• Journal of Bio-Environment Control
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• v.22 no.2
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• pp.182-187
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• 2013

In closed plant production system like plant factory, changes in environmental factors should be identified for conducting efficient environmental control as well as predicting energy consumption. Since high relative humidity (RH) is essential for crop production in the plant factory, transpiration is closely related with RH and should be quantified. In this study, four varieties of lettuces (Lactuca sativa L.) were grown in a plant factory, and the leaf areas and transpiration rates of the plants according to DAT (day after transplanting) were measured. The coefficients of the simplified Penman-Monteith equation were calibrated in order to calculate the transpiration rate in the plant factory and the total amount of transpiration during cultivation period was predicted by simulation. The following model was used: $E_d=a*(1-e^{-k*LAI})*RAD_{in}+b*LAI*VPD_d$ (at daytime) and $E_n=b*LAI*VPD_n$ (at nighttime) for estimating transpiration of the lettuce in the plant factory. Leaf area and transpiration rate increased with DAT as exponential growth. Proportional relationship was obtained between leaf area and transpiration rate. Total amounts of transpiration of lettuces grown in plant factory could be obtained by the models with high $r^2$ values. The results indicated the simplified Penman-Monteith equation could be used to predict water requirements as well as heating and cooling loads required in plant factory system.

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

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.929-938
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• 2020

In this study, we estimated missing evapotranspiration (ET) data at a eddy-covariance flux tower in the Cheongmicheon farmland site using the Artificial Neural Network (ANN). The ANN showed excellent performance in numerical analysis and is expanding in various fields. To evaluate the performance the ANN-based gap-filling, ET was calculated using the existing gap-filling methods of Mean Diagnostic Variation (MDV) and Food and Aggregation Organization Penman-Monteith (FAO-PM). Then ET was evaluated by time series method and statistical analysis (coefficient of determination, index of agreement (IOA), root mean squared error (RMSE) and mean absolute error (MAE). For the validation of each gap-filling model, we used 30 minutes of data in 2015. Of the 121 missing values, the ANN method showed the best performance by supplementing 70, 53 and 84 missing values, respectively, in the order of MDV, FAO-PM, and ANN methods. Analysis of the coefficient of determination (MDV, FAO-PM, and ANN methods followed by 0.673, 0.784, and 0.841, respectively.) and the IOA (The MDV, FAO-PM, and ANN methods followed by 0.899, 0.890, and 0.951 respectively.) indicated that, all three methods were highly correlated and considered to be fully utilized, and among them, ANN models showed the highest performance and suitability. Based on this study, it could be used more appropriately in the study of gap-filling method of flux tower data using machine learning method.

• 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 Soil Science and Fertilizer
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• v.39 no.5
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• pp.274-279
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• 2006

The exact estimation of crop evapotranspiration containing reference or potential evapotranspiration is necessary for decision of crop water requirements. This study was carried out for the evaluation and application of various meteorological elements used for the calculation of reference evapotranspiration (RET) by FAO Penman-Monteith (PM) model. Meteorological elements including temperature, net radiation, soil heat flux, albedo, relative humidity, wind speed measured by meteorological instruments are required for RET calculation by FAO PM model. The average of albedo measured for crop growing period was 0.20, ranging from 0.12 to 0.23, and was slightly lower than 0.23. Determinant coefficients by measured albedo and green grass albedo were 0.97, 0.95 and standard errors were 0.74, 0.80 respectively. Usefulness of deductive regression models was admitted. To assess an influence of soil heat flux (G) on FAO PM, RET with G=0 was compared with RETs using G at 5cm soil depth ($G_{5cm}$) and G at surface ($G_{0cm}$). As the results, RET estimated by G=0 was well agreed with RET calculated by measured G. Therefore, estimated net radiation, G=0 and albedo of green grass could be used for RET calculation by FAO PM.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.230-230
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• 2016

Hydrological modeling is a very complex task dealing with multi-source of data, but it can be potentially benefited from recent improvements and developments in remote sensing. The estimation of actual land surface evapotranspiration (ET), an important variable in water management, has become possible based entirely on satellite data. This study adopted a Surface Energy Balance Algorithm for Land (SEBAL) with the use of MODerate Resolution Imaging Spectrometer (MODIS) satellite products. The SEBAL model is one of the commonly used approach for the ET estimation. A primary advantage of the SEBAL model is rather its minimum requirement for ground-based weather data. The MODIS provides ET (MOD16) product that is based on the Penman-Monteith equation. This study aims to further develop the SEBAL model by employing a more rigorous parameterization scheme including the estimation of uncertainty associated with parameter and model selection in regression model. Finally, the proposed model is compared with the existing approaches and comprehensive discussion is then provided.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.471-479
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• 2011

Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a hydrological factor that has an important role in water cycle. However, there is a limitation to understand it due to heterogeneity of land cover and vegetation. In this study, Mapping EvapoTRanspiration with Internalized Calibration (METRIC) model, one of the energy balance models, and MODerate resolution Imaging Spectroradiometer (MODIS) satellite based well-known Penman-Monteith algorithm were compared. Two ET maps were categorized and compared by land cover classification. The results represented overall applicability of the two models with the highest correlation coefficients in needleleaf and broadleaf forests. This study will be useful to estimate remote sensing based ET maps with high resolution and to figure out spatio-temporal variability and seasonal changes.

• 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 Korea Water Resources Association Conference
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• 2017.05a
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• pp.428-428
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• 2017

In this study, three optimization techniques efficiency is assessed for calibration of the GR4J model for streamflow simulation in Selmacheon, Boryeong Dam and Kyeongancheon watersheds located in South Korea. The Penman-Monteith equation is applied to estimate the potential evapotranspiration, model calibration, and validation is carried out using the readily available daily hydro-meteorological data. The Shuffled Complex Evolution-University of Arizona(SCE-UA), Uniform Adaptive Monte Carlo (UAMC), and Coupled Latin Hypercube and Rosenbrock (CLHR) optimization techniques has been used to evaluate the robustness, performance and optimized parameters of the three catchments. The result of the three algorithms performances and optimized parameters are within the recommended ranges in the tested watersheds. The SCE-UA and CLHR outputs are found to be similar both in efficiency and model parameters. However, the UAMC algorithms performances differently in the three tested watersheds.