APPLICATIONS OF GEOGRAPHIC INFORMATION SYSTEM DATA IN THE UBC WATERSHED MODEL

by

JEANNIE MEI LING L E E B. A. Sc., The University of British Columbia, 1994

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED

SCIENCE

in

THE FACULTY OF GRADUATE STUDIES Department of Civil Engineering

We accept this ̂ hesis as conforming to the required standard

THE UNIVERSITY OF BRITISH COLUMBIA

May 1996

©Jeannie Mei Ling Lee, 1996

In presenting this thesis in partial fulfilment of the requirements for an advanced

degree at the University of British Columbia, I agree that the Library shall make it

freely available for reference and study. I further agree that permission for extensive

copying of this thesis for scholarly purposes may be granted by the head of my

department or by his or her representatives. It is understood that copying or

publication of this thesis for financial gain shall not be allowed without my written

permission.

Department

The University of British Columbia Vancouver, Canada

DE-6 (2/88)

11

ABSTRACT

The suitability of using geographic information system (GIS) data to describe a watershed for

the U B C Watershed Model is investigated. A GIS combines the ability of a database

management system to store, retrieve, and analyse information with the capacity to produce

and manipulate graphical elements on a map. In hydrologic modeling, information from a

GIS is commonly used to describe the physical characteristics of a watershed such as terrain,

forest cover, and soil type. By using a GIS, possible sources of human error and subjectivity

present when manually measuring attributes from maps or aerial photographs can be

removed. 1

The Seymour Watershed located north of Vancouver, B.C. is used as an example in assessing

the value of GIS data for the watershed model and to illustrate the complexities of calibrating

the model. Terrain, ecological, and timber GIS databases for the Seymour Watershed are

used to create a watershed description file (WAT) which is implemented in the model to

produce synthetic watershed hydrographs. These hydrographs are then compared and

calibrated against recorded historical streamflows, resulting in a calibrated Seymour

Watershed model. This model is then used to forecast future streamflows when

meteorological forecasts are also provided.

Although GIS data is not perfect, it is valuable in describing a watershed as input for the

U B C Watershed Model, which is then used to produce calculated hydrographs. The main

Ill

drawback of utilizing a GIS for the Seymour Watershed is the absence of adequate

documentation of some GIS characteristics. Despite the removal of subjectivity and human

error by using GIS data, inaccurate model streamflows remain due to errors in other non-GIS

data such as improper reservoir elevation readings, missing historical streamflow or

meteorological data, and inherent errors in the watershed modeling process.

TABLE OF CONTENTS

Abstract i i

Table of Contents iv

List of Tables vi

List of Figures vii

Acknowledgment ix

1.0 Introduction and Purpose 1

2.0 Literature Review 3 2.1 Perm state runoff model 3 2.2 Ward creek 6 2.3 FIPR hydrologic model 7 2.4 Summary of the past uses of GIS 9

3.0 Overview of the U B C Watershed Model 10 3.1 Required data 11

4.0 Geographical Information Systems 15 4.1 Raster or grid based system 17 4.2 Vector based system 18 4.3 Triangular integrated network 18 4.4 Comparison of raster based and vector based GIS methods 18 4.5 Input of GIS data 20 4.6 GIS applications in hydrology 21

5.0 Outline of Available GIS Data 24 5.1 Overview of the Seymour Watershed GIS data 24 5.2 Detailed description of Seymour Watershed GIS data 26

5.2a terrain database 27 5.2b ecological database 29

5.3 Creating the description file for the Seymour Watershed 30

Figures V . l through V.4 35

6.0 Applying the GIS Watershed File to the U B C Watershed Model :..39 6.1 Abstractions and other adjustments to the original

recorded streamflow 41

6.1a storage flow 43 6.1b intake flow 43

6.2 Compilation of abstracted flows and combination with observed flows 43

Figures VI. 1 through VI. 8 47

7.0 Explanation of the Calibration Process 55 7.1 Stage 1 calibration 56 7.2 Stage 2 calibration 59 7.3 Stage 3 calibration 60 7.4 Optimization routine 60 7.5 Statistics option 61

8.0 Calibration and Discussion of the Seymour Watershed Model 64 8.1 Calibration of the Seymour Watershed model 64 8.2 Assess the meteorological accuracy of the Seymour

Hatchery station 67 8.3 Investigate the accuracy of the determined form of

precipitation 70

Figures V i l l i through Vni.12 73

9.0 Problems Associated with GIS Data, Abstraction Data, Historical Flow, and Meteorological Data 85

9.1 GIS data 85 9.2 Storage reservoir elevation data 87 9.3 AES and WSC data 88

10.0 Results and Conclusions 90 10.1 Results 90 10.2 Conclusions 94

References 99

Appendix 1 Statistics Report for Initial Seymour Watershed Model 101

Appendix 2 Statistics Report from Initial Seymour Watershed Model using Seymour Hatchery AES Station and Grouse Mountain AES Station..'. 104

Appendix 3 Sample Precipitation and Temperature Graphs for 1990 107

Appendix 4 Statistics Report from Seymour Watershed Model using Adjusted Precipitation Temperature 110

vi

LIST OF TABLES

Table V. 1 Summary of Initial Watershed Description File 33

Table VI. 1 Summary of Modified Watershed Description File 40

Table Vffl. 1 Statistical Report Summary for the Initial Calibrated Model 66

Table VIII.2 Statistical Report Summary for the Initial Calibrated Model Using Both Seymour Hatchery and Grouse Mountain AES Stations 68

Table VIII.3 Statistical Report Summary for the Final Calibrated Model

Using Adjusted Precipitation Temperature 72

Table X . 1 Summary of Final Calibration Parameter Values 90

Table X.2 Final Description of the Seymour Watershed 92

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LIST OF FIGURES

Figure V . l initial uncalibrated annual model hydrograph 1989-1990 35

Figure V.2 initial uncalibrated annual model hydrograph 1990-1991 36

Figure V.3 initial uncalibrated annual model hydrograph 1991-1992 37

Figure V.4 initial uncalibrated annual model hydrograph 1992-1993 38

Figure VI. 1 modified uncalibrated annual model hydrograph 1989-1990 47

Figure VI.2 modified uncalibrated annual model hydrograph 1990-1991 48

Figure VI.3 modified uncalibrated annual model hydrograph 1991-1992 49

Figure VI.4 modified uncalibrated annual model hydrograph 1992-1993 50

Figure VI.5 modified uncalibrated annual model hydrograph with adjusted observed flows 1989-1990 51

Figure VI.6 modified uncalibrated annual model hydrograph with adjusted observed flows 1990-1991 52

Figure VI.7 modified uncalibrated annual model hydrograph with adjusted observed flows 1991-1992 53

Figure VI.8 modified uncalibrated annual model hydrograph with adjusted

observed flows 1992-1993 54

Figure Vffl. 1 initial calibrated annual model hydrograph 1989-1990 73

Figure VTJI.2 initial calibrated annual model hydrograph 1990-1991 74

Figure Vffl.3 initial calibrated annual model hydrograph 1991-1992 75

Figure VTJI.4 initial calibrated annual model hydrograph 1992-1993 76

Figure Vffi.5 calibrated annual model hydrograph using both Seymour Hatchery and Grouse Mountain AES stations 1989-1990 77

Figure VtII.6 calibrated annual model hydrograph using both Seymour Hatchery and Grouse Mountain AES stations 1990-1991 78

viii

Figure VIII. 7 calibrated annual model hydrograph using both Seymour Hatchery and Grouse Mountain AES stations 1991-1992 79

Figure VHI.8 calibrated annual model hydrograph using both Seymour Hatchery and Grouse Mountain AES stations 1992-1993 80

Figure VHI.9 final calibrated annual model hydrograph with adjusted precipitation temperature 1989-1990 81

Figure VIII. 10 final calibrated annual model hydrograph with adjusted precipitation temperature 1990-1991 82

Figure VIII. 11 final calibrated annual model hydrograph with adjusted precipitation temperature 1991-1992 83

Figure VIII. 12 final calibrated annual model hydrograph with adjusted precipitation temperature 1992-1993 84

ix

ACKNOWLEDGEMENT

I would like to thank Dr. M . C. Quick for his supervision and advice throughout this research. His invaluable assistance in the computing and research aspects of this project and his expertise with the U B C Watershed Model is much appreciated.

I would also like to thank Lome Gilmour for his work in gathering the necessary GIS information. Finally, I would like to thank my parents and friends for their support and encouragement.

1

INTRODUCTION AND PURPOSE

The purpose of this research is to investigate the suitability of Geographical

Information System (GIS) data to describe a watershed whose hydrologic response

will then be simulated by a hydrologic model. The U B C Watershed Model will be

used to test the applications of GIS in watershed modeling. In addition to the

investigation of the uses of GIS in hydrologic modeling, the complexities in

calibrating the U B C Watershed Model will be illustrated and examined.

GIS information is commonly used to describe the physical characteristics of a

watershed such as terrain, soil, and ground cover properties. The accuracy of these

properties is important because most computerized hydrologic models require details

about the watershed to calibrate the model and to create flow forecasts.

Improvements in estimating these parameters should increase the accuracy in

forecasting watershed flows and may decrease the time needed to calibrate the

hydrologic model. Since GIS information is digitally entered into a database, the

measurements contained in a GIS are devoid of human subjectivity and error. Thus,

it is believed that these measurements are more precise than those obtained by

manually estimating characteristics from maps.

2

The UBC Watershed Model is developed to describe and forecast watershed behavior

in mountainous areas. It requires historical streamflow and weather data plus an

accurate description of the watershed including area, elevation, and forest cover

properties. Presently, the values of these parameters are estimated by physically

examining topographic maps and aerial photographs.

The compatibility of GIS data with the U B C Watershed Model will be appraised

using the Seymour River Watershed. This watershed is selected as a case study

because sufficient GIS data is available from the Greater Vancouver Regional District

watershed management office. The Seymour Watershed is about 126 km and is

located north of Vancouver, B.C. This basin is used as a source of water for domestic

consumption in the Greater Vancouver area. Historical meteorological and

streamflow data are also readily available for this watershed area and river system.

3

II. LITERATURE REVIEW

Information from a geographical information system has been previously applied to

other hydrologic models. Generally, GIS information is used to estimate spatial data

for runoff models and its use eliminates the subjectivity of estimating certain model

input parameters. The following example cases illustrate the functions of GIS in

hydrological analyses.

1. Penn State Runoff Model

Geographical information system data is used in estimating input parameters for the

Penn State Runoff Model (PSR model) (Shamsi, 1993). This model is used to

simulate runoff hydrographs for various durations and frequencies. The Bull Run

Watershed in Union County, Pennsylvania is a rural catchment covering 8.4 square

miles and is used to illustrate the PSR model.

Hydrographs generated by the PSR model are manipulated to create peak flow

presentation and release rate tables which are utilized in the development of a

watershed storm water management plan. A peak flow presentation table is the sum

of individual flow contributions from all sub-basins draining to any given point. The

release rate is defined as the ratio of 'sub-basin pre-development peak flow

4

contribution to watershed peak flow' to 'sub-basin peak flow.' The required physical

input values of the PSR model include area, overland flow width, mean overland flow

slope, percent imperviousness, and stream capacity and travel time. These input

values are estimated using an assortment of basic GIS data layers.

Information in a GIS is divided into separate layers each describing one specific

characteristic. For example, elevation contours are stored as one data layer and slope

segments are stored as another layer. To create a GIS database of elevation and

slope, the layer of elevation contours is superimposed over the data layer for the slope

segments. The final GIS data file contains an array of polygons covering the entire

area, with each polygon representing a constant elevation and slope. A complete

explanation of how a GIS functions is detailed in chapter IV of this report.

The following vector coverages are created for the Bull Run Watershed: subbasins,

streams, roads, soil types, and land use. Coverages of subbasins, streams, and roads

are digitized from topographic maps. Soil types are established from survey maps

and land use coverage is created from aerial photographs. A raster coverage of

elevation is also generated. A vector coverage divides the data layer into an irregular

pattern of polygons and a raster coverage uses a uniform grid of squares to describe

as area. These GIS layers are then manipulated to establish sub-basin area, overland

flow width, overland flow slope, stream length, stream slope, and centroids. Stream

length, slope, and cross section dimensions are used to compute stream capacity and

5

travel time. Other remaining physical parameters, such as imperviousness and runoff

curve numbers, are determined by subsequent processing of the basic layers.

Data compilation is followed by model calibration. The GIS based physical input

parameters are not intended to be altered during the calibration process. Rather,

calibration is to be applied to the adjustment of the most difficult to define hydraulic

parameters. Logically, the watershed parameters can be sub-divided into several

groups. One group depends on physically measurable characteristics and are

definable from GIS data. Other characteristics need to be calibrated from the

hydrological response of the system. Since there is no continuous type rain gauge in

the Bull Run Watershed, only three observed hydrographs are available. The nearest

rain gauge is located approximately 13 miles south of the study area.

The modeled peak flows compare favourably to available Federal Emergency

Management Agency peak flow estimates based on the regional flood-frequency

method developed by the US Army Corps of Engineers (1984). According to Shamsi

(1993), experiences with other similar projects indicate that the GIS based models

calibrate faster than non-GIS based models. This implies that GIS based parameters

are more accurate than those computed from traditional manual measurement

techniques.

