WATER QUALITY TREND AT THE UPPER PART OF JOHOR RIVER IN
RELATION TO RAINFALL AND RUNOFF PATTERN
HASMIDA BINTI HAMZA
A thesis submitted in fulfillment of
the requirement for the award of the degree of
Master of Civil Engineering (Hydraulic and Hydrology by Course Work)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
JUNE, 2009
ii
To my late father, my beloved mother, my ever-loving family and friends
iii
ACKNOWLEDGEMENTS
I wish to express my deep appreciation to my research supervisor Associate
Professor Ir Dr Ayob Katimon for his continual guidance and encouragement during the
preparation of this research thesis.
I also wish to express my thanks to Department of Irrigation and Drainage
(DID), Johor and Syarikat Air Johor (SAJH) for their permission to use the hydrological
and water quality data respectively in this present study. To all of my friends, thank you
very much for their willingness to help, valuable ideas and opinion, support and courage.
I deeply express my appreciation to my beloved mother, Marwa Binti Halik, my
sister, Kak Hasmah and to all of my ever-loving family for their love, care, support,
great patience and everything. Thank you very much!
iv
ABSTRACT
Due to the fact that rainfall and runoff have a vital relationship to water quality,
these hydrological variables are among the most dominants controlling factor on the
variation of water quality. Trend analysis on time series data has proved to be a useful
tool for effective water resources planning, design, and management since trend
detection of hydrological variables such as streamflow and precipitation provides useful
information on the possibility of change tendency of the variables in the future. Both
ARIMA modelling approach (parametric method) and Mann-Kendall test (nonparametric method) were applied to analyses the water quality and rainfall-runoff data
for Johor River recorded for a long period (2004 to 2007). SPSS Statistical Packages
Software was used in performing ARIMA time series modelling and Microsoft Excel
function was used for Mann-Kendall (MK) test. This study aims to investigate the water
quality trend over a long period particularly at the upper part of the Johor River in
relation to rainfall and runoff pattern using both parametric and non-parametric method.
This study focusing on four parameters of nutrient types of pollutant (NH4, turbidity,
colour and SS) and four parameters of chemical types of pollutant (PH, Al, Mn, Fe).
Nutrient and chemical types of pollutant is more significant as major land use
surrounding Johor River is agricultural activities and eroded river corridor problem was
observed. MK test shows that all of the parameters investigated are significant at 95%
confidence limit. Increasing trend was observed for turbidity, colour and SS while
decreasing trend in PH, Al, Mn and Fe. Probability (P) value obtained is zero and others
gave value near to zero shows that all of the water quality parameters have quiet similar
trend with rainfall and flow. The results from the fitted ARIMA models indicate that all
of the water quality parameter investigated was generated by AutoRegressive Integrated
Moving Average (ARIMA) processes ranges from ARIMA (1,1,1) to (2,1,2). Colour,
Turbidity, SS, NH4 and Mn follow a similar trend with rainfall-runoff pattern while PH,
Al and Fe have the opposite trend compare to rainfall-runoff pattern.
v
ABSTRAK
Berdasarkan fakta yang menyatakan bahawa hujan dan air larian mempunyai
hubungan yang sangat penting terhadap kualiti air, pembolehubah-pembolehubah
hidrologi ini adalah di antara faktor kawalan yang paling utama ke atas perubahan kualiti
air. Trend analysis terhadap data siri masa telah dibuktikan sebagai sebuah alat untuk
perancangan sumber air secara efektif, rekabentuk dan pengurusan, kerana pengesanan
corak naik-turun bagi parameter-parameter hidrologi seperti hujan dan pergerakan air
menyediakan maklumat berguna mengenai kemungkinan berlaku perubahan pada
parameter-parameter hidrologi ini pada masa akan datang. Kedua-dua kaedah iaitu
permodelan ARIMA (kaedah parametrik) dan Ujian Mann-Kendall (kaedah bukan
parametrik) telah diaplikasikan untuk menganalisa data kualiti air dan data hujan-air
larian bagi Sungai Johor yang telah direkodkan pada satu tempoh yang panjang (2004
hingga 2007). SPSS Statistical Packages Software telah digunakan dalam menjalankan
permodelan siri masa ARIMA dan Microsoft Excel telah digunakan untuk Ujian MannKendall (MK). Kajian ini bermatlamat untuk mengkaji corak kualiti air berhubungan
dengan corak hujan dan air larian pada tempoh yang panjang terutamanya di bahagian
atas Sungai Johor dengan menggunakan kedua-dua kaedah parametrik dan bukan
parametrik. Kajian ini telah memfokuskan kepada lapan parameter kualiti air. Nutrient
dan bahan pencemar jenis kimia adalah lebih penting memandangkan guna tanah
terbesar di sekitar Sungai Johor ialah aktiviti pertanian dan masalah hakisan tebing
sungai telah dikesan. Ujian Mann-Kendall menunjukkan bahawa semua parameter yang
telah dikaji adalah penting pada 95% had keyakinan. Corak meningkat telah
diperhatikan pada parameter kekeruhan (turbidity), warna (colour) dan endapan terapung
(SS) manakala corak menurun bagi PH, Al, Mn dan Fe. Nilai kebarangkalian (P) yang
diperolehi ialah kosong dan menghampiri kosong menunjukkan bahawa semua
parameter kualiti air yang dikaji mempunyai corak yang hampir sama dengan corak
hujan dan air larian. Keputusan daripada model ARIMA yang terbaik menunjukkan
bahawa semua parameter yang telah dikaji dihasilkan oleh proses AutoRegressive
Integrated Moving Average (ARIMA) di antara ARIMA (1,1,1) hingga (2,1,2). Warna
(colour), kekeruhan (turbidity), endapan terapung (SS), NH4 and Mn mempunyai corak
yang sama dengan corak hujan-air larian manakala PH, Al dan Fe mempunyai corak
yang berlawanan dengan corak hujan-air larian.
vi
TABLE OF CONTENTS
CHAPTER
PAGE
THESIS TITLE
DECLARATION SHEET
i
DEDICATION
ii
ACKNOWLEDGEMENTS
iii
ABSTRACT
iv
ABSTRAK
v
TABLE OF CONTENTS
vi
LIST OF FIGURES
x
LIST OF TABLES
xi
LIST OF SYMBOLS AND ABREVIATIONS
xii
LIST OF APPENDICES
xiii
CHAPTER I
INTRODUCTION
1.1
Introduction
1
1.2
Background of Study
3
1.3
Problem Statement
3
1.4
Aims of Study
7
1.5
Objectives of Study
7
1.6
Scope of Study
7
vii
1.7
Limitation of Study
8
1.8
Significant of Study
8
CHAPTER II
STUDY AREA DESCRIPTION
2.1
Introduction
9
2.2
Study Area
9
2.3
Water Resources
11
2.4
Land Use
11
CHAPTER III
LITERATURE REVIEW
3.1
Introduction
13
3.2
Water quality parameter
13
3.2.1
PH
14
3.2.2
Turbidity
14
3.2.3
Suspended sediment (SS)
14
3.2.4
Trace elements (Fe, Al and Mn)
14
3.2.5
Ammonium (NH4)
15
3.2.6
Colour
15
3.3
Rainfall-runoff Relationship
15
3.4
Time Series Data
17
3.5
Previous studies
17
3.5.1
18
Previous Study on Mann-Kendall Test
3.5.2 Previous Study on Water Quality and ARIMA
Modelling
20
viii
CHAPTER IV
RESEARCH SITE METHODOLOGY
4.1
Introduction
22
4.2
Mann-Kendall Test Procedure
22
4.3
Theory of ARIMA Modeling Approach
23
4.4
Model Assumptions
24
4.4.1
Data Stationarity
24
4.4.2
Normal Distribution
25
4.4.3
Outlier
25
4.4.4
Missing Data
26
4.5
CHAPTER V
ARIMA Modelling Procedures
27
4.5.1
Model Identification
27
4.5.2
Model Parameters Estimation
28
4.5.3
Diagnostic Checking
28
DATA COLLECTION AND ANALYSIS
5.1
Introduction
30
5.2
Types and Sources of Time Series Data
30
5.2.1
Hydrological Data
31
5.2.2
Water Quality Data
31
5.3
Example of Mann-Kendall Test
31
5.4
Example of ARIMA Modelling
34
5.4.1
Model Identification for PH 2004
34
5.4.2
Estimation of Model Parameter for PH 2004
36
5.4.3
Diagnostic Checking for PH 2004
37
5.4.4
Best Fitted ARIMA Model Equation
39
ix
CHAPTER VI
RESULT AND DISSCUSSION
6.1
Rainfall-runoff Pattern
40
6.2
Correlation between Rainfall-Flow and Water Quality
41
6.3
Water Quality-Rainfall and Water Quality-Flow Trend
42
6.4
Best Fitted ARIMA Model
46
6.5
Water Quality Trend Using ARIMA
52
6.5.1
55
CHAPTER VII
Discussion on the Water Quality Trend
CONCLUSION AND RECOMMENDATION
7.1
Introduction
58
7.2
Recommendation
59
REFERENCES
60
APPENDICES
66
x
LIST OF FIGURES
No
Title
Page
1.1
Typical Johore River Corridor
6
1.2
Eroded River Corridor
6
2.1
Map of Johore River
10
2.2
Johore River and Its Surrounding
12
3.1
Schematic representation of watershed hydrology
16
3.2
Block diagram of watershed hydrological processes and storage
17
4.1
Histogram shows outliers in a set of observation
26
5.1
Procedures for determination of descriptive statistic of the
33
variables investigated for Mann-Kendall analysis
5.2
Procedures for determination of z-score of the variables
34
investigated for Mann-Kendall analysis
5.3
Plots of the Original Data for PH Series at Year 2004
35
5.4
Analyse Tools in SPSS for estimation of model parameter
36
5.5
ACF and PACF Plot of Error for Best Fitted Model, PH (2004)
38
5.6
Plot of Residual
38
xi
LIST OF TABLES
No
Title
Page
2.1
Main Land Use at Johor River
11
5.1
Possible ARIMA Model for PH (2004)
37
6.2 (a)
Correlation between Water Quality and Rainfall
42
6.2 (b)
Correlation between Water Quality and Flow
42
6.3
Correlation among Parameter Investigated
42
6.4
Result Summary from Mann Kendall Analyses of Water
43
Quality and Rainfall Parameter
6.5
Result Summary from Mann Kendall Analyses of Water
44
Quality and Flow Parameter
6.6 (a)
Best Fitted ARIMA Model for PH Parameter
46
6.6 (b)
Best Fitted ARIMA Model for Colour Parameter
46
6.6 (c)
Best Fitted ARIMA Model for Turbidity Parameter
47
6.6 (d)
Best Fitted ARIMA Model for Alluminium (Al) Parameter
47
6.6 (e)
Best Fitted ARIMA Model for Iron (Fe) Parameter
48
6.6 (f)
Best Fitted ARIMA Model for Ammonium (NH4) Parameter
48
6.6 (g)
Best Fitted ARIMA Model for Manganese (Mn) Parameter
48
6.6 (h)
Best Fitted ARIMA Model for Suspended Solid (SS) Parameter
49
6.7 (a)
PH Trend
53
6.7 (b)
Iron (FE) Trend
53
6.7 (c)
Alluminium (AL) Trend
53
6.7 (d)
Colour Trend
54
6.7 (e)
Turbidity Trend
54
6.7 (f)
Ammonium (NH4) Trend
54
6.7 (g)
Manganese (MN) Trend
55
6.7 (h)
Suspended Solid (SS) Trend
55
xii
LIST OF SYMBOLS AND ABREVIATIONS
a
Standard deviation

AR coefficient

MA coefficient
ACF
Autocorrelation function
AIC
Aikaike Information Criteria
ARIMA
Autoregressive Integrated Moving Average
at
Residual series
B
Back shift operator
C
Model constant
ck
Autocovariance at lag k
d
Degree of differencing
DID
Department of Irrigation and Drainage
FELDA
MARDI
Malaysian Agricultural Research and Development Institute
Nt
Noise series
OLS
Ordinary Least Square
p
AR parameter
PACF
Partial Autocorrelation Function
Xt
Input series (endogenous series)
Yt
Output series (exogenous series)
xiii
LIST OF APPENDICES
No
Title
Page
A
Flowchart of Methodology
66
B
Daily Hydrological and Water Quality Data
67
C
Output from Microsoft Excel Data Analysis
106
D
Sequence Plot, Histogram, ACF and PACF Plot of the Original
114
Data for PH
E
Sequence Plot, Histogram, ACF and PACF Plot of the Original
117
Data for Colour
F
Sequence Plot, Histogram, ACF and PACF Plot of the Original
121
Data for Turbidity
G
Sequence Plot, Histogram, ACF and PACF Plot of the Original
125
Data for Al
H
Sequence Plot, Histogram, ACF and PACF Plot of the Original
129
Data for Fe
I
Sequence Plot, Histogram, ACF and PACF Plot of the Original
133
Data for NH4
J
Sequence Plot, Histogram, ACF and PACF Plot of the Original
137
Data for Mn
K
Sequence Plot, Histogram, ACF and PACF Plot of the Original
141
Data for SS
L
Example of Output Generated From SPSS
144
M
Diagnostic Checking
148
1
CHAPTER I
INTRODUCTION
1.1
Introduction
Long-term trend of water quality and hydrological parameters (rainfallrunoff) in natural systems reveal information about physical, chemical and biological
changes and variations due to manmade and seasonal interventions.
Trend analysis on time series data has proved to be a useful tool for effective
water resources planning, design, and management (Burn and Hag Elnur, 2002; Gan,
1998; Lins and Slack, 1999; Douglas et al., 2000; Hamilton et al., 2001; Yue and
Hashino, 2003; and others) since trend detection of hydrological variables such as
streamflow and precipitation provides a useful information on the possibility of
change tendency of the variables in the future (Yue and Wang, 2004). Abu Farah
(2006) mentioned that trend analysis is a formal approach in deciding whether an
apparent change in water quality is likely due to random noise or to an actual
underlying change in the ecosystem.
Both parametric and non-parametric tests are commonly used for trend
analysis. Parametric trend tests are more powerful than nonparametric ones, but they
require data to be independent and normally distributed. On the other hand, nonparametric trend tests require only that the data be independent and can tolerate
outliers in the data. One of the widely used non-parametric tests for detecting trends
in the time series is the Mann Kendall test (Mann, 1945; Kendall, 1955). General
2
relationship of the water quality-rainfall and water quality-flow can be obtained by
the probability (P) value. P equal to zero means that the probability that different in
trend of two variables investigated is zero. Hence, they have similar trend. This
method has been used by many researchers (A.W Kenneth and G.P Robert, 2006;
Yue and Wang, 2004; Abu Farah, 2006 and others) from various disciplines. The
majority of the previous studies have assumed that sample data are serially
independent.
It is known that some hydrological time series such as water quality and
streamflow time series may show serial correlation (Yue and Wang, 2004). In such a
case, the existence of serial correlation component such as an autoregressive (AR)
process from a time series will affect the ability of the Mann-Kendall test to assess
the significance of trend (Von Storch, 1995). Besides, these parameters change
continually through time, arise from dynamic processes and consist of random error
components with stochastic variations in space and time (Anpalaki et. al., 2006) that
cannot be explained by normal analytical procedure. Besides, water quality and
hydrological time series with long-term trend, when recorded by any consistent time
interval, will display some measure of auto-correlation. This is expected to affect the
p-values derived from autoregressive and q-values from the moving average model
parameters in a time series ARIMA modeling approach. Autoregressive (AR)
component of the model represent the relationship between present and past
observations. General relationship of water quality parameter and rainfall-runoff
parameter can be obtained by comparing the annual runoff coefficient with AR
coefficient of each parameter investigated.
This approach can also adequately represent the relationship of observed data
using few parameters (Box et.al., 1994). Though the main aim of such effort is
directed towards obtaining suitable dynamic models for predicting future value,
through transfer function modeling approaches, dynamic relationship between
hydrological and water quality parameters could be obtained.
ARIMA modelling approach are extensively used for modelling of seasonal
or nonseasonal time series data from various disciplines, such as hydrology,
economic, environment, politics, etc. (McLeod, 1978), (Lee et.al., 2000), (Liu et. al.,
3
2001), and (Slini et. al., 2002). Both Mann-Kendall test and ARIMA modelling were
performed in this study to determine the water quality and rainfall-runoff trend and
their relationship.
1.2
Background of Study
This study was concerned on the determination of water quality trend at the
upper part of Johore River in relation to rainfall and runoff pattern. Study area
description can be found in Chapter 2 of this report. The study processes are
including site visit, data collection, Mann-Kendall analysis and ARIMA time series
or trend analysis. The methodology procedures were discussed in Chapter 4.
Eight (8) water quality parameters were modelled to see their variation when
rainfall and runoff changed. The water quality parameters investigated are the PH
level, Aluminium (Al), Manganese (Mn), Ferum (Fe), Ammonium (NH4), turbidity,
colour and suspended solid (SS).
The climax of this study occurred on the modelling stage whereas the aims
and the objectives of the study were achieved. Mann-Kendall test and ARIMA
modelling approach was applied in this stage and was performed using Microsoft
Excel function and SPSS Statistical Packages respectively.
1.3
Problem Statement
Johore River is the main river which is situated in Johor River basin. The
resources of this river are increasingly being used as the raw water to satisfy the
clean water supply demand not only for Johore State but also for Singapore. It also a
fascinating place for tourist where the beautiful presence of fireflies glowing in the
thousands here. These mesmerizing insects are found in abundance on the
4
berembang trees that line the banks of the Johore River. Hence, the water quality of
this river should maintain clean.
Based on the Environmental Quality Report (DOE, 2007), Johore River Basin
was categorized as slightly polluted river basin. As reported, Johore River still
considered clean which is fall into Type II (Water Quality Standard Malaysia).
However, two of its tributaries (Lebam River and Tiram River) are categorized as
slightly polluted river. Quick action should be taken to assess the level of water
quality of the Johore River. Therefore, some prevention action could be made to
control the pollution by the aims of maintaining its river water quality status or if
possible improved its water quality.
Other problem is eroded and disturbed of river corridor as shown in Figure
1.1 and Figure 1.2 below. Then, soil detachment process from the river bank may
have the possibility to cause turbid water when some amounts of materials such as
clay, silt, organic and inorganic matter enter the river.
Eroded soil particles also carry associated pollutants that are harmful to the
ecology of receiving water bodies and to human being (Vladimir, 2003). This is
probably implied that the increasing amount of rainfall might have a significant
effect to the water quality parameters (Feng et. al., 2007).
Vladimir (2003) also mentioned that pollution from diffuse sources is driven
by meteorological events that include at atmospheric transport for local, regional,
global and precipitation. Crapper et al. (1999) also suggested that rainfall is probably
the most widely measured meteorological parameter and is one of the major
determinants of erosion. Supported by Feng (2007), mentioned that increasing of
rainfall in a wet season could result in rising of discharge flow and concentration of
Total Suspended Solid (TSS).
Increase suspended sediment and turbidity can directly affect aquatic
organisms, alter stream grade, contribute to flooding, and transport a large nutrient
flux (Sigler et al., 1984). Vladimir (2003), presented works by U.S. Agricultural
Research Centres following the devastating Dust Bowl erosion of farmland during
5
1930s, based on their measurements it was found that general land disturbance by
agriculture can increase erosion rates by two or more orders of magnitude. Fertilizer
used in the agriculture activities also the major pollutant in water quality. Then,
although mining is not widespread as agriculture, water quality impairment resulting
from mining is usually more harmful.
The water quality, however, also will be affected by stream flow volumes,
both concentrations and total loads (Lunchakorn et al., 2008). Their cited such the
research conducted in Finland indicates that changes in stream water quality, in terms
of eutrophication and nutrient transport, are very dependent on changes in stream
flow and a reduction in stream flow might lead to increase in peak concentrations of
certain chemical compounds. Besides, according to the hydrological cycle, when the
precipitation increases, it results in accumulation of rainfall in rivers.
These facts show that rainfall and runoff have a vital relationship to the water
quality. In fact, these hydrological variables are among the most dominants
controlling factor on the variation of water quality. Understanding of this relationship
in Johor River system is a vital key toward an optimal management of its resources.
Analyses on daily water quality data together with the rainfall-runoff data as
the primary investigation might gave a clear view of this relationship. However, it is
difficult to quantify because it involves multi-inter-related variables and these
parameters change continually through time, arise from dynamic processes and
consist of random error components with stochastic variations in space and time that
cannot be modeled or explained by normal analytical procedures. Hence, it is very
difficult to incorporate the effects of all these factors in any single calculation.
Therefore, ARIMA and transfer function modelling which is capable to model the
trend and identified the water quality and rainfall-runoff relationship was applied in
this study.
This study was focusing on four parameters of nutrient types of pollutant
(NH4, turbidity, colour and suspended solid) and four parameters of chemical types
of pollutant (PH, Al, Mn, Fe). Nutrient and chemical types of pollutant is more
significant as major land use surrounding Johore River is agricultural activities.
6
Eroded river bank might also affect by this activities and or by other activities such
as sand mining and land reclamation activities.
Figure 1.1: Typical Johore River Corridor.
Figure 1.2: Eroded River Corridor
7
1.4
Aims of Study
The study aims to investigate the water quality trend over a long period at the
upper part of Johor River. The study also aims to provide some useful information on
the relationship between water quality, rainfall and runoff.
1.5
Objectives of Study
The objectives of this study are as follow:
1. To determine water quality, rainfall and runoff trend of Johor River for the
year of 2004 to 2007 using ARIMA modeling approach.
2. To determine relationship between water quality parameter and rainfallrunoff pattern of the study catchment through transfer function modeling
approach.
3. Determine the most sensitive parameter among the water quality parameter
regarding to the changes in rainfall and runoff
1.6
Scope of Study
1. Site visit to the study area to assess the problems that exist in the site.
2. Data collection of water quality and rainfall-runoff data.
3. Trend analysis by performing ARIMA time series analysis using SPSS
Statistical Packages Software.
4. Statistical analysis for model fitting and diagnostic checking using SPSS
Statistical Packages Software.
5. Z-score determination using Microsoft Excel function for Mann-Kendall
analysis.
8
1.7
Limitation of Study
1. The water quality trend will be predicted for the period of 4 years (20042007) based on available data.
2. Only eight (8) parameters of water quality will be investigated.
3. The study only covered area surrounding the river for about 30 km length of
the Johor River.
4. This study will focused on water quality trend and water quality and rainfall
runoff relationship not the cause and source of pollutant.
5. Daily data will be used for all of the parameter.
1.8
Significant of Study
This study will provide information on the dynamics of the water quality and
hydrologic behavior of Johore River based on past time series data. Output generated
from transfer function model is important because it is only when the dynamic
characteristics of a system are understood that intelligent direction, manipulation,
and control of the system is possible. Understanding of this relationship in Johore
River system is a vital key toward an optimal management of its resources.
It was recognized that water is life itself and without it we cease to exist.
Moreover, the demand of water increases and pollution depletes more of our water
resources. Therefore, this study is one of the initiative to ensure the water quality and
environment been properly manage at Johor River, as it is the most important sources
of raw water to satisfy the clean water supply demand for the entire Johore State and
also Singapore.
Besides, evaluation of water quality parameters is necessary to enhance the
performance of an assessment operation and develop better water resource
management plan of the Johor River. This study is a primary action and the best
measure to ensure a sustainable water resources and environment in this area.
