• Title/Summary/Keyword: Penman equation

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Study on the Water Consumption of Chinese Cabbage by Floating Lysimeter (Floating Lysimeter 에 의한 가을배추의 소비수량 조사연구)

  • 김시원;김선주;김준석
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.29 no.2
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    • pp.23-29
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    • 1987
  • This study was fulfilled by the floating lysimeter method at the experimental farm of Kon-Kuk University from August to November of 1986 to investigate the amount of evapotranspiration by the growing periods, evapotranspiration ratio, amount of watering per one time, days of intermission, soil moisture extraction pattern and crop coefficient of the Chinese cabbage cultivated in the sandy loam soil at the watering point of pF2.O. The results obtained are summarized as follows: 1.The total evapotranspiration during the growing period was 267.2mm, which was 3. 99mm by daily average, and the maximum evapotranspiration showed in the mid ten days of September with the value of 5.81mm I day. 2.The evapotranspiration ratio by the growing stages increased from the last ten days of September and showed maximum in the beginning of October, and the average evapotranspiration ratio was 1.4. 3.The days of watering intermission at the watering point of pF2.O was 2.4 days, and the average yield per plant was 3,228 g. 4. The soil moisture extraction pattern in the initial stage was 78.9 % in the 1st and 2nd soil layer and 21.1 % in the 3rd and 4th layer, and the mid-season stage, the moisture extraction proportion of the under layer accounted for 38.8 % which showed that the root elongated to the lowest soil layer. 5.The average crop coefficient(Kc) of the tested crop during the growing period was 0.67 by Penman equation and 2.36 by Pan Evaporation equation, which showed high difference by the calculation methods, and the changes of crop coefficient by the growing stages by Penman equation was favorable than those calculated by other met-hods.

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A Study on the Development of a Simulation Model for Predicting Soil Moisture Content and Scheduling Irrigation (토양수분함량 예측 및 계획관개 모의 모형 개발에 관한 연구(I))

  • 김철회;고재군
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4279-4295
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    • 1977
  • Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka+Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

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Prediction of Evapotranspiration from Grape Vines in Suwon with the FAO Penman-Monteith Equation (FAO Penman-Monteith 공식을 이용한 수원지역 포도 수체 증발산량 예측)

  • Yun, Seok-Kyu;Hur, Seung-Oh;Kim, Seung-Heui;Park, Seo-Jun;Kim, Jeong-Bae;Choi, In-Myung
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.11 no.3
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    • pp.111-117
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    • 2009
  • Food and Agricultural Organization (FAO) Penman-Monteith (PM) equation is one of the most widely used equations for predicting evapotranspiration (ET) of crops. The ET rate and the base crop coefficients ($K_{cb}$) of the two different grape vines (i.e., Campbell Early and Kyoho) cultivated in Suwon were calculated by using the FAO PM equation. The ET rate of Campbell Early was $2.41\;mm\;day^{-1}$ and that of Kyoho was $2.22\;mm\;day^{-1}$ in August when the leaf area index was 2.2. During this period, the $K_{cb}$ of Campbell Early based on the FAO PM equation was on average 0.49 with the maximum value of 0.72. On the other hand, the $K_{cb}$ of Kyoho was averaged to be 0.45 with the maximum value of 0.64. The seasonal leaf area index for two grape cultivars was measured as 0.15 in April, 0.5 in May, 1.4 in June, 2.2 in July-September, and 1.5 in October. The $K_{cb}$ of Campbell Early showed a seasonal variation, changing from 0.03 in April to 0.11 in May, 0.31 in June, 0.49 in July-September, and 0.33 in October. The magnitudes and the seasonality of $K_{cb}$ of Kyoho were similar to those of Campbell Early.

Estimation of Future Reference Crop Evapotranspiration using Artificial Neural Networks (인공신경망 기법을 이용한 장래 잠재증발산량 산정)

  • Lee, Eun-Jeong;Kang, Moon-Seong;Park, Jeong-An;Choi, Jin-Young;Park, Seung-Woo
    • Journal of The Korean Society of Agricultural Engineers
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    • v.52 no.5
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    • pp.1-9
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    • 2010
  • Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

Characterization of Local Evapotranspiration Based on the Seasonal and Hydrometeorological Conditions (계절과 수문기상학적 조건에 따른 지역 증발산의 특성화)

  • Rim, Chang-Soo;Lee, Jong-Tae;Yoon, Sei-Uei
    • Water for future
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    • v.29 no.2
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    • pp.235-247
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    • 1996
  • Meteorological and soil water content data measured from semiarid watersheds of Lucky Hills and Kendall during the summer rainy and winter periods were used to study the interrelationships between the controlling variables of the evapotranspiration, and to evaluate the effects of variables on daily actual evapotranspiration (ET) estimation. Simple and multiple linear regression (MLR) analyses were employed to evaluate the order of importance of the meteorological and soil water factors involved. The information gained was used for MLR model development. Theavailable energy and vapor pressure deficit were found to be the important variables to estimate actual ET (AET) for both periods and at both watersheds. Therefore, the important variables of evapotranspiration process in these semiarid watersheds appear to be simply the components of energy term in available energy and aerodynamic term in vapor pressure deficit of Penman potential evapotranspiration (PET) equation.

