• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.103-103
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• 2018

인류 문명이 발전되면서 이산화탄소 증가나 표지 피복도의 변화와 같은 인류의 영향으로 인하여 자연현상들은 많은 변화가 발생하고 있다. 특히 고대서부터 현재까지 농작물을 짓기 위해서 관개 수로를 이용한 농업이 발전되어져 왔다. 관개 농업은 작물의 생육과 알맞은 토양환경을 만들기 위해 깨끗한 물을 인공적으로 농업에 이용하기에 물 순환뿐 아니라 에너지 순환에 많은 영향을 미치는 요소이다. 따라서 그에 대한 메커니즘을 이해하는 것을 중요하다. 본 연구에서는 지상기후모형인 Community Land Model(CLM)과 대기기후모형인 Community Atmospheric Model(CAM)을 이용하였으며 관개농업에 관한 과정을 고려할 때와 고려하지 않았을 때 도출된 결과값들을 서로 비교함으로써 관개농업이 지구환경에 미치는 영향을 알아보았다. 관개농업으로 인하여 강수량, 증발산량(Evapotranspiration, ET)과 같은 수문량의 변화뿐 아니라 지하 토양수분과 지하수면 깊이(Water table depth)의 변화량과 같은 모델의 결과들을 시간적 공간적 변화를 나타냄으로써 관개농업이 물 순환에 미치는 영향을 제시하였다. 또한 에너지 순환의 변화에 미치는 영향을 알아보기 위해 잠열(Latent heat) 과 헌혈(Sensible heat)의 변화를 알아내어 관개가 에너지 순환에 미치는 영향을 알아보았다.

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

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• 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 Korea Water Resources Association
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• v.49 no.3
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• pp.227-239
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• 2016

This study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients ($R^2$) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1077-1089
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• 2022

With the construction of Seoul National University (SNU), the Mt. Gwanak watershed has undergone some urbanization. As with other campus catchments, data related to the water cycle is extremely limited. Therefore, this study began by collecting hydrological and meteorological data using Atmos-41, a complex meteorological observation instrument. The observation results of Atmos-41 were validated by analyzing the statistical characteristics and confidence intervals based on the monthly variability of data from the Korea Meteorological Administration. Results of the previous research were used to validate the simulated surface runoff and infiltration using the Storm Water Management Model (SWMM). The potential evapotranspiration (PET) simulated by the SWMM was rectified by comparing it to the Atmos-41 observation data. Multiple regression analysis was employed to adjust for the fluctuations in precipitation, relative humidity, and wind speed because the calculated SWMM PET tends to be underestimated during periods of low temperatures. R² increased from 0.54 to 0.80 when compared to the Atmos-41 PET. The rate of change in the water cycle as a consequence of the SNU's construction resulted in a 15.7% increase in surface runoff, a 14.2% decrease in infiltration rate, and a 1.6% decrease in evaporation.

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.22-35
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• 2016

Global climate change could have an impact on hydrological process of a watershed and result in problems with future water supply by influencing the recharge process into the aquifer. This study aims to assess the change of groundwater recharge rate by climate change and to predict the sustainability of groundwater resource in Pyoseon watershed, Jeju Island. For the prediction, the groundwater recharge rate of the study area was estimated based on two future climate scenarios (RCP 4.5, RCP 8.5) by using the Soil Water Balance (SWB) computer code. The calculated groundwater recharge rate was used for groundwater flow simulation and the change of groundwater level according to the climate change was predicted using a numerical simulation program (FEFLOW 6.1). The average recharge rate from 2020 to 2100 was predicted to decrease by 10~12% compared to the current situation (1990~2015) while the evapotranspiration and the direct runoff rate would increase at both climate scenarios. The decrease in groundwater recharge rate due to the climate change results in the decline of groundwater level. In some monitoring wells, the predicted mean groundwater level at the year of the lowest water level was estimated to be lower by 60~70 m than the current situation. The model also predicted that temporal fluctuation of groundwater recharge, runoff and evapotranspiration would become more severe as a result of climate change, making the sustainable management of water resource more challenging in the future. Our study results demonstrate that the future availability of water resources highly depends on climate change. Thus, intensive studies on climate changes and water resources should be performed based on the sufficient data, advanced climate change scenarios, and improved modeling methodology.

• Ecology and Resilient Infrastructure
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• v.5 no.3
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• pp.134-144
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• 2018

In this study, the historical drought in East Asia was analyzed with the Community Land Model (CLM) and CLM-Variable infiltration capacity (CLM-VIC). The observation dataset, Climate Research Unit (CRU), were also applied to check and estimate the historical drought for 1951 - 2010. The annual precipitation, temperature and evapotranspiration by CRU, CLM and CLM-VIC were investigated before estimating the meteorological drought index, which is the Standardized precipitation evapotranspiration index (SPEI). Three variables by observation and simulations have generally similar spatial pattern in East Asia even though there are some mere differences depending on the local area. These similar patterns are also founded in the results of SPEI by CRU, CLM and CLM-VIC. However, the similarity of SPEI becomes weaker as the drought severity goes severer from D1 to D4.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.34-40
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• 2015

Studies using stable water vapor isotopes have been recently conducted over the past two decades because of difficulties in analysis and sample collection in the past. Stable water vapor isotope data provide information of the moisture transport from ocean to continent, which are also used to validate an isotope enabled general circulation model for paleoclimate reconstructions. The isotopic compositions of groundwater and water vapor also provide a clue to how moisture moves from soil to atmosphere by evapotranspiration. International Atomic Energy Agency designates the stations over the world to observe the water vapor isotopes. To analyze the water vapor isotopes, a cryogenic sampling method has been used over the past two decades. Recently, two types of laser-based spectroscopy have been developed and remotely sensed data from satellites have the global coverage. In this review, measurements of isotopic compositions of water vapor will be introduced and some studies using the water vapor isotopes will also be introduced. Finally, we will suggest the future study in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.486-486
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• 2018

During the post green revolution era, wheat and rice were the main crops of concern to cater the food security issues of Pakistan. The use of semi dwarf high yielding varieties along with extensive use of fertilizers and surface and ground water lead to substantial increase in crop production. However, the higher crop productivity came at the cost of over exploitation of the precious land and water resources, which ultimately has resulted in the dwindling production rates, loss of soil fertility, and qualitative and quantitative deterioration of both surface and ground water bodies. Recently, during the past two decades, severe climate changes are further pushing the Pakistan's wheat-rice system towards its limits. This necessitates a careful analysis of the current crop water requirements and water footprints (both green and blue) to project the future trends under the most likely climate change phenomenon. This was done by using the FAO developed CROPWAT model v 8.0, coupled with the statistically-downscaled climate projections from the 8 Global Circulation Models (GCMs), for the two future time slices, 2030s (2021-2050) and 2060s (2051-2080), under the two Representative Concentration Pathways (RCPs): 4.5 and 8.5. The wheat-rice production system of Punjab, Pakistan was considered as a case study in exploration of how the changing climate might influence the crop water requirements and water footprints of the two major crops. Under the worst, most likely future scenario of temperature rise and rainfall reduction, the crop water requirements and water footprints, especially blue, increased, owing to the elevated irrigation demands originating from the accelerated evapotranspiration rates. A probable increase in rainfall as envisaged by some GCMs may partly alleviate the adverse impacts of the temperature rise but the higher uncertainties associated with the predicated rainfall patterns is worth considering before reaching a final conclusion. The total water footprints were continuously increasing implying that future climate would profoundly influence the crop evapotranspiration demands. The results highlighted the significance of the irrigation water availability in order to sustain and improve the wheat-rice production system of Punjab, Pakistan.