• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.4
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• pp.25-32
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• 2023

KDS (Korean Design Standard) for agricultural drainage is a planning standard that helps determine the appropriate capacity and type of drainage facilities. The objective of this study was to analyze the inundation of the agricultural basin considering the current design standard and the critical rainfall duration. This study used the rainfall durations of 1-48 hour, and the time distribution method with the Chicago and the modified Huff model. For the runoff model, the NRCS (Natural Resources Conservation Service) unit hydrograph method was applied, and the inundation depth and duration were analyzed using area-elevation data. From the inundation analysis using the modified Huff method with different rainfall durations, 4 hours showed the largest peak discharge, and 11 hours showed the largest inundation depth. From the comparison analysis with the current method (Chicago method with a duration of 48 hours) and the modified Huff method applying critical rainfall duration, the current method showed less peak discharge and lower inundation depth compared to the modified Huff method. From the simulation of changing values of drainage rate, the duration of 11 hours showed larger inundation depth and duration compared to the duration of 4 hours. Accordingly, the modified Huff method with the critical rainfall duration would likely be a safer design than the current method. Also, a process of choosing a design hydrograph considering the inundation depth and duration is needed to apply the critical rainfall duration. This study is expected to be helpful for the theoretical basis of the agricultural drainage design standards.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.369-369
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• 2022

기후변화에 관한 정부간 합의체 (IPCC, Intergovernmental Panel on Climate Change) 6차 보고서에서 이번 세기 중반까지 현 수준의 온실가스 배출량을 유지한다면 2021~2040년 중 1.5℃를 초과할 것이다. 이러한 기후의 변화로 인한 기온상승 영향으로 과거와는 달리 산림변화는 과거와 다르게 침엽수는 감소하고 활엽수는 증가하는 추세다. 본 연구에서는 유역 대부분이 산림으로 금강 상류의 용담댐유역 (930.2 km²)을 대상으로 SWAT (Soil and Water Assessment Tool)을 이용하여 장기간 산림변화에 따른 수문 구성요소를 평가하였다. MOD15A2 LAI 및 임상도 자료를 10년 단위 (1980s (1980~1989), 2000s (2000~2009), 2010s (2010~2019))를 이용해 임상별 (침엽수림, 활엽수림, 혼효림) 및 산림 높이를 구축하였다. 임상별 산정된 LAI를 기초로 SWAT의 임상별 LAI 및 수문 검·보정을 통해 용담댐유역 현황을 재현하였다. 모형의 적용성 평가는 R²를 이용하였으며, 임상별 (침엽수, 활엽수, 혼효림) LAI는 0.95, 0.89, 0.90로 증발산량은 0.51, 토양수분은 0.5~0.55로 유량의 경우 0.69로 산정되었다. 산림변화에 따른 1980s는 LAI 자료가 없기에 2000s 및 2010s의 식생 높이 및 LAI를 멱함수로 회귀하여 1980s 엽면적지수를 산정하였다. 기상자료는 2010s로 고정하고 산림 성장이 물순환에 미치는 영향을 1980s 및 2010s의 수문 비교를 시공간적으로 평가할 예정이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.209-209
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• 2022

Reservoirs play a key role in the carbon cycle between terrestrial and marine systems and are pathways that release greenhouse gases(GHGs), CO₂, CH₄, and N₂O, into the atmosphere by decomposing organic matters. Developed countries have been actively conducting research on carbon emission assessment of dam reservoirs for over 10 years under the leadership of UNESCO/IHA, but associated research is very rare in Korea. In particular, the GHGs footprint evaluation, which calculates the change in net carbon emission considering the watershed environment between pre- and post- impoundment, is very important in evaluating the carbon emission of hydroelectric dams. The objective of this study was to estimate the GHG emissions and footprints in Daecheong Reservoir using the G-res Tool, an online platform developed by UNESCO/IHA. The G-res Tool estimates CO₂ and CH₄ emissions in consideration of diverse pathway fluxes of GHGs from the reservoir and characterizes changes in GHG fluxes over 100 years based on the expected lifetime of the dam. The input required to use the G-res Tool include data related to watersheds, reservoirs, and dams, and most were collected through the government's public portal. As a result of the study, the GHG footprint of Daecheong Reservoir was estimated to be 93 gCO₂eq/m²/yr, which is similar to that of other reservoirs around the world in the same climate zone. After impoundment, the CH₄ diffusion emission from the reservoir was 73 gCO₂eq/m²/yr, also similar to those of the overseas reservoirs, but the CH₄ bubbling emission, degassing emission, and CO₂ diffusion emissions were 44, 34, 252 gCO₂eq/m²/yr, respectively, showing a rather high tendency. Since the dam reservoir carbon footprint evaluation is essential for the Clean Development Mechanism evaluation of hydroelectric power generation, continuous research is needed in the future. In particular, experimental studies that can replace the emission factors obtained from the overseas dam reservoirs currently used in the G-res Tool should be promoted.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.27-39
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• 2023

To effectively manage flood risk, it is crucial to assess the stability of flood defense structures like levees under extreme flood conditions. This study focuses on the time-dependent probabilistic assessment of embankment slope stability when subjected to rapid water level drops. We integrate seepage analysis results from finite element analysis with slope stability analysis and employ Monte Carlo simulations to investigate the time-dependent behavior of the slope during rapid drawdown. The resulting probability of failure is used to develop fragility curves for the levee slope. Notably, the probability of slope failure remains low up to a specific water level, sharply increasing beyond that threshold. Furthermore, the fragility curves are strongly influenced by the rate of drawdown, which is determined through hydraulic analysis based on flood scenarios. Climate change has a significant impact on the stability of the water-side slope of the embankment due to water level fluctuations.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.792-803
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• 2023

