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1.
This study presents a multiscale framework for downscaling of the General Circulation Model (GCM) outputs to the mean monthly temperature at regional scale using a wavelet based Second order Voltera (SoV) model. The models are developed using the reanalysis climatic data from the National Centers for Environmental Prediction (NCEP) and are validated using the simulated climatic dataset from the Can CM4 GCM for five locations in the Krishna river basin, India. K-means clustering, based on the multiscale wavelet entropy of the predictors, is used for obtaining the clusters of the input climatic variables. Principal component analysis (PCA) is used to obtain the representative variables from each cluster. These input variables are then used to develop a wavelet based multiscale model using Second order Volterra approach to simulate observed mean monthly temperature for the selected locations in the basin. These models are called W-P-SoV models in this paper. For the purpose of comparison, linear multi-resolution models are developed using Multiple Linear regression (MLR) and are called W-P MLR models. The performance of the models is further compared with other Wavelet-PCA based models coupled with Multiple linear regression models (P-MLR) and Artificial Neural Networks (P-ANN), and, stand-alone MLR and ANN to establish the superiority of the proposed approach. The results indicate that the performance of the wavelet based models is superior in terms of downscaling accuracy when compared with the other models used.  相似文献   

2.
Increasing water demands, higher standards of living, depletion of resources of acceptable quality and excessive water pollution due to agricultural and industrial expansions have caused intense social and political predicaments, and conflicting issues among water consumers. The available techniques commonly used in reservoir optimization/operation do not consider interaction, behavior and preferences of water users, reservoir operator and their associated modeling procedures, within the stochastic modeling framework. In this paper, game theory is used to present the associated conflicts among different consumers due to limited water. Considering the game theory fundamentals, the Stochastic Dynamic Nash Game with perfect information (PSDNG) model is developed, which assumes that the decision maker has sufficient (perfect) information regarding the associated randomness of reservoir operation parameters. The simulated annealing approach (SA) is applied as a part of the proposed stochastic framework, which makes the PSDNG solution conceivable. As a case study, the proposed model is applied to the Zayandeh-Rud river basin in Iran with conflicting demands. The results are compared with alternative reservoir operation models, i.e., Bayesian stochastic dynamic programming (BSDP), sequential genetic algorithm (SGA) and classical dynamic programming regression (DPR). Results show that the proposed model has the ability to generate reservoir operating policies, considering interactions of water users, reservoir operator and their preferences.  相似文献   

3.
During the last decade, a number of models have been developed to consider the conflict in dynamic reservoir operation. Most of these models are discrete dynamic models which are developed based on game theory. In this study, a continuous model of dynamic game and its corresponding solutions are developed for reservoir operation. Two solution methods are used to solve the model of continuous dynamic game, namely the Ricatti equations and collocation methods. The Ricatti equations method is a closed form solution, requiring less computational efforts compared with discrete models. The collocation solution method applies Newton's method or a quasi-Newton method to find the problem solution. These approaches are able to generate operating policies for dynamic reservoir operation. The Zayandeh-Rud river basin in central Iran is used as a case study and the results are compared with alternative water allocation models. The results show that the proposed solution methods are quite capable of providing appropriate reservoir operating policies, while requiring rather short computational times due to continuous formulation of state and decision variables. Reliability indices are used to compare the overall performance of the proposed models. Based on the results from this study, the collocation method leads to improved values of the reliability indices for total reservoir system and utility satisfaction of water users, compared to the Ricatti equations method. This is attributed to the flexible structure of the collocation model. When compared to alternative water allocation models, lower values of reliability indices are achieved by the collocation method.  相似文献   