9
CHAPTER II
STUDY AREA DESCRIPTION
2.1
Introduction
Selecting a suitable study area is important for the beneficial effect of this
study especially to the selected area. Several considerations also required in selecting
the study area such as types and level of problem, availability of data and
information needed, availability of related tools and software to analyse the problem,
and others. This chapter will provide a general description of the study area
including some information of its water resources and land use to look further inside
the area by the means of understanding the important of this present study to the area
of concern.
2.2
Study Area
The Johor River (Figure 2.1), 122.7 km long, drains an area of 2,636 km2. It
originates from Mt. Gemuruh and flows through the southeastern part of Johor and
finally into the Straits of Johor. The catchment is irregular in shape. The maximum
length and breadth are 80 km and 45 km respectively. This present study was
conducted at the upper part of Johor River for about 30 km length from the upstream
10
to the water intake point as this area was believe is the source of pollutant at the
downstream of the river.
About 60% of the catchment is undulating highland rising to a height of
366m while the remainder is lowland and swampy. The highland in the north is
mainly jungle. In the south a major portion had been cleared and planted with oil
palm and rubber. The catchment receives an average annual precipitation of 2,470
mm while the mean annual discharge measured at Rantau Panjang (1,130 km2) has
been 30.5 m3/s during the period 2004 to 2007. The major tributaries are Sayong,
Semanggar, Linggui, Tiram and Lebam Rivers.
Figure 2.1: Map of Johore River
11
2.3
Water Resources
The Johor River basin occupies about 14% of the Johor State of Peninsular
Malaysia. Johor River and its tributaries are important sources of water supply not
only for Johor State but also for Singapore. Syarikat Air Johor Holdings (SAJH) or
Johor Water Company and Public Utility Board of Singapore each draw about 0.25x
106 m3/day of water from Johor River near Kota Tinggi.
2.4
Land Use
Main land use at the study area was summarized in Table 2.1 below. Major
land use is oil palm and other crops. As shown in Figure 2.2, there are many oil palm
plantations and RISDA or FELDA Land Development located in the surrounding
area of Johor River. Sand mining area and vegetable farm can also found there.
Table 2.1: Main Land Use at Johor River
Rank
Land use
Percent (%)
1
Oil Palm and other crops
18.5
2
Forest
16.4
3
Swamps
11.6
4
Urban
5.5
5
Waterbody
0.5
Source: Land and Survey Department, Johore (2006)
12
Figure 2.2: Johore River and Its Surrounding
13
CHAPTER III
LITERATURE REVIEW
3.1
Introduction
Firstly, the definition of the important terms that used will be documented
and describe before go beyond to the review of the water quality trends and
modeling. Hence, fundamental of the study will greatly understand.
3.2
Water quality parameter
There are a number of variables that indicate the quality of water. Some of
the basic variables are water temperature, pH, specific conductance, turbidity,
dissolved oxygen, salinity, hardness, and suspended sediment. However for the
purpose of this study eight (8) parameters are considered. The parameters are the PH,
turbidity, colour, trace element (Fe, Al, Mn), Ammonium (NH4) and suspended
sediment (SS).
3.2.1
PH
PH is a measure of the relative amount of free hydrogen and hydroxyl ions in
the water. Water that has more free hydrogen ions is acidic, whereas water that has
more free hydroxyl ions is basic or alkaline. The values of pH range from 0 to 14
14
(this is a logarithmic scale), with 7 indicating neutral. Values less than 7 indicate
acidity, whereas values greater than 7 indicate a base. The pH of natural waters
hovers between 6.5 and 8.5 (Michaud J.P, 1991).
The presence of chemicals in the water, affects its pH, which in turn can harm
the animals and plants that live there. For example, an even mildly acidulous
seawater environment can harm shell cultivation. This renders pH an important water
quality indicator.
3.2.2
Turbidity
Turbidity is the amount of particulate matter that is suspended in water.
Turbidity measures the scattering effect that suspended solids have on light: the
higher the intensity of scattered light, the higher the turbidity. Materials that cause
water to be turbid include clay, silt, finely divided organic and inorganic matter,
soluble coloured organic compounds, plankton, microscopic organisms and others.
3.2.3
Suspended sediment (SS)
Suspended sediment is the amount of soil moving along within a water
stream. It is highly dependent on the speed of the water flow, as fast-flowing water
can pick up and suspend more soil than calm water. If land is disturbed along a
stream and no protection measures are taken, then excess sediment can harm the
water quality of a stream.
3.2.4
Trace elements (Fe, Al and Mn)
Trace elements are metal and transition metal elements commonly found in
small (less than 1 milligram per liter) concentrations (Michael, 2003). Analytes
commonly detected at most sites in this study included iron (Fe), manganese (Mn)
15
and Alluminium (Al). Trace elements are important indicators of water quality
because, in large concentrations, they are toxic to aquatic life and human.
3.2.5
Ammonium (NH4)
The source of ammonium is from nitrogen element. The nitrogen does not
readily accumulate in the soil and is readily transported to the groundwater in the
form of nirite and to a far lesser degree as ammonium. Subsurface flow may be the
primary transport process that carries nitrogen from the source area to the receiving
water bodies. As cited by Vladimir (2003), the sources of nitrogen include soil
fertilizers (46%), bacteria and legumes (20%), plant residue (17%) and precipitation
(17%).
3.2.6
Colour
Colour is one of the parameter for physical measures of water quality.
Natural sources that effected colour are decay of plant matter, algae growth, minerals
(iron and manganese) and anthropogenic sources such as from paper mills, textile
mills and food processing. Some impacts of colour are usually an aesthetic problem,
both in drinking water and wastewater may be an indication of toxicity and may stain
textiles and fixtures.
3.3
Rainfall-runoff Relationship
The representation of rainfall transformation into runoff (flow) is shown in
Figure 3.1 and Figure 3.2. Runoff generated by precipitation has three components:
1. Surface runoff is a residual of precipitation after all loses have been satisfied.
The loses include interception by surface vegetation, depression storage and
ponding, infiltration into soils, evaporation from soils and open surfaces, and
transpiration by vegetation. The highest loads of particulate pollutants are
16
carried by surface runoff. Furthermore, particulates librated from soil by
rainfall erosion can move from the source area only if there is appreciable
excess rainfall generated from the surface.
2. Interflow is that portion of water infiltrating the soil zone which moves in a
horizontal direction due to lower permeability of subsoils. Typically, the
amount of interflow in the hydrological balance is small and becomes
significant only during spring melt and rain when subsoil is frozen. This type
of flow is not significant in Malaysia region.
3. Groundwater runoff (base flow) is defined as that part of runoff contribution
that originates from springs and wells. In urban areas with sewers, one may
include infiltration inflow into sewers, which can substantial. During
prolonged drought periods, most of the stream flow can be characterized as
groundwater runoff.
Figure 3.1: Schematic representation of watershed hydrology
(Vladimir, 2003: p 147)
17
Figure 3.2: Block diagram of watershed hydrological processes and storage
(Vladimir, 2003: p 108)
3.4
Time Series Data
Time series data is a set of observations obtained by measuring single
variable regularly over a period of time. (SPSS, 1993). One reason to collect time
series data is to try to discover systematic pattern in the series so that mathematical
model can be built to explain the past time behavior of the series.
3.5
Previous studies
Trend analysis and water quality modeling have been applied in hydrological
area for various types of problems basically for the determination of trend for certain
parameter in certain time period. It also a useful tool to determine the relationship or
correlation between parameters. This chapter will present some of the previous
studies on the water quality trend analysis and modeling. For the purpose of
providing an easy way for understanding on this topic, the previous studies will be
18
presented in two separate parts whereas one part for the previous study on water
quality trends using ARIMA modeling and one part using Mann Kendall.
3.5.1
Previous Study on Mann-Kendall Test
T. Yamada et. al., (2007) have successfully conducted a research to
investigate the effects of acid deposition on surface water. They used the
nonparametric Mann–Kendall test to find temporal trends in pH, alkalinity, and
electrical conductivity (EC) in more than 10 years of data collected from five lakes
and their catchments (Lake Kuttara: northernmost; Lake Kamakita: near Tokyo;
Lake Ijira: central; Lake Banryu: western; and Lake Unagiike: southernmost). This
test was successfully detecting the trend of parameters investigated. They found that
the PH of Lake Ijira has declined trend corresponding with the downward trends seen
in PH and alkalinity of the river water trends. They also found that significant
upward trends in the EC of both the lake and stream water. They also found similar
trends for NO3.
Evaluation of spring flow and groundwater base-flow declines from the
groundwater abstraction in the Hillsborough River system of central Florida, USA
was accomplished. This work was successfully done by Kenneth A. W et. al., (2004)
through performing the systematic use of parametric and nonparametric statistical
techniques in their analysis. These techniques include contingency table analysis,
linear regression, Kendall-Theil and Mann-Kendall trend analysis, locally weighted
regression, Pearson correlation, Kendall-tau correlation, Spearman correlation, runs
test, Student‘s t test, and the Kruskall-Wallis test. A Kendall-Thiel trend line was
successfully developed from flow data, which produces a declining slope that is
significant at α=0.05 using the non-parametric Hamed-Rao and modified MannKendall test.
A study by Abu Farah et. al., (2006) observed the characteristics and trends
of the chemical constituents in bulk precipitation and streamwater in a small
mountainous watershed on the Shikoku Island of Japan. Bulk precipitation and
streamwater chemistry data spans from May 1997 to October 2004, and January
19
1996 to October 2004, respectively were tested. Both parametric and non-parametric
statistical analyses were carried out in their study. Nonparametric Seasonal Kendall
Test (SKT) showed a deceasing trend of Ca2+ and an increasing trend of K+ in bulk
precipitation. Despite the decreasing trend of Mg2+, an increasing trend of pH was
found in the streamwater. Non-parametric Mann-Whitney-Wilcoxon Rank Sum test
showed statistically significant increases of NO3− and Ca2+ in streamwater followed
by a moderate thinning operation.
R. Bouza D. et. al., (2008) were analysed thirty-four physical–chemical and
chemical variables in surface water samples collected every month over a period of
24 years. The trend study was performed using the Mann–Kendall Seasonal Test and
the Sen‘s Slope estimator. Results revealed parameter variation over time due mainly
to the reduction in phosphate concentration and increasing pH levels at the Ebro
Basin during the 1981–2004 periods.
Ercan et. Al., (2002) presented trends that were computed for the 31-year
period of monthly streamflows obtained from 26 basins over Turkey. Four nonparametric trend tests which are the Sen‘s T, the Spearman‘s Rho, the Mann-Kendall,
and the Seasonal Kendall known as appropriate tools in detecting linear trends of a
hydrological time series are adapted in their study. Moreover, the Van Belle and
Hughes‘ basin wide trend test is included in the analysis for the same purpose. From
their study, they found that in most cases, the first four tests provide the same
conclusion about trend existence. They concluded that use of the Seasonal Kendall,
which involves a single overall statistic rather than one statistic for each season, is
justified by the homogeneity of trend test. Moreover, some basins located in southern
Turkey show a global trend, implying the homogeneity of trends in seasons and
stations together, based on the Van Belle and Hughes‘ basin wide trend test.
Heejun (2008) presented the spatial patterns of water quality trends for 118
sites in the Han River basin of South Korea were examined for eight parameters
which are temperature, pH, dissolved oxygen (DO), biochemical oxygen demand
(BOD), chemical oxygen demand (COD), suspended sediment (SS), total phosphorus
(TP), and total nitrogen (TN). The researcher mentioned that a non-parametric
seasonal Mann-Kendall‘s test is able to determine the significance of trends for each
20
parameter for each site between 1993 and 2002. The researcher concluded that there
are no significant trends in temperature, but TN concentrations increased for the
majority of the monitoring stations. DO, BOD, COD, pH, SS, and TP show
increasing or decreasing trends with approximately half of the stations exhibiting no
trends. Then, urban land cover is positively associated with increases in water
pollution and included as an important explanatory variable for the variations in all
water quality parameters except pH.
3.5.2
Previous Study on Water Quality and ARIMA Modelling
Axel L. et. al., (2000) presented the study to analyses weekly data samples
from the river Elbe at Magdeburg between 1984 and 1996 to investigate the changes
in metabolism and water quality in the river Elbe since the German reunification in
1990. Modelling water quality variables by autoregressive component models and
ARIMA models reveals the improvement of water quality due to the reduction of
waste water emissions since 1990. The models are used to determine the long-term
and seasonal behaviour of important water quality variables. They found that organic
and heavy metal pollution parameters showed a significant decrease since 1990,
however, no significant change of chlorophyll-a as a measure for primary production
could be found. A new procedure for testing the significance of a sample correlation
coefficient was discussed, which is able to detect spurious sample correlation
coefficients without making use of time-consuming pre-whitening.
M. Power et. Al., 1998 presented the trends in the concentrations of Cd, Cu,
Hg, Ni, Pb and Zn in the estuary of the River Thames between the years 1980 to
1997. They were examined these parameter using linear regression methods to
determine whether stated reductions in metal discharges from all sources to the
estuary have had an effect on water quality. They found that concentrations of all
metals, except Pb, showed exponential declines.
C. Gun et.al., (1997) used the Ivry-sur-Seine explanatory graphical analysis
and statistical time series techniques to analyze the trends and specified time
changes,in a 90-year record of annual average value of Seine river water quality data.
21
They concluded that such a study may now be applied to more rural stations in order
to compare the evolution of water quality and, perhaps, historical monthly average
values to evaluate the seasonality effect on annual trends.
Robert et. al., (2001) was presented the first detailed analysis of HMS data
for Scotland, and identified temporal changes in water quality from 1974 to 1995.
The trend analysis for this application was based on the use of smoothing splines to
fit terms due to long-term trend, variable amplitude seasonality, and a variable slope
flow relationship. They found that nitrate concentrations between rivers are highly
correlated with the amount of arable land, and relationships exist between grassland
cover, orthphosphate-P and suspended solids concentrations and similarly, urban
catchments are highly correlated with ammonium-N, orthophosphate-P and
suspended solids.
Giuseppe et. al., (2005) was presented a proposed water-quality model for the
Lagoon of Venice, Italy. The model is based on the results of an existing,
deterministic, hydraulic-dispersive model of the Lagoon to provide the distribution of
salinity and residence time in the Lagoon of Venice. The water-quality is simulated
by statistic analysis on water-quality data, monthly collected in 30 stations and was
covered a period of 2 years. The Spearman correlation index of salinity and residence
time versus the water-quality variables (nitrogen, phosphorus, and chlorophyll-a and
the trophic index TRIX) has been studied on a yearly average basis and for the
spring–summer periods. The model has been applied to simulate the variation of
nutrients and trophic index distribution in the Lagoon as a consequence of an
increase of hydraulic dissipation at the Lagoon outlets. They concluded that
statistical analysis is effective enough to simulate scenarios, provided that the result
are not much different from the state of the system represented in the database
processed and the dissipations fall in a range of variation that could be acceptable for
the statistic simulation.
22
CHAPTER IV
RESEARCH METHODOLOGY
4.1
Introduction
There are many works, procedures and considerations involved in the process
of determining water quality trend at the upper part of Johor River. For the purpose
of representing the methodology applied in easier way, flowchart of the methodology
applied which aims to summarize the lengthy jobs involved in the present study was
attached in Appendix A.
The objective of this chapter is to represent the methodology applied by
focusing on two important stages involved in the present study which are (1) the
Mann-Kendall test and (2) the univariate ARIMA modelling processes.
4.2
Mann-Kendall Test Procedure
For primary detecting of trends in water quality, Mann–Kendall test was
used. The Mann–Kendall test is a non-parametric test for detection of trends that
accommodates non-normal data distribution (Helsel & Hirsch, 1992). It is a simple
test whereas it required only that the data be independent and can tolerate outliers in
the data. This test was performed using two set of data. Theory behind this test is the
23
comparison of mean and variance to obtain the probability (P) and Z-score for a null
hypothesis. Null hypothesis was provided first whereas assumption was made that
there is no difference between first set of data to the second set of data. This mean
that, the probability of this two set of data to be different is zero or can be write as P
= 0. General relationship of the water quality-rainfall and water quality-flow
respectively were obtained which indicated by the probability (P) value. P equal to
zero means that the different in trend of two variables investigated is zero. Hence,
they have strong relationship with each other.
The Z-score figure indicates in standard deviation units how far from the
mean the figure for the difference between two set of data is located. Positive Z-score
by the Mann-Kendall tests indicates increasing trends while negative Z-score
indicates decreasing trends. Z-score lower than critical Z-score (1.64) or higher than
1.64 indicate significant decreases or increases in trend.
4.3
Theory of ARIMA Modeling Approach
ARIMA is an abbreviation of AutoRegressive Integrated Moving Average
introduced by Box and Jenkins (Box et.al., 1994). As such, some authors refer to this
modeling approach as a Box and Jenkins model. The general ARIMA model contains
autoregressive (AR), Integrated (I) and moving average (MA) parts. The AR part
described the relationship between present and past observations. The MA part
represents the autocorrelation structure of error. The I part represents the differencing
level of the series. With p, d and q as the AR, I AND MA coefficient respectively,
the general form of a stationary ARIMA ( p, d , q ) model for observed time series, Yt ,
can be written as:
p
q
j 1
k 1
Yt    jYt  j  at    kat k
An ARIMA model is written using various notations. For example:
(4.1)
24
ARIMA (1, 0, 0): Yt  C  1Yt 1  at or (1  1 B) yt  C  at
where 1 ,  2 ,..... p are AR coefficients, 1 ,  2 ,.... q are MA coefficients, a t is
residual series, C is the model constant and B is the backshift operator.
4.4
Model Assumptions
Before performing the ARIMA modelling, some assumptions were made
such that :
1.
The data is stationary
2.
The data have normal distribution
3.
No outlier exist in the data
4.
No missing data
4.4.1
Data Stationarity
The stationarity of the daily hydrological and water quality data were
examined by graphical representation of the data. The original data were plotted
against its time interval which is in days. A stationary series is where the series is
statistically in equilibrium; shows by their mean and variance are constant with time.
Besides, the data is considered as stationary when the plotting shows that the data
fluctuates around its constant means and variance (Daniel et. al, 2001, Brockwell and
Davis, 2002, Box et. al., 1994 and Robert and Monnie, 2000). Other graphical
method applied in this present study is by examined the ACF and PACF plot of the
original data. Stationary data have randomly distributed ACF and PACF plot.
The transformation process might be required for the non stationary series
and this can be done using differencing method (Box et.al., 1994) and (Shumway,
25
1988). This process has been considered in ARIMA modelling approach as the I
(Integrated) component or represent as „d‟ in ARIMA notation. The level of
differencing is highly depending on the level of stationarity of the data. The level of
differencing might be 0, 1, 2 or higher than 2. 0 levels means that the differencing
process is not perform to the data. Then level 1 represent the first differencing
process needed and second differencing level needed for level 2. Higher level of
differencing might be applied to the nonstationary and complex data.
4.4.2
Normal Distribution
Normal distribution characteristic can easily identified by examining
histogram of each set of the daily hydrological and water quality data. Data with
normal distribution have a pattern of data distribution which follows a bell shaped
curve. The bell shaped curve has several properties such that the curve concentrated
in the center and decreases on either side. This means that the data has less of a
tendency to produce unusually extreme values, compared to some other distributions.
Besides, the bell shaped curve is symmetric. This tells that the probability of
deviations from the mean is comparable in either direction.
Data transformation is required for the data without normal distribution
behaviour. Two methods of data transformation were applied for this case which is
the normal log transformation method and Box-Cox transformation method (Box and
Cox, 1964). The second method was applied if the normal log transformation method
is not capable to transform the data into normal distribution.
4.4.3
Outlier
An outlier is an observation that lies outside the overall pattern of a
distribution (Moore and McCabe 1999). Usually, the presence of an outlier indicates
some sort of problem. This can be a case which does not fit the model under study or
an error in measurement. Outliers are often easy to spot in histograms. For example,
26
the point on the far left in the above figure is an outlier. This data point should be
removed because it also a sign of nonstationary data.
40
35
30
25
20
15
10
5
0
-6
-5
-4
-3
-2
-1
0
1
2
3
Figure 4.1: Histogram shows outliers in a set of observation
4.4.4
Missing Data
Robert et. al (2000) suggested that data should be replaced by a theoretical
defensible algorithm if some data values are missing is observed in the data series. A
crude missing data replacement method is to plug in the mean for the overall series.
A less crude algorithm is to use the mean of the period within the series in which the
observation is missing. Another algorithm is to take the mean of the adjacent
observations. Missing value in exponential smoothing often applies one step ahead
forecasting from the previous observation. Other form of interpolation employs
linear spines, cubic splines, or step function estimation of the missing data. There are
other methods as well. SPSS provide options for missing data replacement. The
ARIMA approach was applied because it is capable to handle missing value in the
observed data (SSPS, 1993). For ARIMA modelling analysis in SPSS, Kalman
Filtering was performed to the series for the purpose of handling the missing data.
27
4.5
ARIMA Modelling Procedures
The ARIMA modelling procedures was followed the most popular strategy
for building a model which is the one developed by Box and Jenkins (1976), who
defined three major stages of model building: identification, estimation and
diagnostic checking. They (Box and Jenkin, 1976) originally demonstrated the
usefulness of this strategy specifically for ARIMA model building and the general
principles can be extended to all models building (SPSS Trend, 1993).
4.5.1
Model Identification
First stage was conducted to identify the most suitable model to fit the
transformed time series data by examining various types of correlogram which are
the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). It
should also explain that identification is necessarily inexact because at the
identification stage no precise formulation of the problem is available, statistically
‗inefficient‘ methods must be used (Box et. al., 1994). They (Box et. al) suggested
that graphical methods are particularly useful and judgement must be exercised.
Some general guidelines (SPSS, 1993) using graphical method was applied in the
identification process:
i.
Nonstationary series have an ACF that remains significant for half a dozen or
more lags, rather than quickly declining to 0. Difference must be done for
such a series until it is stationary before it can be identified.
ii.
Autoregressive processes have an exponentially declining ACF and spikes in
the first one or more lags of the PACF. The number of spikes indicates the
order of the autoregression.
iii.
Moving average processes have spikes in the first one or more lags of the
ACF and an exponentially declining PACF. The number of spikes indicates
the order of the moving average.
iv.
Mixed (ARMA) processes typically show exponential declines in both the
ACF and the PACF.
28
At the identification stage, the sign of the ACF or PACF and the speed with
which an exponentially declining ACF or PACF approaches 0 are depend upon the
sign and actual value of the AR and MA coefficients (SSPS, 1993).
4.5.2
Model Parameters Estimation
Estimation of model parameters was conducted using Ordinary Least Square
(OLS). For a time series, under OLS method, those values which are chosen for the
parameters will make the smallest sum of the squared residual (Slini et. al., 2002).
Consider the ARIMA (p,d,q) model:
p
q
j 1
k 1
Yt    j Yt  j  at   k at  k
(4.2)
The estimates of the parameters  j , j  1,2,...., p and  k , k  1,2,..., q are chosen so
that the sum of squared residuals written in equation 3 is minimum.


