• Journal of Korea Water Resources Association
• /
• v.55 no.3
• /
• pp.205-215
• /
• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.793-810
• /
• 2022

Synthetic Aperture Radar (SAR) is considered to be suitable for near real-time inundation monitoring. The distinctly different intensity between water and land makes it adequate for waterbody detection, but the intrinsic speckle noise and variable intensity of SAR images decrease the accuracy of waterbody detection. In this study, we suggest two modules, named 'morphology module' and 'edge-enhanced module', which are the combinations of pooling layers and convolutional layers, improving the accuracy of waterbody detection. The morphology module is composed of min-pooling layers and max-pooling layers, which shows the effect of morphological transformation. The edge-enhanced module is composed of convolution layers, which has the fixed weights of the traditional edge detection algorithm. After comparing the accuracy of various versions of each module for U-Net, we found that the optimal combination is the case that the morphology module of min-pooling and successive layers of min-pooling and max-pooling, and the edge-enhanced module of Scharr filter were the inputs of conv9. This morphologic and edge-enhanced U-Net improved the F1-score by 9.81% than the original U-Net. Qualitative inspection showed that our model has capability of detecting small-sized waterbody and detailed edge of water, which are the distinct advancement of the model presented in this research, compared to the original U-Net.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.445-458
• /
• 2010

The effects of geographical and climatic factors on annual and monthly pan and Penman evaporation were analyzed. 52 climatological stations were selected and trend analyses were performed. Furthermore, cluster analysis and multiple linear regression analysis were performed to understand the effects of geographical and climatic factors on pan and Penman evaporation. Based on stepwise multiple linear regression analysis, annual pan evaporation is proved to be mainly controlled by urbanization as geographical factor, and annual pan evaporation is also controlled by temperature, relative humidity, wind speed, and solar radiation as climatic factor. Especially wind speed is considered to be most significant climatic factor which affects pan evaporation. Meanwhile, Penman evaporation is not affected by geographical factors but it is affected by climate factors such as temperature, relative humidity, wind speed, and solar radiation except precipitation. Furthermore, the study results show that only proximity to coast affects pan evaporation trend on July; however, geographical and climatic factors do not affect pan evaporation trends in annual basis and monthly basis (January, April, and October). On the other hand, Penman evaporation trends were not affected by geographical factors in annual and monthly basises.

• Atmosphere
• /
• v.23 no.1
• /
• pp.85-92
• /
• 2013

We estimate atmospheric radiative heating effect of aerosols, based on AErosol RObotic NETwork (AERONET) and lidar observations and radiative transfer calculations. The column radiation model (CRM) is modified to ingest the AERONET measured variables (aerosol optical depth, single scattering albedo, and asymmetric parameter) and subsequently calculate the optical parameters at the 19 bands from the data obtained at four wavelengths. The aerosol radiative forcing at the surface and the top of the atmosphere, and atmospheric absorption on pollution (April 15, 2001) and dust (April 17~18, 2001) days are 3~4 times greater than those on clear-sky days (April 14 and 16, 2001). The atmospheric radiative heating rate (${\Delta}H$) and heating rate by aerosols (${\Delta}H_{aerosol}$) are estimated to be about $3\;K\;day^{-1}$ and $1{\sim}3\;K\;day^{-1}$ for pollution and dust aerosol layers. The sensitivity test showed that a 10% uncertainty in the single scattering albedo results in 30% uncertainties in aerosol radiative forcing at the surface and at the top of the atmosphere and 60% uncertainties in atmospheric forcing, thereby translated to about 35% uncertainties in ${\Delta}H$. This result suggests that atmospheric radiative heating is largely determined by the amount of light-absorbing aerosols.

• Journal of Environmental Impact Assessment
• /
• v.30 no.5
• /
• pp.271-296
• /
• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Journal of Biosystems Engineering
• /
• v.8 no.1
• /
• pp.47-60
• /
• 1983