4.
Despite the widespread application of nonlinear mathematical models, comparative studies of different models are still a huge task for modellers. This is because a large number of trial and error processes are needed to develop each model, so the workload will be multiplied into an unmanageable level if many types of models are involved. This study presents an efficient approach by using the Gamma test (GT) to select the input variables and the training data length, so that the trial and error workload can be greatly reduced. The methodology is tested in estimating solar radiation at the Brue catchment, UK. Several nonlinear models have been developed efficiently with the aid of the GT, including local linear regression, multi-layer perceptron (MLP), Elman neural network, neural network auto-regressive model with exogenous inputs (NNARX) and adaptive neuro-fuzzy inference system (ANFIS). This work is only feasible within the time and resources constraint, due to the GT in reducing huge workload of the trial and error process.  相似文献   

5.
This paper illustrates the application of two decomposition algorithms, generalized Benders decomposition (GBD) and outer approximation (OA), to water resources problems involving cost functions with both discrete and nonlinear terms. Each algorithm involves the solution of an alternating finite sequence of nonlinear programming subproblems and relaxed versions of a mixed-integer linear programming master problem. Three example models, involving capacity expansion of a conjunctively managed surface and groundwater system, are formulated and solved to demonstrate the performance of the algorithms. The results show that OA obtains solutions in far fewer iterations than GBD, but OA requires more computational resources per iteration. As a result, depending on the mixed-integer programming and nonlinear programming solvers available, GBD may be better suited for solving larger planning problems.  相似文献   

6.
A key aspect of large river basins partially neglected in large‐scale hydrological models is river hydrodynamics. Large‐scale hydrologic models normally simulate river hydrodynamics using simplified models that do not represent aspects such as backwater effects and flood inundation, key factors for some of the largest rivers of the world, such as the Amazon. In a previous paper, we have described a large‐scale hydrodynamic approach resultant from an improvement of the MGB‐IPH hydrological model. It uses full Saint Venant equations, a simple storage model for flood inundation and GIS‐based algorithms to extract model parameters from digital elevation models. In the present paper, we evaluate this model in the Solimões River basin. Discharge results were validated using 18 stream gauges showing that the model is accurate. It represents the large delay and attenuation of flood waves in the Solimões basin, while simplified models, represented here by Muskingum Cunge, provide hydrographs are wrongly noisy and in advance. Validation against 35 stream gauges shows that the model is able to simulate observed water levels with accuracy, representing their amplitude of variation and timing. The model performs better in large rivers, and errors concentrate in small rivers possibly due to uncertainty in river geometry. The validation of flood extent results using remote sensing estimates also shows that the model accuracy is comparable to other flood inundation modelling studies. Results show that (i) river‐floodplain water exchange and storage, and (ii) backwater effects play an important role for the Amazon River basin hydrodynamics. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

7.
Abstract

Modelling of the rainfall–runoff transformation process and routing of river flows in the Kilombero River basin and its five sub-catchments within the Rufiji River basin in Tanzania was undertaken using three system (black-box) models—a simple linear model, a linear perturbation model and a linear varying gain factor model—in their linear transfer function forms. A lumped conceptual model—the soil moisture accounting and routing model—was also applied to the sub-catchments and the basin. The HEC-HMS model, which is a distributed model, was applied only to the entire Kilombero River basin. River discharge, rainfall and potential evaporation data were used as inputs to the appropriate models and it was observed that sometimes the system models performed better than complex hydrological models, especially in large catchments, illustrating the usefulness of using simple black-box models in datascarce situations.  相似文献   

8.
The basic principle of the combined model applied consists in the coupling of a long-term management model of the water quantity economy whose stationary conditions are secured by the simulation of long series of measurements on the basis of monthly means of flow. The coupling is performed either by regression models of the water quality (linear and nonlinear single and multiple regressions) especially for conservative, not biochemically convertible substances or with dynamic models of water quality. For the latter case, an example is represented on the basis of a modified STREETER-PHELPS model for eleven segments of different length of a river in the foothills of low mountain ranges. The coupling of the two models permits statements concerning the river basin management with different types of water utilization being taken into account.  相似文献   

