首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 78 毫秒
1.
An approach for nonstationary low‐flow frequency analysis is developed and demonstrated on a dataset from the rivers on the Loess Plateau of China. Nonstationary low‐flow frequency analysis has drawn significant attention in recent years by establishing relationships between low‐flow series and explanatory variables series, but few studies have tested whether the time‐varying moments of low flow can be fully described by the time‐varying moments of the explanatory variables. In this research, the low‐flow distributions are analytically derived from the 2 basic explanatory variables—the recession duration and the recession coefficient—with the assumption that the recession duration and recession coefficient variables follow exponential and gamma distributions, respectively; the derived low‐flow distributions are applied to test whether the time‐varying moments of explanatory variables can explain the nonstationarities found in the low‐flow variable. The effects of ecosystem construction measures, that is, check dam, terrace, forest, and grassland, on the recession duration and recession coefficient are further discussed. Daily flow series from 11 hydrological stations from the Loess Plateau are used and processed with a moving average technique. Low‐flow data are extracted following the pit under threshold approach. Six of the 11 low‐flow series show significant nonstationarities at the 5% significance level, and the trend curves of the moments of low flow are in close agreement with the curves estimated from the derived distribution with time‐dependent moments of the recession duration and time‐constant moments of the recession coefficient. It is indicated that the nonstationarity in the low‐flow distribution results from the nonstationarity in the recession duration in all 6 cases, and the increase in the recession duration is resulted from large‐scale ecosystem constructions rather than climate change. The large‐scale ecosystem constructions are found to have more influence on the decrease in streamflow than on the increase in watershed storage, thus resulting in the reduction of low flow. A high return period for the initial fixed design value decreases dramatically with an increasing recession duration.  相似文献   

2.
The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

3.
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   

4.
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   

5.
Forecast ensembles of hydrological and hydrometeorologial variables are prone to various uncertainties arising from climatology, model structure and parameters, and initial conditions at the forecast date. Post‐processing methods are usually applied to adjust the mean and variance of the ensemble without any knowledge about the uncertainty sources. This study initially addresses the drawbacks of a commonly used statistical technique, quantile mapping (QM), in bias correction of hydrologic forecasts. Then, an auxiliary variable, the failure index (γ), is proposed to estimate the ineffectiveness of the post‐processing method based on the agreement of adjusted forecasts with corresponding observations during an analysis period prior to the forecast date. An alternative post‐processor based on copula functions is then introduced such that marginal distributions of observations and model simulations are combined to create a multivariate joint distribution. A set of 2500 hypothetical forecast ensembles with parametric marginal distributions of simulated and observed variables are post‐processed with both QM and the proposed multivariate post‐processor. Deterministic forecast skills show that the proposed copula‐based post‐processing is more effective than the QM method in improving the forecasts. It is found that the performance of QM is highly correlated with the failure index, unlike the multivariate post‐processor. In probabilistic metrics, the proposed multivariate post‐processor generally outperforms QM. Further evaluation of techniques is conducted for river flow forecast of Sprague River basin in southern Oregon. Results show that the multivariate post‐processor performs better than the QM technique; it reduces the ensemble spread and is a more reliable approach for improving the forecast. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

6.
Prediction of the peak break‐up water level, which is the maximum instantaneous stage during ice break‐up, is desirable to allow effective ice flood mitigation, but traditional hydrologic flood routing techniques are not efficient in addressing the large uncertainties caused by numerous factors driving the peak break‐up water level. This research provides a probability prediction framework based on vine copulas. The predictor variables of the peak break‐up water level are first chosen, the pair copula structure is then constructed by using vine copulas, the conditional density distribution function is derived to perform a probability prediction, and the peak break‐up water level value can then be estimated from the conditional density distribution function given the conditional probability and fixed values of the predictor variables. This approach is exemplified using data from 1957 to 2005 for the Toudaoguai and Sanhuhekou stations, which are located in the Inner Mongolia Reach of the Yellow River, and the calibration and validation periods are divided at 1986. The mean curve of the peak break‐up water level estimated from the conditional distribution function can capture the tendency of the observed series at both the Toudaoguai and Sanhuhekou stations, and more than 90% of the observed values fall within the 90% prediction uncertainty bands, which are approximately twice the standard deviation of the observed series. The probability prediction results for the validation period are consistent with those for the calibration period when the nonstationarity of the marginal distributions for the Sanhuhekou station are considered. Compared with multiple linear regression results, the uncertainty bands from the conditional distribution function are much narrower; moreover, the conditional distribution function is more capable of addressing the nonstationarity of predictor variables, and the conclusions are confirmed by jackknife analysis. Scenario predictions for cases in which the peak break‐up water level is likely to be higher than the bankfull water level can also be conducted based on the conditional distribution function, with good performance for the two stations.  相似文献   

