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

Remco J.J. Dost June, 2005

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

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.

This thesis is dedicated to my parents

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.

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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.

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Table of contents 1.

Introduction ......................................................................................................................................1 1.1. Background .............................................................................................................................1 1.2. Relevance of research .............................................................................................................2 1.3. Research objective ..................................................................................................................3 1.4. Conceptual model ...................................................................................................................4 1.4.1. Hydrologic Information System development....................................................................4 1.4.2. Electric Conductivity routing incorporation ......................................................................4 1.4.3. Water quality visualization and incorporation ...................................................................4 1.5. Methodology...........................................................................................................................5 1.5.1. Literature review ................................................................................................................5 1.5.2. Data collection....................................................................................................................5 1.5.3. Fieldwork............................................................................................................................5 1.5.4. Data analysis.......................................................................................................................5 1.5.5. HIS creation........................................................................................................................5 1.5.6. Assessment .........................................................................................................................6 1.6. Thesis outline..........................................................................................................................7 2. Hydrologic Information Systems .....................................................................................................9 2.1. Present state ............................................................................................................................9 2.1.1. Introduction ........................................................................................................................9 2.1.2. Digital elevation models.....................................................................................................9 2.1.3. Hydrologic parameter extraction......................................................................................11 2.1.4. Available software............................................................................................................14 2.2. HIS Requirements and development ....................................................................................16 2.3. HIS limitations......................................................................................................................17 3. Study area.......................................................................................................................................19 3.1. Introduction...........................................................................................................................19 3.2. Climate..................................................................................................................................20 3.3. Hydrology .............................................................................................................................21 3.4. Geomorphology ....................................................................................................................23 3.5. Geology.................................................................................................................................24 3.5.1. Paleozoic era.....................................................................................................................24 3.5.2. Mesozoic era.....................................................................................................................24 3.5.3. Caenozoic era ...................................................................................................................25 3.6. Land cover ............................................................................................................................26 3.7. Field measurements ..............................................................................................................27 3.7.1. Measure site identification ...............................................................................................27 3.7.2. EC measurements .............................................................................................................28 3.7.3. Discharge measurements ..................................................................................................29 3.7.4. Water quality sampling and analysis................................................................................32 3.8. Field observations.................................................................................................................34 4. Hydrologic Information System.....................................................................................................36 4.1. Introduction...........................................................................................................................36 iii

4.2. Terrain processing ................................................................................................................37 4.2.1. DEM selection ..................................................................................................................37 4.2.2. Hydrologic Digital Elevation Model creation ..................................................................38 4.2.3. Stream definition ..............................................................................................................43 4.2.4 Catchment and watershed processing ...................................................................................44 4.3. Hydrologic Information System creation..............................................................................45 5. Water quality HIS...........................................................................................................................52 5.1. Introduction...........................................................................................................................52 5.2. routing routine ......................................................................................................................53 5.2.1. EC routing methodology ..................................................................................................53 5.2.2. Routine implementation ...................................................................................................56 5.2.3. EC routing results.............................................................................................................58 5.2.4. Model validation and discharge visualization ..................................................................60 5.3. Water quality flux .................................................................................................................61 5.3.1. Flux calculation methodology ..........................................................................................61 5.3.2. Flux implementation.........................................................................................................63 5.3.3. Flux calculation results and visualization ........................................................................63 6. Conclusions and recommendations................................................................................................67 6.1. Conclusions...........................................................................................................................67 6.2. 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

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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 1961-1990. ........................20 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. ...........................................................................42 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. .............................................................44 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. .........................................................................50 Figure 4.13 Layout of the Cochabamba River Hydrologic Information System. ..................................50 Figure 4.14 The Cochabamba River Hydrologic Information System showing flow directions...........51 Figure 5.1 EC routing methodology flow chart. ....................................................................................54 Figure 5.2 Example of the EC routing. ..................................................................................................55 Figure 5.3(a) Rocha network with HydroJunction HydroID`s and downstream trace (b).....................57 Figure 5.4 Visualization of the calculated discharges. ..........................................................................61 Figure 5.5 Calculated discharge EC routing ..........................................................................................62 Figure 5.6 Visualization of the test setup used to determine the fluxes in the network. .......................64 Figure 5.7 Visualization of the calculated fluxes after summation using the HydroTools....................64

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List of tables Table 3-1 (Sub) basin area and discharge. .............................................................................................23 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 HCO3 (b)........................................................................................66

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HYDROLOGIC INFORMATION SYSTEMS AS A SUPPORT TOOL FOR WATER QUALITY MONITORING

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.

