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1.
The discharge regimes of the large rivers of northern Australia are characterized by the occurrence of extreme flood events with far‐reaching environmental and societal impacts. In January 1998 the largest flood ever recorded on the Katherine River, northern Australia, resulted in widespread inundation and resultant damage to the town of Katherine. The occurrence of the flood emphasized the unreliability of the then available flood probability estimates and prompted a palaeoflood approach to estimate the recurrence interval of the event. The location of Katherine is ideal for such a study, as the town is located immediately downstream from Katherine Gorge, which provides the necessary bedrock‐confined channel required for such an approach. In addition, previous work in Katherine Gorge had demonstrated that the gorge sections hold suitable deposits for palaeoflood stage reconstruction. The results of the present study show that at least two flow events with discharges similar to the 1998 flood have occurred within the last 600 years, and that high‐magnitude floods are a general feature of the discharge record of the Katherine River over the last c. 2000 years. Furthermore, because the study was undertaken within a few months of the occurrence of the 1998 flood, it provided the opportunity to evaluate the previously obtained flood discharge estimates and draw attention to the general uncertainties associated with palaeoflood studies. Our results emphasize that palaeoflood stage estimates based on slackwater deposits need to be treated as conservative estimates only. More specifically, with respect to the 1998 event, our study demonstrates that the controls of flood peak were more complex than simply flood routing through the gorge sections. It is clear that the areas downstream from Katherine Gorge made an important contribution to the flood peak of the 1998 event. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

2.
Some recent research on fluvial processes suggests the idea that some hydrological variables, such as flood flows, are upper-bounded. However, most probability distributions that are currently employed in flood frequency analysis are unbounded to the right. This paper describes an exploratory study on the joint use of an upper-bounded probability distribution and non-systematic flood information, within a Bayesian framework. Accordingly, the current PMF maximum discharge appears as a reference value and a reasonable estimate of the upper-bound for maximum flows, despite the fact that PMF determination is not unequivocal and depends strongly on the available data. In the Bayesian context, the uncertainty on the PMF can be included into the analysis by considering an appropriate prior distribution for the maximum flows. In the sequence, systematic flood records, historical floods, and paleofloods can be included into a compound likelihood function which is then used to update the prior information on the upper-bound. By combining a prior distribution describing the uncertainties of PMF estimates along with various sources of flood data into a unified Bayesian approach, the expectation is to obtain improved estimates of the upper-bound. The application example was conducted with flood data from the American river basin, near the Folsom reservoir, in California, USA. The results show that it is possible to put together concepts that appear to be incompatible: the deterministic estimate of PMF, taken as a theoretical limit for floods, and the frequency analysis of maximum flows, with the inclusion of non-systematic data. As compared to conventional analysis, the combination of these two concepts within the logical context of Bayesian theory, contributes an advance towards more reliable estimates of extreme floods.  相似文献   

3.
To date, an outstanding issue in hydrologic data assimilation is a proper way of dealing with forecast bias. A frequently used method to bypass this problem is to rescale the observations to the model climatology. While this approach improves the variability in the modeled soil wetness and discharge, it is not designed to correct the results for any bias. Alternatively, attempts have been made towards incorporating dynamic bias estimates into the assimilation algorithm. Persistent bias models are most often used to propagate the bias estimate, where the a priori forecast bias error covariance is calculated as a constant fraction of the unbiased a priori state error covariance. The latter approach is a simplification to the explicit propagation of the bias error covariance. The objective of this paper is to examine to which extent the choice for the propagation of the bias estimate and its error covariance influence the filter performance. An Observation System Simulation Experiment (OSSE) has been performed, in which ground water storage observations are assimilated into a biased conceptual hydrologic model. The magnitudes of the forecast bias and state error covariances are calibrated by optimizing the innovation statistics of groundwater storage. The obtained bias propagation models are found to be identical to persistent bias models. After calibration, both approaches for the estimation of the forecast bias error covariance lead to similar results, with a realistic attribution of error variances to the bias and state estimate, and significant reductions of the bias in both the estimates of groundwater storage and discharge. Overall, the results in this paper justify the use of the traditional approach for online bias estimation with a persistent bias model and a simplified forecast bias error covariance estimation.  相似文献   

