Human activities exacerbate river network degrading in the Qinhuai River basin.

Rivers are undergoing significant changes under the pressures of natural processes and human activities. However, characterizing and understanding these changes over the long term and from a spatial perspective have proven challenging. This paper presents a novel framework featuring twelve indicators that combine geometric and spatial structures for evaluating changes in river network patterns. Through global principal component analysis, these indicators were integrated into a comprehensive river network pattern index (RNP). Employing Pearson correlation analysis, geographically weighted regression, geographic detector models, and the Shapley Value, the study quantitatively analyzed various stressors' impacts and relative contributions on river network changes from the 1960s to 2015s. The results showed a clear trend of degradation over time, particularly with frequency and density declining by 57 % and 48 %, respectively. The changes across subbasins varied temporally and spatially, with the 1980s emerging as a significant temporal hotspot and six spatial hotspots identified among twenty subbasins. The analysis showed that agriculture was significantly negatively associated with RNP, while the relationship between urbanization and RNP was inverted N-shaped. To address the negative effects of human activities, a shift from uniform management approaches is crucial. In agricultural areas, adopting more intensive farming practices could help mitigate negative impacts on RNP. For highly urbanized regions, city planning should consider the interactions between urbanization and other factors affecting RNP. Overall, incorporating an understanding of RNP's spatial-temporal dynamics and driving factors into spatial planning is critical for creating effective and sustainable management strategies for human-river interactions.

A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance.

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity Ci, and the HCCI of the whole river network is defined as the mean of the Ci for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that Ci showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of Ci in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management.

Field study on endogenous perfluoroalkyl acid release and their spatiotemporal distribution processes induced by inland navigation.

Dense populations and industries in regions with developed inland waterways have caused the significant discharge of perfluoroalkyl acids (PFAAs) into surrounding waterways. Despite being the dominant energy input in the waterways, the impact of ship navigation on endogenous PFAA release is unclear. In this study, a field experiment was carried out in the Wangyu River (Taihu Basin, China) to investigate the spatiotemporal distribution processes of PFAAs in the water column after passage of ships with different tonnages, speeds, and draughts. The results showed that the PFAA contents did not decrease continuously with time but increased with a lag after the passing ship triggered a transient massive dissolution of PFAAs into the overlying water. In addition, PFAA contents in suspended particulate matter (SPM) exhibited a fluctuating downward trends after their peak at the moment of ship passage. Vertically, the PFAA concentrations among the layers of overlying water were relatively homogeneous, whereas SPM exhibited substantial heterogeneity in its distribution and adsorption of PFAAs. Moreover, the differences in jet scouring velocity (u), disturbance duration (t), and draught (h) of ships resulted in large variability in PFAA contents in the water column. Variance partitioning analysis further quantified the effects of u, t, and h on total PFAAs in the water column, with individual contributions of 53 %, 12 %, and 6 %, respectively. Furthermore, the release of endogenous PFAAs induced by ship passage involved rapid and slow processes, the former determining the overall PFAA release and the latter affecting PFAA concentration recovery in the water column. The findings provide in-situ observational data on spatiotemporal variations of PFAAs in multiphase media following ship passage, enhancing our understanding of endogenous pollution in inland waterways.

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods.

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018-2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.