• Water for future
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• v.24 no.4
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• pp.73-84
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• 1991

A number of planning models that are used for preliminary design of detention ponds in urban area were compared with consideration of urbanization effects. The characteristics of hydrological parameters $\alpha$, $\gamma$ which are used in planning models wee analyzed. And a new planning model for detention ponds was suggested. The required storage volumes of the Sinjung I, Myunmock, and Hannam detention pond were calculated by the planning models with the catchment data. The applicability of planning models to estimate the required storage volume of detention pond was investigated. Mori and Rational model have the trend of overstimation of storage volumes of detention ponds, on the other hand Abt & Grigg and Kadoya model show the trend of understimated values, and the rest of the planning models show the reasonable volumes.

• Journal of Soil and Groundwater Environment
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• v.25 no.2_spc
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• pp.55-69
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• 2020

Permeability-analyzing methods commonly involve small-scale drilling, such as pumping or slug test, but it is difficult to identify overall distribution of permeability of the entire target sites due to high cost and time requirement. Spectral induced polarization (SIP) method is known to be capable of providing distributions of both the porosity and the pore size, the two major parameters determining permeability of the porous medium. The relationship between SIP variables and permeability has been studied to identify the hydrological characteristics of target sites. Kozeny-Carman formula has been improved through many experiments to better predict fluid permeability with electrical properties. In this work, the permeability prediction techniques based on SIP data were presented in accordance with the hydrogeological and electrical characteristics of a porous medium. Following the summary of the techniques, various models and related laboratory experiments were analyzed and examined. In addition, the field applicability of the prediction model was evaluated by field case analysis.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.121-131
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• 2015

As the temperature has changed by climate change, changes in its own characteristic values of the chemical substance or the movement and distribution of chemicals take place in accordance with the changes of hydrological and meteorological phenomena. Depending on the impact of climate change on the chemical behavior, it is necessary to understand and predict quantitative changes in the dynamics of the environment of pollutants due to climate change in order to predict in advance the occurrence of environmental disasters, and minimize the impact on the life and the environment after the incident. In this study, we have analysed and compared chemical fate models validated by previous studies in terms of model configuration, application size and input/output factors. The potential models applicable to municipal and industrial areas were selected on the basis of characteristic of each model, availability of input parameters and consideration for climate change, identified the problems, and then presented an approach to improve applicability.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.197-197
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• 2021

최근 기후변화와 하천 제방의 노후화로 인해 수재해 위험이 지속적으로 증가하고 있다. 그러나 기존의 재래적인 하천 제방의 점검은 많은 인력과 예산 소모로 비효율적이며 제방 전구간 점검의 한계, 객관성의 한계 등 많은 한계점들이 존재하여 효과적인 홍수 대응을 위해 새로운 모니터링과 예/경보 시스템의 구축이 반드시 필요한 상황이다. 따라서 본 연구는 인공위성을 이용한 하천 제방 변위 산출과 수문학적 요인과의 관계 분석을 통해 하천 제방 건강상태 모니터링 시스템 방안을 제안하고자 한다. Sentinel-1 SAR 영상과 유럽 우주국(ESA)의 위성 영상 전처리 도구인 SNAP을 이용하여 2020년 여름 붕괴된 남원시의 금곡교 제방의 봄(4~5월), 여름(7~8월)의 변위를 산출하였고, 제방의 위험도 산정을 위해 토양수분관계를 분석하였다. 선행 연구(김상우,2019)에서는 농촌진흥청에서 제공하는 TDR(Time Domain Reflectrometry) 관측값과 Sentinel-1 SAR의 후방 산란계수의 토양수분관계가 일치하는 경향을 제시하여, 본 연구에서는 이를 이용하여 제방 후 방산란계수를 산출하고 변위와 토양수분도의 상관관계를 분석하여 변위 추세와 토양수분도의 추세가 일치하는 경향을 확인하였다. 본 연구 결과를 통해 향후에는 위성을 이용하여 산출한 제방의 변위와 토양수분도의 불확실성을 보완하고 기온, 수위, 토양도, 지하수위와 같은 수문기상학적 데이터의 분석을 통해 초정밀, 자동화 하천 제방 건강상태 모니터링 시스템이 구현 가능할 것으로 기대한다.

• 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.

