• Title/Summary/Keyword: 초강천

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Analyzing Ecological Soundness Considering the Implicit Weight of the Indicator (지표의 내재적 가중치를 고려한 하천의 생태적 건전성 평가)

  • Kim, Hong-Myung;Ha, Sung-Ryong
    • Journal of Environmental Impact Assessment
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    • v.30 no.4
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    • pp.258-269
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    • 2021
  • The purpose of this study is to establish a system to evaluate the ecological soundness of the Geum river basin. The study target area is 14 sub-watersheds of the Geum river basin. For the selection of indicators to ensure transparency and consistency of the evaluation indicators, the ecological soundness indicators were secured by using the indicator adjustment method derived in consideration of the intrinsic weight change characteristics between indicators. The index with the greatest impact on the final composite index was identified as the index of the aquatic ecology among the water quantity, water quality, aquatic ecology, and habitat-riparian environment dimensions. As a result of analyzing the ecological health index of the river, the watershed upstream of the dam (based on the Daecheong -dam) was evaluated to be in relatively good condition until 2014 compared to the base year(2008), and the watershed downstream of the dam was evaluated to be in a poor condition. The annual trend of changes in the ecological soundness index on an annual basis is as follows. In the case of Yongdamdam, Yongdamdamdownstream, Bocheong-chun, Daechungdam, Daechungdamdownstream, and Nonsancheon, although there are differences by time period, the soundness index is in declining. On the other hand, Mujunamdaecheon, Yeongdongcheon, and Gapcheon were evaluated to have improved soundness, while Chogang, Daechungdamupstream, Mihocheon, Gongjugeumgang, and Geumgangestuary were evaluated to deteriorate again after soundness was improved.

Analysis of National Stream Drying Phenomena using DrySAT-WFT Model: Focusing on Inflow of Dam and Weir Watersheds in 5 River Basins (DrySAT-WFT 모형을 활용한 전국 하천건천화 분석: 전국 5대강 댐·보 유역의 유입량을 중심으로)

  • LEE, Yong-Gwan;JUNG, Chung-Gil;KIM, Won-Jin;KIM, Seong-Joon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.23 no.2
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    • pp.53-69
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    • 2020
  • The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R2) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.