4.
The annual peak flow series of the Polish rivers are mixtures of summer and winter flows. In the Part I of a sequence of two papers, theoretical aspects of applicability of seasonal approach to flood frequency analysis (FFA) in Poland are discussed. A testing procedure is introduced for the seasonal model and the data overall fitness. Conditions for objective comparative assessment of accuracy of annual maxima (AM) and seasonal maxima (SM) approaches to FFA are formulated and finally Gumbel (EV1) distribution is chosen as seasonal distribution for detailed investigation. Sampling properties of AM quantile x(F) estimates are analysed and compared for the SM and AM models for equal seasonal variances. For this purpose, four estimation methods were used, employing both asymptotic approach and sampling experiments. Superiority of the SM over AM approach is stated evident in the upper quantile range, particularly for the case of no seasonal variation in the parameters of Gumbel distribution. In order to learn whether the standard two‐ and three‐parameter flood frequency distributions can be used to model the samples generated from the Two‐Component Extreme Value 1 (TCEV1) distribution, the shape of TCEV1 probability density function (PDF) has been tested in terms of bi‐modality. Then the use of upper quantile estimate obtained from the dominant season of extreme floods (DEFS) as AM upper quantile estimate is studied and respective systematic error is assessed. The second part of the paper deals with advantages and disadvantages of SM and AM approach when applied to real flow data of Polish rivers. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

5.
Geomorphic evidence along bedrock-confined reaches of the Salt River in east-central Arizona provides a record of the river's largest flood events. Fine-grained flood slackwater deposits accumulated at channel margin irregularities several metres above the low-flow channel. Discharges associated with flow events responsible for the deposits were estimated by computer flow modelling. These estimates document flood magnitudes in excess of gauged historic streamflows. Relative and radiocarbon dating suggest that a flood record in excess of 600 y is preserved in the slackwater sequences. A prominent flood scar cut into grussy hillslope soils allows the extension of the prehistoric flood record to several thousand years. A maximum discharge estimate of 4600 m3s?1 affixed to the flood scar represents the largest flood event in the record, and is given a minimum recurrence interval of 1000–2000 y. The 1952 flood is the largest historic flow event experienced along the study reach and is estimated at 2900 m3s?1. Two palaeoflood events preserved in the slackwater stratigraphy exceed the 1952 event, and are given recurrence intervals of 300 and 600 y. The latter flood event had an estimated discharge of 3200 m3s?1. It is apparent that discharge estimates affixed to these infrequent, large-magnitude flood events approach a maximum with decreased probabilities (large recurrence intervals). This suggests that a physical limit on discharge may exist within the Salt River drainage basin and is perhaps directly related to drainage basin size.  相似文献   

6.
The estimation of the 100-year flood, or more generally the T-year flood, is a basic problem in hydrology. An important source of uncertainty in this estimate is that caused by the uncertain estimation of parameters of the flood distribution. This uncertainty can have a significant effect on the flood design value, and its quantification is an important aspect of evaluating the risk involved in a chosen level of flood protection. In this paper, simulation is used to determine confidence intervals for the flood design value. The simulation allows verification of Stedinger's formula not only as it applies to confidence intervals, but also verifies the formula as an approximation to percentiles as well.  相似文献   

7.
《水文科学杂志》2013,58(3):582-595
Abstract

This paper explores the potential for seasonal prediction of hydrological variables that are potentially useful for reservoir operation of the Three Gorges Dam, China. The seasonal flow of the primary inflow season and the peak annual flow are investigated at Yichang hydrological station, a proxy for inflows to the Three Gorges Dam. Building on literature and diagnostic results, a prediction model is constructed using sea-surface temperatures and upland snow cover available one season ahead of the prediction period. A hierarchical Bayesian approach is used to estimate uncertainty in the parameters of the prediction model and to propagate these uncertainties to the predictand. The results show skill for both the seasonal flow and the peak annual flow. The peak annual flow model is then used to estimate a design flood (50-year flood or 2% exceedence probability) on a year-to-year basis. The results demonstrate the inter-annual variability in flood risk. The predictability of both the seasonal total inflow and the peak annual flow (or a design flood volume) offers potential for adaptive management of the Three Gorges Dam reservoir through modification of the operating policy in accordance with the year-to-year changes in these variables.  相似文献   

