High-resolution geomorphic information from remote sensing images is a key part of mountain river research. However, the complete information about narrow rivers is difficult to extract automatically and accurately from complex backgrounds, especially with mountain shadows. This research uses a random forest (RF) algorithm with an artificial neural network (ANN), RF-ANN, to analyze remote sensing images. This method supports parallel operations and reduces the scale of the infrared data for noise removal to achieve pixel-level extraction of the river surfaces. The RivWidthCloud (RWC) method is improved using Laplacian and edge algorithms for automatic extraction of the bankfull river widths. The improved RWC method is generalizable since it does not require setting the discriminant threshold manually. The method is then applied to the Huangfuchuan River Basin on the Loess Plateau, China using images from the Chinese GF-1 and ZY-3 satellites as the primary data source to extract the river surfaces and widths of the rivers above level 2. The results show that the RF-ANN method has a 94.7% accuracy for extracting river surfaces. The bankfull river width extraction error is 1.07 m (about 0.5 pixels) and the minimum river width extracted by these methods is 6.1 m (about 3 pixels). R² is 0.93 and the root men square errors (RMSE) is 1.52 for fitting the extracted river widths and the test river widths. For small rivers narrower than 10 m, the extraction error is 18.5%, for widths from 10 to 30 m the error is 8.8%, for widths from 30 to 90 m the error is 2.0%, and for rivers wider than 90 m the error is 0.7%. These results provide accurate datasets for watershed topography research in mountainous and other complex topographic regions.

High-elevation catchments are critical indicators of the earth's response to global warming. This study collected water chemistry data for runoff in the Yellow River source region from 2002 to 2019 to reconstruct the chemical weathering processes. From 2002 to 2019, the temperature in this region increased significantly along with the precipitation and runoff. The annual chemical weathering rates in this region ranged from 30.9 to 78 t·km^-2, with an average of 53.2 t·km^-2, which is more than twice the average chemical weathering rate of all global rivers. The annual chemical weathering processes in this region increased with increasing temperature at a rate of 1.13 t·km^-2/a. In the context of global warming, the Tibetan high-elevation catchments play important roles in the material exchanges between the land and the oceans and contribute to the stabilization of atmospheric CO₂.

The distributed system with high performance and stability is commonly adopted in large scale scientific and engineering computing. In this paper, we discuss a fault-tolerant mechanism under Linux circumstance to improve the fault-tolerant ability of the system, namely a scheme and frame to form the stable computing platform. In terms of the structure and function of the distributed system, active list and file invocation strategies are employed in the task management. System multilevel fault-tolerance can be achieved by repeated processes in a single node and task migration on multi-nodes. Manager node agent introduced in this paper administrates the nodes using the list, disposes of the tasks according to the nodes’ performance, and hence, to be able to make full use of the cluster resources. An evaluation method is proposed to appraise the performance. The analyzed results show the usefulness of the scheme proposed except for some additional overhead of memory consumption.

This paper describes a parallel computing platform using the existing facilities for the digital watershed model. In this paper, distributed multi-layered structure is applied to the computer cluster system, and the MPI-2 is adopted as a mature parallel programming standard. An agent is introduced which makes it possible to be multi-level fault-tolerant in software development. The communication protocol based on checkpointing and rollback recovery mechanism can realize the transaction reprocessing. Compared with conventional platform, the new system is able to make better use of the computing resource. Experimental results show the speedup ratio of the platform is almost 4 times as that of the conventional one, which demonstrates the high efficiency and good performance of the new approach.