• Journal of Soil and Groundwater Environment
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• v.28 no.6
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• pp.45-57
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• 2023

To assess land subsidence estimation and preparedness in the Geum River basin, this study applied GIS techniques and identified six key areas. The Geum River basin has experienced an increase in heavy rainfall since late 2010, and four study areas have shown an increase in groundwater levels. Land subsidence primarily occurred from June to September, with higher rainfall years in 2020 and 2023. Approximately 83.6% of land subsidence in Chungcheongbuk-do province occurred in Cheongju-si, mainly attributed to aging sewage pipes. The regions experiencing population growth have likely led to the construction of underground infrastructures and sewer pipes. Thus, it is considered that various factors, including sewage pipe leaks, precipitation, slope gradient, low drainage density, and groundwater level fluctuations, have contributed to land subsidence. Improving land subsidence estimation involves incorporating additional natural factors and human activities.

• Journal of Korean Society on Water Environment
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• v.36 no.3
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• pp.206-219
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• 2020

As urbanization and impermeable areas have increased, stormwater and non-point pollutants entering the stream have increased. Additionally, in the case of the old town comprising a combined sewer pipe system, there is a problem of stream water pollution caused by the combined sewer overflow. To resolve this problem, many cities globally are pursuing an environmentally friendly low impact development strategy that can infiltrate, evaporate, and store rainwater. This study analyzed the expected effects and efficiency when the LID facility was installed as a measure to improve hydrologic cycle and water quality in the Oncheon stream in Busan. The EPA-SWMM, previously calibrated for hydrological and water quality parameters, was used, and standard parameters of the LID facilities supported by the EPA-SWMM were set. Benchmarking the green infrastructure plan in New York City, USA, has created various installation scenarios for the LID facilities in the Oncheon stream drainage area. The installation and maintenance cost of the LID facility for scenarios were estimated, and the effect of each LID facility was analyzed through a long-term EPA-SWMM simulation. Among the applied LID facilities, the infiltration trench showed the best effect, and the bio-retention cell and permeable pavement system followed. Conversely, in terms of cost-efficiency, the permeable pavement systems showed the best efficiency, followed by the infiltration trenches and bio-retention cells.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.861-869
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• 2005

A rainfall-runoff model should be calibrated so that the model simulates the hydrological behavior of the basin as accurately as possible. In this study, to calibrate the five parameters of the SSARR model, a multi-objective function and the genetic algorithm were used. The solution of the multi-objective function will not, in general, be a single unique set of parameters but will consist of the so-called Pareto solution according to various trade-offs between the different objectives. The calibration strategy using multi-objective function could decrease calibrating time and effort. From the Pareto solution, a single solution could be selected to simulate a specific flow condition.

• Advances in environmental research
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• v.6 no.4
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.31-49
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• 1989

It is of the most urgent necessity to get hydrological time series of long duration for the establishment of rational design and operation criterion for the Agricultural hydraulic structures. This study was conducted to select best fitted frequency distribution for the monthly runoff and to simulate long series of generated flows by multi-season first order Markov model with comparison of statistical parameters which are derivated from observed and sy- nthetic flows in the five watersheds along Geum river basin. The results summarized through this study are as follows. 1. Both two parameter gamma and two parameter lognormal distribution were judged to be as good fitted distributions for monthly discharge by Kolmogorov-Smirnov method for goodness of fit test in all watersheds. 2. Statistical parameters were obtained from synthetic flows simulated by two parameter gamma distribution were closer to the results from observed flows than those of two para- meter lognormal distribution in all watersheds. 3. In general, fluctuation for the coefficient of variation based on two parameter gamma distribution was shown as more good agreement with the observed flow than that of two parameter lognormal distribution. Especially, coefficient of variation based on two parameter lognormal distribution was quite closer to that of observed flow during June and August in all years. 4. Monthly synthetic flows based on two parameter gamma distribution are considered to give more reasonably good results than those of two parameter lognormal distribution in the multi-season first order Markov model in all watersheds. 5. Synthetic monthly flows with 100 years for eack watershed were sjmulated by multi- season first order Markov model based on two parameter gamma distribution which is ack- nowledged to fit the actual distribution of monthly discharges of watersheds. Simulated sy- nthetic monthly flows may be considered to be contributed to the long series of discharges as an input data for the development of water resources. 6. It is to be desired that generation technique of synthetic flow in this study would be compared with other simulation techniques for the objective time series.