• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.583-596
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• 2006

Evaporation is an essential parameter in Global water-energy cycle and the variability of evaporation affects water resources planning and managements. In this study, the temporal variability of pan evaporation data was analyzed and trend analysis of the data using Mann-Kendall test. The relationships among evaporation and rainfall, air temperature, humidity, cloudness were analyzed. Even though the longterm trends of air temperature and rainfall increases, that of evaporation except Jinju and Yeosoo results decreases as worldwide observations. Results demonstrate that decrease of pan evaporation represents increase of terrestrial evaporation as Brutsaert and Parlange(1998)'s analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.47-62
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• 2009

The climate change caused by global warming may affect on the hydro-meteorologic factor such as evaporation (IPCC, 2001). Furthermore, it is also necessary that the effect of climate change according to geographical condition on evaporation should be studied. In this study, considering geographical and topographical conditions, the 6 evaporation equations that have been applied to simulate annual and monthly pan evaporation were compared. 56 climatologic stations were selected and classified, basing on the geographical and topographical characteristics (urbanization, topographical slope, proximity to coast, and area of water body). The evaporation equations currently being used are applied. These evaporation equations are Penman, Kohler-Nordenson-Fox (KNF), DeBruin-Keijman, Priestley-Taylor, Hargreaves, and Rohwer. Furthermore, Penman equation was modified by calibrating the parameters of wind function and was verified using relative error. The study results indicate that the KNF equation compared best with the pan: relative error was 8.72%. Penman equation provided the next-best values for evaporation relative to the pan: relative error was 8.75%. The mass-transfer method (Rohwer) provided the worst comparison showing relative error of 33.47%. In case that there is a close correlation between wind function and wind speed, modified Penman equation provided a better estimate of pan evaporation.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.515-530
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• 2009

Evaporation from surface water bodies is influenced by a number of meteorological parameters. The rate of evaporation is primarily controlled by incoming solar radiation, air and water temperature and wind speed and relative humidity. In the present study, influence of weekly meteorological variables such as air temperature, relative humidity, bright sunshine hours, wind speed, wind velocity, rainfall on rate of evaporation has been examined using 35 years(1971-2005) of meteorological data. Statistical analysis was carried out employing linear regression models. The developed regression models were tested for goodness of fit, multicollinearity along with normality test and constant variance test. These regression models were subsequently validated using the observed and predicted parameter estimates with the meteorological data of the year 2005. Further these models were checked with time order sequence of residual plots to identify the trend of the scatter plot and then new standardized regression models were developed using standardized equations. The highest significant positive correlation was observed between pan evaporation and maximum air temperature. Mean air temperature and wind velocity have highly significant influence on pan evaporation whereas minimum air temperature, relative humidity and wind direction have no such significant influence.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1423-1426
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• 2007

The goal of this research is to develop and apply the integrational model for the pan evaporation and the alfalfa reference evapotranspiration in Republic of Korea. Since the observed data of the alfalfa reference evapotranspiration using lysimeter have not been measured for a long time in Republic of Korea, PM method is used to assume and estimate the observed alfalfa reference evapotranspiration. The integrational model consists of staochastics and neural networks processes respectively. The stochastics process is applied to extend for the short-term monthly pan evaporation and alfalfa reference evapotranspiration. The extended data of the monthly pan evaporation and alfalfa reference evapotranspiration is used to evaluate for the training performance. For the neural networks process, the generalized regression neural networks model(GRNNM) is applied to evaluate for the testing performance using the observed data respectively. From this research, we evaluate the impact of the limited climatical variables on the accuracy of the integrational operation of stochastics and neural networks processes. We should, furthermore, construct the credible data of the pan evaporation and the alfalfa reference evapotranspiration, and suggest the reference data for irrigation and drainage networks system in Republic of Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• 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.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.781-793
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• 2017

In this study, applicabilities of aerodynamic approaches for the estimation of pan evaporation were evaluated on 56 study stations in South Korea. To accomplish this study purpose, previous researchers' evaporation estimation equations based on aerodynamic approaches were grouped into seven generalized evaporation models. Furthermore, four multiple linear regression (MLR) models were developed and tested. The independent variables of MLR models are meteorological variables such as wind speed, vapor pressure deficit, air temperature, and atmospheric pressure. These meteorological variables are required for the application of aerodynamic approaches. In order to consider the effect of autocorrelation, MLR models were developed after differencing variables. The applicability of MLR models with differenced variables was compared with that of MLR models with undifferenced variables and the comparison results showed no significant difference between the two methods. The study results have indicated that there is strong correlation between estimated pan evaporation (using aerodynamic models and MLR models) and measured pan evaporation. However, pan evaporation are overestimated during August, September, October, November, and December. Most of meteorological variables that are used for MLR models show statistical significance in the estimation of pan evaporation. Vapor pressure deficit was turned out to be the most significant meteorological variable. The second most significant variable was air temperature; wind speed was the third most significant variable, followed by atmospheric pressure.

• Water for future
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• v.18 no.3
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• pp.243-251
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• 1985

The distributions of the copper plated(small) pan evaporation in both space and time are analysed with the data observed, and the lake and the potential evaportranspiration are estimated from the climatological data. These value are compared with each other and to the precipitation for deducing the seasonal amounts and variations of water budgets in the selected basins and regions. The meteorological factor which is closely associated with the small pan evaporation are hardly recognizable when they are used as the monthly values. The relationships among the small pan, the Class A pan and the lake evaporation are well correlated with each other with correlation coefficient of above 0.90, so it may be possible to derve other evaporations from knowing one evaporation. The ratio of the Class A pan and the lake evaporation to the small pan evaportion in annual are about 73% and 55%, repectively, except the mountaineous area where the values are about 10% less than those. The evapotranspiration reach about 40∼60% of the annumal precipitation, but in May and October two values are nearly same. The frequencies of the monthly evaportion in class intervals in the regions are also provided.

• 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.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.129-143
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• 2011

In this study, to select the incoming solar radiation equation which is most suitable for the estimation of Penman evaporation, 12 incoming solar radiation equations were selected. The Penman evaporation rates were estimated using 12 selected incoming solar radiation equations, and the estimated Penman evaporation rates were compared with measured pan evaporation rates. The monthly average daily meteorological data measured from 17 meteorological stations (춘천, 강능, 서울, 인천, 수원, 서산, 청주, 대전, 추풍령, 포항, 대구, 전주, 광주, 부산, 목포, 제주, 진주) were used for this study. To evaluate the reliability of estimated evaporation rates, mean absolute bias error(MABE), root mean square error(RMSE), mean percentage error(MPE) and Nash-Sutcliffe equation were applied. The study results indicate that to estimate pan evaporation using Penman evaporation equation, incoming solar radiation equation using meteorological data such as precipitation, minimum air temperature, sunshine duration, possible duration of sunshine, and extraterrestrial radiation are most suitable for 11 study stations out of 17 study stations.