8.
Various regional flood frequency analysis procedures are used in hydrology to estimate hydrological variables at ungauged or partially gauged sites. Relatively few studies have been conducted to evaluate the accuracy of these procedures and estimate the error induced in regional flood frequency estimation models. The objective of this paper is to assess the overall error induced in the residual kriging (RK) regional flood frequency estimation model. The two main error sources in specific flood quantile estimation using RK are the error induced in the quantiles local estimation procedure and the error resulting from the regional quantile estimation process. Therefore, for an overall error assessment, the corresponding errors associated with these two steps must be quantified. Results show that the main source of error in RK is the error induced into the regional quantile estimation method. Results also indicate that the accuracy of the regional estimates increases with decreasing return periods. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

9.
Book reviews     
Abstract

Statistical and deterministic modelling estimates of flood magnitudes and frequencies that can affect flood-plain ecology in the upper Ahuriri River catchment, a mountainous high country catchment in the New Zealand Southern Alps, were evaluated. Statistical analysis of 46 years of historical data showed that floods are best modelled by the generalized extreme value and lognormal distributions. We evaluated application of the HEC-HMS model to this environment by modelling flood events of various frequencies. Model results were validated and compared with the statistical estimates. The SCS curve number method was used for losses and runoff generation, and the model was very sensitive to curve number. The HEC-HMS flood estimates matched the statistical estimates reasonably well, and, over all return periods, were on average approximately 1% greater. However, the model generally underestimated flood peaks up to the 25-year event and overestimated magnitudes above this. The results compared well with other regional estimates, including studies based on L-moments, and showed that this catchment has smaller floods than other similarly-sized catchments in the Southern Alps.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Caruso, B.S., Rademaker, M., Balme, A., and Cochrane, T.A., 2013. Flood modelling in a high country mountain catchment, New Zealand: comparing statistical and deterministic model estimates for ecological flows. Hydrological Sciences Journal, 58 (2), 328–341.  相似文献   

10.
M. Nouh 《水文研究》2006,20(11):2393-2413
Real data on wadi flood flows from Saudi Arabia, Yemen, Oman, Kuwait, UAE, Bahrain and Qatar were used to develop methodologies for the prediction of annual maximum flows and average monthly flows in the Arabian Gulf states. For the prediction of annual maximum floods, three methods have been investigated. In the first method, regional curves were developed and used together with the mean annual flood flow, estimated from the characteristics of the drainage basin, to estimate flood flows at a location in the basin. The second method fits data to various probability distribution functions, with a developed methodology introduced to account for floods generated by more than one system of climate, and the best fitted function was used for flood estimates. In the third method, only floods over a threshold, which depends on characteristics of the drainage basin, were considered and modelled. For the prediction of average monthly flows, stochastic simulation approaches of flood frequency analysis were used. Each of the prediction methods was verified by being applied in 40 different drainage basins. Based on the results obtained, recommendations were made on the best method to be applied (at present) by design engineers in the Arabian Gulf states. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

11.
The specific objective of the paper is to propose a new flood frequency analysis method considering uncertainty of both probability distribution selection (model uncertainty) and uncertainty of parameter estimation (parameter uncertainty). Based on Bayesian theory sampling distribution of quantiles or design floods coupling these two kinds of uncertainties is derived, not only point estimator but also confidence interval of the quantiles can be provided. Markov Chain Monte Carlo is adopted in order to overcome difficulties to compute the integrals in estimating the sampling distribution. As an example, the proposed method is applied for flood frequency analysis at a gauge in Huai River, China. It has been shown that the approach considering only model uncertainty or parameter uncertainty could not fully account for uncertainties in quantile estimations, instead, method coupling these two uncertainties should be employed. Furthermore, the proposed Bayesian-based method provides not only various quantile estimators, but also quantitative assessment on uncertainties of flood frequency analysis.  相似文献   

