Multi-Method Comparative Analysis of Hydroclimatic Trends and Variability in Dry Creek Catchment, South Australia

Abstract

AbstractThe trend and variability of hydroclimatic variables over time are apparent in seasonal creeks, especially those located in urbanized areas. Understanding hydro-climatic trends in urban areas is crucial for the sustainable management of water resources and the environment. This study aimed to explore the spatiotemporal variability and trends of hydroclimate variables as well as the potential connection between rainfall and streamflow in Dry Creek catchment, South Australia. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Innovative Trend Analysis (ITA) were utilized to examine the monotonic and nonmonotonic trends, respectively, and multiple statistical tests were employed to examine the change points in the hydroclimatic time series. Sen’s slope, Simple Linear Regression (SLR), and ITA were used as alternative approaches to assess the magnitudes of change and overcome the limitations in the underlying assumptions of the various methodologies. The variability in the hydroclimate time series was estimated using several indices, such as the coefficient of variation, seasonality indices, flashiness index, and mean zero flow index. The analyses revealed important findings, notably the high variability of rainfall and streamflow during dry periods. Streamflow displayed greater variability compared to rainfall, with high CV values recorded both seasonally and annually. Furthermore, there was a significant upward trend in seasonal rainfall during winter. Additionally, the maximum and mean temperatures demonstrated a statistically significant increase, which can be attributed to global warming and significant urbanization in the catchment area. Comparative analysis has confirmed that the ITA has superior detection capabilities for nonmonotonic trends, outperforming other methods. It excels at presenting graphical representations that accurately depict trends, effectively differentiating between low, medium, and high values. The strong relationship between rainfall and streamflow demonstrated by the Tanh curve suggests that rainfall is the most reliable predictor of streamflow. The outcomes of this investigation are expected to support local governmental organizations and decision-makers in comprehending the spatial and temporal features of rainfall, as well as its correlation with streamflow. This information will further assist in developing flood and drought mitigation strategies backed by empirical evidence. Graphical Abstract