• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5B
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• pp.267-272
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• 2012

To reduce the flood risk caused by unexpected heavy rainfall, many prediction methods for flood have been developed. A major constituent of flood prediction is an accurate rainfall estimation which is an input of hydrologic models. In this study, a regional-scale weather model which can provide relatively longer lead time for flood mitigation compared to the Nowcasting based on radar system will be introduced and applied to the Chongmi river basin located in central part of South Korea. The duration of application of a regional weather model is from July 11 to July 23 in 2006. The estimated rainfall amounts were compared with observations from rain gauges (Sangkeuk, Samjook, and Sulsung). For this rainfall event at Chongmi river basin, Thomson and Kain-Frisch Schemes for microphysics and cumulus parameterization, respectively, were selected as optimal physical conditions to present rainfall fall amount in terms of Mean Absolute Relative Errors (MARE>0.45).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.117-117
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• 2020

The main objective of this study was to setup model and evaluate the model performance for streamflow simulation in Burundi using Soil and Water Assessment Tool (SWAT) model. The total area of Burundi is 27,834 ㎢. The elevation of Burundi ranges from 780 m to 2,700m. The West and East are low lands, while the Central part is high land. The topographic data (30 meters Digital Elevation Model) and land use and land cover data of Burundi were obtained respectively from Shuttle Radar Topography Mission (SRTM) and the Regional Centre for Mapping of Resources for Development (RCMRD). The soil data used was obtained from Food and Agriculture Organization (FAO). The local weather data and discharge data were provided by Burundi Hydro meteorological Service (IGEBU). Mean Areal Precipitation (MAP) and Mean Areal Temperature (MAT) were estimated. The streamflow simulation was done for the period 1980-2017. The calibration and validation of river discharge was performed at a daily time step from 2005 through 2011 as the calibration period and 2012 up to 2017 as the validation period. The findings show that streamflow decreases during Jun to September and increases during March to May and October to December.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.3
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• pp.31-42
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• 1986

The probabilistic model was developed to give a spatial simlation of precipitation series to solve the problem of future need of water resources. The simulation of daily precipitation series at the sub-base stations was induced from the spatial structure of rainfall occurrence probability between the base station and the sub-base stations in the watershed. In this study Hadong was chosen as the base station in Seomjin river basin and Imsil, Boseong, Soonchang, Dongbok, and Gurye were also selected as the sub-base stations. The results of this study are as follows; 1) The separation technique of spatial precipitation state showed effectiveness in the spatial simulation method because the occurrence probability by each precipitation state (Wet-Wet, Dry-Wet, Wet-Dry, and Dry- Dry system) represented the stable value. 2) The daily precipitation series of the sub-base stations which were simulated from those of the base station showed that the simulated annual mean precipitations were similar to the observed data, but the precipitations in summer were decreased slightly. 3) The correlogram and power spectrum of the simulated monthly precipitation for the sub-base stations showed those of the observed sample with good agreement.

• Atmosphere
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• v.23 no.4
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• pp.425-441
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• 2013

This study investigates the projections of water cycle, budget and river discharge over land in the world at the end of twenty-first century simulated by atmosphere-ocean climate model of Hadley Centre (HadGEM2-AO) and total runoff integrating pathways (TRIP) based on the RCP scenario. Firstly, to validate the HadGEM2-AO hydrology, the surface water states were evaluated for the present period using precipitation, evaporation, runoff and river discharge. Although this model underestimates the annual precipitation about 0.4 mm $mon^{-1}$, evaporation 3.7 mm $mon^{-1}$, total runoff 1.6 mm $mon^{-1}$ and river discharge 8.6% than observation and reanalysis data, it has good water balance in terms of inflow and outflow at surface. In other words, it indicates the -0.3 mm $mon^{-1}$ of water storage (P-E-R) compared with ERA40 showing -2.4 mm $mon^{-1}$ for the present hydrological climate. At the end of the twenty-first century, annual mean precipitation may decrease in heavy rainfall region, such as northern part of South America, central Africa and eastern of North America, but for increase over the Tropical Western Pacific and East Asian region. Also it can generally increase in high latitudes inland of the Northern Hemisphere. Spatial patterns of annual evaporation and runoff are similar to that of precipitation. And river discharge tends to increase over all continents except for South America including Amazon Basin, due to increased runoff. Overall, HadGEM2-AO prospects that water budget for the future will globally have negative signal (-8.0~-0.3% of change rate) in all RCP scenarios indicating drier phase than the present climate over land.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.557-568
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• 2013

