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1.
A hybrid model that blends two non‐linear data‐driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non‐parametric resampling technique MBB to obtain innovations, which are then added back to the ANN‐modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN‐based non‐linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN‐based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric‐based hybrid model (LPHM), owing to the effective capturing of the non‐linearities. Moreover, the ANNHM, being a completely data‐driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non‐linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

2.
The simulation of time series is based on estimated statistical parameters of the empirical time series. The Fiering Model generating monthly sums of streamflows is used as an example for the simulation in order to account for the error of the model, theoretically and practically, caused by statistically inprecise parameter estimation. The sensitivity of this model, especially to the correlation coefficients, is analyzed by means of systematic variations of the correlation coefficients, since these are most affected by the error of estimation. No significant dependency could be found comparing the empirical and simulated parameters mean, standard deviation, and skewness. From this follows, that the importance of the correlation coefficients in the Fiering Model is generally overestimated. The results are given for monthly sums of streamflows at four stations with different hydrological characteristics.   相似文献   

3.
The main purpose of this study is to investigate and evaluate the impact of climate change on the runoff and water resources of Yongdam basin, Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The downscaled values are used to modify the parameters of a stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series is fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of 2CO2. This approach is applied to the Yongdam dam basin in the southern part of Korea. The results show that under the condition of 2CO2, about 7.6% of annual mean streamflow is reduced when it is compared with the current condition. Seasonal streamflows in the winter and autumn are increased, while streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern An erratum to this article can be found at  相似文献   

4.
Characterizing the probability distribution of streamflows in catchments lacking in discharge measurements represents an attractive prospect with consequences for practical and scientific applications, in particular water resources management. In this paper, a physically-based analytic model of streamflow dynamics is combined with a set of water balance models and a geomorphological recession flow model in order to estimate streamflow probability distributions based on catchment-scale climatic and morphologic features. The models used are described and the novel parameterization approach is elaborated on. Starting from rainfall data, potential evapotranspiration and digital terrain maps, the method proved capable of capturing the statistics of observed streamflows reasonably well in 11 test catchments distributed throughout the United States, east of the rocky mountains. The method developed offers a unique approach for estimating probability distribution of streamflows where only climatic and geomorphologic features are known.  相似文献   

5.
Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.  相似文献   

6.
A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.  相似文献   

7.
A nonparametric method for resampling multiseason hydrologic time series is presented. It is based on the idea of rank matching, for simulating univariate time series with strong and/or long‐range dependence. The rank matching rule suggests concatenating with higher likelihood those blocks that match at their ends. In the proposed method, termed ‘multiseason matched block bootstrap’, nonoverlapping within‐year blocks of hydrologic data (formed from the observed time series) are conditionally resampled using the rank matching rule. The effectiveness of the method in recovering various statistical attributes, including the dependence structure from finite samples generated from a known population, is demonstrated through a two‐level hypothetical Monte Carlo simulation experiment. The method offers enough flexibility to the modeller and is shown to be appropriate for modelling hydrologic data that display strong dependence, nonlinearity and/or multimodality in the time series depicting the hydrologic process. The method is shown to be more efficient than the nonparametric ‘k‐nearest neighbor bootstrap’ method in simulating the monthly streamflows that exhibit a complex dependence structure and bimodal marginal probability density. Even with short block sizes, this bootstrap method is able to predict the drought characteristics reasonably accurately. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

8.
This work presents the derivation of general streamflow cumulants from daily rainfall time series. The general streamflow cumulants can be used to compute basic streamflow statistics such as mean, variance, coefficient of skewness, and correlation coefficient. Streamflow is considered as a filtered point process where the input is a daily rainfall time series assumed to be a marked point process. The marks of the process are the daily rainfall amounts which are assumed independent and identically distributed. The number of rainfall occurrences is a counting process represented by either the binomial, the Poisson, or the negative binomial probability distribution depending on its ratio of mean to variance. The first three cumulants and the covariance function of J-day averaged streamflows are deduced based on the characteristic function of a filtered point process. These cumulants are functions of the stochastic properties of the daily rainfall process and the basin-response function representing the causal relationship between rainfall and runoff.  相似文献   

9.
Abstract

Wavelet or Fourier analysis is proposed as an alternative nonparametric method to simulate streamflows. An observed series is decomposed into its components at various resolutions and then recombined randomly to generate synthetic series. The mean and standard deviation are perfectly reproduced and coefficient of skewness tends to zero as the number of simulations increases. Normalizing transforms can be used for skewed series. Autocorrelation coefficients and the dependence structure are better preserved when Fourier analysis is used, but the mean and variance remain constant when the simulated and observed series have the same length. Monthly as well as annual flows can be simulated by this technique as illustrated on some examples. Wavelet analysis should be preferred as it generates flow series that exhibit a wider range of required reservoir capacities.  相似文献   

10.
A maximum entropy-Gumbel-Hougaard copula (MEGHC) method has been proposed for monthly streamflow simulation. The marginal distributions of monthly streamflows are estimated through the maximum entropy (ME) method with the first four non-central moments (i.e. mean, standard deviation, skewness and kurtosis) being the constraints. The Lagrange multipliers in ME-based marginal distributions are determined using the conjugate gradient (CG) method which is of superlinear convergence, simple recurrence formula and less calculation. Then the joint distributions of two adjacent monthly streamflows are constructed using the Gumbel-Hougaard copula (GHC) method. The developed MEGHC method has been applied for monthly streamflow simulation in Xiangxi river, China. The goodness-of-fit statistical tests, consisting of K–S test, A–D test, RMSE and Rosenblatt transformation with Cramér von Mises statistic, show that the MEGHC method can reflect dependence structure in adjacent monthly streamflows of Xiangxi river, China. Comparison between simulated streamflow generated by MEGHC and observations indicates the satisfactory performance of MEGHC with small relative errors.  相似文献   

