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Uncertainty Analysis in QUAL2E Model of Zayandeh‐Rood River
Author(s) -
Abrishamchi A.,
Tajrishy M.,
Shafieian P.
Publication year - 2005
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143005x41861
Subject(s) - water quality , reliability (semiconductor) , uncertainty analysis , randomness , sampling (signal processing) , environmental science , quality (philosophy) , biochemical oxygen demand , computer science , environmental engineering , chemical oxygen demand , statistics , mathematics , simulation , wastewater , ecology , power (physics) , physics , philosophy , filter (signal processing) , epistemology , quantum mechanics , computer vision , biology
Water‐quality modeling and prediction is a complicated task because of inherent randomness and uncertainties associated with various processes and variables throughout the stream environment and the lack of appropriate data. Hence, the results of mathematical models are always approximate, lying within an uncertainty. This paper describes and demonstrates the application of the U.S. Environmental Protection Agency's water‐quality model, QUAL2E‐UNCAS, to the Zayandeh‐Rood River in Iran. First‐order reliability analysis is used to examine the variability of predicted water‐quality parameters of total dissolved solids, dissolved oxygen, and biochemical oxygen demand. This analysis also determines key sources of uncertainty affecting prediction of the water‐quality parameters. The results show that reliability analysis can help water‐quality modelers and planners to quantify the reliability of the water‐quality predictions and to carry out more efficiently planned sampling and data collection programs to reduce model‐prediction uncertainty.