T
p
q
t 1
j 1
k 1
S (1 ,.....,  p ), (1 ,.....,  q )   (Yt   i Yt i    t at k ) 2
(4.3)
Equation (4.3) is a complex equation and must be solved iteratively. Analytical
solution is impossible, therefore numerical solution are used. In the present study, the
parameter estimates was calculated with the aid of the SPSS Statistical Packages
Software.
4.5.3
Diagnostic Checking
Then, diagnostic test was conducted to ensure that the essential modeling
assumptions are satisfied for a given model. Graphical method was used by
representing ACF and PACF using residual series as inputs. Randomly distributed
ACF and PACF indicate the fitness of the model chosen.
29
The fitted model also checked using Aikaike Information Criteria (AIC)
whereas the best model have smallest AIC‘s value and supported by larger value of
T-Test and smallest value of standard error.
Plot of residual such histogram of error, versus order, normal probability plot
and versus fits was also conducted. The residual have normal distribution indicate by
the normal curve of the histogram of error. The best fit curve was shown by normal
probability plot. Then randomly distributed of the residual was shown by versus fit.
This proved that our first assumptions whereas the data have normal distribution and
stationary are true. Therefore, the best fitted model is accepted. First and second
stage should be repeated if the characteristics of normally distribution and stationary
of residual is not achieved.
30
CHAPTER V
DATA COLLECTION AND ANALYSIS
5.1
Introduction
This chapter consists of the detail description and discussion on collection
and analysis of the time series data using both non parametric Mann-Kendall test and
parametric ARIMA modelling method. For representing the analysis in easier way,
example of analysis using daily PH data for the year of 2004 was presented. Others
water quality parameters were analysed by applying the similar procedures.
5.2
Types and Sources of Time Series Data
Enough data is required for performing the trend analysis. There are two
types of data that have been used for the analysis which are the hydrological data and
water quality data. In this present study, the averaged daily water quality, rainfall and
runoff data was used. Each of the data follows a similar time interval which is in day.
The data-set covers a period of 4 years which is within the year 2004 to 2007. The
data was attached in Appendix B1 to Appendix B4.
31
5.2.1
Hydrological Data
Rainfall and streamflow (runoff) data was obtained from Department of
Drainage and Irrigation (DID) Malaysia, Johor. Rainfall records from station
1836001 at site Rancangan Ulu Sebol have been extensively used in this study. The
station is located in the north of the Johor River. Then the gauging station for
streamflow is referred to station number 1737451 at site Rantau Panjang, Johor. The
station also located in the north of the Johor River.
5.2.2
Water Quality Data
The water-quality modelled by statistical analysis on water-quality parameter
data, hourly collected in Water Treatment Plant, Semanggar Johor. It is owned by
Syarikat Air Johor Holdings (SAJH) or Johor Water Company. The data available for
seven parameters including pH, Color (TCU), Turbidity (ppm), Al (ppm), Fe(ppm),
NH4 (ppm) and Mn (ppm). While suspended sediment (SS) data was taken from
Department of Drainage and Irrigation (DID) Malaysia, Johor. Data from station
1737551 at site Rantau Panjang have been used.
5.3
Example of Mann-Kendall Test
In this present study, Z-score and P value was obtained using Microsoft Excel
function under Data Analysis toolkit. The simple procedures involved are collect the
data on the two samples, request a z-test analysis from the Microsoft Excel statistics
options, and examine the table of the results. The following notes describe in detail
the procedure.
1. First enter in the data for two samples, for example one column for daily PH
data and one column for the rainfall data. Variable 1 is representing the PH
parameter and Variable 2 for rainfall data.
32
2. Then, using the Descriptive Statistics tool from the Data Analysis option (on
the Tools Menu), the table of Descriptive Statistics for each sample was
generated. The figure for the Variance of each sample was noted down. Such
as illustrated in Figure 5.1.
3. Then from the Data Analysis option, select the last item: Z-test: Two Sample
Means. In the dialogue box, in the Variable 1 Range box, it is indicating the
range of the first sample in Variable 1 column (e.g., $A$1:$A$35). While in
Variable 2 Range, it is indicating the range of the second sample in Variable
2 column (e.g., $B$1:$B$48). In the Hypothesized Mean Difference box,
enter the figure 0. Since the Null Hypothesis, which we are attempting to
refute, says that both samples have similar trend. So that it was hypothesized
that the difference between the means for the two variables is 0.
4. In the Variable 1 Variance (known) box the figure for the Variance for
Variable 1 (noted this down earlier, but this figure can found in the
Descriptive Statistics box generated in the second step described above) aws
entered. In the Variable 2 Variance (known) box the corresponding figure for
the Variance of the Variable 2 was entered.
5. In the Alpha box the number 0.05 should already appear. The Alpha figure
indicates the Confidence Level for this test. A figure of 0.05 states that we
want to be 95 per cent certain of the result or, in other words, that the
probability of being wrong to be .05 or lower.
6. In the Output Range box, type the number of the cell where you want the
Output Table to appear (or alternatively, with the line active in the Output
Range box, click the mouse on an empty cell).
7. Then click on OK. After a couple of seconds, a table appeared in the place
designated by the Output entry. This table has the heading: z-Test: Two
Samples for Means. In the table there are figures for the following items:
Mean, Known Variance, Observations, Hypothesized Mean Difference, z,
33
P(Z<=z) one-tail, z Critical one-tail, P(Z<=z) two-tail, z Critical two tail as
illustrated in Figure 5.2.
For Mann-Kendall analysis, z one tail was used as only one hypothesis is
provided in this study. Positive Z-score by the Mann-Kendall tests indicates
increasing trends while negative Z-score indicates decreasing trends. Z-score lower
than critical Z-score (1.64) or higher than 1.64 indicate significant decreases or
increases in trend. Descriptive statistics generated from the data analysis for each
water quality investigated against rainfall and flow respectively was attached in
Appendix C1 to Appendix C4. While result summary of z-score and P value for each
water quality parameter analyses against rainfall and flow data was presented in the
next chapter.
Figure 5.1: Procedures for determination of descriptive statistic of the variables
investigated for Mann-Kendall analysis.
34
Figure 5.2: Procedures for determination of z-score of the variables investigated for
Mann-Kendall analysis.
5.4
Example of ARIMA Modelling
As mentioned in previous chapter, the ARIMA modelling follows three
important stages which are the model identification, model estimation and diagnostic
checking stages.
5.4.1
Model Identification for PH 2004
As shown in Figure 5.3 (a), the series have characteristics of nonstationary
data indicate by the figure of not constant mean and variance whereas the series not
fluctuate around its mean. Besides, Figure 5.3 (c) also has shown indication of
nonstationary data as the ACF plot has exponential declined pattern. This figure
suggested that differencing process should be applied to the series to transform the
nonstationary data to stationary. Therefore, the I component of the ARIMA model
for this series is exist which might be of order 1 or 2. However, data transformation
35
against normal distribution is not required as the series follows a bell shaped curve
as shown in Figure 5.3 (b).
Possible ARIMA models for the series were observed from the spike detected
in PACF plot and the ACF plot pattern of the original data. As shown in Figure 5.3
(d), possible ARIMA models for PH series for the year of 2004 might be ARIMA
(1,1,0), ARIMA (1,1,1) or ARIMA (1,1,2). This guided by spike at lag 1 at PACF
plot indicates the AR(1) model. Besides, ACF plot pattern suggested that possible
model might be MA(1) or MA(2). Therefore, combination of AR model and MA
model or ARIMA model is best representing the PH series. Table 5.1 summarized
the possible ARIMA model for PH series at year 2004.
6.8
30
6.6
6.4
6.2
20
6.0
5.8
5.6
10
5.4
PH
5.2
Std. Dev = .22
Mean = 5.94
5.0
1
39
134
191
172
229
210
267
248
305
286
343
324
N = 155.00
0
362
Sequence number
63
6.
50
6.
96
153
38
6.
25
6.
13
6.
00
6.
88
5.
75
5.
58
115
63
5.
50
5.
38
5.
25
5.
20
77
PH
(a) Plotting of Original Data
(b) Histogram of Original Data
PH
PH
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(c) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(d) PACF Plot of the Original Data
Figure 5.3: Plots of the Original Data for PH Series at Year 2004
36
In this present study, the graphical representation using ACF and PACF plot
and sequence plot of the original daily water quality data for each parameter were
attached in Appendix D1 to Appendix D3 for PH, Appendix E1 to Appendix E4 for
colour, Appendix F1 to Appendix F4 for turbidity, Appendix G1 to Appendix G4 for
Al, Appendix H1 to Appendix H4 for Fe, Appendix I1 to Appendix I4 for NH4,
Appendix J1 to Appendix J4 for Mn and Appendix K1 to Appendix K3 for SS.
Examining the behaviour of the plot suggested that all of the data are nonstationary
and differencing process is required. Besides, various combinations of ARIMA
models are the possible model that might be generated each of the series.
5.4.2
Estimation of Model Parameter for PH 2004
In this stage, estimation of model parameters for the possible ARIMA model
was conducted using SPSS Statistical Packages Software. To obtain each of the
coefficients, ARIMA time series was performed under Analyse tool in SPSS. By
entering the required information in the box (Figure 5.4) and run the time series
analysis, the ARIMA coefficient was obtained. Appendix L shows example of the
output generated from the SPSS.
Figure 5.4: Analyse Tools in SPSS for estimation of model parameter
37
The possible model was also checked for their Aikaike Information Criteria
(AIC), t-test and standard error. Table 5.1 was summarized the possible ARIMA
models for PH (2004) with their ARIMA model and constant (C) coefficient, AIC, tvalue and standard error. The best fitted model has the characteristics of smallest
AIC value and supported with small standard error and larger t-value. ARIMA
(1,1,2) was selected as the best fitted model for PH (2004) as the model have all of
the characteristics.
Table 5.1: Possible ARIMA Model for PH (2004)
No.
Possible
ARIMA Model
AIC
T-Test
Standard
Error
Coefficient
C
1
110
-671.2972
-2.5574
0.0254
AR(1) = -0.2083
-0.00015
2
111
-686.6417
11.0371
0.0242
AR(1) = 0.7087
0.00011
0.5079
3
5.4.3
112
-689.9701
0.6827
MA(1)= 0.9999
0.0239
AR(1) = 0.2024
1.9153
MA(1)= 0.5659
0.9454
MA(2)= 0.1728
0.00010
Diagnostic Checking for PH 2004
The diagnostic checking was performed to the best fitted model to prove that
the first assumptions are true whereas the series is considered have normal
distribution and stationary. This checking was performed through plotting the ACF
and PACF (Figure 5.5) but this time using residual error as input. The series was
considered passed the diagnostic checking when the ACF and PACF plot of the
residual fluctuate around their confidence limit.
Besides, plot of residual such as histogram of error, versus order, normal
probability plot and versus fits (Figure 5.6) was also conducted. The residual have
normal distribution indicate by the normal curve of the histogram of error. The best
fit curve was shown by normal probability plot. Then randomly distributed of the
residual was shown by versus fit. This proved that our first assumptions whereas the
data have normal distribution and stationary are true. Therefore, the best fitted model
38
is accepted. First and second stage should be repeated if the characteristics of
normally distribution and stationary of residual is not achieved. Plotting of ACF and
PACF for the diagnostic checking for others water quality parameters was attached
in Appendix M.
Error for PH from ARIMA, (1,1,2)
Error for PH from ARIMA, (1,1,2)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
3
2
16
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
Figure 5.5: ACF and PACF Plot of Error for Best Fitted Model, PH (2004)
.1
Error for PH from ARIMA, (1,1,2)
30
20
10
0
0.0
-.1
Std. Dev = .02
Mean = -.001
N = 151.00
-.2
56
.0
44
.0
31
.0
19
.0
06
.0
06
-.0
19
-.0
31
-.0
44
-.0
56
-.0
69
-.0
81
-.0
94
-.0
06
-.1
1
39
20
Error for PH from ARIMA, (1,1,2)
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
362
Sequence number
(a) Histogram of Error
(b) Versus order
Normal P-P Plot of Error for PH from ARIMA
1.00
.06
.04
Error for PH from ARIMA, (1,1,2)
.02
Expected Cum Prob
.75
.50
.25
0.00
0.00
0.00
-.02
-.04
-.06
-.08
-.10
-.12
5.4
.25
.50
.75
5.6
5.8
6.0
1.00
Fit for PH from ARIMA, (1,1,2)
Observed Cum Prob
(c) Normal probability plot
Figure 5.6: Plot of Residual
(d) Versus fits
6.2
6.4
39
5.4.4
Best Fitted ARIMA Model Equation
General ARIMA equation can be writen for the best fitted model. General
ARIMA equation is presented in Equation 4.1 (Chapter 4). Equation for the best
fitted model for PH (2004) can be write as:
(1  B)(1  0.2024 B)Yt  (1  0.5659 B  0.1728 B 2 )at
This equation is useful for further determine the dynamic behaviour of the Johor
River system through transfer modelling. Equation for best fitted model for others
water quality parameters investigated was presented in the next chapter.
40
CHAPTER VI
RESULT AND DISSCUSSION
6.1
Rainfall-runoff Pattern
Plotting the annual runoff coefficient from year 2004 to 2007 is best
representing the rainfall-runoff pattern for the present study. Runoff coefficient is
define as total rainfall over total flow or can be write as:
Runoff Coefficient =
Total Flow
Total Rainfall
(6.1)
Table 6.1 below shows the calculated runoff coefficient for each year from
2004 to 2007. Then by plotting this coefficient against year, rainfall-runoff pattern
was obtained (Figure 6.1). As shown in the figure, rainfall-runoff has declining
pattern at year 2004 to 2005. Then increasing pattern at year 2005 to 2006 and
decreasing again after that. The reason of such pattern is very difficult to address
because it is a natural phenomenon that is unpredictable. In fact, past rainfall event is
not depends on the present rainfall event or we can say in easier way that today
rainfall is not depends on yesterday rainfall.
41
Table 6.1: Calculated Runoff Coefficient
Year
Runoff Coefficient
2004
0.312
2005
0.182
2006
0.437
2007
0.271
Runoff Coefficient
Rainfall-runoff Pattern 2004-2007
0.5
0.4
0.3
0.2
0.1
0.0
2004
2005
2006
2007
Year
Figure 6.1: Rainfall-runoff pattern
6.2
Correlation between Rainfall-Flow and Water Quality
Although rainfall and flow is unpredictable parameter, however their pattern
can be a measure to the water quality trend as changes of this hydrological parameter
is greatly influence the water quality trend. This has been proved using correlation
analysis which aims to assess how the water quality parameter investigated is related
to the hydrological parameters (rainfall and flow). Each water quality parameter was
analysed using Microsoft Excel function. Table 6.2(a), 6.2(b) and Table 6.3
summarized the result obtained from the analysis. As shown, all of the parameter is
significant for both rainfall and flow parameter. Therefore, all of the water quality
parameters investigated was considered correlated with both rainfall and runoff.
42
Table 6.2(a): Correlation between Water Quality and Rainfall
PH
COLOUR
0.0339
0.1386
TURB
AL
FE
NH4
MN
FLOW
SS
0.1628 0.0565 0.0460 0.0601 0.0366 0.1164
0.1628
Table 6.2(b): Correlation between Water Quality and Flow
PH
COLOUR
0.0702
0.2125
TURB
AL
FE
NH4
MN
SS
RAINFALL
0.2150 0.1483 0.0850 0.1411 0.0523 0.8219
0.1164
Table 6.3: Correlation among Parameter Investigated
PH
Colr Turb
AL
FE
NH4
MN
Flow
SS
PH
1
Colr
0.61 1
Turb
0.58 0.88 1
AL
0.45 0.39 0.44
1
FE
0.39 0.31 0.38
0.39 1
NH4
0.57 0.43 0.47
0.52 0.58 1
MN
0.30 0.31 0.29
0.19 0.18 0.26
1
Flow
0.07 0.21 0.22
0.15 0.09 0.14
0.05
1
SS
0.16 0.27 0.25
0.16 0.08 0.16
0.08
0.82
1
Rainfalll 0.03 0.14 0.16
0.06 0.05 0.06
0.04
0.12
0.13
6.3
Rainfall
1
Water Quality-Rainfall and Water Quality-Flow Trend
Table 6.4 and 6.5 below shows the water quality-rainfall and water qualityflow trend obtained from Mann Kendall test. Each of the water quality parameters
investigated shows significant trend at 95% confidence limit. All of the parameter
investigated shows decreasing and increasing trend every year among the period of
2004 to 2007 represented by the z-score. In this analysis, each water quality
parameter were compared to both rainfall and flow parameter to assess their
difference in trend. From the analysis, some of the P value obtained is zero and
43
others gave value near to zero. This means that the water quality-rainfall and water
quality-flow trend is quite the same and the null hypothesis is considered true.
Table 6.4: Result Summary from Mann Kendall Analyses of Water Quality and
Rainfall Parameter
2004
Parameter
N
Z
P
Trend
PH
184
-10.1796
0
Decreasing
COLOUR
184
507.5062
0
Increasing
TURB
184
161.8373
0
Increasing
AL
184
-29.8599
0
Decreasing
FE
184
-29.5724
0
Decreasing
MN
184
-102.225
0
Decreasing
NH4
184
-29.6986
0
Decreasing
N
Z
P
Trend
PH
348
0.1453
0.4422
Increasing
COLOUR
348
42.8084
0
Increasing
TURB
348
26.6931
0
Increasing
AL
348
-8.9039
0
Decreasing
FE
348
-8.7921
0
Decreasing
MN
348
-8.7826
0
Decreasing
NH4
348
-8.8381
0
Decreasing
SS
348
3.9242
4.35E-05
Increasing
N
Z
P
Trend
PH
365
-3.0928
0.0010
Decreasing
COLOUR
365
38.7841
0
Increasing
TURB
365
28.1404
0
Increasing
AL
365
-11.1055
0
Decreasing
FE
365
-10.9848
0
Decreasing
MN
365
-10.9961
0
Decreasing
2005
Parameter
2006
Parameter
44
NH4
365
-10.9689
0
Decreasing
SS
365
13.9046
0
Increasing
N
Z
P
Trend
PH
365
-1.8764
0.0303
Decreasing
COLOUR
365
37.7639
0
Increasing
TURB
365
28.6078
0
Increasing
AL
365
-5.6093
1.02E-08
Decreasing
FE
365
-5.5813
1.19E-08
Decreasing
MN
365
-2.5256
0.0058
Decreasing
NH4
365
-2.5413
0.0055
Decreasing
SS
365
19.3627
0
Increasing
2007
Parameter
Table 6.5: Result Summary from Mann Kendall Analyses of Water Quality and
Flow Parameter
2004
Parameter
N
Z
P
Trend
PH
184
-62.2041
0
Decreasing
COLOUR
184
489.3166
0
Increasing
TURB
184
120.6286
0
Increasing
AL
184
-77.1415
0
Decreasing
FE
184
-77.1415
0
Decreasing
MN
184
-76.8673
0
Decreasing
NH4
184
-76.9991
0
Decreasing
N
Z
P
Trend
PH
348
-2.7518
0.0029
Decreasing
COLOUR
348
42.2207
0
Increasing
TURB
348
21.9268
0
Increasing
AL
348
-5.8589
2.33E-09
Decreasing
FE
348
-5.8211
2.92E-09
Decreasing
MN
348
-5.8175
2.99E-09
Decreasing
2005
Parameter
45
NH4
348
-5.8364
2.67E-09
Decreasing
SS
348
3.4477
0.0003
Increasing
N
Z
P
Trend
PH
365
-18.5699
0
Decreasing
COLOUR
365
37.4085
0
Increasing
TURB
365
18.3391
0
Increasing
AL
365
-22.1198
0
Decreasing
FE
365
-22.0663
0
Decreasing
MN
365
-22.0713
0
Decreasing
NH4
365
-22.0593
0
Decreasing
SS
365
12.3772
0
Increasing
N
Z
P
Trend
PH
365
-8.7155
0
Decreasing
COLOUR
365
36.6921
0
Increasing
TURB
365
17.8916
0
Increasing
AL
365
-10.4151
0
Decreasing
FE
365
-10.4024
0
Decreasing
MN
365
-10.3866
0
Decreasing
NH4
365
-10.4026
0
Decreasing
SS
365
18.4908
0
Increasing
2006
Parameter
2007
Parameter
Notes: Positive Z-scores by the Mann–Kendall tests indicate increasing trends while
negative Z-scores indicate decreasing trends. Z-scores lower than Z-critical (−1.64)
or higher than (+ 1.64) indicate significant decreases or increases.
46
6.4
Best Fitted ARIMA Model
Appendix D to K and Appendix M present the detail plot of mean daily water
quality for the original series with their sample ACF, sample PACF, residual ACF
and residual PACF for each water quality parameter from 2004 to 2007. Evaluation
on the model criteria such that smallest AIC‘s value, smallest standard error and
higher t- value of the possible ARIMA model was gave the best fitted ARIMA model
for each of the water quality parameter from year 2004 to 2007. Best fitted ARIMA
modelling was summarized in Table 6.6 (a) to (h) below.
The ARIMA model ranges from ARIMA (1,1,1) to ARIMA (2,1,2).
Differencing was required to all data series since they are shows nonstationary data
characteristic. Most of the ARIMA models are generated by AR (1) component. The
t-value for all AR (1) coefficient are large and significant at 5% level.
