• Journal of Korea Water Resources Association
• /
• v.53 no.7
• /
• pp.557-567
• /
• 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.

• Journal of Korea Water Resources Association
• /
• v.50 no.1
• /
• pp.37-53
• /
• 2017

Pan evaporation has been used as an indirect method for the estimation of reservoir evaporation. Therefore, in this study, pan evaporation estimation equations using only temperature data were suggested in the case that available meteorological data is limited. A formula for estimating the pan evaporation were suggested by comparing estimated pan evaporation with measured pan evaporation in 12 study areas in Korea. The suggested pan evaporation equations were verified in 44 study areas by comparing not only with temperature-based equations but also with equations using other meteorological data (temperature, wind speed, relative humidity, and sunshine duration). The study results indicate that the suggested equations in this study provide much better pan evaporation estimates, compared with other temperature-based equations. Overall, the suggested equations provide appropriate pan evaporation estimates in most of 56 study areas. Therefore, the suggested equations using only temperature data in this study are considered appropriate for the estimation of pan evaporation in Korea especially in the case that available meteorological data is limited. In the future, using the air temperature and pan evaporation data measured at the reservoir, further research is needed to examine the applicability of suggested equations for the estimation of reservoir evaporation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.351-354
• /
• 2018

본 연구에서는 일 증발접시 증발량 모델링을 위한 다변량 적응 회귀 스플라인 (multivariate adaptive regression splines, MARS) 모델의 성능을 평가하였다. 모델 입력변수 집합은 부산 관측소 (기상청)로부터 수집된 기상자료를 활용하여 증발접시 증발량과의 상관성이 높은 변수들의 조합으로 구성되었으며, 일사량, 일조시간, 평균지상온도, 최대기온의 조합으로 구성된 세 가지 입력집합이 결정되었다. MARS 모델의 성능은 네 가지의 모델성능평가지표를 활용하여 정량적으로 산출되었으며, 그 결과를 인공신경망 (artificial neural network, ANN) 모델과 비교하였다. 입력변수로서 일사량 및 일조시간을 가지는 Set 1의 경우 MARS1 모델이 ANN1 모델보다 우수한 성능을 나타내었으며, Set 2 (일사량, 일조시간, 평균지상온도)의 경우 ANN2 모델, Set 3 (일사량, 일조시간, 평균지상온도, 최대기온)의 경우 MARS3 모델이 상대적으로 우수한 모델 성능을 나타내었다. 모든 분석 모델들을 비교하였을 때, MARS3, ANN2, ANN3, MARS2, MARS1, ANN1 모델의 순서로 우수한 모델 성능을 나타내었으며, 특히 MARS3 모델은 CE = 0.790, $r^2=0.800$, RMSE = 0.762, MAE = 0.587로서 가장 우수한 일 증발접시 증발량 모델링 성능을 나타내었다. 따라서 본 연구에서 적용한 MARS 모델은 지상관측 기상자료를 활용한 일 증발접시 증발량 모델링에서 효과적인 대안이 될 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.445-458
• /
• 2010

The effects of geographical and climatic factors on annual and monthly pan and Penman evaporation were analyzed. 52 climatological stations were selected and trend analyses were performed. Furthermore, cluster analysis and multiple linear regression analysis were performed to understand the effects of geographical and climatic factors on pan and Penman evaporation. Based on stepwise multiple linear regression analysis, annual pan evaporation is proved to be mainly controlled by urbanization as geographical factor, and annual pan evaporation is also controlled by temperature, relative humidity, wind speed, and solar radiation as climatic factor. Especially wind speed is considered to be most significant climatic factor which affects pan evaporation. Meanwhile, Penman evaporation is not affected by geographical factors but it is affected by climate factors such as temperature, relative humidity, wind speed, and solar radiation except precipitation. Furthermore, the study results show that only proximity to coast affects pan evaporation trend on July; however, geographical and climatic factors do not affect pan evaporation trends in annual basis and monthly basis (January, April, and October). On the other hand, Penman evaporation trends were not affected by geographical factors in annual and monthly basises.

• Journal of Korea Water Resources Association
• /
• v.55 no.3
• /
• pp.229-243
• /
• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Korean Journal of Remote Sensing
• /
• v.28 no.5
• /
• pp.521-529
• /
• 2012

The evapotranspiration (ET) is one of the most important factor in the hydrological cycle. In this study, remote sensing based ET algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) was considered. Then, Priestley-Taylor algorithm was used for estimation of potential evapotranspiration in South Korea, and its spatial distribution was analyzed. Overall applicability between estimated potential evapotranspiration and weather station pan evaporation in Nakdong river basin was represented. The results using small pan showed that correlation coefficient in Pohang and Moonkyung Station was 0.70 and 0.55, respectively. However, the results using large pan showed correlation coefficient in Pohang and Moonkyung Station was 0.62 and 0.52, respectively.

• Journal of Wetlands Research
• /
• v.12 no.3
• /
• pp.79-97
• /
• 2010

The purpose of this research is to apply the neural networks models for the hydrologic disaggregation of the yearly pan evaporation(PE) data in Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model(MLP-NNM) and support vector machine neural networks model(SVM-NNM), respectively. And, for the evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. The application of MLP-NNM and SVM-NNM for the hydrologic disaggregation of nonlinear time series data is evaluated from results of this research. Four kinds of the statistical index for the evaluation are suggested; CC, RMSE, E, and AARE, respectively. Homogeneity test using ANOVA and Mann-Whitney U test, furthermore, is carried out for the observed and calculated monthly PE data. We can construct the credible monthly PE data from the hydrologic disaggregation of the yearly PE data, and the available data for the evaluation of irrigation and drainage networks system can be suggested.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4B
• /
• pp.399-412
• /
• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2B
• /
• pp.199-213
• /
• 2008

The goal of this research is to develop and apply the integrational operation method (IOM) for the modeling of the monthly pan evaporation (PE) and the alfalfa reference evapotranspiration ($ET_r$). Since the observed data of the alfalfa $ET_r$ using lysimeter have not been measured for a long time in Republic of Korea, Penman-Monteith (PM) method is used to estimate the observed alfalfa $ET_r$. The IOM consists of the application of the stochastic and neural networks models, respectively. The stochastic model is applied to generate the training dataset for the monthly PE and the alfalfa $ET_r$, and the neural networks models are applied to calculate the observed test dataset reasonably. Among the considered six training patterns, 1,000/PARMA(1,1)/GRNNM-GA training pattern can evaluate the suggested climatic variables very well and also construct the reliable data for the monthly PE and the alfalfa $ET_r$. Uncertainty analysis is used to eliminate the climatic variables of input nodes from 1,000/PARMA(1,1)/GRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Finally, it can be to model the monthly PE and the alfalfa $ET_r$ simultaneously with the least cost and endeavor using the IOM.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.1746-1750
• /
• 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.