Hydrologic Information Systems as a support tool for water quality monitoring. A case study in the Bolivian Andes

Transcription

1 Hydrologic Information Systems as a support tool for water quality monitoring A case study in the Bolivian Andes Remco J.J. Dost June, 2005

2 Hydrologic Information Systems as a support tool for water quality monitoring A case study in the Bolivian Andes by Remco J.J. Dost Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation Environmental System Analysis and Management. Thesis Assessment Board Prof. Z. Su (Chair) Dr. M.S. Krol Dr. B.H.P. Maathuis Ir. P.W.M. Augustijn INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

3 Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

4 This thesis is dedicated to my parents

5 Abstract This research evaluated the use of a GIS as a support tool for water quality modelling by the creation and evaluation of a Hydrologic Information System (HIS) that visualizes the spatial distribution of water quality variables. This is achieved by developing and integrating chemical routing and flux calculations in a geometric network extracted from a DEM using existing routines. The Arc Hydro tools and Arc Hydro data model are used as an extension to ArcGIS to extract topologic variables from a SRTM DEM that are required to build a geometric network. This network was generated in ArcGIS and is the basis of the HIS because its structure of nodes and links allow for network routing. The geodatabase of the network was exported to Excel where the chemical routing was included and tested, followed by the flux calculation methodology creating a HIS. The chemical routing methodology was adapted from Appelo (1993) and uses the electric conductivity at water junctions as a tool to obtain discharges. Using these discharges the fluxes along the river are calculated using a methodology adapted from Chapman (1992) and imported in ArcGIS for visualisation. The River Rocha system in Cochabamba, Bolivia was used as a case study. This research showed that the EC routing and flux calculations can be included and successfully applied in a HIS. The methodology can however be difficult to apply in areas with low discharges, inaccessible terrain or where the natural balance is influenced by human activity. i

6 Acknowledgements I express my sincere gratitude to my first supervisor Dr. Mannaerts, the former head of the WRS department Prof. Meijerink, the WREM program board and Ms. Leurink for providing me with the opportunity to pursue my MSc ambition. I also wish to thank my supervisors Dr. Mannaerts and Dr. Maathuis for their valuable suggestions and support during my MSc research. My sincere gratitude goes to Dr. Valenzuela, former head of the CLASS project in Bolivia, for welcoming me at his project and his full support during fieldwork. At CLASS, I would like to thank Grover, Tatjana, Helga, Pablo, Freddy and Carlos Pedro for their assistance and friendship. I enjoyed our time and cervezas together very much. In this respect I also would like to thank Johan van der Veer for his assistance during fieldwork. I want to thank Drs. De Smeth for his support during laboratory analysis and for operating the ICP and Dr. Whiteaker of CRWR in Austin, USA for kindly sharing his work with me, even though we never met. I would also like to acknowledge my WRS colleagues for their valuable comments and support throughout the MSc study, Job Duim and Benno Masselink for their logistic support, Anke Walet for secretarial support and Anneke Nikijuluw for keeping track of my records. I also wish to thank Boudewijn, Lyande, Jelger, Iris, Raymond and Chris for the enlightening lunch discussions on various topics. Above all, I would like to thank my family for their ongoing support during my career. ii

7 Table of contents 1. Introduction Background Relevance of research Research objective Conceptual model Hydrologic Information System development Electric Conductivity routing incorporation Water quality visualization and incorporation Methodology Literature review Data collection Fieldwork Data analysis HIS creation Assessment Thesis outline Hydrologic Information Systems Present state Introduction Digital elevation models Hydrologic parameter extraction Available software HIS Requirements and development HIS limitations Study area Introduction Climate Hydrology Geomorphology Geology Paleozoic era Mesozoic era Caenozoic era Land cover Field measurements Measure site identification EC measurements Discharge measurements Water quality sampling and analysis Field observations Hydrologic Information System Introduction...36 iii

8 4.2. Terrain processing DEM selection Hydrologic Digital Elevation Model creation Stream definition Catchment and watershed processing Hydrologic Information System creation Water quality HIS Introduction routing routine EC routing methodology Routine implementation EC routing results Model validation and discharge visualization Water quality flux Flux calculation methodology Flux implementation Flux calculation results and visualization Conclusions and recommendations Conclusions Recommendations...69 References...70 Appendix 1 Thornthwaite climate classification...78 Appendix 2 Arc Hydro Data model...80 Appendix 3 Discharge measurements...81 Appendix 4 Water sample analysis...82 iv

9 List of figures Figure 1.1 Methodology flow chart....7 Figure 2.1 a.raster network after processing Strahler second order channels b. Example channel network and definitions...14 Figure 3.1 General location of the study area...19 Figure 3.2 Location of the study area within the Cochabamba district...20 Figure 3.3 Annual average precipitation and temperature over the period Figure 3.4 Overview of study area generated from SRTM and an ASTER image...22 Figure 3.5 Geomorphology of the study area...25 Figure 3.6 Geology of the study area...25 Figure 3.7 Land cover of the study area...26 Figure 3.8 Rocha drainage network with sample sites and special locations...33 Figure 3.9 Anion- cation balance of the water samples...34 Figure 4.1 Correlation of ground control points with the SRTM DEM, the interpolated 20-meter contour line DTM and the interpolated 1-meter contour line DTM...38 Figure 4.2 AGREE method example...39 Figure 4.3 Terra - Aster False Color Composite...40 Figure 4.4 Original SRTM DEM flow accumulation Figure 4.5 Flow accumulation generated using a combination of the original and AGREE DEM...42 Figure 4.6 Effect of area threshold on drainage density...43 Figure 4.7 Extracted channels using different area thresholds Figure 4.8 Catchment processing...45 Figure 4.9 Watershed generation...45 Figure 4.10 Arc Hydro data model components and their relation...46 Figure 4.11 Prepared HydroEdge and HydroJunction feature classes...49 Figure 4.12 Application of the Arc Hydro data model Figure 4.13 Layout of the Cochabamba River Hydrologic Information System Figure 4.14 The Cochabamba River Hydrologic Information System showing flow directions...51 Figure 5.1 EC routing methodology flow chart Figure 5.2 Example of the EC routing Figure 5.3(a) Rocha network with HydroJunction HydroID`s and downstream trace (b)...57 Figure 5.4 Visualization of the calculated discharges Figure 5.5 Calculated discharge EC routing...62 Figure 5.6 Visualization of the test setup used to determine the fluxes in the network Figure 5.7 Visualization of the calculated fluxes after summation using the HydroTools...64 v

10 List of tables Table 3-1 (Sub) basin area and discharge Table 3-2 Elevation and slopes of the geomorphologic units...23 Table 3-3 Land cover in area and % of the total area...27 Table 5-1 Results of the normal (a) and adjusted (b) simulation of the EC routing routine...59 Table 5-2 Validation of the EC routing with the corrected dataset...60 Table 5-3 Flux build up Na (a) and HCO 3 (b)...66 vi

11 1. Introduction 1.1. Background Water is a unique natural resource; it is essential for mankind and, unlike renewable natural resources, the total amount of water in the world is constant and can neither be increased (such as wood) nor diminished (such as oil). Water is however often available in the wrong place (such as fresh water locked up at the poles), at the wrong time (floods and droughts), or with the wrong quality (Meybeck, 1990). From the world water quantity only 2.5% is fresh, from which about 30% is stored in freshwater bodies and groundwater; the remaining 70% is stored at the poles, in the atmosphere, or in other ways (UNESCO, 1978). Freshwater is therefore limited and its quality is under constant pressure due to human influences (WHO, 2004). The quality of a freshwater body can be described by variables addressing its physical and chemical properties (such as discharge and nutrient concentration) and its biological characteristics (such as the amount of bacterial pathogens). The physical and chemical characteristics are determined largely by the climatic, geomorphologic and geochemical conditions prevailing in the drainage basin and the underlying aquifer. The development of biota in surface waters is governed by a variety of environmental conditions, which determine the selection of species as well as the physiological performance of individual organisms and are dependent on the physical and chemical properties, such as water volume and occurrence of trace metals for the physiological functions of living tissue (Chapman, 1992). When describing water quality, the variables used reflect a conceptual understanding based upon different water uses (Meybeck, 1990). Water quality variables can be used to address the suitability of water for a specific purpose e.g. as drinking water or for agricultural use, but are also useful tools for the detection and hydrological interpretation of source areas and for tracing flow paths of water through catchment areas using chemical routing (Appelo, 1982). Therefore, water quality monitoring and assessment are important for water resources management and water quality modelling. Water resources management and water quality modelling are primary related to spatial and dynamic processes. The complexity of spatially distributed hydrological data sets prevailed detailed modelling in the past (Kovar, 1993). This because water quality is affected by a variety of factors, including hydrology, geology, land use and climate, at different temporal and spatial scales. These factors are often measured in different units at different temporal and spatial scales (Chapman, 1992). Diverse data sources (e.g. data obtained from imagery or from field measurements) and thus formats make the analysis a time consuming task because of the data extraction and the data conversions required to be able to model. 1

12 The advances in computer technology made it possible to develop computer applications to address the problem of storing, manipulating and analyzing large volumes of spatial data related to water resources problems (Lyon, 2003; Maidment and Djokic, 2000; Tsihrintzis, 1995). Presently, many organizations use Geographic Information Systems (GIS) to forecast effects related to the spatial variability of data and the community of water resources and GIS specialists who are familiar with these systems is growing (Maidment, 2003; Leipnik, 1993). Since a GIS is capable of combining large volumes of data from a variety of sources, it is an useful tool for many aspects of water quality investigations (Chapman, 1992) and is emerging as a significant support tool for hydrologic modelling (Maidment, 2003). In particular, GIS provides a consistent method for watershed and stream network delineation using digital elevation models (DEM`s) of land-surface terrain (Xinhao, 1998). Standardized GIS data sets for land cover, soil and other properties are developed, and many of these sets are available through the Internet. GIS data preprocessors are developed, that prepare input data for water flow and water quality models. It is therefore now possible to define a Hydrologic Information System (HIS), which is a synthesis of geospatial and temporal data supporting hydrologic analysis and modelling (Maidment, 2003). This research evaluates the use of a GIS as a support tool for water quality modelling by the creation and evaluation of a HIS that visualizes the spatial distribution of water quality variables. This is achieved by developing and integrating load calculations and chemical routing routines in a GIS system and by using existing routines to extract a number of the topological parameters required from a DEM Relevance of research The design of a monitoring program should be based on clear aims and objectives and should ensure that the planned monitoring activities are practicable and that the objectives of the program will be met (Mäkelä et al, 1996). The main elements of the extraction of a drainage network required for water quality monitoring include watershed segmentation, identification of drainage divides and the network of channels, characterization of terrain slope and aspect, and routing of flow of water (Lyon, 2003) as well as on-site measurements, the collection and analysis of water samples, the study and evaluation of the analytical results, and the reporting of the findings (Meybeck, 1996). One specific operation of a water quality program is the preliminary survey. A preliminary survey is short-term and consists of limited activities to determine the spatial water quality variability prior to monitoring program design (Meybeck, 1996). It consists of the selection of sample sites and tests variations in water quality (Mäkelä et al, 1996). To be able to select sample sites, first watershed parameters (such as channel network) need to be determined. The parameterization of a watershed is time-consuming and is subject to errors related to manual operations (Lyon, 2003) using traditional methods (such as digitizing from paper maps and satellite image or aerial photograph interpretation. The automated watershed segmentation and extraction of channel network and sub-watershed properties from raster elevation data represents a rapid way (Garbrecht et al, 2003), and can potentially reduce 2

13 manual errors. Including these methods in the preliminary survey could reduce survey time and watershed parameterization errors. Care must be taken however when selecting a DEM and constructing a hydrological DEM out of it to be used for the extraction procedure; there are various sources for DEM s (topographic maps, radar, optical images), all having advantages and disadvantages over each other (in terms of errors, resolution). Once the watershed parameters are known the water sample sites can be determined and the water quality can be measured at these sites. The quality of water may be described in terms of the concentration of some or all of the organic and inorganic material present in the water, together with certain physical characteristics of the water (Meybeck, 1996) such as discharge; the impact of constituents on a water body depends on this concentration and the load of the constituent (Huber, 1992). The concentration and loads are determined by in situ measurements and by examination of water samples on site or in the laboratory (Meybeck, 1996). To obtain part of these measurements relatively easy, water chemistry can be a tool when using a procedure termed Electric Conductivity (EC) routing (Appelo et al, 1993). When using EC routing discharges can be obtained from the EC of the river and incoming stream water, reducing the amount of discharges to be measured. The only prerequisite is that the EC differ, which is often the case in both large and small rivers (Appelo et al, 1993). Another advantage of the use of this procedure is that all contributions and abstractions within a flow system will be related: in case the quality or discharge changes in one, all others can be revised. As can be concluded from the above, a lot of data needs to be gathered, stored, analyzed and visualized in a preliminary survey, which is time consuming and might be costly. The use of a HIS, being a Geographic Information System including automated parameterization of a watershed and EC routing has the potential to enhance the work of hydrologists when conducting a preliminary survey Research objective The main objective of this study is to develop and evaluate a Hydrologic Information System that visualizes spatially chemical water quality making use of hydrologic parameter extraction and Electric Conductivity routing, as a support tool for water quality monitoring. To reach the main objective of the study, the following research questions need to be answered: - What thematic layers, hydrologic parameters and water quality variables are required to determine spatial water quality of a basin? - Which methods exist to automatically extract the thematic layers and parameters from raster elevation data and can these methods be incorporated in a HIS? - Can the Electric Conductivity routing procedure be incorporated in a HIS? - Can the developed HIS calculate verifiable water quality fluxes using EC routing? 3

14 1.4. Conceptual model A Hydrologic Information System is a Geographic Information System capable of hydrologic analysis (Maidment, 2003), in this research the EC routing and flux calculations. This research consists of three parts: 1. The development of a HIS for a selected study area; 2. The incorporation of the Electric Conductivity routing in the HIS; 3. The calculation and visualization of water quality in the HIS Hydrologic Information System development At first the catchment parameters need to be determined that are required to spatially represent the drainage network of a study area and are needed as input for the EC routing and load calculations. These are determined by a literature study on the topic (Chapter 2). Then the available possibilities for automated extraction of these parameters will be researched, and the possibility of either incorporating these methods in a GIS software package or the use of an existing package will be evaluated. A first result of this part will be the selection of parameters, methods and software to be used for the creation of the HIS. Then a study area must be selected that is suitable for this research and the HIS will be developed for the study area using the selected software and methods Electric Conductivity routing incorporation Once the HIS for the study area is developed, the electric conductivity routing will be incorporated. The EC routing method is a mass balance model that can trace flow paths of water through catchment areas. To be able to do so, the EC and discharge of all water bearing reaches of the drainage network are required; these are collected during fieldwork. Once analyzed, all reaches will be connected in the HIS and all discharges downstream the most upstream junction will be calculated using the routing method and will be verified using field measurements Water quality visualization and incorporation The primary measure of a constituent is its concentration; the impact on a water body however may be influenced by the concentration or the flux. The flux is derived from measurements of both water discharge and concentrations over a period of time (Chapman, 1992). In this research the flux of trace elements is determined as a measure for water quality, because they can be relatively easily measured in the field and analyzed in the laboratory (as in contrary to e.g. pathogens). The calculated discharges and the measured concentrations of the trace elements of the inputs throughout the drainage network and of the main stream upstream the first junction will be used to determine the flux along the main stream till the outlet. The last part of the research is to verify these calculated fluxes using the measured concentrations and to visualize the flux spatially using the GIS software. 4

