• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.143-143
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• 2021

Climate change indicators, mainly frequent drought which has happened since the drought of 1994, 1995, and 2012 causing the devastating effect to the agricultural sector, and could be more disruptive given the context of climate change indicators by increasing the temperature and more variable and extreme precipitation. Changes in frequency, duration, and severity of droughts will have enormous impacts on agriculture production and water management. Since both the possibility of drought manifestation and substantial yield losses, we are propositioning an integrated method for evaluating past and future agriculture drought hazards that depend on models' simulations in the Hung-up watershed. to discuss the question of how climate change might influence the impact of extreme agriculture drought by assessing the potential changes in temporal trends of agriculture drought. we will calculate the temporal trends of future drought through drought indices Standardized Precipitation Evapotranspiration Index, Standardized Precipitation Index, and Palmer drought severity index by using observed data of (1991-2020) from Wonju meteorological station and projected climate change scenarios (2021-2100) of the Representative Concentration Pathways models (RCPs). expected results confirmed the frequency of extreme agricultural drought in the future projected to increase under all studied RCPs. at present 100 years drought is anticipated to happen since the result showing under RCP2.6 will occur every 24 years, RCP4.5 every 17 years, and RCPs8.5 every 7 years, and it would be double in the largest warming scenarios. On another side, the result shows unsupportable water management, could cause devastating consequences in both food production and water supply in extreme events. Because significant increases in the drought magnitude and severity like to be initiate at different time scales for each drought indicator. Based on the expected result that the evaluating the impacts of extreme agricultural droughts and recession could be used for the development of proactive drought risk management, policies for future water balance, prioritize sustainable strengthening and mitigation strategies.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.281-281
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• 2020

이 연구는 농업가뭄이 빈번하게 발생하는 대표적인 국내 농어촌지역에 대하여, 토지이용변화와 잠재증발산량 분석을 통해 관개용수 수요량을 추정하여, 가뭄 수요대응 상시 용수공급체계를 수립하고자 수행되었다. 연구지역은 총 1,164필지(면적 약 289 ha)로 구성되며, 지하수 공공관정에 대한 의존도가 높은(53%) 편이다. 농경지는 537필지(약 46 ha)이며 총면적(약 289 ha)의 약 16% 이고, 논이 약 11%(약 33 ha), 밭이 약 5%(약 13 ha)에 해당한다. 최근 10년(2010-2019년)간 농작물 재배면적을 살펴보면 논벼가 전체 농경지(46 ha)의 65-75%(약 32-33 ha)로 가장 넓다. 논벼를 제외하면, 깨(9-12%; 4-6 ha), 고추(3-9%; 2-5 ha), 고추/깨 윤작(2%; <1 ha), 수수(2-3%; 1 ha) 등이 나머지 면적에서 생산되며, 최근 들어 단호박, 마늘, 살구, 파, 표고버석, 호박 등 재배작물의 종류가 다양해지는 편이다. 작물별 실제 관개용수 수요량을 산정하기 위해 최근 10년간(2010-2019) 작물별 재배면적 변화, 재배기간, 작물계수, 잠재증발산량 등의 자료를 수집하여 분석하였다. 논에 대한 관개용수 수요량의 경우 논(297필지) 면적변화를 기반으로 HOMWRS 프로그램을 이용하여 산정하였다. 밭에 대한 관개용수 수요량은 밭(240개 필지)의 재배작물에 대하여 작물별 증발산량이 밭작물의 관개용수 수요량과 동일하다는 가정 하에 산정하였다. 이 결과, 최근 10년간(2010-2019) 연구지역 관개용수 수요량은 평균 377천 ㎥/년으로 추정되었다. 논은 밭에 비해 약 6배 관개용수 수요량이 많았고, 상세하게는, 논의 관개용수 수요량은 평균 321천 ㎥/년이었고, 반면 밭의 관개용수 수요량은 평균 56천 ㎥/년으로 산정되었다. 밭용수의 경우, 2010년 이래로 농가소득 증대를 위해 밭작물 재배면적이 증가하면서 밭의 용수 수요량은 해마다 증가추세(40~88천 ㎥/년)를 보였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.121-135
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• 2010