Table 6.6 (a): Best Fitted ARIMA Model for PH Parameter
Year
ARIMA
AIC
T-test
Std. Error
Coefficient
C
2004
1,1,2
-689.9701
0.6827
0.0239
AR(1) = 0.2024
0.00010
2005
1,1,1
-1326.3381
1.9153
MA(1) = 0.5659
0.9454
MA(2) = 0.1728
12.8446
0.0280
13.8527
2006
1,1,1
-1293.668
5.2581
1,1,1
-1380.2225
5.5313
0.00011
MA(1) = 0.9988
0.0291
36.8329
2007
AR(1) = 0.6106
AR(1) = 0.3339
0.000069
MA(1) = 0.9288
0.0286
25.4931
AR(1) = 0.3824
-0.00014
MA(1) = 0.8849
Table 6.6 (b): Best Fitted ARIMA Model for Colour Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
111
130.5476
8.1540
0.3655
-1.2554
2005
111
338.2739
7.0451
45.4385
Coefficient
AR(1) = 0.6089
C
-0.0016
MA(1) = 0.9995
0.4112
AR(1) = 0.4306
MA(1) = 0.9527
0.00084
47
2006
111
342.8355
6.5439
0.4172
24.1018
2007
112
289.3926
6.6362
AR(1) = 0.4616
0.00079
MA(1) = 0.8858
0.3721
AR(1) = 0.5256
8.6110
MA(1) = 0.7822
2.2744
MA(2) = 0.1927
0.00094
Table 6.6 (c): Best Fitted ARIMA Model for Turbidity Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
111
163.2783
8.1778
0.4014
10.7914
2005
2006
112
111
269.4063
585.2267
6.7947
112
274.5702
AR(1) = 0.6147
0.3667
AR(1) = 0.5235
2.5349
MA(1) = 0.7799
5.1649
MA(2) = 0.2191
3.3323
3.7860
C
-0.0015
MA(1) = 0.9958
0.6208
25.5008
2007
Coefficient
AR(1) = 0.2323
0.00197
0.00082
MA(1) = 0.8768
0.3649
AR(1) = 0.3515
4.7229
MA(1) = 0.5485
5.8455
MA(2) = 0.3804
0.00106
Table 6.6 (d): Best Fitted ARIMA Model for Alluminium (Al) Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
111
299.9397
4.1761
0.6451
10.0938
2005
111
597.6092
5.9481
111
486.4079
3.5218
0.6312
111
514.5758
2.5754
58.7809
0.00026
AR(1) = 0.3461
0.00136
MA(1) = 0.9696
0.5409
39.1942
2007
AR(1) = 0.3443
C
MA(1) = 0.9967
57.5858
2006
Coefficient
AR(1) = 0.2292
0.0016
MA(1) = 0.9320
0.5669
AR(1) = 0.1619
MA(1) = 0.9675
-0.00136
48
Table 6.6 (e): Best Fitted ARIMA Model for Iron (Fe) Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
112
285.7614
0.4296
0.6147
2005
111
582.1116
111
404.5639
MA(1) = 0.6057
1.3838
MA(2) = 0.2735
4.9829
0.6049
3.0234
111
429.1063
2.1912
AR(1) = 0.2813
C
0.0048
0.0019
MA(1) = 0.9994
0.4716
39.5013
2007
AR(1) = 0.1056
2.5348
5.7056
2006
Coefficient
AR(1) = 0.1968
0.00077
MA(1) = 0.9333
0.4637
67.6924
AR(1) = 0.1259
-0.00123
MA(1) = 0.9752
Table 6.6 (f): Best Fitted ARIMA Model for Ammonium (NH4) Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
111
327.3136
4.0511
0.7160
0.9249
2005
2006
112
111
550.6102
381.4321
0.8811
111
521.9594
AR(1) = 0.3334
0.5852
AR(1) = 0.1430
4.6207
MA(1) = 0.7408
1.5060
MA(2) = 0.2300
2.3465
-0.1605
C
0.00308
MA(1) = 0.9995
0.4499
32.3118
2007
Coefficient
AR(1) = 0.1554
0.0031
0.00018
MA(1) = 0.9077
0.5390
49.8710
AR(1) = -0.0096
-0.0019
MA(1) = 0.9526
Table 6.6 (g): Best Fitted ARIMA Model for Manganese (Mn) Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
111
220.0244
4.0679
0.4883
0.3031
2005
111
385.6385
2.7640
112
-968.2323
7.4961
7.4627
AR(1) = 0.3381
C
-0.00135
MA(1) = 0.9999
0.4429
42.8657
2006
Coefficient
AR(1) = 0.1704
0.00048
MA(1) = 0.9531
0.0453
AR(1) = 0.7291
MA(1) = 1.3892
0.00016
49
-2.9054
2007
111
440.9339
2.0825
MA(2) = -0.3897
0.4791
69.4639
AR(1) = 0.1245
-0.00045
MA(1) = 0.9750
Table 6.6 (h): Best Fitted ARIMA Model for Suspended Solid (SS) Parameter
Year
ARIMA
AIC
T-test
Std. Error
2004
-
-
-
-
2005
212
512.8124
4.8862
0.4876
2006
2007
211
111
410.9504
126.8279
Coefficient
AR(1) = 0.5918
1.8131
AR(2) = 0.2185
5.9127
MA(1) = 0.3257
3.2403
MA(2) = 0.5924
20.1601
0.4619
AR(1) = 1.1109
-6.4319
MA(1) = -0.3467
44.9608
MA(2) = 0.9628
0.7270
0.3354
1.3388
C
AR(1) = 0.2624
-0.0106
0.00164
-0.0045
MA(1) = 0.4453
The complete model equations for the individual year for each water quality
parameter are written in the equations below:
(a) PH
2004: (1  0. 2024B)(1  B)Yt  1  0.5659B  0.1728B 2 )at
2005: (1  0.6106B)(1  B)Yt  0.00011  (1  0.9988B)at
2006: (1  0.3339B)(1  B)Yt  0.000069  (1  0.9288B)at
2007: (1  0.3824B)(1  B)Yt  0.00014  (1  0.8849B)at
50
(b) FE
2004: (1  0.1056B)(1  B)Yt  1  0.6057 B  0.2735B 2 )at
2005: (1  0.2813B)(1  B)Yt  0.0019  (1  0.9994B)at
2006: (1  0.1968B)(1  B)Yt  0.00077  (1  0.9333B)at
2007: (1  0.1259B)(1  B)Yt  0.00123  (1  0.9333B)at
(c) AL
2004: (1  0.3443B)(1  B)Yt  0.00026  (1  0.9967 B)at
2005: (1  0.3461B)(1  B)Yt  0.00136  (1  0.9696B)at
2006: (1  0.2292B)(1  B)Yt  0.0016  (1  0.9320B)at
2007: (1  0.1619B)(1  B)Yt  0.00136  (1  0.9675B)at
(d) MN
2004: (1  0.3381B)(1  B)Yt  0.00135  (1  0.9999B)at
2005: (1  0.1704B)(1  B)Yt  0.00048  (1  0.9531B)at
2006: (1  0. 7291B)(1  B)Yt  1  1.3892B  0.3897 B 2 )at
2007: (1  0.1245B)(1  B)Yt  0.00045  (1  0.9750B)at
(e) NH4
2004: (1  0.3334B)(1  B)Yt  0.00308  (1  0.9995B)at
51
2005: (1  0.1430B)(1  B)Yt  1  0.7408B  0.2300B 2 )at
2006: (1  0.1554B)(1  B)Yt  0.00018  (1  0.9077 B)at
2007: (1  0.0096B)(1  B)Yt  0.0019  (1  0.9526B)at
(f) Colour
2004: (1  0.6089B)(1  B)Yt  0.0016  (1  0.9995B)at
2005: (1  0.4306B)(1  B)Yt  0.00084  (1  0.9527 B)at
2006: (1  0.4616B)(1  B)Yt  0.00079  (1  0.8858B)at
2007: (1  0. 5256B)(1  B)Yt  1  0.7822B  0.1927 B 2 )at
(g) Turbidity
2004: (1  0.6147 B)(1  B)Yt  0.0015  (1  0.9958B)at
2005: (1  0. 5235B)(1  B)Yt  1  0.7799B  0.2191B 2 )at
2006: (1  0.2323B)(1  B)Yt  0.00082  (1  0.8768B)at
2007: (1  0. 3515B)(1  B)Yt  1  0.5485B  0.3804 B 2 )at
(h) SS
2005: (1  0. 5918B  0.2185B 2 )(1  B)Yt  1  0.3257 B  0.5924B 2 )at
2006: (1  1.1109B  0.3467 B 2 )(1  B)Yt  1  0.9628B)at
2007: (1  0.2624B)(1  B)Yt  0.0045  (1  0.44538B)at
52
6.5
Water Quality Trend Using ARIMA Modelling
Plotting on the AR (1) coefficient against year for each water quality
parameters is a best practice to represent the water quality trend of the upper part of
Johor River. Previous study by Worrall and Burt (1999) used the AR (1) coefficient
as a mean to illustrate such a different `memory effect‘ contain in each best-fitted
model for each catchment. They (Worrall and Burt) developed univariate models of
river water quality series taken from three different catchments in the United
Kingdom. The purpose of their study was to explore the fine structure of the existing
data sets as a mean of shedding light on the processes that generate them. A similar
approach was also employed by Ayob, K (1999) to represent the trend of daily flow
of Maridono peat catchment, but instead of different catchment, time series from
different hydrological year were used. Similar approach was also employed here to
represent the water quality trends at the upper part of the Johor River as shown in
Figure 6.7 (a) to (h).
AR(1) Coefficient
PH Trend 2004-2007
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2004
Figure 6.7 (a): PH Trend
2005
Year
2006
2007
53
AR(1) Coefficient
FE Trend 2004-2007
0.3
0.25
0.2
0.15
0.1
0.05
0
2004
2005
2006
2007
Year
Figure 6.7 (b): Iron (FE) Trend
AR(1) Coefficient
AL Trend 2004-2007
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
2004
2005
2006
2007
2006
2007
Year
Figure 6.7 (c): Alluminium (AL) Trend
Colour Trend 2004-2007
AR(1) Coefficient
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2004
Figure 6.7 (d): Colour Trend
2005
Year
54
AR(1) Coefficient
Turbidity Trend 2004-2007
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2004
2005
2006
2007
Year
AR(1) Coefficient
Figure 6.7 (e): Turbidity Trend
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-0.052004
Ammonium (NH4) Trend 2004-2007
2005
2006
2007
Year
Figure 6.7 (f): Ammonium (NH4) Trend
AR(1) Coefficient
MN Trend 2004-2007
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2004
2005
2006
Year
Figure 6.7 (g): Manganese (MN) Trend
2007
55
SS Trend 2004-2007
AR(1) Coefficient
1.2
1
0.8
0.6
0.4
0.2
0
2004
2005
2006
2007
Year
Figure 6.7 (h): Suspended Solid (SS) Trend
6.5.1
Discussion on the Water Quality Trend
The water quality trend, Figure 6.2 (a) to (h) was compared with the rainfall-
runoff pattern (Figure 6.1) to assess either they have similar pattern or not. This
comparison is important to show the water quality and rainfall-runoff relationship.
For parameter MN, NH4, SS, Turbidity and Colour, similar trend was
observed. While for parameter PH, AL, and FE there have the opposite trend to the
rainfall-runoff pattern.
The reason of different behaviour of the water quality trend for PH, Al and Fe
parameters compare to rainfall-runoff pattern are because PH level is greatly
depending on the concentration of soluble hydrogen (H+) and hydroxide (OH-) ion.
Higher concentration of H+ ion will resulted in decreasing PH level (acidic) while
higher concentration of OH- ion will resulted in increasing PH level (alkaline)
(Shayne R. and Nick Umney, 2003). Therefore, increasing amount of rainfall water
is not really increase the PH level and vice versa. Hence the best practice to assess
the PH trend might be by assessing the PH data series within their level of acidity
and alkalinity.
56
For Al and Fe parameters, the reason of the different behaviour is that the
behaviour of trace elements (Fe and Al) is to a large extent determined by their
chemical forms of occurrence. As reported by Gambrell (1994), general chemical
forms of elements include:
1. water-soluble metals, as free ions, inorganic or organic complexes
2. exchangeable metals
3. metals precipitated as inorganic compounds, including insoluble sulphides
4. metals complexed with large molecular-weight humic materials
5. metals adsorbed or occluded to precipitated hydrous oxides and
6. metals bound within the crystalline lattice structure of primary minerals.
As a result, Al and Fe are governed by numerous processes, including sorption/
desorption, precipitation/ dissolution and complexation/ decomplexation. Therefore,
comparing Al and Fe trend with rainfall and runoff pattern alone is not really true.
In year 2005 to 2006, a wide area of land was opened at Kota Tinggi district for
plantation of palm oil and rubber under FELDA and RISDA Land Development
scheme in relation to the Ninth Malaysian Plan (RMK 9). As reported in Ninth
Malaysian Plan (2006-2010), agricultural land use increased from 5.9 million
hectares in 2000 to 6.4 million hectares in 2005, largely due to the expansion in the
hectarage of oil palm, coconuts, vegetables and fruits. Of the total land area, 4.0
million hectares were under oil palm followed by 1.3 million hectares under rubber.
The agro-based industry grew at 4.5 per cent per annum. Total export earnings of the
agro-based industry increased significantly by 8.7 per cent per annum to reach RM
37.4 billion in 2005. Therefore in RMK 9, government decided to increase the
agricultural for every state including Johor. It is expected to grow at higher average
annual rate of 5.0 per cent (The Economic Planning Unit Prime Minister‘s
Department Malaysia, 2006).
Agriculture is a major source of water quality problems (Howard H.G, 2006).
Primary stage of the agricultural activities need large amount of fertilizer in regular
time. In fact, Manganese (Mn) and Ammonium (NH4) are the major element in
57
fertilizer. These types of nutrient are important for the growth of plant. Hence,
increasing trend of this parameter at year 2005 to 2006 was observed, then
decreasing after that as only certain amount of fertiliser is needed in this stage.
Turbidity and colour refers to water clarity. The greater the amount of suspended
solids (SS) in the water, the murkier it appears, and the higher the measured turbidity
and colour. Suspended solids in streams are often the result of sediments carried by
the water. The source of these sediments includes natural and anthropogenic (human)
activities in the watershed, such as natural or excessive soil erosion from agriculture,
forestry or construction, urban runoff, industrial effluents, or excess phytoplankton
growth (The Economic Planning Unit Prime Minister‘s Department Malaysia, 2006).
Some of these activities is believe are the causes of increasing trend of turbidity,
colour and SS trend in the Johor Rivers at year 2005 to 2006.
58
CHAPTER VII
CONCLUSION AND RECOMMENDATION
7.1
Conclusion
Water quality trend at the upper part of Johor River was successfully obtained
using both ARIMA modelling and Mann-Kendall test. Water quality trend without
considering missing data, outlier, normal distribution and stationary was obtained
using Mann-Kendall test. From the test, PH, Al, Fe, NH4 and Mn shows decreasing
trend while colour, turbidity and suspended solid (SS) have decreasing trend since
year 2004 to 2007.
ARIMA modelling was conducted by considering all of the factors that
affecting a trend in water quality and hydrological time series analysis such as the
error, missing data, outlier, seasonal component, stationary and etc. Therefore, water
quality trend at the upper part of Johor River is best presented by plotted AR(1)
coefficient obtained from ARIMA modelling against year than result obtained from
Mann-Kendall test. Hence, it can concluded that Ammonium (NH4), Manganese
(Mn), suspended solid (SS), turbidity and colour have a trend whereas declining
trend at 2004 to 2005, increasing trend at 2005 to 2006 then decrease again after that.
While for PH, Al and Fe, they have the opposite trend.
Besides, Mann Kendall test is best representing the relationship of water
quality-rainfall and water quality-flow alone indicates by zero or near to zero
59
probability. This proved that both rainfall and runoff have a strong relationship with
water quality parameters.
Then, comparison between runoff coefficient plot and water quality trend
from the ARIMA analysis is best described the relationship of water quality, rainfall
and flow together. Ammonium (NH4), Manganese (Mn), suspended solid (SS),
turbidity and colour have a similar trend with the rainfall and runoff whereas
declining trend at 2004 to 2005, increasing trend at 2005 to 2006 then decrease again
after that. While for PH, Al and Fe, they have opposite trend with rainfall and runoff
pattern. Therefore, Mn, NH4, SS, turbidity and colour have strong relationship with
rainfall and runoff. On the other hand, PH, Al and Fe have weak relationship with
rainfall and runoff.
7.2
Recommendation
1. Transfer function modelling is recommended to conduct to determine the
dynamic relationship of the water quality and rainfall-runoff. By doing that, it
can tell us how much amount of water quality parameter will exist when there
is a unit of rainfall or runoff present. Therefore, some methods for controlling
the pollutant from entering the river can be applied.
2. Conduct multiple input-single output analysis because water quality is neither
a static condition of a system, nor can it be defined by the measurement of
only one parameter. There is a range of chemical, physical, and biological
components that affect water quality. Besides, from the analysis all of the
water quality parameters investigated is correlated with each other. Therefore,
combining all of the factors as input and assessing their effect to the rainfall
and flow respectively is expected to give more reliable and beneficial result.
60
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66
APPENDIX A - FLOWCHART OF METHODOLOGY
67
APPENDIX B1 – Daily Water Quality and Hydrological Data for Year 2004
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
PH
.
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.
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Colour
.
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Turb
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Al
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.
Fe
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.
NH4
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Mn
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Flow
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.
SS
0.00
0.00
0.00
0.00
0.00
0.00
0.00
31.00
0.00
4.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
49.00
0.00
0.00
0.00
5.00
77.00
0.00
5.00
25.00
54.00
95.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
68
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
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.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
15.81
9.90
6.67
5.64
5.01
4.46
3.96
3.66
3.41
3.58
6.95
6.96
5.79
5.09
4.03
3.45
3.90
4.15
4.30
4.43
3.81
5.09
9.96
8.65
7.78
6.73
6.61
9.66
115.52
213.14
213.54
192.95
169.32
144.61
114.56
88.46
86.35
84.62
72.24
59.30
53.80
55.68
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
3.00
0.00
0.00
0.00
15.00
0.00
290.00
18.00
0.00
13.00
7.50
0.00
0.00
18.00
19.00
0.00
27.00
9.50
0.00
12.00
0.00
0.00
69
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
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.
.
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.
.
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.
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.
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.
.
.
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.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
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.
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.
.
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.
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.
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.
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.
.
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.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
51.34
45.59
34.94
29.45
37.60
36.98
45.76
40.92
46.12
35.65
23.82
14.05
13.15
26.83
38.85
39.00
35.78
37.16
26.41
23.21
31.62
27.43
20.69
14.99
11.38
10.92
10.89
9.60
7.12
5.92
5.18
5.88
5.33
5.67
4.58
5.08
6.89
9.64
7.72
9.61
9.65
7.81
9.04
0.00
3.00
3.00
0.00
5.00
0.00
10.00
0.00
0.00
0.00
0.00
6.00
25.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.00
0.00
0.00
0.00
4.50
0.00
0.00
0.00
0.00
0.00
0.00
0.00
35.00
17.00
12.00
8.00
0.00
0.00
0.00
17.00
0.00
0.00
70
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
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.
.
.
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.
.
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.
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.
.
.
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.
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.
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.
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.
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.
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.
.
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.
.
.
.
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.
.
.
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.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
11.34
9.46
8.15
7.65
7.14
6.58
4.61
3.83
3.53
3.52
3.50
3.57
3.13
2.77
2.70
2.55
2.68
3.41
3.64
3.14
4.16
4.56
4.81
5.16
5.34
3.99
3.61
4.23
7.84
4.30
2.54
3.25
7.47
4.64
4.87
5.19
0.96
2.70
4.60
4.01
3.37
3.04
2.83
4.00
0.00
0.00
0.00
0.00
0.00
0.00
14.00
0.00
0.00
0.00
0.00
14.00
0.00
0.00
8.50
0.00
0.00
0.00
30.00
4.00
0.00
2.00
2.00
0.00
12.00
11.00
0.00
0.00
0.00
0.00
45.00
0.00
0.00
15.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
71
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5.79
5.85
5.93
5.79
5.66
5.88
5.82
5.87
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1397.17
568.75
337.42
641.83
886.25
937.00
1274.00
774.33
940.17
739.67
919.33
834.64
956.08
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
136.40
182.22
165.75
253.40
188.13
.
.
.
.
.
.
227.54
127.38
158.23
82.93
85.88
109.95
93.46
71.76
69.41
74.02
70.57
76.95
97.20
134.50
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.05
0.06
0.07
0.01
0.02
0.02
0.03
0.02
0.12
0.05
0.02
0.02
0.02
0.07
0.07
0.11
0.10
0.07
0.09
0.03
0.02
0.02
0.05
0.04
0.06
0.02
0.01
0.02
0.03
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.08
0.12
0.20
0.11
0.16
0.05
0.05
0.07
0.05
0.04
0.02
0.06
0.15
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.04
0.03
0.04
0.01
0.02
0.02
0.02
0.04
0.19
0.01
0.02
0.02
0.03
0.14
0.01
0.02
0.13
0.07
0.12
0.08
0.10
0.07
0.11
0.06
0.05
0.06
0.07
0.02
0.09
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.18
0.09
0.12
0.08
0.06
0.04
0.06
0.09
.
.
0.09
0.11
0.13
.
.
0.13
0.18
0.13
0.18
0.15
0.15
0.14
0.19
0.13
0.13
0.10
0.11
0.13
0.13
3.76
5.54
6.19
4.81
2.86
2.44
2.27
2.35
2.40
2.64
3.71
4.32
4.55
4.82
4.31
4.80
4.84
4.61
1.29
0.89
2.68
3.49
4.54
5.38
6.21
7.05
11.80
28.79
58.16
75.10
65.35
23.96
8.40
5.26
4.26
3.81
3.59
3.93
4.01
4.20
4.18
4.04
3.33
3.00
0.00
0.00
4.50
1.00
23.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
3.00
89.00
3.00
5.00
25.00
60.00
26.00
0.00
7.00
85.00
21.00
40.00
0.00
10.00
5.00
0.00
0.00
0.00
6.00
0.00
0.00
5.00
0.00
4.00
2.00
1.50
5.00
17.00
72
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
5.88
5.66
5.84
5.93
5.88
5.92
5.86
5.86
5.84
5.85
5.91
5.94
5.98
6.12
6.06
6.13
6.22
6.22
.
.
.
.
.
.
.
6.22
6.13
6.20
6.19
6.06
6.05
6.24
6.10
6.06
6.11
6.19
6.02
5.94
5.94
5.81
5.97
6.01
6.18
1037.33
1574.25
1051.07
793.00
773.08
723.17
582.08
506.75
704.92
657.83
576.67
579.42
589.90
533.25
641.07
739.25
701.42
378.83
479.08
290.40
.
488.00
747.45
639.42
458.75
580.75
972.67
882.42
854.55
1153.00
901.78
683.20
666.25
742.92
931.50
817.67
1643.25
1610.75
1723.09
1647.75
918.17
710.58
774.11
148.96
176.21
97.30
94.98
102.66
95.46
72.07
83.27
85.50
80.17
74.41
71.76
111.85
84.81
88.65
77.68
77.84
64.34
187.10
79.80
63.67
75.59
114.42
74.48
57.66
71.49
131.76
122.24
144.26
.
167.00
87.15
89.03
148.71
85.43
202.48
228.00
210.17
237.45
242.25
114.52
107.40
107.00
0.06
0.05
0.02
0.06
0.06
0.04
0.03
0.04
0.06
0.04
0.02
0.02
0.08
0.06
0.02
0.10
0.09
0.09
0.01
0.04
0.01
0.02
0.01
0.01
0.01
0.00
0.04
0.01
0.01
0.03
0.03
0.04
0.05
0.03
0.03
0.02
0.01
0.01
0.04
0.07
0.02
0.01
0.00
0.09
.
0.11
0.09
0.04
0.06
0.09
0.11
.
0.13
0.07
0.06
0.11
0.17
0.07
0.13
0.18
0.08
0.07
0.11
0.08
0.18
0.10
0.09
0.11
0.08
0.12
0.10
0.14
0.10
0.10
0.09
0.15
0.08
0.11
0.11
0.13
0.06
0.19
.
.
0.03
0.05
0.21
.
0.19
0.04
0.07
0.04
0.06
0.16
0.18
0.09
0.01
0.01
0.10
0.15
0.06
0.14
0.22
0.07
0.00
0.02
0.01
0.06
0.03
0.02
0.02
0.01
0.13
0.04
0.04
0.05
0.09
0.12
0.12
0.02
0.07
0.04
0.06
0.06
0.11
0.09
0.13
0.01
0.01
0.17
0.21
0.13
0.14
0.18
0.10
0.12
0.21
0.15
0.12
0.12
0.11
0.11
0.07
0.08
0.10
0.16
0.14
0.07
0.10
0.03
0.14
0.12
0.07
0.09
0.11
0.19
0.21
0.17
0.23
0.16
0.15
0.11
0.04
0.04
0.03
0.13
0.09
0.10
0.21
0.22
0.05
0.07
1.87
3.33
2.74
3.36
3.51
4.09
4.40
4.68
5.02
5.30
4.12
3.20
3.09
2.85
2.75
1.65
0.90
0.28
0.03
1.18
1.76
2.27
0.87
1.85
2.66
3.13
3.11
2.63
2.29
-0.90
0.97
2.82
2.30
3.18
3.30
4.07
5.16
6.24
7.33
8.42
9.51
10.01
7.29
0.00
12.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
40.00
4.00
0.00
10.00
0.00
17.00
0.00
0.00
0.00
18.00
0.00
0.00
26.50
0.00
0.00
0.00
0.00
4.50
23.00
13.00
0.00
15.50
35.00
0.00
0.00
0.00
0.00
0.00
73
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
6.16
5.89
.