15 1.5. Methodology Literature review The literature review consists of the collection and review of articles related to this research, especially with regard to those parameters that are required to visualize water quality using the concentration and flux and what GIS packages and/or tools have (part) of these capabilities. Reviewed will be if a particular GIS software and/or tools can be used for this research, or whether a combination can be used and/or part has to be developed and how. The various sources of input parameters (such as a DEM) will be reviewed on usefulness for this research. Based on the literature review a study area will be selected and various literature will be used throughout this research in support of measure techniques and GIS development Data collection Existing relevant data of the study area will be collected such as satellite imagery, geology, geomorphology, topology and land use data from local organizations or from public domain data sources Fieldwork During fieldwork data is collected required for this research. Ground control points (GCP s) will be collected to georeference satellite imagery and DEM s. Elevation data is collected to assess the elevation accuracy of the DEM s available for this research. The Rocha River needs to be surveyed to determine what tributaries yield base flow and what other sources and sinks exist along the network. Both water quality and quantity data will be collected from these. To be able to perform the EC routing, measurements are taken up- and downstream every junction, where downstream a sufficient mixing length is taken into account that is determined in-situ. The water quantity is determined using the velocity-area and salt dilution methods. For the determination of the water quality, water samples are collected that will be analyzed at the laboratory at ITC. To be able to use the EC routing procedure, the quantity and quality measurements must be connected, i.e. measurements are taken in succession starting upstream moving downstream. Because the entire network cannot be surveyed in one day, the last measurement of one day is repeated the next day before proceeding downstream to ensure the connectivity Data analysis At ITC the data collected in the field will be analyzed. The salt dilution and velocity-area data is used to calculate discharges. The water samples are analyzed in the laboratory for the trace elements; an inductively coupled plasma spectrometer (ICP) will be used for the cations and a photo spectrometer for the anions. The satellite images and DEM will be georeferenced. The elevation accuracy of the available DEM s will be assessed using the elevations measured in the field; the most accurate DEM is then used to extract the required drainage network using the selected GIS and/or tools HIS creation Once all data is available the HIS will be build. First the drainage network is extracted from the selected DEM and all tributaries, sources and sinks are connected, using the selected GIS 5

16 software and/or tools. The created drainage network will be compared with the field survey to exclude the non-base flow yielding tributaries. Then the quantity and quality measurements are connected to the network as attributes of the network at the junction and measure and sample points, and the database is exported to Excel. Here the EC routing and flux calculations are incorporated using Excel functionality. For the EC routing, we consider a mixing model, linked in series. Capital letters Q and A indicate discharge and water quality in the main stream, small letters q and a indicate similarly discharge and water quality of contributing streams, springs, or seepage zones (Appelo, 1993). The continuity equation: And Q i+1 * A i+1 = Q i * A i + q i,i+1 * a i,i+1 (1.1) Q i+1 =Q i + q i,i+1 (1.2) The EC routing is used to calculate the discharges of all reaches within the network. Then the fluxes along the network are calculated. Mathematically, the flux Φ is calculated using (Chapman, 1992): Φ = t 2 t1 C( t) Q( t) δ t (1.3) where, Φ is flux (mass/time) Q is water discharge (volume/time); C is concentration (quantity per volume); t is time. Now the concentrations and fluxes of the network are calculated, the Excel spreadsheet is exported back to the database. The spatial water quality is now visualized using the tools of the GIS Assessment To be able to assess the HIS as a support tool for water quality monitoring, first the EC routing is validated; the calculated discharges are compared with the measured values to indicate accuracy. Then the fluxes are calculated and validated by comparing them with the measured ones. If either one does not yield verifiable results, the developed HIS is considered not to be suitable as a support tool for water quality monitoring for this case study. An overview of the methodology is presented as a flow chart in Figure

17 Pre-fieldwork Model study and data collection: - Literature study - Study area data Network monitoring: - Survey network - Selection discharge measure sites - Selection of water quality sample sites Fieldwork Data collection: - GCP - Discharge - General water quality variables - Water samples Field data analysis: - Discharge - Water samples (Laboratory) Topographic parameter extraction: - Tool selection - DEM selection - Drainage extraction Post-fieldwork HIS development: - Connected drainage network - EC routing - Flux calculation HIS validation Conclusion Figure 1.1 Methodology flow chart Thesis outline Chapter 2 is a literature review related to Hydrologic Information Systems in general and related to this research. Chapter 3 describes the study area in Cochabamba, Bolivia the data gathered and the preparation of the base data a priori of the building of the HIS. 7

18 Chapter 4 describes the creation of the HIS using the base data and the coupling of the HIS with existing modelling components. Chapter 5 describes the incorporation of the EC routing and the flux calculation in the HIS. Chapter 6 describes the conclusions and recommendations. 8

19 2. Hydrologic Information Systems 2.1. Present state Introduction In 1992, Dodson wrote in the Handbook of hydrology (Maidment, 1992) A major problem that hydrologists and drainage engineers will face during the next several years is information overload - the inability to deal with the available information using present methods. In those days, the spreadsheet was the major interface for modelling and data management. At present the state of water resources and watershed modelling has advanced the use of Geographic Information Systems (GIS) (Maidment et al, 2000) using it s the simulation capability of physical, chemical and biological processes (Lyon, 2003). The four functions of a GIS are (1) data acquisition and pre-processing, (2) data management, storage and retrieval, (3) data management and analysis and (4) product generation (Meijerink et al, 1994) which make it a useful support tool for water resources applications with a focus on water resources modelling (Maidment et al, 2000). At present a large number of GIS systems produced by various organizations are available for hydrologists. Because hydrologic models require different types of data depending on the processes modelled (Cruise et al, 1993), not every GIS is suitable for a specific model. According to Lyon (2003), the main elements of the extraction of a drainage network required for water quality monitoring include watershed segmentation, identification of drainage divides and the network of channels, characterization of terrain slope and aspect, and routing of flow of water. Techniques are available for the extraction of these parameters from a digital representation of the topography, a Digital Elevation Model (DEM), whereas the manual determination is a tedious, time-consuming, error-prone and often highly subjective process (Martz et al, 2003). This research focuses on the development and integration of load calculations and chemical routing routines and requires coupled stream reaches as a main input (Appelo et al, 1999). The present state for the extraction of these reaches is discussed in this chapter Digital elevation models The major issues with the derivation of drainage networks from a DEM is related to the quality and resolution of the DEM and the to the methodology used to derive this information (Garbrecht et al, 2000). A DEM cannot accurately reproduce drainage features that are at the same scale as its spatial resolution; this results in shorter channels and channel area capturing. Therefore the resolution must be selected relative to the size of the drainage features. Zhang and Montgomery (1994) evaluated DEM resolutions between 2 and 90 meters and concluded that grid sizes of 10 meters would be sufficient for many hydrologic 9

20 models. This will however require vast computational power for large watersheds and a DEM with such a resolution must be available for the studied area. At present there are many sources to generate elevation data that can be ordered by principle of generation: ground survey, existing topographical maps, optical stereo, airborne laser scanning and radar-based. Perizza (2004) states that Surveying is the science of measuring the physical features of the earth using specialized equipment and procedures to obtain highly accurate results. The accuracy is however dependent on the type of specialized equipment used. A barometer uses air pressure to determine elevation and with its accuracy of 1 to 1 ½ meter is mainly used at areas where no high accuracy is required; it does not measure its location (Jonkers, 1984), but can be integrated into satellite positioning system receivers. Total stations or theodolites with trigonometric leveling methods can also be used; these need an initial position with known x, y and z positions to measure and cannot measure beyond distances larger that 150 meters but do so with a precision of 10 centimeter per 100 meters (Jonkers, 1984). Another way of ground surveying is with the use of a satellite positioning system. Measurements with a satellite positioning system can be compared to triangulation; it relies on the measurement of distances to fixed positions, in this case to satellites orbiting about 20,183 km above the earth (Van Sickle, 2004). At this moment there are two operational satellite positioning systems, the Global Positioning System GPS (USA), the GLONASS system (Russia) and one European system (Galileo) under development. In general we can distinguish between measurements with a single satellite positioning system receiver and a differential receiver. A single handheld receiver has a positional accuracy between 7 and 15 meters or between 3 and 5 meters when capable of receiving correction signals from the WAAS or EGNOS satellites and an elevation accuracy of 3 meters when equipped with a barometric altimeter (Burrows, 2004). A differential receiver system consists of two receivers; one system at a known position that sends corrective factors to a roving system that occupies unknown positions in the same geographic area. These factors can be communicated in real-time using a GSM or radio link or may be applied in post-processing that result in sub meter accuracy (Van Sickle, 2004) or into centimetre or better precision for kinematic receiver systems (Leick, 2004) that correct for ionosphere influences. Another way of obtaining a DEM is by photogrammetric analysis. Although most photogrammetric applications used involve aerial photographs, a large number of researchers have investigated the extraction of elevation and/or the production of DEMs from high spatial resolution imagery in the visible and near-infrared spectrum (Toutin, 2001). Terrain elevations are extracted by analyzing the area of overlap of a stereo pair. There are two views of the same terrain in this area, taken from different vantage points, where the relative position of features positioned closer to the camera at higher elevations change more from photo to photo than the features at a lower elevation. This change is called parallax, and the measurement of it determines terrain elevations (Lillesand, 1994). Every platform has different specifications in terms of resolution, swath width and accuracy of elevation 10

21 extraction; an Ikonos image for example has a swath width of 11 kilometer, spatial resolution of 1 to 4 meters (Bakker, 2004) and an across-track elevation extraction accuracy of 1.5 to 2 meters (Toutin, 2001), while a Landsat TM image has a swath width of 185 kilometer, spatial resolution of 30 meter (Bakker, 2004) and an adjacent-track elevation extraction accuracy of 45 to 70 meters (Toutin, 2001) when images of different overpasses are used to create a stereo pair. Other commonly used data sources are radar and LIDAR (LIght Detection And Ranging) systems; Interferometric Synthetic Aperture Radar (IFSAR) image processing such as the STAR-3i, can provide digital elevation data with a spatial resolution of 5 meters and a meter vertical resolution, while a commercial LIDAR system can provide a spatial resolution of 5 meter with an accuracy of 0.30 meter in x, y and z with a 95% confidence level (Rijkswaterstaat, 2000). Readily available DEM`s can be obtained via a number of public domain sources that provide data sets via the Internet. A number of widely used sources are reviewed here. The GTOPO30 data set is a global DEM with a spatial resolution of 1 kilometer that is created from various datasets such as the DTED (Digital Terrain Elevation Data) and the Digital Chart of the World (DCW). The absolute vertical accuracy of GTOPO30 varies by location according to the source data from 30 meter to 160 meter (Miliaresis et al, 1999). The GLOBE (Global Land One-kilometer Base Elevation) data set is another global DEM with a spatial resolution of 1 kilometer and an overall vertical resolution exceeding 20 meter up to 300 meter at Antarctica. This dataset is an improved version of the GTOPO30 and makes use of that data source as well as other local data sources (Hastings et al, 1998). The Shuttle Radar Topography Mission (SRTM) data set is a 1 and 3 arc second dataset recorded in 2000 by IFSAR and the overall vertical accuracy requirement for the mission was 16 meter (Rabus et al, 2003). An investigation to quantify the magnitude of this error on bare soil revealed absolute errors ranging from 4 to 1.1 meter (Kellndorfer et al, 2004). The dataset is publicly available in a 3-arc second resolution (near-global) and a 1-arc resolution (North-American continent). In conclusion it can be said that there a numerous sources for DEM`s, with varying production methods, resolutions, availabilities and qualities and the selection of a DEM depends on its availability, scale of the watershed, watershed coverage, accuracy required and various other aspects such as financial and temporal space available for DEM generation and model limitations Hydrologic parameter extraction Identifying surface drainage in the presence of depressions or sinks, flat areas and flow blockages as a result of data noise, interpolation errors and systematic production errors in DEMs (Maidment et al, 2000) or natural flat areas or sinks (Vogt et al, 2002) result in difficulties. It is therefore common to remove these prior to using any methodology for 11

22 drainage identification (Maidment et al, 2000) and a number of methods are developed to hydrologically correct DEMs. Some of the approaches fill up local sinks to the level of the lowest grid cell on the rim of the sink with a defined flow direction; this implies that all sinks are an underestimation of elevation (Martz et al, 1998). However, some sinks arise from obstruction of flow paths by overestimation of elevation and these sinks should be removed by breaching the obstruction; a combined filling and breaching methodology is presented by Garbrecht et al in Several methods for defining surface drainage across flat areas have also been developed and range from arbitrary flow direction assignment and landscape smoothing (Maidment et al, 2000) to the burning-in off digitized rivers into the original DEM enforcing the resulting drainage network along the known lines, the AGREE DEM method (Vogt et al, 2003). The effect of these approaches will vary with the grid cell size of the DEM (Saunders, 1999). Once the corrections are made a flow direction and accumulation grid can be extracted prior to the definition of the drainage network. At this moment there are a number of raster-based methodologies to derivate drainage information from DEMs. First an aspect and flow direction map is derived; then the flow accumulation is calculated. A simple and widely used methodology is the steepest descent, or D-8, method for flow routing (Maidment et al, 2000). The D-8 method (Fairfield and Leymarie, 1991) evaluates individual raster cells by examining the elevation of itself and the eight surrounding cells and assigns flow to the lowest neighbouring cell (the steepest path from the central cell). This is a reliable method provided that the topographic data (DEM) processed has a sufficiently fine resolution to represent the major features of the land surface geometry (Shaw, 2004) and for zones of convergent flows and along well-defined valleys (Vogt et al, 2003). For overland flow analysis a partitioning of flow into multiple directions and thus multiple receiving cells may be better according to Desmet et al (1996). Such a methodology is the fractional, or F8, method that partitions flow from one cell to all its neighbors by weighting flow according to relative slope (Quinn et al, 1991). Uncertainty associated with the weighting schemes prompted development of a flow tube analogy in which flow is resolved for each cell using both aspect and gradient (Mackay et al, 1998). This methodology, the D-Infinity method, was proposed by Tarboton (1997) in which flow dispersion is reduced by dividing the flow between a maximum of two neighboring down slope grid cells. The digital elevation model networks (DEMON) method by Costa-Cabral and Burges (1994) use an algorithm developed by Lea (1992) that uses aspect associated with each pixel to specify flow directions. A comparison between previous described methods by Tarboton (1997) shows that the DEMON and D-Infinity methods perform similar and better that the D-8 and F8 methods when using the framework of that specific research. Another new method for flow partitioning is the mass flux method; this method is similar to the D- infinity method, and treats each pixel as a control volume and uses a rigorous mass balance to partition flow between two neighbor pixels, with the exception of single-pixel peaks and pixels on drainage divides (Rivix, 2004). Where results from different methods differ, the choice of methods becomes important (Tarboton, 1997), therefore the final method selection 12