This study is to assess the effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water supply using the SLURP. Before the future analysis, the SLURP model was calibrated using the 6 years daily streamflow records (1998-200398 and validated using 3 years streamflow data (2004-200698 for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang8 and Gosam98located in Anseongcheon watershed. The calibration and validation results showed that the model was able to simulate the daily streamflow well considering the reservoir operation for paddy irrigation and flood discharge, with a coefficient of determination and Nash-Sutcliffe efficiency ranging from s 7 to s 9 and 0.5 to s 8 respectively. Then, the future potential climate change impact was assessed using the future wthe fu data was downscaled by nge impFactor method throuih bias-correction, the future land uses wtre predicted by modified CA-Markov technique, and the future ve potentiacovfu information was predicted and considered by the linear regression bpowten mecthly NDVI from NOAA AVHRR ima ps and mecthly mean temperature. The future (2020s, 2050s and 2e 0s) reservoir inflow, the temporal changes of reservoir storaimpand its impact to downstream streamflow watershed wtre analyzed for the A2 and B2 climate change scenarios based on a base year (2005). At an annual temporal scale, the reservoir inflow and storaimpchange oue, anagricultural reservoir wtre projected to big decrease innautumnnunder all possiblmpcombinations of conditions. The future streamflow, soossmoosture and grounwater recharge decreased slightly, whtre as the evapotransporation was projected to increase largely for all possiblmpcombinations of the conditions. At last, this study was analysed contribution of weather, vegetation and land use change to assess which factor biggest impact on agricultural reservoir and stream watershed. As a result, weather change biggest impact on agricultural reservoir inflow, storage, streamflow, evapotranspiration, soil moisture and groundwater recharge.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.489-508
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• 2018

As climate changes, it is necessary to predict changes in the habitat environment in order to establish more aggressive adaptation strategies. The bioclimatic classification which clusters of areas with similar habitats can provide a useful ecosystem management framework. Therefore, in this study, biological habitat environment of Northeast Asia was identified through the establishment of the bioclimatic zones, and the impac of climate change on the biological habitat was analyzed. An ISODATA clustering was used to classify Northeast Asia (NEA)into 15 bioclimatic zones, and climate change impacts were predicted by projecting the future spatial distribution of bioclimatic zones based upon an ensemble of 17 GCMs across RCP4.5 and 8.5 scenarios for 2050s, and 2070s. Results demonstrated that significant changes in bioclimatic conditions can be expected throughout the NEA by 2050s and 2070s. The overall zones moved upward, and some zones were predicted to be greatly expanded or shrunk where we suggested as regions requiring intensive management. This analysis provides the basis for understanding potential impacts of climate change on biodiversity and ecosystem. Also, this could be used more effectively to support decision making on climate change adaptation.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.1
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• pp.16-22
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• 2013

Water is the most important resource for the maximum water use efficiency and yield of maize. Water has to be applied moderately based on the water requirement of maize. Crop water requirement (WR) is a function of the potential evapo-transpiration (PET) and crop coefficient (Kc). PET can be estimated by the climate data measured at the weather station in the production region. Kc was measured by the NIAST (RDA) through lysimeter experiments. In this study, the growth stage of maize was divided into five ones (G-1: Apr. 25 ~ May 20, G-2: May 21 ~ Jun. 20, G-3: Jun. 21 ~ Jul. 20, G4: Jul. 11 ~ Jul. 25, G5: Jul. 26 ~ Aug. 20). The average PET during maize growing season of the 45 areas was 2.85 mm $day^{-1}$. The highest water requirement was at the G-3 stage among the maize growth stages. The mean water requirement (MWR) according to growth stage was 1.74 ~ 2.42 (average 2.02), 2.99 ~ 4.21 (average 3.41), 3.82 ~ 5.25 (average 4.41), 3.05 ~ 4.31 (average 3.48), and 2.62 ~ 3.49 (average 3.01) mm $day^{-1}$ in the G-1, G-2, G-3, G-4 and G-5 stage, respectively. The total water requirement (TWR) according to growth stage was 45.37 ~ 63.04 (average 52.56), 92.54 ~ 130.59 (average 105.77), 76.46 ~ 105.09 (average 88.14), 45.73 ~ 64.67 (average 52.20), and 68.25 ~ 90.75 (average 78.33) mm in the G-1, G-2, G-3, G-4 and G-5 stage, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.27 no.4
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• pp.269-274
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• 1994

