• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.312-317
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• 2006

지역규모의 기후변화 모의결과를 이용하여 금강유역 용담댐의 홍수기 치수안전도에 대한 민감도분석을 수행하였다. 기후변화 모의에 사용된 SNURCM(Seoul National University Regional Climate Model)은 미국 National Center for Atmospheric Research의 Community Climate System Model의 전지구모형을 기반으로 spectral nudging 기법을 사용한 공간해상도 30 km, 연직 21층의 지역기후모형이다. 기후변화 시나리오로는 SRES 'B1'이 사용되었으며 과거 control run에 대한 기후모의 정확도 분석을 통하여 SNURCM 기상자료를 관측치와 비교한 결과 면적강우량을 다소 과소추정하였고 이점을 감안하여 SNURCM의 일 모의결과에 보정 계수를 적용하였다. 하천유출량은 SSARR 모형을 이용하여 SNURCM 모의가 수행된 전체기간을 $1980{\sim}1999$년과 $2000{\sim}2019$년으로 20년씩 나누어 용담댐 일 유입량을 산정하여 통계분석을 실시하였고 과거와 미래 20년 동안을 비교하여 본 결과 (1) 유량의 평균보다는 분산이 미래 20년 동안 증가하여 가뭄과 홍수에 대한 위험도가 증가함을 알 수 있었고, (2) 특히 연최대유량 또한 미래 20년 동안 상당히 증가하여 홍수기 치수대책이 더욱 중요해질 것으로 판단되었다. 마지막으로 용담댐 운영은 범용 시스템분석 도구인 STELLA(System Thinking Experimental Learning Laboratory with Animation) 상에서 GUI로 구현하여 유입량 변화에 따른 용담댐 치수안전도 변화를 모의해 보았다. 용담댐의 홍수기 운영은 저수지 수위가 제한수위를 초과하기 시작하면 Rigid ROM 발효하여 방류량을 결정하도록 구성하였고, 무효방류(spill)가 일어나는 현상을 실패로 가정하여 이에 대한 신뢰도(reliability), 회복도(resiliency), 그리고 심도(vulnerability)를 치수안전도 지표로 계산하였다. 전체기간을 1980년${\sim}$1999년, 2000년${\sim}$2019년, 2000년${\sim}$2009년, 그리고 2010년${\sim}$2019년까지 총 4구간으로 나누어 결과를 도출하였으며 예상한 바와 같이 후반기 20년 동안에 세 가지 지표가 취약해 지는 것을 확인할 수 있었고, 특히 2000년부터 2009년까지 10년 동안에는 더욱 취약해짐을 확인할 수 있었다.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.23-32
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• 2011

This research uses a distributed model, Vflo which can devide subwater shed into square grids and interpret diverse topographic elements which are obtained through GIS processing. To use the distributed model, soil wetness information was extracted through Tasseled Cap transformation from LANDSAT 7 $ETM^+$ satellite data and then they were applied to each cell of the test area, unlike previous studies in which have applied average soil condition of river basin uniformly regardless of space-difference in subwater shed. As a resut of the research, it was ascertained the spatial change of soil wetness is suited to the distributed model in a subwater shed. In addition, we derived out a relation between soil wetness of image collection time and 10 days-preceded rainfall and improved the feasibility of weights obtained by the relation equation.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1059-1065
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• 2008

Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.109-112
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• 2007

본 연구에서는 댐 하류지역의 물관리를 위한 기초자료를 제공하기 위해 금강수계의 댐 하류지역인 논산지점을 대상으로 기 개발된 수위-유량 곡선식에 의해 추정된 유출량과 RRFS에 의해 추정된 유출량을 실제 분석하고자하는 시점에서 도보측정에 의해 측정된 유출량과 비교하였다. 이러한 유출분석을 위해 유출분석의 기초자료인 논산유역의 면적강우량을 산정하고자 8개의 강우관측지점을 선정하고 티센계수를 구하였다. 각 방법별 유출량의 비교를 위해 도보측정에 의한 유출량은 2005년부터 2006년까지 각각 39회와 40회에 걸쳐 추정되었고 기 개발된 수위-유량곡선식에 의한 유출량은 국가수자원관리 종합시스템에서 제공하는 수위-유량곡선식을 사용하여 추정되었으며 RRFS에 의한 유출량은 매개변수의 민감도분석과 보정에 의해 추정되었다. 이들 방법에 의한 유출량 중에서 도보측정에 의한 유출량을 참값으로 가정하고 수위-유량곡선식에 의한 유출량과 RRFS에 의한 유출량, 각각에 대한 상대평균제곱근오차와 상대절대오차를 구하였다. 이러한 오차분석 결과에서 2005년과 2006년 모두 RRFS에 의한 유출량이 기 개발된 수위-유량 곡선식에 의한 유출량보다 도보 측정에 의한 유출량에 더 근접하는 좋은 결과를 나타내었다. 따라서 향후 댐 하류 주요지점을 대상으로 유출분석을 실시할 경우 본 연구와 같은 분석 절차를 거쳐야 할 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.277-277
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• 2023

