• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.4
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• pp.51-61
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• 2002

The objective of this study is to develop GLEANS-PADDY model to predict nutrients loading from paddy-field areas. This model is developed by modifying the GLEANS model which is used for uplands, and composed of hydrology and nutrient submodels. The optimal field size for CLEANS-PADDY model application is about up to 50 ha with mild slope, relatively homogeneous Soils and spatially rainfall, and a single crop farming. The CLEAMS model is modified to handle ponded soil surface condition and saturated soil profile in paddy field. In the hydrology submodel of the CLEAMS-PADDY model. the ponded depth routing method is used to handle the ponded water condition of paddy field. To compute potential evapotranspiration the FAO-24 Corrected Blaney-Criddle method is used for paddy field instead of Penman-Monteith method in the CLEAMS model. In the nutrients submodel of the CLEAMS-PADDY model, the soil was assumed saturated and soil profile in the root zone was divided into oxidized and reduced zones.

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

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.247-250
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• 2003

Crop evapotranspiration rates of the garlic and potato were measured in a lysimeter at National Jeju Agricultural Experiment Station, Rural Development Administration, Korea. The crop coefficients were calculated using the values of the actually measured evapotranspiration(ETcrop) and the reference crop evapotranspiration (ETo) estimated by the Penman-Monteith equation. The maximum crop coefficients of the potato and garlic were 1.07 and 1.31 respectively. A water requirement model using the moisture accounting method is presented. The moisture accounting method is illustrated by the example (Table 2). As soon as the accumulated deficit exceeds 22 mm, a further irrigation is supplied.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.889-902
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• 2020

This study was performed in Seolmacheon basin to evaluate the adequacy of GLDAS (Global Land Data Assimilation System) and GLEAM (Global Land Evaporation Amsterdam Model) evapotranspiration data. The verification data necessary for the evaluation of adequacy were calculated after processing the latent heat flux data produced in the Seolmacheon basin with the Koflux program. In order to gap-fill the empty period, alternative evapotranspiration was calculated in three ways: FAO-PM (Food and Agriculture Organization-Penman Monteith), MDV (Mean Diurnal Variation) and Kalman Filter. This study selected Kalman Filter method as the data gap-filling method because it showed the best Bias and RMSE among the three methods. The amount of GLDAS spatial evapotranspiration was calculated as Noah (version 2.1) with a time interval of 3 hours and a spatial resolution of 0.25°. The amount of GLEAM spatial evapotranspiration was calculated using GLEAM (version 3.1a). This study evaluated the spatial evapotranspiration of GLDAS and GLEAM as the evapotranspiration based on eddy covariance. As a result of evaluation, GLDAS spatial evapotranspiration showed better results than GLEAM. Accordingly, in this study, the GLDAS method was proposed as a method for calculating the amount of spatial evapotranspiration in the Seolmacheon basin.

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

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.183-183
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• 2017

Pan evaporation (Epan) is an important indicator of water and energy balance. Despite global warming, decreasing annual Epan has been reported across different continents over last decades, which is claimed as pan evaporation paradox. However, such trend is not necessarily found in seasonal data because the level of contributions on Epan vary among meteorological components. This study investigates long-term trend in seasonal pan evaporation from 1908 to 2016 across South Korea. Meteorological variables including air temperature (Tair), wind speed (U), vapor pressure deficit (VPD), and solar radiation (Rs) are selected to quantify the effects of individual contributing factor to Epan. We found overall decreasing trend in Epan, which agrees with earlier studies. However, mixed tendencies between seasons due to variation of dominant factor contributing Epan were found. We also evaluated the reference evapotranspiration based on Penman-Monteith method and compared this with Epan to better understand the physics behind the evaporation paradox.

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

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1067-1071
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• 2008

장기적인 수자원정책을 수립하기 위해서는 강수, 증발산, 유출 등의 물수지의 변동성을 평가하는 것이 중요하다. 특히 기후변화로 인한 기온 증가는 증발산량에 영향을 미칠 것이다. 따라서 기후변화에 따른 수자원의 영향을 신뢰성 있게 평가하기 위해서는 증발산량의 산정방법에 대한 불확실성을 평가하는 것이 필요하다. 본 연구에서는 다섯 가지의 증발산량 산정방법에 대해 기온 및 강수변화에 따라 증발산량 계산과 유출량산정에 미치는 영향을 평가하였다. 안동댐 유역에 대해 준분포형 수문모형인 SLURP를 이용하여 기온과 강수변화에 따른 5가지 증발산량 산정방법의 민감도를 분석하였다. SLURP 모형에서는 Penman-Monteith method, Morton CRAE method, Spittlehouse/Black method, Granger method, Linacre method의 다섯 가지방법을 제시하고 있고, 관측 자료에 대해 검 보정을 수행한 결과 5개의 증발산량 산정법 모두 안동댐 유역에 대해 잘 모의하는 것으로 나타났다. 기온과 온도를 변화시킨 합성시나리오에서 Linacre 방법이 다른 방법들과 비교하여 높은 민감도를 나타내었는데 증발산량 산정법별 구조적 차이가 원인 것으로 판단되어 추가적인 연구가 진행 중이다. 결과적으로 각 증발산량 산정방법에 따른 민감도 차이는 기후변화 영향평가 결과의 불확실성을 제시하는 척도가 될 것이다.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.