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Assessment of Gis-Interpolation Techniques for Groundwater Evaluation: a Case Study of the Sandveld, Western Cape, South Africa

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Assessment of Gis-Interpolation Techniques for Groundwater Evaluation: a Case Study of the Sandveld, Western Cape, South Africa ASSESSMENT OF GIS-INTERPOLATION TECHNIQUES FOR GROUNDWATER EVALUATION: A CASE STUDY OF THE SANDVELD, WESTERN CAPE, SOUTH AFRICA ZAHN MÜNCH Thesis presented in partial fulfilment of the requirements for the degree of Master of Natural Sciences at the University of Stellenbosch Supervisor: Prof H.L. Zietsman December 2004 AUTHOR’S DECLARATION I, the undersigned, hereby declare that the work contained in this thesis is my own original work and that I have not previously in its entirety or in part submitted it at any university for a degree. Signature: ___________________________________ Date: 25 November 2004 . ABSTRACT The Sandveld, a potato growing area of the Western Cape is subject to significant groundwater abstraction for both municipal and agriculture purposes. The climate is arid and sensitive and important ecosystems in the area are showing varying degrees of impact. Management measures are needed to ensure ongoing sustainable development of the area. In this study, different interpolation techniques were evaluated to calculate values for unsampled variables rainfall and groundwater elevation. Local deterministic techniques as well as geostatistical techniques were used. It was found that geostatistical techniques, especially with collateral information, such as topography, provided a more accurate result. For environmental studies of this nature, Kriging is recommended as interpolation technique. The underlying data will determine the selection of the particular type of Kriging. Data was extracted from a customized relational database, geoMon, used for data capture, retrieval, processing and reporting. Ease of data extraction facilitated analysis. The interpolated grids were applied in two scenarios: Recharge calculations and quantification as well as a new classification approach according to Resource Directed Measures (RDM). Management classes were defined based on GIS-derived data. OPSOMMING Die Sandveld, ‘n aartappelverbouingsgebied in die Wes-Kaap, ondergaan aansienlike grondwateronttrekking vir beide munisipale sowel as landbou doeleindes. Die klimaat is dor en droog en sensitiewe en belangrike ekosisteme in die area ondervind wisselende impakvlakke. Bestuursmaatreëls word benodig om volhoubare ontwikkeling van die area te verseker. In hierdie studie is verskillende interpolasie tegnieke om onbekende waardes vir veranderlikes gebruik in grondwater evaluasie te bereken, evalueer. Lokale deterministiese tegnieke sowel as geostatistiese tegnieke is gebruik. Geostatistiese tegnieke, veral gebruik saam met addisionele inligting soos topografie, bereken meer akkurate resultate. Vir omgewingsstudies van hierdie aard, word Kriging aanbeveel as interpolasie tegniek. Eienskappe van die onderliggende data word gebruik om die tipe Kriging aan te dui. Data is onttrek uit ‘n gebruikersaangepaste databasis, geoMon, wat gebruik is vir datavaslegging, onttrekking, prosessering en verslaggewing. Die gemak waarmee data onttrek kon word het analise vergemaklik. Geïnterpoleerde data is gebruik vir grondwateraanvullingsberekeninge en -kwantifisering sowel as ‘n nuwe benadering tot klassifikasie volgens hulpbron gerigte maatreëls. Bestuursklasse gebaseer op GIS-afgeleide data, is gedefinieer. ACKNOWLEDGEMENTS I would like to thank the following people for their support in many ways during the process of writing this thesis: o Cecil, for encouragement and support o Eric, Alex and Jo-Jo, for not complaining about all the hours in front of the computer o Julian Conrad (GEOSS), for providing a workplace, data, time and input o Professor Larry Zietsman, for not growing weary with the ever changing topics o Edith Katzenellenbogen, for editorial input, crossing the T’s and dotting the I’s. Above all, thank you to God my Creator without whom none of this would have been possible. CONTENTS Tables ……………………………………………………………………….. iii Figures ……………………………………………………………………… iv Abbreviations ……………………………………………………………….. v CHAPTER 1: INTRODUCTION ………………………………………… 1 1.1 Setting the scene: The Sandveld ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 1 1.2 Foundation for research ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 1 1.3 Aims and objectives ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 3 1.4 Literature review ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 3 1.5 Focus on groundwater ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 5 1.6 Research design ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 6 1.7 Location of study area ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 8 1.8 Description of data used for analysis ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 9 1.8.1 Groundwater level and chemistry ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 10 1.8.2 Rainfall data ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 11 1.8.3 Elevation data ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 11 1.8.4 Geology data ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 12 1.8.5 Land cover data ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 12 1.8.6 Rivers and catchments ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 13 1.9 Document organization ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 13 CHAPTER 2: GEOMON – A RELATIONAL DATABASE … … … … ... 