In this study, Calculated the nonpoint sources(NPS) load per unit area about various rainy events in vineyard of Nakdong River basin. NPS monitoring and calculation for NPS load per unit area were estimated from 'Investigation method of precipitation discharge(National Institute of Environmental Research, 2007)'. The evaluation of applicability for NPS load per unit by compared with prior research data and Total Maximum Daily Load(TMDL) data. Five target areas were each $2000m^2$, $1800m^2$, $1943m^2$, $2484m^2$, $864m^2$ and located in Gyeongsangbukdo Gyeongju, Gyeongsangbukdo Sangju, Gyeongsangnamdo Hapcheon in Korea. Since fruits were the only crop on the target area, the characteristics of stormwater discharge at survey sites could be evaluated independently. A total of 115 rainfall events in the Orchard area during five years(2008-2012) was surveyed, and 38 of them became stormwater discharge. In the Nakdong River watershed, average of event mean concentrations(EMCs) in Orchard area for biochemical oxyzen demand(BOD), Chemical oxyzen demand(COD), total nitrogen(T-N), total phosphorus(T-P) were 2.0mg/L, 10.1mg/L, 3.195mg/L, 0.578mg/L, respectively. NPS load per unit area in Orchard area showed BOD : $2.0kg/km^2{\cdot}day$, COD : $10.2kg/km^2{\cdot}day$, T-N : $3.220kg/km^2{\cdot}day$, T-P : $0.606kg/km^2{\cdot}day$.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Journal of Korean Society on Water Environment
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• v.22 no.2
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• pp.314-320
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• 2006

Diffuse pollution from an urban area contributes to the significant pollution loading to a receiving water body. In this paper, rainfall runoffs from an urban basin with combined sewer systems located in the city of Daejeon were monitored to measure the rainfall runoff discharge rates and pollutant concentrations. Strong first flush effects were observed for all monitored rainfall runoffs. The first flush effects were closely related to rainfall intensity, while suspended solids were closely related to pollutant constituents. The observed averaged Event Mean Concentrations (EMCs) of Combined Sewer Overflows (CSOs) were 536.1 mg SS/L, 467.7 mg CODcr/L, 142.7 mg BOD/L, 16.5 mg TN/L, and 13.5 mg TP/L. Storage volumes for containing the first flush to improve water quality of the receiving stream can be estimated based on suspended solid concentration. In this study, retainment of the first flush equivalent to 5mm of precipitation could reduce diffuse pollution loading induced by CSOs to a receiving water body by up to 80% of suspended solid loading.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.490-499
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• 2000

Several studies on development of water quality treatment systems by wetlands are on going because of their benefits of low construction cost and high efficiency of waste water treatment. The objectives of this study were to review the necessary contents of survey and design factors for constructing constructed wetlands and to examine the required wetland area to treat non-point source pollution through case studies. The measurement of water quality and quantity in precipitation period is needed to analyse the inflow characteristics of the non-point pollution and to determine the amount of design flow. The design inflow for constructing constructed wetland was determined to the total runoff from 30mm of daily rainfall in the AMC(III) condition of the SCS method and is similar 70% of the annual mean runoff. The natural type wetland system with 0.1m of water depth and 5 hours of detention time was applied. From the results of the case studies, 70% of inflow could be treated and 1∼3% of wetland area of the total basin is needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.