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2. Ward Creek

The Ward Creek Watershed located in Baton Rouge, Louisiana is a highly developed

urban basin (Cruise and Greene, 1995). The 12.12 km 2 area is mostly mixed

residential with some industrial and commercial areas. Local engineers are interested

in developing an interface that further incorporates the spatial analysis capabilities of

a GIS within the hydrologic analysis of an urban watershed.

The researchers intend to use a GIS to create a spatial database that most accurately

represents the hydrologic characteristics of the watershed. No previous GIS work has

been done on this area and none of the needed physical data is available in digital

form. The data acquired from various sources are of different scales and resolutions

and must be manually geo-coded. Topography, soils, land use, pervious and

impervious areas, storm drain system, stream channel, and street network of the urban

watershed are coded into separate layers of data. The relational functions within the

GIS are used to create a table of attributes corresponding to each information layer.

Conclusions drawn from this project indicate that the coordinate values defining the

locations and boundaries of features in the GIS can be used for spatial analysis. The

coordinate values are used to identify which hydrologic response areas contribute to

the flow at a particular inlet, the dimensions of the response areas, the overland

distance from these areas to the inlets, and the storm drain that connects the particular

7

inlet to the next downstream inlet. The investigation of the effects that spatial

location has on the discharge show that when changes to individual lots are made,

even though there is little or no impact on the flow at the outlet, there is a larger

impact at the nearest inlet receiving the flow from the lot surface. These results are

obtained without any calibration of the hydrologic model.

3. FIPR Hydrologic Model

The Florida Institute of Phosphate Research (FIPR) funded the development of an

integrated hydrologic model to more adequately represent mine reclamation

hydrology ( Burdge, Ross, Tara, 1993). The impetus of this model is the need for

more accurate, reliable, and standardized quantitative hydrologic assessments of long

term surface and groundwater effects associated with large scale mining impacts. A

typical application for the FIPR Hydrologic Model (FH model) consists of five tasks:

digital data gathering, GIS operations for model input, model input data processing,

hydrologic simulation, and output post-processing such as statistical analysis and

producing graphics.

The F H model is ideally run on watersheds that are less than 10 km with sub-basins

less than 1 km 2. It is considered a small scale application and requires fine GIS

resolution namely, topographic resolution of 1 meter contour intervals. Soils are

8

broken down by hydrologic classifications and land use conditions are resolved into a

simplified classification scheme.

The GIS is contained within the model to perform the spatial data referencing and

analysis function for generating model input. Hydrologic codes perform the

calculations for time-dependant hydrologic simulation. There are four principal

functions of the GIS in the F H model: perform the complex map overlaying to

develop input data for the hydrologic models; provide the linkage mechanism

between models with different spatial representations; provide the conversion of

maps into common projections and scales; and provide limited post-simulation

graphical output display.

The significant points of the FEPR Hydrologic model are numerous. It is claimed that

the GIS implementation of the FH model provides a means to standardize model

parameter selection by omitting any user subjectivity. This model is also equipped

with a complete user interface within the GIS to integrate the input data for modeling.

Finally, the GIS application is able to display, interpret, and perform further analysis

on the extensive output data generated by the model.

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4. Summary of the Past Uses of GIS

Of the three models reviewed, GIS is used most extensively in the Florida Institute

Hydrologic Model where the geographic information system is integrated into the

model. GIS is utilized to estimate input parameters, provide a link between models

of different representations, convert various maps into common scales and

projections, and create a graphical display. The Perm State Model uses GIS data to

estimate input characteristics only. The model of Ward Creek uses GIS to represent

the hydrologic characteristics of an urban watershed and is primarily used to forecast

the effect of land development on storm drain flows.

In all cases, GIS data is used to accurately describe the physical characteristics of a

watershed. This research on the use of GIS in the U B C Watershed Model will be

similar to the Perm State Runoff Model in that GIS data will be used to estimate input

parameters for a hydrologic model. It is hoped that the use of GIS for the Seymour

Watershed will conclude with encouraging results similar to those of the Perm State

Model, namely model flows comparable to observed flows.

10

OVERVIEW OF THE UBC WATERSHED MODEL

As described in the user manual, the U B C Watershed Model creates a computational

representation of watershed behavior. This computer model calculates daily

watershed outflow due to snowmelt and rainfall using maximum and minimum daily

temperatures and precipitation data. In addition to streamflow values, the model also

provides information on the accumulation and depletion of snowpack, soil moisture

budget, soil and groundwater storage values, contributions to runoff from various

portions of the watershed, and surface and sub-surface components of runoff. Given

continuous meteorological input data, the U B C Watershed Model can operate

continuously, accumulating and depleting the snowpack and producing estimates of

streamflow. For calibration and verification purposes, the model uses historical

meteorological and streamflow records as reference data and calculates performance

statistics on total flow and hydrograph shape reconstitution.

The watershed model is essentially designed for short term river flow forecasting.

The accuracy of the forecast is dependant on how representative the meteorological

data is to date, the accuracy of the meteorological forecast, and the current

assessment of the snowpack, soil, and groundwater storage. Since the model operates

continuously when given continuous meteorological input, it is possible to operate

the model for longer term forecasting using projected future weather patterns. Once

11

an initial watershed status is specified, this long term forecasting capability assesses

the range of possible outcomes for a whole season of snowmelt and rain runoff. Such

seasonal forecasts are updated with recorded data as the season progresses thus

gradually narrowing the range of possible outcomes. An extension of this mode of

operation is used to estimate complete years of data when the streamflow records are

non-existent but meteorological data is available.

1. Required Data

The U B C Watershed Model requires a watershed to be represented as several

consecutive horizontal bands of increasing elevation. Although up to twelve bands

may be used, the operational manual suggests that four to eight bands are sufficient

for most watersheds. The altitude intervals for each elevation band can be freely

selected, though it is suggested to select bands which coincide with changes in the

natural features of the watershed such as lake elevations, rock bluffs, or forest type.

Each band is then distinguished by its physical characteristics: mean elevation, area,

forested area, forest density, fraction of north/south orientation, glaciated area,

glaciated orientation, and impermeable fraction of the soil. In addition, each band is

assigned a meteorological station where temperature and precipitation data will be

retrieved and used in calculations to estimate snowpack accumulation, snowmelt,

evaporation, soil moisture status, and finally runoff which is calculated in terms of

fast, medium, and slow contributions.

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Mean band elevation is measured in meters and band area is measured in square

kilometers. The forest properties of the watershed are depicted by the forested area,

represented as a decimal fraction of the total band area, and the density of the forest.

Forest density is a measure of the fraction of band area that is shaded by the forest.

Relative north/south orientation of the band is measured on a scale between zero and

one. Zero denotes a northerly orientation and one, a southerly orientation. For

example, a value of 0.2 indicates a 20% southerly orientation. Glacier characteristics

of the watershed are described by the glaciated area, measured in square kilometers,

and the glacier orientation which is measured on the same scale as band orientation.

Impermeablility is represented as the decimal fraction of the band that is

impermeable. Also included in the description of the watershed are indexes

indicating which meteorological station a band will use for temperature,

precipitation, and evapotranspiration calculations. A fourth index adjusts the

precipitation at each band, either increasing or decreasing the precipitation.

In addition to the physical description of the watershed, historical data is needed.

Streamflow and meteorological data of at least one full year are required for

calibration and operation of the U B C model. The U B C Watershed Model uses

meteorological information to generate synthetic streamflows which are compared

with the historically observed flows to calibrate the hydrologic model. The accuracy

and representativeness of the recorded data, both flow and meteorological, are crucial

13

to the performance of the model. The recorded observations should represent typical

annual flow and meteorological trends and events to correctly simulate the response

of a real watershed by the computer model. Generally, when more years of data are

available, the calibration and forecasting performance of the model improves.

Flow data must be measured in cubic meters per second and read either daily or

hourly. Generally, daily data is preferred over hourly data. Meteorological data

consists of a maximum and minimum temperature in degrees Celsius and

precipitation in millimeters. These readings are also be recorded either hourly or

daily. Another important stipulation of the watershed model is the recorded

meteorological data be multiplied by a factor of 10. This includes both temperatures

and precipitation measurements.

For this research, daily stream flow data is obtained from the Water Survey of

Canada (WSC) and meteorological data is obtained from Environment Canada which

was previously named Atmospheric Environment Services (AES). In order for a

meteorological or streamflow data file to be recognized as such by the U B C

Watershed Model, it must be identified by a name and the extension ' .AES ' for

meteorological data or ' . W S C for flow data.

For each watershed, the U B C Watershed Model allows up to 5 AES stations but only

one streamflow gauge station. The multiple meteorological stations are necessary for

14

large watersheds or watersheds which are particularly mountainous. Precipitation in

mountainous watersheds can vary, sometimes greatly, between valley and summit

areas. By using various AES stations, lower elevation bands can be represented by a

different station than higher elevation bands. Each meteorological station must have

a unique name and a measured elevation in meters.

15

GEOGRAPHICAL INFORMATION SYSTEMS

A geographical information system is a computer system which links a database

management system to a number of spatially distributed features that can be

represented on a map. It combines the power of a database management system to

store, retrieve, and analyse information with the ability to produce and manipulate

graphical elements on a map.

Geographical information system technology was first used in the 1960's to perform

spatial operations. One of the earliest applications of this emerging technology in

water resource engineering is reported by Solomon et al in 1968 who used the

"square grid system" for computer estimation of precipitation, temperature, and

runoff in Newfoundland (Muzik, Pomeroy, 1990). The use of GIS flourished in the

late 1970's and in recent years has rapidly grown in number and complexity of

applications. In the area of water resource planning and design, a major effort is

focused on the development and utilization of microcomputer-based GIS systems.

The modern GIS is an evolutionary result of other computer systems and is a merger

between database and graphics software. As stated before, GIS programs are capable

of producing maps and have developed this ability from computer-aided drafting

(CAD) and thematic mapping. Computer-aided drafting is an automated drafting

16

program with the capability of dividing a drawing into a number of trait layers and

printing only selected layers. The major limitation of C A D is the inability to

interrelate distinct layers of information beyond a visual basis. In other words, an

icon or characteristic on a C A D map cannot be spatially related to other icons or

characteristics beyond a visual relationship on a map. In comparison, thematic

mapping shades areas of a map based on the values of a single variable and is also

unable to relate the values of different variables beyond a visual basis. Since

thematic mapping is purely a graphic system, it can only be used as a display system

to show results of a spreadsheet or database.

A relational database management system allows the storage and retrieval of

information from text records. Attributes of each record can be grouped into subsets

to meet a specified criteria. Geographically referenced databases associate each

record to a specific spatial location. The geographical database describes each object

in terms of their position in a specified coordinate system, their associated attributes

which are unrelated to position, and their spatial or topological interrelations which

describe how the objects are linked together in the greater system. The final

geographical information system product combines the analytic capabilities of a

database management system with a high resolution computer graphics system.

Generally, a GIS divides a map into distinct information layers where each layer is

subdivided into an array of discrete units. Each unit contains a characteristic value or

trait, which is unique from neighbouring units. Also, the location of each GIS unit

17

and its relation to other units are contained within the unit information. When

creating a graphical output map of GIS data, attribute polygons are either coloured in

coordination with characteristic values or values are marked on the polygons

themselves. Over time, the GIS has developed three distinct forms to represent a

database: raster or grid-based; vector or contour-based line networks; and triangular

integrated networks.

1. Raster or Grid Based System

The raster method separates different geographical attributes into distinct layers and

divides each layer into a regularly sized grid pattern. For instance, one grid layer

contains soil type, another grid layer contains vegetation type, and so on. Each

enclosed square is described by the characteristics of its centre coordinates. The

location and interrelation of each grid square unit is indicated by the position of the

grid unit in the overall grid sequence. If the surface is thought of as a visual image

with the dots having various colours and intensities similar to a computer monitor

screen, the use of the term 'raster image' is revealed. The size of the grid pattern may

vary for each attribute layer depending on the detail required to adequately represent

the characteristic.

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2. Vector Based System

Instead of a grid, the vector based format uses a topological data structure of points,

lines, and polygons defined by x, y coordinates to describe a map area. A topological

data structure defines the elements of a map such that the spatial relationship between

line segments, points, and areas are known. If a point on a map is randomly selected,

it is possible to determine i f that point is within a particular polygon and i f that

polygon is adjacent to a particular line segment. As with the raster method, distinct

attributes are separated into different map layers.

3. Triangular Integrated Network

A triangular integrated network (TIN) is a subset of a more general polygonal

description of attribute regions. It is mainly used for topography where a network of

irregularly spaced points are connected by lines to produce a triangular patchwork to

indicate topographical traits. Each triangle is treated as a planar facet indicating

significant peaks and valleys. Map attributes are also separated into distinct layers.

