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Kim S.B.,National Water Research Institute | Shin H.J.,K water Institute | Park M.,Han River Environmental Research Center | Kim S.J.,Konkuk University
Paddy and Water Environment | Year: 2015

The Soil and Water Assessment Tool model was applied to assess the potential climate change impact on snowmelt and the non-point source pollution discharges in a 6,640.0 km2 high-elevation watershed of South Korea. For the snowmelt parameters of the model, Terra Moderate Resolution Imaging Spectroradiometer image was used to obtain the snow cover depletion curve. The model was calibrated using 11 years of data from 2000 to 2010 that included daily runoff and monthly sediment, total nitrogen (TN), and total phosphorus (TP). The climate change impacts on snowmelt in the watershed were evaluated for Special Report on Emission Scenarios A2, A1B, and B1, scenarios (HadCM3) and Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios (HadGEM3-RA). With the temperature increase of 4.33 °C during the snowmelt period in 2080s (2060–2099) RCP 8.5 scenario based on baseline period (1981–2010), the future snowmelt decreased to 39.9 % during snowmelt period (November–April). Turning the reduced snowmelt discharges into rain-runoff discharges under the 45.7 % increase of precipitation caused increase of future sediment, TN, and TP loads to 53.0, 118.2, and 137.5 % respectively. The future increases of TN and TP loads can stimulate the algal bloom and the eutrophication of the dam reservoir. © 2014, The International Society of Paddy and Water Environment Engineering and Springer Japan. Source


Shin H.J.,K water Institute | Park M.,Han River Environmental Research Center | Kim S.J.,Konkuk University
Paddy and Water Environment | Year: 2016

This study is to evaluate the future potential climate impact on snow hydrology using SLURP model for a 6661.0 km2 mountainous watershed of South Korea. For the model test, the NOAA AVHRR images were analyzed to prepare snow-related data of the model. Snow cover areas were extracted using channels 1, 3, and 4, and the snow depth was spatially interpolated using snowfall data of 11 ground meteorological stations. With the snowmelt parameters (snow cover area, snow water equivalent, and snow depth), the model was calibrated for 2 sets (2002–2003, 2004–2005), and verified for 2 sets (1997–1998 and 2001–2002) using the calibrated parameters. The average Nash–Sutcliffe efficiencies during the full year period (December to November) and snowmelt period (December to April) were 0.60 and 0.66, respectively. The future climate data of CCCma CGCM2 SRES A2 and B2 scenarios were adjusted and downscaled using change factor method. By the future impact of climate change, the annual dam inflows were projected to change maximum −29.3 and −30.4 % for 2090s A2 scenario and 2030s for B2 scenario, respectively. The future dam inflow increased in winter season (December to February) up to 222.0 %, while other periods decreased up to 54.8 %. The future snowmelt increased in December and January by the future temperature increase of 3.9 °C in minimum. The future snowmelt for the 2 months affected the dam inflows during the winter season. © 2015, The International Society of Paddy and Water Environment Engineering and Springer Japan. Source


Kim M.,Ulsan National Institute of Science and Technology | Baek S.,Ulsan National Institute of Science and Technology | Ligaray M.,Ulsan National Institute of Science and Technology | Pyo J.,Ulsan National Institute of Science and Technology | And 2 more authors.
Water (Switzerland) | Year: 2015

Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows. © 2015 by the authors. Source

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