Assessment of Metals Loading in an Acid Mine Drainage Watershed
- Authors
- Pak, Gijung; Jung, Minjae; Kim, Hwansuk; Mallari, Kristine Joy B.; Chung, Gunhui; Kim, Sungpyo; Kim, Young; Oa, Seongwook; Yoon, Jaeyoung
- Issue Date
- 3월-2016
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- Hydrology; Metals; WARMF model
- Citation
- MINE WATER AND THE ENVIRONMENT, v.35, no.1, pp.44 - 54
- Indexed
- SCIE
SCOPUS
- Journal Title
- MINE WATER AND THE ENVIRONMENT
- Volume
- 35
- Number
- 1
- Start Page
- 44
- End Page
- 54
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/89442
- DOI
- 10.1007/s10230-015-0336-6
- ISSN
- 1025-9112
- Abstract
- Watershed-scale modeling can be useful in identifying the main environmental factors and the physical mechanisms responsible for acid mine drainage (AMD) formation, attenuation, and impacts. Since flow rates and water quality of the AMD and receiving streams are related to the rainfall-runoff relationship and associated contaminant dissolution, we thought that hydrologic analysis of the mined area and surrounding drainage basin should be the starting point in documenting the source and fate of AMD contaminants. Further modeling of AMD pollutants could then be performed in terms of metal concentrations and loading at the watershed scale. In this study, monitoring was conducted in the Geopung mine watershed; the watershed analysis risk management framework (WARMF) model was used to evaluate the effect of AMD contributions to downstream metal concentrations. The hydrologic model of the basin was calibrated and verified with rainfall and streamflow data, and the water quality model was calibrated for the dissolved concentrations of metals (Cd, Cu, Zn, and Pb), using discharge data gathered in 2009. There was a strong correlation (r = 0.93) between the observed and simulated runoff values plus high Nash-Sutcliffe model efficiency (NSE = 0.89) and low average percent difference between predicted and measured values (%Diff = 0.46). Subsequent model validation using data gathered in 2010 also showed good agreement (%Diff = 9.76; NSE = 0.77; r = 0.91) between the observed and simulated values. For the metals, the model was calibrated using data from 2010; the correlation between the observed and simulated values was quite good (r = 0.80-0.41).
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- Appears in
Collections - Graduate School > Department of Environmental Engineering > 1. Journal Articles
- College of Science and Technology > Department of Environmental Engineering > 1. Journal Articles
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