Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches

Abstract

Climate change and complex anthropogenic activities have raised significant concerns regarding Precipitation-Runoff Relationships (PRR). Traditional methods, assuming stationary and linear conditions, often fail to adequately capture these intricate links. To address the limitations, we proposed an integrated framework, employing the Driving indices for Precipitation-Runoff relationships within the nonStationary and nonLinear theory approaches (DPRS and DPRL) to identify the possible driving mechanisms in PRR. The framework is validated across five sub-basins (WRB1-WRB5) within the Wei River Basin, known for its high spatiotemporal variability and intense anthropogenic activities. Spatiotemporal dynamics, nonstationary processes, and nonlinear interactions among various factors are assessed, including climate forcing, groundwater, vegetation dynamics, and anthropogenic influences. DPRS and DPRL assessments revealed that baseflow significantly influences PRR but with high uncertainty. Potential evapotranspiration plays a dominant role in driving negative PRR changes in WRB5 (weakening the correlation between precipitation and runoff), while vegetation dynamics negatively affect PRR with lower uncertainty. Anthropogenic influences represented by Impervious Surface Ratio (ISR), Night-Time Light (NTL), and population density (POP) exhibit varying driving levels, with ISR having the strongest and direct impact, closely linked to urbanization processes and scale within the study cases. The mutual validation of DPRS and DPRL confirms the dominance of baseflow in the Wei River Basin, with urbanization contributing to high ISR, NTL, and POP driving levels in WRB2 and WRB3. Afforestation policies intensify vegetation dynamics’ impact in WRB4 and WRB5. This framework extends its utility to analyze various land evapotranspiration and soil moisture content at different depths in the PRR, supported by a physically-based hydrological model. Basin complexity is further employed to validate the reliability of the assessment outcomes. These insights contribute to a more comprehensive understanding of hydrological processes and facilitate informed decisions for sustainable water resource management within the basin.