4. Comparison of Raster Based and Vector Based GIS Methods

The most commonly used forms of GIS are grid based and vector based formats. In

the past when comparing grid and vector based systems, a grid based system was

preferred at times because it is more efficient in data storage than a vector based

19

system (Zhang et al., 1990). A case in point: the digital representation of a soil map

in a vector based file contains both soil series codes and the corresponding Cartesian

coordinates. In contrast, a raster based coverage contains soil series codes only, with

the Cartesian coordinates implicitly expressed by the order of the grid pixels. The

larger the coverage, the more storage space a vector based GIS requires compared to

a raster based GIS. Most older raster based geographic information systems fail to

adequately include multiple attributes capability so only one type of thematic

attribute can be stored at a time. The same data layer is stored as many times as the

number of modeling parameters thus reducing the advantage in storage efficiency.

Modern GIS systems no longer suffer these problems and inefficiencies.

The vector method is more complicated but generally gives higher resolution than the

raster method. In the raster format, the resolution is a function of the size of the grid

cell and the rate of change of the data. Usually, the smaller the grid cell, the higher

the resolution but at the cost of increased data to be handled.

To illustrate the difference between raster and vector data storage systems, an

elevation contour map is examined. Elevation is a spatial parameter with a

continuous function. Contours define points of uniform level or form discrete areas

of uniform elevation. The boundaries and contours are usually complex and

curvilinear in form. The vector approach stores the boundaries either as sets of

coordinates or as polygons. The alternative raster method applies an orthogonal grid

20

to the elevation data and each grid cell is attributed with an elevation value. This

value is assumed to be constant throughout the grid cell. The vector method is more

complicated, but generally gives higher resolution. In contrast, the raster method is

simpler and often computationally faster.

5. Input of GIS Data

The method of entering data into the GIS varies and affects the cost of database

creation and sometimes the data structure. In the past for example, the semi­

automatic digitizer usually imposed a vector or chain structure, while scan digitizers

imposed a grid structure (Clarke, 1986). Nowadays, systems are increasingly able to

handle data from both vector and grid data structures by automatic raster-to-vector or

vector-to-raster conversion. Other forms of data input include individual grid cell

encoding and the use of remote sensing imagery. Stored data can be map and non-

map data. Non-map data consists of characteristics such as soil type, land cover and

use, etc.

While the underlying assumption of any GIS application is that the database of

physical information is available, the acquisition and compilation of this information

is not a trivial task. Often, appropriate data is only available in map form, so that

even with modern digitizing hardware the process is highly labour intensive. The

eventual payoff comes from the multiple ways in which the data can be used once it

21

is made digitally accessible in a GIS. Consequently, a geographical information

system is only cost efficient and effective i f there are demands for several different

analyses on the same database.

6. GIS Applications in Hydrology

Since so much of hydrology is linked to processes at the surface of the earth, the

connection to the topographic, computer-based methodology of a geographic

information system is a predictable step in the evolution of hydrologic engineering.

Hydrologic applications of GIS systems range from synthesis and characterization of

hydrologic tendencies to the prediction of response to hydrologic events.

Several previous attempts have been made to incorporate GIS in hydrologic

modeling. These attempts can be summarized in four categories (Cruise and Greene):

1. calculation of input parameters for existing hydrologic models

2. mapping and display of hydrologic variables

3. watershed surface representation

4. identification of hydrologic response units

Presently, the majority of GIS applications in hydrologic analyses fall into the first

two categories.

22

To exemplify of the amount of GIS data required to run a hydrologic model, the

integrated hydrologic model sponsored by the Florida Institute of Phosphate Research

is used (Ross, Tara). This model is developed to aid in the design of phosphate mine

reclamation. It is a combination of a commercial geographical information system,

an evapotranspiration code, and surface and ground water models. For the spatial

data requirements, eight map layers are obtained or created in digital form for the

GIS. The necessary layers are:

1. land use and vegetative cover

2. soil type

3. surface topography

4. hydrological routing elements such as streams and rivers

5. watershed and sub-basin delineation

6. water table contours

7. potentiometric surface contours

8. ground water confining layer elevations

In a typical application of this hydrologic model, individual map layers are converted

to a common scale and projection before any analysis. Then the first task of the GIS

is to determine average conditions over each sub-basin. Next, attributes or

characteristics tagged to a map feature on the land form maps are compared. The

results of these comparisons are numerical values such as physical or empirical

infiltration coefficients for the surface water model. Attributes are then averaged

23

over the smallest grid or parcel description in the model and the results are written to

a data file to be read prior to hydrologic simulation.

24

OUTLINE OF AVAILABLE GIS DATA

The Greater Vancouver Regional District (GVRD) is in the process of developing an

extensive geographical information system for its three local watersheds: Seymour,

Capilano, and Coquitlam. A l l the water for Vancouver and its surrounding suburbs is

withdrawn from these three river systems. At present, the Seymour Watershed is the

only G V R D watershed that has a complete GIS database which includes terrain and

ecological information. Therefore, the Seymour Watershed is selected as the

research area.

In March 1993, a team of engineering consultants headed by Acres International Ltd.

published an ecological inventory pilot study on the Jamieson-Orchid-Elbow

Drainage area, located in the northern portion of the Seymour Watershed. The pilot

study covers a wide range of aspects: climate, forest hydrology, sediment transport,

terrain and terrain stability, biogeoclimatic classification, forest fire hazards, forest

health, fisheries, and wildlife. From this pilot study and discussions with the G V R D

mapping technologist, the required data from the Seymour Watershed is identified

and extracted from the plethora of available GIS information.

1. Overview of the Seymour Watershed GIS Data

The requested GIS data constitutes snow course or accumulation data, terrain maps, a

25

drainage network map, and biogeoclimatic data. Terrain maps consist of many layers

of which the selected required layers are surficial materials, texture, material

thickness, and slope angle characteristics. Biogeoclimatic classification at the site-

series level is based on vegetation, soil, and other physiographic data. This group of

data is divided into two sub-sets: site type and forest ecosystem. Information

considered as site type are land form, elevation, and average slope and aspect. Forest

ecosystem data include soil depth, water table depths, and soil moisture. Other useful

biogeoclimatic information are percent coverage of all trees and understorey

vegetation, stand composition, successional stage, and disturbance regime.

The supplied GIS data is collected by using the ARCMNFO Geographical Information

System. The necessary information is simply selected from the large GIS database

via a Windows interface. To combine the data onto a few large spreadsheets, a

selection of map layers has to be projected and scaled to a uniform GIS polygon size.

For example, in a particular area the forest density polygon area is 10 but the soil

is classified as two 5 m 2 polygon areas. The final spreadsheet to describe this area

constitutes two polygons of 5 m each.

The final collection of data is stored in three Excel spreadsheets however, some of

the originally requested information is not provided. The absent data is not supplied

due to the inordinate amount of time and effort needed to assemble this data which is

not specifically required for the operation of the U B C Watershed Model. A l l the

26

essential information for the hydrological model is contained in a set of vast

spreadsheets: one for terrain, one for ecosystems, and one for timber. The terrain and

ecosystem databases describe the ground surface and the ecological properties^

respectively. The timber database is an appendage of the ecosystem spreadsheet and

details the specifics of the forest properties.

2. Detailed Description of Seymour Watershed GIS Data

The terrain and ecosystem databases are quite similar to each other. Both data

spreadsheets are at least 20 columns long with thousands of entries. The first three

columns of the terrain and ecosystem spreadsheets identify the GIS polygon, and

indicate area in square meters and perimeter in meters. The polygon identification

value is unique throughout each spreadsheet however, identification numbers are not

compatible between any of the three GIS data sheets.

Both spreadsheets also indicate the elevation span of each polygon. The terrain

database gives the mean elevation and the elevation range of the polygon.

Comparatively, the ecosystem database gives the mean, minimum, and maximum

elevations of the polygon. In addition, the terrain spreadsheet indicates slope for each

polygon; the ecosystem spreadsheet denotes the maximum, minimum, and mean

slopes of each polygon

27

a. terrain database

The terrain database describes the terrain, geological activities, aspect, and sediment

transport in the watershed. Terrain is depicted by using terrain unit symbols to

indicate which materials are present in a polygonal area and which materials are

more abundant than others. Stratigraphic units are also utilized to show where

surficial materials overlie a different material layer. Texture is included in this

database and indicates the coarseness of the material and ranges from clay and silt,

62.5 um and less, to diamicton, a mass of pebbles and larger clasts in a matrix of

fines.

Materials and surface expression are divided into three sections to denote the

dominant, secondary, and tertiary layers. Not all polygons require three layers of

description but when more than one material is present, a blank, 7', or 7/' is used to

separate each layer. A blank indicates an equal amount of dominant and secondary

materials. A 7' indicates that the dominant is more extensive than the secondary and

7/' means that the dominant is much more extensive than the secondary. These

dividers are also present between secondary and tertiary materials.

Materials are distinguished into six categories: colluvium, fluvial sediments, 'active'

fluvial sediments, till, organic sediments, and bedrock. Colluvium are products of

gravitational slope movements including talus, landslide and debris flow deposits.

Fluvial sediments are sands and gravels transported and deposited by streams and

28

rivers. Active fluvial sediments are in active deposition zones. Till is material

deposited by glaciers without modification by flowing water and typically consists of

pebbles, cobbles, and boulders in a matrix of silty sand. Organic sediments are

materials resulting from the accumulation of decaying vegetative matter. Materials

classed as bedrock are either outcrops of rock or bedrock within a few centimeters of

the surface.

Surface expression is specified in 14 classes: moderate slope, blanket, cone, fan,

hummocky, gentle slope, moderately steep slope, rolling topography, plain, ridges,

steep slope, undulating topography, veneer, and mantle of variable thickness. These

categories are too numerous to be described here but, in general, each class pertains

to the range of slope, amount of hillocks and hollows, and the overall shape of an

area.

Geological processes are expressed as the occurrence of avalanche, failing, rapid

mass movement, and gullying. Failing includes sliding or slumping and is defined as

a slope experiencing slow mass movement. Rapid mass movement is exemplified by

debris flows, debris slides, avalanches, and rockfalls. Gullying and avalanches are

self-explanatory. Information on slide count and slide density failure per hectare are

also provided. Slide count is the number of slide initiation points in each GIS

polygon and slide density failure is the slide area expressed per hectare for each

polygon.

29

Aspect is given for each GIS area in azimuth, clockwise from north. Sediment

transport is indicated as the volume of fine sediment in cubic meters per hectare per

year. A category for drain class is also given which relates to the permeability of the

soil.

b. ecological database

The ecological spreadsheet characterizes the vegetative cover and assigns a timber

identification number for each polygon area. Identification numbers in the ecological

database do not coincide with the timber identification numbers. Area and perimeter

are also supplied for each polygon. Site series, site type, variants, and successional

stage are given to describe the type of vegetation, soil, and climate characteristics

present. A site series is a group of sites with uniform soil moisture and nutrients.

Site type is a division of a site series that is uniform in soil or landform characteristics

and variants describe the usual local climate. Successional stage is the average age of

forest growth and is depicted in seven categories: initial, shrub herb, pole sapling,

young forest, mature forest, old growth, and no vegetation. Initial is growth two years

and younger. Shrub herb is between 2 to 15 years; pole saplings are between 15 to 40

years. Young forest is 40 to 100 years, mature forest is 100 to 250 years, and old

growth is over 250 years old. No vegetation is obviously no growth. Site disturbance

is also provided which indicates fire, landslide, harvesting, etc.

30

Each ecological GIS polygon is assigned a timber identification number used in the

timber spreadsheet to describe the forest cover of the area. The timber database

illustrates the type, age, and size of trees in great detail and is an extremely large

spreadsheet. The important data columns from this spreadsheet are crown closure,

crown closure class, and density.

Crown closure is the percent of ground area covered by the vertically projected tree

crown area. The tree crown is commonly known as the limbed portion of the tree.

Crown closure class is a measurement of the crown closure. Classes range from zero

to ten and represent percent intervals from 0 to 100%. Class zero is from 0 to 5%,

class 10 is from 95% to 100%, and the consecutive classes in between represent

successive increments of 9%. Density is the quantitative measure of tree cover in an

area expressed as the number of tree stems per hectare.

3. Creating the Description File for the Seymour Watershed

To create the watershed description file (WAT) for the U B C Watershed Model, the

first step is to sort the terrain spreadsheet data by elevation. After much

consideration, it is decided that perimeter, geological processes, slide values, and

sediment transport information are not needed to model the watershed. Furthermore,

secondary and tertiary materials and their corresponding surface expressions are

omitted because the U B C model does not benefit from such an in-depth soil

31

description. The terrain database is reduced to dominant material, surface

expression, slope, aspect, elevation, area, and polygon identification numbers.

Then, the information is arranged and summarized into 50 m intervals from 250 m to

1650 m. Characteristics of each elevation interval are a result of a weighted average

of each polygon trait according to its area. The same procedure of data reduction and

sorting is done to the ecosystem spreadsheet. The only practical information in this

database is the successional stage, area, timber identification numbers, elevation, and

slope. Forest characteristics are eventually added to the ecosystem spreadsheet in

accordance to their timber identification values. These characteristics are then

extracted from the timber spreadsheet.

Next, the area of each 50 m elevation band is compared between the ecosystem and

terrain databases. Most of the band areas compare well between the two

spreadsheets, so another process of elevation band amalgamation is performed.

