Search

Scientific understanding of the scale effects of landscape structure on water quality is essential for national land planning and water environment management. However, there is no consensus on the optimal spatial landscape scale affecting water quality. To this end, we developed a novel mix scale division method, which is consist of the intersection of sub-basin, stream buffer zone and circular buffer zone. Based on the water quality data from 113 monitoring stations of the Yellow River Basin, we quantitatively identified the spatial scale effects of various landscape indexes on water quality, as well as the nonlinear response of the water quality along the gradient of landscape indicators in the Yellow River, by using the Redundancy analysis (RDA) and Generalized Additive Model (GAM). We show that landscape indexes related to buildup and agricultural lands had significant impact on water quality changes, and this impact was more influential during the rainy season compared with the dry season. We also identified that the optimal scale and landscape pattern corresponding to the main non-point source factors, i.e., COD Cr , total nitrogen (TN), and total phosphorus (TP). The variations of these three water quality indicators were predominated by buildup land (B-CA) at sub-basin (with a contribution percentage of 43.7%), agricultural land (A-CA) at the scale of stream buffer (bs) = 1000 m and circular buffer (bc) = 20 km (50.2%), and the largest patch index of agriculture (A-LPI) at the scale of bs = 1000m and bc = 25 km (41.7%), respectively. The B-CA>50000 ha at the sub-basin scale and A-LP = 50% at the mixed scale (bs = 1000m, bc = 25 km) would accelerate the deterioration of water quality. Results implied that water quality could be improved by implementing landscape regulation within an appropriate scale and considering the non-linear effects of landscape structure on water quality. • Developing a novel mixed buffer zone division method to analyze the influence range of landscape structure on water quality. • Locating the optimal scales of different landscape indices on water quality. • The nonlinear effect of the landscape on the water quality is found. • Water quality degradation in YRB is susceptible to human activities, particularly the agricultural landscape. [ABSTRACT FROM AUTHOR]

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010–2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions. [ABSTRACT FROM AUTHOR]

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO 3 –N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts. • XGBoost-SHAP uncovers key drivers in semi-arid aquatic integrity evolution. • Innovative integration of XGBoost-SHAP with FCM maps aquatic spatial patterns. • AT dominates the distribution of aquatic biological integrity. • Distinct north-south trends in aquatic biological integrity of semi-arids revealed. [ABSTRACT FROM AUTHOR]