5.84
5.76
5.84
5.81
5.93
6.15
5.93
5.94
5.87
5.78
5.60
5.89
5.84
5.71
5.70
5.74
5.71
5.74
6.07
5.75
.
5.86
5.82
5.86
5.75
5.79
5.59
5.59
5.69
6.01
.
.
.
.
.
.
.
.
.
.
589.27
1183.50
1764.58
1797.86
1386.80
1440.78
1496.55
1139.33
941.08
1153.42
877.67
1601.18
1276.08
1464.50
942.42
989.43
1550.33
600.00
1645.08
1379.25
797.00
710.00
712.75
530.83
564.75
591.33
955.33
1435.67
1486.36
1402.00
789.83
484.25
630.75
.
.
.
.
.
.
.
.
.
.
74.33
257.53
.
138.82
180.40
194.11
192.18
137.48
127.23
168.00
147.98
239.36
130.03
204.92
126.27
156.63
251.98
115.00
221.93
192.50
93.60
91.00
77.03
77.67
68.33
72.05
174.08
196.00
254.55
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.01
0.00
0.03
0.01
0.00
0.00
0.03
0.04
0.03
0.08
0.03
0.03
0.03
0.07
0.05
0.01
0.02
0.03
0.04
0.04
0.03
0.03
0.03
0.02
0.01
0.02
0.02
0.07
0.03
0.11
0.06
0.02
0.02
.
.
.
.
.
.
.
.
.
.
0.09
0.05
0.13
0.10
0.10
0.09
0.10
0.02
0.08
.
.
.
.
.
.
.
.
.
.
.
0.18
0.12
0.07
0.12
0.20
0.13
0.10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.04
0.01
0.05
0.00
0.17
0.05
0.03
0.03
0.00
0.20
0.09
0.07
0.08
0.16
0.15
0.06
0.09
0.16
0.14
0.13
0.10
0.03
0.03
0.01
0.08
0.06
0.06
.
0.08
.
0.15
0.07
0.10
.
.
.
.
.
.
.
.
.
.
0.09
0.03
.
0.16
0.16
0.17
0.23
0.13
0.16
.
0.19
0.11
0.11
0.23
0.21
0.15
0.08
0.18
0.09
0.16
0.10
0.14
0.12
0.03
0.05
0.08
0.08
0.22
.
.
0.14
0.05
0.06
.
.
.
.
.
.
.
.
.
.
4.76
10.30
26.16
10.67
9.91
11.65
13.02
11.61
10.93
11.00
11.60
37.43
30.17
42.51
38.85
90.03
73.84
87.61
80.99
91.27
68.59
64.31
62.25
59.85
57.93
27.20
10.30
17.68
26.63
52.93
56.22
37.29
29.70
52.37
52.38
38.52
21.83
15.38
16.68
26.18
42.50
52.72
51.99
5.00
0.00
10.00
8.00
3.00
0.00
0.00
3.00
0.00
44.00
1.00
63.00
0.00
0.00
5.00
7.00
47.00
3.00
2.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
45.00
48.00
48.00
0.00
0.00
58.00
0.00
0.00
0.00
0.00
5.00
33.00
44.00
15.00
15.00
0.00
7.00
74
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5.72
5.75
5.89
5.92
5.99
5.87
5.88
5.98
6.01
6.01
6.00
5.94
6.06
5.88
5.91
5.96
5.90
.
5.92
5.85
5.91
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1134.40
548.08
374.26
580.42
874.75
1013.50
858.42
743.58
529.33
967.08
652.17
803.08
632.83
736.75
736.75
924.55
895.25
1570.17
1146.83
743.67
.
385.58
530.33
618.70
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
108.69
79.68
76.23
69.97
120.31
137.67
78.00
79.97
70.18
161.50
73.22
85.07
77.55
91.22
114.48
133.48
118.67
167.08
146.92
85.04
.
60.88
67.26
62.07
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.07
0.03
0.07
0.06
0.04
0.03
0.05
0.07
0.06
0.04
0.01
0.01
0.02
0.01
0.04
0.03
0.02
0.04
0.05
0.07
.
0.02
0.02
0.03
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.15
0.14
0.03
0.08
0.10
0.09
0.07
0.02
0.08
0.08
0.13
0.05
0.06
0.05
0.04
0.06
0.06
0.07
0.08
0.18
.
0.07
0.09
0.10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.07
0.08
0.05
0.08
0.03
0.04
0.03
0.07
0.07
0.07
0.06
0.04
0.05
0.07
0.06
0.05
0.04
0.08
0.08
0.15
.
0.05
0.07
0.10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.04
0.07
0.05
0.12
0.10
0.06
0.11
0.14
0.15
0.16
0.04
0.10
0.03
0.15
0.13
0.11
0.04
0.15
0.11
0.12
.
0.10
0.13
0.11
50.36
47.49
44.77
45.71
43.64
47.69
36.15
42.40
45.19
64.61
77.82
85.00
85.46
70.42
70.03
62.98
62.26
75.19
90.74
97.18
93.67
89.28
88.74
88.17
90.28
88.60
83.21
78.75
73.32
70.79
68.60
63.49
59.67
61.55
67.28
71.94
80.26
76.56
84.20
75.46
56.53
46.04
42.43
1.00
11.00
10.00
13.00
2.00
0.00
8.00
14.00
0.00
0.00
25.00
12.00
0.00
58.00
0.00
22.00
0.00
36.00
5.50
5.00
7.50
4.50
24.00
0.00
0.00
15.00
0.00
0.00
2.00
0.00
0.00
14.00
5.00
0.00
5.00
0.00
0.00
0.00
0.00
0.00
14.00
10.00
0.00
75
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
5.95
5.92
6.07
6.05
5.98
5.86
6.02
5.87
6.08
6.20
5.95
6.01
5.97
6.00
5.94
5.93
6.24
6.19
6.19
5.96
6.08
6.05
.
6.25
6.23
6.04
565.75
630.78
971.83
788.63
598.11
.
.
.
386.27
467.44
555.17
505.00
651.08
946.75
557.22
593.82
575.38
526.56
425.17
403.67
320.83
439.00
335.17
545.33
557.50
1458.67
66.43
92.87
74.39
80.66
88.81
.
115.83
204.51
153.36
85.03
70.48
69.47
89.75
110.21
79.72
81.56
82.58
124.80
61.30
62.50
.
.
.
64.31
80.83
217.33
0.02
0.09
0.03
0.02
0.02
0.12
0.05
0.04
0.04
0.04
0.11
0.02
0.01
0.03
0.01
0.02
0.06
0.02
0.04
0.05
0.04
0.03
0.04
0.07
0.04
0.07
0.06
0.07
0.09
0.05
0.07
.
0.12
0.11
0.07
0.05
0.08
0.03
0.07
0.05
0.10
0.08
0.07
0.03
0.08
0.10
0.05
0.07
0.06
0.10
0.12
0.13
0.05
0.08
0.08
0.06
0.05
.
0.09
0.14
0.05
0.10
0.07
0.10
0.06
0.04
0.04
0.04
0.12
0.10
0.10
0.07
0.08
0.06
0.12
0.10
0.10
0.14
0.09
0.11
0.11
0.10
0.12
.
0.14
0.17
0.10
0.11
0.12
0.07
0.10
0.10
0.08
0.04
0.12
0.08
0.13
0.15
0.13
0.11
0.08
0.12
0.13
0.18
33.94
31.56
33.32
26.52
20.48
36.04
28.83
35.65
31.08
23.23
17.95
16.27
16.02
16.15
15.05
14.42
14.14
13.75
13.36
13.16
32.34
21.28
22.41
22.21
20.93
13.93
0.00
0.00
3.00
0.00
16.00
7.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
28.00
3.00
20.00
70.00
30.00
76
APPENDIX B2 - Daily Water Quality and Hydrological Data for Year 2005
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
PH
6.09
5.94
5.74
5.84
5.74
5.58
5.75
5.75
5.74
5.67
5.64
5.77
5.69
5.61
5.73
5.87
5.59
5.66
6.01
5.76
5.74
5.65
5.82
5.81
5.92
5.84
5.90
6.07
6.17
6.13
6.04
5.71
5.84
5.86
6.15
5.98
6.08
Colour
2622.75
1921.50
839.67
684.17
868.00
634.50
639.00
1188.17
1213.50
751.67
492.33
338.67
348.00
407.50
414.67
397.20
915.80
372.67
380.40
440.60
469.50
406.00
333.00
358.80
495.60
414.80
398.50
430.33
330.17
549.83
467.00
484.83
370.67
242.83
369.17
282.00
321.33
Turb
286.00
205.50
205.58
151.08
112.58
71.78
63.29
133.43
156.33
92.86
50.23
35.07
39.67
53.64
41.25
38.98
39.98
38.73
47.63
61.55
74.49
61.31
39.68
61.00
50.04
81.20
94.06
52.07
47.93
51.06
55.51
65.71
63.79
61.70
54.30
35.08
56.86
Al
0.12
0.12
0.06
0.04
0.06
0.07
0.03
0.00
0.01
0.02
0.01
0.00
0.03
0.09
0.01
0.00
0.01
0.01
0.03
0.02
0.01
0.01
0.02
0.01
0.03
0.03
0.02
0.02
0.06
0.04
0.02
0.03
0.02
0.01
0.02
0.02
0.03
Fe
0.11
0.11
0.11
0.09
0.18
0.10
0.04
0.03
0.09
0.07
0.03
0.00
0.16
0.12
0.01
0.08
0.04
0.07
0.07
0.11
0.11
0.04
0.06
0.09
0.07
0.13
0.06
0.21
0.07
0.10
0.10
0.12
0.10
0.04
0.13
0.05
0.11
NH4
0.10
0.12
0.05
0.09
0.11
0.11
0.07
0.02
0.01
0.01
0.01
0.01
0.08
0.13
0.02
0.03
0.04
0.01
0.07
0.04
0.04
0.04
0.04
0.05
0.04
0.10
0.12
0.06
0.05
0.08
0.06
0.07
0.04
0.00
0.07
0.00
0.09
Mn
0.18
0.18
0.22
0.10
0.12
0.16
0.09
0.07
0.09
0.06
0.05
0.05
0.10
0.13
0.08
0.06
0.05
0.06
0.12
0.05
0.04
0.04
0.05
0.12
0.07
0.08
0.14
0.06
0.10
0.16
0.06
0.08
0.08
0.06
0.09
0.08
0.09
Flow
92.13
130.16
185.89
220.11
223.34
196.68
148.90
110.75
76.67
66.84
73.82
70.89
72.94
61.78
48.17
40.57
35.57
31.87
28.53
18.87
9.32
7.47
6.60
5.72
4.95
4.33
4.10
3.62
5.08
4.91
3.98
2.97
2.63
2.51
2.35
2.63
3.78
SS
Rainfall
.
73.00
849.10
1.50
1214.80
28.00
1438.40
1.50
1459.30
0.00
1285.60
40.00
973.30
20.00
707.10
0.00
429.80
0.00
356.10
0.00
408.00
0.00
390.40
0.00
401.10
0.00
319.90
0.00
228.30
0.00
180.20
0.00
150.60
0.00
130.30
0.00
112.00
0.00
64.60
0.00
23.50
0.00
17.40
0.00
14.80
0.00
12.30
0.00
10.60
0.00
9.10
0.00
8.40
0.00
8.30
0.00
10.80
0.00
10.40
0.00
7.40
0.00
6.00
0.00
5.00
0.00
5.00
0.00
5.00
0.00
5.40
0.00
7.80
0.00
77
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
6.00
6.13
5.69
6.03
6.20
6.32
6.14
5.91
6.05
6.05
5.97
6.08
6.15
6.22
6.23
6.14
6.17
5.96
5.87
.
5.95
6.25
6.06
6.09
6.03
5.93
.
6.17
6.34
6.05
6.05
6.29
6.16
6.15
6.08
6.01
6.06
6.04
6.04
6.06
269.80
347.33
299.67
515.00
355.00
313.50
358.50
380.00
728.00
281.80
319.00
431.00
294.33
392.00
424.40
450.17
548.83
338.83
373.40
.
370.33
382.83
417.33
339.00
370.67
695.60
.
328.20
445.75
743.67
584.17
300.17
219.67
443.00
379.00
454.33
434.83
446.33
276.17
256.00
37.31
35.62
32.61
58.31
38.98
32.25
62.49
83.45
121.09
49.62
33.29
33.57
24.37
52.98
56.77
66.16
66.58
37.76
40.88
.
39.30
52.67
69.53
42.41
60.33
115.16
.
44.07
88.35
90.50
66.38
44.01
31.86
39.21
34.64
42.38
41.08
51.27
41.69
40.63
0.01
0.01
0.04
0.01
0.02
0.02
0.03
0.03
0.02
0.01
0.01
0.00
0.01
0.04
0.05
0.05
0.03
0.07
0.05
.
0.03
0.04
0.05
0.11
0.01
0.02
.
0.03
0.09
0.04
0.03
0.03
0.03
0.04
0.02
0.03
0.02
0.06
0.03
0.02
0.02
0.06
0.08
0.09
0.11
0.07
0.05
0.07
0.10
0.05
0.06
0.06
0.05
0.06
0.09
0.09
0.05
0.04
0.43
.
0.05
0.08
0.09
0.08
0.03
0.09
.
0.07
0.16
0.08
0.09
0.04
0.04
0.03
0.04
0.04
0.00
0.10
0.08
0.03
0.02
0.01
0.03
0.09
0.06
0.03
0.04
0.00
0.02
0.00
0.05
0.02
0.04
0.04
0.08
0.10
0.06
0.04
0.04
.
0.04
0.07
0.10
0.03
0.06
0.00
.
0.04
0.14
0.11
0.12
0.06
0.02
0.00
0.00
0.01
0.01
0.07
0.08
0.04
0.04
0.04
0.07
0.11
0.08
0.02
0.04
0.11
0.11
0.07
0.08
0.08
0.04
0.05
0.25
0.13
0.13
0.08
0.11
.
0.08
0.07
0.10
0.09
0.10
0.15
.
0.06
0.10
0.10
2.43
0.10
0.06
0.00
0.02
0.07
0.05
0.11
0.12
0.10
4.59
4.63
6.86
9.98
13.12
12.25
7.81
4.74
2.79
2.36
2.16
2.02
1.89
2.71
10.32
18.49
21.29
16.05
10.27
7.62
8.91
8.09
6.56
2.36
0.83
1.15
1.42
1.55
3.18
4.11
3.21
2.88
3.99
7.09
6.77
4.60
3.46
2.11
0.92
0.70
9.60
9.50
15.50
26.40
38.60
35.00
18.60
10.00
5.50
5.00
4.00
4.00
4.00
5.50
28.20
62.30
75.10
51.30
27.40
17.90
22.00
19.40
14.90
4.80
2.00
2.40
3.00
3.30
6.60
8.50
6.50
5.60
8.40
16.30
15.40
9.50
7.00
4.20
1.90
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
26.00
23.50
0.00
0.00
0.00
4.00
0.00
0.00
41.00
0.00
0.00
0.00
0.00
21.00
26.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
78
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
6.04
6.04
6.14
6.22
6.03
6.03
6.15
6.16
6.30
6.11
6.19
6.34
5.82
5.94
6.05
6.07
5.90
6.05
6.19
6.09
6.03
6.16
6.12
6.05
6.16
6.10
6.15
5.89
6.03
6.15
6.04
6.00
6.16
6.22
6.26
5.90
5.97
6.01
6.23
6.34
274.83
387.60
226.80
250.50
326.33
392.00
302.17
265.50
321.50
755.67
501.33
474.33
764.83
770.80
539.17
444.50
538.67
515.33
571.67
432.67
402.33
380.00
333.50
395.33
451.50
711.50
557.83
518.50
458.83
641.33
442.33
488.33
457.17
326.17
356.33
601.33
745.50
494.67
372.00
325.33
44.63
39.83
30.23
29.00
35.31
57.16
46.22
27.34
32.18
73.31
58.93
68.78
646.57
235.34
68.58
91.36
87.99
54.53
78.27
59.18
74.97
50.04
35.32
41.73
52.45
103.26
59.48
84.78
67.98
76.58
57.17
72.41
48.83
40.80
37.29
101.96
64.01
71.24
44.93
63.56
0.02
0.02
0.03
0.03
0.04
0.06
0.03
0.03
0.02
0.18
0.03
0.06
0.11
0.11
0.03
0.06
0.00
0.00
0.02
0.05
0.04
0.06
0.04
0.02
0.27
0.04
0.02
0.03
0.03
0.06
0.05
0.03
0.02
0.03
0.03
0.03
0.05
0.04
0.02
0.01
0.06
0.03
0.04
0.06
0.05
0.08
0.03
0.03
0.06
0.04
0.03
0.14
0.12
0.12
0.03
0.07
0.07
0.06
0.07
0.05
0.03
0.07
0.04
0.04
0.04
0.20
0.06
0.08
0.11
0.09
0.08
0.10
0.10
0.12
0.03
0.10
0.09
0.08
0.07
0.04
0.03
0.04
0.02
0.04
0.05
0.10
0.05
0.01
0.01
0.01
0.03
0.11
0.09
0.11
0.07
0.04
0.04
0.01
0.04
0.06
0.09
0.07
0.03
0.01
0.00
0.12
0.06
0.20
0.08
0.08
0.05
0.05
0.05
0.12
0.05
0.09
0.10
0.09
0.12
0.04
0.04
0.06
0.11
0.10
0.03
0.07
0.10
0.10
0.08
0.10
0.03
0.10
0.12
0.11
0.10
0.09
0.12
0.10
0.11
0.11
0.09
0.09
0.08
0.08
0.08
0.07
0.06
0.12
0.09
0.13
0.08
0.11
0.10
0.15
0.11
0.09
0.11
0.12
0.11
0.08
0.65
0.89
0.62
0.49
0.43
0.44
0.52
0.54
0.64
0.51
0.55
0.72
1.04
0.92
1.76
2.05
2.11
2.13
2.16
2.27
2.34
2.35
2.30
1.91
1.47
1.17
0.97
0.87
0.83
0.80
0.79
0.79
0.81
0.83
0.84
0.84
0.85
0.89
1.02
1.29
1.00
1.80
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.30
1.70
3.40
4.00
4.00
4.00
4.00
4.40
5.00
5.00
4.80
3.70
2.70
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.50
0.00
0.00
0.00
4.00
0.00
0.00
0.00
4.00
10.00
0.00
57.00
2.00
0.00
0.00
5.00
6.00
0.00
0.00
0.00
0.00
0.00
7.00
0.00
0.00
0.00
0.00
0.00
6.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
26.00
0.00
31.00
79
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
6.35
6.48
6.35
6.19
6.08
6.19
6.05
5.88
6.17
6.06
5.80
5.92
5.82
5.97
5.75
6.05
6.09
6.24
6.04
6.14
6.07
6.19
6.25
6.22
6.00
6.14
6.12
6.07
5.94
5.78
5.94
5.96
5.99
6.04
6.08
6.04
5.92
6.07
6.05
6.02
352.67
700.33
716.00
626.00
1080.83
1103.33
838.67
873.00
1085.00
622.17
1594.17
780.83
872.83
814.33
854.33
1520.00
639.50
586.50
469.00
518.50
554.83
416.33
483.83
770.33
884.17
825.80
891.00
1150.67
1368.00
1079.33
802.67
720.67
717.83
456.83
629.00
742.67
822.00
296.33
291.33
591.83
65.18
131.60
123.73
127.87
168.96
182.29
88.78
100.03
137.62
148.72
214.00
110.60
97.86
89.93
99.61
151.17
85.72
67.86
69.09
68.78
64.99
61.98
53.50
82.05
119.08
121.73
114.62
183.20
189.67
148.83
91.94
73.84
164.94
107.51
82.67
84.91
74.48
33.83
39.91
65.88
0.03
0.04
0.08
0.10
0.03
0.03
0.03
0.01
0.05
0.10
0.13
0.09
0.03
0.03
0.27
0.06
0.09
0.13
0.09
0.08
0.03
0.03
0.04
0.04
0.05
0.10
0.12
0.08
0.04
0.05
0.08
0.05
0.12
0.11
0.12
0.02
0.03
0.04
0.05
0.05
0.06
0.07
1.45
0.10
0.06
0.04
0.04
0.07
0.34
0.16
0.41
0.20
0.08
0.12
0.02
0.07
0.17
0.16
0.10
0.28
1.08
0.07
0.08
0.07
0.04
0.14
0.13
0.12
0.11
0.05
0.08
0.09
0.11
0.11
0.14
0.06
0.08
0.06
0.11
0.08
0.08
0.04
0.25
0.08
0.06
0.07
0.06
0.01
0.32
0.18
0.12
0.13
0.06
0.14
0.02
0.03
0.15
0.15
0.05
0.06
0.08
0.05
0.06
0.04
0.06
0.10
0.17
0.11
0.07
0.05
0.05
0.14
0.07
0.10
0.14
0.07
0.06
0.10
0.09
0.07
0.13
0.13
0.13
0.10
0.06
0.09
0.11
0.17
0.13
0.16
0.12
0.17
0.10
0.12
0.10
0.23
0.13
0.14
0.04
0.23
0.11
0.11
0.11
0.14
0.13
0.14
0.19
0.15
0.11
0.08
0.11
0.11
0.13
0.10
0.12
0.11
0.10
0.10
0.11
0.12
1.62
1.87
1.82
1.75
1.55
1.41
1.30
1.08
3.29
4.49
1.93
5.33
3.75
2.84
2.68
4.56
2.55
1.67
1.48
1.62
1.66
1.75
1.63
1.04
0.74
0.70
0.85
0.77
0.67
0.69
0.79
1.31
0.79
0.73
0.84
1.07
1.24
1.56
1.54
1.64
3.00
3.80
4.00
3.20
3.00
3.00
2.60
2.10
6.60
9.50
3.90
11.40
7.60
5.60
5.30
9.60
5.10
3.20
3.00
3.00
3.00
3.00
3.20
2.10
1.30
1.00
2.00
1.50
1.00
1.00
1.50
2.50
1.20
1.50
2.00
2.00
2.30
3.00
3.00
3.20
0.00
0.00
22.00
8.00
0.00
0.00
13.00
3.00
0.00
35.00
0.00
10.00
45.00
3.00
0.00
65.00
0.00
7.00
7.00
0.00
0.00
0.00
28.00
0.00
0.00
0.00
2.00
0.00
0.00
0.00
63.00
19.00
0.00
0.00
4.00
0.00
0.00
0.00
20.00
0.00
80
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
6.06
6.03
6.09
5.92
5.95
6.14
6.08
6.13
5.97
6.08
6.16
6.04
6.00
5.95
6.31
6.24
6.06
6.14
6.23
6.32
6.38
6.29
6.22
6.01
6.13
6.15
6.25
6.14
5.96
6.10
6.23
5.80
6.22
6.30
5.73
5.99
5.62
6.31
6.40
6.16
457.50
442.17
891.00
672.50
623.00
890.67
792.17
536.83
618.50
789.50
649.83
866.00
687.50
920.33
721.17
571.00
629.33
622.17
32.14
450.83
397.17
512.33
512.83
622.33
546.50
635.00
682.17
1140.00
758.67
521.00
546.83
737.67
963.50
1220.33
882.67
863.33
793.33
781.83
831.67
969.17
54.22
127.41
95.74
63.39
82.60
99.87
69.37
61.26
135.52
162.64
85.12
63.60
107.39
107.92
78.90
61.79
52.80
51.93
47.02
47.15
46.23
50.34
51.17
60.41
69.48
69.68
74.32
121.36
67.23
50.36
51.95
98.91
133.34
137.78
283.75
145.98
99.05
85.20
200.23
171.39
0.06
0.12
0.10
0.05
0.04
0.03
0.04
0.10
0.05
0.07
0.09
0.00
0.04
0.05
0.07
0.06
0.03
0.04
0.03
0.02
0.01
0.02
0.02
0.04
0.04
0.06
0.03
0.03
0.04
0.05
0.05
0.07
0.06
0.04
0.02
0.08
0.05
0.07
0.06
0.04
0.05
0.07
0.11
0.09
0.10
0.09
0.17
0.19
3.21
0.22
0.12
0.08
0.08
0.11
0.11
0.05
0.07
0.08
0.06
0.07
0.08
0.11
0.11
0.20
0.08
0.07
0.09
0.08
0.07
0.06
0.09
0.18
0.12
0.14
0.14
0.10
0.07
0.19
0.26
0.18
0.04
0.12
0.11
0.09
0.09
0.08
0.08
0.14
0.27
0.16
0.12
0.15
0.10
0.88
0.14
0.07
0.08
0.18
0.06
0.16
0.06
0.07
0.08
0.10
0.06
0.09
0.08
0.07
0.07
0.07
0.11
0.13
0.13
0.11
0.08
0.10
0.05
0.11
0.19
0.10
0.08
0.13
0.12
0.09
0.10
0.12
0.12
0.18
0.16
0.16
0.10
0.12
0.14
0.11
0.16
0.11
0.09
0.08
0.12
0.10
0.11
0.14
0.13
0.13
0.11
0.10
0.10
0.11
0.23
0.13
0.15
0.14
0.16
0.15
0.14
0.14
0.09
0.14
0.17
0.08
1.85
1.07
1.13
1.58
1.66
1.68
1.99
2.16
2.41
4.73
1.99
1.77
2.81
1.80
1.87
1.92
1.94
1.95
1.94
1.94
2.12
2.74
1.82
2.67
1.63
1.63
2.25
2.72
3.18
3.07
2.64
2.55
2.78
3.31
2.80
3.09
2.25
1.91
1.84
1.89
3.90
2.20
2.20
3.00
3.00
3.00
4.00
4.00
4.80
9.90
3.90
3.50
5.70
3.60
4.00
4.00
4.00
4.00
4.00
3.60
4.00
5.70
5.50
4.00
3.00
3.00
4.50
5.40
6.00
6.00
5.20
5.00
5.40
6.90
5.50
6.20
4.40
4.00
3.80
4.00
7.00
0.00
0.00
0.00
0.00
0.00
25.00
0.00
0.00
0.00
0.00
0.00
13.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
7.00
0.00
0.00
0.00
22.00
0.00
0.00
0.00
0.00
0.00
0.00
9.00
0.00
19.00
4.00
17.00
0.00
20.00
0.00
81
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
6.07
5.01
6.05
5.91
6.13
6.22
6.27
5.92
5.99
5.50
5.96
6.06
6.22
6.12
5.98
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
889.50
802.50
590.83
741.33
580.33
739.33
615.17
1774.20
804.00
917.50
1202.67
617.11
807.83
870.50
770.00
.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
304.83
126.86
101.91
80.16
52.41
82.47
162.24
312.75
74.62
115.40
125.79
70.94
57.80
69.34
87.24
.
