Hydrologic Terrain Processing Using Parallel Computing

Abstract: Topography in the form of Digital Elevation Models (DEMs), is widely used to derive information for the modeling of hydrologic processes. Hydrologic terrain analysis augments the information content of digital elevation data by removing spurious pits, deriving a structured flow field, and calculating surfaces of hydrologic information derived from the flow field. The increasing availability of large terrain datasets with very small ground sample distance (GSD) poses a challenge for existing algorithms that process terrain data to extract this hydrologic information. This paper will describe a parallel algorithm that has been developed to enhance hydrologic terrain pre-processing so that larger datasets can be more efficiently computed. This paper describes a Message Passing Interface (MPI) parallel implementation for Pit Removal. This key functionality is used within the Terrain Analysis Using Digital Elevation Models (TauDEM) package to remove spurious elevation depressions that are an artifact of the raster representation of the terrain. The parallel algorithm works by decomposing the domain into stripes or tiles where each tile is processed by a separate processor. This method also reduces the memory requirements of each processor so that larger size grids can be processed. The parallel pit removal algorithm is adapted from the method of Planchon and Darboux that starts from a large elevation then iteratively scans the grid, lowering each grid cell to the maximum of the original elevation or the lowest neighbor. The MPI implementation reconcile

The Initial Design of Data Sharing Infrastructure for the Critical Zone Observatory

The Critical Zone Observatory (CZO) program is a multi-institutional collaborative effort to advance scientific understanding of environmental interactions from bedrock to the atmospheric boundary layer across scales and disciplines. To create a comprehensive hydrogeochemical portrait of experimental sites the observatories collect large volumes of data. Publishing, analyzing and archiving these data in a consistent and integrated manner across all CZO sites is challenging due to the inherent heterogeneity in data collection and processing techniques. We present the initial design and a prototype of the CZO data sharing infrastructure. While each CZO site maintains its own data management system, the integrated infrastructure design specifies formats and protocols for presenting the information on CZO web sites, where it can be browsed by users as well as automatically harvested into a centralized data system. The latter validates, archives and converts the data into standards-compliant data services, which can be consumed by various client applications