Sustainable Water Resources In Arid Land Integrating Remote Sensing and GIS Analysis; a case study from Red Sea Hills; Sudan Mohamed.Babiker@nrsc.no I- INTRODUCTION 1 REMOTE SENSING 1 GEOGRAPHIC INFORMATION SYSTEM (GIS) 2 STUDY AREA 3 II- OBJECTIVES 4 MAIN OBJECTIVE 4 SPECIFIC OBJECTIVES 5 III- METHODS 5 TASK 1: GENERATING DIGITAL ELEVATION MODEL 5 TASK 2: EXTRACTING HYDROGEOLOGICAL PARAMETERS AND ANALYSIS 5 TASK 3: ESTABLISHING A GIS DATABASE AND INTEGRATED ANALYSIS 7 IV- ANTICIPATED RESULTS AND DELIVERABLES 7 REFERENCES 7 I- INTRODUCTION Groundwater is extremely important to sustain life in arid and semi-arid areas, as surface water sources are either lacking or unreliable. Hard rocks in which the primary porosity and hydraulic conductivity are low underlay many of these areas. Successful identification of sites for groundwater prospecting most often depends principally on the location of narrow fracture zones in the bedrock or location of weathered zones with high hydraulic conductivity. A successful approach to groundwater exploration in these areas must make maximum use of existing hydrogeological and other data to optimise target areas for exploitation. The sustainable abstraction rate from groundwater in arid and semi-arid areas has to be based on a thorough knowledge of the total local hydrogeological conditions, including the usually low recharge rates. In areas new to exploration, the acquisition of data is costly and time consuming and it is important that the data is fully analysed to best effect to justify the cost of acquiring them. As arid lands cover a large portion of the global land surface and are most often located in developing countries, access to a safe and reliable water supply is an important index of development. Several hundred million people in rural Africa are still without this basic commodity. Current development policies and resources allocations in developing countries will not alleviate this problem in the near future, not only due to continuing population growth but also even more due to lack of basic knowledge of dry land hydrology as well as the lack of an operational cost-efficient prospecting methodologies. Remote Sensing The application of remote sensing to groundwater study is one of its most complex uses. The complexity results from the fact that the object of study, groundwater, is not portrayed directly on the 1
data. Most applications of remotely sensed data, however, deal with surface phenomena, because it is the surface that is being photographed or imaged by satellite or aircraft. The subsurface hydrological conditions can be inferred from surface indicators, such as areal geological features and structures, vegetation, streamflow characteristics, soil and soil moisture anomalies, vegetation types and distribution, springs, etc (Jakson, 1983). All these features can be identified in remotely sensed data. The identification depends on the spatial and spectral resolution of the data as well as the size of the feature. Some of these features can be identified on standard processed false colour images, and even relatively simple, digital image enhancement techniques make significant improvement for the identification of these features. Many features that could indicate the occurrence of groundwater are visible on satellite data, especially in areas with limited regolith cover. These include types of rock, fracture zones, drainage patterns, various types of unconsolidated deposits, vegetation, etc. All these parameters will be investigated in this study and the result will be compared with the existing water resources. The process involved in using satellite data in prospecting for groundwater is mapping the above mentioned features, analysing them, and then identifying areas with favourable conditions for groundwater availability. The type of analysis varies depending on the features to be identified e.g. lineament analysis covers their location, concentration, length and orientation. Many have used lineaments extracted from different remotely sensed data, as a groundwater indicator e.g. (Kock, 1993) in Red Sea Hills Sudan using large format camera, (Gustafsson, 1993) in SE Botswana using SPOT, (Krishnamurthy, 1996) in Tiruchirappalli district in India, using images from the Indian Remote Sensing Satellite, (Mabee, et al., 1994) on Georgetown Island in the USA, using a variety of aerial platforms and (Salman, 1983) around Qena Province, Egypt, using Landsat (MSS). Drainage network analysis includes pattern analysis that usually gives an indication of the structure and composition of the rocks, or drainage basin analysis, which has a direct relation to the hydrologic behaviour of basins. Drainage networks can be treated in two ways: (1) to study the pattern for the degree of the structural control upon the drainage network (Kock 1993), or (2) to analyse drainage density and drainage frequency and to relate these to hydrological behaviour (Smith, 1997). The second approach is mainly dependant on the image resolution or the photo scale. The value of the drainage density can therefore be different for the same area if satellite images of different resolutions are used (Babiker, 1999). Geographic Information System (GIS) The integration of multiple datasets, with various indications of groundwater availability, can decrease the uncertainty and lead to safer decisions. Geographic Information systems (GIS) are ideal for capture, storage, retrieval and analysis of various factors indicating groundwater availability. However, the integration and manipulation of various digital datasets can be made with such ease that 2