An experiment was carried out to investigate the effects of irrigation period on the yield and quality of melon (Cucumis melo L.) cultivated in green house 1992~1993. Four different irrigation period was applied ; from transplanting to harvest(Irrigation I), from initial flowering to 35 days after flowering(Irrigation II), from initial flowering to 20 days after flowering(Irrigation III), from initial flowering to 5 days after flowering(Irrigation IV). The water was applied by drip irrigation when the soil water potential of 15cm depth reach at -0.5bar. The results obtained were as follows ; 1. The amount of water applied during the periods were 170.5mm, 145.0mm,126.9mm and 78.8mm for irrigation period I, II, III and IV, respectively. 2. Average evapotranspiration during the cultivation of melon was 3.31mm/day. Evapotranspiration was the highest at the period from 5days after flowering to 15days after flowering. 3. The total yield was increased with the higer amount of water applied, and the yield was in the order of Irrigation I, II, III and IV. However, the yield with economically high quality was the highest in Irrigation III. 4. The sugar content and quality of Net form were decreased with higher irrigation amount.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1057-1072
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• 2017

In order to investigate the effect of air temperature reduction on an urban neighborhood park, air temperature data from five inside locations (forest, pine tree, lawn, brick and pergola) depending on surface types and three outside locations (Suwon, Maetan and Kwonsun) depending on urban forms were collected during the summer 2016 and compared. The forest location had the lowest mean air temperature amongst all locations sampled, though the mean difference between this and the other four locations in the park was relatively small ($0.2-0.5^{\circ}C$). In the daytime, the greatest mean difference between the forest location and the two locations exposed to direct beam solar radiation (brick and lawn) was $0.5-0.8^{\circ}C$ (Max. $1.6-2.1^{\circ}C$). In the nighttime, the mean difference between the forest location and the other four locations in the park was small, though differences between the forest location and locations with grass cover (pine tree and lawn) reached a maximum of $0.9-1.7^{\circ}C$. Comparing air temperature between sunny and shaded locations, the shaded locations showed a maximum of $1.5^{\circ}C$ lower temperature in the daytime and $0.7^{\circ}C$ higher in the nighttime. Comparing the air temperature of the forest location with those of the residential (Kwonsun) and apartment (Maetan) locations, the mean air temperature difference was $0.8-1.0^{\circ}C$, higher than those measured between the forest location and the other park locations. The temperatures measured in the forest location were mean $0.9-1.3^{\circ}C$ (Max. $2.0-3.9^{\circ}C$) lower in the daytime than for the residential and apartment locations and mean $0.4-1.0^{\circ}C$ (Max. $1.3-3.1^{\circ}C$) lower in the nighttime. During the hottest period of each month, the difference was greater than the mean monthly differences, with temperatures in the residential and apartment locations mean $1.0-1.6^{\circ}C$ higher than those measured in the forest location. The effect of air temperature reduction on sampling locations within the park and a relatively high thermal environment on the urban sampling locations was clearly evident in the daytime, and the shading effect of trees in the forest location must be most effective. In the nighttime, areas with a high sky view factor and surface types with high evapotranspiration potential (e.g. grass) showed the maximum air temperature reduction. In the urban areas outside the park, the low-rise building area, with a high sky view factor, showed high air temperature due to the effect of solar (shortwave) radiation during the daytime, while in the nighttime the area with high-rise buildings, and hence a low sky view factor, showed high air temperature due to the effect of terrestrial (longwave) radiation emitted by surrounding high-rise building surfaces. The effect of air temperature reduction on the park with a high thermal environment in the city was clearly evident in the daytime, and the shading effect of trees in the forest location must be most effective. In the nighttime, areas with high sky view factor and surface types (e.g., grass) with evapotranspiration effect showed maximum air temperature reduction. In the urban areas outside the park, the high sky view factor area (low-rise building area) showed high air temperature due to the effect of solar (shortwave) radiation during the daytime, but in the nighttime the low sky view factor area (high-rise building area) showed high air temperature due to the effect of terrestrial (longwave) radiation emitted surrounding high-rise building surfaces.

