• Water for future
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• v.24 no.4
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• pp.59-66
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• 1991

Abstract The purpose of the synthetic of monthly river flows based on the short-term observed data by means of multivariate stochastic models is to provide abundunt input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, multivariate Thomas-Fiering and Matalas models for synthetic generation based on stream flows in neihboring basin were employed to check if it can be applide in the modeling of monthly flows. Statistical parameters estimated by Method of Moment and Fourier Series Analysis respectively were reproduced for statistical features. For comparisons the statistical parameters of the generated monthly flow by each model were compared with those of the observed monthly flows. Results of this study suggest that the application of Matalas model for synthetic generation of monthly river flows can be adapted.

• Water for future
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• v.12 no.2
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• pp.63-69
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• 1979

The sequences of monthly streamflows constitute a non-statonary time series. The purely stochastic model has been applied to data generation of non-stationary time series. Tow different mothods--single site and multisite generation--have been used on the hydrologic time series. In this study the synthetic generation method by bivariate analysis, studied by Thomas Fiering, one of multi-site models, has been applied to the historical data on monthly streamflows at two sites in Nakdong River, and also for validity of this model the single site Thomas Fiering model applied. Through statistical analysis it has been shown that the performance of bivariate Thomas Fiering model was better than that of the other. By comparison of mean and standard deviaion between the historical and the generated, and cross correlogram interpretation, it has been known that the model used herein has good performance to simultaneously generate the monthly streamflows at two sites in a river hasin.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.131-138
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• 2000

A past monthly data is not faithful so much for a short term. But, the stochastic generation technique was provide of a long-term data. Thus this study is used a data which generated a monthly inflow amounts data by Thomas-Fiering model. This model is needed a certain process which determination of distribution, decision of continuous durability, etc. It was generated a inflow data every one month as Thomas-Fiering method. The generated inflow data was used input data for a monthly cumulative analysis. This analysis obtained a storage capacities which would be required during droughts having various return periods. It was presented a equation of fitting regression that was carried out regression analysis of 5, 10, 20, 50 years period.

• Water for future
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• v.18 no.4
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• pp.335-345
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• 1985

The purpose of stochastic models for synthetic generation of river flows based on the short-term observed data is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. Among many of such models the Monte Carlo Method of synthetic generation, which is usually known to be appropriate for annual data generation, is employed to check if it can be applied for the generation of monthly flows. For the purpose of comparisons the statistical parameters of the generated monthly flows by Monte Carlo model based on the appropriate probability distribution for each month were compared with those of the generated flows by Thoms-Fiering multiseason model and with those of the observed monthly flows. On the other hand, the statistical parameters of the annual river flows obtained by adding the generated monthly flows year by year based on the Monte Carlo and Thomas-Fiering models were compared with those of the annual flows generated directly by annual Monte Carlo model with reference to those for the observed annual river flows. Based on the above comparative studies, the discussions are made and conclusions derived.

• Water for future
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• v.19 no.2
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• pp.167-180
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• 1986

Disaggregation model has recently become a major technique in the field of synthetic generation and the model is possibly one of the most widely acepted tools in stochastic hydrology. The application of disaggregation model is evaluated with the streamflow data at the Waegwan and Hyunpung stage gaugin station on the main stem of the Nakdong River. The disaggregation process of annual streamflow data and the method of parameter estimation for the model is reviewed and the statistical analysis of the generated monthly streamflows such as a computation of moment estimation of covariance and correlogram analysis is made. The results, disaggregated monthly streamflow, obtained by Disaggregation Basic Model for single site are compared with the historical streamflow data and also with the other model, Thomas-Fiering Model. The generated monthly streamflow data by two models have been investigated and verified by comparision of mean and standard deviation between the historical and generated data.

• Water for future
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• v.18 no.1
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• pp.75-83
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• 1985

The applicability of ARMA(1, 1) multiseason model, which is in the beginning stage of active researches in the field of synthetic generation is evaluated with the streamflow data at the Nakdong stage gauging station on the main stem of the Nakdong River. The method of parameter estimation for the modelis reviewed and the statistical analysis of the generated seasonal streamflows such as corrlogram analysis and the computation of moments is made. The results obtained by ARMA(1, 1) multiseason model are compared with the historical streamflow data and also with those by two other multiseason models, namely, Thomas-Fiering model and Matalas AR(1) multiseason model. The seasonal streamflows grnerated by three multiseason models were annually summed up to form respective annual flow series whose statistics were compared with those of the annual flow series generated by three annual models, namely, AR(1), Matalas AR(1), and ARMA(1, 1) annual models. The possibility of ARMA(1, 1) multiseason model for the simultaneous generation of seasonal and annual streamflows is also evaluated.

• Water for future
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• v.7 no.1
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• pp.71-77
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• 1974

This paper reviews several different single site generation models for further development of a model for generating the Synthetic sequences of streamflow in the continuous streams like main streams in Korea. Initially the historical time series is looked using a time series technique, that is correlograms, to determine whether a lag one Markov model will satisfactorily represent the historical data. The single site models which were examined include an empirical model using the historical probability distribution of the random component, the linear autoregressive model(Markov model, or Thomas-Fiering model) using both logarithms of the data and Matala's log-normal transformation equations, and finally gamma distribution model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.3
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• pp.63-74
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• 1986

A methodology for the probabilistic determination of active storage capacity of an impounding reservoir is proposed with due considerations to the durations and return periods of the low flow series at the reservoir site. For more reliable probabilistic analysis the best-fit stochastic generation model of Monte Carlo type was first selected for the generation of monthly flow series, the models tested being the Month Carlo Model based on the month-by-month flow series (Monte Carlo-A Type), Monte Carlo Model based on the standardized sequential monthly flow series (Monte Carlo-B Type), and the Thomas-Fiering Model. Monte Carlo-B Model was final1y selected and synthetic monthly flows of 200 years at Hong Cheon dam site were generated. With so generated 200 years' monthly flows partial duration series of low flows were developed for various durations. Each low flow series was further processed by a nonsequential mass analysis for specified draft rates. This mass analysis furnished the storage-draft-recurrence interval relationship which gives the reservoir storage requirement for a specified water demand from the reservoir during a drought of given return period. Illustrations are given on the application of these results in analyzing the water supply capacity of a particlar reservoir, existing or proposed.

• Water for future
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• v.18 no.3
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• pp.253-264
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• 1985

Basically, there are two ways viewing the reservoir storage-yield relationship., The most common viewpoint is the determination of the storage required at a given reservoir to supply a required yield. This type of problem is usually encountered in the planning and early design phases of a reservoir. The second viewpoint is the determination of yield from a given amount of storage. This often occurs in the final design phases or in re-evaluation of an existing reservoir for a more comprehensive analysis. The purpose of this study is to improve the present methodology estimating the storage-yield relationship for a reservoir design or a reservoir operation. The Residual Mass curve Technique, the slightly modified version of Low Flow Techniques and the Transition Probability Matrix Technique are reviewed and examined for the best fit technique to find the reservoir storage-yield realtionship. The historical data during 1917~1940 at the proposed Hongchun damsite and the synthetic data simulated by Thomas-Fiering model are utilized to examine the reservoir storge-yield relationship with three techniques in detail. After the three techniques which estimate the reservoir storage-yield relationship were reviewed extensively, it was concluded that the Residual Mass Curve Technique and the slightly modified version of Low Flow Techniques were suitable for a preliminary design, but the Transition Probability Matrix Technique Provided satisfactory results as a final design technique because it reflected the variation of a monthly yield as well as seasonlly.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.