the original objective of the analysis and the quality of the input data may be lost in the process. Nevertheless, as the hydrogeological community is moves away from hard decisions to soft decisions, i.e. probabilities, the GIS is becoming an invaluable tool in which to create a decision platform. The integration of remote sensing and GIS provide a powerful combination as a number of remote sensing platforms (optical and radar) now provide data that can be used to create Digital Elevation Models (DEM), and image processing software provides the tools to create and analyse them. GIS software has hydrological tools that provide opportunities to delineate and analyse hydrological response units. STUDY AREA The study covers the Sinkat district, one of three administrative units in Sinkat Province, which in turn is one of the four Provinces comprising the Red Sea State. The Red Sea State lies wholly within the arid part of the Sudan where more than 95% of the land receives less than 200 mm of rain per year (El Tom, 1991) figure 1. Figure 1 The Sudan map showing the Red Sea Region location Sinkat district lies between latitudes 18 0 :10 / and 19 0 :12 / N and longitudes 36 0 :05 / and 37 0 :20 / E covering an area of approximately 7950 km 2, most of which is dominated by the Red Sea Hills. Perennial streams in the district are completely absent and its geology is dominated by the largely impermeable Pre-Cambrian Basement Complex rocks (AbuFatima, 1992). This makes rainwater, though limited and variable, an important resource in the district. The physical limitations of climate, coupled with solid geology and the hilly nature of the terrain impose limitations on the natural water resources and thus on man s economic activities. The distribution of the available water resources is governed by the drainage system of seasonal 3
watercourses known as khors. It is along the sides and in the beds of these khors that trees, the main grazing resource, are found where agriculture is practised, and where hand-dug wells provide the main water supply. These khors are thus the main focus of human activity and settlement in the district. The total population of the district in 1983 was 66,709 persons of whom 77.6% were classified as rural and the remaining 22.4% as urban (comprising the populations of Sinkat and Gebeit Towns, the only two urban centres in the district). Sinkat Town (7,884 persons in 1983) is the district and province capital. Gebeit Town (7018 persons) is mainly a military base and major railway station. Apart of these towns and Erkowit, the largest village in the district, settlements are very small and widely scattered reflecting the marginal character of the natural resources which limit the development of large human settlements. The main water supplies in the district are wells located in or beside the khors. They are three main types; bore holes with machine pumps of which a few are found in Sinkat and Gebeit towns, bore holes with hand-pumps and, most common and widely spread, traditional hand-dug wells (Babiker, 1999) Photo 1. The most common method for water collection is manual retrieval (ropes and buckets) from hand-dug open wells, while piped water supplies are very limited and restricted to the town centres of Sinkat and Gebeit. The wells are highly dependent on rainfall for their water quantity and quality, some wells may become dry or increase in salinity during longer drought periods. Migration with animals in search for water and pasture constitutes an important element of the Hadendowa life style and their resilience and adaptation to drought and the uneven distribution of water and grazing resources. They undertake a longer seasonal migration of 100-120 km between the coastal area and the western part of their territory following the rainy seasons and daily migrations of shorter 10-20 km distances around a camp (Egemi, 1995). II- OBJECTIVES Photo1 Hand dug well with superstructure at Sinkat khor Main Objective The principle aim of this project is to investigate and combine the hydrological parameters (slope, catchment area, drainage network) derived from Digital Elevation Model (DEM) generated from 4