23 to be used in this research will be based on the DEM used, the completeness of a software that can be used (e.g. in terms of methods available in one package) and the results of a method in comparison with drainage patterns derived from satellite images. Using the flow accumulation grid, channels can be defined using the critical source area (CSA) or the flow accumulation value method. The CSA value is a user defined minimum drainage area below which a permanent channel is defined (Mark, 1984) and the flow accumulation value the user defined minimum accumulation value below which a permanent channel is defined. Areas or accumulations smaller than this threshold value are considered land surface draining the channels (Olivera et al, 2002). These concepts control the watershed segmentation process and all resulting spatial and topologic drainage network and sub catchment characteristics (Garbrecht et al, 1999). Before the channels are defined an outlet is included by the user, indicating the end of the drainage network. The drainage divides are defined by tracing the cells that drain to that outlet (Colombo et al, 2000). Once the channel network is extracted from a DEM it is displayed as a series of raster cells. According to Horton (1945) a network consists of a set of channel links connected by network nodes; for a network to be useful for hydrologic modelling these individual channel links and adjacent contributing areas must be identified and associated with topologic information for upstream and downstream connectivity (Garbrecht et al, 2000). This channel ordering and node indexing is fundamental to the automation of flow routing management and the node index numbers can be used to link to tabulated attributes of the network channels (Garbrecht et al, 1997) such as discharge and quality, making it a requirement for this research. Automated channel indexing can be performed using a raster and a vector GIS approach. A raster GIS approach has been presented by Garbrecht and Martz (1997); they developed a numerical model that uses the Strahler (1957) and Shreve (1967) method for ordering and indexing. First Strahler orders are determined by a cell-by-cell trace of the raster network in a downstream direction beginning at the nodes where channel links originate and ending at the outlet node (Figure 2.1 a), storing beginning and ending coordinates of each link in an attribute table. The subsequent Shreve node indexing uses this table to search and index the nodes starting at the outlet node following a left hand pattern until the outlet node is reached again (Figure 2.1 b) and stores the index number in a table. 13

24 a b Figure 2.1 a.raster network after processing Strahler second order channels b. Example channel network and definitions. Source Garbrecht, A vector GIS approach has been presented by Maidment (2002); he uses the GIS database functionality of ArcGIS to populate and interconnect features in different data layers. The backbone of this approach is a geometric network database: topologically connected link and node features that represent a hydrologic system as a linear network. As input a set of streams, watersheds, water bodies and points (such as gauge locations) can be used: the streams can be digitized channels or vectorized raster channels. The GIS software indexes and connects the features automatically: a unique numerical integer identifier is assigned to all features within a database, after which the nodes and links are connected. The flow direction of the network is assigned automatically and is equal to the direction of digitization or extraction and can be changed manually. An advantage of the vector approach is that the data is represented in a GIS form, in contrary to the tabular form of the raster approach; a disadvantage is that specific software needs to be used. In conclusion it can be said that there are different methods for the extraction and indexing of drainage channels and related hydrologic parameters, and that the selection is dependent on the total requirement of the research Available software A number of methodologies for the extraction and indexing of drainage channels and related hydrologic parameters have been introduced in the previous section. This section gives an overview of a number of existing models, GIS/HIS packages and/or tools that make use of these methodologies and therefore have potential to be used in this research. Reviewed will be the possibilities for the visualization of water quality and electric conductivity routing in particular. ANUDEM has been designed to produce accurate digital elevation models with sensible drainage properties from comparatively small elevation and streamline data sets (Hutchinson, 14

25 2005). ANUDEM has no water quality functionality but is compatible with Arc/Info, Idrisi and GRASS formats for input and output data files. RiverTools (Rivix, 2004) is a GIS application for analysis and visualization of digital terrain, watersheds and river networks and is designed to work with other GIS applications via file format support. Rivertools uses the D8, D-Infinity and mass flux flow grid methods river to extract flow direction. The network can be indexed using the Relief and Shreve-Strahler order and exported with these as attributes. A hydrologic model called Topoflow can be used as a plug-in to RiverTools to create a hydrologic modelling and visualization environment; electric conductivity routing is not supported, but could be programmed using the IDL language. Tardem is a suite of programs for the analysis of Digital Elevation Data developed by Tarboton (Tarboton, 2000). This software can be used standalone or as a tool of ArcGIS and uses the D8 and Dinf methodology and produces output that can be imported in a GIS. Tardem has no hydrological module but is compatible with ArcGIS. TOPOG (Arora, 2005) is a stand alone physically-based, distributed parameter, catchment hydrological model that has been developed jointly by CSIRO Land and Water and the Cooperative Research Centre for Catchment Hydrology: electric conductivity routing is not supported and cannot be added. TOpographic PArameteriZation (TOPAZ) (Garbrecht et al, 1999) is a software package that consists of 6 interdependent programs for automated analysis of landscape topography from digital elevation models. TOPAZ is not a GIS in the traditional sense, but performs numerical processing of raster DEMs and produces a number of data layers and attribute tables; the primary objective is the rapid and systematic identification and quantification of topographic features in support of investigations related to e.g. land-surface processes, hydrologic and hydraulic modelling, assessment of land resources, and management of watersheds. The DEM processing is based on the D8 method and the critical source area (CSA) concept. TOPAZ has no water quality functionality but is compatible with Arc/Info and Idrisi. The Better Assessment Science Integrating point and Nonpoint Sources (BASINS) and the Soil and Water Assessment Tool (SWAT) are customized ArcView GIS applications that integrate environmental data, analysis tools and modelling systems. They use the same automatic watershed delineation tool that expands the ArcView and Spatial Analyst extension functions to operate the watershed delineation. The DEM processing of these tools are based on the D8 method and the flow accumulation value method (Perakum, 2004). Electric conductivity routing is not included in either tool. MIKE BASIN is a water resources model that operates within ArcView and includes a tool for automatic catchment delineation from DEM's and addresses water allocation, conjunctive use, reservoir operation and water quality issues (dhisoftware, 2005). Technically, MIKE 15

26 BASIN is a quasi-steady-state mass balance model, allowing for routed river flows with a water quality component. Electric conductivity routing is not included. ArcHydro (Maidment, 2002) is a geospatial and temporal data model for water resources that operates within ArcGIS and supports hydrologic simulation models. The complete model consists of five categories to divide water resources elements: network, drainage, channel, hydrography and time series. The Arc Hydro tools are used to derive several data sets that collectively describe the drainage patterns of a catchment. First a raster analysis is performed to generate data on flow direction, flow accumulation, stream definition, stream segmentation, and watershed delineation. This data is then used to develop a vector representation of catchments and drainage lines and a geometric network is constructed. This method uses the D8 method and the flow accumulation value method. Electric conductivity routing is not supported, but could be included using the possibilities of the database and geometric network and Visual Basic programming. The Integrated Land and Water Information System (ILWIS) (ITC, 2005) is GIS/Remote Sensing software with hydrologic flow options such as Fill sinks, DEM optimization, Flow direction and Flow accumulation. ILWIS has no water quality functionality, but this could be included. The Watershed Modelling System (WMS) (HEC, 1999) is a comprehensive graphical modelling environment for all phases of watershed hydrology and hydraulics that includes tools for automated basin delineation and can import, create and manipulate GIS data. WMS is a modelling suite, but electric conductivity routing is not supported and cannot be added. In conclusion it can be said that all software reviewed is capable of the (partial) extraction of hydrological parameters, but do not support electric conductivity routing HIS Requirements and development As can be concluded from the present state of HIS, the main elements of the extraction of a drainage network required for water quality monitoring include watershed segmentation, identification of drainage divides and the network of channels, characterization of terrain slope and aspect, and routing of flow of water (Lyon, 2003). This can be done automatically using various techniques (Fairfield and Leymarie, 1991; Vogt et al, 2003; Quinn et al, 1991; Tarboton, 1997; Lea, 1992; Rivix, 2004) on a hydrological corrected DEM. The DEM selection is dependent on its availability, scale of the watershed, watershed coverage, accuracy required and various other aspects such as financial and temporal space. A number of tools and (GIS) software packages are available at present that can extract these components. The use of a GIS is preferred because of its various data handling capabilities (Meijerink et al, 1994). Because at this moment there are no tools or (GIS) software packages that use the electric conductivity routing method it has to be developed. An indexed drainage network is required; 16

27 this can be extracted using previous mentioned techniques. The continuity equation (Appelo, 1993) can then be incorporated using other programming tools. Out of the reviewed packages Arc Hydro is selected for this research because of a number of advantages over the other tools and (GIS) software packages. Arc Hydro is imbedded in the ArcGIS package that is an integrated collection of GIS software products for building a complete GIS. It uses a geometric network database structure that does the indexing, connecting and assigning of flow direction automatically (Maidment, 2002). To be able to use these possibilities, two ArcGIS extensions have to be added to the standard functionality (ESRI, 2005): the network Analyst is a powerful extension for routing, and provides a framework for network-based spatial analysis and the spatial analyst extension that adds a comprehensive set of advanced spatial modelling and analysis tools to the ArcGIS Desktop and is a prerequisite for Arc Hydro. Once the indices are generated, they are stored in a Microsoft Access database that can be directly linked to Microsoft Excel for easy data manipulation. ArcGIS uses the Visual Basic standard interface language and a model builder interface, which provides a graphical modelling framework for designing and implementing geoprocessing models that can include tools, scripts, and data (ESRI, 2005), adding to the development possibilities. This functionality means that the HIS can be accessed using four different interfaces: ArcGIS, Access, Excel and by programming Visual Basic, which is a great advantage over the other packages. Microsoft Excel is very good for developing prototypes (Merwade et al, 2002), and will be used as such in this research to develop the prototype EC routing method using the indices stored in the database to calculate discharges and fluxes, that will then be stored in the database again and visualized using the ArcMap program that has powerful cartographic possibilities. A case study area is required to provide a drainage network and EC and discharge measurements to build and test the EC routing method. The prerequisites for the electric conductivity routing method are a steady state flow regime and the reaches must have a different EC (Appelo, 1993). From the possible ITC fieldwork areas the Rocha River basin area in Cochabamba, Bolivia, is chosen because the differences in geology and land cover between the sub basins result in the required different EC values in the network (Chapter 3) HIS limitations There are a number of limitations in the HIS due to its design. The choice of Arc Hydro means that only the D8 method (Fairfield and Leymarie, 1991) can be used to calculate flow accumulation, even though other methods are proven to perform better (Tarboton, 1997). A related limitation is that one single threshold is used for the channel network definition using the critical source area (Maidment, 2002). Since both ArcHydro and ArcGIS have no EC routing routine incorporated, the routine has to be developed. Other limitations are due to the focus of the research: the EC routing method. Only steady state conditions can be used (Appelo, 1993) and thus advanced modelling of runoff and 17

28 groundwater system interaction is not included. The 1-dimensional fluid flow system does not include diffusion, dispersion and decay functions, nor does it contain a temporal function. There are also a number of limitations due to the choice of study area. There is no or limited area coverage for the DEM sources: only the SRTM DEM covers the entire study area. This DEM is not a surface model, thus the land cover might influence the accuracy of the topographic variable extraction. There are no permanent monitoring stations, thus the fluxes cannot be calculated over time. 18

29 3. Study area 3.1. Introduction Bolivia is a country in South America (Figure 3.1) and is divided into six physiographic regions; the Andes, the Altiplano, the Yungas, the Highland Valley, the Gran Chaco and the Tropical lowlands (Saavedra, 2000). In the Highland valley lays the Cochabamba department, one of the nine departments of Bolivia (Cartagena, 2004) and within this department the city of Cochabamba. The city of Cochabamba is a progressive and active city with a growing population of over 800,000 and its name is derived from the Quechua words khocha and pampa meaning `swampy plane`. Cochabamba lies in the Cochabamba valley, a fertile green bowl 25 km long by 10 km wide (Swaney, 2001) between S, W and S, W and has an elevation around 2550 m.a.s.l. Figure 3.1 General location of the study area The study area is the basin of the Rocha River (Figure 3.2), the main collecting river for the Cochabamba valley (Villaroel, 2004). The river starts just East of the city and flows through it to the West and eventually to the South out of the Cochabamba department, the boundary of this study. The Rocha River was chosen for this study because of the expected variations in electric conductivity between the river and its contributories due to differences in the geology and land use of and within the different basins. 19

30 Isiboro Pampa Grande Santa Rosa Corani-San Mateo-Ibirizu Rocha-Maylanco Cochabamba District Tapacari Santivañez Cliza-Sulty Ayopaya Caine Julpe-Mizque Legend Cochabamba district Study area Figure 3.2 Location of the study area within the Cochabamba district 3.2. Climate According to the national meteorology agency SENAMHI (Servicio Nacional de Meteorologia e hidrologia), the average annual precipitation is mm and de average temperature 17.7 C over the period of 1961 to 1990 (Figure 3.3). Most of the precipitation occurs in the wet season, from November until March. Monthly average climatic data Precipitation (mm) Precipitation (mm) January February March April May June July August September Month October November December Temperature ( C) Temperature ( C) Figure 3.3 Annual average precipitation and temperature over the period (Senamhi, 2004). A scheme for classifying climates was derived by Thornthwaite (Appendix 1a); it is a generic method that uses temperature and precipitation to define the boundaries of climatic types (Allaby, 2002). Using this scheme, a potential evaporation and a moisture index is calculated 20

31 and used to determine the climate classification in terms of moisture and temperate provinces (Appendix 1b). The moisture province is determined using a precipitation - efficiency Index (P-E index) that is a value that indicates the amount of water that is available for plant growth. It is determined by a summation of the monthly P-E indices that are calculated using the formula: Where, P-E monthly = 115(r/t 10) 10/9 (3.1) r = mean monthly rainfall (inches) t = mean monthly temperature ( F) The P-E index of 27.9 indicates a humid climate type, B 1. The temperate province is determined using the thermal efficiency index (T E index) that is a value for the amount of energy, as heat, that is available for plant growth in the course of the year. Because temperature and evaporation are so closely linked, the T- E index is used as the potential Evapotranspiration to determine the climate type. It is determined by a summation of the monthly T-E indices that are calculated using the formula: Where, T-E monthly = (t-32)/4 (3.2) t = mean monthly temperature ( F) The T-E index of 95.9 indicates a mesothermal climate type, denominates as B`3. In addition, the classification adds code letters that qualify these main categories by referring to the amount and distribution of precipitation associated with them. These additional qualifications are based on an aridity index for moist climates and a humidity index for dry climates. Since the climate has been typified as a moist climate, the aridity index is used. The aridity index indicates the amount of water available to plants and is equal to the difference between precipitation and evaporation, or in other words the effective precipitation (EP). Effective precipitation is calculated using the formula: Where, EP = r/t (3.3) r =mean annual precipitation (mm) t = mean monthly temperature ( C) An EP of 27.3 shows a moderate water deficiency in winter. Thus using the Thornthwaite classification scheme, the climate can be best described as a humid mesothermal climate with a moderate water deficiency in winter Hydrology The river Rocha starts in the Rocha-Maylanco basin and has many tributaries. Most generate storm runoff only and not base flow. Because for this study a steady state flow regime is 21

