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

Authors: WU, LEI - Northwest A&f University; YEN, HAW - Texas Agrilife Research; Arnold, Jeffrey; MA, XIAOYI - Northwest A&f University

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/3/2020
Publication Date: 3/14/2020
Citation: Wu, L., Yen, H., Arnold, J.G., Ma, X. 2020. Is the correlation between hydro-environmental variables consistent with their own time variability degrees in a large-scale loessial watershed? Science of the Total Environment. 722:137737. https://doi.org/10.1016/j.scitotenv.2020.137737.
DOI: https://doi.org/10.1016/j.scitotenv.2020.137737

Interpretive Summary: Timing of streamflow and pollutants transported to water bodies is critically important to understanding and predicting environmental outcomes. However, little is known about the correlation and time variability of different environmental variables. In this study, the SWAT+ model was applied to the Yanhe River Watershed in China to analyze the time variability of rainfall, runoff, and ammonium-nitrogen. All variables were found to have different degrees of time variability at different scales, which will affect the allocation and implementation of management practices in a watershed. This variability should be accounted for when developing conservation plans to mitigate environmental concerns in a watershed.

Technical 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.