• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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• pp.63-72
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• 1994

The objective of the study is to develop weather generators for daily rainfall and small pan evaporation and to test the applicability with recorded data. Daily rainfall forecasting model(DRFM) was developed that uses a first order Markov chain to describe rainfall seque- nces and applies an incomplete Gamma function to predict the amount of precipitation. Daily evaporation forecasting model(DEFM) that adopts a normal distribution function to generate the evaporation for dry and wet days was also formulated. DRFM and DEFM were tested with twenty year weather data from eleven stations using Chi-square and Kolmogorov and Smirnov goodness of fit tests. The test results showed that the generated sequences of rainfall occurrence, amount of rainfall, and pan evaporation were statistically fit to recorded data from eleven, seven, and seven stations at the 5% level of significance. Generated rainfall data from DRFM were very close in frequency distri- bution patterns to records for stations all over the country. Pan evaporation for rainy days generated were less accurate than that for dry days. And the proposed models may be used as tools to provide many mathematical models with long-term daily rainfall and small pan evaporation data. An example is an irrigation scheduling model, which will be further detailed in the paper.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.766-770
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• 2005

Cascade-Correlation Neural Networks Model(CCNNM) is used to estimate daily evaporation using limited climatical variables such as atmospheric temperature, dewpoint temperature, relative humidity, wind speed, sunshine duration and radiation. DeBruln equation is applied to estimate daily free-surface evaporation. It is converted into pan evaporation using pan coefficient. The results of CCNNM shows better than those of Debruin equation. This research represents that the strong nonlinear relationship such as evaporation modeling can be generalized by the CCNNM ; a special type of Backpropagation algorithm Neural Networks Model.

• Progress in Superconductivity and Cryogenics
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• v.22 no.1
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• pp.24-28
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• 2020

Heat leakage is an important parameter to reflect heat insulated performance of cryogenic vessel. According to the current standard requirements, it needs to measure the daily evaporation rate to indicate heat leakage. The test needs-over 24h after cryogenic vessel in heat equilibrium as standard required, therefore test efficiency is poor and new efficient method is required to cut test time. First of all, the volume of instantaneous evaporated gas and heat leakage are calculated by the current standard corresponding to the maximum allowable daily evaporation rate of cryogenic vessel. Depending on the relationship between real daily evaporation rate and maximum allowable daily evaporation rate of cryogenic vessel, we designed a new test method based on the pressure changes over time in cryogenic vessel to determine whether its heat insulated performance meets requirements or not. Secondly, the heat transfer process was analyzed in measurement of cryogenic vessel, and the heat transfer equations of whole system were established. Finally, the test was completed in four hours; meanwhile the heat leakage and daily evaporation rate of cryogenic vessel are calculated basing on test data.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.243-255
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• 2010

The spatial distribution of pan evaporation and pan evaporation trends have been studied. In this study, pan evaporation data from 1973 to 1990 for 56 climatological stations were analyzed. In addition to annual average daily pan evaporation, monthly average daily pan evaporation in April, July, October and January were analyzed, considering seasonal effect. The study results indicate that in case of annual average daily pan evaporation, 38 stations out of 56 stations show decreasing trend. In case of average daily pan evaporation in January, 33 stations show decreasing trend. In April, 38 stations show increasing trend. In July, 47 stations show decreasing trend. In October, 35 stations show increasing trend. Therefore, on the whole, pan evaporation tended to decrease in January, July, and annual basis. On the other hand, pan evaporation tended to increase in April and October. Furthermore, pan evaporation trend in each individual region shows also different trend even though the region is located nearby, indicating that there are geographical and topographical effects on pan evaporation trend. Pan evaporation data and climatic data from 1973 to 2006 for 11 climatological stations were used for trend analysis. Climatic variables such as temperature, relative humidity and wind speed show same or opposite trend direction compared with pan evaporation in annual or monthly basis. Annual and monthly solar radiation trends show the same direction compared with pan evaporation; however, annual and monthly precipitation trends show the opposite direction compared with pan evaporation.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1439-1442
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• 2010

Time-lag recurrent neural networks model (Time-Lag RNNM) 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 ($W_{mean}$) and mean relative humidity ($RH_{mean}$). And, for the performances of Time-Lag RNNM, 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 Time-Lag RNNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE using Time-Lag RNNM, 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 Time-Lag RNNM.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.336-339
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• 2012

Accurate forecasting of pan evaporation (PE) is very important for monitoring, survey, and management of water resources. The purpose of this study is to develop and apply Kohonen self-organizing feature maps neural networks model (KSOFM-NNM) to forecast the daily PE for the dry climate region in south western Iran. KSOFM-NNM for Ahwaz station was used to forecast daily PE on the basis of temperature-based, radiation-based, and sunshine duration-based input combinations. The measurements at Ahwaz station in south western Iran, for the period of January 2002 - December 2008, were used for training, cross-validation and testing data of KSOFM-NNM. The results obtained by TEM 1 produced the best results among other combinations for Ahwaz station. Based on the comparisons, it was found that KSOFM-NNM can be employed successfully for forecasting the daily PE from the limited climatic data in south western Iran.

• Journal of ILASS-Korea
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• v.26 no.1
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• pp.1-8
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• 2021

Droplet evaporation has been known as a common phenomenon in daily life, and it has been widely used for many applications. In particular, the influence of the different heated substrates on evaporation flux and flow characteristics is essential in understanding heat and mass transfer of evaporating droplets. This study aims to simulate the droplet evaporation process by considering variation of thermal property depending on the substrates and the surface temperature. The commercial program of ANSYS Fluent (V.17.2) is used for simulating the conjugated heat transfer in the solid-liquid-vapor domains. Moreover, we adopt the diffusion-limited model to predict the evaporation flux on the different heated substrates. It is found that the evaporation rate significantly changes with the increase in substrate temperature. The evaporation rate substantially varies with different substrates because of variation of thermal property. Also, the droplet evaporates more rapidly as the surface temperature increases owing to an increase in saturation vapor pressure as well as the free convection effect caused by the density gradient.

• Journal of Environmental Science International
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• v.22 no.1
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• pp.119-129
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• 2013

We compared equilibrium evaporation($E_{equili}$) eddy-covariance($E_{eddy}$) with soil moisture data($E_{SMseries}$) which were measured with a 2 hours sampling interval at three points for a humid forest hillslope from May 5th to May 31th in 2009. Accumulations of $E_{eddy}$, $E_{equili}$ for the study period were estimated as 2.52, 3.28 mm and those of $E_{SMseries}$ were ranged from 1.91 to 2.88 mm. It suggested that the eddy-covariance method considering the spatial heterogeneity of soil evaporation is useful to evaluate the soil evaporation. Method A, B and C were proposed using mean meterological data and daily moisture variation and the computations were compared to eddy-covariance method and equilibrium evaporation. The methods using soil moisture data can describe the variations of soil evaporation from eddy-covariance through simple moving average analysis. Method B showed a good matched with eddy-covariance method. This indicated that Dry Surface Layer (DSL) at 14:00 which was used for method B is important variable for the evaluation of soil evaporation. The total equilibrium evaporation was not significantly different to those of the others. However, equilibrium evaporation showed a problem in estimating soil evaporation because the temporal tendency of $E_{equili}$ was not related with the those of the other methods. The improved understanding of the soil evaporation presented in this study will contribute to the understandings of water cycles in a forest hillslope.