32 required, part of fieldwork was to identify the rivers that contribute base flow to the Rocha River. Following field observations (Chapter 5), the study area is determined to be the river Rocha system till the end of the Cochabamba district. This includes the (sub)basins of the following rivers that have base flow (upstream to downstream): the Rocha, the Tamborada, the Old Rocha, the Alcantarilla, the Chujlla, the Khora, the Viloma, the Tapacari and the Arce (Figure 3.4) Viloma Alcantarilla Chujlla Khora Old Rocha Tapacari Tamborada Angostura reservoir Arce Kilometers 1:1,000,000 UTM, WGS 84, Zone 19 South Legend Waterbody Watershed Cochabamba city Rocha River Tributary Figure 3.4 Overview of study area generated from SRTM and an ASTER image (Chapter 4). When comparing the sub basins contribution to the study area in terms of size, it can be noted that the largest basins are the Tamborada and Arce basins, followed by the Tapacari, Viloma, Old Rocha, and then the Alcantarilla, Chujlla and Khora (Table 3-1). The Tamborada tributary discharges into the Angostura reservoir prior to the junction with the Rocha River and does therefore add little water to the system. The major tributaries in terms of discharge are the Arce, Tapacari and Viloma rivers (Table 3-1). Basin Area Km 2 % of total Q (m 3 /s) Rocha Tamborada Old Rocha Alcantarilla Chujlla Khora Viloma * Tapacari Arce

33 Table 3-1 (Sub) basin area (Figure 3.4) and discharge (Chapter 5) *estimated from field observation Geomorphology The morphology of channel cross sections is well understood; the shape and size of alluvial channel cross sections are closely related to the geology of the terrain and the flows responsible for them. As flow increases in the downstream direction, channel width and mean depth tend to increase, while the water surface slope decreases (Mosley, McKerchar, Maidment, 1992). This can also be observed when following the Rocha River system from its origin until the end of the Cochabamba district and thus the study area. When looking at the geomorphology of the study area (CLASS, 2003), 4 main units can be distinguished: mountains, fans, valleys and plains. Within the mountain unit 6 sub-units: peak, range, volcanic, hill, slope and glacial (deposits) can be distinguished, and within the valley unit 3 sub-units: valley, basin and terrace (Figure 3.5). Unit Elevation (Meters) Slope (%) Mountain peak > 80 Mountain range Mountain volcanic Mountain hill Mountain slope > 80 Mountain glacial (deposits) Valley valley Valley basin Valley terrace Fans Plain < 2 Table 3-2 Elevation and slopes of the geomorphologic units (CLASS, 2003). From the geomorphology map can be observed that the study area is mostly a mountainous area, where the terrain slopes from mountain peaks into small valleys (Figure 3.5 and Table 3-2). The Rocha system originates at the glacial valleys and joins with the Tamborada at the Cochabamba plain. The Tamborada River originates from the mountain range and fan in the East and moves through the glacial valleys and plains while joined by small streams from the surrounding fans to join with the Rocha River. After this junction, the Rocha continues to move over the plain, to be joined by the small rivers Chujlla and Khora that originate from the mountain range and fan North of Cochabamba. The system then moves South, to be joined by the Viloma River that originates from the mountain range in the North. The system continues through the valleys where it meets the river Tapacari, which originates from the mountain range in the East. Continuing to the South, the Rocha joins with the river Arce that originates from the large sub basin in the South East of the study area dominated by mountain 23

34 ranges, before moving out of the study area. There is no geomorphic information available concerning the Southern part of the Arce sub basin Geology The South American continental core or platform consists of the Guyana and Brazilian shields which occupy the east-central portion of the continent. Away from the shields, the Phanerozoic mountain chains developed towards the edge of the continent: the Caribbean mountain system in the north, the Andean Cordillera in the north-west, west and south-west and the Magellan system in the south (Martin, 1981). The Cochabamba area is situated in the central part of the Eastern Cordillera; the map presented (Figure 3.6) is adapted from the Bolivian Geological Department (GEOBOL, 1978). A geological description was made by the same department and the Swedish Geological Department (GEOBOL, 1994). The description here is mainly based on this source. There are outcrops of Paleozoic sedimentary rocks (Permian, Silurian and Ordovician) formed in a back arc and/or foreland environment. The Mesozoic rocks (Cretaceous) correspond to back arc or retro arc environments. The Tertiary deposition occurred in a continental foreland environment Paleozoic era The oldest rocks in the study area are from the Ordovician period and are mainly quartzitic sandstones, siltstones and slates from the Capinota, Anzaldo, Amutara and San Benito Formation (Fm). On top of this formation are deposits from the Silurian period. These are syntectonically deposited diamictites (Cancaffiri Fm) overlain by shales (Uncia Fm), siltstones and sandstones (Catavi Fm). Small areas of bioclastic limestones, marl and carboneous shales (Copacabana Fm) can also be found within the study area and where deposited during the Permian period Mesozoic era During the Cretaceous period the alluvial-fluvial conglomerates and sandstones of the Toro Toro Fm were deposited. These are followed by limestones, sandstones and marls of the EI Molino Fm. 24

35 Legend Fan_complex Mountain-Glacial Plain Valley-Basin Mountain-Hill Valley-Terrace Kilometers 1:1,000,000 UTM, WGS 84, Zone 19 South Mountain-Peak Mountain-Range Mountain-Volcanic Mountain_Slope Valley-Valley Rocha River Tributary Figure 3.5 Geomorphology of the study area (CLASS, 2003) Legend Quaternary pebbles, gravel, sand, silt, clay Ordovician quartzitic sandstone, siltstone, slates Silurian diamictites, shales, sandstone, siltstone Cretaceous sandstone, limestone, marl Permian limestone, carboneous shales Kilometers 1:1,000,000 UTM, WGS 84, Zone 19 South Tertiary lavas, tuff Tertiary sandstone, gypsiferous marl, tuff, clay Rocha River Tributary Figure 3.6 Geology of the study area (GEOBOL, 1978) Caenozoic era From the Tertiary period there is a marked lithological unconformity (Morochata Fm), which consists of lava and tuff and marks the culmination of the Upper Cretaceous sedimentation 25

36 (Tertiary). During the Pleistocene - Holocene the Cochabamba basin was filled with sandstones and gypsum (Tertiary) and with deposits of alluvial fans and fluvio-lacustrine material; pebbles, gravel, sand, silt and clay (Quaternary) Land cover Because no detailed land cover information could be collected during fieldwork, the land cover map of the study area is adapted from the European Commission s Institute for Environment and sustainability (IES) Global Land Cover 2000 dataset (Eva et al, 2003). This 1 km resolution global dataset is created using the SPOT Vegetation Instrument that is one of the first sensors that is specifically designed for global vegetation monitoring. It has a 2000 km swath enabling a daily acquisition of data and samples data in the visible, near and middle infrared. This dataset was selected because it has been highly appreciated by renowned organizations around the world, such as the UN (IES, 2004). The classification scheme for the legend is based on vegetation structural categories (Eiten, 1968) and is produced by the FAO and UNEP (Di Gregorio and Jansen, 2000). The classes are grouped as: Forest: Tree canopy cover is greater than 40% and height greater than 5 meters. Shrublands: Shrub canopy cover is greater than 20% and canopy height less than 5 meters. Grasslands: Herbaceous cover greater than 10%. Tree and shrub canopy cover less than 20%. Agricultural lands: Mosaic of pasture, cultivation and degraded natural vegetation. Barren surfaces: Areas with less than 10% vegetation cover, often volcanic or with high saline content. Water: Fresh open water Urban: Buildings, roads and other structures of anthrogenic origin Legend Rocha River Bare soil Kilometers 1:1,000,000 UTM, WGS 84, Zone 19 South Tributary Shrubland Grassland Forest Agriculture Open water Urban Figure 3.7 Land cover of the study area (Eva et al, 2003) 26

37 From the land cover map can be observed that the by far largest part of the study area is covered by natural grassland (Figure 3.7 and Table 3-3); this can be assigned to the mountainous character of the area (Figure 3.5) that prohibits large-scale agriculture and forestry. The deposits of alluvial fans and fluvio-lacustrine material (Figure 3.6) in the Cochabamba (North) and Punata-Cliza (North-East) basins made it suitable for agricultural practices and urban settlements due to its fertile properties and plain topography. A large area of the Punata-Cliza basin has bare soil due to high saline content due to poor irrigation practices (Metternicht, 1996). Shrublands can be found mainly in the small river valleys and the main forested area can be found North of Cochabamba on the mountain range and fans of the Tunari mountain, the Tunari national park. Landuse Area (Km2) % of total Agriculture Bare soil Forest Grassland Shrubland Urban Water Table 3-3 Land cover in area and % of the total area (Figure 3.7) Field measurements Before the routines were developed, all essential EC and discharge data was measured and samples for chemical analysis where taken during a field campaign. Due to possible discharge fluctuations at the observation points these measurements ideally should be done simultaneous, but logistically this was not possible. Therefore first a preliminary survey was done to identify the observation points. Depending on the amount of discharge, a measurement technique was selected; the velocity-area or dilution gauging method. When the velocity-area method was used, cross-sections were taken and marked at the selected measure sites. After this preliminary work, the actual measurements were done. From upstream to downstream all measurements were performed in a four day span; to ensure continuity, the last measurements of day 1, 2 and 3 where repeated first the next measure day before moving downstream. The amount of water samples that could be transported to the laboratory in the Netherlands to be analyzed was limited and therefore the decision was made to prioritize the tributaries. These were all sampled and the remaining samples were taken at the other discharge points Measure site identification To be able to use the EC routing routine, it is important that all water sources and sinks are accounted for. Therefore, the River Rocha was surveyed during fieldwork from its source East of Cochabamba downstream until the outlet of the study area was reached (Figure 3.4), in an attempt to identify these sources and sinks. 27

38 At the time of the survey limited data was available; a few topographic maps and satellite images both not covering the entire study area and field conditions proved to be difficult. Most junctions where difficult to reach and surveying the entire river system by foot was not possible because due to terrain conditions some areas were inaccessible. Therefore next to the available data the knowledge of local hydrologists was used to identify measure sites. The survey revealed that the main water source of the system is the base flow of the tributaries; a total of 7 tributaries yielded base flow. There are also 8 sewage point sources (mainly in the Cochabamba city area), one reservoir outlet that discharge at a regular time interval via the Khora tributary and there is one water diversion from another basin into the Rocha System via a pipeline. When a stretch along the stream could not be reached, measurements were taken at a location before and after this stretch. Changes in EC between these two measurements indicate that a water source or sink may be present in that area. Preferably the identified observation sites should be measured simultaneous; then the entire network can be processed. Due to the extent of the area, this was not possible and the network was measured in four days. Every last measurement of one day was repeated the next before moving downstream to ensure continuity EC measurements To be able to apply the routine, the EC is required downstream and upstream of every junction of both the stream and its tributary or point source. Special attention was required for the EC measurement downstream, because sufficient mixing length had to be taken into account. The mixing length is the length downstream the junction required for the full mixing of the water. If insufficient mixing length is kept, the EC value measured is not correct and consequently the discharge calculation will also be incorrect. There are a number of empirical equations to determine the mixing length; these however require estimations on discharge and the Chezy coefficient (see section 3.7.3). The mixing length was therefore determined in-situ; the EC was monitored moving downstream of a junction until a constant value was observed. This indicates a complete mixing of the streams and thus sufficient mixing length and the measurement was taken at that point. The EC was measured with a Hach Sension 156 portable multi-parameter meter. This meter has a temperature correction function that allows for a linear or non-linear correction. The non-linear coefficient has been pre-determined by the manufacturer from measurements using aqueous NaCl solutions. For most freshwater samples this is the best setting (Hach, 2000) and this setting was therefore used. The linear function corrects for a selected reference temperature with 2% per 1ºC. The meter was calibrated before use with a NaCl standard with a known electrolytic conductivity of 1000 µs/cm (at 25ºC), which is a standard that can be used if the expected measure range is 0-10,000 µs/cm. Substances that may affect EC measurements are gasses such as ammonia or carbon dioxide when measuring very low conductivity levels (<2 µs/cm) and high amounts of hydroxides such as in boiler water. These substances and conditions are not present in the study area and therefore corrections are not required. The EC`s measured at the junctions are listed in Table

39 Discharge measurements Required for the EC routine is one discharge at the beginning of the stream. To be able to validate the routine, ideally measured discharges at all junctions are required. However, due to time limitations, one discharge at 14 of the 16 junctions could be measured. The remaining junctions were not measured because they could not be accessed, or the discharge was so low that they were skipped to save time to measure the larger tributaries in order to obtain a better discharge distribution. At the selected junctions one discharge was measured, either up- or downstream the junction or of the stream input. The discharge that could be measured easiest (in terms of accessibility and method) was measured using the velocity-area or dilution gauging method. When the discharge of the stream input was measured, the EC routine was also used to determine the discharge of the stream. The velocity area method for the determination of discharge in open channels consists of measurements of stream velocity and wetted cross-sectional area that is determined by measuring depth of flow and distance between observation verticals across the channel (Herschy, 1995). The stream velocity was determined using either a float or current meter and the cross-sectional area with a total station (theodolite). In general, vertices are spaced on the basis of the following criteria: Equidistant: the distance between the vertices is equal; Equal flow: the vertices are spaced such that areas of equal flow velocity are separated; Bed profile: the vertices are spaced to make allowance for irregularities of the bed. According to Herschy (1995), the spacing should be arranged such that no segment contains more than 10% of the total flow; increasing the width of the segments would increase the uncertainty of the discharge measurement. The vertices were spaced according to the bed profile; when a change in slope occurred, a vertical was taken. The flow velocity was calculated using a propeller type current meter or a float, depending on the water level. Any level below 28 cm was to shallow to measure with the available current meter, thus then a float was used. When the current meter was used, the stream was divided into sections of equal velocity and measurements were taken mid-section of these areas. Taking measurements mid-section of the cross-section verticals would be to time consuming, and the differences within the velocities per stream measured are small. The velocity was measured at 0.2, 0.6 and 0.8 of the depth from the surface if there was sufficient depth; otherwise it was measured at 0.6 alone. Measurements were continued until 3 revelation recordings were done within 10 % of each other. These were then averaged and the velocity was calculated using the calibrated equation of the current meter (equation 3.4 or 3.5). or If n=<0.66 then v = * n (3.4) If 0.66=<n=<10.13 then v = *n (3.5) 29

40 where, n = amount of propeller revelations (revelation/second); v = velocity (m/s). When the water level was to shallow to use the current meter, the float method was used. The float method consists of observing the time required for a float to traverse the course of known length so that (Herschy, 1995): where, v = (L/t) * 0.9 (3.6) v is the float velocity (m/s); L is the distance traveled (m); t is the time of travel over distance L (s); 0.9 is a correction factor for vertical velocity distribution. Ideally 3 cross-sections are selected and averaged to determine the wetted area, one at the beginning, the centre and end of the reach L. If the site conditions were such that this was not possible, then cross-sections were taken at the beginning and end of the reach only. As a float local material was used, such as small branches and sticks. A total of 5 measurements were taken in the centre of the stream; the highest and lowest value was omitted and the remaining averaged to determine flow velocity. A factor of 0.9 was applied to correct for the vertical velocity distribution for a smooth stream bed. The discharge was then calculated using the following equation: Q = v * A (3.7) where, Q is discharge (m 3 /s); v is flow velocity (m/s); A is the wetted area (m 2 ). When the water depth was to shallow to use a current meter and the stream to wide to use a float, the dilution gauging method was used to determine the discharge. The principle of dilution gauging is the addition of a suitable selected tracer to the flow at an injection point. Downstream of this point the discharge may be calculated from the determination of the dilution of the tracer due to dispersion (Herschy, 1995). In general, two methods can be distinguished: the constant rate injection method and the integration (gulp or sudden injection) method. The integration method was used during fieldwork because it is easier to set up and perform than the injection method. The integration method is well described by Herschy (1995): A volume V of a solution of concentration C 1 of a suitably chosen tracer is injected over a short period into a cross-section located at the beginning of the measuring reach, in which the discharge Q remains constant 30