9.
Evaluation of stochastic reservoir operation optimization models   总被引：5，自引：0，他引：5  
This paper investigates the performance of seven stochastic models used to define optimal reservoir operating policies. The models are based on implicit (ISO) and explicit stochastic optimization (ESO) as well as on the parameterization–simulation–optimization (PSO) approach. The ISO models include multiple regression, two-dimensional surface modeling and a neuro-fuzzy strategy. The ESO model is the well-known and widely used stochastic dynamic programming (SDP) technique. The PSO models comprise a variant of the standard operating policy (SOP), reservoir zoning, and a two-dimensional hedging rule. The models are applied to the operation of a single reservoir damming an intermittent river in northeastern Brazil. The standard operating policy is also included in the comparison and operational results provided by deterministic optimization based on perfect forecasts are used as a benchmark. In general, the ISO and PSO models performed better than SDP and the SOP. In addition, the proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.  相似文献   

10.
ABSTRACT

A set of linked optimization models was used to evaluate planning and operation of the proposed Pamba-Achankovil-Vaippar (PAV) water transfer project in India. The shortage of water for irrigation in the Vaippar basin has led to the need for water import. The project consists of three reservoirs. The models were applied at three levels. At Level-1, the projections of water requirement for irrigation in the Vaippar basin at Reservoir-1 were estimated using an LP model. Level-2 was operated at three sub-levels: the first was the determination of the export requirements from the Pamba basin (Reservoir-2) to the Achankovil basin (Reservoir-1); the second was determining the capability of Reservoir-2 to export and sizing of the three reservoirs to meet the above targets was the third sub-level. Integrated reservoir operation and canal irrigation water distribution were done at Level-3. DP models were employed at levels-2 and 3. The linked LP, DP and simulation models were found effective for planning water transfers.  相似文献   

11.
This paper presents a chance-constrained programming model for optimal control of a multipurpose reservoir and its modification to a model for single reservoir design. An algorithm is developed for solving complex stochastic problems of multipurpose reservoir planning and design. The complexity of the problem is resolved by a two-step algorithm: (1) transformation of chance constraints on the state and control variables is performed at the first step; and (2) the choice of optimum control or optimal reservoir storage is carried out in the second step. The method of iterative convolution is chosen for the first step, while linear programming is selected for the second step. The algorithm allows the use of random inflows and random demands together with other deterministic demands. The reservoir design problem is presented as a modified optimal control problem. The procedure is illustrated with an example of a hypothetical reservoir design problem with three different types of downstream releases (hydropower production, municipal water supply, and irrigation).  相似文献   

12.
In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict drought in the Büyük Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Büyük Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy.  相似文献   

13.
Analytical properties of the solutions of the inverse problem of gravimetry are studied in the context of the approximative approach and method of linear integral representations. A new effect of the inheritance of specific analytical properties in the solutions, which is named the effect of hidden equivalence, is revealed and investigated. This effect significantly influences the interpretation informativity in the complex models of a medium, and it should be taken into account in the geological interpretation of gravity data. Hidden equivalence is studied for both linear and nonlinear inverse problems. As an example of a nonlinear problem, the inverse problem of structural gravimetry is analyzed. The correlation between the boundary equations and boundary values of the harmonic functions is demonstrated. Methods are suggested to allow for the effects that occur by expanding the approximative approach for complex conditions (the models of the media with spatially distributed parameters) by the dynamical and criterion principles.  相似文献   

