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How much complexity is needed to simulate watershed streamflow and water quality? A test combining time series and hydrological models
Author(s) -
Chien Huicheng,
Mackay D. Scott
Publication year - 2013
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.10066
Subject(s) - streamflow , watershed , environmental science , hydrology (agriculture) , water quality , hydrological modelling , scale (ratio) , computer science , geology , ecology , geography , climatology , drainage basin , cartography , geotechnical engineering , machine learning , biology
Modelled hydrologic processes are represented in a set of numerical equations; the complexity of which can be measured by the total number of variables needed. A single dominant hydrologic process could control the hydrologic response of a watershed, and so the identification of the corresponding dominant variable(s) would aid in identifying a parsimonious model and in collecting more reliable data. By accounting for both model complexity and serial correlation in the variables, a model is used to identify the dominant variables for representing watershed scale streamflow, sediment transport and phosphorus yields. Long‐term water quantity and quality data were used to show that rainfall and non‐linear soil water storage were the dominant variables for weekly streamflow, suspended sediment and particulate phosphorus. Model accuracy did not consistently improve when other statistically significant variables were included. The results suggest that improved model performance may not justify the added model complexity. As such, identification of dominant variables would be the priority for developing parsimonious hydrologic models, especially at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.