Is the correlation between hydro-environmental variables consistent with their own time variability degrees in a large-scale loessial watershed?

Abstract

Temporal scale is an important keyword in environmental hydrology but little information is available in the relationship between correlation and time variability degree of hydro-environmental variables at a watershed scale, which makes it difficult to design effective real-time management strategies. Here we take the Yanhe River Watershed as a study case to simulate and inventory the fractal characteristics of correlation and time variability degree of runoff, rainfall, and NH4+-N at different time scales, focusing on the long-term series of 1984–2012. (i) The coupled modeling framework based on SWAT (Soil and Water Assessment Tool), statistics and fractal theory is a time series analysis method that is particularly suitable for the evaluation of long-range correlation of non-linear time series. The Nash-Sutcliffe Efficiency coefficient (NSE), R2 and PBIAS during the calibration and verification period proved the reliability and acceptability of the established SWAT model in modeling multi-time scale runoff and NH4+-N load in the upstream catchment of Ganguyi hydrological station. (ii) Runoffs at all time scales showed positive correlations with rainfall although the significant level had a certain time scale differences. More interestingly, the correlation between NH4+-N loss and runoff at different time scales was significantly higher than that of rainfall. (iii) Each hydro-environmental variable has different fractal and time variation characteristics at different time scales, and the correlation levels between different hydrological variables are not completely consistent with their own time variability degrees at different time scales. These findings point to a fundamental challenge in managing regions with leading infiltration-excess runoff and uneven nutrient loading because the meteorological and hydrological variables in these regions exhibit the strongest temporal variability, which will affect the effective allocation and implementation in management practices.