41 for the duration of the gauging. The injection is often performed by a simple steady emptying of a flask of tracer solution. At a second cross section downstream from this reach, beyond the mixing length, the concentration of added tracer, C 2, is determined over a period of time sufficiently long to ensure that all the tracer has passed through the second (sampling) cross-section. If all the tracer injected passes through the sampling cross-section, the discharge is calculated from the following equation. where, T0 M = VC1 = Q C2( t) dt (3.8) M is the mass of the tracer injected; V is the volume of injected solution; C 1 is the concentration of tracer in the injected solution; Q is the river discharge; C 2 (t) is the concentration of added tracer at the fixed sampling point over the time interval dt; t is the elapsed time, taking as the origin the instant at which the injection started; t 0 is the time interval of the first molecule of tracer at the sampling cross-section. Equation 5.8 requires that the value of the integral t0 C 2( t) dt (3.9) be the same at every point of the sampling cross-section. This condition is satisfied if the injected solution is well mixed with the flow in the river. In practice, the presence of the tracer is no longer detectable at any point in the sampling cross-section at a certain time t 0 + T. The value of T is known as the time of passage of the tracer cloud through the sampling cross-section. Let 1 T C2 = C2 ( t) dt (3.10) T t0 The practical condition of good mixing is that C 2 is identical at all points in the cross-section, hence from equations (3.8) and (3.10). V C T C 1 1 Q = (3.11) 2 A chemical tracer, common salt (NaCl), was used for the integration method. The amount of salt required per m 3 /s of stream discharge depends on the mixing length and background conductivity (natural conductivity measured when no salt solution is present), but as a rule of thumb 0.2 kg of salt per m 3 /s is considered sufficient (Herschy, 1995). The exact amount was determined in-situ; first one test injection was done and the amount of salt and the mixing 31

42 length was adjusted if required. A low peak on the time-concentration graph indicates that more salt was required; a steep rise of the graph would mean not sufficient mixing length was taken into account. Using these discharge measurement techniques, the Rocha system was surveyed entirely (Appendix 3) Water quality sampling and analysis To be able to calculate water quality fluxes (section 5.4), the concentration of selected water quality variables have to be determined. The quality of surface water is determined by interactions with soil, rocks, groundwater and the atmosphere and might be affected by human influences. The bulk of solutes in surface water are derived from soils and groundwater interaction. The primary variables for measuring water quality therefore focuses on a combination of chemical and physical variables that reflect the characteristics of the water studied (Chaweepan, 2003). In this research general variables and trace elements are determined as a measure for water quality, because they can be relatively easily measured in the field and analyzed in the laboratory (as in contrary to e.g. pathogens). The general variables measured in the field are the EC (section 3.7.2), ph and the temperature. The trace elements where analyzed at ITC and therefore water samples were taken; due to limited resources (limited sample amount), sample sites where selected using a systematic sampling approach. One sample was taken at the beginning of the Rocha River to be able to determine the background concentration. Then samples were taken up- and downstream the junctions with the tributaries and of the tributaries to determine the main inflow fluxes; the remaining samples were taken at a number of the discharge points (Figure 3.8) that are low in discharge but high in EC. The samples were taken as discrete grab samples; a bucket was used to collect the sample at the centre of the stream. Then two plastic vessels of 250 ml were filled with the sample, from which one was acidified until ph 2.0 was reached using nitric acid (HNO 3 ) to preserve the sample. Before the samples were analyzed in the laboratory, they were first filtered using 0.45 µm pore diameter filters because larger particles are considered suspended matter that in principle can be filtered out of the water before it is used. An ICP-AES (Inductively Coupled Plasma Atomic Emission Spectrometry) was used to analyze the cations because its sensitivity, freedom from interferences and multi-element detection capabilities on a single sample. The sample is carried into the plasma in an acidified aqueous solution by argon gas flow. On reaching the plasma, the sample is vaporized and ionized at temperatures ranging from 6000 K K and the subsequent spectral emissions are measured (Warwick, 2005). The ICP-AES was used to measure Al, Ca, Na, Fe, K, Mg, Mn, Li and Si. The anions could not be analyzed by the ICP-AES and were therefore analyzed using a spectrophotometer in the ITC laboratory. The spectrophotometer is an instrument used to measure the intensity of wavelengths in a spectrum of light compared with the intensity of 32

43 light from a standard source (spectrophotometer, 2005) and was used to measure Cl, SO 4, NO 3 and HCO 3. The accuracy of the analysis can be checked using the anion-cation balance. Because the solution must be electrically neutral, the sum of the cations in meq/l must be equal to the sum of the anions in meq/l (Hounslow, 1995). S 31 F S 32 F S 30 F S 29 F S 33 F S 34 F S 35 F S 38 F S 37 F S 36 F Rocha dam S 9 F S 11 F S 23 F S 20 F S 18 F S 7 F S 24 FS 21 F S 15 F S 17 F S 16 F Viloma Junction S 40 F S 39 F S 42 F S 41 F Oxydation plant S 6 F S 5 F S 4 F S 3 F S 2 F Pipeline Legend Kilometers UTM, WGS 84, Zone 19 South Drainage network Sample points Figure 3.8 Rocha drainage network with sample sites and special locations The charge balance is expressed as a percentage: cations anions % Difference = (3.12) cations + anions If the balance calculated using equation 3.12 is below 5%, the analysis is assumed to be good. If the balance is much higher than according to Hounslow (1995) this can be explained by: Poor analysis; Other constituents are present that are not used to calculate the balance; The water is very acid; Organic ions are present in significant quantities. 33

44 When analyzing the charge balance (Figure 3.9), only 38% of the samples are within 5% difference and thus considered reliable. However, 64% of the samples are within 10%; the reason for this can be the long time between sampling and analyzing that might have its effect on the sample composition. Because not all samples could be filtered in the field due to lack of time, acid extraction might have occurred. Some of the samples have a difference of more than 10%; when checking the locations of these samples it can be observed that they are at or close to sewage discharge points. Therefore there might be a large amount of organic ions present that could cause this large difference. Normally the samples with a difference larger than 5% would be omitted. However, because this research studies the use of a HIS for preliminary water quality surveys, here all samples are used because in a real survey they would be used as an aid in the development of a sampling scheme for further studies. anion-cation balance Difference (%) Sample ID Figure 3.9 Anion- cation balance of the water samples indicating the accuracy of sample analysis. The measured general variables and analysis results are stored in a separate feature class, the XYTraceElement class, to keep the dataset orderly. The results of the water analysis can be found in Appendix Field observations During fieldwork a number of observations were made that could influence the results of this research. A number of these observations are related to the nature of the fieldwork area. The dry season starts in general in April (Figure 3.3) and the routing measurements were done at this time. There might therefore still be surface runoff draining in the system causing unfavourable conditions (unsteady instead of the required steady state conditions). Due to the nature of the climate (section 3.2) and human influences (collection of the Tamborada discharge in the Angostura reservoir (Figure 3.4)) the discharge was low (<1 m 3 /s (Appendix 3)) until the junction with the Viloma River. This makes the routing methodology sensitive to even small (< 1m 3 /s) missed inputs and/or outputs. The mountainous nature of the terrain 34

45 (Figure 3.5) and lack of infrastructure make it plausible that indeed inputs and outputs have been missed, because the Rocha River could not be surveyed entirely. There are more human influences that are influential. The Rocha River passes Cochabamba City (Figure 3.7) were there are a number of raw sewage inputs and there is an oxidation plant draining in the Rocha River (Figure 3.8). These might cause irregular EC measurements due to a decline in oxygen concentration and the release of ammonia and nitrite (Chapman, 1992), interrupting the routing. A dam diverting the Rocha River to a temporarily reservoir (Figure 3.8) was made a few days before the measurements, altering the intended measure scheme such that the routing had to be stopped at the dam and restarted at the junction with the Chujjla River. 35

46 4. Hydrologic Information System 4.1. Introduction Now the study area is selected, the Hydrologic Information System can be developed using the selected data model Arc hydro. The ArcGIS Hydro data model Arc Hydro (Maidment, 2002) is a geospatial and temporal data model for surface water resources that operates within ArcGIS and supports hydrologic simulation models. Arc Hydro defines a set of water resources feature classes (classes with Point, multipoint, polyline, polygon, annotation or network features) in ArcGIS (such as watersheds, monitoring points) and the relations between these classes and stores them in a geodatabase. A geodatabase is a special form of a relational database that stores geospatial coordinate data of a GIS layer in one field in a relational data table (Clark et al, 2001). The complete model consists of five categories to divide water resources elements: network, drainage, channel, hydrography and time series. This model is used for this research because it can automatically transfer a geodatabase into a Hydrologic Information System and therefore has great potential to be used for water resources studies. The model uses an ArcGIS geometric network to trace the flow of water through a stream network and can relate drainage, stream confluences, water discharge and monitoring points required in this research using ArcGIS relationship classes. A geometric network is a connectivity relationship between a collection of feature classes in a feature dataset and consist of two fundamental components: edges and junctions. An edge is a type of network element that has a length and through which commodities flow: in this case stream reaches. A junction occurs at the beginning, end and intersection of two or more edges or along an edge and allows the transfer of flow between them: in this case the confluence of stream reaches, water discharge points and monitoring points. Edges are always polylines and junctions are always points and in a network they are topologically connected to each other (ESRI, 2004). The data model also generates a number of feature classes not required for this research that can be omitted and leaves room for the creation and connection of user defined feature classes. An ArcHydro toolset was developed by Maidment (2002) to help create and manage Arc Hydro data. These tools are used to derive several data sets that collectively describe the drainage patterns of a catchment and that are used to create the geometric network. To be able to apply the hydro data model, first a number of input feature classes have to be created. First a geodatabase is created and a digital elevation model of the study area is selected and added to the database. Then a raster analysis is performed on the DEM using Arc Hydro tools to generate data on flow direction, flow accumulation, stream definition, stream segmentation, and watershed delineation. This data is then used to develop a vector 36

47 representation of the catchment, drainage lines and drainage confluences, start and ending points (junctions), the basis of the geometric network. Because the EC routing routine will be performed on the Rocha River only, only the Rocha confluences in the study area will be maintained for this study, all other drainage will be ignored. Because there are a number of other water sources, such as sewage inputs, next a feature class is created containing the water discharge points. Additional parameters have been measured up- and downstream of every confluence of the Rocha River in the study area (Chapter 5), such as EC and constituent concentrations, and these measurements are stored in a feature class containing these monitoring points. A geometric network is then created and the Arc Hydro model applied resulting in the Rocha River Hydrologic Information System. The ArcHydro procedure descriptions in this chapter are mainly based on the work of Maidment (2002) Terrain processing DEM selection Elevation information for the study area is available from a number of sources. Digital contour maps are available with an interval of 20 meters (IGM, 1985) and 1 meter (Cochabamba municipality, 1990), but these do not cover the entire study area. SPOT and ASTER satellite images are available to extract a DEM using photogrammetric analysis, but the available scenes do not cover the entire study area either. The only sources of data that covers the study area are the global elevation data sets, such as the Space Shuttle Radar Topography (SRTM), GTOPO30 and GLOBE datasets. From these datasets the SRTM dataset has the highest spatial resolution and is therefore preferred to be used in this research because it can more accurately reproduce drainage features as explained in Chapter 2. The raw SRTM dataset does however have a number of uncertainties that need to be evaluated and, if possible, corrected before it can be used. According to Tulu (2005), these uncertainties can be divided into three groups. The first one characterizes the InSAR parameters during data acquisition, the second deals with post-processing steps and the last one contains the influences of land cover. A number of these uncertainties can be assessed by visual inspection, such as data voids due to radar shadows and water bodies. These can be corrected by replacing the voids with data from another set, or by interpolating the edge values. This may however include new uncertainties, and therefore the result is as good as the data used. Uncertainties that are not apparent such as processing blunders and land cover influences can be evaluated by cross comparing the SRTM data with ground data, but there will always be uncertainties due to the mission accuracies. The absolute accuracy is according to the mission specification <=45 meter horizontally and <=16 meter vertically. The vertical quality of the SRTM data of the study area is assessed by cross comparing the dataset without voids with ground control points sampled in the field with a handheld GPS. The GPS used is a Garmin GPS etrex Summit that has a barometric altimeter accuracy of 10 feet (+/- 3 meter) and a horizontal accuracy of 15 meter. The GPS was calibrated on a benchmark before measurement. Figure 4.1 shows the correlation of the ground control 37

48 points with the SRTM and the DTM`s derived from 20-meter contour lines and 1-meter contour lines for comparison. Correlation GCP - SRTM elevation Correlation GCP-1-mtr contour DTM SRTM elevation (m) GCP elevation (m) y = 0,9699x + 74,158 R 2 = 0,9808 Contour DTM elevation (m) GCP elevation (m) y = 0,9706x + 50,571 R 2 = 0,8413 Correlation GCP-20-mtr contour DTM Contour DTM elevation (m) GCP elevation (m) y = 0,955x + 91,439 R 2 = 0, mtr contour 1-mtr contour SRTM Average (m) Min (m) 0,4 2,8 0,1 Max (m) 47,6 42,6 77 STD Figure 4.1 Correlation of ground control points with the SRTM DEM, the interpolated 20-meter contour line DTM and the interpolated 1-meter contour line DTM The SRTM data has the best correlation of 98% and a slope close to 1, with an average error and standard deviation of 10 meters, this despite a few outliers that could not be omitted, and therefore is within the missions accuracy specification of a vertical accuracy smaller than 16 meters. The DTM`s derived from the contour maps are less accurate with an average error of 26 meters and a standard deviation of respectively 10 and 13. In conclusion it can be said that from the available datasets the SRTM has the highest accuracy and covers the entire study area: it is therefore selected for this research. The relative vertical and horizontal accuracy of the SRTM DEM will be checked after the extraction of the drainage by comparison with drainage digitized from a georeferenced satellite image, because the SRTM DEM is not a surface model and land cover (vegetation, urban areas) might introduce errors Hydrologic Digital Elevation Model creation The purpose of the creation of a hydrologic correct DEM is to perform an initial analysis of the terrain to identify and correct errors that may propagate to later stages of the analysis or 38

49 may even block further processing. These errors such as flow loops and sinks can occur naturally or are a result of the terrain averaging into a raster cell and have to be removed. In ArcHydro a pit filling method is applied, optionally preceded by the AGREE method. The pit filling procedure changes the relative elevation of the DEM for selected areas, and in ArcGIS the elevation of all the cells in a pit are raised to the minimum elevation of the surrounding cells so that water can flow across the terrain surface, the commonly called flooding approach (Olivera, 2002;Colombo et al, 2001). AGREE is a surface reconditioning system that adjusts the surface elevation of the DEM to be consistent with a vector stream or ridge line coverage (Hellweger, 1997). A quick overview of the procedure is described by Hellweger (1997): 1. Drop/raise the elevation of the cells corresponding to the vector lines a certain amount. 2. Buffer the vector lines. 3. Assign elevation to the cells inside the buffer so that there is a straight line path from the vector line to the original elevation just outside the buffer. 4. Drop/raise the elevation of the cells corresponding to the vector lines a certain amount. Figure 4.2 AGREE method example When using the AGREE method, the original DEM is lowered at the position of the drainage and within a user determined buffer with a user determined elevation. The actual terrain differences within this channel do however not change; the procedure creates a channel, and should therefore only be used in parts where difficulties are expected, such as flat or urban 39