These new elevation bands will result in a wider range of elevation for each interval.

The selection of new elevation intervals is done by examing the successional stage

and percent of total watershed area of consecutive 50 m elevation bands. A change in

forest pattern is often a good indication where the final watershed bands should fall.

Finally, the watershed bands are selected as roughly equal area bands combined with

any obvious changes in ecosystem characteristics. The final elevation bands are 250

m to 450 m. 500 m to 750 m. 800 m to 900 m, 950 m to 1050 m, 1100 m to 1200 m,

32

and 1250 m to 1650 m, inclusive. Both terrain and ecosystem spreadsheets are

divided into these elevation intervals.

Discrepancies in the final elevation band areas between the two different databases

are corrected by slicing GIS entries with very large areas into smaller portions of

different elevations. In other words, the area of a large polygon is partitioned into

smaller horizontal strips according to the elevation range of the polygon. The

relationship between area and elevation in a polygon is assumed to be linear hence,

the slope from maximum to minimum elevation is assumed constant. These

segments are then added to other elevation bands that are low in comparative areas.

With the final watershed bands defined, the characteristics from both terrain and

ecosystem databases are averaged by weighted area. Successional stage and all

identification numbers are deleted since they are no longer required. The completed

WAT file consists of mean elevation, total area, forested fraction, shaded fraction,

relative orientation, and drain class for each band.

Eventually, drain class is discarded because no documentation was found to explain

the class values and there is no apparent correlation between soil type and drain class.

Percent of bedrock or rock replaces drain class as a measure of the impermeability in

the elevation band.

33

The initial watershed description file (WAT) is as follows:

band number

band elevation

band area

forested fraction

shaded fraction

relative orientation

fraction of rock

1 308 m 19.3 km 2 0.99 0.50 0.99 0.015

2 606 m 22.8 km 2 0.96 0.55 0.80 0.046

3 828 m 24.3 km 2 0.95 0.52 0.99 0.058

4 978 m 19.5 km 2 0.97 0.50 0.94 0.227

5 1127 m 20.3 km 2 0.94 0.41 0.95 0.306

6 1306 m 19.6 km 2 0.86 0.23 0.98 0.627

Table V . l

In addition to GIS information, AES and streamflow data are necessary to operate the

watershed model. The meteorological station data consists of maximum and

minimum daily temperature, daily precipitation, and the station elevation and is

easily attained from Environment Canada. Of the numerous stations in the Seymour

Watershed area, the North Vancouver Seymour Hatchery station, station number

110N666, is considered the most suitable for this research area. The North

Vancouver Grouse Mountain Resort, station number 1105658, is eventually used in

this report as a supplemental AES station to examine the accuracy of the Seymour

Watershed calibration.

34

Streamflow data is obtained from the Water Survey of Canada office for the Seymour

River North Vancouver station. This station is located in Lynn Valley and measures

the Seymour River a few kilometers north of the Burrard Inlet, where the river finally

discharges.

Figures V . l through V.4 are the synthetic hydrographs produced by the U B C

Watershed Model using the initial Seymour Watershed description file. Each graph

displays the calculated and observed streamflow for each hydrologic year, from 1989

to 1993. Although the model is yet to be calibrated, these calculated hydrographs are

useful to illustrate the first step in the progression of refining the accuracy of the

watershed model.

35

36

37

38

39

APPLYING THE GIS WATERSHED FILE TO THE

UBC WATERSHED MODEL

The official drainage basin for the Seymour River North Vancouver streamflow

gauge station is 176 km 2. From the GIS analysis which measures only to the Seymour

Dam, the Seymour Watershed is 126 km 2 . The 50 km 2 drainage area difference is the

area from the dam downstream to the streamflow gauge station. In order to

accurately recreate the observed streamflow with the synthetic model flow, the total

area of the Seymour Watershed description (WAT) file must be increased.

By examining topographical maps of the Seymour Watershed basin, most of the area

variance is from elevations in the lower watershed bands. After much consideration

and discussion, the 50 km 2 of additional area is added to the model in the following

increments: 15 km 2 for band 1,13 km 2 for band 2, 10 km 2 for band 3, 7 km 2 for band

4, and 5 km 2 for band 5. The highest band, six, is not increased.

The impermeable fractions of all six watershed bands are much lower than expected.

Since these values are estimated by using the percent of bedrock at the surface, they

are not necessarily representative of the actual impermeability. Areas consisting of

materials other than rock can also be rather impervious. After performing an initial

run of the watershed model, it is observed that insufficient flow is entering the fast

40

runoff component. The modeled hydrograph seems to be generally low compared to

the observed graphs and too much flow is entering the groundwater component. To

compensate for this problem, a blanket value of 0.2 is added to the impermeable

fraction of each band. These alterations result in a modified WAT file:

band number

band elevation

band area forested fraction

shaded fraction

relative orientation

impermeable fraction

1 308 m 34.3 km 2 0.99 0.50 0.99 0.215

2 606 m 35.8 km 2 0.96 0.55 0.80 0.246

3 828 m 34.3 km 2 0.95 0.52 0.99 0.258

4 978 m 26.5 km 2 0.97 0.50 0.94 0.427

5 1127 m 25.3 km 2 0.94 0.41 0.95 0.506

6 1306 m 19.6 km 2 0.86 0.23 0.98 0.827

Table VT.1

Each watershed hydrograph is plotted from October 1 to September 30. Observed

data is available from 1989 to 1993 so a total of four hydrological years are used for

calibration.

Found at the end of this chapter are figures VI. 1 through VI.4 which are the

uncalibrated model hydrographs from the U B C Watershed Model implementing the

newly modified WAT file. These graphs are very similar in appearance to the

previous streamflow graphs which used the initial watershed description file.

41

Generally, the modified WAT file produces streamflows slightly larger than the flows

from the initial WAT file. This is the desired result of increasing the drainage area

and increasing the impermeability of each watershed description band.

When compared to observed streamflow graphs, the calculated hydrographs generally

follow the overall shape of the historical hydrographs. Significant peaks in the.

observed flow coincide with peaks in the calculated flow. These similarities indicate

that the preliminary watershed hydrographs are on the right track in modeling the

Seymour Watershed. The most noticeable discrepancy between the model and the

observed data is the amount of streamflow at any point in the hydrograph. In general,

the calculated flow is substantially more than the observed flow. Some of this

variance is caused by abstractions for domestic consumption and storage in the

reservoir. In the following sections, calculations will be described which correct the

historical flow data for these influences.

1. Abstractions and Other Adjustments to the Original Recorded

Streamflow

The Greater Vancouver Regional District regulates flow from the Seymour Lake

reservoir by storing flow from the Seymour River and controlling discharges exiting

the Seymour Dam, located at the south end of the lake. Flows are regularly removed

from the river and used for domestic consumption or held as lake storage. The

42

observed streamflow from the Water Survey of Canada is measured downstream from

this dam and is not a true measure of the natural river flow. The U B C Watershed

Model uses meteorological data to calculate natural, unregulated flows which occur

upstream from the dam. To compare the modeled flow with the observed flow, the

amount of removed flow must be added to the original observed flow.

Discharge files obtained from the G V R D are in either text or Excel spreadsheet

format and cover the years 1989 to 1994. Each annual data file contains the date,

reservoir elevation, weir elevation, head over crest, spillway flow, 24" hollow cone

valve, 37" HB valve east and west, and intake flow. Elevations are measured in feet

and flows are in either million gallons per day or cubic feet per second. The hollow

cone and HB valves are used to release water into the river for fishery requirements.

The information that is needed from the discharge files are date, reservoir elevation,

and intake flow. The reservoir elevation is recorded manually on a daily basis but is

not measured at a specific, recurrent time of the day. The measurement reading may

be taken in the morning one day and in the late afternoon, the next day. Since there is

no method to account for this randomness, there is no correction technique and it is

hoped that it has little affect on the accuracy of the hydrologic model.

43

a. storage flow

Flow in or out of storage is calculated by multiplying the change in reservoir

elevation by the lake area. Since no elevation-storage curve of the reservoir is

available, a static value of lake area is implemented. In reality, when the surface

elevation of the lake changes, the area also changes. This incremental area change is

a small percentage of the entire lake area and is assumed to be insignificant. Lake

area is estimated from the GIS database of the Seymour Watershed by extracting

polygons which are identified as lake segments. The total surface area of the

reservoir is 3.8 km 2 or 41 million square feet.

b. intake flow

Intake flow is the water used for domestic consumption purposes such as drinking

water. It is generally a uniform amount of water withdrawn from the reservoir and

varies from 5 to 10 ft3/s. Both storage and intake flow must be converted into cubic

meters per second in order to add these flows to the observed WSC flow.

2. Compilation of Abstracted Flows and Combination with Observed Flows

Storage and intake flows are combined into one spreadsheet file to represent the total

flow removed from the Seymour River. This flow file is then converted into a format

compatible with the WSC file format. Two different computer programs are created

44

to perform this task and to combine the removed streamflow values with the observed

values.

Streamflows entered in a WSC file are arranged in 9 columns of data. The first

column is an identification number for the specific row of data. It consists of 8

characters: three for the last three digits of the year, two for the month, one for the

row number, and two for the total number of days in the month. For example,

989011031 represents the first row of data for the month of January, which has 31

days, in the year 1989. Flow values are displayed to one tenth of a unit. An

important feature for the format of the WSC file in the U B C Watershed Model is that

the end of the ID number must be 16 spaces from the left edge, the first data value in

the row must run from space 17 to 24, and the following data values must be

contained within 8 spaces starting from space 25 to 33. A l l data values are right

justified within each input space. If the data file is not in this format, the watershed

model will not read the historical streamflow values properly resulting in an erratic

observed hydrograph.

The first computer program, written in Visual Basic 3.0, is created to transform

abstraction data from a text file into a WSC compatible format. Before this

transformation program begins, the file of removed flows must be manually

transformed from a spreadsheet format to two columns of continuous text: one for the

day, from 1 to 365 or 366, and the other for the flow value. Also, a change in year

45

must be indicated as a data entry. In other words, data for 1989 would start with

1989 as the first row followed by the two columns of data beneath it. Only one year

of data is manually converted from spreadsheet to text format at a time, then

consecutive years are joined together as two long columns of text. A dummy data

entry, such as 'xxxx', is required at the end of the completed text file to indicate the

end of file. When these modifications are complete, the program is employed to

convert the text file into a WSC format. The final file containing all flow

abstractions is named 'removed.wsc.'

The second computer program is used to add the removed flows to the original WSC

file, 'seynvan.wsc' This program creates the file 'add.wsc' which is the corrected

Seymour River flow. Before applying this addition program, the first line of

'seynvan.wsc' which is used as a gauge station descriptor or identification line must

be deleted. This addition program is also written in Visual Basic 3.0.

A new run of the watershed model produces hydrographs with flows that are more

similar to the observed historical flows. Figures VI.5 through VI. 8 display the

hydrographs produced by the watershed model and the hydrographs of the new

observed streamflows which include the flows removed or added for storage or

intake. The model flows are not altered from the previous graphs of figures VI. 1 to

V I A

46

By taking into consideration abstracted river flows, the overall observed flow

increases. However, there are also new irregularities which arise with the addition of

removed flows. There are instances where the observed streamflow drops below zero

which are due to sudden changes in reservoir elevation. These unexpected

fluctuations in surface elevation are likely due to effects of wind on the lake surface

or errors in reservoir level records which will be investigated in chapter 9. In

addition, there are remaining occurrences where the model flow does not follow the

observed graph.

Thus far, all of these observations are from an uncalibrated watershed model. An

uncalibrated model retains the default watershed parameters and compares the initial,

unaltered model hydrographs to the observed hydrographs. The following chapter

will detail the calibration process where the synthetic hydrographs are adjusted for

total yearly or monthly volumes and graph shape in an attempt to resemble the

historical hydrographs.

47

48

49

50

51

52

53

55

VII. EXPLANATION OF THE CALIBRATION PROCESS

Once the description of the watershed is complete and the WSC file has been

adjusted for storage and other abstractions, calibration of the model can commence.

According to the U B C Watershed Model manual, the calibration of the watershed

involves adjusting parameter values in a WAT file until the annual and monthly

volumes and patterns of calculated runoff approximate historical records. The WAT

file contains a large array of parameters but the adjustment of only a few of these

parameters is necessary for the calibration of the model. A majority of these

parameters have default values which have been pre-calibrated and do not require

further alteration.

Calibration parameters can be adjusted manually or they can be tuned using the

optimization routine provided in the U B C Watershed Model. When using the

optimization routine, it is best to alter a limited number of parameters at one time.

Some variables are dependant on other variables, so calibrating many parameters at

once is not effective. The general calibration procedure is iterative and consists of

three steps:

1. adjust particular parameter values in the WAT file

2. run the U B C Watershed Model

56

3. evaluate the results of the model using the 'graphics' and 'statistics' options

to determine the degree to which the estimated hydrograph agrees with

recorded streamflow behavior

This process is repeated until suitable parameter values are established.

