• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.463-477
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• 2015

The recent unusual climate and extreme weather events have frequently given unexpected disaster and damages, facing difficulties in the management of water resources. In particular, climate change could result in intensified typhoons, and this would be the worst case scenario that can happen. The primary objective of this study is to identify the patterns of typhoon-induced precipitation and the associated synoptic pattern. This study focused on analyzing precipitation patterns over the South Korea using historic records as opposed to a specified season or duration, and further investigates the potential connection with heavy rainfall to synoptic patterns. In this study, we used the best track data provided by the Regional Specialized Meteorological Center of Japan for 40 years from 1973 to 2012. The patterns of the typhoon-induced precipitation were categorized into four groups according to a given typhoon track information, and then the associated synoptic climatology patterns were further investigated. The results demonstrate that the typhoon-induced precipitation patterns could be grouped and potentially simulated according to the identified synoptic patterns. Our future work will focus on developing a short-term forecasting model of typhoon-induced precipitation considering the identified climate patterns as inputs.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.213-225
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• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.545-553
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• 2021

The frequency by water disaster in urban areas are increasing continuously due to climate change and urbanization. Countermeasures are being conducted to reduce the damage caused by water disasters. An analysis based on permeability, one of the parameters that affect runoff, is needed to predict quantitative runoff in urban watersheds and study runoff reduction. In this study, the SWAT model was simulated for the oncheon stream basin, a representative urban stream in Busan. The permeability map was prepared by calculating the CN values for each hydrologic response unit. Based on the permeability map prepared, EPA SWMM analyzed the effect of LID technology application on the water cycle in the basin for short-term rainfall events. The LID element technology applied to the oncheon stream basin was rooftop greening in the residential complex, and waterproof packaging was installed on the road. The land cover status of the land selected based on the permeability map and the application of LID technology reduced the outflow rate, peak flow rate, and outflow rate and increased the infiltration. Hence, LID technology has a positive effect on the water cycle in an urban basin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.434-442
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• 2019

Low Impact Development(LID) techniques has been attracting attention as a countermeasure to solve frequent flood damage in urban areas. LID is a techniques for returning to the natural hydrological cycle system by infiltrating the runoff from the impervious surface into the soil. The Bio-retention, one of the LID element technology has outflow reduction effect by reserving and infiltrating storm water runoff from watersheds. Recently, a number of studies have been carried out as interest in the reduction of storm water runoff and non-point pollutants in Bio-retention has increased. However, quantitative analysis on the outflow reduction of Bio-retention applied to small watershed is insufficient. In this study, Bio-retention model was constructed in a small watershed using K-LIDM which is capable of hydrologic analysis. When the storage capacity was increased or dividing the Bio-retention and watershed, the outflow reduction effect was 20% according to the storage capacity increase and 5~15% in the distributed Bio-retention system. The results of this analysis will be used as the basic data of future Bio-retention research related to watershed characteristics, vegetation type and soil condition.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.354-374
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• 2016

This study explored spatiotemporal variability of water quality in correspondence with hydrometeorological factors in the five stations of Paldang Reservoir located in the Han River during 4 years from May 2012 to December 2015. Variability of basic water quality factors were largely related with seasonal fluctuations of hydrology. Temperature stratification occurred in the deep dam station, and prolonged hypoxia was observed during the draught year. Nitrogen nutrients were increased with decreasing inflow in which changing pattern of $NH_4$ reversed to $NO_3$ by the effect of treated wastewater effluent. Phosphorus increase was manifest during the period of high inflow or severe drought. Chl-a variation was reversely related with both flow change and AGP(algal growth potential) variations. Our study demonstrated that water quality variability in Paldang Reservoir was largely attributed to both natural and operational changes of inflow and outflow (including water intake) based on major pollution source of the treated wastewater (total amount of $472{\times}10^3m^3d^{-1}$) entering to the water system from watershed. In the process of water quality variability, meteorological (e.g., flood, typhoon, abnormal rainfall, scorching heat of summer) and hydrological factors (inflow and discharge) were likely to work dynamically with nutrients pulse, dilution, absorption, concentration and sedimentation. We underline comprehensive limnological study related to hydro-meteorolology to understand short- and long-term water quality variability in river-type large reservoir and suggest the necessity of P-free wastewater treatment for the effective measure of reducing pollution level of Paldang drinking water resource.