• Title/Summary/Keyword: Penman evaporation

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Neural Networks-Genetic Algorithm Model for Modeling of Nonlinear Evaporation and Evapotranspiration Time Series 1. Theory and Application of the Model (비선형 증발량 및 증발산량 시계열의 모형화를 위한 신경망-유전자 알고리즘 모형 1. 모형의 이론과 적용)

  • Kim, Sung-Won;Kim, Hung-Soo
    • 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.

Comparison of reference evapotranspiration estimation methods with limited data in South Korea

  • Jeon, Min-Gi;Nam, Won-Ho;Hong, Eun-Mi;Hwang, Seonah;Ok, Junghun;Cho, Heerae;Han, Kyung-Hwa;Jung, Kang-Ho;Zhang, Yong-Seon;Hong, Suk-Young
    • Korean Journal of Agricultural Science
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    • v.46 no.1
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    • pp.137-149
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    • 2019
  • Accurate estimation of reference evapotranspiration (RET) is important to quantify crop evapotranspiration for sustainable water resource management in hydrological, agricultural, and environmental fields. It is estimated by different methods from direct measurements with lysimeters, or by many empirical equations suggested by numerous modeling using local climatic variables. The potential to use some such equations depends on the availability of the necessary meteorological parameters for calculating the RET in specific climatic conditions. The objective of this study was to determine the proper RET equations using limited climatic data and to analyze the temporal and spatial trends of the RET in South Korea. We evaluated the FAO-56 Penman-Monteith equation (FAO-56 PM) by comparing several simple RET equations and observed small fan evaporation. In this study, the modified Penman equation, Hargreaves equation, and FAO Penman-Monteith equation with missing solar radiation (PM-Rs) data were tested to estimate the RET. Nine weather stations were considered with limited climatic data across South Korea from 1973 - 2017, and the RET equations were calculated for each weather station as well as the analysis of the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The FAO-56 PM recommended by the Food Agriculture Organization (FAO) showed good performance even though missing solar radiation, relative humidity, and wind speed data and could still be adapted to the limited data conditions. As a result, the RET was increased, and the evapotranspiration rate was increased more in coastal areas than inland.

A Study on Evaporation Estimation of Tank Model (Tank 모형의 증발산량 산정에 관한 연구)

  • Jung, Il-Won;Koo, Bo-Young;Bae, Deg-Hyo
    • Proceedings of the Korea Water Resources Association Conference
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    • 2006.05a
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    • pp.1746-1750
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    • 2006
  • 다양한 목적의 장기유출분석에 많이 적용되고 있는 4단 Tank 모형의 증발산관련 입력자료는 증발접시자료를 이용하거나 또는 장기간의 유량과 강수량의 차이로 정의되는 월별 손실량을 계산한 결과를 사용하고 있다. 증발접시자료는 자료 구득문제와 신뢰성 문제 등으로 인해 적용사례가 적고 통상 인근 관측지점의 손실량을 계산하고 이를 전이하여 적용하고 있다. 그러나 이러한 일증발산량 산정방법은 장기적인 유량 자료를 보유한 인근 관측지점이 있어야 적용할 수 있다는 점과 관측지점의 자료 신뢰성에 따라 유출결과에 큰 영향을 미칠수 있는 한계가 있다. 따라서 본 연구에서는 이러한 문제점을 개선하기 위하여 Hamon 방법과 Jensen-Haise 방법 및 FAO Penman-Monteith 방법을 검토하여 Tank 모형 계산에 필요한 실제증발산량을 산정할 수 있는 방안에 대해 모색하였다. 분석결과 유역별 실제손실량은 지형적인 영향을 받는 것으로 분석되었으며, 이를 통해 잠재증발산량을 실제증발산량으로 보정하는 월별보정계수를 지형인자로부터 추정하는 방법을 제안하였다.