The full calibration procedure is done in three stages, each dealing with different

groups of parameters but following the same iterative pattern of modification and

evaluation. Stage one is concerned with the meteorological distribution parameters

and is initially carried out in a very simplified manner to determine a working basis

for further calibration. Stage two deals with the time distribution of runoff and stage

three assesses gradients of behavior in the watershed. Stages two and three are

refining processes which should not be attempted until the annual and monthly

volumes of runoff, calculated in stage one, correspond closely to the historical

streamflow. After completing stages two and three, the user may return to the first

stage to achieve greater parameter refinement. Visual comparison of estimated and

observed flows are useful especially when calibrating the model for runoff timing.

1. Stage 1 Calibration

The parameters manipulated in stage one calibration are EOLMID, EOLHI,

POGRADL, P O G R A D M , POGRADU, POSREP, and PORREP. The first five

variables are found in the section 'distribution of meteorological variables' in the

57

WAT file and the last two variables are within the 'AES station elevations and

parameters' section. POGRADL is the precipitation gradient for elevations below

EOLMTD. POGPvADM is the precipitation gradient for elevations above EOLMTD

but below EOLHI. POGRADU is the precipitation gradient for elevations above

EOLHT. EOLMTD is often taken as the elevation of the middle of the barrier height

and EOLHI is estimated as two-thirds the barrier height.

Stage one calibration begins by setting EOLMTD and EOLHI to values greater than

the maximum elevation of the watershed. The other calibration parameters initially

remain at their default values and should not be changed until a preliminary run of

the model has been performed. After running the U B C Watershed Model, the

'statistics' function is used to provide statistical analysis on how well the model

hydrographs estimate the total flow, shape, and timing of the observed hydrographs.

Next, examine the statistics file, comparing the total observed flow and the total

estimated flow values. These values represent the monthly and annual runoff figures

taken from the WSC file and the U B C Model, respectively. Their comparison

indicates how accurate the precipitation gradients distribute precipitation over the

watershed. When necessary, adjust annual and monthly runoff statistics by modifying

the value of POGRADL. This parameter is increased if the calculated flow is too low

and decreased i f the flow is too high. Re-run the watershed model with the new

parameter values and compare annual runoff values again. This process is repeated

until observed and calculated flows are similar.

58

As stage one calibration proceeds, evidence may indicate the necessary refinement of

POSREP and PORREP. These two parameters are AES adjustment factors for

snowfall and rainfall data, respectively. Normally, in watersheds where the available

data for the basin is reliable and elevation is accurately represented, these parameters

remain unchanged at zero. This indicates that the measured precipitation data is not

changed in the watershed model. In some situations, there may be strong evidence

that the measured precipitation should be increased or decreased to match the

calculated watershed values.

At a later stage in calibration it may become apparent from the hydrologic response,

snow course information, or a combination of the two, that precipitation gradients are

higher or lower at various elevations. To account for this, P O G R A D M can be

activated by setting EOLMID to an elevation value below which POGRADL will

govern and above which P O G R A D M will govern.

Once annual and monthly volumes are close to the desired levels, the next step is to

determine i f the runoff time distribution is similar between the results generated by

the U B C model and those from historical data. The 'Graphics' option on the main

menu provides a visual representation of the shape of the runoff. Using the display

options, the user can observe the characteristics of the estimated and observed flows.

59

2. Stage 2 Calibration

The important factors in stage two calibration are COIMPA, POPERC, PODZSH,

POUGTK, and PODZTK. COIMPA is the fraction of impermeable area in a band

and controls the amount of water entering the sub-surface. POPERC allocates how

much of this volume of water entering the sub-surface can be stored in the

groundwater, PODZSH divides the groundwater component into an upper and deep

zone component, and POUGTK and PODZTK are the routing time constants for

these two zones, respectively. The volumes of runoff and the shapes of the

groundwater recession flows are then examined, particularly at the end of the summer

for POUGTK and through the winter for the longer recession PODZTK.

The procedure for stage two calibration starts with determining the routing time

constant for the deep zone groundwater reservoir from recorded winter flows. Then,

the required seasonal allocation to deep zone groundwater storage is determined.

Next, determine the upper groundwater routing time constant and adjust the

groundwater percolation to calibrate allocation to upper groundwater storage. The

fraction of impermeable area in a band, the soil moisture deficit production, and

impermeable area recessional parameter may also require adjustment. Any of these

steps can be separately repeated to improve the calibration of the model.

60

3. Stage 3 Calibration

Stage three of the calibration process deals with gradients of behavior in the

watershed, identified by the fraction of impermeable area in a band. It is a fine

tuning process that can only be carried out when stages one and two achieve a high

level of model performance.

4. Optimization Routine

Calibration of the Seymour Watershed is performed using the 'optimization method'.

The optimization method is chosen over manual calibration of the watershed because

it removes the tedium of having to vary parameters and then re-running the watershed

model. It is helpful i f the user has some knowledge of the hydrological processes

involved in watershed modeling. Through experience and judgment, the user can

define reasonable value ranges for the parameters and let the computer do the work

of finding the best values for them.

The optimization module investigates three groups of parameters separately. The

first group, precipitation distribution, adjusts precipitation gradients. This

optimization routine is performed until the estimated volumes begin to converge with

the observed volumes. The next group to be optimized is the water distribution group

which distributes the rain and snowmelt to groundwater, interflow, and surface runoff

61

through the soil moisture budget. The third group of parameters, routing constants,

adjusts the time constants for each component of flow, controlling the length of time

taken to pass through the watershed.

Once a value range is defined for each parameter, the optimization routine randomly

selects values from the range for the set of tagged parameters in each group, executes

the model with these values, and calculates the coefficient of efficiency, the

coefficient of efficiency compensated for volume error, the coefficient of

determination, and the estimated total flow for the period. This procedure can be run

for a number of iterations, at the end of which the ten best efficiencies and their

corresponding parameters are saved to a file. The WAT file is then updated with the

most efficient parameter values. This updated WAT file is then used to optimize the

next optimization group. When all the selected groups are processed, the resultant

WAT file contains the parameter values that give the best efficiency for the

watershed. The entire optimization process can be repeated to obtain better

calibration efficiencies.

5. Statistics Option

The U B C Watershed Model provides a 'statistics' option to calculate various

statistical data for each month of a calibrated WAT file. Statistics include the mean

observed flow averaged over the specified period, mean estimated flow averaged over

62

the period, total observed flow, total estimated flow, difference between the observed

flow and the estimate flow, coefficient of efficiency, and coefficient of

determination.

The coefficient of efficiency, e!, relates how well the estimated hydrograph compares

in shape and total flow to the historical hydrograph. It is calculated as:

e! = l - ( £ ( Q ^ - Q ^ ) 2

£(Qobs /' " Qobs avg )

where Q o b s a v g = ZQobs

n

n = number of days for daily runs or hours for hourly runs

Qobs / = observed flow on day or hour /

Qest / = estimated flow on day or hour /

An efficiency of 1.0 indicates a perfect fit between the observed and the calculated

hydrographs. An efficiency less than 1.0 is a result of imperfect shape, total flow, or

timing. Negative values for the coefficient of efficiency can also be attained. The

calculated efficiency is more sensitive to large peaks than small underlying flows.

The coefficient of determination, d!, is a factor which relates how well the shape of

the estimated hydrograph corresponds to the shape of the observed hydrograph. It is

independant of total flow differences between the two hydrographs however, timing

63

does affect the value of this statistic. For example, i f two hydrographs are identical

except one has more volume than the other, the coefficient of determination is 1.0. If

one of these hydrographs is shifted slightly to the left, the coefficient of

determination decreases. The coefficient of determination is calculated as follows:

d! = l - £ ( Q ^ - ( b * C U ; + a))2

savg . ^XQobs ;' " Qobs avg )

where a = (SQ o b s/-b * Z Q ^ ) n

b = S f C U , * O^fJ) - S Q ^ * ZQnhw * (1/n) £ ( Q e s u ) 2 - ( l / n ) * Z ( Q e s t / ) 2

n = number of days for daily runs or hours for hourly runs

Qobs / = observed flow on day or hour i

Qesi / = estimated flow on day or hour /

64

VIII. CALIBRATION AND DISCUSSION OF THE

SEYMOUR WATERSHED MODEL

1. Initial Calibration of the Seymour Watershed Model

The calibration of the Seymour Watershed requires the modification of some

parameter values. EOLMTD is changed to 850 m and EOLHI is 1000 m. The

precipitation gradients POGRADL and P O G R A D M are both calibrated to 1, but

POGRADU remains at zero. POSREP is adjusted to -0.67 and PORREP is adjusted

to 0.11.

The low precipitation gradient factors are consistent with research done by Loukas

and Quick. They found that the precipitation of the Seymour Valley is similar to the

precipitation found in the neighbouring mountainous areas. In the Seymour River

Watershed, precipitation was found to increase with elevation up to a height of 260m.

Beyond this elevation, the rainfall dramatically drops and then a further slight

increase in rainfall, leveling off at the upper elevations. This process is apparent in

the values of POGRADL, POGRADM, and POGRADL.

65

In the second stage of calibration, only POPERC and PODZSH are altered. POPERC

is changed to 9 and PODZSH is changed to 0.7. The other stage two calibration

parameters are unaltered and remain at their default values.

A statistical analysis is performed on the Seymour WAT file for each year from 1989

to 1993 and for the four years overall. The entire statistical output is found in

appendix 1 located at the end of this report. For each month starting from October

1989 to September 1993, the mean observed and estimated flows, total observed and

estimated flows, difference between observed and estimated flows, coefficient of

efficiency, and coefficient of determination are calculated and displayed. At the end

of the statistical report, the above values are calculated for each year from 1989 to

1993 and for the full four year period.

The coefficient of determination for 1989 to 1993 is 0.5314 and the coefficient of

efficiency is 0.5245. The difference between observed annual flow and estimated

flow is 6.3% of Q o b s . The statistical results for each one year interval are as shown in

table VIII. 1.

Figures VIII. 1 to VIII.4, found at the end of this chapter, are the final calibrated

model hydrographs of the Seymour Watershed. These graphs are created using the

modified watershed description file, the observed streamflows including abstractions,

and the calibrated watershed parameters.

66

period interval

coefficient of determination

coefficient of

efficiency

observed flow [m3/sl

model flow fm3/sl

observed -estimated

fm3/sl

percentage flow

difference 1989 -

1990

0.6422 0.6176 6137 5269 868 14% Qobs

1990-

1991

0.4706 0.4596 8334 7291 1043 12.5% Q o b s

1991 -

1992

0.5929 0.5914 5959 6254 -295 -5% Qobs

1992-

1993

0.5168 0.4927 4983 5006 -23 -0.5% Q o b s

1989-

1993

0.5314 0.5245 25412 23820 1592 6.3% Q o b s

Table Vm.1

The graphical output of the U B C Watershed Model indicates that the calculated

hydrograph is not consistent in accurately predicting the observed hydrograph and

there are times when the observed flow is negative. The negative historical flows are

most likely due to inaccurate reservoir elevation values which can be altered to avoid

negative flow values. However, these negative flows remain in the observed data

because they are infrequent and do not drastically affect the shape of the hydrograph.

The modeled flow is less reactive than the observed data. It appears that the late-

winter, early-spring months from January to April have the most discrepancies.

During these problematic months, the estimated flow is low and flat while the

observed flow shows numerous peaks. These disparities suggest that the model is not

correctly responding to the precipitation data which may be due to an inappropriately

used meteorological station. In other words, the streamflow station may be located in

67

an area that does not correlate on a meteorological basis to the information provided

by the environmental station. A supplemental station will be used to evaluate this

possibility.

2. Assess the Meteorological Accuracy of the Seymour Hatchery Station

The data from the North Vancouver Grouse Mountain Resort station is used to ensure

that the Seymour Hatchery station is a good representative for the meteorological

events of the Seymour Watershed. The hatchery station is located in a valley at an

elevation of 210 m but the Grouse Mountain station is near the summit at 1128 m.

Sometimes valley stations do not accurately exemplify the weather outside of the

local area and are heavily affected by orthographic lifting of the clouds.

Since the Grouse station is 1128 m in elevation, it is used to represent the highest

bands in the watershed, 5 and 6. The other bands are unchanged and continued to use

the Seymour Hatchery station. IOTSTA, IOPSTA, and IOESTA are the parameters

that require changing to implement the secondary meteorological station.

The new WAT file, containing the Grouse Mountain station, is calibrated using the

optimization routine. With the use of two AES stations, there are two snow

adjustment factors and two rain adjustment factors; a pair each for the Seymour

Hatchery and the Grouse Mountain stations. The final calibration results in a similar

68

WAT file as before but with altered snow adjustment factors, rain adjustment factors,

and POGRADL. For the hatchery station, the snow adjustment factor is -0.67 and for

the Grouse Mountain station, the snow adjustment factor is now 0.90. The rain factor

for the hatchery is 0.2 and the rain factor for Grouse Mountain is -0.80. POGRADL

is calibrated to 2.

