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CHALLENGES IN MECHANISTIC AND EMPIRICAL MODELLING OF STORMWATER: REVIEW AND PERSPECTIVES
Author(s) -
AlAmin Shams,
AbdulAziz Omar I.
Publication year - 2013
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.1804
Subject(s) - stormwater , surface runoff , empirical modelling , trace (psycholinguistics) , empirical research , process (computing) , urban runoff , environmental science , computer science , storm , hydrology (agriculture) , engineering , geography , ecology , mathematics , meteorology , biology , linguistics , philosophy , statistics , geotechnical engineering , programming language , operating system
Much research has been conducted to quantify stormwater runoff and quality using both mechanistic (i.e. process‐based) and empirical (i.e. data‐driven) techniques. Mechanistic models generally include the mathematical representation of relevant physico‐chemical processes to generate storm runoff quantity and quality. Empirical approaches analyse available data for potential response and predictor variables to trace the interactions of major processes and develop data‐driven explanatory and/or predictive relationships. This paper reviews major, mostly unresolved, challenges with both mechanistic and empirical modelling of stormwater, sheds light on the scientific gaps with conventional practices, and offers important perspectives by taking the highly urbanized Miami River Basin of Florida as an analytical example. Appreciating the varying levels of process complexity in different urban river basins, we discuss the relative applicability of mechanistic and empirical methods for robust predictions of stormwater quantity and quality. Copyright © 2013 John Wiley & Sons, Ltd.