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A Study on the Estimation of Monthly Average River Basin Evaporation (월(月) 평균유역증발산량(平均流域蒸發散量) 추정(推定)에 관(關)한 연구(硏究))

  • Kim, Tai Cheol;Ahn, Byoung Gi
    • Korean Journal of Agricultural Science
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    • v.8 no.2
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    • pp.195-202
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    • 1981
  • The return of water to the atmosphere from water, soil and vegetation surface is one of the most important aspects of hydrological cycle, and the seasonal trend of variation of river basin evaporation is also meaningful in the longterm runoff analysis for the irrigation and water resources planning. This paper has been prepared to show some imformation to estimate the monthly river basin evaporation from pan evaporation, potential evaporation, regional evaporation and temperature through the comparison with river basin evaporation derived from water budget method. The analysis has been carried out with the observation data of Yongdam station in the Geum river basin for five year. The results are summarized as follows and these would be applied to the estimation of river basin evaporation and longterm runoff in ungaged station. 1. The ratio of pan evaporation to river basin evaporation ($E_w/E_{pan}$) shows the most- significant relation at the viewpoint of seasonal trend of variation. River basin evaporation could be estimated from the pan evaporation through either Fig. 9 or Table-7. 2. Local coefficients of cloudness effect and wind function has been determined to apply the Penman's mass and energy transfer equation to the estimation of river basin evaporation. $R_c=R_a(0.13+0.52n/D)$ $E=0.35(e_s-e)(1.8+1.0U)$ 3. It seems that Regional evaporation concept $E_R=(1-a)R_C-E_p$ has kept functional errors due to the inapplicable assumptions. But it is desirable that this kind of function which contains the results of complex physical, chemical and biological processes of river basin evaporation should be developed. 4. Monthly river basin evaporation could be approximately estimated from the monthly average temperature through either the equation of $E_w=1.44{\times}1.08^T$ or Fig. 12 in the stations with poor climatological observation data.

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Development of pan coefficient model for estimating evaporation: focused on Seoul station (증발량 산정을 위한 증발접시계수 산정모형 개발: 서울지점을 중심으로)

  • Rim, Chang-Soo
    • Journal of Korea Water Resources Association
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    • v.53 no.7
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    • pp.557-567
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    • 2020
  • The six current models for estimating pan coefficient were applied to test the applicability of models in Seoul, South Korea. The models are Cuenca's model, Snyder's model, Pereira et al.'s model, Allen et al.'s model, Orang's model, and Raghuwanshi and Wallender's model. The estimated pan coefficients were compared with measured one. The measured pan coefficient was obtained by using measured pan evaporation and FAO Penman-Monteith reference evapotranspiration. Estimated evaporation by using estimated pan coefficients was compared with measured one. Furthermore, model for estimating pan coefficient in Seoul was developed. When applying 6 current models for 10 m, 15 m and 20 m fetch distances, pan coefficient estimates from Snyder's model were most similar to measured pan coefficients for all fetch distances. On the other hand, pan coefficient estimates from Pereira et al.'s model were most different from measured one. Therefore, model for estimating pan coefficient in Seoul was developed by modifying Snyder's model. When applying developed model, estimated monthly average evaporation was 92.1 mm for 10 m, 15 m and 20 m fetch distances and measured one was 91.9 mm, indicating that evaporation estimate from developed model is closest to measured one, compared with those of current models.

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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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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Probabilistic Medium- and Long-Term Reservoir Inflow Forecasts (I) Long-Term Runoff Analysis (확률론적 중장기 댐 유입량 예측 (I) 장기유출 해석)

  • Bae, Deg-Hyo;Kim, Jin-Hoon
    • Journal of Korea Water Resources Association
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    • v.39 no.3 s.164
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    • pp.261-274
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    • 2006
  • This study performs a daily long-term runoff analysis for 30 years to forecast medium- and long-term probabilistic reservoir inflows on the Soyang River basin. Snowmelt is computed by Anderson's temperature index snowmelt model and potenetial evaporation is estimated by Penman-combination method to produce input data for a rainfall-runoff model. A semi-distributed TOPMODEL which is composed of hydrologic rainfall-runoff process on the headwater-catchment scale based on the original TOPMODEL and a hydraulic flow routing model to route the catchment outflows using by kinematic wave scheme is used in this study It can be observed that the time variations of the computed snowmelt and potential evaporation are well agreed with indirect observed data such as maximum snow depth and small pan evaporation. Model parameters are calibrated with low-flow(1979), medium-flow(1999), and high-flow(1990) rainfall-runoff events. In the model evaluation, relative volumetric error and correlation coefficient between observed and computed flows are computed to 5.64% and 0.91, respectively. Also, the relative volumetric errors decrease to 17% and 4% during March and April with or without the snowmelt model. It is concluded that the semi-distributed TOPMODEL has well performance and the snowmelt effects for the long-term runoff computation are important on the study area.