11.
Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

12.
The classical least-squares (LS) algorithm is widely applied in practice of processing observations from Global Satellite Navigation Systems (GNSS). However, this approach provides reliable estimates of unknown parameters and realistic accuracy measures only if both the functional and stochastic models are appropriately specified. One essential deficiency of the stochastic model implemented in many available GNSS software products consists in neglecting temporal correlations of GNSS observations. Analysing time series of observation residuals resulting from the LS evaluation, the temporal correlation behaviour of GNSS measurements can be efficiently described by means of socalled autoregressive moving average (ARMA) processes. For a given noise realisation, a well-fitting ARMA model can be automatically estimated and identified using the ARMASA toolbox available free of charge in MATLAB® Central.In the preliminary stage of applying the ARMASA toolbox to residual-based modelling of temporal correlations of GNSS observations, this paper presents an empirical performance analysis of the automatic ARMA estimation tool using a large amount of simulated noise time series with representative temporal correlation properties comparable to the GNSS residuals. The results show that the rate of unbiased model estimates increases with data length and decreases with model complexity. For large samples, more than 80% of the identified ARMA models are unbiased. Additionally, the model error representing the deviation between the true data-generating process and the model estimate converges rapidly to the associated asymptotical value for a sufficiently large sample size with respect to the correlation length.  相似文献   

13.
S. Mohan  P. K. Sahoo 《水文研究》2008,22(6):854-862
The number of drought events derived from the historic streamflow or rainfall series will be limited and produce results that are not very reliable. This study proposes a drought simulation methodology that uses a long sequence of synthetically generated monthly streamflow/rainfall series, from which it is possible to drive a large sample of drought events and the prediction of drought characteristics will be reliable. The modified Herbst method has been used to identify droughts in the generated streamflow and rainfall series. The drought simulation procedure is illustrated with a case study of the Bhadra reservoir catchment in Karnataka State, India. Monthly droughts were derived from both historic and generated monthly streamflow and rainfall series. The important drought characteristics were determined and the suitable probability distribution for each parameter was arrived at after studying seven different probability models. The use of the probability curves thus derived has been illustrated with examples (referred to in Part 1 as ‘point droughts’). Similarly, the development and application of stochastic models for the prediction of regional drought parameters have been illustrated with examples in the accompanying paper (Part 2: regional droughts). Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

14.
Hydrologic risk analysis for dam safety relies on a series of probabilistic analyses of rainfall-runoff and flow routing models, and their associated inputs. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated in order to evaluate the probability of dam overtopping. Typically, parametric density estimation methods have been applied in this setting, and the exhaustive Monte Carlo simulation (MCS) of models is used to derive some of the distributions. Often, the distributions used to model some of the random variables are inappropriate relative to the expected behaviour of these variables, and as a result, simulations of the system can lead to unrealistic values of extreme rainfall or water surface levels and hence of the probability of dam overtopping. In this paper, three major innovations are introduced to address this situation. The first is the use of nonparametric probability density estimation methods for selected variables, the second is the use of Latin Hypercube sampling to improve the efficiency of MCS driven by the multiple random variables, and the third is the use of Bootstrap resampling to determine initial water surface level. An application to the Soyang Dam in South Korea illustrates how the traditional parametric approach can lead to potentially unrealistic estimates of dam safety, while the proposed approach provides rather reasonable estimates and an assessment of their sensitivity to key parameters.  相似文献   

15.
Stochastic weather generators have evolved as tools for creating long time series of synthetic meteorological data at a site for risk assessments in hydrologic and agricultural applications. Recently, their use has been extended as downscaling tools for climate change impact assessments. Non‐parametric weather generators, which typically use a K‐nearest neighbour (K‐NN) resampling approach, require no statistical assumptions about probability distributions of variables and can be easily applied for multi‐site use. Two characteristics of traditional K‐NN models result from resampling daily values: (1) temporal correlation structure of daily temperatures may be lost, and (2) no values less than or exceeding historical observations can be simulated. Temporal correlation in simulated temperature data is important for hydrologic applications. Temperature is a major driver of many processes within the hydrologic cycle (for example, evaporation, snow melt, etc.) that may affect flood levels. As such, a new methodology for simulation of climate data using the K‐NN approach is presented (named KnnCAD Version 4). A block resampling scheme is introduced along with perturbation of the reshuffled daily temperature data to create 675 years of synthetic historical daily temperatures for the Upper Thames River basin in Ontario, Canada. The updated KnnCAD model is shown to adequately reproduce observed monthly temperature characteristics as well as temporal and spatial correlations while simulating reasonable values which can exceed the range of observations. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

16.
Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non‐steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two‐stage genetic algorithms were used to construct nonlinear time series models of 10‐day streamflows of the Wu‐Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

17.
An attempt is made in this study to search for a model which is capable of reproducing the reservoir storage components and runs' characteristics along with the mean, standard deviation, skewness and correlation coefficients of historical monthly streamflows. The model based upon the method of fragments and a scheme devised for annual values is found to yield satisfactory results and is, therefore, recommended for use.  相似文献   

18.
A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improvement in one hour lead time forecasts at higher measurement densities.  相似文献   

19.
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20.
Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.  相似文献   

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