• KCID journal
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• v.14 no.1
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• pp.80-88
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• 2007

The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a near-global scale to generate the most complete high-resolution digital topographic database of Earth. SRTM consisted of a specially modified radar system that flew onboard the Space shuttle SRTM consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission on February 2000. Since 2004, in a GLCF (Global Land Cover Facility, http;//glcf.umiacs.umd.edu/) web-site, products of SRTM including 1Km and 90m resolutions for outside US and a 30m resolution for the US have been provided. This study is to assess the accuracy of SRTM-DEM in comparing with NGIS-DEM generated from NGIS digital topographic map(1:25,000) in Guem river watershed. For the Geum river watershed, SREM-DEM elevation ranged from 0 to 1,605m and NGIS-DEM ranged from 6 to 1,610m, and the average elevation of SRTM-DEM was 226.7m and 218.9m for NGIS-DEM, respectively. NGIS-DEM was subtracted from SRTM in three zones -Zone I (0~100m), Zone II (100~400m), Zone III (over 400m)- to estimate difference between SRTM and NGIS-DEM. As the results, the differences of these DEM were 5.2m (11.6%) in Zone I, 8.8m (3.8%) in Zone II, 12.5m (2.1%) in Zone III. Although there were differences between SRTM-DEM and NGIS-DEM, SREM-DEM would be possible to be utilized as DEM data for the region where DEM is not prepared.

• KCID journal
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• v.14 no.2
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• pp.194-206
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• 2007

The number of all agricultural reservoir is 18,000, but the ratio of reservoir is 53% before 1945, 35% from 1946 to 1971 in Korea. Therefore, it may have been required that new management system and maintenance techniques are introduced. In this study, there are many facilitie(50.0%), that have been over 50 years, and reservoirs that have been over 30 years is 98.3% in study. So, this study may suggest that reservoir must be considered as new concept through the change of usage and the unification. On the other hand, reservoir works must be developed as amenity resources, other circumstances and district values.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.433-446
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• 2021

In this study, the future flood inundation changes under a climate change were simulated in the Tonle Sap basin in Cambodia, one of the countries with high vulnerability to climate change. For the flood inundation simulation using the rainfall-runoff-inundation (RRI) model, globally available geological data (digital elevation model [DEM]; hydrological data and maps based on Shuttle elevation derivatives [HydroSHED]; land cover: Global land cover facility-moderate resolution imaging spectroradiometer [GLCF-MODIS]), rainfall data (Asian precipitation-highly-resolved observational data integration towards evaluation [APHRODITE]), climate change scenario (HadGEM3-RA), and observational water level (Kratie, Koh Khel, Neak Luong st.) were constructed. The future runoff from the Kratie station, the upper boundary condition of the RRI model, was constructed to be predicted using the long short-term memory (LSTM) model. Based on the results predicted by the LSTM model, a total of 4 cases were selected (representative concentration pathway [RCP] 4.5: 2035, 2075; RCP 8.5: 2051, 2072) with the largest annual average runoff by period and scenario. The results of the analysis of the future flood inundation in the Tonle Sap basin were compared with the results of previous studies. Unlike in the past, when the change in the depth of inundation changed to a range of about 1 to 10 meters during the 1997 - 2005 period, it occurred in a range of about 5 to 9 meters during the future period. The results show that in the future RCP 4.5 and 8.5 scenarios, the variability of discharge is reduced compared to the past and that climate change could change the runoff patterns of the Tonle Sap basin.