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Uncertainty Considerations in Calibration and Validation of Hydrologic and Water Quality Models
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Transactions of the ASABE. 58(6): 1745-1762. (doi: 10.13031/trans.58.10710) @2015
Authors: Jorge A. Guzman, Adel Shirmohammadi, Ali M. Sadeghi, Xiuying Wang, Maria L. Chu, Manoj K. Jha, Prem B. Parajuli, R. Daren Harmel, Yogesh P. Khare, Jairo E. Hernandez
Keywords: Hydrologic modeling, Model calibration, Model validation, Spatiotemporal, Uncertainty.
Abstract. Hydrologic and water quality models (HWQMs) are increasingly used to support decisions on various environmental issues and policy directions for present and future scenarios, at scales varying from watershed to continental levels. Uncertainty associated with such models may affect the ability of the models to accurately evaluate the response of complex systems, leading to misguided assessments and risk management decisions. Current well-known HWQMs contain numerous input parameters, many of which are not known with certainty, and in other cases model users can hardly recognize the genesis of uncertainty. Uncertainty in data, model structure, and model parameters can propagate throughout model runs, causing the model output to substantially deviate from the expected response of the natural system. Various uncertainty assessment methods have been used with different HWQMs, creating concerns about an adequate approach for handling uncertainty in these models and how such an approach can be implemented across various discretization complexities and scales. In this article, our primary intention is to review uncertainty in the currently used HWQMs and to provide guidance and useful information for researchers and investigators. In this regard, we explore the genesis of uncertainty in hydrologic and water quality modeling (i.e., spatiotemporal scales, model representation, model discretization, model parameterization) and provide strategies for assessing uncertainty in hydrologic and water quality modeling on local and global scales when interpreting the model output.
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