• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.889-902
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• 2020

This study was performed in Seolmacheon basin to evaluate the adequacy of GLDAS (Global Land Data Assimilation System) and GLEAM (Global Land Evaporation Amsterdam Model) evapotranspiration data. The verification data necessary for the evaluation of adequacy were calculated after processing the latent heat flux data produced in the Seolmacheon basin with the Koflux program. In order to gap-fill the empty period, alternative evapotranspiration was calculated in three ways: FAO-PM (Food and Agriculture Organization-Penman Monteith), MDV (Mean Diurnal Variation) and Kalman Filter. This study selected Kalman Filter method as the data gap-filling method because it showed the best Bias and RMSE among the three methods. The amount of GLDAS spatial evapotranspiration was calculated as Noah (version 2.1) with a time interval of 3 hours and a spatial resolution of 0.25°. The amount of GLEAM spatial evapotranspiration was calculated using GLEAM (version 3.1a). This study evaluated the spatial evapotranspiration of GLDAS and GLEAM as the evapotranspiration based on eddy covariance. As a result of evaluation, GLDAS spatial evapotranspiration showed better results than GLEAM. Accordingly, in this study, the GLDAS method was proposed as a method for calculating the amount of spatial evapotranspiration in the Seolmacheon basin.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.38-46
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• 1999

Canopy structure conductances of a Norway spruce forest in the Solling Hills(Central Germany) and Central Forest Biosphere Reserve(320km to the north-west from Moscow) were derived from LE(latent heat flux) and H(sensible heat flux) fluxes measured(by Eddy correlation technique and energy balance method) and modelled(by one dimensional non-steady-state) SVAT(soil-vegetation-atmosphere-transfer) model(SLODSVAT) using a rearranged Penman-Monteith equation("Big-leaf" approximation) during June 1996. They were compared with canopy stomatal conductances estimated by consecutive intergrating the stomatal conductance of individual needles over the whole canopy("bottom-up" approach) using SLODSVAT model. The result indicate a significant difference between the canopy surface conductances derived from measured and modelled fluxes("top-down" approach) and the stomatal conductances modelled by the SLODSVAT("bottom-up" approach). This difference was influenced by some nonphysiological factors within the forest canopy(e.g. aerodynamic and boundary layer resistances, radiation budget, evapotranspiration from the forest understorey). In general, canopy surface conductances derived from measured and modelled fluxes exceeded canopy stomatal conductance during the whole modelled period, The contribution of the understorey's evapotranspiration to the total forest evapotranspiration was small (up to 5-9% of the total LE flux) and was not depended on total radiation balance of forest canopy. Ignoring contribution of the understorey's evapotranspiration resulted in an overestimation of the canopy surface conductance for a spruce forest up to 2mm/s(about 10-15%).

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1141-1180
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• 2022

The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.96-96
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• 2021

수문기상인자 중 하나인 증발산량은 수자원 계획 및 관리 시 고려되며, 특히 물수지 모형 등의 입력자료로 활용된다. 우리나라를 포함한 각국 기상청 및 국제기구에서는 직접 관측이 아닌 FAO56 Penman-Monteith(PM)을 통해 증발산량을 산출하고 있다. FAO56 PM 방법은 복사(radiation), 대기온도(air temperature), 습도(humidity), 풍속(wind speed) 등의 기상인자로부터 기준증발산량(reference evapotransipiration)을 추정하며, 상대적으로 높은 정확성을 보여준다. 그러나 FAO56 PM 방법은 많은 기상인자를 요구하므로 미계측 유역을 포함한 일부지역에 대한 증발산량 자료 구축이 어려운 실정이다. 또한, 기준증발산량의 특성이 시간에 따라 변화하므로 비정상성(nonstationary)을 고려한 분석이 요구된다. 본 연구에서는 온도인자 기반의 대체모형(surrogate model)을 개발하여 기준증발산량의 비정상성을 고려하고자 한다. 한강유역에 위치한 관측소를 대상으로 모형을 개발하였으며, 시간에 따라 변동하는 기준증발산량의 특성을 고려하기 위해 Bayesian 추론기법을 통해 매개변수를 시간에 따라 추정하였다. 또한, 본 연구에서는 대체모형으로 산정된 증발산량을 활용해 가뭄지수인 EDDI(evaporative demand drought index)를 제시하였다. 가뭄 모니터링 및 조기 경보 안내를 위해 개발된 EDDI를 활용하여 기존 가뭄보다 빠르게 진행되는 초단기 가뭄(flash drought)를 평가하였다. 본 연구에서 개발된 모형은 미계측 지역에서도 적용이 가능하므로 수자원분야에서 활용성이 높을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.184-184
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• 2020

