Calculating catchment area with divergent flow based on a regular grid
Computers & Geosciences
Scale dependence in terrain analysis
Mathematics and Computers in Simulation - Special issue: selection of papers presented at the MSSA/IMACS 11th biennial conference on modelling and simulation, Newcastle, New South Wales, Australia, November 1995
Parallel Flood Modeling Systems
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Efficient Flow Computation on Massive Grid Terrain Datasets
Geoinformatica
An efficient depression processing algorithm for hydrologic analysis
Computers & Geosciences
An efficient depression processing algorithm for hydrologic analysis
Computers & Geosciences
Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Implementation and performance optimization of a parallel contour line generation algorithm
Computers & Geosciences
Parallel scanline algorithm for rapid rasterization of vector geographic data
Computers & Geosciences
Parallel contributing area calculation with granularity control on massive grid terrain datasets
Computers & Geosciences
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Advanced digital photogrammetry and remote sensing technology produces large terrain datasets (LTD). How to process and use these LTD has become a big challenge for GIS users. Extracting drainage networks, which are basic for hydrological applications, from LTD is one of the typical applications of digital terrain analysis (DTA) in geographical information applications. Existing serial drainage algorithms cannot deal with large data volumes in a timely fashion, and few GIS platforms can process LTD beyond the GB size. High throughput computing (HTC), a distributed parallel computing mode, is proposed to improve the efficiency of drainage networks extraction from LTD. Drainage network extraction using HTC involves two key issues: (1) how to decompose the large DEM datasets into independent computing units and (2) how to merge the separate outputs into a final result. A new decomposition method is presented in which the large datasets are partitioned into independent computing units using natural watershed boundaries instead of using regular 1-dimensional (strip-wise) and 2-dimensional (block-wise) decomposition. Because the distribution of drainage networks is strongly related to watershed boundaries, the new decomposition method is more effective and natural. The method to extract natural watershed boundaries was improved by using multi-scale DEMs instead of single-scale DEMs. A HTC environment is employed to test the proposed methods with real datasets.