• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.495-504
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• 2008

Evapotranspiration (ET) is an important state variable while simulating daily streamflow in hydrological models. In the estimation of ET, for example, when using FAO Penman Monteith equation, the LAI (Leaf Area Index) value reflecting the conditions of vegetation generally affects considerably. Recently in evaluating the vegetation condition as a fixed quantity, the remotely sensed LAI from MODIS satellite data is available, and the time series values of spatial LAI coupled with land use classes are utilized for ET evaluation. Four years (2001-2004) of MODIS LAI was prepared for the evaluation of Penman Monteith ET in the continuous hydrological model, SLURP (Semi-distributed Land Use-based Runoff Processes). The model was applied for simulating the dam inflow of Chungju watershed ($6661.3km^2$) located in the upstream of Han river basin. For four years (2001-2004) dam inflow data and meteorological data, the model was calibrated and verified using MODIS LAI data. The average Nash-Sutcliffe model efficiency was 0.66. The 4 years watershed average Penman Monteith ETs of deciduous, coniferous, and mixed forest were 639.1, 422.4, and 631.6 mm for average MODIS LAI values of 3.64, 3.50, and 3.63 respectively.

• Journal of Korea Water Resources Association
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• v.37 no.2
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• pp.133-144
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• 2004

A Tank Model composed of 4 tanks with soil moisture structure was applied to Daecheong Dam and Soyanggang Dam watersheds. Calibration and verification were repeated 332 and 472 times for each watershed using SCE-UA global optimization method for different calibration periods and objective functions. Four different methods of evapotranspiration calculation were used and evaluated. They are pan evaporation, 1963 Penman, FAO-24 Penman-Monteith, and FAO-56 Penman-Monteith methods. Tank model with soil moisture structure showed better results than the standard tank model for daily rainfall-runoff simulation. Two types of objective function for model calibration were found. Proper calibration period are 3 years, in which dry year and flood year are included. If a calibrationperiod has an inadequate runoff rate, the period should be more than 8 years. The four methods of eyapotranspiraton computation showed similar results, but 1963 Penman method was slightly inferior to the other methods.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.35 no.6
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• pp.518-524
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• 1990

Actual evapotranspiration was measured over rice paddy field by Bowen ratio heat balance method and based on this, investigated was the reliability of actual evapotranpiration estimation from Class-A Pan and small pan evaporation and reference evapotranspiration calculated by modified Penman-Monteith model. Crop coefficients based on Class-A Pan and small pan evaporation and reference evapotranspiration by modified Penman-Monteith model were averaged to be 1.57. 1.10 and 1.49 over the whole rice growing season, respectively. Their respective coefficients of variation were 28.7. 22.7 and 12.8 percent, respectively. Crop coefficient based on modified Penman-Monteith model varied in good agreement with the trend of leaf area development, being greatest around heading stage.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1087-1091
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• 2008

Evapotranspiration (ET) is an important factor while simulating daily streamflow in hydrological models. The LAI (Leaf Area Index) value reflecting the conditions of vegetation generally affects considerably in the estimation of ET, for example, when using FAO Penman Monteith equation. Recently in evaluating the vegetation condition as a fixed quantity, the remotely sensed LAIs from MODIS satellite data are avaliable, and the time series values of spatial LAI coupled with land use classes are utilized for ET evaluation. The 4 years (2001-2004) MODIS LAI data were prepared for the evaluation of continuous hydrological model, SLURP (Semi-distributed Land Use-based Runoff Processes). The model was applied for simulating the dam inflow of Chungjudam watershed ($6661.58\;km^2$) located in the upstream of Han river basin of South Korea. From the model results, the FAO Penman Monteith ET was affected by the MODIS LAIs. Especially for the ET of deciduous forest, the Total ET was 33.9 % lager than coniferous forest for the 3.8 % lager of LAI. The watershed average LAI caused a 7.0 % decrease in average soil moisture of the watershed and 14.3 % decrease of ground water recharge.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.233-242
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• 2010

SEBAL (Surface Energy Balance Algorithm for Land) developed by Bastiaanssen (1995) is an image-processing model comprisedof twenty-five sub models that calculates spatial evapotranspiration (ET) and other energy exchanges at the surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near infrared. In this study, the model was applied to Gyeongancheon watershed, the main tributary of Han river Basin. ET was computed on apixel-by-pixel basis from an energy balance using 4 years (2001-2004) Landsat and MODIS images. The scale effect between Landsat (30 m) and MODIS (1 km) was evaluated. The results both from Landsat and MODIS were compared with FAO Penman-Monteith ET. The absolute errors between satellite ETs and Penman-Monteith ET were within 12%. The spatial and temporal characteristics of ET distribution within the watershed were also analyzed.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.411-417
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• 2017

