• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.11-19
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• 2019

In this study, the actual evapotranspiration products of Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM) and MOD16, which are satellite- and reanalysis-based dataset, were validated at the flux tower sites (i.e., CFK and SMK) managed by Korea Institute of Hydrological Survey, and the uncertainty and correlation analysis were conducted using Triple Collocation (TC) method. The result of validation with the flux tower showed better agreement in the order of GLEAM> GLDAS>MOD16. At the result of three combinations (S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16), the order of best to worst is GLEAM, GLDAS, MOD16, and flux tower for CFK (GLDAS> GLEAM>MOD16>flux tower for SMK). Since the error variance and correlation coefficients of the flux tower show relatively worse performance in TC analysis than the other products, By applying TC method to three products (GLDAS vs. GLEAM vs. MOD16), the uncertainty of each dataset were evaluated at the Korean Peninsula, As a results, the GLDAS and GLEAM performed reasonable performance (low error variance and high correlation coefficient), whereas results of MOD16 showed high error variance and low correlation coefficient at the cropland.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.227-227
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• 2016

일반적으로 가뭄은 신뢰성 높고 활용이 쉬운 강수량 자료를 활용하여 판단되고 있으나, 복합적인 대응을 하기 위해서는 증발산량, 토양수분 등 다양한 변수를 고려해야 한다. 이러한 수문기상정보들은 관측자료의 자료 확보기간이 통계 분석을 하기에 짧거나, 시공간적 대표성 부족 등의 단점이 있다. 이러한 문제점을 극복하기 위해 지면모델이 대안으로 널리 활용중이나, 이를 실제로 가뭄에 활용한 응용연구는 상대적으로 부족한 실정이다. 본 연구에서는 미국 NASA의 전지구지표자료동화체계 GLDAS (Global Land Data Assimilation System) 산출물을 활용하여 지면모델 기반의 수문기상정보를 국내 가뭄감시 연구에 적용하고자 하였다. 이를 위해, GLDAS 프로젝트를 통해 제공되는 다중모델 기반의 증발산량, 토양수분 결과를 비교 분석하고 이를 직접 활용할 수 있는 가뭄판단 지수에 적용하여 성능을 검토하였다. 이를 통해 GLDAS 산출 정보가 가뭄판단에 있어 발휘하는 성능을 평가함으로써, 향후 본원에서 구축할 지면 모델 앙상블 시스템의 가뭄감시정보 산출의 효과를 간접적으로 검토하고자 한다.

• 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.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.883-893
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• 2019

In this study, crop coefficients were calculated in two different methods and the results were evaluated. In the first method, appropriateness of GLDAS-based evapotranspiration was evaluated by comparing it with observed data of Cheongmi-cheon (CMC) Flux tower. Then, crop coefficient was calculated by dividing actual evapotranspiration with potential evapotranspiration that derived from GLDAS. In the second method, crop coefficient was determined by using MLR (Multiple Linear Regression) analysis with vegetation index (NDVI, EVI, LAI and SAVI) derived from MODIS and in-situ soil moisture data observed in CMC, In comparison of two crop coefficients over the entire period, for each crop coefficient GLDAS K_c and SM&VI K_c, shows the mean value of 0.412 and 0.378, the bias of 0.031 and -0.004, the RMSE of 0.092 and 0.069, and the Index of Agree (IOA) of 0.944 and 0.958. Overall, both methods showed similar patterns with observed evapotranspiration, but the SM&VI-based method showed better results. One step further, the statistical evaluation of GLDAS K_c and SM&VI K_c in specific period was performed according to the growth phase of the crop. The result shows that GLDAS K_c was better in the early and mid-phase of the crop growth, and SM&VI K_c was better in the latter phase. This result seems to be because of reduced accuracy of MODIS sensors due to yellow dust in spring and rain clouds in summer. If the observational accuracy of the MODIS sensor is improved in subsequent study, the accuracy of the SM&VI-based method will also be improved and this method will be applicable in determining the crop coefficient of unmeasured basin or predicting the crop coefficient of a certain area.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.61-70
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• 2019

The modified Kling-Gupta efficiency fusion method to merge actual evapotranspiration was proposed and compared with the simple Taylor skill's score method using Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MODIS Global Evapotranspiration Project (MOD16), and the flux tower on three different land cover types over the Korean peninsula and China. In the results of the weights estimated from two actual evapotranspiration merging techniques (i.e., STS and KGF), the weights of reanalysis data (i.e, GLDAS and GLEAM) in cropland and grassland showed similar performance, while the results of weights are different according to the merging techniques in forest. Both two merging techniques showed better results than original dataset in grassland and forest. However, there were no improvement in cropland compared to the other land cover types. The results of the KGF method slightly improved compared to those of the STS in grassland and forest.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.835-848
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• 2021