50 areas. As an example, consider Figure 4.2. First the pixel at the location of the stream is lowered with a user defined value (in this example 5 feet). Then the surface is gradually heightened from the stream pixel to the end of the user selected buffer (in this example 5 pixels, including the stream pixel) and connects to the original surface again. The smooth value lowers the terrain between the stream pixel and the end of the buffer, the sharp value lowers the stream pixel. Because the AGREE method is followed by the pit filling method, using large lowering values will not affect the DEM much, because it will be raised again during the filling method. Therefore, the values used for the AGREE parameters depend on the nature of the DEM and the issues that are being resolved; a trial and error approach is needed before satisfactory results are obtained (Maidment, 2002). After the pit filling method the flow direction and flow accumulation is calculated using the now hydrologic corrected DEM. The flow direction is calculated using the D8 (8 flow directions) method introduced by O`Callaghan and Mark (1984) and the flow accumulation grid contains the accumulated number of cells upstream of a cell, for each cell in the input grid Cochabamba City Kilometers Figure 4.3 Terra - Aster False Color Composite (Band 3n, 2 and 1 as RGB) satellite image of 19 November 2000 used to digitize the main stream and its tributaries. The accuracy of the resulting accumulation was tested by comparing the output grid with drainage lines digitized from a georeferenced Terra ASTER image (Figure 4.3). Only the river Rocha and its tributaries that yielded water during fieldwork are digitized until the end 40

51 of the study area within the extent of the satellite image. The other streams are omitted to recreate the flow system at time of fieldwork. The difference in shape and the distance from the original position is used to determine which input DEM yields the best output flow accumulation grid. To evaluate whether the use of the AGREE method has a significant influence on this flow accumulation grid the SRTM DEM was used in different forms. The SRTM is not a digital surface model (model representing the actual surface), but an elevation model where vegetation, water surface and human interventions (such as urban areas) influence the elevation. Therefore flow accumulation errors are expected in the Cochabamba city area and the forested areas of the study area due to the poor representation of the terrain in the original SRTM. This is caused by the buildings and trees that raise the terrain above the actual surface. First the flow accumulation grid was calculated using the SRTM DEM without using the agree method first (Figure 4.4a). A visual interpretation shows that the flow accumulation lines do not exactly follow the digitized lines (Figure 4.4c), but that the horizontal error remains within 500 meter. The exceptions are at the junction with the Tapacari (Figure 4.4c) and the urban area (Figure 4.4b), where there are major differences (> 500 meters) most likely caused by vertical differences between the DEM and the actual surface. When using the AGREE method for the entire network, the main flow accumulation is almost equal to the digitized one. The maximum difference is 250 meter, but in general the error remains within one pixel (less then 90 meters difference). The AGREE method created a sharp and smooth drop of 5 meter using a buffer of 5 cells. When we use the AGREE method only for the urban area (Figure 4.5a), the error does not exceed 90 meter in the AGREE area (Figure 4.5b), and remains equal to the error as observed without the AGREE method (Figure 4.5c). I therefore conclude that with the morphology of this particular study area the SRTM can be used to create the flow accumulation grid when the AGREE method is used for the alluvial section that contains the urban area of Cochabamba city. Even though using the AGREE method might introduce errors caused by visual interpretation mistakes while digitizing, the apparent average error of 500 meter (<6 pixels) I find acceptable for this study, due to lack of ground truth and higher resolution data. In general it can be observed that this crucial step can not be generalized and has to be evaluated for every area where it is applied. 41

52 a. Original SRTM DEM flow accumulation b. Detailed view urban area Kilometers UTM, WGS 84, Zone 19 South Legend Digitized drainage Flow accumulation Value High : Low : Kilometers c. Detailed view non-urban area Kilometers Figure 4.4 Original SRTM DEM flow accumulation a. Original and AGREE in city area SRTM DEM flow accumulation Kilometers UTM, WGS 84, Zone 19 South Legend Digitized drainage Flow accumulation Value High : Low : b. Detailed view urban area with AGREE. End urban area and AGREE section Kilometers c. Detailed view non-urban area without AGREE Kilometers Figure 4.5 Flow accumulation generated using a combination of the original and AGREE DEM. The AGREE method has been applied to the City area (b) only. The other areas therefore show no difference to the accumulation using the original SRTM DEM (c). 42

53 Stream definition Next the channel network is defined. ArcHydro uses the critical source area, where the user defines a minimum drainage area below which a permanent channel is defined (Mark, 1984). The effect of different contributing area thresholds (As) on channel and drainage density is illustrated by Colombo (2000) and shows that the density decreases when the threshold increases (Figure 4.6). Figure 4.6 Effect of area threshold on drainage density (Colombo, 2000) A number of scientists (Tarboton, 1989; Willgoose, 1991) have discussed and reported a power law relationship between the mean channel slope (S), and the contributing area (A) with a constant (c) and scaling exponent (). S=cA - (4.1) Unfortunately, this relationship could not be used to determine the proper threshold area due to large differences in the main slope (from 0.5% to 513%, average 11%) that result in different hydrologic behaviour and the fact that only a single threshold can be used. Therefore, the most acceptable threshold to be used for this research is defined as the largest threshold that still defines the smallest digitized channel, derived by trail and error, starting with the default threshold of 1% of the total drainage area that can be used as either area size or number of pixels. The best result was obtained using an area threshold of 62 Km2, about 0.5% of the total study area (Figure 4.7a). When the 1% threshold was used, not all drainage in the urban area was represented (Figure 4.7c), but with the 0.5% threshold it was (Figure 4.7b). This threshold is therefore used for this research. The next step is the stream segmentation function where a grid of stream segments is created that have a unique identification value. A segment is defined either as a head segment, or it may be defined as a segment between two segment junctions (Maidment, 2002). This is required for the next step when the sub-catchments are calculated. 43

54 a. Channel extraction A=62Km2 b. Detailed view urban area when A= 62 Km Kilometers c. Detailed view urban area when A = 80 Km2 Kilometers Legend UTM, WGS 84, Zone 19 South Channel A=62Km2 Digitized drainage Kilometers Figure 4.7 Extracted channels using different area thresholds Catchment and watershed processing Catchment processing determines the outline of the catchments within the SRTM data area using the stream definition files and the (sub-) watershed(s) of the area of interest, using user defined (sub-) watershed outlet point(s). First a grid is created in which each cell carries a value (grid code) indicating to which catchment the cell belongs (Figure 4.8a). These values are carried by the stream grid that drains that area, defined during the stream definition process. This catchment grid is then converted into a catchment polygon feature class by combining the adjacent cells in the grid that have the same grid code and by vectorizing the aggregated area (Figure 4.8b). Single cell polygons and the "orphan" polygons generated as the artifacts of this process are dissolved automatically, so that at the end of the process there is just one polygon per catchment. The stream definition is now vectorized and each line in the feature class carries the identifier of the catchment in which it resides (Figure 4.8c). These codes are now used to aggregate the catchments into the head catchments (Figure 4.8d). Finally the drainage points are determined; this is a point file of drainage points associated to the catchments (Figure 4.8d). Now the catchments are determined, the watershed(s) of interested, in this case the Rocha watershed can be defined by defining the outlet. Because the study area is smaller then the entire catchment the outlet is defined at the end of the study area. To not affect the sub watershed parameters, such as size, the study area outlet is defined at the outlet of the last sub watershed in the study area (Figure 4.9a). 44

55 a. Catchment grid. b. Catchment polygons. c. Stream definition segments. d. Head catchments with channels and points. Figure 4.8 Catchment processing. To limit the drainage network to the study area, the extracted drainage segments are masked with the watershed boundary using the Clip function in ArcGIS (Figure 4.9b). Now all watershed parameters are extracted from the SRTM that represent the main network. a. Catchment and watershed b. Watershed with drainage lines and points Figure 4.9 Watershed generation 4.3. Hydrologic Information System creation As stated earlier, the Arc Hydro data model is applied to a geodatabase containing a number of hydrologic feature classes and a geometric network to create the HIS required for this study. Arc Hydro is composed of five major components, with four components representing geospatial Feature Datasets and one that contains time series. These components are the Hydrography, Network, Drainage, Channel and Time Series packages (Figure 4.10). 45

56 Hydrography Drainage Channel Network Time series Figure 4.10 Arc Hydro data model components and their relation (From Arc Hydro GIS for water Resources. Maidment, 2002). Combined these components are the Arc Hydro geodatabase structure, where the different feature data sets are related using Relationship classes between the feature classes (Appendix 2). Not all feature data sets and feature classes that are part of the data model are relevant for this research. The following describes the data sets and the feature classes used for this research in more detail. The Hydrography feature data set stores 12 different map-related features that can be represented differently cartographically. This research makes use of the following two features: WaterDischarge: A point along the drainage network that discharges into the network. MonitoringPoint: A point where water quantity and quality is measured. A relationship ties the MonitoringPoint features to the HydroJunctions in the network. The other features are not used, even tough they are present in the study area (such as Bridge and Waterbody), because they are not relevant for the EC routing. The Network feature dataset contains the HydroNetwork, and is therefore the most important dataset in this research. The HydroNetwork is a used for tracing the flow through the network and connects monitoring points to junctions in the network. The HydroNetwork resembles the cartographic representation of the stream network, but small differences might occur where the HydroNetwork has been adjusted to insure correct network topology (Whiteaker, 2004). The network feature dataset stores 7 features from which 3 are used in this research: HydroEdge: Linear network features, in this case the drainage. HydroJunction: Point network features, in this case stream confluences, monitoring and discharge points. HydroNetwork_Junctions: A generic junction created at the ends of edges, except where other junctions already exist. 46

57 The other features represent a schematic layout of the HydroNetwork or contain event attributes that describe a linear or point event, not used in this research. The drainage feature dataset describes the drainage features typically derived from a DEM. The dataset contains 5 classes, from which 4 are used: Watershed: The drainage area defined for a specific study area. Catchment: A drainage area defined by applying a set of rules to the landscape. DrainagePoint: A point located at the outlet of a (sub)basin. DrainageLine: The drainage generated using extraction procedures on a DEM. The Basin feature defines a drainage area defined for administrative purposes and is therefore not used. The Channel feature dataset contains 3 classes, the CrossSection, CrossSectionPoint and ProfileLine class that are used to define a section of a river that can be used for modelling. The cross sections in this research are used to calculate discharge and are not used in other models; this feature dataset is therefore not used. The last feature dataset is the Time Series package. The time series are used to store time series of data in a table connected to a monitoring point. This is an interesting package that could display time series of discharge and water quality. However, since I do not use temporal datasets, this package is also not used. The geodatabase created so far contains the drainage lines as hydro edges and the beginning, ending and connecting nodes as hydro junctions. However, not all drainage yields base flow and there can also be other discharge sources, such as city sewage. To be able to use the EC routing routine for the entire study area, all discharge points have to be accounted for. During fieldwork, an attempt was made to map and measure all discharge points, by surveying the Rocha River upstream to downstream, from the beginning of the study area until the end. This survey is described further in the next chapter, but yielded a number of discharge points originating from drainage, a water diversion pipeline, industrial and city sewage systems and a sewage plant that need to be incorporated into the geometric network. Other measurements performed during fieldwork that need to be incorporated into the hydro network are required for the EC routing routine and for the calculation of the constituent loads (Chapter 5). EC measurements where taken up- and downstream of confluences and discharge points in combination with one discharge measurement, either up- or downstream. Additional EC measurements where taken along the Rocha river for verification purposes. Water samples were taken along the Rocha River and from its tributaries. The discharge and measure and sample sites are assigned to different feature classes because they serve different purposes in this research; in this way the points can be separately modelled en cartographically represented. The discharge points contribute to the total discharge of the network and are stored in the WaterDischarge simple feature class. The 47

58 sample and measure points are stored in the MonitoringPoint simple feature class. The Arc Hydro data model allows for this incorporation of the discharge, measure and sample points into the hydro network, by defining these points in a separate feature class: MonitoringPoint and WaterDischarge in the Hydrography feature dataset. These classes can be relationally connected to junctions in the hydro network for analyses or represent cartographic point locations. This connection is established with the creation of a junction at the location of these points. Any object in the Arc Hydro framework obtains an identifier in the geodatabase: the HydroID. By linking the Discharge and Monitoring point with the Junction HydroID` s by using a Relationship class, data can be transferred between the attribute tables. Before the Arc Hydro data model can be applied, first all data has to be prepared. The drainage lines and drainage points that are used to create the HydroEdges and Junctions are edited. Because this study is performed on the Rocha River only, all drainage that did not yield base flow or that is not a 2 nd order tributary is removed from the network. This is done to simplify the EC routing routine incorporation (Figure 4.11a) and resulted in the final HydroEdge feature class. Now the discharge, measure and sample sites are added; the point measurements from the field are converted to the WaterDischarge and MonitoringPoint feature classes and the measurement values added as an attribute. The measure locations do however not coincide with the derived drainage. An advantage of working with different feature classes and relationship classes is that we can leave the measure points at their original position. To link them to the network we create new junctions in the HydroJunction feature class. These are positioned up and downstream of the confluences in the case of the monitoring points (Figure 4.9c), and in the case of the discharge points a junction is created at the discharge point and an edge connected to it with a monitoring point (Figure 4.11b). In this way the discharge points and stream confluences are connected uniformly to simplify the EC routing incorporation. When the HydroEdges and HydroJunctions are prepared, the HydroNetwork is created by applying the ArcGIS Geometric Network operation: now all feature classes are prepared. The Arc Hydro data model can now be applied using the Case Schema Operation. The case schema operation converts a geodatabase into a more specific database using a blue print, in this case the Arc Hydro data model. The operation first checks if all feature classes, relations and tables in the model exists in the geodatabase and if this is not the case, these are created and added to the feature datasets (Figure 4.12a). Then the attribute fields of the existing feature classes are updated; if non-existent they are created, if they already exists they are assigned by the user to a field from the model (Figure 4.12b). For example, if the features do not have a HydroID, this field is assigned and populated by the Arc Hydro model; if the feature class already as a Name field with for example the names of the streams, the user can assign this field to the Name field of the feature class as defined by the data model. Now the data model is applied, the attribute fields can be filled using the ArcHydro toolset. 48

59 a b Kilometers UTM, WGS 84, Zone 19 South Legend HydroEdge WaterDischarge MonitoringPoint HydroJunction c Meters ,000 Meters Figure 4.11 Prepared HydroEdge and HydroJunction feature classes. First the flow direction field of the Edges is set by entering predefined values into the flow direction field; then the flow direction can be visualized and possible errors identified using the functionality of the ArcGIS Network Analyst (Figure 4.14). Then the relations between the channels and points are updated using the Arc Hydro function Generate From/To Node for Lines. This function fills the FROM_NODE and TO_NODE fields for each line feature in the "Line" feature class. The functions Find Next Downstream Line and Find Next Downstream Junction are the applied to find the next downstream element in a linear feature class, and assigns the HydroID of this downstream feature to the NextDownID field in the feature class. This allows features to "communicate" with each other without the presence of a network, passing and summing values or other information as desired; the functionality required for the incorporation of the routing. Next the Relationship Classes are made active by linking the HydroJunctions to the Discharge and MonitoringPoint feature classes by assigning the HydroID of the Junctions to the JunctionID field of the corresponding point features. Then a number of fields are populated to clarify the various features, such as the Name field (used to enter sample ID`s, GPS points etc.) and FType field (used to indicate point type such as sample, discharge, EC monitoring point or stream confluence). 49

