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A Hydrologic/Water Quality Model Applicati1 1
Author(s) -
Engel Bernard,
Storm Dan,
White Mike,
Arnold Jeff,
Arabi Mazdak
Publication year - 2007
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2007.00105.x
Subject(s) - documentation , hydrological modelling , context (archaeology) , computer science , protocol (science) , quality (philosophy) , agency (philosophy) , water quality , adaptation (eye) , risk analysis (engineering) , quality assurance , model selection , systems engineering , management science , engineering , operations management , business , philosophy , alternative medicine , external quality assessment , pathology , optics , biology , paleontology , epistemology , climatology , programming language , medicine , physics , geology , machine learning , ecology
This paper presents a procedure for standard application of hydrologic/water quality models. To date, most hydrologic/water quality modeling projects and studies have not utilized formal protocols, but rather have employed ad hoc approaches. The procedure proposed is an adaptation and extension of steps identified from relevant literature including guidance provided by the U.S. Environmental Protection Agency. This protocol provides guidance for establishing written plans prior to conducting modeling efforts. Eleven issues that should be addressed in model application plans were identified and discussed in the context of hydrologic/water quality studies. A graded approach for selection of the level of documentation for each item was suggested. The creation and use of environmental modeling plans is increasingly important as the results of modeling projects are used in decision‐making processes that have significant implications. Standard modeling application protocols similar to the proposed procedure herein provide modelers with a roadmap to be followed, reduces modelers’ bias, enhances the reproducibility of model application studies, and eventually improves acceptance of modeling outcomes.