7.
Many civil infrastructures are located near the confluence of two streams, where they may be subject to inundation by high flows from either stream or both. These infrastructures, such as highway bridges, are designed to meet specified performance objectives for floods of a specified return period (e.g. the 100 year flood). Because the flooding of structures on one stream can be affected by high flows on the other stream, it is important to know the relationship between the coincident exceedence probabilities on the confluent stream pair in many hydrological engineering practices. Currently, the National Flood Frequency Program (NFF), which was developed by the US Geological Survey (USGS) and based on regional analysis, is probably the most popular model for ungauged site flood estimation and could be employed to estimate flood probabilities at the confluence points. The need for improved infrastructure design at such sites has motivated a renewed interest in the development of more rigorous joint probability distributions of the coincident flows. To accomplish this, a practical procedure is needed to determine the crucial bivariate distributions of design flows at stream confluences. In the past, the copula method provided a way to construct multivariate distribution functions. This paper aims to develop the Copula‐based Flood Frequency (COFF) method at the confluence points with any type of marginal distributions via the use of Archimedean copulas and dependent parameters. The practical implementation was assessed and tested against the standard NFF approach by a case study in Iowa's Des Moines River. Monte Carlo simulations proved the success of the generalized copula‐based joint distribution algorithm. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

8.
Comparisons of flow time series between preimpact and postimpact periods have been widely used to determine hydrological alterations caused by reservoir operation. However, preimpact and postimpact periods might also be characterized by different climatological properties, a problem that has not been well addressed. In this study, we propose a framework to assess the cumulative impact of dams on hydrological regime over time. The impacts of the Three Gorges Dam (TGD) on the flow regime of the Yangtze River were investigated using this framework. We reconstructed the unregulated flow series to compare with the regulated flow series during the same period (2010 to 2015). Eco‐surplus and eco‐deficit and the Indicators of Hydrologic Alteration (IHA) parameters were used to examine hydrological regime change. Among 32 IHA parameters, Wilcoxon signed‐rank test and principal component analysis identified the October median flow, 1‐ and 3‐day maximum flows, 1‐day minimum flow, and rise rate as representative indicators of hydrological alterations. Eco‐surplus and eco‐deficit showed that the reservoir also changed the seasonal regime of the flows by reducing autumn flow and increasing winter flow. Changes in annual extreme flows and October flows lead to negative ecological implications downstream of the TGD. Ecological considerations should be taken into account during operation of the TGD in order to mitigate the negative effects on the fluvial ecosystem in the middle reach of Yangtze River. The framework proposed here could be a robust method to assess the cumulative impacts of reservoir operation over time.  相似文献   

9.
Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.  相似文献   

10.
In this study, we investigated rainfall, run‐off, and sediment transport dynamics (414 run‐off events and 231 events with sediment information) of a humid mountain badland area—the Araguás catchment (Central Pyrenees, Spain)—from October 2005 to September 2016. Use of this long‐term database allows characterization of the hydrological response, which consist of low‐magnitude/high‐frequency events and high‐magnitude/low‐frequency events, and identification of seasonal dynamics and rainfall‐run‐off thresholds. Our results indicate that the Araguás catchment, similarly to other humid badlands, had high hydrological responsiveness (mean annual run‐off coefficient: 0.52), a non‐linear relationship of rainfall with run‐off (common in Mediterranean environments), and seasonal hydrological and sedimentological dynamics. We created and validated a multivariate regression model to characterize the hydrological variables (stormflow and peak discharge) and sedimentological variables (mean and maximum suspended sediment concentrations and total suspended sediment load). In summer and at the beginning of autumn, the response was mainly related to rainfall intensity, suggesting a predomination of Hortonian flows. In contrast, in spring and winter, the responses were mainly related to the antecedent conditions (previous rainfall and baseflow), suggesting the occurrence of saturated excess flow processes, and the contribution of neighbouring vegetated areas. The multivariate analysis also showed that total sediment load is better predicted by a multivariate regression model that integrates pre‐event, rainfall, and run‐off variables. In general, our models provided more accurate predictions of small‐magnitude/high‐frequency events than high‐magnitude/low‐frequency events. This study highlights the high inter‐ and intra‐annual variability response in humid badland areas and that long‐term records are needed to reduce the uncertainty of hydrological and sedimentological responses in Mediterranean badland areas.  相似文献   