12.
Evaluation of dam overtopping probability induced by flood and wind   总被引：5，自引：4，他引：1  
This study develops a probability-based methodology to evaluate dam overtopping probability that accounts for the uncertainties arising from wind speed and peak flood. A wind speed frequency model and flood frequency analysis, including various distribution types and uncertainties in their parameters, are presented. Furthermore, dam overtopping probabilities based on monthly maximum (MMax) series models are compared with those of the annual maximum (AMax) series models. An efficient sampling scheme, which is a combination of importance sampling (IS) and Latin Hypercube sampling (LHS) methods, is proposed to generate samples of peak flow rate and wind speed especially for rare events. Reservoir routing, which incorporates operation rules, wind setup, and run-up, is used to evaluate dam overtopping probability.  相似文献   

13.
Summary TheGumbel's theory of largest values has been applied to the estimation of probability of occurrence and of return periods of largest earthquakes in the European area. For this study shallow shocks from the period 1901–1955 and from 15 earthquake zones were used. For each zone the largest magnitudes corresponding to one-year intervals were arranged in order of increasingM, grouped in classes and then the probabilitiesF(x j) were calculated. The data plotted on the probability paper fit a straight line fairly well. The extrapolated lines yield the possibility of estimating large magnitudes which will be exceeded with a given probability, e.g. 1%. Such values were compared with largest magnitudes observed during the period 1901–1955. Their return periods indicate that in most regions the largest probable shock already occurred. Following the procedure ofEpstein-Lomnitz the coefficients and were calculated and compared with corresponding values ofa andb of the magnitude-frequency relation.  相似文献   

14.
All river engineering schemes require flood discharge estimates as part of the design and appraisal process. Unfortunately, continuous measurement of flood discharges is limited to those river sites with instrumented gauging stations, which constitute only a small proportion of channel reaches where information is required. Therefore, considerable research effort has been devoted to the development of reliable indirect techniques of flood discharge estimation. Research on the interrelationship of stream channel geometry and river discharge has provided the basis for an indirect method of flood estimation – the channel-geometry method – which employs river channel dimensions alone to estimate discharge characteristics at ungauged river sites. Channel-geometry equations are developed empirically by relating streamflow data from gauging stations and channel dimensions measured from natural river reaches in the vicinity of the gauge, and take the form of power function relations. Once regional channel-geometry equations have been defined, a channel width or channel capacity measurement is the only variable needed to estimate the flood flow characteristics at a specified river site. The method is useful as an alternative to traditional catchment-based approaches or as a rapid reconnaissance technique. In addition to the application for flood discharge prediction, channel-geometry equations could prove helpful in the management of river channels, first, by providing a basis for assessing local deviations in the channel form–discharge relation, deviations which could be employed as indicators of the sensitivity of particular stretches of river channel to change, and secondly, in the computation of natural channel dimensions for use in river channel design and river restoration.  相似文献   

15.
Bayes estimate of the probability of exceedance of annual floods   总被引：1，自引：1，他引：1  
In this paper Lindley's Bayesian approximation procedure is used to obtain the Bayes estimate of the probability of exceedence of a flood discharge. The Bayes estimates of the probability of exceedence has been shown by S.K. Sinha to be equivalent to the estimate of the probability of exceedence from the predictive or Bayesian disribution, of a future flood discharge. The evaluation of complex ratios of multiple integrals common in a Bayesian analysis is not necessary using Lindley's procedure. The Bayes estimates are compared to those obtained by the method of maximum likelihood and the method of moments. The results show that Bayes estimates of the probability of exceedence are larger as expected, but have smaller posterior standard deviations.  相似文献   

16.
Selection of a flood frequency distribution and associated parameter estimation procedure is an important step in flood frequency analysis. This is however a difficult task due to problems in selecting the best fit distribution from a large number of candidate distributions and parameter estimation procedures available in the literature. This paper presents a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion—second order variant (AICc), Bayesian Information Criterion (BIC) and a modified Anderson–Darling Criterion (ADC). It has been found from the Monte Carlo simulation that ADC is more successful in recognizing the parent distribution correctly than the AIC and BIC when the parent is a three-parameter distribution. On the other hand, AIC and BIC are better in recognizing the parent distribution correctly when the parent is a two-parameter distribution. From the seven different probability distributions examined for Tasmania, it has been found that two-parameter distributions are preferable to three-parameter ones for Tasmania, with Log Normal appears to be the best selection. The paper also evaluates three most widely used parameter estimation procedures for the Log Normal distribution: method of moments (MOM), method of maximum likelihood (MLE) and Bayesian Markov Chain Monte Carlo method (BAY). It has been found that the BAY procedure provides better parameter estimates for the Log Normal distribution, which results in flood quantile estimates with smaller bias and standard error as compared to the MOM and MLE. The findings from this study would be useful in flood frequency analyses in other Australian states and other countries in particular, when selecting an appropriate probability distribution from a number of alternatives.  相似文献   

17.
Abstract

Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes.