In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.263-263
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• 2020

댐 유역 수문자료는 댐의 효율적인 운영, 중장기 댐 운영 계획, 수자원 관리, 댐 저수량 예보 등에 사용된다. 댐의 주요 수문자료로는 일반적으로 유입량인 강수량과 유량으로 구성되어있으며 유출량인 방류량, 증발량, 토양수분량으로 구분한다. 현재 강수량, 유량, 방류량은 지속적으로 계측하고 있으나 증발산량과 토양의 저류량 등은 실제적으로 측정의 어려움을 가지고 있다. 본 연구에서는 실측자료를 물수지 방정식에 대입해 발생한 잔차를 통해 산출한 증발산량과 비교적 정확성이 높다고 알려져 있는 GLDAS NOAH 지형 모형자료에서 산정된 증발산량간의 비교를 수행하였다. 또한 이렇게 각각 산정한 증발산량으로 월별로 물수지 분석을 정량화하여 분석하였다. 유량자료는 후영교 수위관측소의 자료, 강수량은 괴산군(청천면사무소) 강수량관측소 외 15개소 자료, 댐 방류량자료 등의 실측자료를 사용하였으며, 증발량은 GLDAS NOAH 지표면 model을 이용하여 산정하였다. 저수지 토양수분량은 자료가 없어 고려하지 않았다. 2019년 괴산댐 유역의 총 유입량은 218.54 백만㎥이며, 증발량을 고려한 총 유출량은 200.50 백만㎥으로 나타나 댐의 저류량은 18.05 백만㎥으로 나타났다. 그러나 실제 저수지의 수위-저수용량 곡선식을 이용하여 계산된 총 저류량은 0.06 백만㎥으로 상당한 차이를 보이고 있다. 이 원인으로 1. 증발량 추정자료 사용, 2. 토양저류량 미 고려, 3. 자료가 없는 취수량 미 고려 4. 유량, 방류량, 강수량 자료 오차 등이 있는 것으로 판단된다. 한편, GLDAS NOAH 지표면 model을 이용한 연 저수지증발량과 물 수지 방적식을 이용한 연 저수지증발량은 각각 0.79 백만㎥, 18.84 백만㎥으로 나타나, 역시 차이를 보인다. 이는 물 수지 방정식을 이용한 연 저수지증발량은 토양수분증발량 미 고려에 따른 것과 GLDAS NOAH 지표면 model자료는 직접적인 실측 자료가 아닌 추정 자료로 다소 오차가 있을 것으로 생각된다. 댐 유역 물의 이동을 추적하고 이를 정량적으로 나타내는 것은 결과적으로 효율적인 댐 운영을 가능하게 한다. 그러나 최근 실시되는 유량측정과는 달리 물 수지 분석의 주요 인자인 증발량과 토양수분량 등은 측정이 전무하여 여러 가지 방법으로 추정하는 현실이다. 추후 이러한 수문자료를 실측하여 제공한다면 댐 관리 및 중장기 댐 운영 계획 수립 등 효율적인 댐 운영에 대단히 유용할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.187-187
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• 2015

인공위성을 이용한 토양수분의 측정은, 범지구적인 물순환 분석에 있어서, 수문학적인 인자들의 시공간적인 변화를 예측, 분석하는데 있어 가장 효율적인 방법으로 제안되어왔다. 현재 국/내 외 적으로 사용하는 토양수분 위성은 Soil Moisture and Ocean Salinity (SMOS), Advanced SCATerometer (ASCAT)이 많이 사용되고 있으며, 더불어 일본에서 최근에 발사 된 Advanced Microwave Scanning Radiomter 2 (AMSR2) 센서를 통한 토양수분도 데이터도 적극 활용 되고 있다. 각 위성은 토양수분을 산출 하는 알고리즘, 파장대 그리고 위성 통과 시간 등이 각기 다르므로, 이러한 위성의 데이터를 사용하기 위해서는 지점 데이터와의 검증이 필수적으로 필요하게 된다. 이에따라 본 연구에서는 위성 데이터와 Global Land Data Assimilation System (GLDAS)와의 비교를 통해 각 위성데이터의 동아시아 지역에서의 효용성을 평가하였다. 동아시아의 건조한 지역에서는 SMOS가 가장 좋은 토양수분 데이터 결과를 보여주었으며, 다른 많은 지역에서는 ASCAT이 우세한 결과를 보여주었다. 하지만 한반도 지역의 특정 지역에서는 AMSR2의 토양수분 값이 ASCAT을 뛰어넘는 좋은 결과를 보여주는 결과가 도출되었다. 추가적으로, SMOS의 경우 Radio Frequency Interference (RFI)의 영향으로 한반도지역 토양수분을 측정하는 것에는 많은 무리가 있음을 알 수 있었다.