11.
The application of stationary parameters in conceptual hydrological models, even under changing boundary conditions, is a common yet unproven practice. This study investigates the impact of non‐stationary model parameters on model performance for different flow indices and time scales. Therefore, a Self‐Organizing Map based optimization approach, which links non‐stationary model parameters with climate indices, is presented and tested on seven meso‐scale catchments in northern Germany. The algorithm automatically groups sub‐periods with similar climate characteristics and allocates them to similar model parameter sets. The climate indices used for the classification of sub‐periods are based on (a) yearly means and (b) a moving average over the previous 61 days. Classification b supports the estimation of continuous non‐stationary parameters. The results show that (i) non‐stationary model parameters can improve the performance of hydrological models with an acceptable growth in parameter uncertainty; (ii) some model parameters are highly correlated to some climate indices; (iii) the model performance improves more for monthly means than yearly means; and (iv) in general low to medium flows improve more than high flows. It was further shown how the gained knowledge can be used to identify insufficiencies in the model structure. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

12.
Top‐kriging is a method for estimating stream flow‐related variables on a river network. Top‐kriging treats these variables as emerging from a two‐dimensional spatially continuous process in the landscape. The top‐kriging weights are estimated by regularising the point variogram over the catchment area (kriging support), which accounts for the nested nature of the catchments. We test the top‐kriging method for a comprehensive Austrian data set of low stream flows. We compare it with the regional regression approach where linear regression models between low stream flow and catchment characteristics are fitted independently for sub‐regions of the study area that are deemed to be homogeneous in terms of flow processes. Leave‐one‐out cross‐validation results indicate that top‐kriging outperforms the regional regression on average over the entire study domain. The coefficients of determination (cross‐validation) of specific low stream flows are 0.75 and 0.68 for the top‐kriging and regional regression methods, respectively. For locations without upstream data points, the performances of the two methods are similar. For locations with upstream data points, top‐kriging performs much better than regional regression as it exploits the low flow information of the neighbouring locations. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

13.
The estimation of sub‐daily flows from daily flood flows is important for many hydrological and hydraulic applications. Flows during flood events often vary significantly within sub‐daily time‐scales, and failure to capture the sub‐daily flood characteristic can result in an underestimation of the instantaneous flood peaks, with possible risk of design failure. It is more common to find a longer record of daily flow series (observed or modelled using daily rainfall series) than sub‐daily flow data. This paper describes a novel approach, known as the steepness index unit volume flood hydrograph approach, for disaggregating daily flood flows into sub‐daily flows that takes advantage of the strong relationship between the standardized instantaneous flood peak and the standardized daily flood hydrograph rising‐limb steepness index. The strength of this relationship, which is considerably stronger than the relationship between the standardized flood peak and the event flood volume, is shown using data from six rivers flowing into the Gippsland Lakes in southeast Australia. The results indicate that the steepness index unit volume flood hydrograph approach can be used to disaggregate modelled daily flood flows satisfactorily, but its reliability is dependent on a model's ability to simulate the standardized daily flood hydrograph rising‐limb steepness index and the event flood volume. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

14.
Uncertainty and variability in bivariate modeling of hydrological droughts   总被引：2，自引：1，他引：1  
There are two kinds of uncertainty factors in modeling the bivariate distribution of hydrological droughts: the alteration of predefined critical ratios for pooling droughts and excluding minor droughts and the temporal variability of univariate and/or bivariate characteristics of droughts due to the impact of human activities. Daily flow data covering a period of 56 hydrological years from two gauging stations from a humid region in South China are used. The influences of alterations of threshold values of flow and critical ratios of pooling droughts and excluding minor droughts on drought properties are analyzed. Six conventional univariate models and three Archimedean copulas are employed to fit the marginal and joint distributions of drought properties, the Kolmogorov–Smirnov and Anderson–Darling methods are used for testing the goodness-of-fit of the univariate model, and the Cramer-von Mises method based on Rosenblatt’s transform is applied for the test of the bivariate model. The change point analysis of the copula parameter of bivariate distribution of droughts is first made. Results demonstrate that both the statistical characteristics of each drought property and their bivariate joint distributions are sensitive to the critical ratio of excluding minor droughts. A model can be selected to fit the marginal distribution for drought deficit volume or maximum deficit, but it is not determined for drought duration with the lower ratios of the pooling and excluding droughts. The statistical uncertainty of drought duration makes the modeling of bivariate joint distribution of drought duration and deficit volume or of drought duration and maximum deficit undermined. Change points significantly occurred in the period from the late 1970s to the middle 1980s for a single drought property and the copula parameter of their joint distribution due to the impact of human activities. The difference between two subseries separated by the change point is remarkable in the magnitudes of drought properties and the joint return periods. A copula function can be selected to optimally fit the bivariate distribution, provided that the critical ratios of pooling and excluding droughts are great enough such as the optimal value of 0.4 in the case study. It is valuable that the modeling and designing of the bivariate joint correlation and distribution of drought properties can be performed on the subseries separated by the change point of the copula parameter.  相似文献   