This study aimed to assess the impact of weather events on the sorghum-sudangrass hybrid (Sorghum bicolor L.) cultivar production trend in the central inland region of Korea during the monsoon season, using time series analysis. The sorghum-sudangrass production data collected between 1988 and 2013 were compiled along with the production year's weather data. The growing degree days (GDD), accumulated rainfall, and sunshine duration were used to assess their impacts on forage production (kg/ha) trend. Conversely, GDD and accumulated rainfall had positive and negative effects on the trend of forage production, respectively. Meanwhile, weather events such as heavy rainfall and typhoon were also collected based on weather warnings as weather events in the Korean monsoon season. The impact of weather events did not affect forage production, even with the increasing frequency and intensity of heavy rainfall. Therefore, the trend of forage production for the sorghum-sudangrass hybrid was forecasted to slightly increase until 2045. The predicted forage production in 2045 will be 14,926 ± 6,657 kg/ha. It is likely that the damage by heavy rainfall and typhoons can be reduced through more frequent harvest against short-term single damage and a deeper extension of the root system against soil erosion and lodging. Therefore, in an environment that is rapidly changing due to climate change and extreme/abnormal weather, the cultivation of the sorghum-sudangrass hybrid would be advantageous in securing stable and robust forage production. Through this study, we propose the cultivation of sorghum-sudangrass hybrid as one of the alternative summer forage options to achieve stable forage production during the dynamically changing monsoon, in spite of rather lower nutrient value than that of maize (Zea mays L.).

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.260-260
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• 2011

SWAT is semi-distributed and continuous-time distributed simulation watershed model, which can simulate point and nonpoint source pollutants as well as hydrology and water quality. It was developed to predict the effects of alternative management decisions on water, sediment, and chemical yields with reasonable accuracy. It is able to predict and manage hydrology, sediments, nutrients, and pesticides with Best Management Practices (BMPs) in a watershed. SWAT model also has potential for use in ungauged basins to predict streamflow and baseflow from saturated source area in watersheds. According to various cultivation practices and climate change, SWAT model is available to analyze relative change in hydrology and water quality. In order to establish optimum management of water quality, both monitering and modeling have been conducted actively using SWAT model. As SWAT model is computer program to simulate a lot of natural phenomena, it has limitation to predict and reflect them with on hundred percent accuracy. Thus, it is possible to analyze the effect of BMPs in the watershed where users want to simulate hydrology and water quality only if model accuracy and applicability are assessed first of all and the result of it is well for the study watershed. For assessment of SWAT applicability, most researchers have used $R^2$ and Nash and Sutcliffe Efficiency (NSE). $R^2$ and NSE are likely to show different results according to a warm up period and sometimes its results are very different. There have been hardly any studies of whether warm up period can affect simulation results in SWAT model. In this study, how warm up period has a effect on SWAT results was analyzed and a appropriate warm up period was suggested. Lots of SWAT results were compared after using measured data of Soyanggang-dam watershed and applying various warm up period (0 ~ 10 year(s)). As a result of this study, when there was no warm up period, $R^2$ and NSE were 0.645, 0.602 respectively, when warm up period was 2 years, $R^2$ and NSE were 0.648, 0.632, and when warm up period was 4 years, $R^2$ and NSE were 0.663, 0.652 separately. Through this study, sensitive analysis of warm up period in SWAT model was conducted, and this study could give a guideline able to simulate hydrology and water quality for more accuracy than before as users change a lot of warm up periods as well as any simulation parameters.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.691-699
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• 2021

The global surface temperature has risen critically over the past century and according to the IPCC Fifth Assessment Report 2014, existing risks in natural and human systems will worsen. Coastal erosion is mostly caused by climate change and among all the coastal areas at risk, Benin, which is part of the Gulf of Guinea, has been ranked very highly as a vulnerable region. Therefore, in this review, we focus on the evolution of coastline change in Cotonou of Benin, summarizing its resultant impacts and applied measures around the coast area by reviewing previous studies. Signs of coastal erosion in Cotonou appeared in 1963. After 39 years, the east shoreline of Cotonou has retreated by 885 m, resulting in the disappearance of more than 800 houses. To solve this problem, Benin authorities built seven groynes in 2013, and have increased the number of the structure as a way to interrupt water flow and limit the movement of sediment. Over the region, shorelines appeared preserved accordingly. In contrast, areas located further east, where groynes were not installed, have suf ered from intensive erosion at a rate of 49 m/yr. In the future, as a next step, the effectiveness of groynes should be studied with local and broader perspectives.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.315-326
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• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.20-25
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• 2020

Spatial simulation of crop growth often requires application of management conditions to each cell. In particular, it is of great importance to determine the temperature conditions during the nursery period for rice seedlings, which would affect heading date projections. The objective of this study was to determine the value of TMPSB, which is the parameter of ORYZA2000 model to represent temperature increase under a plastic tunnel during the rice seedling periods. Candidate values of TMPSB including 0℃, 2℃, 5℃, 7℃ and 9℃ were used to simulate rice growth and yield. Planting dates were set from mid-April to mid-June. The simulations were performed at four sites including Cheorwon, Suwon, Seosan, and Gwangju where climate conditions at rice fields common in Korea can be represented. It was found that the TMPSB values of 0℃ and 2℃ resulted in a large variation of heading date due to low temperature occurred in mid-April. When the TMPSB value was >7℃, the variation of heading date was relatively small. Still, the TMPSB value of 5℃ resulted in the least variation of heading date for all the planting dates. Our results suggested that the TMPSB value of 5℃ would help reasonable assessment of climate change impact on rice production when high resolution gridded weather data are used as inputs to ORYZA2000 model over South Korea.