• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.4
• /
• pp.229-237
• /
• 2019

This study was conducted to estimate annual reference evapotranspiration (ET₀) for the agro-climatic zones for rice paddy fields in South Korea between 1980 and 2015. The daily ET₀ was estimated by applying the Penman-Monteith method to meteorological data from 61 weather stations provided by Korean Meteorological Administration (KMA). The average of annual ET₀ from 1980 to 2015 was 1334.1±33.89 mm. The ET₀ was the highest at the Southern Coastal Zone due to their higher air temperature and lower relative humidity. The ET₀ had significantly increased with 2.81 mm/yr for the whole zones over 36 years. However, the change rate of it was different among agro-climatic zones. The annual ET₀ highly increased in central zones and eastern coastal zones. In terms of correlation coefficient, the temporal change of the annual ET₀ was closely related to variations of four meteorological factors (i.e., mean, minimum temperatures, sunshine duration, and relative humidity). The results demonstrated that whole Korean agro-climatic zones have been undergoing a significant change in the annual ET₀ for the last 36 years. Understanding the spatial pattern and the long-term variation of the annual ET₀ associated with global warming would be useful to improve crop and water resource managements at each agro-climatic zone of South Korea.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.459-469
• /
• 2010

This study is to develop a grid-based daily runoff model considering seasonal vegetation canopy condition. The model simulates the temporal and spatial variation of runoff components (surface, interflow, and baseflow), evapotranspiration (ET) and soil moisture contents of each grid element. The model is composed of three main modules of runoff, ET, and soil moisture. The total runoff was simulated by using soil water storage capacity of the day, and was allocated by introducing recession curves of each runoff component. The ET was calculated by Penman-Monteith method considering MODIS leaf area index (LAI). The daily soil moisture was routed by soil water balance equation. The model was evaluated for 930 $km^2$ Yongdam watershed. The model uses 1 km spatial data on landuse, soil, boundary, MODIS LAI. The daily weather data was built using IDW method (2000-2008). Model calibration was carried out to compare with the observed streamflow at the watershed outlet. The Nash-Sutcliffe model efficiency was 0.78~0.93. The watershed soil moisture was sensitive to precipitation and soil texture, consequently affected the streamflow, and the evapotranspiration responded to landuse type.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.4
• /
• pp.238-249
• /
• 2019

The evapotranspiration is estimated based on weather factors such as temperature, wind speed and humidity, and the Hargreaves equation is a simple equation for calculating evapotranspiration using temperature data. However, the Hargreaves equation tends to be underestimated in areas with wind speeds above 3 m s^-1 and overestimated in areas with high relative humidity. The study was conducted to determine Hargreaves equation coefficient in 82 regions in Korea by comparing evapotranspiration determined by modified Hargreaves equation and the Penman-Monteith equation for the time period of 2008~2018. The modified Hargreaves coefficients for 50 inland areas were estimated to be 0.00173~0.00232(average 0.00196), which is similar to or lower than the default value 0.0023. On the other hand, there are 32 coastal areas, and the modified coefficients ranged from 0.00185 to 0.00303(average 0.00234). The east coastal area was estimated to be similar to or higher than the default value, while the west and south coastal areas showed large deviations by area. As results of estimating the evapotranspiration by the modified Hargreaves coefficient, root mean square error(RMSE) is reduced from 0.634~1.394(average 0.857) to 0.466~1.328(average 0.701), and Nash-Sutcliffe Coefficient(NSC) increased from -0.159~0.837(average 0.647) to -0.053~0.910(average 0.755) compared with original Hargreaves equation. Therefore, we confirmed that the Hargreaves equation can be overestimated or underestimated compared to the Penman-Monteith equation, and expected that it will be able to calculate the high accuracy evapotranspiration using the modified Hargreaves equation. This study will contribute to water resources planning, irrigation schedule, and environmental management.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.2
• /
• pp.97-104
• /
• 2019

