Detection of seafloor channels using Bathymetry data in Geographical Information Systems Kundu.S.N, Pattnaik.D.S Department of Geology, Utkal University, Vanivihar, Bhubaneswar. Orissa. snkundu@gmail.com ABSTRACT Hydrological analysis in GIS of hypsometric data detects streams and channels based on the resolution of input data. This has been depicted for onland terrains for automatic detection of streams and channels. However, such techniques can be applied to bathymetry data over the sea to detect channel systems which extend from where rivers terminate at the shore. The main purpose of this study was to use General Bathymetric Charts of the Oceans (GEBCO) bathymetry data detection of channels in the Arabian Sea, western India. This article demonstrated the processing of bathymetry data for hydrographical network analysis and deduces seafloor channels which extend from the shelf to the abyssal plains. Existence of such channels impacts the fact that seafloor sedimentation transport along these channels contribute to depositional environments which act as source kitchen and reservoir for biogenic oil and gas reserves, discovery of which could change the energy scenario of the world. Keywords: GIS, Bathymetry, DEM 1. Introduction In hydrological studies, digital elevation models (DEM) are used for automated detection of drainage network, catchment boundary and also assist in estimation of several catchment parameters such as slope, contours, aspects etc (Rahman et al. 2010). The computation for DEM pixels are based on the flow routing model introduced by O Callaghan and Mark (1984) and referred as the D8 Method. In this D8 method, each pixels discharges in to one of its eight neighbors in direction of steepest descent. In the beginning, this method was problematic when grid cells lacking a down slope neighbor occurred in the DEM referred to as a sink, resulted in flow paths that terminated at the grid cell with the lowest elevation, producing a discontinuous drainage pattern. Jensen and Domingue (1988) developed a new procedure to eliminate all sinks prior to the assignment of flow directions. The automation of the extraction methods permits determining the hydrological and physical parameters efficiently, at all scales. The increased development in the geographical information domain drives in several applications to the usage of the GIS s powerful tools allowing the manipulation, storage, analysis and display of spatial referenced data. Advances over the past two decades in development of geospatial tools for processing bathymetry have enabled the observation that has revolutionized our understanding of deep water systems and processes (Sager et al., 2004). GIS works wonders for management of geographical and thematic data, also in 821
modeling, calculation and communication abilities (Goodchild et al. 1996). ESRI ArcGIS s extension spatial analyst provides structured modules for hydrological analysis from a DEM. GEBCO provides global bathymetry in a gridded form for most of the world s oceans in 30 arc second scale which conforms to roughly 1 km resolution at the equator. 2. Data Sources Figure 1: The study area Topography, which influences spatial and temporal variability of the hydrologic process, is represented by a DEM that can be used for automated processing. In this case, where the study area is offshore, bathymetry in lieu of topography forms the major factor, from which we can analyze subsea features and morphology through DEM derivatives and hydrographical network extraction. The data used in this application, was downloaded from the British Oceanographic Data Centre (BODC, www.bodc.ac.uk) where global GEBCO data is available for free (Monahan, 2009). The resolution of the data is 1 km for the study area which is appropriate for hydrological studies of submarine channels which 822
are much larger in dimension as compared to on land hydrological networks. The GEBCO data are available in geo tiff format defined in geographical coordinates of The Word Geodetic System 84 (WSG84). The units of depth are in meters. To make use of the methodology, the chosen area was offshore west India in the Arabian sea between the north parallels 15 and 22, and Meridians between 65 and 75 (Figure 1). ESRI ArcGIS 9.2 spatial analyst (ESRI, 2007) was used for the channel network delineation extracted from the physical characteristics of watercourses. Moreover, GIS allows visualizing the spatial information and interpret through overlay analysis in relation to other datasets like onshore topography (Figure 1). The bathymetry data in form of a DEM is the only input for the whole study. 