digitised topographic maps with hydrogelogical parameters (lineaments, geological formation and soil texture) extracted from high resolution satellite data (Landsat 7ETM+). These parameters will be analysed using GIS to locate groundwater potential areas in arid lands. Specific Objectives To generating a Digital elevation model (DEM) digitised from topographic maps, and extracting the topographical and geomorphologic features that influences the hydrological behaviour of the landscape e.g. slope, drainage area and drainage density. To map the lineaments features from Landsat 7 ETM+, analysis them according to their location, orientation and concentration and study their hydrological significant. To create geological maps from Landsat 7 ETM+ for the study area and classify the area into units according to their hydrological behaviour. To establish a GIS database for analysing and integrating the above data. To analyse within the GIS framework the parameters of the response units and to evaluate through correlation their relative importance. III- METHODS Task 1: Generating Digital Elevation Model Research over the past decades has demonstrated the feasibility of extracting topographic information of hydrological interest directly from digital elevation models (DEM), a mathematical representation of landscape topographic features. Techniques are available for extracting slopes properties, drainage areas, drainage divides, drainage network and other data (Martz and Grbrecht, 1993). These techniques are faster and provide more precise and reproducible measurements than traditional manual techniques applied to topographic maps. As such they have the potential to greatly assist in the parameterisation of hydrological surface-runoff models, especially for large watersheds (i.e. greater than 10 km 2 ) where the manual determination of the drainage network and subwatershed properties is a tedious time-consuming, error-prone, and often highly subjective process. The automated techniques also have the advantage of generating digital data that can be readily imported and analysed by a geographical information system (GIS) where hydrological tools can further their analysis. Task 2: Extracting Hydrogeological Parameters and analysis 1- Mapping lineaments and analysis: mapping of linear features on remote sensing data has been an integral part of many groundwater exploration programmes in hard rock terrain. Many workers have investigated lineament data with respect to their groundwater potential, several with an emphasis 5
on arid or semi-arid hard rock areas (Krishnamurthy, et al. 1996), (Kock 1993), (Sander, 1997) and (Smith, et al. 1997). Linear features play a very important role in groundwater recharge and flow in hard rock terrains which depends on their distribution, orientation and density. Well yields often show a positive correlation with linear features or with their intersections (Waters, et al., 1990). A lineament is defined as a mapable, simple or composite linear feature of a surface whose parts are aligned in a rectilinear or slightly curvilinear relationship and which differs from the patterns of adjacent features and presumably reflects a subsurface phenomenon. Lineaments usually represent joints, zones of joint concentration, dike, and dike swarms, faults or shear zones. Thus lineaments can be zones either of weakness or of resistance in the crust, which have been etched out or sculptured by the agents of erosion. All types of lineament can be distinguished on satellite images and aerial photographs depending on their resolution and scale. Some lineaments which are clearly visible on satellite images, are often not recognisable in the field. This is because they either correspond to wide strips of more highly fractured rock and/or they do not show any displacement and thus show no lithologic or structural contrast on either side of the plane which indicates the existence of a fault. The easiest recognisable structural features on satellite images are vertical fracture zones corresponding to simple extension fractures, normal faults, transcurrent faults or strike slip faults. Thrust faults with relatively low-angle planes and curved traces are more difficult to detect because they have usually been less well enhanced by erosion. Horizontal extension fractures usually play a secondary role in regulating groundwater flow. They are generally formed near the surface due to stress release during the removal of the lithologic overburden. These exfoliation structures do not penetrate the crust very deeply and are basically irrelevant in terms of regional groundwater circulation. In contrast, open fractures or fractures filled with coarse rock fragments, represent potential infiltration sites for rainfall and runoff as well as being excellent water transmitters. All the factors mentioned above have direct implications for the process of mapping lineaments and need to be considered when interpreting the resulting mapped data. In this study the concentration will be on the topographical negative straight lineaments, representing joints, faults and fractures. Landsat ETM 7+ will used for mapping the lineaments. Different techniques will be used for extracting the lineaments, such as liner stretching, band combination, filtering and edge enhancement (Mather, 1987). These are the most common techniques and can be found image analysis packages e.g. ERDAS IMAGINE. 2- Geological Mapping: Knowledge of the geology and the tectonic setting of the area permits a better interpretation of the various hydrogeological observations such as drainage pattern, water infiltration and water chemistry. 6