최근 기후변화의 영향으로 호우의 발생빈도가 증가하고 있는 추세이며, 도시지역의 호우는 돌발적이고 국지적인 특성을 가지고 있어 인명과 재산피해 역시 증가하고 있으며, 급격한 도시화로 인한 구조적으로 홍수에 취약한 실정이다. 국지성 도시호우는 저층(1 km 내외)에서 형성되는 강우가 지배적이며, 기존의 대형레이더는 높은 산 정상에 설치되어 1.5 km 이상의 강우관측을 중심으로 운영됨에 따라 저층강우의 탐지 및 변동성 관측에 취약하여, 이에 대형 레이더에서 뿐만 아니라 도시단위의 국지성 호우관측에 대응할 수 있는 소형 레이더 기반 고정밀 강우관측 마련 및 운영 기술이 필요하다. 현재 K-water는 부산 에코델타 스마트시티에 도시 물재해 플랫폼 구현의 일환으로 돌발강우사전 탐지 및 도시의 신속·정확한 강우 관측을 위하여 높은 시공간 해상도를 제공하는 이중편파X 밴드 소형 강우레이더를 설치하고, 효율적 운용을 위해 각 고도각에서의 빔 차폐율을 확인하고 이를 고려한 최적 관측전략을 수립하였다. 또한 Z-Phi 방법을 이용한 반사도 감쇠 보정 기술을 개발하였으며, 강우 추정을 위해 하이브리드 고도면 합성 기법(HSR) 기법을 적용하고 검증하였다. 이후 소형 레이더의 정량적 추정강수를 이용하여 강우예측 정보를 생산하기 위해 이류모델을 적용하고, 비슬산과 소형 합성 레이더 추정강수로 선행 10분에서 180분까지 예측할 수 있도록 개발하였다. 또한, 지상강우관측 자료와의 정확도 비교 평가를 수행하고, 행정구역 및 표준유역의 예측 평균강우량을 생산하여 부산 에코델타 스마트시티 도시 물재해 통합관리 시스템과 연계운영을 위한 후속 과업을 수행중에 있다.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

• Journal of Korean Society of Forest Science
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• v.104 no.3
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• pp.403-410
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• 2015

This study was conducted to analyze the forest environmental characteristics on a total of 20 forest environmental factors affecting the debris flow against 272 sites of risk areas. In the case of environmental factors, it showed the high risk of debris flow under the following conditions such as soil depth of less than 30cm, west slope, altitude of 200~300 m, mountain average slope of $25{\sim}30^{\circ}$, sandy loam, igneous rocks, and composite slope. Among the rainfall factors, 50~100 mm of maximum hourly rain fall and 300 mm of maximum rain fall per day have been shown the high risk of debris flow. Furthermore, the high risk of debris flow was related to the river-bed average slope of $10{\sim}20^{\circ}$, the river-bed average width of >10 m, the small amount of debris in river-bed (less than 20% of river-bed structure), the drainage density of >$1km/km^2$, the 40~60% of area with more than $20^{\circ}$ slope, and the 40~60% of areas with risk grade 2 of landslide. In addition, forest environmental factors including the driftwood, soil erosion control structures, age-class 3, crown density (density), and mixed forest were important factors causing the high risk of debris flow.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.559-567
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• 2010

This study was conducted to investigate the hydrological characteristics of groundwater level change and rainfall hydrological runoff processes caused by tunnel construction at Milbot bog located in Mt. Cheonseong. Data were collected from July 2004 to May 2008. The results were summarized as follows: The occurrence time of the direct runoff caused by unit rainfall at the Milbot bog were tended to be slower than those at general mountainous basin. Also, runoff did not sensitively respond to amount of rainfall at the most of the long and short term hydrograph. The annual runoff rates from 2004 to 2008 were 0.26, 0.13, 0.16, 0.25 and 0.27, respectively, slightly increased after 2005 regardless of the tunnel construction. Thus, the function of Milbot bog will be weakened, and it supposed to be changed to land in the future because of increasing annual runoff. The annual runoff rate for 4 years was 0.19, which is greatly lower than that of general mountainous basin. The recession coefficient of the direct runoff in short term hydrograph was ranged to 0.89~0.97, which is much larger than that of the general mountainous basin, 0.2~0.8. The recession coefficient of base flow ranged from 0.93 to 0.99, which are similar to general mountainous watershed's values. Groundwater level of Milbot bog increased or decreased in proportion to rainfall intensity, and in the descending time after the groundwater level was reached at peak point, it tends to be decreased very slowly. Also, groundwater level increased or decreased maintaining relatively high value after precedent rainfall. Groundwater level was highest during summer with heavy rainfall, but was lowest during winter. Average groundwater levels decreased annually from 2004 to 2008, -8.48 cm, -14.60 cm, -20.46 cm, -20.11 cm, -28.59 cm, respectively. Therefore, it seems that the Milbot bog is becoming dry and losing its function as a bog.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.65-81
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• 2021

In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R² ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R² was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R² was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.