Fertilization intensities at the buffer zones of ponds regulate nitrogen and phosphorus pollution in an agricultural watershed

The sudden increase in water nutrients caused by environmental factors have always been a focus of attention for ecologists. Fertilizer inputs with spatio-temporal characteristics are the main contributors to water pollution in agricultural watersheds. However, there are few studies on the thresholds of nitrogen (N) and phosphorus (P) fertilization rates that affect the abrupt deterioration of water quality. This study aims to investigate 28 ponds in Central China in 2019 to reveal the relationships of basal and topdressing fertilization intensities in surrounding agricultural land with pond water N and P concentrations, including total N (TN), nitrate (NO3--N), ammonium (NH4+-N), total P (TP), and dissolved P (DP). Abrupt change analysis was used to determine the thresholds of fertilization intensities causing sharp increases in the pond water N and P concentrations. Generally, the observed pond water N and P concentrations during the high-runoff period were higher than those during the low-runoff period. The TN, NO3--N, TP, DP concentrations showed stronger positive correlations with topdressing intensities, while the NH4+-N concentrations exhibited a higher positive correlation with basal intensities. On the other hand, the NO3--N concentrations had a significant positive correlation with the topdressing N, basal N, and catchment slope interactions. Significant negative correlations were observed between all water quality parameters and pond area. Spatial scale analysis indicated that fertilization practices at the 50 m and 100 m buffer zone scales exhibited greater independent effects on the variations in the N and P concentrations than those at the catchment scale. The thresholds analysis results of fertilization intensities indicated that pond water N concentrations increased sharply when topdressing and basal N intensities exceeded 163 and 115 kg/ha at the 100 and 50 m buffer zone scales, respectively. Similarly, pond water P concentrations rose significantly when topdressing and basal P intensities exceeded 117 and 78 kg/ha at the 50 m buffer zone scale, respectively. These findings suggest that fertilization management should incorporate thresholds and spatio-temporal scales to effectively mitigate pond water pollution.

Landscape patterns regulate non-point source nutrient pollution in an agricultural watershed

Landscape pattern critically affects hydrological cycling and the processes of non-point source nutrients pollution. However, little is known about the quantitative relationship between landscape characteristics and the river water quality, and very few studies have addressed the abrupt changes in river water quality with the gradient of landscape metrics. The present study was conducted in a typically intensive agriculture watershed of eastern China including 13 sub-watersheds with different landscape pattern metrics. We adopted redundancy analysis, nonparametric deviance reduction approach, bootstrap sampling and other statistical methods to reveal the quantitative relationship between landscape pattern metrics and water quality variables; then, the phenomenon of an abrupt change in river water quality was explored with different landscape pattern gradients. The results show that landscape pattern significantly affects river water quality, and this effect was quite different in dry and rainy seasons. In the studied watershed, landscape pattern metrics could respectively explain 71.1% and 55.3% of the total variance in the river water quality in dry and rainy seasons. The configuration metrics of landscape pattern had a stronger ability than their composition metrics to explain the variance in water quality. In the dry season, largest patch index of forestland (LPI_for), the most important landscape index, explained 37.9% of the total variance in water quality. While, in the rainy season, the most important landscape index was the largest patch index of farmland (LPI_far), and it could explain 32.4% of that variance. In the studied watershed, when the LPI_for was <35% or LPI_far was over than 50%, water quality would typically change abruptly, at which the probability of a change in river water would suddenly rise substantially.