Environmental and growth factors such as light intensity, vapor pressure deficit, and leaf area index are important variables that can change the transpiration rate of plants. The objective of this study was to compare the transpiration rates estimated by modified Penman-Monteith model and artificial neural network. The transpiration rate of paprika (Capsicum annuum L. cv. Fiesta) was obtained by using the change in substrate weight measured by load cells. Radiation, temperature, relative humidity, and substrate weight were collected every min for 2 months. Since the transpiration rate cannot be accurately estimated with linear equations, a modified Penman-Monteith equation using compensated radiation (Shin et al., 2014) was used. On the other hand, ANN was applied to estimating the transpiration rate. For this purpose, an ANN composed of an input layer using radiation, temperature, relative humidity, leaf area index, and time as input factors and five hidden layers was constructed. The number of perceptons in each hidden layer was 512, which showed the highest accuracy. As a result of validation, $R^2$ values of the modified model and ANN were 0.82 and 0.94, respectively. Therefore, it is concluded that the ANN can estimate the transpiration rate more accurately than the modified model and can be applied to the efficient irrigation strategy in soilless cultures.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.86-92
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• 1999

Evapotranspiration is one of important variables affecting ecosystem processes such as vegetation distribution and growth. It acts as a limiting factor for natural water resource management. The transpiration of vegetation is mainly determined by climatic factors. The lower slope of the study area was densely forested with Pinus densiflora S. et Z. of 8 m height, and the upper slope was covered with poorly grown Pinus densiflora S. et Z. and Quercus trees. The amount of evapotranspiration was estimated to 590.3 mm/yr by annual water budget method. The canopy resistance of Penman-Monteith model was determined as 99 s/m. Seasonal evapotranspiration can be estimated with the calculated evaporation and the canopy resistance. The amount of evapotranspiration peaked in May. That is considered from both the direct evaporation of intercepted rainfall and the transpiration of vegetation during the dry spring season.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.535-539
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• 2009

Evapotranspiration (ET) is one of the main factor to understand the hydrologic cycle on land surfaces of entire globe. It accounts for about 65% of precipitation returning to the atmosphere. Accurate estimation of the ET is essential to many applications of water resources management, hydrology, meteorology, climatology, and agriculture. Over the past few decades, there have been extensive efforts to develop and validate a number of ET models. Priestley-Taylor (P-T) and Food and Agriculture Organization Penman-Monteith (P-M) models are generally recognized as simple, but great operational approaches to estimate ET over different land cover types. In this study, we compare/validate different models of increasing complexity, P-T, P-M, and Common Land Model (CLM) in croplands, IA.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.73-88
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• 2007

The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.285-299
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• 2023

Estimating the evapotranspiration is very important factor for effective water resources management, and FAO Penman-Monteith (FAO P-M) model has been applied for reference evapotranspiration estimation by many researchers. However, because various input data are required for the application of FAO P-M model, understanding the effect of each input data on FAO P-M model is necessary. Therefore, in this study, for 56 study stations located in South Korea, the effects of 8 meteorological factors (maximum and minimum temperature, wind speed, relative humidity, solar radiation, vapor pressure deficit, net radiation, ground heat flux), energy and aerodynamic terms of FAO P-M model, and elevation on FAO P-M reference evapotranspiration (RET) estimation were analyzed. The relative sensitivity analysis was performed to determine how 10% increment of each specific independent variable affects a reference evapotranspiration under given set of condition that other independent variables are unchanged. Furthermore, to select the 5 representative stations and perform the monthly relative sensitivity analysis for those stations, 56 study stations were classified into 5 clusters using cluster analysis. The study results showed that net radiation was turned out to be the most sensitive factor in 8 meteorological factors for 56 study stations. The next most sensitive factor was relative humidity, solar radiation, maximum temperature, vapor pressure deficit and wind speed, followed by minimum temperature in order. Ground heat flux was the least sensitive factor. In case of ground surface condition, elevation showed very low positive relative sensitivity. Relativity sensitivities of energy and aerodynamic terms of FAO P-M model were 0.707 for energy term and 0.293 for aerodynamic term respectively, indicating that energy term was more contributable than aerodynamic term for reference evapotranspiration. The monthly relative sensitivities of meteorological factors showed the seasonal effects, and also the relative sensitivity of elevation showed different pattern each other among study stations. Therefore, for the application of FAO P-M model, the seasonal and regional sensitivity differences of each input variable should be considered.