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Estimation of Potential Water Resources in Mega Cities in Asia

  • Takuya, Komura;Toshitsugu, Moroizumi;Kenji, Okubo;Hiroaki, Furumai;Yoshiro, Ono
    • Proceedings of the Korea Water Resources Association Conference
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    • 2008.05a
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    • pp.75-81
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    • 2008
  • The water shortage in mega cities in Asia, which face a rapid growth in urban population, is an outstanding problem. It is important, therefore, to accurately estimate the water balance in each city in order to use the limited water resources effectively. In this study, we estimated the potential water resources in し sixteen mega cities in Asia. The target cities were Delhi and Calcutta, India; Colombo, Sri Lanka; Dhaka, Bangladesh; Yangon, Myanmar; Bangkok, Thailand; Kuala Lumpur, Malaysia; Singapore; Jakarta, Indonesia; Hanoi, Vietnam; Beijing and Hong Kong, the People's Republic of China; Seoul, the People's Republic of Korea; Manila, the Philippines, and Sapporo and Tokyo, Japan. The potential water resources were estimated by subtracting the actual evaporation from the amount of rainfall. The actual evaporation was estimated using the potential evaporation obtained by Hamon's equation which requires the air temperature and the possible hours of sunshine. When the results of Hamon's and Penman's evaporation equations were compared, a considerable error appeared in the low latitude region. The estimation using Hamon's equation was corrected with the linear regression line of Hamon's and Penman's equations. A classification of the land cover was carried out based on satellite photographs of the target cities, and the volume of surface runoff for each city was obtained using the runoff ratios which depended on the land cover. As a result, the potential water resources in the above mega cities in Asia were found to be greater than the world average. However, the actual water resources which are available for one person to use are probably very limited.

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Estimation on Trends of Reference Evapotranspiration of Weather Station Using Reference Evapotranspiration Calculator Software (Reference Evapotranspiration Calculator Software를 이용한 기상관측소 기준증발산 추정)

  • Choi, Wonho;Choi, Minha;Oh, Hyunje;Park, Jooyang
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.2B
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    • pp.219-231
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    • 2010
  • The Reference Evapotranspiration Calculator Software (REF-ET) supports computational guidelines for the reference evapotranspiration using seventeen FAO Penman-Monteith (PM) equations simultaneously such as the ASCE and FAO standardized forms. The REF-ET can conveniently consider missing data predictions and regional site characterizations, when reference ET is computed on monthly, daily, and hourly time steps. The applicability of the REF-ET was estimated to simulate the reference ET using hourly weather data from Seoul weather station for 29 years. The result found that the FAO24-Rd and 1957-Makk equations closely concerned with solar radiation parameter which were the most highly correlated to reference ET computed by pan coefficient. In addition, the 1957-Makk equation was identified as the most correct computational method for reference ET by analysis of bias and root mean square error. The 1957-Makk equation could predict the reference ET within the error of less than 1.06 mm/day, though all the other equations tended toward overestimation of predicting the reference ET in comparison with refecence ET of pan. The results of this study suggest that the REF-ET will be applicable to support reference ET estimation for a variety of field condition and time-scale.

Estimation of Potential Evapotranspiration using LAI (LAI를 고려한 잠재증발산량 추정)

  • Kim, Joo-Hun;Kim, Kyung-Tak
    • Journal of the Korean Association of Geographic Information Studies
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    • v.8 no.4
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    • pp.1-13
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    • 2005
  • In the process of a hydrology circulation, evapotranspiration is considered a very important factor to build a plan for the development of water resources and to operate water resources system. This study purposes to estimate daily potential evapotranspiration quantity in consideration of energy factors of the surface by using spatial information such as Landsat TM (ETM+) data, DEM and Landcover. Kyounan-cheon, Han River is selected as a target area, and landcover is divided by vegetation and non-vegetation covered area. Penman-Monteith equation which considers leaf-area index is used to estimate potential evapotranspiration quantity of vegetation covered area. The combination method (energy burget and aerodynamic method) is used in non-vegetation covered area. Among the input data for estimating potential evapotranspiration, NDVI, SR and Albedo is formed by Landsat, TM and ETM+ from 1986 through 2002. ground heat flux is estimated by using NDVI distribution map, LAI distribution map is drawn by using SR distribution map. The result of estimation shows that the average potential evapotranspiration in the whole basin is about 1.8-3.2mm/day per each cell. THe results of estimating potential evapotranspiration quantity by each landcover are as follows; water surface 3.6-4.9mm/day, city 1.4-3.1mm/day, bareland 1.4-3.5mm/day, grassland 1.7-3.7mm/day, forest 1.7-3.0mm/day and farmland 1.8-3.6mm/day. The potential evapotranspiration quantity is underestimated in comparison with observed evaporation data by evaporation pan, but it is considered that it has physical propriety.

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Prediction of Transpiration Rate of Lettuces (Lactuca sativa L.) in Plant Factory by Penman-Monteith Model (Penman-Monteith 모델에 의한 식물공장 내 상추(Lactuca sativa L.)의 증산량 예측)

  • Lee, June Woo;Eom, Jung Nam;Kang, Woo Hyun;Shin, Jong Hwa;Son, Jung Eek
    • 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.

Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model (역전파 신경망 모델을 이용한 기준 작물 증발산량 산정)

  • Kim, Minyoung;Choi, Yonghun;O'Shaughnessy, Susan;Colaizzi, Paul;Kim, Youngjin;Jeon, Jonggil;Lee, Sangbong
    • 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.