Statistics are also run for this new WAT file for the years 1989 to 1993. The full

statistical report is found in appendix 2. This report uses the same calculations and

time periods as the report produced for the previous watershed model which used

only the Seymour Hatchery AES station. For the four year period, the coefficient of

determination is 0.5432, the coefficient of efficiency is 0.5423, and the observed flow

exceeds the estimated flow by 2.4% of Q o b s . A statistical summary is shown in the

following table.

period interval

coefficient of determination

coefficient of

efficiency

observed flow [m3/sl

model flow [m3/sl

observed -estimated

[m3/sl

percentage flow

difference 1989 -

1990

0.6447 0.6348 6137 5304 833 13.5% Q o b s

1990-

1991 ,

0.4984 0.4920 8334 7440 894 11% Qobs

1991 -

1992

0.5964 0.5932 5959 6340 -381 -6% Q o b s

1992-

1993

0.5011 0.4811 4983 5687 -704 -14% Qobs

1989-

1993

0.5432 0.5423 25412 24804 608 2.4% Q o b s

TableVin.2

69

The statistical results from the WAT file using the Grouse Mountain station does not

indicate a significant improvement from the original WAT file which used only the

Seymour Hatchery station. In general, the coefficient of determination and the

coefficient of efficiency improve when the mountain station is added but the annual

flow errors increase. By using the supplemental station, the greatest improvement in

the coefficient of efficiency is 0.324 occurring in 1990 to 1991 and the highest

increase in flow difference is 13.5% Q o b s in 1992 to 1993. After comparing the

statistics of the new WAT file with the original WAT file over the 1989 to 1993

period, the lack of general improvement is evident: overall coefficient of

determination improves by 0.0118, coefficient of efficiency increases by 0.0178, and

total flow error for the four year period decreases from 1591.8 m3/s to 608.3 m3/s.

Figures VIII. 5 through VIII. 8 display the calculated hydrographs using both the

Seymour Hatchery and Grouse Mountain meteorological stations. The observed

hydrographs are unchanged from previous graphs and include abstracted flows.

A visual inspection of the new and original hydrographs indicate that the Grouse

Mountain station does not improve the lack of reaction during the January to April

period. The new model hydrographs do not indicate the peaks in flow that are

present in the observed hydrographs but still generally resembles the original model

hydrograph. This implies that the Grouse :Mountain station and the Seymour

Hatchery station are showing the same precipitation response. There is no advantage

70

in implementing a supplemental AES station in this model. The diversion of the

model hydrograph from the observed hydrograph may be due to a slight temperature

inversion which is investigated in the following section.

3. Investigate the Accuracy of the Determined Form of Precipitation

A temperature inversion is when a pocket of cold air is trapped in a topographical

depression by lighter, warm air. Since this model is using one meteorological station

located in the Seymour valley, the recorded station temperature is possibly lower than

the temperature in the rest of the watershed which is higher in elevation. An

unidentified inversion causes the model to underestimate the rain precipitation and

over-estimate the amount of snowfall. To investigate this potential problem, the

rainfall and snowfall calculated by the model is plotted with the maximum and

minimum temperatures for each year of available data. Appendix 3 contains a

sample plot of these parameters for January through March, 1990 and a plot of the

observed flow, calculated flow, rainfall, and snowfall for the same period.

From these graphs, a temperature inversion in the valley is discovered. Rainfall

events are present during January to April which the model does not incorporate as

streamflow but the observed data records an increase in flow. The temperatures

during these rain events are close to zero and much of the precipitation is being

miscalculated as snow by the hydrologic model. To correct this situation, the

71

parameter which represents the value added to the temperature before determining

the form of precipitation, POTASR, is increased.

With the modification of the temperature parameter, the calibration constants of

POSREP and PORREP are altered again. Calibration runs using different POTASR

values found that the optimal amount of added temperature is 3 °C. A new run of the

optimization routine results in a POSREP of -0.44 and a PORREP of 0.014. Notice

that the absolute value of both the snow and rain AES adjustment factors have

decreased from the previous calibrated WAT file. This indicates that the historical

precipitation data is incorporated by the model with less prior alterations.

This new WAT file produces synthetic hydrographs which resemble the observed

graphs more than the previous Seymour WAT file which did not adjust the POTASR

factor. There is a clear improvement in the problematic months of January through

April. Figures VIII.9 through VIII. 12 are the annual hydrographs produced by this

final, improved WAT file.

The statistical results have also improved by using the new Seymour WAT file and

are displayed in full in appendix 4. A statistical summary is shown in table VIII.3 on

the next page.

72

period interval

coefficient of determination

coefficient of

efficiency

observed flow [m3/sl

model flow [m3/sl

observed -estimated

[m3/s]

percentage flow

difference 1989-1990

0.6697 0.6665 6137 5857 280 4 . 6 % Q o b s

1990-1991

0.5408 0.5342 8334 8449 -115 -1.4% Qobs

1991 -1992

0.5830 0.5668 5959 6713 -754 -12.6% Q o b s

1992-1993

0.5428 0.5153 4983 5322 -339 - 6 . 8 % Q o b s

1989-1993

0.5754 0.5672 25412 26341 -929 - 3 . 6 % Q o b s

Table Vffl.3

73

74

75

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84

85

PROBLEMS ASSOCIATED WITH GIS DATA,

ABSTRACTION DATA, HISTORICAL FLOW, AND

METEOROLOGICAL DATA

Much of the data used in this project requires some adjustments and assumptions

before it is entered into the watershed model. The GIS, storage flow, streamflow, and

meteorological databases each have particular problems, such as missing or

inaccurate data, which must be addressed.

1. GIS Data

The geographic information system data has proved to be very useful in describing

the Seymour Watershed but requires some modifications to the area of large GIS

polygons and clarification in definitions of some terrain characteristics. In the GIS

database, there are some polygons with excessively large areas which negatively

affect the accuracy of the description of the watershed. The mean elevation of a vast

area does not give an accurate description of the polygon since it is unlikely that the

area lies laterally along the mean elevation with little above or below this level. It is

more likely that a sizable but equal amount of area lies above the mean elevation as

there is below it. These large polygons skew the area within an elevation band, hence

86

all large GIS polygons were divided into smaller polygon slices of varying mean

elevations. These new subdivided areas are utilized to supplement elevation bands

which do not have equal terrain and ecosystem areas. Each polygon is visualized as a

large rectangle where the area grows linearly with the increase in elevation from

minimum to maximum. This is a necessary assumption since there is no information

to indicate how area relates to a change in elevation for each polygon.

Another problem with the GIS database is the lack of available information on some

watershed characteristics, namely drain class. Unlike other classifications like crown

closure and successional stage, drain class does not have any accessible

documentation to explain its definition or numerical values. The only information

obtained about drain class is from the mapping technologist who believed it was

related to soil permeability. A direct measurement of imperviousness would be very

useful for the U B C Watershed Model.

Since the drain class definition is unavailable, the percent of impervious area in a

band is approximated by the percent of bedrock present in an elevation band. As

stated before, this severely underestimates the amount of impervious area and an

additional percentage is required. Without previous experience working with the

U B C Watershed Model, it is difficult to estimate how much additional

imperviousness is needed and identify which bands require supplementation.

87

2. Storage Reservoir Elevation Data

Reservoir elevation measurements are required to calculate the flow that enters or

exits lake storage. These daily measurements are read manually by a technician who

records values at the Seymour Dam. Usually, the surface elevation rapidly increases

due to rain events then gradually decreases as water is withdrawn from the reservoir

during dry periods. Occasionally, the elevation significantly increases or decreases

one day then, the next day, suddenly reverts back to levels similar to the days prior to

the sudden change in elevation. In actuality, these elevation fluctuations are almost

impossible because it indicates a large volume rapidly entering storage which exits

the next day in the same abrupt manner. The result of these jumps is an erratic

observed watershed flow. Regularly, the observed hydrograph will drop below zero

or suddenly increase in a dramatic spike. The behavior of these unusual readings

indicate an error in measurement or some behavior of the reservoir surface that has

yet to be considered.

Seiching or periodic oscillating of the water surface is a possible cause for these

inaccuracies. These oscillations are due to a drag force exerted by the wind on the

surface of the lake. The actual stress felt by the water surface is influenced by the

wind strength, the stability of the meteorological boundary layer over the water

surface, the variability of the wind speed over the lake, the length of fetch, the degree

of wave development, and the amount of wave energy dissipation at the shores of the

88

lake. Of these influences, wind speed is the dominant factor since stress is related to

the square of the wind velocity. This drag force pushes water towards the far-side of

the lake resulting in a standing wave at the reservoir surface. When the wind stops,

the surface oscillates until the water level is returned to a stable position. If elevation

measurements are taken while the lake level is unstable, an inaccurate reading results.

Some unexpected readings are due to these periodic surface oscillations, but either

instrumental or human error is the most probable cause for most measurement

irregularities. Since readings are done manually, it is conceivable that values are

misread by the technician. The only way to eradicate this inconsistency is to

automate the task of measurement taking which is considered too costly.

In an attempt to correct these errors, lake elevations which are considered

inconsistent with adjacent daily values are changed to represent an average of

succeeding and preceding daily measurements. The elevation of Seymour Lake is

plotted for all available years so that these significant variances can be identified and

altered. Once this is complete, the observed flow is rarely negative.

3. AES and WSC Data

The meteorological and streamflow data files contain one significant problem:

missing data values. Sometimes weeks of precipitation, temperature, or flow data are

89

absent, indicated by a reading of '-9999'. If a substantial amount of data is missing

from an annual file, the entire year of information is useless. But i f only a few

sporadic days of data values are absent, they can be estimated and replaced.

To replace missing data for maximum or minimum temperatures, an average

temperature is taken from the available adjacent days of data. Usually, either the

maximum or minimum temperature is absent per day so a sudden change in

temperature is still indicated by the available values. Missing streamflow data is

averaged in a similar manner as the temperature. Maximum and minimum flow

values are not provided but it is assumed that daily streamflow is similar to its

preceding and succeeding days. Absent precipitation data is substituted with a zero

since an average of adjacent days is not an accurate replacement. Unlike temperature

or streamflow, precipitation can widely vary each day and no supplemental

information is available to indicate a change.

90

X. RESULTS AND CONCLUSIONS

1. Results

The final calibrated parameters for the Seymour Watershed are:

EOLMTD 850 m

EOLHI 1000 m

POGRADL F

P O G R A D M F

POGRADU 0 ^

snow adjustment factor -0.44

rainfall adjustment factor 0.014

impermeable fraction varies per band

ground water percolation 9 mm

deep zone share 0.7

upper groundwater runoff 30 days*

deep zone share time 150 days*

evapotranspiration 100 mm*

impermeable area factor 100 mm1

temperature increase before determining form of precipitation 3.0 °C

Table X.1

* indicate an unchanged default value

Notice POGRADL and P O G R A D M both equal 1. This means that for elevations

below 1000 m, EOLHI, the precipitation increases 1% for every 100 m increase in

elevation. Above 1000 m the precipitation gradient is zero. The low gradient values

are in agreement with research done by Loukas and Quick where it was found that

91

precipitation in the Seymour Valley is similar to the precipitation in the higher

elevations of the adjacent mountains.

The snow adjustment factor of -0.44 means that the recorded snowfall is decreased by

44 % to match the calculated watershed value. In contrast, the observed rain is

increased by 1.4 %, the rainfall adjustment factor, to correspond with the estimated

watershed value. If these two parameters remained unchanged at zero, the available

data is considered reliable and representative for the basin. The rainfall adjustment

factor of 0.014 is minimal enough to indicate that the historical data is accurate. The

value of -0.44 for the snowfall factor indicates that the historical snowfall data is not

perfect but still valid.

The impermeable fraction for each band is not altered from the initial watershed

description. The amount of groundwater percolation, indicating the maximum

capacity of sub-surface storage, is 9 mm where any excess runoff enters interflow.

Deep zone share value of 0.7 divides the groundwater into an upper component and

deep zone component: 30 % upper zone and 70 % deep zone. The upper groundwater

runoff time constant is 30 days and deep zone share time constant is 150 days. These

variables indicate the volumes of runoff and the shapes of the groundwater recession

flows at the end of the summer as upper groundwater and as deep zone share through

the winter.

92

Evapotranspiration is measured as the actual evapotranspiration from the potential

value. This is compared to how much moisture has satisfied the soil demands. The

impermeable area modification factor is compared with how much moisture has

satisfied the soil demands. These two parameters are used in an exponential decay

function to reconstitute summer rainfall runoff.

The final description of the Seymour Watershed is as follows:

band 1 2 3 4 5 6

mid band elevation

308 m 606 m 828 m 978 m 1127 m 1306 m

band area 34.3 km 2 35.8 km 2 34.3 km 2 26.5 km 2 25.3 km 2 19.6 km 2

forested fraction 0.99 0.96 0.95 0.97 0.94 0.86

density of canopy 0.50 0.55 0.52 0.50 0.41 0.23

orientation 0.99 0.80 0.99 0.94 0.95 0.98

glaciated area 0 0 0 0 0 0

glacier orientation

0 0 0 0 0 0

impermeable fraction

0.215 0.246 0.258 0.427 0.506 0.827

AES temp, station 1 1 1 1 1 1

AES ppt. station 1 1 1 1 1 1

ppt. adjustment 0 0 0 0 0 0

AES evap. station 1 1 r 1 1 1

AES station North Vancouver Seymour Hatchery WSC station North Vancouver Seymour Arm

Table X.2

93

It is difficult to determine i f the GIS data improved the amount of time needed to

calibrate the UBC Watershed Model since data from other sources were not used.

