Hydrodynamic conditions are important controlling factors in the evolution of vegetation and ecosystems, especially aquatic ecosystems. This study established a conceptual vegetation evolution model to explore the succession law of vegetation patches in the effective area of a channel, including the evolution of vegetation coverage (Ce) and the corresponding longitudinal dispersion coefficient (Ke). 2D shallow-water equations were implemented to calculate flow variables, and the equivalent Manning coefficient was used to reflect the effects caused by vegetation patches. New vegetation emerged in regions where the bed shear stress is lower than the critical bed shear stress, whereas the original vegetation was removed in regions where the bed shear stress is higher than the critical value. Two typical cross-sections and two initial Ce were considered to better understand the succession trend of vegetation patches under different external conditions. The findings showed that Ce and Ke increased to a constant value with increasing simulation duration, whereas a higher critical bed shear stress, defined by the threshold value (TV), was linked to a higher final vegetation coverage (Cf) and final longitudinal dispersion coefficient (Kf). Furthermore, different initial vegetation distributions, including coverage and position, caused little effect on the Cf in the rectangular channel, but the Cf in the parabolic channel was linearly affected by the averaged bed shear stress at the initial patches. The maximum Kf in both channels occurred with regularly distributed initial patches on one side of the bank. The vegetation patches in all scenarios evolved from block-shaped to strip-shaped and finally formed a stable vegetated landscape. This conceptual vegetation evolution model will improve our understanding of the influence of hydrodynamic conditions on the vegetation evolution in aquatic ecosystems.
The evolution of the velocity caused by submerged vegetation in river courses with regular cross sections has been poorly investigated using analytical methods. Fully understanding the longitudinal profiles of the velocity is significant and essential to study the hydrodynamic processes taking place in wetland ecosystems, which is the most important indicator of energy transfer and material exchange in fluvial ecosystems. Based on the experimental data in the literature, the streamwise velocity gradually is found to decrease in the submerged vegetation and increase in the free surface layer. A four-panel analytical model derived from exponential decay theory was developed to predict the streamwise velocity occurring both upstream and within vegetation along the main flow direction in a rectangular open channel with a submerged vegetation patch. We aim at the development of velocity, which is averaged across the vegetation and free surface layers separately, and the length scales shown in the velocity evolution. The explicit calculation expression for each parameter in the analytical model was provided, and sensitivity analysis was conducted by changing the parameters to check how the predicted velocity changed. The change in the drag coefficient of vegetation (CD) and the velocity at the leading edge of the vegetation (U0) influenced the development of velocity. The former mainly enlarges the velocity difference between the vegetation and free surface layers after the leading edge as it increases, and the latter mainly diminishes the velocity difference before the leading edge as it increases. The error statistics is conducted and shows that the predicted and measured velocities are consistent within a relative error of 11%, indicating that the partitioned model is applicable in predicting longitudinal velocity in flows with submerged vegetation of various flexibility. The theoretical analysis of velocity development paves the way for the future studies on the flow turbulent structure and sediment transport in partially vegetated rivers.
The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.
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