Browsing by Author "Srivastava, Anurag"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Comparison of Two Algorithms for Removing Depressions and Delineating Flow Networks From Grid Digital Elevation Models
    Srivastava, Anurag (Virginia Tech, 2000-02-10)
    Digital elevation models (DEMs) and their derivatives such as slope, flow direction and flow accumulation maps, are used frequently as inputs to hydrologic and nonpoint source modeling. The depressions which are frequently present in DEMs may represent the actual topography, but are often the result of errors. Creating a depression-free surface is commonly required prior to deriving flow direction, flow accumulation, flow network, and watershed boundary maps. The objectives of this study were: 1) characterize the occurrence of depressions in 30m USGS DEMs and assess correlations to watershed topographic characteristics, and 2) compare the performance of two algorithms used to remove depressions and delineate flow networks from DEMs. Sixty-six watersheds were selected to represent a range of topographic conditions characteristic of the Piedmont and Mountain and Valley regions of Virginia. Analysis was based on USGS 30m DEMs with elevations in integer meters. With few exceptions watersheds fell on single 7.5minute USGS quadrangle sheets, ranged in size from 450 to 3000 hectares, and had average slopes ranging from 3 to 20 percent. ArcView (3.1) with the Spatial Analyst (1.1) extension was used to summarize characteristics of each watershed including slope, elevation range, elevation standard deviation, curvature, channel slope, and drainage density. TOPAZ (ver 1.2) and ArcView were each used to generate a depression-free surface, flow network and watershed area. Characteristics of the areas 'cut' and 'filled' by the algorithms were compared to topographic characteristics of the watersheds. Blue line streams were digitized from scanned USGS 7.5minute topographic maps (DRGs) then rasterized at 30 m for analysis of distance from the derived flow networks. The removal of depressions resulted in changes in elevation values in 0 - 11% of the cells in the watersheds. The percentage of area changed was higher in flatter watersheds. Changed elevation cells resulted in changes in two to three times as many cells in derivative flow direction, flow accumulation and slope grids. Mean fill depth by watershed ranged from 0 to 10 m, with maximum fill depths up to 40 m. In comparison with ArcView, TOPAZ, on average affected 30% fewer cells with less change in elevation. The significance of the difference between ArcView and TOPAZ decreased as watershed slope increased. A spatial assessment of the modified elevation and slope cells showed that depressions in the DEMs occur predominantly on or along the flow network. Flow networks derived by ArcView and TOPAZ were not significantly different from blue line streams digitized from the USGS quadrangles as indicated by a paired t test. Watershed area delineated by ArcView and TOPAZ was different for almost all watersheds, but was generally within 1%. Conclusions from this study are: 1) The depressions in 30 m DEMs can make up a significant portion of the area especially for flatter watersheds; 2) The TOPAZ algorithm performed better than ArcView in minimizing the area modified in the process of creating a depressionless surface, particularly in flatter topography; 3) Areas affected by removing depressions are predominantly adjacent to the stream network; 4) For every elevation cell changed, slopes are changed for two to three cells, on average; and 5) ArcView and TOPAZ derived flow networks closely matched the blue line streams.
  • Thumbnail Image
    Stabilized Explicit Time Integration for Parallel Air Quality Models
    Srivastava, Anurag (Virginia Tech, 2006-08-18)
    Air Quality Models are defined for prediction and simulation of air pollutant concentrations over a certain period of time. The predictions can be used in setting limits for the emission levels of industrial facilities. The input data for the air quality models are very large and encompass various environmental conditions like wind speed, turbulence, temperature and cloud density. Most air quality models are based on advection-diffusion equations. These differential equations are moderately stiff and require appropriate techniques for fast integration over large intervals of time. Implicit time stepping techniques for solving differential equations being unconditionally stable are considered suitable for the solution. However, implicit time stepping techniques impose certain data dependencies that can cause the parallelization of air quality models to be inefficient. The current approach uses Runge Kutta Chebyshev explicit method for solution of advection diffusion equations. It is found that even if the explicit method used is computationally more expensive in the serial execution, it takes lesser execution time when parallelized because of less complicated data dependencies presented by the explicit time-stepping. The implicit time-stepping on the other hand cannot be parallelized efficiently because of the inherent complicated data dependencies.