• Journal of Wetlands Research
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• v.19 no.3
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• pp.345-352
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• 2017

The average global temperature on Earth has increased by about $0.85^{\circ}C$ since 1880 due to the global warming. The temperature increase affects hydrologic phenomenon and so the world has been suffered from natural disasters such as floods and droughts. Therefore, especially, in the aspect of water deficit, we may require the accurate prediction of water demand considering the uncertainty of climate in order to establish water resources planning and to ensure safe water supply for the future. To do this, the study evaluated future water balance and water deficit under the climate change for Anseong river basin in Korea. The future rainfall was simulated using RCP 8.5 climate change scenario and the runoff was estimated through the SLURP model which is a semi-distributed rainfall-runoff model for the basin. Scenario and network for the water balance analysis in sub-basins of Anseong river basin were established through K-WEAP model. And the water demand for the future was estimated by the linear regression equation using amounts of water uses(domestic water use, industrial water use, and agricultural water use) calculated by historical data (1965 to 2011). As the result of water balance analysis, we confirmed that the domestic and industrial water uses will be increased in the future because of population growth, rapid urbanization, and climate change due to global warming. However, the agricultural water use will be gradually decreased. Totally, we had shown that the water deficit problem will be critical in the future in Anseong river basin. Therefore, as the case study, we suggested two alternatives of pumping station construction and restriction of water use for solving the water deficit problem in the basin.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korea Water Resources Association
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• v.48 no.2
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• pp.115-126
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• 2015

In recent years, debris flow disaster has occurred in multiple locations between high and low mountainous areas simultaneously with a flooding disaster in urban areas caused by heavy and torrential rainfall due to the changing global climate and environment. As a result, these disasters frequently lead to large-scale destruction of infrastructures or individual properties and cause psychological harm or human death. In order to mitigate these disasters more effectively, it is necessary to investigate what causes the damage with an integrated model of both disasters at once. The objectives of this study are to analyze the mechanism of debris flow for real basin, to determine the PMP and run-off discharge due to the DAD analysis, and to estimate the influence range of debris flow for fan area according to the scenario. To analyse the characteristics of debris flow at the real basin, the parameters such as the deposition pattern, deposit thickness, approaching velocity, occurrence of sediment volume and travel length are estimated from DAD analysis. As a results, the peak time precipitation is estimated by 135 mm/hr as torrential rainfall and maximum total amount of rainfall is estimated by 544 mm as typhoon related rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.368-368
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• 2020

최근 초대형화 되어 나타나고 있는 이상홍수와 지진 등에 의한 저수지 붕괴와 같은 대규모 비상상황 발생으로 하류지역 주민의 생명과 재산의 피해가 발생하고 있다. 국내의 경우 1996년 이후로 지속적으로 발생하고 있는 이상홍수로 인해 1998년에는 40개,1999년에는 5개의 소규모 저수지가 붕괴되었으며 최근 2013년과 2014년에도 저수지가 붕괴되는 상황이 발생했다. 댐붕괴의 원인은 구조물의 자연적 노화, 극심한 강우나 홍수, 지진, 제체전도, 파이핑, 침윤발생, 월류 및 파랑 등에 의한 자연적 상황 등이 요인이 될 수 있으며, 시공결함, 사고 또는 전쟁과 같은 인위적인 요인으로 발생할 수도 있다. 과거에 설계 및 시공기술이 부족하였거나 경제적인 이유로 부실하게 건설되어 있는 댐이 세계적으로 산재되어 있어 잠재적인 위험을 상당수 내재하고 있는 실정이다. 본연구는 댐의 점진적인 파괴에 의해 발생하는 유출수문곡선을 구하고 파괴의 성질을 예측 및 홍수파를 수리학적으로 추적하기위해 BREACH 모형과 DAMBRK 모형을 사용했으며 극한홍수(PMF)조건과와 최대지진발생(MCE)조건을 적용하여 원주시 관내 저수지 붕괴 모의 시나리오를 구축했다. 저수지 붕괴에 따른 유출수문곡선을 유도하기 위해서 본 연구에서는 기존의 EAP보고서 자료를 참고하여 붕괴지속시간, 붕괴부 평균폭, 붕괴부 측벽면 경사의 변화에 따라 다양한 모의를 수행함으로써 발생되는 붕괴부 유량 수문곡선을 도출하여 각각의 조건들이 붕괴파 형성에 미치는 영향에 대한 분석을 실시하였다. 그 결과 저수지의 붕괴시 첨두유출량에 민감한 영향을 주는 인자는 붕괴지속시간과, 붕괴부 평균폭으로서 이들 값이 붕괴유출량 변화에 많은 영향을 주는 것으로 나타났다. 최대지진발생(MCE)조건 해석결과 홍수류의 범람으로 인해 홍수파가 하류측으로 진행할수록 완만히 감소하며, 하천 중·상류부 인근 제내지로 홍수류의 범람이 발생하는 것으로 검토되었으며, 극한홍수(PMF)조건 해석결과 최대지진발생(MCE)조건과 같이 홍수파가 하류측으로 진행할수록 완만히 감소하는 특성을 보이며, 하천 전체 구간에서 인근제내지로 홍수류의 범람이 발생하는 것으로 검토되었다. 본 연구는 침수구역 피해규모 산정 및 비상대처계획도를 작성시 기초데이터가 되어 상황별 피해예상지역에 대해 응급행동요령, 주민대피계획비상대처계획을 수립하여 지역 주민생활에 안정을 기여하고자 한다.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of the Korean Geotechnical Society
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• v.30 no.6
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• pp.23-39
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• 2014