.
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.
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0.04
0.05
0.04
0.05
0.05
0.08
0.06
0.13
0.08
0.04
0.04
0.05
0.03
0.03
0.06
.
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.
0.15
0.08
0.01
0.08
0.09
0.05
0.17
0.53
0.12
0.10
0.08
0.08
0.08
0.08
0.14
.
.
.
.
.
.
.
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.
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.
.
.
.
.
.
0.12
0.06
0.04
0.11
0.08
0.06
0.11
0.18
0.16
0.08
0.10
0.08
0.07
0.07
0.10
.
.
.
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0.07
0.08
0.11
0.11
0.11
0.12
0.12
0.18
0.16
0.14
0.15
0.11
0.12
0.13
0.14
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1.64
0.89
0.50
0.79
0.86
1.18
1.46
1.63
1.79
1.19
0.68
0.92
1.12
0.82
0.72
1.15
1.42
1.49
1.57
1.73
1.84
1.93
1.92
2.21
1.92
1.77
4.84
4.01
2.48
2.69
2.98
2.85
3.26
2.87
2.74
4.08
3.50
2.93
2.68
2.27
3.00
1.60
1.00
1.70
2.00
2.10
3.00
3.00
3.70
2.40
1.00
1.80
2.20
1.10
1.70
2.10
3.00
3.00
3.00
3.00
3.80
4.00
4.00
4.00
4.00
3.20
10.20
8.30
4.80
5.40
6.00
5.80
6.60
5.90
5.60
8.30
7.10
6.00
5.40
4.50
0.00
0.00
0.00
0.00
24.00
6.00
0.00
30.00
10.00
0.00
0.00
0.00
0.00
0.00
0.00
5.00
2.00
0.00
10.00
0.00
0.00
0.00
0.00
0.00
62.00
0.00
0.00
10.00
2.00
0.00
23.00
7.00
0.00
48.00
0.00
0.00
4.00
0.00
0.00
0.00
82
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
.
.
.
.
.
.
6.04
6.37
6.29
6.15
6.30
6.16
6.00
5.90
5.79
5.74
5.57
5.69
5.56
5.58
5.64
5.66
6.06
6.52
6.42
6.39
6.61
6.32
6.12
6.57
6.22
6.28
6.46
6.31
6.44
6.18
6.42
5.76
6.24
6.73
.
.
.
.
.
.
633.00
565.33
1154.00
1481.00
909.33
831.67
701.50
474.40
671.00
50.66
2642.67
1186.67
1048.17
1209.83
727.17
710.50
587.33
773.83
834.50
600.00
1080.83
1428.83
730.67
1096.00
756.67
555.67
547.17
511.50
708.67
843.50
618.50
850.67
782.17
660.33
.
.
.
.
.
.
88.28
60.47
119.77
149.67
105.72
79.97
83.99
82.26
81.94
117.36
245.92
105.71
116.02
134.58
85.04
64.47
60.18
89.43
87.23
51.91
101.92
210.92
140.92
117.72
82.33
57.66
54.28
51.58
114.11
99.30
87.59
106.63
100.58
63.72
.
.
.
.
.
.
0.04
0.04
0.04
0.07
0.09
0.86
0.05
0.03
0.12
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.07
0.04
0.02
0.02
0.02
0.06
0.02
0.06
0.06
0.05
0.02
0.04
0.07
0.03
0.07
0.09
0.07
0.03
.
.
.
.
.
.
0.09
0.16
0.13
0.09
0.23
0.07
0.06
0.11
0.29
0.08
0.10
0.08
0.07
0.07
0.18
0.16
0.20
0.10
0.06
0.02
0.04
0.09
0.06
0.15
0.21
0.17
0.09
0.10
0.09
0.05
0.15
0.21
0.18
0.10
.
.
.
.
.
.
0.10
0.12
0.10
0.14
0.23
0.12
0.05
0.11
0.22
0.09
0.13
0.09
0.09
0.07
0.17
0.13
0.14
0.11
0.07
0.10
0.06
0.02
0.06
0.15
0.19
0.10
0.09
0.12
0.10
0.09
0.12
0.21
0.21
0.10
.
.
.
.
.
.
0.08
0.11
0.15
0.16
0.21
0.16
0.08
0.15
0.18
0.13
2.45
0.13
0.19
0.10
0.10
0.14
0.15
0.17
0.13
0.12
0.11
0.21
0.11
0.17
0.16
0.15
0.15
0.17
0.17
0.10
0.12
0.14
0.14
0.15
1.93
2.32
2.36
3.17
2.62
3.74
2.01
1.83
4.21
6.34
5.01
2.85
2.10
1.87
1.81
1.90
8.46
5.21
4.08
6.10
3.57
2.21
1.77
2.35
2.76
2.07
3.65
7.04
6.93
7.68
4.28
2.18
1.54
1.24
2.79
2.76
2.39
2.34
1.87
1.77
4.00
4.90
7.60
6.30
5.20
4.90
4.00
4.00
9.10
14.00
10.70
5.60
4.20
3.90
3.60
4.00
20.80
11.40
8.60
13.30
7.30
4.30
3.40
4.70
5.70
4.00
7.60
16.00
15.80
18.20
9.00
4.40
3.00
2.10
5.60
5.50
4.50
4.40
4.00
3.50
0.00
0.00
0.00
0.00
10.00
15.00
4.00
7.00
11.50
0.00
0.00
0.00
7.50
0.00
0.00
43.00
10.00
14.00
0.00
9.00
0.00
0.00
17.00
7.00
13.00
4.00
4.00
4.00
0.00
0.00
0.00
0.00
22.00
0.00
0.00
0.00
0.00
4.00
0.00
10.00
83
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
6.06
6.29
6.63
6.54
6.65
6.45
6.33
6.02
6.01
6.20
6.10
6.46
5.70
5.56
5.53
5.52
5.70
6.00
5.89
6.23
5.88
5.97
6.11
5.89
5.59
5.78
5.94
6.23
6.42
6.05
6.04
5.94
6.14
6.07
6.24
6.59
6.23
6.51
6.56
6.56
1312.67
981.43
1252.33
549.67
449.17
678.00
774.00
1060.67
1791.67
1728.83
1844.67
1523.67
1437.33
1809.00
1776.50
733.60
705.50
474.33
489.50
424.33
715.67
1309.83
873.00
785.83
1392.33
1381.83
768.17
503.33
606.33
999.50
1434.00
650.00
882.00
986.67
469.00
388.00
747.83
1676.33
864.83
515.67
141.33
102.06
99.49
67.24
65.71
93.47
69.14
127.80
177.33
338.58
337.17
307.00
360.33
205.57
168.00
74.10
65.13
62.81
53.14
92.73
212.04
196.67
89.57
79.66
132.42
102.98
62.46
48.05
75.99
144.72
163.32
63.25
108.29
91.31
52.26
55.86
84.03
155.17
90.35
55.38
0.04
0.04
0.04
0.05
0.07
0.03
0.02
0.02
0.02
0.08
0.05
0.15
0.17
0.12
0.06
0.04
0.04
0.04
0.04
0.06
0.12
0.07
0.10
0.03
0.03
0.01
0.06
0.03
0.05
0.08
0.04
0.02
0.01
0.01
0.01
0.03
0.07
0.09
0.08
0.02
0.12
0.10
0.07
0.18
0.15
0.13
0.10
0.14
0.06
0.16
0.23
0.39
0.28
0.23
0.08
0.05
0.12
0.13
0.10
0.21
0.45
0.19
0.21
0.11
0.08
0.05
0.14
0.16
0.13
0.08
0.09
0.11
0.11
0.08
0.07
0.13
0.12
0.27
0.16
0.09
0.06
0.11
0.10
0.13
0.14
0.10
0.09
0.11
0.07
0.16
0.21
0.33
0.26
0.15
0.07
0.08
0.06
0.13
0.11
0.09
0.37
0.32
0.22
0.08
0.11
0.11
0.10
0.12
0.11
0.06
0.15
0.06
0.11
0.08
0.07
0.10
0.16
0.14
0.13
0.09
0.17
0.19
0.17
0.14
0.09
0.13
0.12
0.13
0.14
0.15
0.24
0.36
0.20
0.20
0.15
0.17
0.14
0.14
0.10
0.05
0.26
0.18
0.14
0.17
0.19
0.12
0.07
0.11
0.14
0.15
0.19
0.09
0.13
0.14
0.14
0.05
0.14
0.18
0.14
0.13
0.95
1.23
1.28
1.69
1.61
1.63
2.02
2.70
1.22
4.42
26.91
38.92
98.94
132.02
147.38
120.72
64.69
24.40
10.16
6.78
22.30
36.98
42.53
29.03
19.40
7.65
17.30
4.36
2.95
7.17
7.94
5.29
4.60
7.72
5.58
2.40
2.92
6.05
5.80
2.54
2.00
2.20
2.60
3.40
3.40
3.20
4.00
3.90
2.30
9.80
106.30
173.30
610.30
863.50
963.40
780.30
346.80
91.70
27.20
17.00
80.10
159.60
192.20
115.30
66.30
57.10
18.30
9.00
5.90
17.10
18.90
11.40
9.70
19.00
12.50
4.70
5.90
13.50
12.60
5.00
0.00
0.00
0.00
0.00
0.00
0.00
14.00
3.00
14.00
0.00
110.00
0.00
36.00
0.00
0.00
0.00
0.00
0.00
47.00
37.00
3.00
7.00
6.00
0.00
0.00
0.00
0.00
0.00
23.00
24.00
23.00
22.00
0.00
0.00
11.00
24.50
0.00
0.00
0.00
0.00
84
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
6.54
6.43
6.64
6.42
6.21
6.31
7.02
6.20
6.39
6.77
6.19
6.26
6.28
6.03
5.98
5.90
6.20
6.61
6.33
6.38
6.15
6.21
6.14
6.28
6.16
6.04
6.13
6.09
6.14
6.08
6.41
.
.
.
.
.
.
.
.
.
466.00
530.67
617.50
1272.50
1700.83
1874.50
1667.17
1172.33
995.33
468.50
575.00
660.83
892.67
2028.17
955.00
680.17
747.83
474.33
385.62
571.50
439.80
456.83
893.00
763.67
710.17
843.00
872.00
2076.17
756.40
774.00
730.17
.
.
.
.
.
.
.
.
.
53.68
54.34
64.98
171.82
265.08
246.33
141.33
124.97
109.40
144.88
148.40
137.46
121.87
251.31
69.91
56.47
66.69
75.88
76.14
61.33
48.21
43.93
80.30
92.42
252.92
153.61
187.82
307.83
175.24
75.74
72.90
.
.
.
.
.
.
.
.
.
0.02
0.27
0.03
0.07
0.08
0.08
0.03
0.04
0.03
0.07
0.08
0.06
0.13
0.03
0.04
0.02
0.01
0.03
0.06
0.05
0.07
0.02
0.04
0.02
0.05
0.05
0.12
0.12
0.07
0.03
0.02
.
.
.
.
.
.
.
.
.
0.07
0.15
0.11
0.21
0.13
0.15
0.12
0.12
0.10
0.21
0.30
0.11
0.10
0.05
0.06
0.05
0.06
0.13
0.13
0.16
0.19
0.09
0.11
0.08
0.11
0.12
0.36
0.17
0.12
0.11
0.10
.
.
.
.
.
.
.
.
.
0.09
0.10
0.10
0.16
0.12
0.12
0.07
0.14
0.11
0.15
0.24
0.12
0.15
0.09
0.06
0.06
0.04
0.10
0.14
0.19
0.14
0.08
0.14
0.21
0.05
0.10
0.30
0.20
0.12
0.10
0.06
.
.
.
.
.
.
.
.
.
0.13
0.13
0.16
0.17
0.13
0.13
0.10
0.16
0.16
0.08
0.10
0.13
0.17
0.12
0.10
0.13
0.08
0.06
0.09
0.16
0.11
0.12
0.12
0.12
0.07
0.06
0.18
0.17
0.10
0.16
0.13
.
.
.
.
.
.
.
.
.
1.81
1.52
1.51
4.19
14.01
14.48
10.17
9.75
13.08
34.68
50.84
41.39
27.58
35.95
13.64
3.73
5.76
3.74
2.99
2.20
1.73
1.49
1.54
1.58
3.77
6.90
4.58
25.49
30.36
8.05
3.34
2.30
1.34
3.48
2.59
1.31
0.89
1.14
7.04
3.51
3.60
3.00
3.00
9.30
42.80
44.90
26.90
25.20
38.90
147.10
245.30
186.20
109.20
154.30
41.70
12.40
7.60
7.50
6.00
4.20
3.40
2.80
3.00
3.00
8.00
16.10
10.10
98.00
122.90
20.70
6.80
4.50
2.60
7.20
5.20
2.50
2.00
2.50
16.40
7.10
0.00
10.00
35.00
0.00
0.00
20.00
21.50
31.00
21.00
0.00
0.00
0.00
8.00
0.00
0.00
4.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
27.00
12.00
0.00
63.00
0.00
4.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
33.00
7.00
7.50
0.00
85
358
359
360
361
362
363
364
365
.
.
.
.
.
.
.
6.01
.
.
.
.
.
.
.
520.33
.
.
.
.
.
.
.
55.05
.
.
.
.
.
.
.
0.03
.
.
.
.
.
.
.
0.04
.
.
.
.
.
.
.
0.06
.
.
.
.
.
.
.
0.03
2.14
5.16
4.92
1.93
1.04
1.38
1.99
1.70
4.20
11.20
10.50
3.90
2.00
2.70
3.00
?
58.00
0.00
0.00
0.00
0.00
4.00
1.00
12.00
86
APPENDIX B3 – Daily Water Quality and Hydrological Data for Year 2006
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
PH
6.35
6.44
6.30
6.20
6.18
6.15
6.34
5.99
5.79
5.87
6.24
5.80
5.72
5.89
5.78
5.67
5.88
5.88
6.14
5.39
6.10
6.30
5.89
6.00
6.18
6.40
6.52
6.33
6.13
6.41
5.98
6.15
6.08
5.54
5.91
5.92
6.40
6.08
6.36
6.27
6.16
5.98
Colour
1146.00
549.50
506.33
480.00
441.00
935.50
952.17
1474.00
1387.00
2348.50
1647.00
1065.83
1089.33
811.17
623.33
588.67
498.67
18.71
412.00
466.00
883.67
530.50
411.50
207.00
275.00
142.67
278.17
155.50
244.17
286.67
306.33
223.05
240.33
265.50
395.00
569.83
389.17
166.17
304.50
282.83
705.33
738.17
Turb
123.72
61.80
50.70
51.01
55.75
100.86
304.55
497.33
361.08
288.33
170.67
99.77
100.27
95.08
60.31
56.17
46.70
45.13
44.28
52.36
106.16
114.73
51.57
42.97
25.52
26.01
32.79
29.48
39.33
44.28
34.15
28.40
25.10
21.88
32.81
48.03
46.75
47.24
35.97
29.45
83.80
87.28
Al
0.04
0.04
0.03
0.03
0.03
0.02
0.03
0.23
0.26
0.16
0.07
0.15
0.04
0.04
0.04
0.03
0.07
0.07
0.03
0.03
0.04
0.03
0.03
0.04
0.04
0.03
0.04
0.02
0.01
0.01
0.02
0.02
0.02
0.02
0.01
0.03
0.02
0.01
0.02
0.03
0.05
0.09
Fe
0.11
0.17
0.10
0.07
0.10
0.14
0.15
0.62
0.61
0.43
0.07
0.27
0.08
0.12
0.14
0.11
0.14
0.15
0.18
0.11
0.06
0.06
0.08
0.06
0.08
0.03
0.06
0.03
0.09
0.04
0.06
0.09
0.05
0.03
0.05
0.05
0.04
0.04
0.08
0.06
0.15
0.18
NH4
0.16
0.25
0.13
0.12
0.08
0.13
0.10
0.49
0.49
0.35
0.07
0.33
0.09
0.04
0.11
0.12
0.14
0.19
0.18
0.08
0.09
0.07
0.09
0.05
0.09
0.03
0.06
0.03
0.08
0.04
0.06
0.08
0.04
0.09
0.11
0.11
0.06
0.04
0.10
0.07
0.16
0.15
Mn
0.15
0.11
0.07
0.08
0.12
0.15
0.13
0.29
0.31
0.25
0.06
0.23
0.13
0.13
0.11
0.16
0.07
0.09
0.13
0.12
0.14
0.10
0.06
0.04
0.09
0.05
0.09
0.05
0.09
0.08
0.02
0.08
0.07
0.04
0.05
0.08
0.07
0.07
0.11
0.07
0.07
0.10
Flow
16.17
15.13
16.31
17.85
16.86
14.85
27.04
59.81
177.01
245.46
236.59
203.13
175.98
153.94
124.92
92.82
68.38
53.93
46.88
44.07
48.20
45.01
40.32
35.80
32.57
29.60
27.68
25.80
22.83
23.11
24.30
23.23
23.55
23.87
23.71
22.21
21.50
21.50
20.39
19.82
22.26
25.33
SS
?