3. Methodology & Processes Bathymetry data processing and DEM creation for hydrological stream network delineation involves a slightly modified workflow as compared to topography data (Figure 2). Figure 2: Workflow for Hydrological stream network detection 3.1 Data Preparation Special care is taken to accommodate the relative digital numbers for elevation as bathymetry includes negative values. The GEBCO DEM contained negative elevation values indicating depth. Such negative values were unacceptable in spatial analyst as it is designed to handle topographic elevation data (i.e. above mean sea level). To achieve this, a value equal to the maximum depth of the region was added to the DEM. This makes the minimum depth of the region to zero, which is meant only for processing purpose. For all depth reporting purpose, values in the original data was used. 3.2 Hydrological Network Extraction This method creates a regular transition from the overbank to the stream centerline in the DEM to make water enter the stream. The flow is then forced at the cell of the network extraction and will appear in better adequacy with the terrain reality. Then, depressions and pits are filled by increasing the elevation of the pit cells to a level of the surrounding 823
terrain in order to determine flow directions. These depressions are attributed to the interpolation process using which the GEBCO data was gridded. Figure 3: Flow Direction raster with directional cell values (D8 algorithm) Flow direction raster (Figure 3) is determined using the D8 algorithm which takes into account the eight neighboring cells of the considered cell and computes the maximum slope with respect to its neighbors. The direction of this slope is the flow direction and the neighboring cell is the accumulation point. Cells with a high flow accumulation are areas of concentrated flow and are used to identify stream channels (Figure 4) based on a threshold. Defining a threshold is critical to the dimension of the hydrological network which can be extracted from such processing. Starting with a threshold of 1000 km2, the network detection process was iterated with an increment of 500 km2, and the resulting channel network for each was visually evaluated. Based on the extent and seafloor physiography of the study area, we optimized the threshold to be 2500 km2, which suited the resolution of the data and the morphology of the seafloor. The channels were ordered as per Strahler (Strahler 1952) to establish confluence of tributaries from the continental slope region towards the deep (Figure 5). 824
Figure 4: Flow accumulation raster (higher values indicate channels) 4. Observations and Conclusion The study applies hydrological analyses for the automated detection of seafloor channels in a GIS using bathymetry data. This application on the study area disclosed the existence of submarine channel complexes that originate where rivers terminate on land. This is in contrary to the belief that rivers always terminated at the sea (Figure 5). The findings provide a strong evidence of existence of channels and their connectivity to river channel systems. On land sediment transport is therefore possible beyond the coastline to the seep ocean floor though these channels. Suitable transport and deposition of such sediments can result in hydrocarbon accumulation in deepwater reservoirs. This is corroborated in the findings of recent research (Kolla, 2007). Further hydrological parameters assisting in detailed understanding of these channel systems can also be developed and applied in GIS using high resolution bathymetry. 825
Figure 5: Derived channel network with Strahler ordering and onland streams 5. References 1. ESRI Inc (2007), ARC/INFO Windows Version 9.2. Environmental Systems Research Institute (ESRI), 380 New York Street, Redlands, CA 92373 8100, USA 2. Goodchild MF, Kemp KK, Theriault M, Roche Y (1996), Systèmes d information géographique, Notes de cours Volume1, Notions de base, LATIG, Département de géographie, Université Laval, Québec [Geographical Information Systems, Support for course, Volume 1, Basis notions, LATIG]. Department of Geography, University Laval, Quebec 3. Jensen SK, Dominique JO (1988), Extracting topographic structure from digital elevation data geographical information system analysis. Photogrammetric Engineering & Remote Sensing 54(11), pp 1593 1600 826
4. Kolla, V. (2007), A review of sinuous channel avulsion patterns in some major deep sea fans and factors controlling them, Marine and Petroleum Geology 24, pp 450 469. 5. Monahan D. (2009), Bathymetry, In: John H. Steele, Karl K. Turekian, and Steve A. Thorpe, Editor(s) in Chief, Encyclopedia of Ocean Sciences, Academic Press, Oxford, pp 297 304 6. O Callaghan J, Mark DM (1984), The extraction of drainage networks from digital elevation data. Computer Vision Graphics Image Processes 28(3):pp 323 344 7. Rahman, M.M., Arya, D.S. and Goel, N.K. (2010), Limitation of 90 m SRTM DEM in drainage network delineation using D8 method a case study in flat terrain of Bangladesh, Applied Geomatics, 2, pp 49 58. 8. Strahler, A. N. (1952), "Hypsometric (area altitude) analysis of erosional topology", Geological Society of America Bulletin 63 (11), pp 1117 1142. 9. Sager, W. W., W. R. Bryant, and E. H. Doyle, eds. (2004), Special issue: High resolution studies of continental margin geology and geohazards: AAPG Bulletin, 88(6), pp 699 873. 827