A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate

Silicate weathering, which is of great importance in regulating the global carbon cycle, has been found to be affected by complicated factors, including climate, tectonics and vegetation. However, the exact transfer function between these factors and the silicate weathering rate is still unclear, leading to large model–data discrepancies in the CO2 consumption associated with silicate weathering. Here we propose a simple parameterization for the influence of vegetation cover on erosion rate to improve the model–data comparison based on a state-of-the-art silicate weathering model. We found out that the current weathering model tends to overestimate the silicate weathering fluxes in the tropical region, which can hardly be explained by either the uncertainties in climate and geomorphological conditions or the optimization of model parameters. We show that such an overestimation of the tropical weathering rate can be rectified significantly by parameterizing the shielding effect of vegetation cover on soil erosion using the leaf area index (LAI), the high values of which are coincident with the distribution of leached soils. We propose that the heavy vegetation in the tropical region likely slows down the erosion rate, much more so than thought before, by reducing extreme streamflow in response to precipitation. The silicate weathering model thus revised gives a smaller global weathering flux which is arguably more consistent with the observed value and the recently reconstructed global outgassing, both of which are subject to uncertainties. The model is also easily applicable to the deep-time Earth to investigate the influence of land plants on the global biogeochemical cycle.</p

A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation

The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional &ldquo;node-arc&rdquo; unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks