• Korean Journal of Soil Science and Fertilizer
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• v.29 no.1
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• pp.34-43
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• 1996

Determining the actual evapotranspiration(ETa) of a crop, and appropriate water management of the crop based on the ETa are very important For increasing the yield. The present study aimed at determining ETa and crop coefficient of sesame growing under different climatic conditions with the transparent thin polyethylene film mulch(0.03 mm thick) and without this mulch. Bottomless cylindrical lysimeters(105cm in diameter, 120cm in height, protruded 20 cm above the soil surface) were installed on the field of sandy loam "Bonyang series" soil with a moderate drainage. The determination of ETa was performed by measuring each component of a model equation, $ETa=(R+I)-\{Ro+(D1+D2)\}+C{\pm}{\Delta}S$. Sesame, cv. "Ansan" was sown in two rows with the spacing of $50{\times}15cm$ on May 10 in 1991 and 1992. The mulching covers 80% of the soil surface. Sesame consumed the water of 139.0 mm(1.53 mm/day) and 171.2 mm(1.59 mm/day) in ETa without the film mulch, but that of 132.6 mm(1.46 mm/day) and 199.8 mm(1.85 mm/day) with its mulching through both years of 1991 and 1992, respectively. The ETa's accounted for 52 and 69% of the potential evapotranspiration(ETp) in the mulched crop, and 54 and 59% of ETp in the non-mulched crop 1991 through 1992, respectively. Its ETa's were much more and their gap between the mulching and non-mulching treatment was larger in 1992 than in 1991 as a result of the better climatic condition of 1992.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.161-170
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• 2008

Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with $r^2$value of 0.69 and root mean square error of $0.5\;ton\;ha^{-1}$. Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

• Korean Journal of Soil Science and Fertilizer
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• v.32 no.4
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• pp.344-356
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• 1999

A new and relatively simple equation for the soil water content-pressure head curve, ${\theta}$(h) is described in this paper. The particular form of the equation enables one to derive closed-form analytical expressions for the relative hydraulic conductivity, Kr, when substituted in the predictive conductivity models of Y. Mualem. Hopmans' equation is presented as an experimental method. The experienced method, $ET_a=K_sK_cET_o$ is introduced to estimate the actual evapotranspiration, $ET_a$(or $ET_c$). Using $ET_c$ and coil water data measured automatically in a weighing lusimeter, $K_s$ and $K_c$ values are estimated. Recently, FAO has introduced calculation procedures for the soil water(stress) coefficient, Ks in "Guidelines for computing crop water requirements".

• Journal of Soil and Groundwater Environment
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• v.24 no.2
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• pp.23-37
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• 2019

To evaluate the available groundwater supply to the agricultural water demand in the future with the climate change scenarios for 40 sub-regions in Jeju Island, groundwater recharge and the available groundwater supply were estimated using water balance analysis method. Groundwater recharge was calculated by subtracting the actual evapotranspiration and direct runoff from the total amount of water resources and available groundwater supply was set at 43.6% from the ratio of the sustainable groundwater capacity to the groundwater recharge. According to the RCP 4.5 scenario, the available groundwater supply to the agricultural water demand is estimated to be insufficient in 2020 and 2025, especially in the western and eastern regions of the island. However, such a water shortage problem is alleviated in 2030. When applying the RCP 8.5 scenario, available groundwater supply can't meet the water demand over the entire decade.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.295-295
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• 2023

물수지 분석은 우리가 사용하는 물의 순환과정을 파악하여 우리 생활에 필요한 물을 안정적으로 공급하고 관리하기 위한 기초 자료이다. 물관리 기본법 제11조에도 유역 간의 물관리는 조화와 균형을 이루고 있는 기본원칙으로 설명되고 있으며 지속가능한 개발, 이용과 보전을 도모하고 물로인해 발생하는 재해를 예방하기 위해서는 유역단위로 관리되어야 함을 원칙으로 두고 있다. 최근 들어 국내에서는 강수량과 유량에 대한 조사가 급격히 발전함에 따라 정확도 높은 관측이 수행되고 있는 반면에 증발산량 같은 경우에는 경험식에 의존하여 측정자료를 산정하고 있는 실정이다. 증발산량은 눈에 보이지 않아 비교적 중요성을 인지하고 있지 못하나 강수량의 약 30~40%를 차지함으로 오차를 무시하기 어려우며 보다 정확한 관측이 필요하다. 실측으로는 증발접시가 있지만 물이 항상 차 있어야 하며, 팬의 가열, 강수 등 관측값 보정이 필요하다. 최근 기술의 발전으로 에디공분산 방법이 장비로 구현할 수 있게 되었으며 이러한 방법은 기존의 장비에서 발생되는 근본적인 문제점을 해결하였다. 특히 증발과 증산을 모두 측정이 가능하며 실제 증발산량 측정이 가능하다. 환경부에서는 에디공분산을 활용한 증발산량 관측소 13개소를 운영하고 있으며 관측소 인근 실제 유량측정하고 있는 지역과 연계하여 실측 기반의 물수지 검토를 수행해보고자 한다.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.209-218
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• 2020

