Fractal domain refinement of models simulating hydrological time series. Issue 9 (4th July 2022)

• Record Type: Journal Article
• Authors: Habib, Abrar; Butler, Adrian P.; Bloomfield, John P.; Sorensen, James P. R.
• Is Part Of: Hydrological sciences journal. Volume 67:Issue 9(2022)
• Page Start: 1342
• Page End: 1355
• Publication Date: 2022-07-04
• Subjects: hydrological models -- long-term memory -- fractal domain refinement; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2022.2084342
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: Fractal analysis of a time series provides information on how the series varies across all (possible) temporal scales with respect to a given statistical measure. Dynamic hydrological models are typically optimized/calibrated using performance criteria defined in the time domain; however, the performance of models in simulating the fluctuation structure of a time series is seldom investigated. We use a multi-objective pattern search algorithm to calibrate a combined 15-minute resolution recharge–groundwater flow model. The non-dominated simulations of the model are then analysed in the fractal domain using robust detrended fluctuation analysis. The results show that some non-dominated simulations can be eliminated based on poor performance in the fractal domain, hence ensuring that the fluctuation structure of the optimized simulations is captured; this was named fractal-domain-refinement. Furthermore, some recharge parameters are sensitive to fractal-domain-refinement. This gives insights into which parameters are sensitive to the fractal behaviour of the simulated variable.