15.
This study develops a novel approach for modelling and examining the impacts of time–space land‐use changes on hydrological components. The approach uses an empirical land‐use change allocation model (CLUE‐s) and a distributed hydrological model (DHSVM) to examine various land‐use change scenarios in the Wu‐Tu watershed in northern Taiwan. The study also uses a generalized likelihood uncertainty estimation approach to quantify the parameter uncertainty of the distributed hydrological model. The results indicate that various land‐use policies—such as no change, dynamic change and simultaneous change—have different levels of impact on simulating the spatial distributions of hydrological components in the watershed study. Peak flow rates under simultaneous and dynamic land‐use changes are 5·71% and 2·77%, respectively, greater than the rate under the no land‐use change scenario. Using dynamic land‐use changes to assess the effect of land‐use changes on hydrological components is more practical and feasible than using simultaneous land‐use change and no land‐use change scenarios. Furthermore, land‐use change is a spatial dynamic process that can lead to significant changes in the distributions of ground water and soil moisture. The spatial distributions of land‐use changes influence hydrological processes, such as the ground water level of whole areas, particularly in the downstream watershed. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

16.
This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

17.
Periods of summertime low flows are often critical for fish. This study quantified the impacts of forest clear‐cutting on summertime low flows and fish habitat and how they evolved through time in two snowmelt‐dominant headwater catchments in the southern interior of British Columbia, Canada. A paired‐catchment analysis was applied to July–September water yield, the number of days each year with flow less than 10% of mean annual discharge, and daily streamflow for each calendar day. The postharvest time series were divided into treatment periods of approximately 6–10 years, which were analysed independently to evaluate how the effects of forestry changed through time. An instream flow assessment using a physical habitat simulation‐style approach was used to relate streamflow to the availability of physical habitat for resident rainbow trout. About two decades after the onset of logging and as the extent of logging increased to approximately 50% of the catchments, reductions in daily summertime low flows became more significant for the July–September yield (43%) and for the analysis by calendar day (11–68%). Reductions in summertime low flows were most pronounced in the catchment with the longest postharvest time series. On the basis of the temporal patterns of response, we hypothesize that the delayed reductions in late‐summer flow represent the combined effects of a persistent advance in snowmelt timing in combination with at least a partial recovery of transpiration and interception loss from the regenerating forests. These results indicate that asymptotic hydrological recovery as time progresses following logging is not suitable for understanding the impacts of forest harvesting on summertime low flows. Additionally, these reductions in streamflow corresponded to persistent decreases in modelled fish habitat availability that typically ranged from 20% to 50% during the summer low‐flow period in one of the catchments, suggesting that forest harvest may have substantial delayed effects on rearing salmonids in headwater streams.  相似文献   

18.
Estimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.  相似文献   

19.
The estimation of flood frequency is vital for the flood control strategies and hydraulic structure design. Generating synthetic flood events according to statistical properties of observations is one of plausible methods to analyze the flood frequency. Due to the statistical dependence among the flood event variables (i.e. the flood peak, volume and duration), a multidimensional joint probability estimation is required. Recently, the copula method is widely used for multivariable dependent structure construction, however, the copula family should be chosen before application and the choice process is sometimes rather subjective. The entropy copula, a new copula family, employed in this research proposed a way to avoid the relatively subjective process by combining the theories of copula and entropy. The analysis shows the effectiveness of the entropy copula for probabilistic modelling the flood events of two hydrological gauges, and a comparison of accuracy with the popular copulas was made. The Gibbs sampling technique was applied for trivariate flood events simulation in order to mitigate the calculation difficulties of extending to three dimension directly. The simulation results indicate that the entropy copula is a simple and effective copula family for trivariate flood simulation.  相似文献   

20.
This paper presents a copula technique to develop time-variant seismic fragility curves for corroded bridges at the system level and considers the realistic time-varying dependence among component seismic demands. Based on material deterioration mechanisms and incremental dynamic analysis, the time-evolving seismic demands of components were obtained in the form of marginal probability distributions. The time-varying dependences among bridge components were then captured with the best fitting copula function, which was selected from the commonly used copula classes by the empirical distribution based analysis method. The system time-variant fragility curves at different damage states were developed and the effects of time-varying dependences among components on the bridge system fragility were investigated. The results indicate the time-varying dependence among components significantly affects the time-variant fragility of the bridge system. The copula technique captures the nonlinear dependence among component seismic demands accurately and easily by separating the marginal distributions and the dependence among them.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号