상온통풍(常溫通風)을 이용(利用)한 In-bin drying에 대(對)한 많은 실험결과(實驗結果)에 의(依)하면 우리나라 10월(月)의 기상조건(氣象條件)은 저온건조(低溫乾燥) system에 적합(適合)한 건조능력(乾燥能力)을 가지고 있는 것으로 나타났다. 최근(最近) Computer를 이용(利用)한 Simulation model이 개발(開發)되어 건조현상(乾操現象)에 관(關)한 경제적(經濟的)이고 효율적(效率的)인 분석(分析)이 가능(可能)하게 되었다. 이러한 분석결과(分析結果)에 의(依)하면 초기함수율(初期含水率)이 높은 벼를 Full-bin을 이용(利用)한 상온통풍건조(常溫通風乾操)를 할 경우(境遇) 건조기간(乾燥期間)이 길어지며 bin내(內)의 상층부(上層部) 곡물(穀物)이 변질(變質)되는 문제점(問題點)이 발생(發生)하였다. 또한 벼의 수확작업체계(收穫作業體系)가 관행(慣行) 및 Binder작업체계(作業體系)에서 점차(漸次) Combine작업체계(作業體系)로 전환(轉換)되어감에 따라 포장(圃場)에서의 건조(乾燥)가 어려우며 예취(刈取), 탈곡작업과정(脫穀作業過程)에서의 기계적(機械的)인 곡물(穀物) 손실(損失)을 줄이기 위(爲)하여 함수율(含水率)이 비교적(比較的) 높은 벼를 수확(收穫)하여야 한다. 본(本) 연구(硏究)의 목적(目的)은 상온통풍건조(常溫通風乾燥)에 있어서 건조능력(乾燥能力)을 증가(增加)시키기 위(爲)하여 곡물(穀物)을 일정기간(一定期間) 나누어서 bin에 넣고 건조(乾燥)를 하는 Layer drying의 Simulation model을 개발(開發)하고 이 Model에 수원지방(水原地方)의 7년간(年間) 평균(平均) 기상자료(氣象資料)를 입력(入力)시켜 곡물(穀物)의 초기함수율(初期含水率), 투입량(投入量), 투입기간(投入期間)의 변화(變化)에 따른 Layer drying현상(現象)을 규명(糾明)하는데 있다. Simulation에 사용(使用)된 bin의 크기는 직경(直徑) 2m, 깊이 1.5m이며 송풍(送風)팬의 용량(容量)은 0.5HP이었다. Simulation분석(分析) 결과(結果)를 요약(要約)하면 다음과 같다. (1) Layer drying의 Simulation model을 개발(開發)하였으며 이 Model은 벼의 상온통풍건조(常溫通風乾燥) 실험(實驗)에서 함수량(含水量) 변화(變化)의 이론치(理論値)와 실제실험치(實際實驗値)가 잘 일치(一致)하였다. (2) 곡물투입기간(穀物投入期間) 1일(日)을 Full-bin drying으로 간주(看做)할 때 Layer drying의 건조성능(乾燥性能)은 Full-bin보다 높은 것으로 나타났다. (3) 연속송풍(連續送風)(24시간(時間))을 할 경우(境遇) 곡물투입기간(穀物投入期間)이 증가(增加)함에 따라 건조기간(乾燥期間)의 감소경향(減少傾向)은 단속송풍(斷續送風)인 경우(境遇)보다 적었지만 건조기간(乾燥期間)은 단축(短縮)되었다. 그러나 건조(乾燥)에너지의 소모(消耗)는 단속송풍(斷續送風)일 때보다 크게 나타났다. (4) 단속송풍(斷續送風)(9 : 00AM~6 : 00PM)일 경우(境遇) 곡물투입기간(穀物投入期間)을 증가(增加)시키면 건조기간(乾燥期間)이 크게 줄어 들었다. (5) 곡물(穀物)의 초기함수율(初期含水率)이 21% 이하(以下)일 경우(境遇) 연속(連續) 및 단속송풍(斷續送風)에서 건조기간(乾燥期間)의 차이(差異)가 별로 없었다. (6) 곡물(穀物)의 초기함수율(初期含水率)이 높으면 Full-bin drying에서는 상부층(上部層)에 곡물(穀物)이 변질(變質)될 우려(憂慮)가 있으나 Layer drying에서는 곡물투입량(穀物投入量)을 조절(調節)하면 이것을 방지(防止)할 수 있었다. (7) 건조(乾燥)가 완료(完了)된 후(後) 층별(層別) 최종곡물(最終穀物) 함수율(含水率)은 모든 건조조건(乾燥條件)에서 동일(同一)하였으나 bin의 하부층(下部層)은 과건조(過乾燥) 물상(物象)을 일으켰다.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1413-1425
• /
• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Ecology and Environment
• /
• v.51 no.1
• /
• pp.105-123
• /
• 2018

Currently, satellite images act as essential and important data in water resources, environment, and ecology as well as information of geographic information system. In this paper, we will investigate basic characteristics of satellite images, especially application examples in water resources. In recent years, researches on spatial and temporal characteristics of large-scale regions utilizing the advantages of satellite imagery have been actively conducted for fundamental hydrological components such as evapotranspiration, soil moisture and natural disasters such as drought, flood, and heavy snow. Furthermore, it is possible to analyze temporal and spatial characteristics such as vegetation characteristics, plant production, net primary production, turbidity of water bodies, chlorophyll concentration, and water quality by using various image information utilizing various sensor information of satellites. Korea is planning to launch a satellite for water resources and environment in the near future, so various researches are expected to be activated on this field.

• Journal of Korea Water Resources Association
• /
• v.39 no.5 s.166
• /
• pp.453-466
• /
• 2006

The objective of this study was to analyze the changes in the hydrological environment in Soyanggang-dam watershed due to climate change results (in yews 2050 and 2100) which were simulated using CCCma CGCM2 based on SRES A2 and B2. The SRES A2 and B2 were used to estimate NDVI values for selected land use using the relation of NDVI-Temperature using linear regression of observed data (in years 1998$\sim$2002). Land use change based on SRES A2 and B2 was estimated every 5- and 10-year period using the CA-Markov technique based on the 1985, 1990, 1995 and 2000 land cover map classified by Landsat TM satellite images. As a result, the trend in land use change in each land class was reflected. When land use changes in years 2050 and 2100 were simulated using the CA-Markov method, the forest class area declined while the urban, bareground and grassland classes increased. When simulation was done further for future scenarios, the transition change converged and no increasing trend was reflected. The impact assessment of evapotranspiration was conducted by comparing the observed data with the computed results based on three cases supposition scenarios of meteorological data (temperature, global radiation and wind speed) using the FAO Penman-Monteith method. The results showed that the runoff was reduced by about 50% compared with the present hydrologic condition when each SRES and periods were compared. If there was no land use change, the runoff would decline further to about 3$\sim$5%.