Impacts of the multichannel river network on plume dynamics in the Pearl River estuary were examined using a high-resolution 3-D circulation model. The results showed that during the dry season the plume was a distinct feature along the western coast of the estuary. The plume was defined as three water masses: (a) riverine water (<5 psu), (b) estuarine water (12-20 psu), and (c) diluted water (>22 psu), respectively. A significant amount of low-salinity water from Hengmen and Hongqimen was transported through a narrow channel between the QiAo Island and the mainland of the Pearl River delta during the ebb tide and formed a local salinity-gradient feature (hereafter referred to as a discharge plume). This discharge plume was a typical small-scale river plume with a Kelvin number K 5 0.24 and a strong frontal boundary on its offshore side. With evidence of a significant impact on the distribution and variability of the salinity and flow over the West Shoal, this plume was thought to be a major feature of the Pearl River plume during the dry season. The upstream multichannel river network not only were the freshwater discharge sources but also played a role in establishing an estuarine-scale subtidal pressure gradient. This pressure gradient was one of the key dynamical processes controlling the water exchange between discharge and river plumes in the Pearl River estuary. This study clearly showed the role of the river network and estuary interaction on river plume dynamics.

No abstract

No abstract

The discovery of causal relationships between observed variables has received much attention in the past. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and structural equation models, bayesian networks are widely applied to analyze the structures. In reality, many causal relationships are more or less nonlinear, raising some doubts as to the applicability and usefulness of purely linear methods. In this paper, we generalize the basic linear model to nonlinear model, and propose a two-step method, which first make use of the feature-selection based approach to obtain the d-separation equivalence class, undetermined causal directions are then found by nonlinear regression and pairwise independence tests. In addition to theoretical algorithm we empirically demonstrate the power of the proposed method through experiments.