This paper investigated actual crop evapotranspiration (ET_c) of tomato and paprika planted in test beds of the greenhouse. Crop water requirement (CWR) is the amount of water required to compensate ET_c loss from the crop. The main objectives of the study are to assess whether the actual crop watering (ACW) was adequate CWR of tomato and paprika and which amount of ACW should be irrigated to each crop. ET_c was estimated using the Penman-Monteith model (P-M) for each crop. ACW was calculated from the difference of amount of nutrient supply water and amount of nutrient drainage water. ACW and CWR of each crop were determined, compared and assessed. Results indicated CWR-tomato was around 100 to 1,200 ml/day, while CWR-paprika ranged from 100 to 500 ml/day. Comparison of ACW and CWR of each crop found that the difference of ACW and CWR are fluctuated following day of planting (DAP). However, the differences could divide into two phases, first the amount of ACWs of each crop are less than CWR in the initial phase (60 DAP) around 500 ml/day and 91 ml/day, respectively. Then, ACWs of each crop are greater than the CWR after 60 DAP until the end of cultivation approximately 400 ml/day in tomato and 178 ml/day in paprika. ET_c assessment is necessary to correctly quantify crop irrigation water needs and it is an accurate short-term estimation of CWR in greenhouse for optimal irrigation scheduling. Thus, reducing ACW of tomato and paprika in the greenhouse is a recommendation. The amount of ACW of tomato should be applied from 100 to 1,200 ml/day and paprika is 100 to 500 ml/day depend on DAP.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1803-1818
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• 2021

As the frequency of drought increases due to climate change, it is very important to have a monitoring system that can accurately determine the situation of widespread drought. However, while ground-based meteorological data has limitations in identifying all the complex droughts in Korea, satellite remote sensing data can be effectively used to identify the spatial characteristics of drought in a wide range of regions and to detect drought. This study attempted to analyze the possibility of using remote sensing data for drought identification in South Korea. In order to monitor various aspects of drought, remote sensing and ground observation data of precipitation and potential evapotranspiration, which are major variables affecting drought, were collected. The evaluation of the applicability of remote sensing data was conducted focusing on the comparison with the observation data. First, to evaluate the applicability and accuracy of remote sensing data, the correlations with observation data were analyzed, and drought indices of various aspects were calculated using precipitation and potential evapotranspiration for meteorological drought monitoring. Then, to evaluate the drought monitoring ability of remote sensing data, the drought reproducibility of the past was confirmed using the drought index. Finally, a high-resolution drought map using remote sensing data was prepared to evaluate the possibility of using remote sensing data for actual drought in South Korea. Through the application of remote sensing data, it was judged that it would be possible to identify and understand various drought conditions occurring in all regions of South Korea, including unmeasured watersheds in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.32-32
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• 2023

Incidences of urban flood and extreme heat waves (due to the urban heat island effect) are expected to increase in New Zealand under future climate change (IPCC 2022; MfE 2020). Increasingly, the mitigation of such events will depend on the resilience of a range Nature-Based Solutions (NBS) used in Sustainable Urban Drainage Schemes (SUDS), or Water Sensitive Urban Design (WSUD) (Jamei and Tapper 2019; Johnson et al 2021). Understanding the impact of changing precipitation and temperature regimes due climate change is therefore critical to the long-term resilience of such urban infrastructure and design. Cuthbert et al (2022) have assessed the trade-offs between the water retention and cooling benefits of different urban greening methods (such as WSUD) relative to global location and climate. Using the Budyko water-energy balance framework (Budyko 1974), they demonstrated that the potential for water infiltration and storage (thus flood mitigation) was greater where potential evaporation is high relative to precipitation. Similarly, they found that the potential for mitigation of drought conditions was greater in cooler environments. Subsequently, Jaramillo et al. (2022) have illustrated the locations worldwide that will deviate from their current Budyko curve characteristic under climate change scenarios, as the relationship between actual evapotranspiration (AET) and potential evapotranspiration (PET) changes relative to precipitation. Using the above approach we assess the impact of future climate change on the urban water-energy balance in three contrasting New Zealand cities (Auckland, Wellington, Christchurch and Invercargill). The variation in Budyko curve characteristics is then used to describe expected changes in water storage and cooling potential in each urban area as a result of climate change. The implications of the results are then considered with respect to existing WSUD guidelines according to both the current and future climate in each location. It was concluded that calculation of Budyko curve deviation due to climate change could be calculated for any location and land-use type combination in New Zealand and could therefore be used to advance the general understanding of climate change impacts. Moreover, the approach could be used to better define the concept of urban infrastructure resilience and contribute to a better understanding of Budyko curve dynamics under climate change (questions raised by Berghuijs et al 2020)). Whilst this knowledge will assist in implementation of national climate change adaptation (MfE, 2022; UNEP, 2022) and improve climate resilience in urban areas in New Zealand, the approach could be repeated for any global location for which present and future mean precipitation and temperature conditions are known.

• Ecology and Resilient Infrastructure
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• v.4 no.2
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• pp.115-121
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• 2017

This study estimated potential natural vegetation in Gayasan National Park through the occurrence probability distribution by using geographic information system (GIS). in Gayasan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. The presence of the Gaya National Park Vegetation survey results showed that 128 communities were distributed. The analyzed relationship between actual vegetation and distribution factors such as elevation, aspect, slope, topographic index, annual mean temperature, warmth index and potential evapotranspiration in Gayasan national park. The probability distribution of potential natural vegetation communities at least 0.3 odds were the advent of Pinus densiflora communities with the highest 55.80%, Quercus mongolica community is 44.05%, 0.09% is Quercus acutissima communities, Quercus variabilis communities are found to be 0.06%. If you want to limit the factors that affect the distribution of vegetation by factors presented in this study, the potential natural vegetation of the Gaya National Park was expected to appear in Quercus mongolica community (43.1%) and Pinus densiflora communities (56.9%).