However, it is reasonable to conclude that the GIS watershed information was very

useful in describing some characteristics of the Seymour Watershed. Without it, all

watershed characteristics must be measured manually.

After examining the statistical analysis and resulting hydrograph of the Seymour

Watershed WAT file, it is concluded that GIS data can adequately describe some of

the physical characteristics of a watershed for the U B C Watershed Model. The

calculated model flow follows the historical flow well. It is not a perfect correlation

but the main characteristics of the measured hydrographs are replicated.

Flow discrepancies from the observed data occurring between January and April of

the initial calibrated watershed model are due to a temperature inversion present in

the Seymour Valley. The meteorological station used by the model is located in the

valley and occasionally records temperatures which are lower than those in the

remaining higher elevations of the watershed. An attempt to correct this error with

the use of a supplemental AES station was not successful. The inversion was finally

amended by increasing the value added to the temperature before determining the

form of precipitation, POTASR.

94

Despite the benefits of using GIS to describe the watershed, there are some

inaccuracies and obstacles in utilizing the information. Where large GIS polygons

are used to describe an area, an assumed linear relationship between area and

elevation increase is adopted to divide the polygon into smaller slices which better

represents the area. A more serious problem with using GIS data is the absence of

adequate documentation for some of the characteristics and values. Without

sufficient background explanation, some watershed characteristics are estimated from

crude assumptions and measurements. It is also important to note that the use of GIS

does not erase inaccuracies present in other data used in the watershed model such as

meteorological data and reservoir elevation records.

2. Conclusions

A Geographical Information System is very useful for the description of a watershed

in the U B C Watershed Model. Without a GIS, watershed characteristics such as area

and forest cover must be measured manually which is subjective and vulnerable to

human error. There is a vast amount of information available in a GIS but the most

practical databases for the watershed model are focused on terrain and ecology. In

this investigation, GIS information is used to establish the elevation, area, forested

fraction, shaded fraction, relative orientation, and the impermeable fraction of each

descriptive watershed band in the Seymour Watershed.

95

Most of the GIS information requires some manipulation before it can be applied to

the watershed model. Original data from the GIS is transferred to a spreadsheet and

is then sorted into elevation bands. Band characteristics are summarized to

accurately describe each elevation interval of the watershed. Since only the

minimum, maximum, and mean elevations of each GIS polygon are available, very

large polygons are divided into a number of smaller areas of varying elevations to

accurately depict the watershed area. These smaller polygons are then distributed to

the elevation band that it best represents.

The U B C Watershed Model is able to simulate observed hydrographs in the Seymour

Watershed with a coefficient of efficiency of 0.5672 and a coefficient of

determination of 0.5754 for the period between 1989 to 1993. The discrepancy

between the total observed flow and the total calculated flow is 929 m3/s or 3.6 % of

the observed flow for the four year period. Upon visual comparison of the annual

hydrographs, the calculated flow is quite similar to the observed flow. The main

hydrograph peaks are present and the general shape and tendencies of the observed

graph are reproduced.

Although the coefficients for efficiency and determination are not extremely high, the

similarities between the modeled hydrograph and the observed hydrograph lead to the

conclusion that the data from a GIS can be used to correctly depict the physical

characteristics of a watershed in the U B C Watershed Model. Normally, expected

96

values for these coefficients are greater than 0.75 for rain dominated watersheds and

greater than 0.85 for snowmelt dominated basins. The discrepancies between

estimated and observed flows and the low coefficients of efficiency and

determination are not entirely due to errors within the GIS data. Inaccuracies and

additional problems present in other data sources, such as impermeability values and

storage flow, also affect the accuracy of the model.

In general, the GIS data is plainly displayed and explanations of most characteristics

and values are given or are available from noted sources. However, there is one trait

with no available information which may be useful to the U B C Watershed Model,

namely drain class. An explanation of drain class may provide direct information on

the impermeability of an area. Instead, the impermeable fraction of the Seymour

Watershed is derived from the fraction of rock in each elevation band. This greatly

underestimates the actual amount of impermeable area and requires some correction.

The amount of additional impermeability required by each band cannot be

determined from calculations or measurements but is derived from the user's

previous experience with the watershed model. A user with experience calibrating

the UBC Watershed Model will be knowledgeable of the expected fraction of

impermeability of the average watershed. Generally, the amount of impermeable

area increases with elevation and is not less than 0.10 but can be up to 1.0, indicating

complete impermeability.

97

It is important to ensure that all the water produced in the watershed is accounted for

in the recorded streamflow data. The Seymour Watershed contains a lake which is

used as a storage reservoir and a source of domestic water for the Vancouver area.

These abstracted flows must be added to the historical streamflow to accurately

represent the natural flow produced by the watershed.

Storage flow is calculated from the change in reservoir elevation which is manually

measured at a random time of the day. Occasional errors in these elevation

recordings are apparent when calculating storage flow. Expected normal storage

flow should dramatically increase over a few days of rain as the reservoir fills with

water and then gradually decrease as flow is released from storage. At times, the

calculated storage flow suddenly increases or decreases one day but is followed by

flow readings similar to days prior to the sharp change. This flow pattern is most

likely due to an erroneous reservoir elevation reading which can be affected by

human error, the time of the day, and lake surface oscillations as previously

explained.

In order to calibrate the model against observed streamflow, the model watershed

area must equal the actual observed area. The area of each band may be altered to

accurately reproduce the real watershed outflow. In the case of the Seymour

Watershed, the total area according to the GIS data is less than the area accounted by

98

the Water Survey of Canada. The Water Survey of Canada is the source for historical

streamflow of the Seymour River and measures the entire watershed area contributing

to the Seymour River North Vancouver flow gauge station. The flow gauge is

located downstream of the Seymour Dam; however, the GIS data measures the

watershed area down to the dam only. The difference in area due to the distance

between the dam and the gauge station must be accounted for to produce an accurate

model calibration. From a topographical map, this additional area downstream of the

dam is identified and added to the appropriate elevation bands.

Presently, GIS data cannot be used as a direct input to the U B C Watershed Model

without prior manipulation. The next developmental step in the evolution of GIS and

the U B C Watershed Model is to create a detailed computer interface which

systematically corrects or adjusts the GIS information in preparation for the

watershed model.

Ultimately, GIS information is very valuable in describing most of the physical

attributes of a watershed. A Geographical Information System can completely

eliminate any need for manual measurements from topographical or aerial maps.

Although using a GIS diminishes the subjectivity of measuring physical watershed

characteristics, the accuracy of the watershed model is still affected by the accuracy

of other measurements.

99

REFERENCES

Acres International Limited et al. G V W D Watershed Ecological Inventory Pilot Study (Jamieson-Orchid-Elbow Drainage). Final Report. March 18, 1993.

Bhaskar, Nagashwar R., Wesley P. James, and Ravikumar S. Devulapalli. "Hydrologic Parameter Estimation Using Geographic Information System." Journal of Water Resources Planning and Management. Vol.18, No.5, September/October, 1992: 492-511.

Brooks, Norman H. , et al. Mixing in Inland and Coastal Waters. San Diego: Academic Press, Inc., 1979.

Burdge, Jeffrey, Mark A. Ross, and Patrick D. Tara. "New Directions in Integrated Hydrologic Modeling with GIS." Engineering Hydrology. Proceedings of the Symposium, Hydraulics Division on the American Society of Civil Engineers, July 25-30, 1993: 563-568. Edited by Chin Y . Kuo. New York: American Society of Civil Engineers, 1993.

Cruise, J. F. And R. G. Greene. "Urban Watershed Modeling Using Geographic Information System." Journal of Water Resources Planning and Management. Vol.121, No.4, July/August, 1995: 318-325.

DeVantier, Bruce A. And Arlen D. Feldman. "Review of GIS Applications in Hydrologic Modeling." Journal of Water Resources Planning and Management. Vol.119, No.2, March/April, 1993: 246-259.

French, Steven P., and Lyna L. Wiggins. GIS: Assessing Your Needs and Choosing a System. Chicago: The American Planning Advisory Service, 1991.

Haan, C. T., D. L. Nofziger, and H. Zhang. "Hydrologic Modeling with GIS: an Overview." American Society of Agricultural Engineers. Vol.6(4) July 1990: 453-457.

Hendricks, L. A. , et al. " A Geographical Information System (GIS) User Interface for Delineating Wellhead Protection Areas." Ground Water. Vol.31, No.3, May-June, 1993: 480-488.

Loukas, Athanasios, and Michael C. Quick. "24-H Design Storm for Coastal British Columbia." Journal of Hydraulic Engineering. Vol.121, No. 12, December, 1995: 889-899.

100

Ministry of Forests. Province of British Columbia. Forest Inventory Manual. Forest Classification/Sampling and Environmentally Sensitive Areas. Vol.2. 1992.

Muzik, I. And S. J. Pomeroy. " A Geographic Information System for Prediction of Design Flood Hydrographs." Canadian Journal of Civil Engineering. Vol.17 (1990): 965-973.

Ross, Mark A. And Patrick D. Tara. "Integrated Hydrologic Modeling with Geographic Information Systems." Journal of Water Resources Planning and Management. Vol.119, No.2, March/April, 1993: 129-137.

Shamsi, Uzair M . " A GIS Application to Hydrology." Engineering Hydrology. Proceedings of the Symposium, Hydraulics Division on the American Society of Civil Engineers, July 25-30, 1993: 371-375. Edited by Chin Y. Kuo. New York: American Society of Civil Engineers, 1993.

University of British Columbia Mountain Hydrology Group. U B C Watershed Model Manual. Version 4.0. Vancouver: Mountain Hydrology Group, Civil Engineering, U B C , 1995.

APPENDIX 1

Statistics Report for Initial Seymour Watershed Model

11018D15Ea3LslOhlOV |s4B U.B.C. |s4B WATERSHED MODEL I I |s4B STATISTICS REPORT

sl2h8V a95CDate: 07-12-1996 Time: 14:56:40

STATISTICS FOR THE OCT 1 , 1989 - SEP 30 , 1993 WATER YEAR(S) Mean Qobs

cms/d Mean Qest

cms/d Tot Qobs

cms Tot Qest

cms Tot Qobs

-Tot Qest Coeff.of

Eff Coeff.of

Det 1989 OCT 28.2 29.5 874.3 914.6 -40.3 0.6262 0.6272 NOV 31.4 36.9 941.4 1107.1 -165.7 0.8069 0.8279 DEC 15.5 20.3 481.5 627.9 -146.4 0.6708 0.6940 1990 JAN 19.1 11.1 591.2 344.3 246.9 0.4185 0.7113 FEB 14.6 3.4 409.5 95.2 314.3 -0.3839 0.3149 MAR 18.1 3.9 560.8 119.7 441.1 -1.7588 0.2483 APR 22.9 22.3 687.6 670.2 17.4 -1.0350 0.1422 MAY 17.9 13.5 555.0 419.3 135.7 -0.9831 0.0945 JUN 23.5 19.8 705.1 593.8 111.3 0.6300 0.6910 JUL 3.8 3.0 119.0 92.0 27.0 0.2593 0.3811 AUG 3.0 4.5 93.6 140.7 -47.1 -0.3912 0.3871 SEP 3.9 4.8 117.6 144.4 -26.8 0.4266 0.5478 OCT 25.6 26.0 792.1 805.8 -13.7 0.2289 0.2367 NOV 59.4 69.5 1782.1 2086.2 -304.1 0.5626 0.5792 DEC 18.0 8.5 557.4 264.3 293.1 0.1184 0.4197

1991 JAN 19.1 4.5 592.3 139.8 452.5 -0.0868 0.6174 FEB 49.2 22.9 1377.1 640.2 736.9 -0.0062 0.2461 MAR 8.5 4.8 263.9 150.2 113.7 -0.2533 0.0001 APR 23.8 23.7 712.5 709.8 2.7 0.4171 0.4440 MAY 21.4 29.5 662.2 913.8 -251.6 0.2559 0.5306 JUN 15.5 8.3 464.7 248.4 216.3 -1.3685 0.1448 JUL 6.8 5.5 212.0 169.1 42.9 -0.1473 0.1681 AUG 27.6 28.1 856.2 872.6 -16.4 0.7169 0.7204 SEP 2.1 9.7 61.7 290.6 -228.9 -1.6898 0.7301