수자원 계획 및 관리 시 증발산량의 정량적 분석은 필수적으로 고려되는 사항 중 하나이다. 일단위 이하의 잠재증발산량 산정은 세계식량기구(FAO)가 Penman-Monteith 방법을 기반으로 개발한 FAO56 PM 방법을 주로 활용하며, 이는 다른 방법에 비하여 높은 정확성과 적용성이 뛰어나다. 그러나 FAO56 PM 방법의 입력 매개변수는 다양한 기상자료이며, 장기간의 신뢰성 높은 자료를 구축하는 것은 어려운 실정이다. 이에 본 연구에서는 증발산량 공식인 Hargreaves 공식을 활용하여 FAO56 PM 방법으로 산정된 잠재증발산량과 기온차 사이의 시계열 관계를 재구성한 회귀분석 기법을 개발하였다. 개발된 모형에 유역면적을 적용하여 유역면적별 잠재증발산량을 산정하였으며, 이를 기존의 잠재증발산량과의 비교를 통해 모형의 적합성을 평가하였다. 결과적으로, 복잡한 잠재증발산량식을 단순한 대체모형(surrogate model)으로 제시함으로써 효율적인 증발산량 정량적 평가와 제한적인 기상자료 조건에 보편적 활용이 가능하다. 향후 연구에서는 회귀분석방법에 Bayesian 추론기법을 활용하여 구성함으로 잠재증발산량의 불확실성을 정량적으로 표현하고자 한다.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.2
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• pp.13-23
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• 2006

In Korea, upland irrigation generally depends on the ground water or natural rainfall since irrigation water supplied from dams is mainly used for paddy irrigation, and only limited amount of irrigation water is supplied to the upland area. For the stable security of upland irrigation water, storage level of irrigation dams was simulated by the periods. A year was divided into 4 periods considering the irrigation characteristics. Through the periodical management of storage level, water utilization efficiency in irrigation dams could be enhanced and it makes available to secure extra available water from existing dams without new development of water resources. Two study areas, Seongju and Donghwa dam, were selected for this study. Runoff from the watersheds was simulated by the modified tank model and the irrigation water to upland crops was calculated by the Penman-Monteith method. The analyzed results showed that relatively sufficient extra available water could be secured for the main upland crops in Seongju area. In case of Donghwa area, water supply to non-irrigated upland was possible in normal years but extra water was necessary in drought years such as 1998 and 2001.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.327-327
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• 2019

The study will quantify the total uncertainties in streamflow and precipitation projections for Upper Awash River Basin located in central Ethiopia. Three hydrological models (GR4J, CAT, and HBV) will be used to simulate the streamflow considering two emission scenarios, six high-resolution GCMs, and two downscaling methods. The readily available hydrometeorological data will be applied as an input to the three hydrological models and the potential evapotranspiration will be estimated using the Penman-Monteith Method. The SCE-UA algorithm implemented in PEST will be used to calibrate the three hydrological models. The total uncertainty including the incremental uncertainty at each stage (emission scenarios and model) will be presented after assessing a total of 24 (=$2{\times}6{\times}2$) high-resolution precipitation projections and 72 (=$2{\times}6{\times}2{\times}3$) streamflow projections for the study basin. Finally, the primary causes that generate uncertainties in future climate change impact assessments will be identified and a conclusion will be made based on the finding of the study.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.273-280
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• 2018

Accurate estimation of evapotranspiration is mightily important for understanding and analyzing the hydrological cycle. There are various methods for estimating evapotranspiration and each method has its own advantages and limitations. Therefore, it is necessary to develop an optimal evapotranspiration product by combing different evapotranspiration products. In this study, we developed an optimal evapotranspiration by fusing two satellite- and model-based evapotranspiration estimates, including revised remote sensing-based Penman-Monteith (RS-PM) and Modified Satellite-Based Priestley-Taylor (MS-PT) methods, Global Land Data Assimilation System (GLDAS), and Global Land Evaporation Amsterdam Model (GLEAM). The statistical analysis (i.e., correlation coefficients, index of agreement, MAE, and RMSE) of combined evapotranspiration product showed to be improved compared to the individual model results. After confirming the overall results, in future studies, advanced data fusion techniques will be used to obtained improved results.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.