• Water for future
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• v.13 no.4
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• pp.41-50
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• 1980

The prupose of this study are the theoretical examination of Gamma distribution function and its application to hydraulic engineering, that is studying the simulation of monthly streamflow by the Gamma distribtution function model(Gamma Model) based on Monte Carlo technique. In the analysis, monthly streamflow data in the Nak Dong River, the Han River, and the Keum River were used and the data were changed to modular coefficient in order to make the analysis convenient. At first, the fitness of monthly streamflow to 2-Parameter Gamma distribution was tested by Chi-square and Kolmogrov-Smironov test, by which it was found the monthly streamflow were fit well to this Gamma distribution function. Then, the Gamma Model based on the Gamma distribution and Monte Carlo Method was used in the simulation of monthly streamflow, and simulateddata showed that all their stastical characteristics were preserved well in the simulation. Consequently, it can be concluded that the Gamma Model is suitable for the simulation of monthly streamflow series directly by using the Mote Carlo technique.

• Journal of Wetlands Research
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• v.10 no.1
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• pp.1-9
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• 2008

This study describes the WASMOD, water balance model which can consider the snowmelting. The pilot study basin is the Soyang River basin with outlet at Soyang Dam Site and compute long-term monthly streamflow, The advantage of the WASMOD is that the input data is simple and the user can operate easily. To optimize for the parameters of the model, the WASMOD used VA05A of automatic fitting technique. The observed and simulated monthly streamflow hydrographs were compared. The model performance on corrleation coefficient between the observed and the simulated streamflow for the verification periods was above 0.89. It was shown that the WASMOD reproduces the observed monthly streamflow hydrographs very well. This evidence suggests that the WASMOD might be appropriate for the simulation of monthly streamflow

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.488-488
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• 2015

Intermittent streamflow is common phenomenon in arid and semi-arid regions. To manage water resources of intermittent streamflows, stochactic simulation data is essential; however the seasonally stochastic modeling for intermittent streamflow is a difficult task. In this study, using the periodic Markov chain model, we simulate intermittent monthly streamflow for occurrence and the periodic gamma autoregressive and copula models for amount. The copula models were tested in a previous study for the simulation of yearly streamflow, resulting in successful replication of the key and operational statistics of historical data; however, the copula models have never been tested on a monthly time scale. The intermittent models were applied to the Colorado River system in the present study. A few drawbacks of the PGAR model were identified, such as significant underestimation of minimum values on an aggregated yearly time scale and restrictions of the parameter boundaries. Conversely, the copula models do not present such drawbacks but show feasible reproduction of key and operational statistics. We concluded that the periodic Markov chain based the copula models is a practicable method to simulate intermittent monthly streamflow time series.

• Water for future
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• v.18 no.4
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• pp.317-325
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• 1985

In the analysis, monthly streamflow records atthe gauging station in Nakdong, Han and Geum river were used. Also, the fitness of monthly streamflow to Gamma and Long-normal distribution was tested by Kolomogorv-Smirnov test. The results obtained in this study can be summarized as follws (1) The fitness of monthly streamflow to two-parameter Gamma distribution was tested by Kolomorov-Smirnov test, which fits well to this Gamma distribution (2) The Run-length and Run-sum were simulated by the Gamma model. In this result, run-length and Run-sum of monthly streamflow were fit for Gamma model (3) The mean decreases (increases) the expected surplus (deficit) Run-Sum of the monthly streamflow. The higher the truncation level of negative Run-length and Run-sum the larger is the effect of mean.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.2
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of Korea Water Resources Association
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• v.34 no.3
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• pp.275-285
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• 2001

Since the majority of streamflow during dry periods is provided by groundwater storage, the streamflow depends on a basin moisture state recharged from rainfall during wet periods. This hydrologic characteristics dives good condition to predict long-term streamflow if the basin state like groundwater recharge is known in advance. The objective of this study is to examine groundwater recharge effect to monthly streamflow, and to attempt monthly streamflow prediction using estimated groundwater recharge. The ground water recharge is used as an independent variable with streamflow and precipitation to construct multiple regression models for the prediction. Correlation analysis was performed to assess the effect of groundwater carry-over to streamflow and to establish the associations among independent variables. The predicted streamflow shows that the multiple regression model involved groundwater recharge gives improved results comparing to the model only using streamflow and precipitation as independent variables. In addition, this paper shows that the prediction model with the effect of groundwater carry-over taken into account can be developed using only precipitation.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.5
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• pp.29-38
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• 2006

Geographic Information System has extended to higher assessment of water resources. GIS linking with hydrological model becomes a trend in water resource assessment modeling. One of the most popular models is SWAT2000 which have effectiveness in multi-purpose processes for predicting the impact of land management practices on water, sediments and chemicals yields in large complex watershed with varying soils, land uses, and management conditions over long period of time. In this study, SWAT2000 model was applied to Gap stream watershed in Daejeon city where TMDL (Total Maximum Daily Load) Regulation would be implanted. The Gap Stream watershed was partitioned into 8 subbasins, however, only 3 out of 8 subbaisns were observed for having practical gauged data on the basis of streamflow from the year of 2002 to 2005. Gauged streamflow data of Indong, Boksu and Hoeduck stations were used for calibration and validation of the SWAT Streamflow simulation. Estimation Efficiency Analysis (COE), Regression Analysis ($R^{2}$), Relative Error (R.E.) were used for comparing observed streamflow data of the 3 subbasins on the daily and monthly basis with estimated streamflow data in order to fix optimized parameters for the best fitted results. COE value for the daily and monthly streamflow was ranged from 0.45 to 0.96. $R^{2}$ values for daily and monthly streamflow ranged from 0.51 to 0.97. R.E. values for total streamflow volume ranged from 3 % to 22.5 %. The accuracy of the model results shows that the SWAT2000 model can be applicable to Korean watersheds like the Gap Stream watershed that needs to be partitioned into a number of subbasins for TMDL regulation.

• 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.9 no.2
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• pp.87-100
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• 1976

These studies are aimed at the analysis of systematic variation pattern of water resources in Korean river catchments and the development of their simulation models from the stochastic analysis of monthly and annual hydrologic data as main elements of water resources, i.e. rainfall and streamflow. In the analysis, monthly & annual rainfall records in Soul, Taegu, Pusan and Kwangju and streamflow records at the main gauging stations in Han, Nakdong and Geum river were used. Firstly, the systematic variation pattern of annual streamflow was found by the exponential function relationship between their standard deviations and mean values of log-annual runoff. Secondly, stochastic characteristics of annual rainfall & streamflow series were studied by the correlogram Monte Carlo method and a single season model of 1st-order Markov type were applied and compared in the simulation of annual hydrologic series. In the simulation, single season model of Markov type showed better results than LN-model and the simulated data were fit well with historical data. But it was noticed that LN-model gave quite better results in the simulation of annual rainfall. Thirdly, stochastic characteristics of monthly rainfall & streamflow series were also studied by the correlogram and spectrum analysis, and then the Model-C, which was developed and applied for the synthesis of monthly perennial streamflow by lst author and is a Markov type model with transformed skewed random number, was used in the simulation of monthly hydrologic series. In the simulation, it was proved that Model-C was fit well for extended area in Korea and also applicable for menthly rainfall as well as monthly streamflow.