Modeled 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 was used to show that rainfall and nonlinear 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.Key words: complex hydrologic model, dominant variable; parsimonious statistical model; long-term hydrologic data; time series model, water quantity and quality model. This article is protected by copyright. All rights reserved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.