship is observed prior to 1940. Correlation analysis in the 2–6 year band between the seasonal precipitation and the selected climatic indices revealed strong positive correlations with the ENSO, the NAO, and the NAM in eastern and western Canada for the post-1940 period. For the period prior to 1940, the correlation tend be negative for all the indices whatever the region. A particular feature in the correlation analysis results is the consistently stronger and positive NAM–precipitation correlations in all the regions since 1940. The cross-wavelet spectra and the correlation analysis in the 2–6 year band suggest the presence of a change point around 1940 in Canadian seasonal precipitation—that is found to be more likely related to NAM dynamics.  相似文献   

14.
Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre-processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi-variate bias correction (MBC) methods—initially proposed to correct the inter-variable correlation or multi-variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution-based single-site bias correction methods are used for comparison. The effects of multi-site correction on hydro-meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi-site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi-site methods consistently reproduce watershed-averaged meteorological variables better than single-site methods, especially for extreme values. In terms of representing hydrological extremes, the multi-site methods generally perform better than the single-site methods, although the benefits vary according to the hydrological index. However, when applying the multi-site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi-site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.  相似文献   

15.
In recent decades, copula functions have been applied in bivariate drought duration and severity frequency analysis. Among several potential copulas, Clayton has been mostly used in drought analysis. In this research, we studied the influence of the tail shape of various copula functions (i.e. Gumbel, Frank, Clayton and Gaussian) on drought bivariate frequency analysis. The appropriateness of Clayton copula for the characterization of drought characteristics is also investigated. Drought data are extracted from standardized precipitation index time series for four stations in Canada (La Tuque and Grande Prairie) and Iran (Anzali and Zahedan). Both duration and severity data sets are positively skewed. Different marginal distributions were first fitted to drought duration and severity data. The gamma and exponential distributions were selected for drought duration and severity, respectively, according to the positive skewness and Kolmogorov–Smirnov test. The results of copula modelling show that the Clayton copula function is not an appropriate choice for the used data sets in the current study and does not give more drought risk information than an independent model for which the duration and severity dependence is not significant. The reason is that the dependence of two variables in the upper tail of Clayton copula is very weak and similar to the independent case, whereas the observed data in the transformed domain of cumulative density function show high association in the upper tail. Instead, the Frank and Gumbel copula functions show better performance than Clayton function for drought bivariate frequency analysis. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

16.
Climate extremes in South Western Siberia: past and future   总被引：1，自引：1，他引：0  
In this study, the temporal and spatial trends of ten climate extreme indices were computed based on observed daily precipitation and on daily maximum and minimum temperatures at 26 weather stations in South Western Siberia during the period 1969–2011 and, based on projected daily maximum and minimum temperatures, during 2021–2050. The Mann–Kendall test was employed to analyze the temporal trend and a combination of multiple linear regressions and semivariogram functions were used to evaluate the regional spatial trends and the local spatial variability of climate extremes, respectively. The results show that the temperature-based climate extremes increase at a 0.05 significance level while none of the precipitation-based climate extremes did. Spatially, dominant gradients are observed along latitude: The northern taiga vegetation zone experiences a colder and wetter climate while the southern forest steppe zone is drier and hotter. Over time, a tendency towards homogenization of the regional climate is observed through a decrease of the spatial variability for most climate extreme indices. In the future, the most intense changes are anticipated for the bio-climate indicators “growing season length” and “growing degree days” in the north, while the warming indicators, “warm day” and “warm night” are expected to be high to the south.  相似文献   

17.
Subimal Ghosh 《水文研究》2010,24(24):3558-3567
The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

18.
The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.  相似文献   

19.
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   

20.
Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Ni?o Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models’ outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO.  相似文献   

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