• Journal of Korea Water Resources Association
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• v.39 no.4 s.165
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• pp.299-311
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• 2006

In this paper it is outlined the methodology of estimating the parameters of water balance analysis method for calculating recharge, using ground water level rises in monitoring well when values of specific yield of aquifer are not available. This methodology is applied for two monitoring wells of the case study area in northern area of the Jeiu Island. A water balance of soil layer of plant rooting zone is computed on a daily basis in the following manner. Diect runoff is estimated by using SCS method. Potential evapotranspiration calculated with Penman-Monteith equation is multiplied by crop coefficients($K_c$) and water stress coefficient to compute actual evapotranspiration(AET). Daily runoff and AET is subtracted from the rainfall plus the soil water storage of the previous day. Soil water remaining above soil water retention capacity(SWRC) is assumed to be recharge. Parameters such as the SCS curve number, SWRC and Kc are estimated from a linear relationship between water level rise and recharge for rainfall events. The upper threshold value of specific yield($n_m$) at the monitoring well location is derived from the relationship between rainfall and the resulting water level rise. The specific yield($n_c$) and the coefficient of determination ($R^2$) are calculated from a linear relationship between observed water level rise and calculated recharge for the different simulations. A set of parameter values with maximum value of $R^2$ is selected among parameter values with calculated specific yield($n_c$) less than the upper threshold value of specific yield($n_m$). Results applied for two monitoring wells show that the 81% of variance of the observed water level rises are explained by calculated recharge with the estimated parameters. It is shown that the data of groundwater level is useful in estimating the parameter of water balance analysis method for calculating recharge.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.575-585
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• 1997

A daily runoff system was constructed to support decision making for water use in the downstream region of multipurpose dams in the Han River. The daily runoff system used the modified model from NWSRFS by Tabios III et al. (1986), and potential evapotranspiration was computed from Penman equation. DWOPER was used for channel routing. While the North Han River is the main river reach in the channel routing system, the South Han River and the Soyang River became tributaries. The rainfall-runoff model was calibrated and verified for five subbasins. Daily runoffs were simulated with the hydrometeorological data in 1986 and1990. The simulations were performed from April to November, and the sum of computed discharges for seven or thirty days were compared with actual releases of the downstream dams. It showed the average absolute errors of 8.7 ~31.6%. The sum of total discharges were 10% or less. While stage errors were produced by 0.5 m or less at Yoju station in the ease of simulation from April to August in 1986, the errors kept under 0.2 m since September. In the simulation for 1990, we compared two simulation results. One is produced from real internal and downstream boundary conditions and the other is one from internal and downstream boundary conditions fixed arbitrarily. The two cases showed similar results.

• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.653-666
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• 2011

The purposes of this study are to suggest flood vulnerability assessment method on climate change with evaluation of this method over the 5 river basins and to present the uncertainty range of assessment using multi-model ensemble scenarios. In this study, the data related to past historical flood events were collected and flood vulnerability index was calculated. The vulnerability assessment were also performed under current climate system. For future climate change scenario, the 39 climate scenarios are obtained from 3 different emission scenarios and 13 GCMs provided by IPCC DDC and 312 hydrology scenarios from 3 hydrological models and 2~3 potential evapotranspiration computation methods for the climate scenarios. Finally, the spatial and temporal changes of flood vulnerability and the range of uncertainty were performed for future S1 (2010~2039), S2 (2040~2069), S3 (2070~2099) period compared to reference S0 (1971~2000) period. The results of this study shows that vulnerable region's were Han and Sumjin, Youngsan river basins under current climate system. Considering the climate scenarios, variability in Nakdong, Gum and Han river basins are large, but Sumjin river basin had little variability due to low basic-stream ability to adaptation.