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Urban living accounts for approximately 50% of the worldwide population and this percentage will continue to rise in the decades to come. Urbanizing landscapes and their associated processes represent key aspects of coupled natural and human systems. Additionally, they can influence and cause impacts on regional broad-scale basin hydrology, stream and riparian ecosystems, and water quality. For this research, it is hypothesized that streamflow and water quality time series data for urbanizing basins may exhibit signs of complexity such as long range correlations, state transitions, and chaotic dynamics. To explore this hypothesis, we will use tools from statistical physics and ecology such as multifractal detrended fluctuation analysis, critical slowing down, and chaos-based time series analysis. We will apply these tools to streamflow, rainfall, and water quality data for nearly 40 stream gauges located in the metropolitan areas of cities across the US. It is expected that this analysis will reveal signs of complex behavior different from that already observed in natural basins, as well as new and unique behavior. The results from this research could help support and provide empirical evidence for developing improved data-driven models.


Session R49, Data Processing: Reconstruction, Gap Filling, and Error Corrections


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