A simple and effective method for quantifying spatial anisotropy of time series of precipitation fields

The spatial shape of a precipitation event has an important role in determining the catchment's hydrological response to a storm. To be able to generate stochastic design storms with a realistic spatial structure, the anisotropy of the storm has to be quantified. In this paper, a method is proposed to estimate the anisotropy of precipitation fields, using the concept of linear Generalized Scale Invariance (GSI). The proposed method is based on identifying the values of GSI parameters that best describe isolines of constant power on the two-dimensional power spectrum of the fields. The method is evaluated using two sets of simulated fields with known anisotropy and a measured precipitation event with an unknown anisotropy from Brisbane, Australia. It is capable of accurately estimating the anisotropy parameters of simulated nonzero fields, whereas introducing the rain-no rain intermittency alters the power spectra of the fields and slightly reduces the accuracy of the parameter estimates. The parameters estimated for the measured event correspond well with the visual observations on the spatial structure of the fields. The method requires minimum amount of decision making and user interaction, making it suitable for analyzing anisotropy of storm events consisting of long time series of fields with a changing spatial structure.