• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.260-260
• /
• 2011

SWAT is semi-distributed and continuous-time distributed simulation watershed model, which can simulate point and nonpoint source pollutants as well as hydrology and water quality. It was developed to predict the effects of alternative management decisions on water, sediment, and chemical yields with reasonable accuracy. It is able to predict and manage hydrology, sediments, nutrients, and pesticides with Best Management Practices (BMPs) in a watershed. SWAT model also has potential for use in ungauged basins to predict streamflow and baseflow from saturated source area in watersheds. According to various cultivation practices and climate change, SWAT model is available to analyze relative change in hydrology and water quality. In order to establish optimum management of water quality, both monitering and modeling have been conducted actively using SWAT model. As SWAT model is computer program to simulate a lot of natural phenomena, it has limitation to predict and reflect them with on hundred percent accuracy. Thus, it is possible to analyze the effect of BMPs in the watershed where users want to simulate hydrology and water quality only if model accuracy and applicability are assessed first of all and the result of it is well for the study watershed. For assessment of SWAT applicability, most researchers have used $R^2$ and Nash and Sutcliffe Efficiency (NSE). $R^2$ and NSE are likely to show different results according to a warm up period and sometimes its results are very different. There have been hardly any studies of whether warm up period can affect simulation results in SWAT model. In this study, how warm up period has a effect on SWAT results was analyzed and a appropriate warm up period was suggested. Lots of SWAT results were compared after using measured data of Soyanggang-dam watershed and applying various warm up period (0 ~ 10 year(s)). As a result of this study, when there was no warm up period, $R^2$ and NSE were 0.645, 0.602 respectively, when warm up period was 2 years, $R^2$ and NSE were 0.648, 0.632, and when warm up period was 4 years, $R^2$ and NSE were 0.663, 0.652 separately. Through this study, sensitive analysis of warm up period in SWAT model was conducted, and this study could give a guideline able to simulate hydrology and water quality for more accuracy than before as users change a lot of warm up periods as well as any simulation parameters.

• Journal of Korea Water Resources Association
• /
• v.51 no.6
• /
• pp.481-490
• /
• 2018

Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.

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

• The Journal of Engineering Geology
• /
• v.28 no.4
• /
• pp.541-552
• /
• 2018

The implementation of drought measures in the upstream areas of river basins is seldom considered with respect to water supply. However, the demand for such measures is increasing rapidly owing to the occurrence of severe droughts, and interventions on streams and the water supply are needed. Physical interventions are an option to prevent streams from becoming dry and to maintain stream water flow, but dam construction is challenging because of environmental and ecological considerations. Here, a feasibility study was conducted to assess the potential effects of sand dams, which are widely used in arid regions in Africa. The SWAT-K model, which is a hydrologic model used for Korean watersheds, is used to estimate the flow rate of water in an ungauged watershed. The changes in water storage of the sand-dammed reservoir and in downstream flow rates are estimated for two types of sand dam (natural and dredged). The results show that sand dams are capable of increasing the downstream flow rate during normal conditions and of mitigating water supply problems caused by the withdrawal of water during drought periods.

• Journal of Korea Water Resources Association
• /
• v.47 no.4
• /
• pp.371-384
• /
• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.44 no.5
• /
• pp.637-648
• /
• 2024

The frequency of extreme weather events such as heavy and extreme rainfall has been increasing due to global climate change. Accordingly, it is essential to quantify hydrometeorological variables for efficient water resource management. Among the various hydro-meteorological variables, Land Surface Temperature (LST) and Evapotranspiration (ET) play key roles in understanding the interaction between the surface and the atmosphere. In Korea, LST and ET are mainly observed through ground-based stations, which also have limitation in obtaining data from ungauged watersheds, and thus, it hinders to estimate spatial behavior of LST and ET. Alternatively, remote sensing-based methods have been used to overcome the limitation of ground-based stations. In this study, we evaluated the applicability of the National Aeronautics and Space Administration's (NASA) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST and ET data estimated across Korea (from July 1, 2018 to December 31, 2022). For validation, we utilized NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data and eddy covariance flux tower observations managed by agencies under the Ministry of Environment of South Korea. Overall, results indicated that ECOSTRESS-based LSTs showed similar temporal trends (R: 0.47~0.73) to MODIS and ground-based observations. The index of agreement also showed a good agreement of ECOSTRESS-based LST with reference datasets (ranging from 0.82 to 0.91), although it also revealed distinctive uncertainties depending on the season. The ECOSTRESS-based ET demonstrated the capability to capture the temporal trends observed in MODIS and ground-based ET data, but higher Mean Absolute Error and Root Mean Square Error were also exhibited. This is likely due to the low acquisition rate of the ECOSTRESS data and environmental factors such as cooling effect of evapotranspiration, overestimation during the morning. This study suggests conducting additional validation of ECOSTRESS-based LST and ET, particularly in topographical and hydrological aspects. Such validation efforts could enhance the practical application of ECOSTRESS for estimating basin-scale LST and ET in Korea.