Citation Svensson, C., Kjeldsen, T.R., and Jones, D.A., 2013. Flood frequency estimation using a joint probability approach within a Monte Carlo framework. Hydrological Sciences Journal, 58 (1), 1–20.  相似文献   

18.
Compositional Bayesian indicator estimation   总被引：1，自引：1，他引：0  
Indicator kriging is widely used for mapping spatial binary variables and for estimating the global and local spatial distributions of variables in geosciences. For continuous random variables, indicator kriging gives an estimate of the cumulative distribution function, for a given threshold, which is then the estimate of a probability. Like any other kriging procedure, indicator kriging provides an estimation variance that, although not often used in applications, should be taken into account as it assesses the uncertainty of the estimate. An alternative approach to indicator estimation is proposed in this paper. In this alternative approach the complete probability density function of the indicator estimate is evaluated. The procedure is described in a Bayesian framework, using a multivariate Gaussian likelihood and an a priori distribution which are both combined according to Bayes theorem in order to obtain a posterior distribution for the indicator estimate. From this posterior distribution, point estimates, interval estimates and uncertainty measures can be obtained. Among the point estimates, the median of the posterior distribution is the maximum entropy estimate because there is a fifty-fifty chance of the unknown value of the estimate being larger or smaller than the median; that is, there is maximum uncertainty in the choice between two alternatives. Thus in some sense, the latter is an indicator estimator, alternative to the kriging estimator, that includes its own uncertainty. On the other hand, the mode of the posterior distribution estimator, assuming a uniform prior, is coincidental with the simple kriging estimator. Additionally, because the indicator estimate can be considered as a two-part composition which domain of definition is the simplex, the method is extended to compositional Bayesian indicator estimation. Bayesian indicator estimation and compositional Bayesian indicator estimation are illustrated with an environmental case study in which the probability of the content of a geochemical element in soil being over a particular threshold is of interest. The computer codes and its user guides are public domain and freely available.  相似文献   

19.
Abstract

The two-parameter EV1 distribution adequately describes New Zealand's flood series. Contour maps of [Qbar]/A0.8 and Q100[Qbar] are presented, where [Qbar] is the mean annual flood, A is the basin area and Q100 is the 1% annual exceedance probability flood. The maps are based directly on measured discharge series from a large sample of river recording stations. Thus when basins are ungauged, or have just a short record, an estimate of a design flood QT with specified annual exceedance probability (1/T) can be obtained using map estimates of [Qbar]/A0.8 and Q100[Qbar], without having first to estimate rainfall statistics for the basin, a particularly difficult task in sparsely instrumented mountainous areas. These maps succinctly summarize a great deal of hydrological information and permit improved flood frequency estimates.  相似文献   

20.
The capability of a simple kinematic‐storage model (KSM) is analysed to be used as a tool for a Decision Support System for the evaluation of probability inundation maps in near real time in poorly gauged areas. KSM simulates the floodplain as a storage and assumes no exchange of momentum with the channel. For the in‐bank flow, the storage is modified through a coefficient for taking the variations of channel cross sections into account. The generalized likelihood uncertainty estimation approach is used for addressing the probability flood maps along with their associated uncertainties. The model is tested on two river reaches along the Tiber River in Central Italy where observed inundation maps are available for two recent flood events. Despite the inherent uncertainties present in the input data and in the model structure, the results show that the model reproduces reasonably well, in terms of both precision and accuracy, the observed inundated areas. Tests were performed at different digital elevation model resolutions, showing a small effect of the geometry on the maximum performance obtained. The very low computational times, the parsimony of the model and its low sensitivity to the quality of the geometry representation of the channel and the floodplain makes KSM very appealing for flood forecasting and early warning system applications in poorly gauged and inaccessible areas. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

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