Geological mapping from enhanced satellite images with special emphasis on mineral and petroleum exploration has been a major field of research during the last two decades. Good geological discrimination can be carried out in arid and semi arid areas using various types of image enhancement techniques as vegetation cover is sparse or lacking. Landsat 7ETM+ will also be used in this study for the geological mapping. The digital image processing of remotely sensed data provides flexibility in data handling, due to the fact that it can be numerically manipulated. There are many processing techniques that can be applied to remotely sensed data for geological applications. The most common techniques are rationing, principlecomponent analysis, contrast stretching and filtering (Krishnamurthy, et al., 1992), (Kenea, 1997) and (Mather, 1998). Task 3: Establishing a GIS Database and Integrated Analysis The last task will be to establish the GIS database from the data extracted in tasks 1-4. There are many programmes can handle this kind of information e.g. ERDAS IMAGINE which is available at University of Bergen. IV- Anticipated Results and Deliverables 1. Digital elevation model for the study area showing the different hydrological parameters. 2. Lineament maps showing their length, orientation and concentration and their role in the hydrological behaviour of the drainage network. 3. Geological maps showing the different geological units in the study area and their influence on the hydrogeology of the area. 4. Database from the above data and any other data from field or any other resources that could help to better understand the hyrogeology and hydrology of the study area. 5. Analysis of the data in a GIS to reach the main objective of the study which is to identif areas favourable for the exploitable groundwater occurrence. 6. A better understanding of the parameters that influence distribution of groundwater resources in arid land. References AbuFatima, M., 1992, Magmatic and tectonic evolution of the granite-greenstone sequeneces of the Sinkat area, Red Sea Province, NE Sudan, M.Ph Thesis, University of Portsmouth, UK, 276 p. Babiker, M., 1999, Water resources assessment in arid land using remote sensing, Sinkat District, Red Sea Hills, Sudan, Cand. Scient. Thesis University of Bergen, Norway, VIII, 136 s. Bannari, A., Morin, D., Bonn, F. and Huete, A. H., 1995, A Review of Vegetation Indices, Remote sensing Reviews, 13, 95-120 Egemi, O. A. M., 1995, The political ecology of subsistence crisis in the Red Sea Hills, Sudan, Ph. D. thesis University of Bergen 208 s. El Tom, M. A., 1991, The Climate of the Red Sea region of the Sudan: an outline, the RESAP, University of Khartoum, Sudan, 19 p. 7
Gustafsson, P., 1993, Satellite Data and GIS as Tools in Groundwater Expolorayion in a Semi-arid Area, Thesis Type, Geology, Chalmers University of Technology / University of Goteborg, 159 p. Huete, A. R., Liu, H. Q., Batchily, K. and Leeuwen, W. v., 1997, A Comparison of Vegetation Indices over a Global Set of TM Image for EOS-MODIS, Remote sensing of enviroment, 59, 3, 440-451 Kenea, N. H., 1997, Improved geological mapping using Landsat TM data, Southern Red Sea Hills, Sudan: PC and IHS decorrection stretching, INT. J. Remote Sensing, 18, 6, 1233-1244 Kock, M., 1993, Relationships Between Hydrogeological features and geomorphic-tectonic characteristics of the Red Sea Hills of Sudan based on space images, Ph.D. Thesis, Boston University, 218. Krishnamurthy, J., Manavalan, P. and Saivasan, V., 1992, Application of digital enhancement techniques for groundwater exploration in Hard-rock terrain, INT. J. Remote Sensing, 13, 15, 2925-2942 Lillesand, T. M. and Kiefer, R. W., 1994, Remote sensing and image interpretation, John Wiley & Sons, Inc., 750 p. Mabee, S. B., Hardcastle, K. C. and Wise, D. U., 1994, A Method of Collecting and Analyzing Lineaments for Regional -Scale Fractured-Bedrock Aquifer Studies, Ground Water, 32, 6, 884-894 Martz, L. W. and Grbrecht, J., 1993, Automated Extraction of Drainage Network and Watershed Data from Digital Elevation Models, Water resources Bulletin, 29, 6, 901-908 Mather, P. M., 1987, Computer processing of remotely-sensed images : an introduction, John Wiley & Sons Inc., 352 p. Mather, P. M., 1998, An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills, Sudan, INT. J. Remote Sensing, 19, 4, 587-604 Myneni, R., Hall, F., Sellers, P. and Marsak, A., 1995, The interpretation of specral vegetation indexes, IEEE Transaction on geoscience and remote sensing, 33, 2, 481-486 Rondeaux, G., Steven, M. and Baret, F., 1996, Optimization of Soil-adjusted Vegetation, Remote sensing of encironment, 55, 95-107 Salman, A. -A. B., 1983, Using Landsat imagery interpretation for underground water prospecting around Qena Province, Egypt, INT. J. Remote Sensing, 4, 2, 179-189 Ward, R. C. and Robinson, M., 1990, Principles of hydrology, McGraw-Hill, 365 p. Waters, P., Greenbaum, D., Smart, P. and Osmaston, H., 1990, Application of Remote sensing to Groundwater Hydrology, Hardwood, 223-264 8