14.
Modeling of sediment transport in relation to changing land-surface conditions against a background of considerable natural variability is a challenging area in hydrology. Bayesian dynamic linear models (DLMs) however, offer opportunities to account for non-stationarity in relationships between hydrologic input and basin response variables. Hydrologic data are from a 40 years long record (1951–1990) from the 5905 km2 Yadkin River basin in North Carolina, USA. DLM regressions were estimated between log-transformed volume-weighted sediment concentration as a response and log-transformed rainfall erosivity and river flow, respectively, as input variables. A similar regression between log-transformed river flow and log-transformed basin averaged rainfall was also analyzed. The dynamic regression coefficient which reflects the erodibility of the basin decreased significantly between 1951 and 1970, followed by a slowly rising trend. These trends are consistent with observed land-use shifts in the basin. Bayesian DLMs represent a substantial improvement over traditional monotonic trend analysis. Extensions to incorporate multiple regression and seasonality are recommended for future applications in hydrology.  相似文献   

15.
Understanding low flow variability is critical for assessing water quality and health of riverine ecosystems in a river basin. Low flows are dependent on human water abstraction as well as the climate variability. This paper investigates the changing nature of low flows and their association with large-scale climate variability for different watersheds in the State of Texas, USA. For this purpose, we employed trend, wavelet analysis and linear as well as nonlinear correlations to identify important changes in low flow characteristics for three stream-gauging stations selected from different (i.e. Brazos, Colorado and Trinity) river basins located in Texas for the time period of 1916–1959 and 1960–2003. We also investigated the teleconnections between low flow variables and the large-scale climate indices (NINO 3.4, SOI and PDO) using cross wavelet analysis as well as their linear and non-linear correlation relationship. Our results indicated that the low flow magnitudes have shown considerable different characteristics for selected river basins during two separate time periods (1916–1959 and 1960–2003). Based on cross wavelet analysis, we identified that the low flows in selected stations of Colorado and Trinity River basins are likely to be influenced by all three large-scale climate indices. In addition to that, we identified that low flows are more nonlinearly associated with climate indices. Among the selected River basins, the stronger association between low flows and large scale climate indices are observed for Trinity River basin. The results from this study can help in better understanding of low flow hydrology and their potential relationship with large scale indices.  相似文献   

16.
Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.  相似文献   

17.
General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.  相似文献   

18.
The processes that occur in wetlands and natural lakes are often overlooked and not fully incorporated in the conceptual development of many hydrological models of basin runoff. These processes can exert a considerable influence on downstream flow regimes and are critical in understanding the general patterns of runoff generation at the basin scale. This is certainly the case for many river basins of southern Africa which contain large wetlands and natural lakes and for which downstream flow regimes are altered through attenuation, storage and slow release processes that occur within the water bodies. Initial hydrological modelling studies conducted in some of these areas identified the need to explicitly account for wetland storage processes in the conceptual development of models. This study presents an attempt to incorporate wetland processes into an existing hydrological model, with the aim of reducing model structural uncertainties and improving model simulations where the impacts of wetlands or natural lakes on stream flow are evident. The approach is based on relatively flexible functions that account for the input–storage–output relationships between the river channel and the wetland. The simulation results suggest that incorporating lake and wetland storage processes into modelling can provide improved representation (the right results for the right reason) of the hydrological behaviour of some large river basins, as well as reducing some of the uncertainties in the quantification of the original model parameters used for generating the basin runoff. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

19.
The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.  相似文献   

20.
For many practical reasons, the empirical black‐box models have become an increasingly popular modelling tool for river flow forecasting, especially in mountainous areas where very few meteorological observatories exist. In this article, precipitation data are used as the only input to estimate river flow. Using five empirical black‐box models—the simple linear model, the linear perturbation model, the linearly varying gain factor model, the constrained nonlinear system model and the nonlinear perturbation model–antecedent precipitation index—modelling results are compared with actual results in three catchments within the Heihe River Basin. The linearly varying gain factor model and the nonlinear perturbation model yielded excellent predictions. For better simulation accuracy, a commonly used multilayer feed‐forward neural network model (NNM) was applied to incorporate the outputs of the individual models. Comparing the performance of these models, it was found that the best results were obtained from the NNM model. The results also suggest that more reliable and precise predictions of river flow can be obtained by using the NNM model while also incorporating the combined outputs of different empirical black‐box models. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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