The purpose of this study was to establish the estimation method of irrigation water amount for sewage treated water reuse for agricultural purpose. To calculate the irrigation water amount, we adopted Penman-Monteith for potential evapotranspiration estimation and applied crop coefficient and irrigation efficiency factor. We developed the irrigation water amount calculation program using C language in Xcode environment. The target district for calculation is having 259 ha of agricultural land located near the Jinyeong Clear Water Circulation Center in Hanrim-myeon, Gimhae city. The meteorological data of the study area were obtained from Changwon weather station from 1986 to 2017. Calculated average and maximum of annual mean potential evapotranspiration were 2.72 mm/day and 6.22 mm/day, respectively. We used K-S (Kolmogorov-Smirnov) for goodness-of-fit test to find optimal probability distribution of annual mean and maximum evapotranspiration. As a result, the normal distribution was selected for the appropriate distribution. The annual mean and maximum potential evapotranspiration for 10-year return period by applying normal distribution were 2.88 mm/day and 6.76 mm/day, respectively. Assuming that the irrigation efficiency is 80%, the irrigation water requirement was calculated as $36.05m^3/day/ha$ and $84.45m^3/day/ha$, respectively, when annual mean and maximum potential evapotranspiration were applied. The actual irrigation water amount can be calculated by applying the crop coefficient and cropping days for the study area based on the developed irrigation water amount estimation program in this study.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.724-730
• /
• 2004

본 연구에서는 GIS와 연계되는 SWAT 모형을 이용하여 소양강댐 유역의 일 유출량을 모의하였으며, 모형에서 제공하는 단일 지점을 이용하는 기본 방법과 다지점 강우를 이용하기 위한 방법으로 나누어 비교하였다. 모형의 민감도 분석을 통해 매개변수를 최적화 하였고, 잠재 증발산량을 산정하기 위하여 Penman-Monteith 방법을 이용하였다. 과거의 관측 수문곡선을 SWAT 모형에 의해 모의된 일 유출 수문곡선과 비교한 결과, 두 가지 방법 모두 총 유출체적은 물수지에 기본을 둔 모형의 특성상 잘 일치 하였다. 그러나 갈수기와 홍수기의 일 유출 수문곡선은 다지점의 강우자료를 이용한 경우가 더 적합함을 알 수 있었다. 또한 SWAT 모형이 장기 일 유출량 모의에 적용 가능함을 확인하였다.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.39 no.4
• /
• pp.114-121
• /
• 1997

In order to provide basic information for the estimation of evapotranspiration in the ponded direct seeding paddy field, both field lysimeter experiment and model prediction were performed to estimate daily ET. Various methods were used to predict daily reference crop ET and crop coefficients. Measure4 mean daily ET during the 1995 growing season varied from 5.9 to 6.1 mm depending on the species, while it varied from 5.1 to 5.5 mm in 1996. Model predicted mean daily ET during the 1995 growing season varied from 3.9 to 4.9 mm depending on the prediction model, while it varied from 3.5 to 4.7 mm in 1996. The smaller ET values both measured and predicted in 1996 were caused by the low values of temperature, sunshine hours, and solar radiation. Crop coefficients varied from 1.20 to 1.50 in 1995 depending on the prediction model, while it varied from 1.10 to 1.47 in 1996. Comparison of the seven reference crop ET prediction methods used in this study shows that the Penman-Monteith method and the FAO-Radiation method gave the lowest ET while the corrected Penman method and the Hargreaves method gave the largest ET. Since crop coefficients vary to a large extent based on the prediction methods, reference crop ET prediction method should be carefully selected in irrigation planning.