102 s0B| s0B| s0B|

OCT 1.6 2.7 49.1 83.6 -34.5 -0.1279 o.oocfe 0

NOV 36.2 40.2 1086.2 1206.8 -120.6 0.4669 0.4783 DEC 22.7 22.9 705.1 710.8 -5.7 0.4693 0.4733 1992 JAN 43.8 31.2 1357.8 968.5 389.3 0.5848 0.6775 FEB 24.3 22.5 704.8 653.8 51.0 0.4097 0.4232 MAR 10.0 16.0 308.5 496.9 -188.4 -0.1364 0.6689 APR 24.9 28.4 747.7 851.5 -103.8 0.4794 0.5404 MAY 9.9 13.3 306.4 413.5 -107.1 -1.7485 0.2818 JUN 6.0 5.4 179.8 163.1 16.7 0.3290 0.3827 JUL 5.3 4.5 165.4 139.4 26.0 0.2576 0.2958 AUG 5.1 3.6 158.0 111.6 46.4 -0.0051 0.3563 SEP 6.3 15.2 189.8 454.6 -264.8 -0.7909 0.3146 OCT 20.9 25.9 649.4 801.8 -152.4 0.6859 0.7043 NOV 21.7 24.1 651.3 723.8 -72 .5 0.1491 0.2121

a59RFile DEC

: SEYMOUR6.STAall5CPage 1 of 5.6 4.6 173.1

2 143.6 29.5 -0.4575 0.2676

1993 JAN 12.3 2.4 382.2 75.2 307.0 -0.0759 0.6907 FEB 8.7 2.0 242.4 55.6 186.8 -0.5930 0.6922 MAR 22.2 23.6 689.5 731.6 -42.1 0.5665 0.5727 APR 28.3 37.8 849.4 1135.0 -285.6 0.4406 0.6600 MAY 24.6 21.1 763.2 652.7 110.5 -0.8603 0.0673 JUN 11.8 10.2 353.5 305.0 48.5 0.3325 0.4204 JUL 4.7 6.4 146.7 198.6 -51.9 -0.1409 0.2768 AUG 0.6 4.2 19.3 131.1 -111.8 -1.3429 0.2520 SEP 2.1 1.7 62.5 52.2 10.3 -0.1298 0.4321 Defined

Period Period 17.4

(891001-16.3

930930) 25411.9 23820.1 1591.8 0.5245 0.5314

Defined Period

Period 16.8

(891001-14.4

900930) 6136.6 5269.2 867.4 0.6176 0.6422

Defined Period

Period 22.8

(901001-20.0

910930) 8334.2 7290.8 1043.4 0.4596 0.4706

Defined Period

Period 16.3

(911001-17.1

920930) 5958.6 6253.9 -295.3 0.5914 0.5929

Defined Period

Period 13.7

(921001-13.7

930930) 4982.5 5006.2 -23.7 0.4927 0.5168

APPENDIX 2

Statistics Report from Initial Seymour Watershed Model Using Sey

Hatchery AES Station and Grouse Mountain AES Station

11018D15Ea3LslOhlOV |s4B U.B.C. |s4B WATERSHED MODEL I I |s4B STATISTICS REPORT

sl2h8V a95CDate: 07-12-1996 Time: 14:59:50

STATISTICS FOR THE OCT 1 , 1989 - SEP 30 , 1993 WATER YEAR(S) Mean Qobs

cms/d Mean Qest

cms/d Tot Qobs

cms Tot Qest

cms Tot Qobs

-Tot Qest Coeff.of

Ef f Coeff.of

Det 1989 OCT 28.2 24.6 874.3 762.6 111.7 0.6027 0.6448 NOV 31.4 33.4 941.4 1002.7 -61.3 0.8062 0.8201 DEC 15.5 19.0 481.5 587.7 -106.2 0.6596 0.6767 1990 JAN 19.1 12.4 591.2 383.7 207.5 0.4930 0.6945 FEB 14.6 3.6 409.5 99.5 310.0 -0.3592 0.3159 MAR 18.1 5.0 560.8 154.3 406.5 -1.4636 0.1656 APR 22.9 22.9 687.6 685.8 1.8 -0.2755 0.1375 MAY 17.9 20.3 555.0 629.4 -74.4 -0.3144 0.0864 JUN 23.5 20.2 705.1 605.8 99.3 0.6246 0.6670 JUL 3.8 3.3 119.0 102.5 16.5 0.2750 0.4391 AUG 3.0 4.2 93.6 128.9 -35.3 0.1366 0.3681 SEP 3.9 5.4 117.6 160.9 -43.3 0.0472 0.2533 OCT 25.6 23.9 792.1 741.7 50.4 0.2187 0.2226 NOV 59.4 65.5 1782.1 1964.5 -182.4 0.6117 0.6255 DEC 18.0 8.9 557.4 275.7 281.7 0.1400 0.4206 1991 JAN 19.1 4.8 592.3 148.6 443.7 -0.0633 0.6188 FEB 49.2 25.2 1377.1 704.7 672.4 0.0354 0.2465 MAR 8.5 5.0 263.9 155.1 108.8 -0.1700 0.0232 APR 23.8 24.1 712.5 721.7 -9.2 0.4903 0.5277 MAY 21.4 27.6 662.2 855.3 -193.1 0.4895 0.6301 JUN 15.5 20.3 464.7 608.0 -143.3 -0.3997 0.2130 JUL 6.8 9.0 212.0 279.0 -67.0 0.3098 0.5023 AUG 27.6 22.9 856.2 710.1 146.1 0.6913 0.7251 SEP 2.1 9.2 61.7 275.2 -213.5 -1.1497 0.7136

105 s0B| s0B|

sOBI

1 0 6

OCT 1.6 3.1 49.1 95. 0 -45 .9 -0. 0957 0. 0240 NOV 36.2 40.2 1086.2 1206. 3 -120 .1 0. 4377 0. 4502

DEC 22.7 23.8 705.1 739. 2 -34 .1 0. 4745 0. 4774

1992 JAN 43.8 33.3 1357.8 1032. 8 325 .0 0. 5927 0. 6591 FEB 24.3 25.5 704.8 740. 4 -35 .6 0. 4081 0. 4159 MAR 10.0 19.4 308.5 600. 4 -291 .9 -1. 1475 0. 6314 APR 24.9 24.3 747.7 729. 7 18 .0 0. 4578 0. 5675 MAY 9.9 14.5 306.4 450. 3 -143 .9 -1. 6688 0. 3854 JUN 6.0 4.9 179.8 148. 3 31 .5 0. 3744 0. 4273 JUL 5.3 4.1 165.4 125. 9 39 .5 0. 2486 0. 3702 AUG 5.1 3.2 158.0 100. 4 57 .6 0. 0448 0. 4169 SEP 6.3 12.4 189.8 372. 0 -182 .2 -0. 1099 0. 3234 OCT 20.9 23.8 649.4 736. 5 -87 . 1 0. 6756 0. 6893 NOV 21.7 25.0 651.3 749. 3 -98 .0 0. 1430 0. 2111

a59RFile DEC

: SEYGROUS.STAall5CPage 1 of 5.6 4.9 173.1

2 150. 6 22 .5 -0. 5072 0. 2732

1993 JAN 12.3 2.5 382.2 78. 0 304 .2 -0. 0637 0. 6925 FEB 8.7 2.4 242.4 66. 6 175 .8 -0. 4734 0. 4773 MAR 22.2 23.8 689.5 738. 6 -49 .1 0. 6108 0. 6155 APR 28.3 35.3 849.4 1059. 1 -209 .7 0. 5380 0. 6860 MAY 24.6 27.3 763.2 846. 0 -82 .8 -0. 4030 0. 0005

JUN 11.8 24.3 353.5 728. 2 -374 .7 -2. 0642 0. 4317

JUL 4.7 8.9 146.7 277. 3 -130 .6 -0. 0658 0. 3684

AUG 0.6 5.7 19.3 176. 1 -156 .8 -2. 6602 0. 2657

SEP 2.1 2.7 62.5 80. 3 -17 .8 -0. 2805 0. 2852

Defined Period

Period 17.4

(891001-17.0

930930) 25411.9 24770. 8 641 .1 0. 5424 0. 5434

Defined Period Period 16.8

(891001-14.5

900930) 6136.6 5303. 9 832 .7 0. 6348 0. 6447

Defined Period Period 22.8

(901001-20.4

910930) 8334.2 7439. 7 894 .5 0. 4920 0. 4984

Defined Period Period 16.3

(911001-17.3

920930) 5958.6 6340. 4 -381 .8 0. 5932 0. 5964

Defined Period Period 13.7

(921001-15.6

930930) 4982.5 5686. 8 -704 .3 0. 4811 0. 5011

107

APPENDIX 3

Sample Precipitation and Temperature Graphs for 1990

108

109

110

APPENDIX 4

Statistics Report from Seymour Watershed Model Using Adjusted

Precipitation Temperature

11018D15Ea3LslOhlOV

s4B U.B.C. sOB| S4B WATERSHED MODEL sOB|

111

I S4B STATISTICS REPORT sOBI

sl2h8V a95CDate: 07-15-1996 Time: 11:50:10

STATISTICS FOR THE OCT 1 , 1989 - SEP 30 , 1993 WATER YEAR(S)

Mean Qobs Mean Qest cms/d cms/d

Tot Qobs Tot Qest Tot Qobs Coeff.of Coeff.of cms cms -Tot Qest Eff Det

1989 OCT

NOV

DEC

1990 JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

1991 JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

28.2

31.4

15.5

19.1

14.6

18.1

22.9

17.9

23.5

3.8

3.0

3.9

25.6

59.4

18.0

19.1

49.2

8.5

23.8

21.4

15.5

6.8

27.6

26.9

38.7

22.1

20.0

11.9

8.7

21.9

12.8

17.8

3.1

4.4

4.4

25.5

81.1

17.0

12.5

38.2

7.2

29.2

23.2

7.3

4.9

25.2

874.3

941.4

481.5

591.2

409.5

560.8

687.6

555.0

705.1

119.0

93.6

117.6

792.1

1782.1

557.4

592.3

1377.1

263.9

712.5

662.2

464.7

212.0

856.2

834.1

1161.5

684.7

620.3

334.5

270.1

656.2

396.3

534.1

96.0

136.1

133.3

792.0

2432.3

527.2

387.0

1068.8

224.4

875.4

720.6

218.2

150.5

780.2

40.2

-220.1

-203.2

-29.1

75.0

290.7

31.4

158.7

171.0

23.0

-42.5

-15.7

0.1

-650.2

30.2

205.3

308.3

39.5

•162.9

-58.4

246.5

61.5

76.0

0.6321

0.7913

0.6230

0.6686

0.3456

-0.5065

-0.9906

-0.8805

0.5722

0.2521

-0.1159

0.5826

0.1530

0.5451

0.4695

0.3642

0.2338

0.2560

0.3385

0.5494

-1.6792

-0.1141

0.7178

0.6405

0.8175

0.6630

0.6793

0.3823

0.3355

0.1210

0.0982

0.6946

0.4008

0.3911

0.6093

0.1611

0.6073

0.4852

0.6061

0.2651

0.3377

0.4868

0.5677

0.1664

0.1709

0.7210

112 OCT 1.6 2.4 49.1 74.8 -25.7 -0.0713 0.0003 NOV 36.2 42.9 1086.2 1287.4 -201.2 0.3512 0.4067 DEC 22.7 31.4 705.1 973.1 -268.0 0.1521 0.3676 1992 JAN 43.8 46.9 1357.8 1455.1 -97.3 0.6441 0.6495 FEB 24.3 27.2 704.8 788.7 -83.9 0.3314 0.4029 MAR 10.0 14.0 308.5 434.5 -126.0 0.2915 0.6396 APR 24.9 20.2 747. 7 604.7 143.0 0.4339 0.5430 MAY 9.9 10.8 306.4 335.2 -28.8 -0.8629 0.3175 JUN 6.0 4.6 179.8 138.3 41.5 0.2742 0.3556 JUL 5.3 3.8 165.4 117.7 47.7 0.1239 0.2492 AUG 5.1 3.0 158.0 93.9 64.1 -0.1796 0.3221 SEP 6.3 13.7 189.8 410.0 -220.2 -0.4332 0.3159 OCT 20.9 23.3 649.4 721.6 -72 .2 0.6889 0.6979 NOV 21.7 26.9 651.3 806.9 -155.6 0.1793 0.2842

a59RFile DEC

: SEYMOUR7.STAall5CPage 1 of 5.6 7.8 173.1

2 242.7 -69.6 -0.6045 0.2007

1993 JAN 12.3 6.1 382.2 188.4 193.8 0.2961 0.7101 FEB 8.7 3.5 242.4 96.7 145.7 -0.1482 0.7475 MAR 22.2 31.3 689.5 969.6 -280.1 0.4726 0.6255 APR 28.3 38.5 849.4 1155.3 -305.9 0.4300 0.6778 MAY 24.6 16.7 763.2 517.2 246.0 -2.0215 0.0126 JUN 11.8 9.3 353.5 277. 7 75.8 0.3025 0.4157 JUL 4.7 5.8 146.7 178.7 -32.0 0.0604 0.2761 AUG 0.6 3.8 19.3 116.6 -97.3 -0.9105 0.2433 SEP 2.1 1.7 62.5 50.3 12.2 -0.1108 0.3827 Defined

Period Period 17.4

(891001-18.0

930930) 25411.9 26340.8 -928.9 0.5672 0.5754

Defined Period

Period 16.8

(891001-16.0

900930) 6136.6 5857.2 279.4 0.6665 0.6697

Defined Period

Period 22.8

(901001-23.1

910930) 8334.2 8448.6 -114.4 0.5342 0.5408

Defined Period

Period 16.3

(911001-18.3

920930) 5958.6 6713.3 -754.7 0.5668 0.5830

Defined Period

Period 13.7

(921001-14. 6

930930) 4982.5 5321.8 -339.3 0.5153 0.5428