22.20
32.70
46.60
37.60
19.90
156.80
541.60
1882.90
2619.00
2528.10
2179.40
1888.50
1646.60
1317.70
942.10
647.10
469.40
381.10
350.40
397.30
360.50
321.80
301.00
236.60
174.10
151.60
130.60
14.00
101.00
98.10
102.30
105.70
109.30
107.70
91.20
83.40
83.50
71.90
65.80
91.70
125.50
Rainfall
3.00
0.00
10.00
0.00
19.00
0.00
60.00
50.00
80.00
100.00
33.00
0.00
0.00
0.00
0.00
8.00
0.00
0.00
0.00
0.00
34.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
31.00
0.00
0.00
0.00
0.00
24.00
0.00
0.00
15.00
6.50
0.00
0.00
87
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
5.82
5.63
6.19
6.41
6.08
6.05
6.48
6.49
6.14
5.62
6.09
5.90
6.02
6.33
6.17
5.89
5.88
6.00
6.20
6.09
6.09
6.32
6.39
6.36
6.53
6.36
6.50
6.64
6.44
6.40
6.25
6.16
6.41
6.34
6.45
6.24
6.24
6.23
6.14
6.32
6.57
6.28
6.40
6.40
6.23
442.83
629.33
822.67
946.00
361.83
307.50
360.83
344.33
351.17
233.83
187.58
335.50
391.50
377.83
587.33
411.67
404.50
292.40
447.33
358.33
302.33
387.67
370.67
343.33
377.83
287.50
248.50
293.67
278.17
317.00
288.00
285.67
321.67
322.33
266.50
392.83
564.83
713.00
773.00
618.50
903.83
561.00
413.83
272.33
356.33
39.39
59.85
85.71
198.55
49.48
30.70
32.27
45.37
37.63
60.84
55.21
47.91
33.77
33.88
60.06
46.23
56.64
38.69
43.04
32.84
33.77
41.96
41.28
38.71
40.45
34.84
28.83
28.83
29.64
33.73
33.14
32.34
33.61
26.75
31.84
34.60
55.71
70.58
85.72
67.29
73.52
49.70
39.93
31.83
34.75
0.01
0.03
0.05
0.04
0.03
0.02
0.02
0.01
0.00
0.01
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.04
0.02
0.04
0.01
0.01
0.01
0.03
0.04
0.03
0.01
0.02
0.01
0.01
0.04
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.03
0.05
0.02
0.02
0.10
0.06
0.09
0.10
0.09
0.07
0.07
0.05
0.06
0.07
0.04
0.07
0.09
0.08
0.05
0.05
0.05
0.04
0.08
0.05
0.09
0.05
0.03
0.09
0.07
0.07
0.11
0.09
0.05
0.04
0.09
0.02
0.10
0.10
0.15
0.10
0.05
0.07
0.08
0.10
0.08
0.07
0.14
0.04
0.12
0.07
0.07
0.11
0.14
0.11
0.05
0.08
0.06
0.12
0.07
0.04
0.06
0.18
0.10
0.08
0.07
0.07
0.09
0.10
0.06
0.13
0.07
0.05
0.08
0.06
0.08
0.21
0.18
0.09
0.05
0.08
0.08
0.09
0.10
0.19
0.12
0.13
0.07
0.09
0.16
0.12
0.11
0.07
0.19
0.17
0.08
0.17
0.10
0.13
0.09
0.05
0.10
0.10
0.09
0.04
0.04
0.04
0.13
0.12
0.10
0.11
0.08
0.09
0.09
0.02
0.00
0.00
0.00
0.01
0.08
0.06
0.09
0.09
0.03
0.07
0.09
0.02
0.05
0.11
0.10
0.09
0.12
0.13
0.08
0.07
0.10
0.11
0.10
0.09
0.14
23.75
21.67
25.17
26.94
21.59
19.64
18.71
18.16
17.87
17.74
17.77
17.51
17.51
18.70
28.73
35.30
35.21
30.17
26.86
25.32
24.36
22.24
19.41
19.90
20.81
21.71
23.44
27.55
28.90
28.70
26.05
23.03
21.71
20.91
23.53
23.88
19.55
20.91
26.23
27.67
28.21
29.25
29.93
25.76
23.73
108.00
85.40
124.00
143.60
84.60
64.20
54.80
49.60
46.50
45.40
45.70
43.40
43.40
55.20
164.30
266.10
296.20
182.10
142.40
125.30
114.70
91.70
62.00
67.40
76.90
86.50
104.90
150.30
165.70
163.40
133.60
100.00
86.10
77.30
105.40
109.50
63.40
77.80
135.30
151.90
158.00
170.10
177.90
130.40
107.90
10.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
72.00
0.00
20.00
4.50
2.00
0.00
0.00
0.00
7.00
18.00
22.00
88
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
6.57
6.47
6.46
6.35
6.49
6.66
6.42
6.37
6.37
6.45
6.67
6.55
6.38
6.47
6.53
6.36
6.33
6.35
6.34
6.36
6.46
6.28
6.36
6.12
6.40
6.37
6.57
6.25
6.15
6.22
6.47
6.42
6.38
6.20
6.33
6.16
5.88
6.13
6.03
5.99
5.96
6.18
6.04
6.43
6.16
338.33
828.17
1060.83
785.50
784.33
554.33
788.17
831.24
1085.80
809.00
1109.33
1353.00
1395.83
1461.83
1055.00
749.40
608.67
662.67
1016.67
788.80
846.67
672.33
615.67
960.33
937.00
1097.20
1216.67
1739.60
1073.50
871.00
1065.00
1677.00
1314.83
1248.17
1206.33
897.50
908.00
722.67
1901.00
992.83
849.50
883.67
785.67
1232.00
841.50
36.69
96.42
99.33
81.54
75.92
67.97
101.06
101.20
112.73
138.80
184.73
188.83
130.27
161.48
142.89
68.60
61.16
79.43
127.05
110.49
91.73
66.07
61.79
102.89
87.72
223.66
151.83
227.90
149.50
114.66
125.45
166.52
154.92
115.27
204.25
164.67
117.02
108.07
212.83
136.34
69.93
113.83
110.56
148.42
128.31
0.02
0.03
0.02
0.02
0.03
0.03
0.07
0.03
0.03
0.01
0.04
0.03
0.04
0.07
0.11
0.02
0.02
0.04
0.01
0.02
0.06
0.04
0.03
0.04
0.04
0.08
0.03
0.03
0.07
0.07
0.04
0.05
0.02
0.03
0.02
0.10
0.11
0.06
0.09
0.02
0.02
0.02
0.02
0.09
0.09
0.08
0.14
0.12
0.07
0.11
0.10
0.24
0.09
0.10
0.14
0.10
0.14
0.07
0.25
0.45
0.06
0.13
0.21
0.11
0.07
0.29
0.23
0.07
0.28
0.12
0.23
0.12
0.08
0.27
0.25
0.14
0.07
0.05
0.13
0.06
0.32
0.36
0.19
0.24
0.05
0.02
0.03
0.05
0.32
0.35
0.08
0.16
0.07
0.07
0.12
0.14
0.25
0.09
0.09
0.12
0.14
0.23
0.08
0.21
0.69
0.00
0.31
0.20
0.19
0.12
0.46
0.41
0.10
0.30
0.13
0.27
0.21
0.06
0.38
0.46
0.23
0.07
0.06
0.15
0.10
0.25
0.42
0.26
0.27
0.05
0.08
0.16
0.16
0.28
0.25
0.08
0.07
0.13
0.13
0.12
0.11
0.14
0.13
0.08
0.14
0.15
0.12
0.11
0.22
0.36
0.06
0.09
0.10
0.13
0.19
0.18
0.15
0.10
0.20
0.13
0.13
0.20
0.11
0.23
0.25
0.12
0.15
0.10
0.12
0.13
0.25
0.23
0.06
0.00
0.00
0.00
0.05
0.06
0.00
0.00
27.40
27.52
29.86
27.59
24.17
?
18.92
16.45
14.82
18.05
31.53
42.76
27.33
34.72
34.44
17.84
23.58
17.53
21.23
19.61
17.91
15.57
14.71
15.47
19.28
25.34
23.45
33.15
29.95
23.62
22.30
26.74
24.80
26.16
31.40
37.44
41.84
38.15
33.93
31.90
24.99
21.05
18.97
21.39
22.23
148.50
149.80
177.10
150.90
112.50
?
57.00
34.10
19.70
50.00
206.90
340.00
158.40
262.70
276.50
106.60
46.60
43.60
80.70
63.80
47.60
26.20
18.90
25.70
60.40
125.80
104.90
216.20
178.50
106.50
92.30
119.50
141.20
134.60
195.30
294.90
331.60
311.50
296.80
225.20
121.90
78.90
57.40
82.50
91.60
0.00
40.00
0.00
8.00
9.00
8.50
0.00
4.50
6.50
5.50
5.00
13.00
30.00
0.00
0.00
3.50
0.00
7.00
0.00
0.00
0.00
3.00
0.00
18.00
11.00
35.00
34.00
9.00
0.00
10.00
0.00
6.50
4.00
0.00
0.00
45.00
6.00
0.00
0.00
0.00
0.00
44.00
0.00
2.00
0.00
89
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
6.14
6.17
6.40
6.72
6.33
6.46
6.42
6.39
6.30
6.28
6.49
6.46
6.21
6.28
6.33
6.17
6.42
6.39
6.43
6.36
6.40
6.40
6.36
6.29
6.27
6.33
6.29
6.44
6.39
6.14
6.25
6.26
6.21
6.20
6.31
6.46
6.40
6.20
6.48
6.37
6.30
6.39
6.40
6.52
6.52
1808.83
967.33
1129.00
1234.67
906.67
1003.67
915.83
1335.83
2032.83
1301.67
952.50
803.50
1009.67
973.17
2579.67
1547.17
1670.00
1106.83
1155.67
784.86
794.86
684.71
653.43
747.14
629.71
1547.71
1296.86
1335.29
1767.71
991.86
731.71
666.43
636.57
659.86
586.71
483.29
574.57
966.71
604.29
842.71
946.29
1103.43
782.29
893.86
893.86
180.00
118.82
111.86
137.10
115.72
101.88
113.23
149.03
207.67
135.21
120.25
123.59
122.58
146.65
315.50
268.58
214.83
118.34
110.24
72.22
107.51
84.87
74.36
91.83
95.12
199.08
165.83
201.17
228.17
173.00
95.56
68.72
76.16
95.68
81.64
88.84
80.97
104.78
81.23
101.60
95.56
203.00
117.73
102.63
112.28
0.08
0.05
0.01
0.03
0.03
0.03
0.08
0.10
0.08
0.02
0.03
0.03
0.04
0.02
0.16
0.10
0.12
0.04
0.02
0.03
0.01
0.04
0.03
0.04
0.04
0.03
0.03
0.05
0.04
0.08
0.09
0.05
0.01
0.03
0.03
0.01
0.04
0.04
0.03
0.03
0.01
0.06
0.04
0.05
0.05
0.24
0.29
0.17
0.24
0.13
0.07
0.36
0.43
0.15
0.19
0.17
0.12
0.08
0.09
0.43
0.41
0.18
0.06
0.12
0.05
0.09
0.14
0.09
0.15
0.15
0.12
0.13
0.16
0.07
0.15
0.23
0.13
0.07
0.07
0.15
0.07
0.13
0.30
0.13
0.08
0.12
0.16
0.16
0.08
0.13
0.05
0.09
0.14
0.20
0.11
0.21
0.26
0.34
0.16
0.20
0.15
0.11
0.13
0.12
0.37
0.34
0.20
0.08
0.21
0.08
0.07
0.10
0.06
0.22
0.12
0.21
0.11
0.18
0.06
0.11
0.09
0.11
0.13
0.08
0.18
0.09
0.12
0.22
0.11
0.05
0.06
0.16
0.25
0.11
0.14
0.01
0.02
0.14
0.16
0.10
0.05
0.20
0.26
0.08
0.14
0.13
0.10
0.06
0.08
0.24
0.38
0.15
0.11
0.18
0.09
0.08
0.14
0.08
0.11
0.12
0.13
0.09
0.14
0.10
0.10
0.09
0.10
0.08
0.06
0.13
0.11
0.13
0.23
0.10
0.17
0.12
0.15
0.13
0.10
0.11
22.13
19.22
18.82
18.78
16.63
16.40
16.92
18.31
24.66
22.75
18.01
16.27
15.64
17.22
27.51
36.15
22.54
28.16
36.95
20.55
19.77
18.05
18.36
17.78
18.25
23.69
23.17
27.11
34.10
40.71
43.11
37.07
28.48
25.40
22.73
20.71
19.21
19.01
19.61
20.24
18.44
22.80
21.31
18.54
17.18
90.60
59.70
56.00
55.40
35.60
33.50
38.20
51.00
118.10
97.20
48.20
32.40
26.90
41.30
150.30
293.20
304.10
165.00
94.80
73.50
65.30
48.50
51.70
45.90
50.60
107.50
101.60
145.90
240.60
324.60
342.10
304.50
161.20
126.00
97.00
75.20
59.70
57.70
63.70
70.10
52.10
97.70
81.60
53.30
40.60
2.00
0.00
2.00
0.00
0.00
14.00
3.00
0.00
0.00
0.00
0.00
36.00
4.00
15.00
0.00
0.00
0.00
3.00
0.00
0.00
0.00
0.00
0.00
22.00
0.00
13.00
7.50
0.00
33.00
12.00
0.00
0.00
0.00
12.00
0.00
6.00
14.50
11.50
7.00
4.50
0.00
0.00
4.00
0.00
0.00
90
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
6.59
6.48
6.37
6.47
6.23
6.41
6.52
6.39
6.28
6.49
6.45
6.38
6.33
6.41
6.32
6.52
6.36
6.38
6.46
6.44
6.57
6.56
6.60
6.04
6.51
6.37
6.45
6.45
6.51
6.35
6.31
6.40
6.36
6.34
6.43
.
.
.
.
.
.
.
.
.
.
507.86
1858.29
646.00
980.14
981.67
1035.17
889.33
873.00
724.67
1241.33
1261.00
856.33
1670.83
1742.17
1233.33
833.50
1143.00
727.67
996.17
1366.67
1597.67
1043.67
1488.50
1286.67
913.50
2430.33
1677.33
799.83
743.83
1031.67
930.17
669.67
1025.67
2024.83
2076.17
.
.
.
.
.
.
.
.
.
.
0.05
114.43
84.37
143.33
99.99
98.12
161.15
85.02
104.66
130.43
122.57
85.72
173.25
202.92
144.58
122.18
129.54
75.74
112.26
128.50
149.28
100.64
185.90
116.45
106.49
278.33
175.83
75.93
82.07
123.79
117.35
77.41
123.80
186.92
245.00
.
.
.
.
.
.
.
.
.
.
0.05
0.04
0.05
0.02
0.02
0.02
0.02
0.05
0.08
0.05
0.02
0.03
0.02
0.08
0.05
0.09
0.07
0.02
0.02
0.03
0.03
0.03
0.05
0.06
0.04
0.08
0.10
0.04
0.03
0.04
0.04
0.08
0.04
0.04
0.05
.
.
.
.
.
.
.
.
.
.
0.18
0.14
0.09
0.17
0.08
0.07
0.06
0.15
0.30
0.21
0.17
0.09
0.07
0.20
0.17
0.43
0.31
0.05
0.08
0.14
0.10
0.10
0.18
0.25
0.06
0.13
0.28
0.10
0.15
0.12
0.19
0.22
0.13
0.11
0.13
.
.
.
.
.
.
.
.
.
.
0.14
0.11
0.11
0.17
0.10
0.06
0.07
0.13
0.21
0.15
0.22
0.19
0.07
0.20
0.16
0.38
0.27
0.09
0.12
0.10
0.13
0.10
0.21
0.20
0.11
0.21
0.21
0.13
0.21
0.12
0.20
0.20
0.13
0.11
0.13
.
.
.
.
.
.
.
.
.
.
0.09
0.08
0.12
0.08
0.08
0.07
0.09
0.09
0.15
0.16
0.03
0.09
0.12
0.16
0.21
0.23
0.17
0.13
0.09
0.09
0.13
0.12
0.23
0.08
0.12
0.13
0.13
0.12
0.17
0.16
0.18
0.18
0.21
0.20
0.16
.
.
.
.
.
.
.
.
.
.
15.76
14.87
18.13
14.20
20.85
20.11
17.12
14.69
13.18
12.61
18.13
21.70
26.61
31.78
29.98
23.99
22.22
18.23
15.80
15.37
15.11
14.70
16.61
17.36
14.91
18.53
23.68
18.09
15.23
14.82
18.17
22.12
23.38
19.76
22.34
23.44
24.08
19.28
16.62
15.52
14.24
13.70
15.51
22.81
23.63
27.90
20.20
14.70
49.90
76.40
68.90
40.00
18.80
8.10
5.30
50.20
85.70
139.70
199.50
178.50
110.60
91.40
50.50
28.30
24.40
22.10
18.60
36.00
42.30
20.60
54.60
107.20
49.20
23.30
19.80
49.90
90.30
103.90
92.60
65.40
104.80
111.70
60.80
35.60
25.80
15.20
11.10
26.50
97.80
106.70
0.00
36.00
0.00
0.00
0.00
4.00
0.00
0.00
0.00
4.00
0.00
4.00
22.00
10.00
0.00
0.00
0.00
0.00
0.00
0.00
19.00
0.00
0.00
56.00
0.00
0.00
0.00
0.00
0.00
43.00
0.00
7.00
0.00
17.00
10.00
0.00
0.00
0.00
0.00
0.00
14.50
0.00
0.00
0.00
14.00
91
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
6.35
6.47
6.50
6.57
6.43
6.28
6.25
6.33
6.34
6.52
6.27
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
707.67
588.00
607.00
556.17
881.00
2002.50
1296.33
875.33
1304.67
882.83
943.17
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
78.28
64.67
56.31
59.90
98.92
202.34
111.88
143.83
228.08
190.35
137.76
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.06
0.01
0.03
0.03
0.04
0.11
0.06
0.02
0.06
0.06
0.10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.14
0.10
0.10
0.16
0.10
0.11
0.23
0.14
0.25
0.15
0.34
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.13
0.18
0.19
0.19
0.12
0.10
0.32
0.28
0.21
0.28
0.29
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.11
0.12
0.12
0.12
0.19
0.22
0.25
0.13
0.26
0.17
0.15
.
.
.
.
.
.
.
.
.
.
.
.
.
20.56
22.14
23.59
22.17
29.67
39.30
33.81
21.80
17.84
16.52
16.47
17.79
20.30
19.39
16.85
14.60
17.35
19.29
17.25
13.44
13.56
13.49
13.00
13.03
13.10
16.74
28.24
31.28
30.89
41.39
36.00
29.05
31.79
37.04
42.66
48.55
79.72
88.24
72.80
48.17
31.33
24.13
21.15
20.04
20.78
73.60
90.60
106.20
90.90
175.10
315.20
249.10
87.10
46.70
34.50
34.20
46.20
70.90
61.60
37.70
18.00
42.10
60.60
41.30
10.70
9.70
10.00
7.10
7.40
7.50
39.20
158.60
193.70
192.30
329.50
298.70
168.40
200.10
292.80
338.20
406.80
784.80
887.70
692.40
404.50
214.40
112.10
80.00
68.10
75.90
0.00
14.00
60.00
24.00
0.00
0.00
5.00
0.00
2.50
1.00
0.00
7.00
0.00
0.00
0.00
0.00
4.00
0.00
0.00
10.00
0.00
0.00
0.00
0.00
0.00
1.00
10.00
32.50
24.00
0.00
23.00
0.00
3.00
5.00
44.00
25.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
92
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
.
.
.
.
.
.
.
.
.
6.21
6.36
6.71
6.59
6.48
6.51
6.45
6.41
6.35
6.56
6.60
6.43
6.60
6.21
6.72
6.55
6.65
6.66
6.42
6.58
6.18
6.13
6.37
6.49
6.48
6.86
6.21
6.01
6.45
6.23
6.30
6.03
6.02
6.02
6.00
6.24
.
.
.
.
.
.
.
.
.
577.17
556.17
461.40
424.83
377.50
298.50
241.33
347.67
370.33
478.67
506.17
937.33
874.50
613.33
506.83
483.50
421.83
393.50
512.00
518.33
1347.17
798.33
609.00
443.50
749.50
784.00
2140.80
2342.67
1396.50
1251.50
1120.50
1564.00
1385.00
926.00
906.50
1443.33
.
.
.
.
.
.
.
.
.
78.16
60.30
44.25
38.81
32.36
22.78
33.78
46.13
36.67
48.09
52.03
93.95
90.12
76.34
53.78
49.74
46.79
40.40
54.81
53.13
207.83
72.40
56.03
66.93
108.75
104.40
261.75
291.17
185.08
138.00
122.52
163.58
151.75
92.57
110.98
152.17
.
.
.
.
.
.
.
.
.
0.05
0.06
0.05
0.04
0.03
0.02
0.02
0.01
0.01
0.03
0.01
0.04
0.04
0.02
0.02
0.02
0.04
0.04
0.06
0.01
0.05
0.04
0.02
0.03
0.05
0.02
0.15
0.06
0.04
0.03
0.03
0.05
0.06
0.07
0.04
0.03
.
.
.
.
.
.
.
.
.
0.21
0.23
0.09
0.09
0.13
0.11
0.07
0.08
0.12
0.13
0.05
0.10
0.11
0.10
0.08
0.11
0.07
0.06
0.09
0.10
0.15
0.10
0.09
0.15
0.17
0.13
0.25
0.27
0.12
0.11
0.10
0.10
0.08
0.09
0.08
0.13
.
.
.
.
.
.
.
.
.
0.20
0.26
0.12
0.10
0.12
0.08
0.08
0.10
0.18
0.10
0.08
0.15
0.13
0.10
0.10
0.12
0.12
0.08
0.11
0.17
0.16
0.15
0.10
0.14
0.14
0.13
0.21
0.21
0.11
0.17
0.09
0.15
0.19
0.12
0.12
0.12
.
.
.
.
.
.
.
.
.
0.12
0.18
0.17
0.13
0.07
0.07
0.11
0.11
0.11
0.12
0.11
0.13
0.13
0.10
0.14
0.13
0.11
0.09
0.14
0.10
0.13
0.12
0.09
0.10
0.16
0.13
0.34
0.21
0.18
0.21
0.19
0.19
0.20
0.15
0.14
0.13
17.11
15.91
16.05
18.14
17.70
19.76
16.43
14.20
14.32
19.02
25.11
28.57
27.86
25.63
24.33
24.91
24.71
20.92
16.09
15.68
16.43
19.71
25.56
25.77
25.51
22.95
21.26
21.69
23.33
24.28
20.24
16.03
15.28
16.30
14.43
45.77
28.07
54.39
51.39
41.42
37.70
44.67
46.89
44.18
44.92
39.80
29.40
30.50
49.40
65.30
45.30
33.70
15.00
16.20
58.50
123.20
162.00
153.80
128.70
114.40
120.90
118.50
77.80
30.90
27.10
33.80
66.40
128.00
130.20
127.20
99.30
81.00
85.70
103.60
113.90
70.50
30.20
23.70
32.60
16.60
167.30
372.70
474.90
438.00
333.60
307.30
358.30
380.90
351.80
361.00
0.00
18.00
0.00
0.00
0.00
0.00
0.00
10.00
0.00
0.00
0.00
0.00
0.00
0.00
9.00
0.00
0.00
0.00
0.00
0.00
2.00
1.00
0.00
0.00
1.50
0.00
25.00
0.00
0.00
0.00
1.50
1.50
37.00
50.50
0.00
28.00
32.50
0.00
27.00
54.00
45.00
5.00
5.00
15.00
0.00
93
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
6.24
6.10
6.35
6.11
6.39
6.60
6.22
6.02
6.31
6.25
6.78
6.52
6.05
6.09
6.51
6.23
5.76
6.36
7.01
6.28
6.43
6.20
5.92
5.28
6.52
6.42
6.43
6.33
6.14
5.98
5.96
6.13
6.49
6.34
6.48
6.17
6.21
6.41
6.46
6.29
6.13
6.29
6.32
6.35
6.00
898.50
812.17
641.33
579.00
1240.50
997.83
1092.83
2119.50
781.50
453.00
667.83
969.00
2656.67
1743.83
1239.17
708.17
841.92
700.25
430.67
437.75
812.00
534.58
889.17
877.33
598.00
476.00
424.00
998.00
953.50
644.50
1062.33
1414.83
911.33
984.00
1410.00
2325.50
1474.00
915.50
1703.67
2196.83
3673.50
1704.33
2243.83
2189.17
1249.50
105.76
121.65
68.93
83.91
123.36
105.96
152.41
191.31
83.48
55.43
114.33
203.53
411.33
204.33
132.43
155.42
151.42
147.33
100.95
93.08
163.25
117.67
95.92
80.13
58.73
50.51
117.91
92.64
92.64
70.21
155.89
148.50
100.80
137.01
166.00
311.58
141.08
83.18
193.47
293.92
412.42
346.00
267.25
215.00
124.83
0.05
0.03
0.07
0.04
0.07
0.05
0.03
0.06
0.05
0.01
0.06
0.09
0.08
0.06
0.07
0.03
0.05
0.06
0.06
0.07
0.07
0.05
0.06
0.02
0.03
0.04
0.04
0.02
0.04
0.03
0.07
0.03
0.04
0.09
0.06
0.11
0.08
0.03
0.05
0.04
0.06
0.06
0.07
0.09
0.05
0.09
0.05
0.13
0.08
0.09
0.11
0.14
0.15
0.17
0.07
0.14
0.30
0.16
0.12
0.17
0.09
0.11
0.12
0.18
0.08
0.11
0.13
0.15
0.10
0.09
0.11
0.13
0.09
0.11
0.17
0.19
0.09
0.11
0.17
0.20
0.39
0.28
0.10
0.18
0.12
0.17
0.26
0.11
0.13
0.14
0.13
0.12
0.14
0.20
0.16
0.13
0.14
0.15
0.17
0.15
0.10
0.19
0.18
0.11
0.17
0.06
0.07
0.07
0.07
0.05
0.05
0.07
0.14
0.09
0.11
0.09
0.11
0.13
0.09
0.13
0.13
0.11
0.12
0.15
0.18
0.43
0.23
0.12
0.18
0.11
0.13
0.09
0.12
0.09
0.17
0.16
0.12
0.12
0.09
0.18
0.14
0.14
0.15
0.16
0.12
0.09
0.32
0.27
0.20
0.13
0.05
0.07
0.10
0.06
0.07
0.09
0.10
0.22
0.17
0.10
0.09
0.09
0.15
0.15
0.10
0.16
0.18
0.11
0.12
0.21
0.34
0.36
0.21
0.23
0.17
0.26
0.27
0.31
0.23
0.19
51.14
52.56
42.41
37.42
36.20
33.01
?