60 a b Figure 4.12 Application of the Arc Hydro data model. Finally a number of feature data sets are added that contain additional information used in this research that are not part of the Hydro data model. The Soil set contains the soil and geomorphology classes and the WaterQuality set contains the original sample locations. Now the base Hydrologic Information System for the Rocha River is ready (Figure 4.13 andfigure 4.14) and the EC routing can be incorporated. Figure 4.13 Layout of the Cochabamba River Hydrologic Information System. 50

61 a b Kilometers UTM, WGS 84, Zone 19 South Legend WaterDischarge MonitoringPoint HydroNetwork_Junctions HydroEdge HydroJunction c Meters Meters Figure 4.14 The Cochabamba River Hydrologic Information System showing flow directions. 51

62 5. Water quality HIS 5.1. Introduction The water quality HIS is created by incorporation of the electric conductivity (EC) routing methodology and flux calculation in the developed base HIS of the Rocha River system. The EC routing is adapted from Appelo (1993) and is used to calculate the discharge of a stream and its inputs (its tributaries and point sources) using the EC differences between them. The method can only be applied in steady state (base flow) conditions, because other water sources such as overland flow and precipitation are ignored in this method. For the EC routing, we consider a mixing model, linked in series (Appelo, 1993): And Where, Q i+1 * A i+1 = Q i * A i + q i,i+1 * a i,i+1 (5.1) Q i+1 =Q i + q i,i+1 (5.2) Q is discharge in the stream (volume/time); A is water quality in the stream (mass/volume); q is discharge of the inputs (volume/time); a is water quality of the inputs (mass/volume). When the EC routing is in place, it can be used to calculate the discharge of the stream and inputs within the entire network, using a single discharge measurement of either the stream or input at the most upstream junction. This is achieved by passing down the calculated discharge of the stream from this junction to the next downstream junction where it is used to calculate the discharge of the input and the stream downstream the junction and so on until the outlet of the study area is reached. Once all discharges are calculated, the flux of selected measured variables can be calculated. Mathematically, the flux Φ (load) is calculated using (Chapman, 1992): where, Φ = t 2 t1 C( t) Q( t) δ t (5.3) Φ is flux (mass/time) Q is water discharge (volume/time); C is concentration (quantity per volume); t is time. 52

63 This equation can only be applied when the discharge and the constituent concentration are known during a certain period of time at the point of interest. The EC routing and flux calculation routines can be embedded into the HIS using the Visual Basic programming language, but since this research focuses on the creation and testing of a prototype only, the routines will be applied using Excel instead. Therefore, the junction data will be exported from ArcGIS to a geodatabase format and then imported in Excel. Here the routing and flux calculations are applied and the results are exported to a geodatabase format again. The results are then imported in the HIS and visualized routing routine EC routing methodology To incorporate the EC routing routine in the HIS, the mixing model (section 5.1, equations 5.1 and 5.2) has to be included in the ArcHydro data model. Drainage networks are represented in the data model by the HydroNetwork (chapter 4.3), which connects streams and stream junctions through a network of edges and junctions. Movement of water (or contaminants in the water) can be simulated using ArcHydro Tools by passing attribute values of e.g. discharge, down a network using the NextDownID field of the feature class. This simulation makes it possible to include the mixing model at the junctions. The EC of the stream and inputs is known at the junctions, thus if one discharge is known, the others can be calculated. The calculated discharge downstream of a junction can be passed down the network to the next junction to be used as input in the mixing model, and so on. The hydrologic information to be processed are the input parameters of the mixing model: the discharge (Q) and the EC as the water quality parameter. As stated earlier, the EC up-, downstream and of the input is required at all junctions and the discharge has to be passed down the network using the feature classes of the HydroNetwork, the junctions and edges. There are four types of values used in the HydroNetwork that can be used to calculate and pass down values: received values, incremental values, total values and passed values (Whiteaker, 2003). Received values are those received from adjacent upstream features. Junctions can only receive values from adjacent upstream edges and an edge only from its upstream junction. As an example, consider the network in Figure 5.2. When routing, Edge 1 and 2 flow into Junction 3 and edge 3 and 4 into Junction 5. The received values of Junction 5 consists therefore of values from Edges 1 and 2 and the received values of Junction 5 from Edges 3 and 4. Incremental values are at a features location and can be positive (addition) or negative (withdrawal) values. Total values are obtained by combining the received and incremental values using one of the operations available in Hydro Tools: sum, average, minimum, maximum, count, median, mode, standard deviation or weighted average. Passed values are passed from a feature to the next downstream feature. Using these value types, the routing routine can be simulated even though actual incorporation in the HIS would require additional programming work. 53

64 calculated downstream discharge pass values down Received upstream discharge Junction EC EC routing Received EC calculated downstream discharge End procedure No Changes? Yes Figure 5.1 EC routing methodology flow chart. First the attribute table is adjusted; one attribute field is added as an incremental field to the HydroJunction feature Class: EC. Then one received field is added: received upstream discharge, one total field: calculated downstream discharge and another incremental field: input discharge. The EC field is filled with the EC field measurements, where HydroJunctions with two edges store the EC measured downstream that junction. The received upstream discharge and input discharge fields are empty, but will be filled using the routing routine. The calculated downstream discharge field contains one measured value as a start point: the discharge upstream the first HydroJunction with two edges. Now the routing routine is incorporated: first the calculated downstream discharge value is passed down the network to fill the received upstream discharge field in the next features field until it s passed down the entire network. Then the incremental EC field is passed down the network and processed at the HydroJunctions with two edges. However, instead of using one of the available operations (such as SUM), the routing equations are used. The adjusted equation 5.4 use the received incremental EC values, the junction s incremental EC value and the received upstream discharge field to calculate the discharge downstream that is then stored in the calculated downstream discharge field: Q down ( ECup ECinput ) = Qup * (5.4) ( EC EC ) down input 54

65 Where, Q down = Discharge downstream of the junction (m 3 /s); Q up = Discharge upstream of the junction (m 3 /s); EC down = EC downstream of the junction (µs/cm); EC up = EC upstream of the junction (µs/cm); EC input = EC of the stream input (µs/cm). Then discharge of the stream input is calculated using adjusted equation 5.5 and stored in the input discharge field. Q input = Q Q (5.5) down up Where, Q down = Discharge downstream of the junction (m 3 /s); Q up = Discharge upstream of the junction (m 3 /s); Q input = Discharge of the stream input (m 3 /s). The process now repeats itself: first the total field calculated downstream discharge is passed down to fill the received upstream discharge field in the next features field until it s passed down the entire network. Then the adjusted equations are used to calculate the discharge downstream and the input discharge. This is repeated until no more changes occur indicating the entire network has been processed. Figure 5.1 displays a flow chart of this procedure. Junction 1 Edge 1 Junction 2 Junction 3 Edge 2 Junction 4 Edge 3 Edge 4 Junction 5 Edge 5 Junction 6 Figure 5.2 Example of the EC routing. As an example, consider Figure 5.2: first the calculated downstream discharge field (containing the measured discharge of Junction 1) is passed down the network until it fills the received upstream discharge field from Junctions 3, 5 and 6. Then the EC field is passed 55

66 down the network. The discharge downstream Junction 3 is calculated using the EC of Junctions 1, 2 and 3 and the received upstream discharge field of Junction 3. The resulting downstream discharge is stored in the calculated downstream discharge field of Junction 3 and the calculated input in the input discharge field of Junction 2. The process now repeats itself: the calculated downstream discharge field is passed down the network to fill the received upstream discharge fields. This changes the fields of Junctions 5 and 6 into the calculated downstream discharge field of Junction 3. The received upstream discharge field of Junction 3 does not change. Then the EC field is passed down the network and the discharge downstream Junction 3 is recalculated using the EC of Junctions 1, 2 and 3 and the received upstream discharge field of Junction 3: the result is stored in the calculated downstream discharge field of Junction 3, but is equal to the value first calculated. The discharge downstream Junction 5 is calculated using the EC of Junctions 3, 4 and 5 and the received upstream discharge field of Junction 5: the result is stored in the calculated downstream discharge field of Junction 5 and in the input discharge field of Junction 4. Now the calculated downstream discharge field is passed down the network again, and the field of Junction 6 is filled with the value of Junction 5. Recalculation of the downstream discharge fields would not result in any changes, thus the procedure is completed and stopped. The routine has now calculated the discharges at the junctions of the main stream. This methodology is applied to the geodatabase using ArcGIS and the HydroTools for data preparation; the routine is then simulated using Excel. The output is stored in the database again and imported in ArcGIS. The procedure is then validated by comparing field discharge measurements with the calculated ones downstream the junctions Routine implementation To be able to use the previous described methodology, first the dataset had to be adjusted. The features in the HydroJunction class that are not part of the routing routine (such as nodes indicating the beginning of a tributary) were moved to the HydroNetwork_Junctions feature class. This to simplify the routine by reducing the entries in the HydroJunction attribute table. Then the EC field was added to the HydroJunction attribute table and filled using the Consolidate Attributes tool from the ArcHydro toolbox. This tool allows consolidating the source attribute in the source layer based on a relationship between the source layer and the target layer (Maidment, 2002). The relationship used is the HydroJunctionHasMonitoringPoint Relationship class that was created when the ArcHydro data model was applied to the Hydro database (section 4.3). The EC values are now copied from the source, the MonitoringPoint feature class, to the target, the HydroJunction feature class, using the HydroID as the key value. Next the received upstream discharge field and the calculated downstream discharge fields are added. To be able to validate the results, also a discharge measured field is added that contains all discharges measured in the field. This field is partly filled with the Consolidate Attributes tool using the HydroJunctionHasMonitoringPoint Relationship class. To fill the remainder (the discharge points) first a new Relationship class HydroJunctionHasWaterDischarge was created. Using the Consolidate Attributes tool, the 56

67 remainder was filled with the measured discharges from the WaterDischarge feature class. Now the dataset is ready, the HydroJunction attribute table is exported to Excel (using the Export function) where the EC routing routine will be simulated. In Excel, the HydroJunction attribute table is imported; the routing routine will be simulated in different worksheets so that the data can be retrieved and returned to the original database file without changing it, as would be the case when using programming in ArcGIS. Because in Excel there are no routing options, this is simulated by ordering the Junctions up- to downstream. First the network analyst in ArcGIS was used to trace the network downstream while the Junction HydroID`s were displayed as labels (Figure 5.3). The HydroID`s were then entered manually in Excel in the correct order. Because the EC downstream was stored in an extra HydroJunction below the HydroJunction at the junction, this HydroID was also noted down to be able to retrieve the EC at this location. Using these HydroID`s the EC values were extracted from the database worksheet using the VLOOKUP function in Excel. This function searches for a value in the first column of a table array and returns a value in the same row from another column in the table array. Then the discharge columns were added, were only the first most upstream measured discharge is entered. Using equation 5.4 the discharge downstream is now calculated, and the input discharge is calculated using equation Legend Kilometers UTM, WGS 84, Zone 19 South HydroJunction Drainage network Downstream trace Figure 5.3(a) Rocha network with HydroJunction HydroID`s and downstream trace (in red) (b). 57

68 This results in a table that simulates the routing. The calculated discharges are then returned to the imported database Worksheet and thus ArcGIS where the results can be visualized EC routing results Using the proposed methodology, the EC routing routine is applied (Table 5-1a). The network is considered without backwater effects, and seepage and evaporation are ignored. The observations along the network are not done simultaneous, but the network was measured in 4 days. Therefore multiple start values are used in the received upstream discharge field, one for every first measurement of every day. When analysing the results a number of errors can be observed in the form of negative values indicating water withdrawal resulting in an unrealistic discharge development through the network (Figure 5.5a). These errors can be explained as errors caused by the difference of the theoretic model and field conditions. The input at HydroID 5490 is discharge from a waste water oxidation plant which results in a decline in oxygen concentration and a release of ammonia and nitrite downstream (Chapman, 1992), causing irregular EC measurements; the EC downstream is higher than the EC upstream and of the EC of the stream input. Another error that can be explained from field conditions is that downstream the junction with the Viloma River the EC of the Rocha River is higher than upstream and of the input. This error is most likely caused by a lack of measurements. Due to the inaccessibility of the terrain, the downstream measurement (HydroID 5477) was taken 1.8 kilometers downstream, the upstream measurement (HydroID 5474) 8.5 kilometers upstream and the input (HydroID 5476) 8.2 kilometers upstream the actual junction. Any sources or sinks within these stretches are not measured, and most likely at least one is present resulting in the EC differences. The errors (indicated as special locations in Figure 3.8) cannot be corrected, because there is no possibility to remeasure the erroneous points in the field. However, the routine can be adjusted such that the procedure is stopped at erroneous calculations and proceeds again further downstream. This can be achieved by entering a new measured discharge start value in the calculated downstream discharge field following every error to be able to continue the process. This means that the methodology (section 5.2.1) has to be adjusted such that multiple measured discharge start values can be entered before (known errors) and after (errors that become visible when using the routine) the routine is used to calculate discharge. The dataset has been adjusted accordingly and a total of 6 start values are now entered (Table 5.1b). The results (Figure 5.5b) show the discharge build-up through the Rocha River, where interruptions indicate calculation errors. 58

69 a b Junction number Junction number Junction HydroID HydroID upstream HydroID side HydroID downstream Ec upstream (µs/cm) EC input (µs/cm) EC downstream (µs/cm) Received Q upstream (m 3 /s) Calculated Q input (m 3 /s) Calculated Q downstream (m 3 /s) * (3) * (8) (5)* (14)* Junction HydroID HydroID upstream HydroID side HydroID downstream Ec upstream (µs/cm) EC input (µs/cm) EC downstream (µs/cm) Received Q upstream (m 3 /s) Calculated Q input (m 3 /s) Calculated Q downstream (m 3 /s) (3)* (8)* (9)** (5)* (14)* (15)** Table 5-1 Results of the normal (a) and adjusted (b) simulation of the EC routing routine. Yellow indicates inserted values (*daily start value **start value to correct error, () refers to cross section Appendix 3), orange calculated values using equation 5.13, blue calculated values using equation 5.14 and green passed values. 59