Estimation of Reference Evapotranspiration Based on Remote Sensing: Nakdong River Hydrologic Survey (원격탐사 기반 기준 증발산 산정 모의: 낙동강 유역조사 분석)

  • Sur, Chan-Yang;Lee, Jong-Jin;Park, Jae-Young;Choi, Min-Ha
    • Proceedings of the Korea Water Resources Association Conference
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    • 2012.05a
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    • pp.67-70
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    • 2012
  • 현재 국내외에서는 양질의 증발산을 관측하여 활용하기 위해 증발접시 (evaporation pan), 침루계 (lysimeter) 등을 이용하여 실측하거나 Flux Tower에서 Eddy covariance technique, Bowen ratio method 등을 이용하여 경험적으로 산정하고 있다. 이러한 방법으로 산정되는 증발산은 크게 두 분류로 나눌 수 있다. 일반적인 기후 상태에서 유역의 토양이 증발산에 방해를 받지 않을 정도로 충분히 물을 포함하고 있고, 식생이 조밀한 상태에서의 증발산량을 의미하는 잠재 증발산과 실제 산정치인 실제 증발산으로 나눌 수 있다 (Thornthwait, 1939). 본 연구에서는 유역의 잠재 증발산을 산정하여 실제 증발산과 비교를 통해 적용성을 확인하고자 한다. 잠재 증발산을 산정하는 방법은 Moderate Resolution Imaging Spectroradiometer (MODIS) 인공위성 데이터를 이용한 원격탐사 기술을 적용하여 산정한다. 원격탐사 기술은 지상 관측의 단점을 보완한 것으로써, 날씨, 인간 활동 등 주변 외부 환경의 영향에 민감하게 반응하여 공간적인 분포 현황을 파악하는 것이 어려운 지상 관측의 한계점을 대체하기 위한 방법이다. 이들 방법으로는 가장 널리 쓰이는 Penman-Monteith (Penman, 1948; Monteith, 1965), 일별 최대, 최저, 평균 기온을 이용한 Hargreaves 방법 (Hargreaves, 1985)과 Priestley-Taylor 방법 (Priestley and Taylor, 1972) 등의 세 가지 방법을 소개하였다. 세 가지 방법으로 산정된 잠재 증발산을 통해 해당 유역의 잠재 증발산의 공간적인 거동을 파악해 볼 수 있다.

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Simulation of Soil Hydrological Components in Chuncheon over 30 years Using E-DiGOR Model

  • Aydin, Mehmet;Jung, Yeong-Sang;Yang, Jae-E.;Lee, Hyun-Il;Kim, Kyung-Dae
    • Korean Journal of Soil Science and Fertilizer
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    • v.45 no.4
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    • pp.484-491
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    • 2012
  • The hydrological components of a sandy loam soil of nearly level in Chuncheon over 30 years were computed using the E-DiGOR model. Daily simulations were carried out for each year during the period of 1980 to 2009 using standard climate data. Reference evapotranspiration and potential soil evaporation based on Penman-Montheith model were higher during May to August because of the higher atmospheric evaporative demand. Actual soil evaporation was mainly found to be a function of the amount and timing of rainfall, and presumably soil wetness in addition to atmospheric demand. Drainage was affected by rainfall and increased with a higher amount of precipitation and soil water content. Excess drainage occurred throughout rainy months (from July to September), with a peak in July. Therefore, leaching may be a serious problem in the soils all through these months. The 30-year average annual reference evapotranspiration and potential soil evaporation were 951.5 mm and 714.2 mm, respectively. The actual evaporation from bare soil varied between 396.9-528.4 mm and showed comparatively lesser inter-annual variations than drainage. Annual drainage rates below 120 cm soil depth ranged from 477.8 to 1565.9 mm. The long-term mean annual drainage-loss was approximately two times higher than actual soil evaporation.