• Proceedings of the Korea Water Resources Association Conference
• /
• 1991.07a
• /
• pp.102-113
• /
• 1991

한정된 수자원을 관리하기 위한 물관리시스템을 구축운영하는데 수문학적 물손실의 관점에서 증발산은 매우 중요한 요소이다. 본 연구에서는 이러한 증발산이 실제 유역에서 변화하는 것을 일별로 추정하기 위해서 유역일증발산 모형을 개발하여 실제증발산을 추정하였다. 실제증발산은 잠재증발산을 먼저 산정하고 이를 이용하여 강우차단에 의한 증발, 토양증발, 지표하 증산으로 나누어 추정하였다. 잠재증발산 추정방법은 토양-식생-대기의 관계를 정교히 모식할 수 있는 Penman-Monteith 방법을 채택하였고, 강우차단에 의한 증발은 Bultot와 Dupriez가 제안한 모형을, 토양증발은 Ritchie가 제시한 모형을 채택하였다. 증산은 토양층을 상부·하부층으로 나누어 상부층증산과 하부층 증산으로 분리 추정하였으며, 증발산과 토양수분의 관계는 Thornthwait-Mather의 관계를 이용하여 추정하였다. 이 모형의 타당성을 임업연구원에서 운영하고 있는 2개의 시험유역을 대상으로 검토하였다. 그결과 실제일증발산을 년단위로 계산한 실제증발산값은 물수지 개념에 의한 실제증발산결과와 비교할 때 약 8~30% 오차를 수반하고 있었다. 이러한 결과는 모형의 실제유역의 적용에 있어서 입력되는 여러 매개변수가 가정에 의해 결정된 것이기 때문일 것이라고 판단된다.

• Journal of Korea Water Resources Association
• /
• v.53 no.9
• /
• pp.701-715
• /
• 2020

This study compared several reference evapotranspiration estimated using eight methods such as FAO-56 Penman-Monteith (FAO PM), Hamon, Hansen, Hargreaves-Samani, Jensen-Haise, Makkink, Priestley-Taylor, and Thornthwaite. In addition, by analyzing the monthly deviations of the results by the FAO PM and the remaining seven methods, monthly optimized correction coefficients were derived and the improvement effect was evaluated. These methods were applied to 73 automated synoptic observation system (ASOS) stations of the Korea Meteorological Administration, where the climatological data are available at least 20 years. As a result of evaluating the reference evapotranspiration by applying the default coefficients of each method, a large fluctuation happened depending on the method, and the Hansen method was relatively similar to FAO PM. However, the Hamon and Jensen-Haise methods showed more large values than other methods in summer, and the deviation from FAO PM method was also large significantly. When comparing based on the region, the comparison with FAO PM method provided that the reference evapotranspiration estimated by other methods was overestimated in most regions except for eastern coastal areas. Based on the deviation from the FAO PM method, the monthly correction coefficients were derived for each station. The monthly deviation average that ranged from -46 mm to +88 mm before correction was improved to -11 mm to +1 mm after correction, and the annual average deviation was also significantly reduced by correction from -393 mm to +354 mm (before correction) to -33 mm to +9 mm (after correction). In particular, Hamon, Hargreaves-Samani, and Thornthwaite methods using only temperature data also produced results that were not significantly different from FAO PM after correction. It can be also useful for forecasting long-term reference evapotranspiration using temperature data in climate change scenarios or predicting evapotranspiration using monthly or seasonal temperature forecasted values.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.6
• /
• pp.111-121
• /
• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2B
• /
• pp.199-213
• /
• 2008

The goal of this research is to develop and apply the integrational operation method (IOM) for the modeling of the monthly pan evaporation (PE) and the alfalfa reference evapotranspiration ($ET_r$). Since the observed data of the alfalfa $ET_r$ using lysimeter have not been measured for a long time in Republic of Korea, Penman-Monteith (PM) method is used to estimate the observed alfalfa $ET_r$. The IOM consists of the application of the stochastic and neural networks models, respectively. The stochastic model is applied to generate the training dataset for the monthly PE and the alfalfa $ET_r$, and the neural networks models are applied to calculate the observed test dataset reasonably. Among the considered six training patterns, 1,000/PARMA(1,1)/GRNNM-GA training pattern can evaluate the suggested climatic variables very well and also construct the reliable data for the monthly PE and the alfalfa $ET_r$. Uncertainty analysis is used to eliminate the climatic variables of input nodes from 1,000/PARMA(1,1)/GRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Finally, it can be to model the monthly PE and the alfalfa $ET_r$ simultaneously with the least cost and endeavor using the IOM.