An artificial rainwater reservoir installed in urban areas for recycling rainwater is an eco-friendly facility for reducing storm water effluence. However, in order to recycle the rainwater directly, the artificial rainwater reservoir requires an auxiliary system that can remove non-point source pollutants included in the initial rainfall of urban area. Therefore, the conventional soil filtration technology is adopted to capture non-point source pollutants in an economical and efficient way in the purification system of artificial rainwater reservoirs. In order to satisfy such a demand, clogging characteristics of the sand filter layers with different grain-size distributions were studied with real non-point source pollutants. For this, a series of lab-scale chamber tests were conducted to make a prediction model for removal of non-point source pollutants, based on the clogging theory. The laboratory chamber experiments were carried out by permeating two types of artificially contaminated water through five different types of sand filter layers with different grain-size distributions. The two artificial contaminated waters were made by fine marine-clay particles and real non-point source pollutants collected from motorcar roads of Seoul, Korea. In the laboratory chamber experiments, the concentrations of the artificial contaminated water were measured in terms of TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) and compared with each other to evaluate the performance of sand filter layers. In addition, the accumulated weight of pollutant particles clogged in the sand filter layers was estimated. This paper suggests a prediction model for removal of non-point source pollutants with theoretical consideration of the physical characteristics such as the grain-size distribution and composition, and change in the hydraulic conductivity and porosity of sand filter layers. The lumped parameter ${\theta}$ related with the clogging property was estimated by comparing the accumulated weight of pollutant particles obtained from the laboratory chamber experiments and calculated from the prediction model based on the clogging theory. It is found that the lumped parameter ${\theta}$ has a significant influence on the amount of the pollutant particles clogged in the pores of sand filter layers. In conclusion, according to the clogging prediction model, a double-sand-filter layer consisting of two separate layers: the upper sand-filter layer with the effective particle size of 1.49 mm and the lower sand-filter layer with the effective particle size of 0.93 mm, is proposed as the optimum system for removing non-point source pollutants in the field-sized artificial rainwater reservoir.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.227-240
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• 2006

In the several decades, various storage facilities have been developed and operated to supply water resource, flood control or environmental preservation etc. Then, how those man-maid storage facilities affect on the downstream water and environment and how the hydrologists can evaluate those features for water resources problem-solving are high-concentrated problems in this field. Most large watersheds in Korea contain various types of artificial facilities such dams, reservoirs, in-land ponds, wetlands etc. But the study to develop the technology for achieving the effect of the variances and properties of the long term streamflow caused by the artificial storage facilities have been on the simple watershed models and experimental modeling in the real fields. In this paper, we introduce the procedure and methods to consider the above problems based on continuous and semi-distributed featured SWAT model. At the first, we describe the elements and mechanisms of storage facilities in SWAT model to see how we can apply that in proper and appropriate manner for real field problems. Then, we applied the process to a sample watershed, Taewha River basin which covers the most of Ulsan region. Specially, we concentrate on our effort to the effect of upper reservoirs on down stream long term flows based on various scenario basis. The result was described and analysed in spacial and temporal variations on that basin using the precise manner.