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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.

1.2.

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

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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.

1.3.

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?

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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. 1.4.1.

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. 1.4.2.

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. 1.4.3.

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.

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1.5.

Methodology

1.5.1.

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. 1.5.2.

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. 1.5.3.

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. 1.5.4.

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. 1.5.5.

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

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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: Qi+1 * Ai+1 = Qi * Ai + qi,i+1 * ai,i+1

(1.1)

Qi+1=Qi + qi,I+1

(1.2)

And

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): t2

Φ = C (t )Q(t )δt

(1.3)

t1

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. 1.5.6.

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 1.1.

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Pre-fieldwork


Model study and data collection: - Literature study - Study area data

Fieldwork

Network monitoring: - Survey network - Selection discharge measure sites - Selection of water quality sample sites

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.

1.6.

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.

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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.

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2. Hydrologic Information Systems 2.1.

Present state

2.1.1.

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. 2.1.2.

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

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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

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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 1.74 15 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. 2.1.3.

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


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 1996. 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 Dinfinity 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


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


a

b

Figure 2.1 a.Raster network after processing Strahler second order channels b. Example channel network and definitions. Source Garbrecht, 1997.

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. 2.1.4.

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


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


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.

2.2.

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


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).

2.3.

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


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


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 17°10’ S, 67°00’ W and 18° 20’ S, 66°30’ 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


Isiboro Pampa Grande Corani-San Mateo-Ibirizu

Santa Rosa

Rocha-Maylanco

Tapacari

Santivañez

Cochabamba District

Cliza-Sulty

Julpe-Mizque Ayopaya

Caine

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 482.5 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 25

120

20

100 80

15

60

10

40

5

20

0

Ja Fe nua br r y u M ary ar c Ap h r M il ay Ju ne J Se Au uly pt gu em st O b N cto er ov b D em er ec b em er be r

0

Temperature (°C)

Precipitation (mm)

140

Precipitation (mm)

Month

Temperature (°C)

Figure 3.3 Annual average precipitation and temperature over the period 1961-1990 (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


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: P-Emonthly = 115(r/t – 10)10/9

(3.1)

Where, r = mean monthly rainfall (inches) t = mean monthly temperature (°F) The P-E index of 27.9 indicates a humid climate type, B1. 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: T-Emonthly = (t-32)/4

(3.2)

Where, 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: EP = r/t

(3.3)

Where, 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.

3.3.

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


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).

8080000

Alcantarilla Chujlla Khora

820000

Old Rocha

8060000

8060000

Viloma

Tamborada

Tapacari

8040000

8040000

Angostura reservoir

8020000

8020000

Arce

8000000 7980000

850000

8080000

790000

Legend Waterbody Watershed 0 4.5 9

18

Cochabamba city

27 Kilometers

Rocha River Tributary

1:1,000,000 UTM, WGS 84, Zone 19 South

730000

760000

790000

820000

8000000

760000

7980000

730000

850000

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 Rocha Tamborada Old Rocha Alcantarilla Chujlla Khora Viloma Tapacari Arce

Area Km2

% of total 7961 2191 124 90 63 63 241 990 2198

100 28 2 1.136 0.792 0.787 3 12 28

22

Q (m3/s) 17.17 0.81 0.06 0.26 0.3 0.31 3* 5.76 9.29


Table 3-1 (Sub) basin area (Figure 3.4) and discharge (Chapter 5) *estimated from field observation.

3.4.

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 Mountain – peak Mountain – range Mountain – volcanic Mountain – hill Mountain – slope Mountain – glacial (deposits) Valley – valley Valley – basin Valley – terrace Fans Plain

Elevation (Meters) 2400 – 5000 2200 – 4800 4000 – 4400 2500 – 4000 2400 – 4500 2200 – 4300 2100 – 5000 2700 – 4400 2600 – 3000 2200 – 3400 2500 – 3700

Slope (%) > 80 50 - 80 20 - 50 20 - 50 > 80 5 - 50 5 - 50 20 - 50 2-5 5 - 50