Spatiotemporal investigation of scaling properties of precipitation using high resolution data

Abstract Natural systems, driven by complex processes, exhibit variations in their behavior across different time scales. These variations often follow power-law relationships, providing valuable insights into the underlying physical mechanisms. This study investigates the scaling and multifractal behavior of precipitation using high-resolution (1-minute) data from nine stations across diverse climatic zones in Istanbul. By using high-resolution data, the study investigates precipitation scaling at sub-daily time scales, which is crucial for understanding short-duration rainfall events and their potential impacts on urban areas like Istanbul. The research employs a combination of spectral scaling, detrended fluctuation analysis (DFA), wavelet variance, and multifractal analysis to examine precipitation scaling. The calculated scaling exponents were then compared to identify potential interdependencies. Notably, the multifractal analysis revealed an apparent multifractal character in the precipitation data for all stations. The results of the study indicate that the precipitation data exhibits multifractal characteristics at all stations, with spectral scaling exponents ranging from 0.58 to 0.98 and Hölder exponents tending towards 0.5, indicating weak positive serial correlations. The findings of this research have important implications for various hydrological applications, including the development of intensity-duration-frequency (IDF) curves and the evaluation of global circulation models (GCMs).