Spatiotemporal characterization and relationships of meteorological factors, river water quality and quantity in the Weihe River Basin, China

Weihe River Basin (WRB), China. In this study, the Soil and Water Assessment Tool (SWAT) model was applied to simulate hydrological processes. The water yields (WYLD) for the sub-basins corresponding to water quality monitoring sections were successfully calculated. Principal Component Analysis (PCA) combined with parametric tests were used to investigate the spatiotemporal variation of indicators describing the natural environment. Redundancy analysis (RDA) was employed to analyze the degree of correlation and influence between different indicators. Temporal analysis indicated that climate change exhibited greater variability in autumn than in other seasons. The distribution pattern of principal component factor 2 (PC2), representing non-point source pollution, indicated no significant seasonal variation during summer and winter. Spatial analysis revealed that the mean water yields of sub-basins belonging to different spatial reaches were similar within the same period. Ammonia nitrogen (NH 3 -N), pH, turbidity (TU), and conductivity ( σ ) exhibited strong spatial heterogeneity. Precipitation (P) and temperature exhibited positive correlations with both flow rate (Q) and water yields, and precipitation exerted a greater influence on flow rate than on water yield. Additionally, nitrogen concentration showed no significant relationship with water quantity. Increases in precipitation, flow rate and temperature all reduce the solubility of oxygen in the water column. This study elucidates the spatiotemporal distribution and correlations among natural environmental factors in the basin, offering practical significance for the integrated management of climate change and water environment issues. • SWAT model replenished to water quantity time series. • Principal Component Analysis on the temporal characterization of indicators. • Application of statistical tests to the spatial characterization of indicators. • Relationships of indicators were analyzed.