70 Model validation and discharge visualization For validation purposes the discharge up- or downstream the stream or of the stream input (see section 3.7.3) was measured at 14 junctions. Due to the errors addressed in section 5.2.3, 6 of these measurements were used as input of the routing and 4 for the validation (Table 5-2). Validation is done by comparing the measured with the calculated discharges. HydroID Cross section Q measured (m 3 /s) Q calculated (m 3 /s) Differences (%) 5442* * Table 5-2 Validation of the EC routing with the corrected dataset (Table 5-1b): calculated versus measured discharges. *discharge measured is calculated using EC routing and the measured input. The cross section refers to the location of the measurement (Appendix 3). When plotting the measured discharges (Appendix 3) against the discharges calculated using the EC routing routine and the corrected dataset (Table 5-1b) the correlation is high (99.9%). However, the slope of 1.49 indicates that the trend is not good; ideally the slope should be 1. The single measurement with a high value has a strong weight in this result, highly influencing the trend line. When plotting the measured discharges against the discharges calculated again, but only using the measured values smaller then 1 m3/s (Figure 5.5d), the correlation is 99.9%, and the slope is The differences are however over 23% (Table 5-2), indicating that there is no trend, but the number of observations is very low thus these results are not reliable. Validating the incorrect dataset (Table 5-1a) gives the same correlation (Figure 5.5c) because the validation points follow the same start points in both datasets. To be able to visualize the results spatially in ArcGIS, the data was imported in ArcGIS from Excel. First in Excel the calculated discharge values are copied to the corresponding fields in the ArcHydro database worksheet. Since the downstream parameters are stored in the junction downstream the actual junction, the calculated downstream discharge values are copied to the downstream junctions linked to the monitoring points. The database is then copied to the attribute table of the HydroJunction feature class. Here a new field Qec is created that contains all discharge values resulting from the EC routine. This field is created to be used in the next section, the load calculation and makes it easier to compare the calculated and measured discharge values. Using the display options of ArcGIS, the calculated discharges are now visualized for the entire network (Figure 5.4). Due to the large differences in discharge, 1 liter/second versus 17 m 3 /second, labels are used to display the values. Now all discharges are calculated using the EC routing routine, the flux of selected measured variables can be calculated and visualized. 60

71 a b Kilometers UTM, WGS 84, Zone 19 South Legend Drainage network 8.6 Discharge (m3/s) c Meters ,500 Meters Figure 5.4 Visualization of the calculated discharges Water quality flux Flux calculation methodology The flux of a selected constituent can be calculated over a certain period using equation 5.3. Because fluxes are calculated for a certain time period, continuous measurements of both water discharge and constituent concentration are required. This is rarely done and it is usually necessary to rely on water quality information obtained at fixed intervals. Because the water discharge can be recorded continuously (using a river stage recorder) it is possible to create a calibration curve for a monitoring site. The actual concentration occurring between different chemical analyses can be obtained by extrapolation (Chapman, 1992). For this research, one sample was taken and analyzed per selected sample site (section 3.7.4), thus temporal constituent concentration information is not available. Therefore the constant flux hypothesis is used (Chapman, 1992): the flux, i =Q i C i measured at the sample site t i, is supposedly constant during the time interval t j. The total flux calculated for the whole period is then Φ = i. If we take 1 second as the time period, the flux calculation becomes: Φ = Q i C i (5.6) Where, Φ is flux (mg/s); Q i is water discharge (m 3 /s); C i is constituent concentration (mg/m 3 ). 61

72 a b Discharge EC routing Discharge EC routing Q (m3/s) Q (m3/s) Distance (m) Q Routing Distance (m) Q Routing c d Discharge measured vs calculated y = x R 2 = Discharge measured vs calculated y = x R 2 = Q calculated (m3/s) Q calculated (m3/s) Q measured (m3/s) Q measured (m3/s) Figure 5.5 Calculated discharge EC routing with errors (a) and corrected (b) and verification with the incorrect (c) and corrected dataset (d). 62

73 Using the discharges calculated (section 5.3) and concentrations of selected constituents (section 3.7.4) determined, the fluxes can be calculated using equation 5.6. The flux at the outlet of the watershed can be determined by aggregating the fluxes along the network, providing that the fluxes are determined for all the inputs and at the main stream before the first junction. Multiplying this flux with a user defined time period gives the total load of the constituent for the watershed for this time period. The results of the flux aggregation are compared with the fluxes of the sample site along the Rocha River for validation. As an example, the fluxes of Na + and HCO 3 - are calculated Flux implementation The flux calculation is incorporated in the attribute table of the HydroJunction feature class. First two new fields are created that contain the constituent concentrations in the HydroJunction attribute table; the Na_concentration and HCO3_concentration fields. Since the water quality parameters are stored in the XYTraceElement feature class (section 3.7.4), first a new Relationship Class is created to be able to fill these new attribute fields; the HydroJunctionHasXYTraceElement Relationship Class. Using this relationship and the ArcHydro Consolidate Attributes tool, the Na_concentration and HCO3_concentration fields are filled. The flux at the outlet is calculated by summation of the fluxes at inputs and the flux upstream the first junction. Therefore, first these fluxes are calculated using equation 5.6 and stored in new fields NaFlux and HCO3Flux. Using the Total function of the Accumulate Attributes ArcHydro Tool the fluxes are accumulated in newly created fields NaFluxOutlet and HCO3FluxOutlet. For the validation two new fields are created, the NaVal and HCO3Val fields that contain the calculated fluxes at the sample sites along the Rocha River Flux calculation results and visualization Ideally, the fluxes of the selected constituents at the outlet of the watershed should be calculated using the methodology described in section However, due to sample limitations not all inputs have been measured. During fieldwork the decision was made to prioritize sampling the tributaries of the Rocha System over the other inputs. Consequently, the upper part of the Rocha (basically the part upstream the junction with the Tamborada) lacks sample sites and thus cannot be used for the flux calculation. Further more the erroneous discharge calculations of the Oxidation plant and the Viloma River limit the suitable section from the junction with the Old River Rocha until the junction with the Khora River, were it not that this section was dammed off (section 5.2.3). This leaves no representable section to be used, and therefore the flux at the outlet could not be calculated due to lack of data. To test the methodology, the flux at the outlet was calculated anyway using the assumptions that the Rocha River system is uninterrupted and that the erroneous inputs and inputs without sample site do not yield discharge (Figure 5.6 and Table 5-3). 63

74 a Kilometers UTM, WGS 84, Zone 19 South Legend Drainage network 1,400 HCO3 (gr/s) Na (gr/s) b c Meters ,000 2,000 Meters Figure 5.6 Visualization of the test setup used to determine the fluxes in the network. a Kilometers UTM, WGS 84, Zone 19 South Legend Drainage network 2,300 Na (gr/s) HCO3 (gr/s) b c Meters ,200 Meters Figure 5.7 Visualization of the calculated fluxes after summation using the HydroTools The result shows that the flux is passed down the network totalling the inputs resulting in the total flux at the outlet (Figure 5.7 and Table 5-3). When comparing the fluxes at the outlet 64

75 calculated with Excel with the fluxes aggregated using Arc Hydro tools, the results are identical. The 1 gram/second deviation between the Na+ fluxes is due to rounding of. The fluxes can be used to determine the watershed constituent load over a certain period when multiplied by a time factor. 65

76 a b Junction number Junction HydroID HydroID upstream HydroID side HydroID downstream Received Q upstream (m 3 /s) Calculated Q input (m 3 /s) Calculated Q downstream (m 3 /s) HCO3 concentration upstream (mg/l) HCO3 concentration input (mg/l) HCO3 flux upstream (gr/s) HCO3 flux input (gr/s) HCO3 flux downstream (gr/s) HCO3 flux outlet Arc Hydro (gr/s) Junction number Junction HydroID HydroID upstream HydroID side HydroID downstream Received Q upstream (m 3 /s) Calculate d Q input (m 3 /s) Calculated Q downstream (m 3 /s) Na concentration upstream (mg/l) Na concentration input (mg/l) Na flux upstream (gr/s) Na flux input (gr/s) Na flux down stream (gr/s) Na flux outlet Arc Hydro (gr/s) Table 5-3 Flux build up Na (a) and HCO3 (b). Yellow indicates input, green the flux at the outlet calculated with Excel, red calculated with Arc Hydro tools. 66

77 6. Conclusions and recommendations 6.1. Conclusions As mentioned in Chapter 1, the main objective of this study was to develop and evaluate a Hydrologic Information System (HIS) that visualizes spatially chemical water quality making use of hydrologic parameter extraction and Electric Conductivity (EC) routing, as a support tool for water quality monitoring. As can be concluded from the present state of HIS (Chapter 2, section 2.1.1), the main elements of the extraction of a drainage network required for water quality monitoring include watershed segmentation, identification of drainage divides and the network of channels, characterization of terrain slope and aspect, and routing of flow of water (Lyon, 2003). Next to that, as discussed in Chapter 3, section the primary variables for measuring water quality focus on a combination of chemical and physical variables that reflect the characteristics of the water studied (Chaweepan, 2003) and must be related to the objectives of the monitoring program (Chapman, 1992). Because this research focuses on EC routing and flux calculations, general variables (EC, ph and temperature) and trace elements are selected as a measure for water quality, because they can be relatively easily measured in the field and analyzed in the laboratory. The EC is in this case essential because it is required for the EC routing routine. A number of tools and (GIS) software packages are available at present that can automatically extracted thematic layers and hydrologic parameters using various techniques (Fairfield and Leymarie, 1991; Vogt et al, 2003; Quinn et al, 1991; Tarboton, 1997; Lea, 1992; Rivix, 2004) and a hydrological corrected Digital Elevation Model (DEM) (Chapter 2, section and 2.1.3). The use of the ArcHydro tools as an extension to ArcGIS, the ArcHydro data model and Excel made it possible not only to embed the tools required to extract the required layers and parameters in a GIS, but also to develop a prototype of a HIS incorporating the EC routing and flux calculation procedures. The selection of the extraction tools and the DEM introduced a number of errors and limitations. The extracted drainage network has an average horizontal error of 500 meters when compared to a network interpreted from satellite imagery (Figure 4.4). Using the AGREE method (Hellweger, 1997), this error reduces to an average maximum of 90 meters (the Cochabamba city area, Figure 4.4b), but requires knowledge of a drainage network prior to the extraction. A related limitation is that one single threshold is used for the channel network definition using the critical source area (Chapter 4, section 4.2.3). As a result, a 67

78 number of streams, including the Khora and Chujlla, are under represented (Figure 4.6b). Using a smaller single threshold value would result in a better representation of these streams, but also into more primary channels than that are actually present. A larger value would not represent these streams at all (Figure 4.7c). The application of the ArcHydro data model (Chapter 4, section 4.3) automatically creates relations between feature classes and missing fields (such as HydroID) that are used for the prototype HIS developed. A disadvantage is that the data model also has functionality not required resulting in the creation of fields and feature classes that are part of the model (such as the feature class Bridge ) that have to be deleted. The resulting geodatabase can however still be altered to the needs of the user and the ArcHydro data model is therefore an excellent base for a HIS. The major achievement of this research is the development and implementation of a prototype HIS that includes an EC routing and a flux calculation routine (Chapter 5). The prototype is an intrinsic model that uses the functionality of ArcGIS, ArcHydro tools and Excel in combination with the developed methodology to calculate discharges and fluxes of selected constituents throughout the network. The prototype proves that the routines can be incorporated an applied. The nature of the study area made it difficult to apply the model and produce verifiable results. When comparing the calculated and measured discharges, the correlation is high (R 2 =99.9%, and the slope is 1.04) (Figure 5.5d). The differences are however in excess of 23% (Table 5-2), indicating that there is no trend. There are a number of reasons that can explain this. One of the conditions for the EC routing is that there must be steady state conditions. Because the observations are done at the beginning of the dry season it is uncertain if this condition is met. The beginning of the dry season marked the end of the fieldwork period, which left little time to make observations. Only one routing of the entire network could be done and consequently the amount of observations is low leaving little possibilities for testing and validating the methodology. The inaccessibly of the terrain made it difficult to identify junctions with point sources and sinks and presumably not all are accounted for. The general low discharge along the main stream made it sensitive to human influences (such as sewage inputs and the oxidation plant input) and to missed junctions even with low discharges. This results in under- and over estimations of the calculated discharges throughout the network. The assumption that there is no in- or exfiltration along the channel also introduces errors. The validation therefore is more an indication of the missed sources and sinks then of the accuracy of the routing. 68

79 Due to sample limitations and because the EC routing did not lead to calculated discharges at every junction, the fluxes could not be calculated for every junction either (Chapter 5, section 5.3.3). Instead, the fluxes of the selected variables sodium and bicarbonate at the outlet of the watershed are calculated by summation of the available data using the functionality of the ArcHydro tools. Multiplication of the resulting fluxes with a time period results in the constituents load over that period of time. Even though the methodology could not be verified due to lack of data, the assumption that there is no dispersion or decay will most likely result in a difference between the actual and calculated loads using this methodology. In conclusion, it can be said that the developed prototype HIS has a number of advantages and disadvantages. The model can be used under steady state conditions only and not all factors influencing discharge and flux calculations are incorporated (channel bed in- and exfiltration, decay, etc), which will influence the results. The major advantage is that once the model has been setup, the routines can be applied and the results visualized easily. Changes along a network can be easily updated, and the HIS is therefore potentially useful for water quality monitoring Recommendations As stated earlier, the selection of the extraction tools and the DEM introduced a number of errors and limitations. Using a higher resolution DEM and/or converting the DEM into a DSM could reduce the horizontal error of the extracted drainage network. Using other software packages with more advanced extraction methods prior to the application of the ArcHydro model could also reduce the error. For example, the use of RiverTools would include the D-infinity method for drainage extraction and ILWIS would include a variable sub-basin threshold selection for the channel definition. The EC routing methodology should be used during steady state conditions with sufficient base flow and it is therefore recommended to use the methodology in more humid climates. When very low flows are present in combination with contaminant sources (such as sewage outlets) the EC routing method becomes difficult to apply or at least would require a high density of observations in order to be able to detect all sources, sinks and EC changes in the network. Using automatic data loggers (that record level/discharge and EC) would increase the amount of observations and would in combination with regular measurement of constituents allow for the calculation of loads. Using automatic data loggers will also ensure simultaneous measurements throughout the network as required for the routing. Including processes as channel bed in- and exfiltration, decay, diffusion and dispersion using more advanced intrinsic modelling and/or DLL`s should create a more complete model producing more accurate results. Finally, it s recommended to adjust the ArcHydro data model to match the conceptual model of the research by altering the UML diagrams of the model with a suitable software (e.g. Visio) if the research covers multiple datasets requiring the same (or similar) database design. 69

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88 Appendix 1a. Thornthwaite climate classification Moisture provinces Climate type A perhumid Moisture index (inches) 100 or more B4 humid B3 humid B2 humid B1 humid C2 moist subhumid 0 20 C1 dry subhumid 20 0 D semiarid E arid Temperate provinces Climate type Potential evapotranspiration inches E frost D tundra C1 microthermal C2 microthermal B1 mesothermal B2 mesothermal B3 mesothermal B4 mesothermal A megathermal centimeters Moist climates Aridity index (A, B, C2) Type Index r little or no water deficiency s moderate water deficiency in summer w moderate water deficiency in winter s2 large water deficiency in summer > 33.3 w2 large water deficiency in winter > 33.3 Dry climates Humidity index (C1, D, E) Type Index d little or no water surplus 0 10 s moderate water surplus in winter w moderate water surplus in summer s2 large water surplus in winter >20 w2 large water surplus in summer >20 78

89 Appendix 1b. Thornthwaite climate classification Cochabamba, Bolivia Month Mean precipitation (mm) Mean precipitation (Inch) Mean temperature ( C) Mean temperature ( F) T-E index EP P-E Index January February March April May June July August September October November December Annual

90 Appendix 2. ArcHydro Data model Structure of the ArcHydro data model (from Arc Hydro GIS for water resources. Maidment, 2002). The components are organized in the ArcHydro geodatabase into four feature data sets (channel, drainage, hydrography and network) and one table (Time series). 80