14 2.1 Information systems and relational databases ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 14 2.2 Database requirements ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 15 2.3 Database description ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 15 2.4 Database structure ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 16 2.4.1 BHBASE ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 16 2.4.2 WLMON ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 17 2.4.3 CHEMMON ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 17 2.4.4 BHImage ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 17 2.5 Functionality of geoMon ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 17 2.5.1 Data capture ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 18 2.5.2 Analysis ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 18 2.5.3 Reporting ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 20 CHAPTER 3: INTERPOLATION – WHICH GRID TO CHOOSE … ... 22 3.1 Interpolation methods ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 22 3.1.1 Local interpolation methods ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 23 3.1.1.1 Thiessen polygon ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 23 3.1.1.2 Inverse distance weighted interpolation ¼ ¼ ¼ ¼ ¼ 23 3.1.1.3 Splines ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 24 3.1.1.4 Comments on local interpolations ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 24 ii 3.1.2 Geostatistical methods … … … … … … … … … … … … … … … .. 24 3.1.2.1 Kriging … … … … … … … … … … … … … … … … … ... 25 3.1.2.2 Semi-variogram computation … … … … … … … … … 26 3.2 Interpolating rainfall data … … … … … … … … … … … … … … … … … .. 27 3.2.1 Thiessen polygons … … … … … … … … … … … … … … … … … . 27 3.2.2 Inverse distance weighting … … … … … … … … … … … … … … 28 3.2.3 Spline … … … … … … … … … … … … … … … … … … … … … … 28 3.2.4 Kriging … … … … … … … … … … … … … … … … … … … … … .. 29 3.2.5 Spatial prediction using other parameters … … … … … … … … . 30 3.2.6 Rainfall results – local methods … … … … … … … … … … … … 30 3.2.7 Rainfall results – deterministic interpolators … … … … … … … 32 3.3 Water levels – how to achieve a good result … … … … … … … … … … . 35 CHAPTER 4: GROUNDWATER RECHARGE ¼ .¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 39 4.1 Recharge methodology ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 42 4.2 Recharge per catchment ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 44 CHAPTER 5: RDM – DEFINING MANAGEMENT CLASSES … .… ... 47 5.1 Resource directed measures ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 47 5.1.1 Classification ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 48 5.1.2 Setting the reserve ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 49 5.1.3 Resource quality objectives ..¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 49 5.2 Determine the reserve using RDM ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 50 5.2.1 Classification ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 50 5.2.1.1 Delineating groundwater response units (GRUs) ¼ .. 50 5.2.1.2 Level of modification ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 50 5.2.1.3 Importance ¼ ...¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 54 5.2.1.4 Vulnerability (DRASTIC) ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 55 5.2.1.5 Defining management classes ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 57 5.3 Classification ± the outputs ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 58 5.3.1 Geohydrological response units (GRUs) ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 58 5.3.2 Level of modification ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 61 5.3.3 Importance ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 62 5.3.4 Vulnerability ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 63 5.3.5 Management classes based on present ecological status ¼ ¼ ... 64 5.4 Rainfall and recharge calculations per GRU ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 66 CHAPTER 6: DISCUSSION AND CONCLUSION ¼ .¼ .¼ ¼ ¼ ¼ ..¼ . 67 References ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 70 Personal Communications ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 77 Additional Sources ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 78 Appendix A: Recharge ratings ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 79 Appendix B: Interpolation data ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 82 Appendix C: DRASTIC ratings and weights ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ .. 87 Appendix D: geoMon Tables ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ... 89 iii TABLES Table 1.1 Study area catchment names, main rivers and areas ¼ ¼ ¼ ¼ ¼ ¼ ¼ . 9 3.1 Significance testing using different deterministic interpolation techniques ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ..¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 34 3.2 Comparison of analysis of variance of different Kriging techniques used to derive groundwater elevations ¼ ¼ ¼ ¼ ¼ ...¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 36 4.1 Recharge statistics per quaternary catchment ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 46 4.2 Comparison of variance statistics for recharge calculated from different rainfall input data sets ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ 46 5.1 Summary of ratings per factor influencing
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