?
?
?
?
?
64.63
73.51
71.54
71.80
74.29
78.04
78.86
71.46
66.72
69.77
68.93
61.81
54.43
44.44
36.98
33.60
34.47
35.08
35.22
35.40
39.25
38.96
38.89
40.90
42.72
36.39
36.10
65.60
145.15
321.17
365.62
327.00
288.71
434.90
452.50
338.60
306.00
301.50
253.90
?
?
?
?
?
?
601.20
709.90
686.00
689.00
719.30
764.80
774.80
684.80
626.90
664.20
654.00
566.40
475.50
357.00
305.10
296.70
297.50
298.70
299.00
299.50
315.10
312.90
313.20
324.90
338.80
302.10
258.60
612.50
1535.70
3362.00
3789.80
3423.60
3051.80
2.00
0.00
3.00
17.50
20.00
20.00
0.00
8.00
0.00
13.00
40.00
0.00
11.00
0.00
44.50
0.00
0.00
20.00
7.00
0.00
20.50
4.50
0.00
2.00
0.00
12.00
0.00
10.50
1.00
0.00
5.00
33.00
0.00
8.00
7.00
0.00
0.00
37.00
36.00
310.00
54.00
35.00
30.00
5.00
0.00
94
358
359
360
361
362
363
364
365
5.73
6.31
5.98
6.36
6.59
5.87
6.34
6.57
1280.00
703.17
721.67
1541.50
1212.17
1095.83
800.67
520.00
121.88
66.44
144.98
145.75
126.92
119.50
82.37
57.23
0.07
0.04
0.03
0.07
0.08
0.09
0.07
0.04
0.09
0.10
0.09
0.15
0.20
0.11
0.10
0.10
0.11
0.11
0.09
0.14
0.30
0.12
0.14
0.13
0.14
0.15
0.11
0.15
0.18
0.14
0.13
0.08
250.92
214.72
192.46
221.77
250.08
250.61
208.63
234.86
2674.10
2301.60
2066.40
2374.20
2665.90
2671.30
2510.30
2252.20
73.00
75.00
32.00
38.50
0.00
0.00
0.00
0.00
95
APPENDIX B4– Daily Water Quality and Hydrological Data for Year 2007
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
PH
5.77
5.78
6.02
6.71
6.10
6.35
6.20
6.42
6.32
6.26
6.22
6.04
6.17
6.33
6.06
6.08
6.10
6.30
6.26
5.82
5.73
5.69
6.03
6.22
5.90
5.93
5.92
5.81
6.04
6.21
6.01
6.03
5.91
5.80
5.96
5.69
5.79
6.00
6.33
5.69
5.67
6.07
Colour
375.17
321.17
328.17
447.33
639.83
497.33
542.33
482.33
552.50
520.17
1385.00
2237.00
3164.00
2939.17
2345.67
1424.67
889.83
622.83
485.17
496.17
918.00
1066.00
587.33
546.17
639.40
728.33
614.17
849.83
852.33
432.33
572.00
564.83
517.50
406.00
464.33
437.17
429.00
535.17
430.50
385.83
426.17
398.33
Turb
37.14
36.28
36.58
49.13
77.13
52.99
44.78
35.20
44.91
54.16
183.83
485.67
402.75
330.73
218.75
129.38
81.21
57.03
47.78
59.53
105.61
104.11
61.70
56.30
63.41
68.22
58.53
96.38
93.25
57.89
54.14
57.80
52.83
40.35
47.24
48.38
43.51
53.69
38.58
37.06
43.37
46.72
Al
0.05
0.06
0.06
0.03
0.06
0.06
0.04
0.03
0.04
0.03
0.03
0.07
0.11
0.04
0.09
0.06
0.04
0.03
0.03
0.05
0.08
0.05
0.03
0.03
0.03
0.04
0.04
0.04
0.04
0.02
0.03
0.03
0.01
0.03
0.06
0.01
0.04
0.02
0.04
0.02
0.01
0.01
Fe
0.10
0.13
0.08
0.07
0.11
0.13
0.16
0.09
0.10
0.07
0.11
0.16
0.25
0.12
0.13
0.11
0.09
0.09
0.09
0.10
0.11
0.10
0.10
0.10
0.10
0.09
0.09
0.11
0.08
0.05
0.09
0.09
0.05
0.10
0.09
0.05
0.07
0.07
0.10
0.08
0.13
0.08
NH4
0.15
0.13
0.10
0.09
0.10
0.13
0.14
0.15
0.10
0.09
0.13
0.13
0.20
0.13
0.14
0.15
0.09
0.12
0.09
0.09
0.13
0.12
0.10
0.09
0.09
0.08
0.13
0.11
0.08
0.06
0.10
0.10
0.05
0.09
0.13
0.10
0.07
0.10
0.15
0.12
0.10
0.09
Mn
0.12
0.14
0.06
0.08
0.10
0.13
0.09
0.15
0.13
0.11
0.20
0.23
0.33
0.18
0.17
0.19
0.12
0.13
0.09
0.09
0.13
0.14
0.11
0.12
0.14
0.08
0.13
0.17
0.13
0.15
0.14
0.14
0.13
0.15
0.16
0.08
0.07
0.12
0.15
0.10
0.06
0.10
Flow
107.52
84.62
69.26
60.91
60.28
56.39
48.83
40.94
34.59
33.22
43.79
184.20
544.76
518.19
365.11
259.13
190.98
149.14
117.31
102.96
101.47
94.11
82.94
72.44
62.04
54.78
57.70
61.35
60.13
49.72
56.38
40.87
33.93
29.75
26.56
24.87
23.93
22.59
21.51
20.69
19.98
19.26
SS
1958.20
1686.90
1459.20
1321.70
1310.90
1240.80
1090.50
927.10
796.20
765.20
976.70
2485.80
4309.10
4204.40
3537.50
3009.60
2609.60
2305.80
2054.80
1919.70
1903.50
1814.30
1664.10
1509.30
1340.30
1211.30
1264.80
1329.50
1308.10
1240.80
1109.50
925.40
781.10
681.00
598.10
550.50
521.20
479.40
446.30
419.80
396.70
374.90
Rainfall
1.50
17.00
15.00
1.50
0.00
4.50
1.50
14.50
19.00
155.00
315.00
60.00
50.00
0.00
0.00
0.00
0.00
0.00
47.00
0.00
0.00
3.50
1.00
12.50
22.50
15.00
3.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
96
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
6.19
5.94
5.97
6.15
6.08
6.22
5.64
5.99
5.72
5.63
5.66
5.53
5.51
5.67
5.92
5.90
5.86
6.02
5.97
6.00
5.94
6.06
6.00
5.93
5.83
5.96
6.01
5.97
6.07
6.08
6.08
6.01
5.89
5.29
6.00
5.89
5.89
5.75
5.79
5.97
5.96
5.88
5.92
6.04
5.90
479.50
535.67
1006.83
853.83
779.67
1202.83
1858.33
1510.17
685.17
588.17
509.00
493.33
418.67
798.33
815.17
1247.00
1216.20
866.67
1476.50
1015.00
879.50
934.67
1008.50
744.17
680.50
579.67
484.83
686.83
811.17
568.17
589.33
642.83
1649.00
1138.00
990.33
1303.83
1211.17
2046.17
1501.20
743.83
699.83
848.67
512.17
530.50
543.17
49.30
70.34
95.77
92.36
76.18
140.20
165.25
127.76
73.88
57.43
59.51
48.78
43.96
78.71
85.23
147.50
116.92
149.50
153.33
102.75
80.58
96.13
80.83
66.73
66.17
55.53
55.44
73.90
68.94
56.11
57.40
111.45
272.67
130.55
89.76
171.76
117.65
240.48
133.05
76.54
70.13
89.58
52.99
54.30
66.44
0.02
0.03
0.04
0.03
0.03
0.05
0.03
0.03
0.06
0.03
0.02
0.03
0.02
0.00
0.01
0.01
0.05
0.04
0.03
0.03
0.02
0.01
0.03
0.02
0.04
0.05
0.03
0.02
0.00
0.00
0.01
0.03
0.08
0.02
0.04
0.06
0.03
0.02
0.02
0.06
0.04
0.03
0.02
0.01
0.01
0.08
0.07
0.14
0.01
0.08
0.20
0.08
0.12
0.11
0.06
0.06
0.11
0.09
0.02
0.07
0.05
0.07
0.20
0.07
0.10
0.07
0.05
0.09
0.08
0.09
0.08
0.03
0.05
0.05
0.05
0.05
0.12
0.20
0.10
0.09
0.15
0.16
0.12
0.16
0.07
0.06
0.06
0.06
0.03
0.03
0.06
0.10
0.15
0.01
0.12
0.17
0.14
0.12
0.11
0.07
0.07
0.16
0.06
0.02
0.10
0.04
0.12
0.12
0.07
0.13
0.06
0.04
0.09
0.05
0.09
0.06
0.05
0.05
0.07
0.06
0.05
0.10
0.14
0.11
0.07
0.12
0.14
0.04
0.14
0.08
0.05
0.09
0.04
0.03
0.04
0.08
0.12
0.09
0.01
0.15
0.15
0.16
0.14
0.13
0.10
0.09
0.11
0.12
0.13
0.10
0.06
0.13
0.23
0.15
0.16
0.09
0.06
0.12
0.12
0.11
0.12
0.08
0.10
0.08
0.08
0.10
0.11
0.12
0.08
0.10
0.11
0.10
0.09
0.14
0.09
0.05
0.12
0.11
0.07
0.07
18.49
18.53
19.95
22.61
23.42
24.90
23.79
20.57
17.99
16.45
15.63
16.00
16.26
16.64
18.75
17.38
17.51
23.42
23.99
22.73
21.08
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
354.60
356.70
395.60
480.20
505.30
551.30
517.00
416.20
344.00
316.20
305.70
309.80
313.20
319.10
361.30
332.40
337.60
505.30
522.90
483.80
432.60
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.00
0.00
0.00
0.00
2.00
0.00
8.00
0.00
0.00
0.00
0.00
0.00
5.00
7.50
0.00
14.00
22.00
24.00
0.00
4.00
0.00
0.00
0.00
0.00
0.00
0.00
10.00
0.00
1.00
0.00
5.00
1.00
1.00
0.00
13.00
5.00
0.00
23.00
0.00
2.00
0.00
0.00
0.00
2.00
95.00
97
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
5.88
5.71
5.72
6.02
6.03
5.96
5.95
5.90
5.95
5.93
5.79
5.92
5.93
5.86
5.87
5.73
5.82
5.82
5.83
5.96
6.02
6.01
6.06
5.94
5.95
6.05
6.06
6.06
6.02
5.90
5.88
5.87
5.82
5.81
5.64
5.87
5.96
5.82
5.84
5.67
5.80
5.77
6.01
5.97
5.92
829.33
1833.33
925.00
798.33
1045.67
776.33
1541.83
1429.83
1110.00
851.00
1078.17
679.67
1395.00
2313.17
2121.33
2951.83
1380.83
1194.33
1445.83
1200.67
1116.33
1456.83
1250.67
1465.50
933.17
663.83
758.33
1092.67
1490.00
2025.17
2765.17
1380.17
1032.17
671.67
462.67
1252.17
1039.83
657.33
400.67
386.67
483.50
633.33
1831.50
1387.67
805.83
250.25
342.33
122.54
115.06
104.69
77.28
164.06
141.00
107.58
86.41
118.95
81.64
137.78
432.08
211.25
213.00
121.21
112.88
159.50
139.78
197.29
124.85
146.44
184.08
99.25
74.47
86.29
102.98
169.83
215.75
285.75
141.33
107.62
71.89
69.22
131.15
116.00
68.10
48.41
54.90
57.55
118.88
180.83
128.51
67.20
0.02
0.04
0.01
0.02
0.02
0.03
0.03
0.03
0.02
0.03
0.04
0.04
0.02
0.04
0.02
0.03
0.03
0.02
0.05
0.02
0.02
0.12
0.00
0.02
0.03
0.07
0.04
0.02
0.03
0.04
0.03
0.05
0.02
0.03
0.00
0.13
0.02
0.02
0.02
0.00
0.03
0.05
0.00
0.03
0.01
0.19
0.14
0.01
0.05
0.06
0.07
0.05
0.06
0.01
0.11
0.08
0.03
0.08
0.09
0.11
0.13
0.05
0.04
0.04
0.06
0.06
0.05
0.07
0.06
0.06
0.07
0.08
0.07
0.05
0.08
0.06
0.10
0.05
0.06
0.03
0.05
0.05
0.06
0.09
0.05
0.04
0.07
0.10
0.08
0.04
0.07
0.07
0.02
0.07
0.08
0.11
0.08
0.07
0.08
0.09
0.08
0.04
0.05
0.08
0.07
0.01
0.03
0.05
0.06
0.06
0.07
0.09
0.04
0.10
0.03
0.07
0.08
0.10
0.02
0.06
0.10
0.09
0.04
0.07
0.01
0.08
0.07
0.09
0.09
0.04
0.05
0.04
0.10
0.11
0.03
0.14
0.26
0.04
0.10
0.18
0.10
0.10
0.07
0.09
0.13
0.15
0.08
0.12
0.14
0.03
0.18
0.05
0.06
0.10
0.14
0.13
0.10
0.04
0.13
0.14
0.13
0.10
0.13
0.17
0.09
0.13
0.12
0.07
0.09
0.10
0.05
0.09
0.12
0.11
0.12
0.05
0.20
0.11
0.14
0.04
22.39
18.94
.
.
.
.
.
20.81
17.22
16.14
14.80
14.86
27.88
30.72
31.06
28.80
25.02
22.85
20.70
18.30
19.01
20.29
18.80
16.28
16.05
18.20
22.82
27.95
21.45
17.76
16.16
.
.
21.42
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
423.80
330.00
311.50
299.50
301.90
627.80
705.50
713.70
657.40
52.60
487.50
420.20
352.20
369.50
407.00
364.00
313.60
310.50
352.10
485.70
632.70
444.00
340.90
312.70
367.80
473.20
443.20
.
.
.
.
.
.
.
.
.
.
.
0.00
0.00
26.00
0.00
61.00
0.00
0.00
2.00
0.00
0.00
40.00
72.00
0.00
3.00
3.00
49.00
0.00
1.50
0.00
3.50
36.00
0.00
0.00
22.00
5.00
0.00
24.00
0.00
2.00
0.00
0.00
0.00
12.00
10.00
29.00
0.00
0.00
0.00
2.00
0.00
22.00
0.00
0.00
0.00
0.00
98
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
5.97
5.75
5.78
5.94
5.95
5.87
5.85
5.74
5.62
5.73
5.74
5.50
5.81
5.76
5.86
5.87
5.70
5.74
5.75
5.83
5.84
5.93
5.86
5.39
5.77
5.45
5.65
5.46
5.88
6.08
5.51
5.94
5.64
5.64
6.00
5.74
5.72
5.73
5.79
5.51
5.78
5.81
5.73
5.70
5.99
797.33
1394.33
746.17
991.33
647.83
2002.33
2791.00
1999.67
1206.83
1033.00
931.00
1336.00
1875.50
1994.33
1366.67
1027.17
1227.50
1216.17
949.50
1095.17
1130.83
829.33
1322.17
1458.67
878.00
1040.00
2208.17
1719.67
873.33
1612.83
1653.33
1361.67
1325.67
1203.67
1235.50
2514.50
2043.20
904.00
737.67
680.00
995.83
702.33
639.50
1381.83
2673.33
81.38
145.65
115.83
125.75
85.64
224.92
268.17
216.00
111.23
98.06
125.30
161.67
198.00
221.33
120.54
102.23
157.12
108.25
100.29
113.97
116.72
80.98
148.82
112.38
74.79
107.18
249.42
179.67
94.87
215.42
154.83
147.67
139.42
123.63
174.15
294.42
201.64
98.96
80.29
83.85
100.73
82.58
65.22
147.88
266.92
0.02
0.03
0.06
0.04
0.02
0.02
0.03
0.09
0.01
0.02
0.04
0.03
0.09
0.04
0.03
0.01
0.01
0.01
0.04
0.02
0.06
0.05
0.07
0.02
0.01
0.01
0.02
0.00
0.05
0.04
0.04
0.02
0.02
0.00
0.01
0.02
0.04
0.05
0.04
0.00
0.00
0.02
0.02
0.02
0.04
0.06
0.07
0.02
0.06
0.03
0.11
0.08
0.02
0.05
0.08
0.09
0.20
0.14
0.09
0.09
0.08
0.08
0.03
0.11
0.09
0.11
0.07
0.09
0.01
0.05
0.05
0.04
0.00
0.08
0.08
0.08
0.05
0.07
0.08
0.09
0.07
0.08
0.08
0.08
0.07
0.04
0.03
0.03
0.08
0.07
0.07
0.03
0.08
0.07
0.01
0.15
0.08
0.03
0.03
0.03
0.15
0.15
0.10
0.06
0.10
0.02
0.05
0.04
0.05
0.09
0.15
0.07
0.09
0.01
0.05
0.05
0.01
0.00
0.10
0.06
0.07
0.04
0.06
0.03
0.07
0.07
0.08
0.10
0.09
0.13
0.04
0.06
0.05
0.11
0.09
0.10
0.11
0.09
0.16
0.08
0.12
0.10
0.04
5.62
0.05
0.13
0.13
0.12
0.12
0.12
0.09
0.05
0.00
0.11
0.13
0.14
0.09
0.13
0.04
0.11
0.08
0.12
0.14
0.14
0.12
0.09
0.04
0.04
0.00
0.08
0.08
0.10
0.15
0.11
0.13
0.22
0.12
0.10
0.10
0.14
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.00
23.00
33.00
0.00
44.00
0.00
0.00
5.00
10.00
8.00
13.00
45.00
0.00
7.00
0.00
2.00
0.00
14.00
0.00
0.00
0.00
2.00
3.00
0.00
2.00
32.00
0.00
10.00
0.00
100.00
0.00
28.00
0.00
35.00
0.00
2.00
0.00
0.00
8.00
0.00
0.00
4.00
34.00
1.00
0.00
99
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
5.94
6.01
5.58
5.65
5.69
5.42
5.67
5.96
5.53
5.77
5.57
5.60
5.75
5.77
5.93
5.91
5.86
5.97
5.75
5.83
5.78
5.95
5.86
5.89
5.69
5.70
5.39
5.54
5.83
5.79
5.67
5.91
5.01
5.41
5.56
.
.
.
.
.
.
.
.
.
.
1439.83
910.00
851.83
1020.83
1121.00
699.33
805.67
710.83
684.17
636.83
694.17
1939.33
725.67
857.00
564.67
649.17
662.50
684.40
744.40
1043.83
765.50
1297.17
2164.50
808.17
607.27
1234.17
1810.00
978.80
1103.33
850.80
1202.17
2608.60
1818.83
1514.17
1978.50
.
.
.
.
.
.
.
.
.
.
143.08
99.37
132.56
98.44
125.08
75.33
84.64
69.31
69.23
74.32
73.16
70.08
67.71
80.56
56.29
61.50
66.08
67.32
101.77
129.31
82.03
128.18
218.08
93.83
74.64
188.92
287.75
112.05
105.11
93.02
116.02
245.20
174.91
149.25
203.33
.
.
.
.
.
.
.
.
.
.
0.02
0.03
0.00
0.03
0.06
0.01
0.01
0.03
0.03
0.01
0.00
0.00
0.00
0.05
0.02
0.02
0.00
0.02
0.01
0.04
0.03
0.03
0.01
0.00
0.01
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0.03
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0.07
.
.
.
.
.
.
.
.
.
.
0.05
0.07
0.05
0.10
0.08
0.05
0.06
0.04
0.06
0.06
0.05
0.06
0.06
0.08
0.06
0.06
0.05
0.12
0.04
0.08
0.06
0.09
0.08
0.05
0.04
0.05
0.07
0.07
0.07
0.12
0.04
0.07
0.04
0.10
0.11
.
.
.
.
.
.
.
.
.
.
0.08
0.07
0.04
0.11
0.03
0.05
0.06
0.10
0.09
0.06
0.05
0.07
0.03
0.08
0.07
0.04
0.08
0.10
0.05
0.10
0.07
0.08
0.07
0.08
0.06
0.03
0.08
0.07
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0.12
0.05
0.08
0.04
0.09
0.07
.
.
.
.
.
.
.
.
.
.
0.11
0.09
0.05
0.11
0.04
0.15
0.06
0.11
0.11
0.04
0.02
0.03
0.09
0.12
0.10
0.07
0.07
0.10
0.03
0.24
0.12
0.13
0.18
0.01
0.03
0.05
0.07
0.23
0.14
0.15
0.05
0.09
0.00
0.17
0.07
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
16.35
13.31
13.62
15.27
13.42
14.09
15.32
16.31
15.22
21.11
26.69
27.30
24.73
18.75
15.02
?
13.65
.
.
.
.
44.41
33.27
21.18
22.54
26.24
22.96
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
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.
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.
.
.
.
.
.
.
0.00
0.00
0.00
0.00
0.00
14.00
0.00
0.00
0.00
5.00
0.00
0.00
0.00
0.00
0.00
7.00
0.00
42.00
0.00
0.00
2.00
1.00
0.00
0.00
64.00
43.00
0.00
0.00
0.00
10.00
0.00
2.00
25.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
0.00
0.00
100
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5.89
5.78
5.39
5.42
5.60
5.79
5.42
5.36
5.48
5.76
5.84
5.53
5.90
5.67
5.98
6.16
6.09
6.14
5.90
5.99
6.11
6.00
5.86
5.77
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1786.17
1968.83
3562.33
3118.67
1044.67
690.83
682.00
1439.00
1022.50
789.67
707.20
818.00
591.00
671.00
610.17
1128.17
744.00
625.33
699.67
962.50
740.17
755.50
663.67
812.67
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
163.03
202.17
381.33
288.42
110.15
65.90
72.20
199.52
113.61
77.43
72.41
89.03
104.83
58.36
92.02
131.98
76.31
59.76
73.13
90.04
82.12
75.91
76.65
89.64
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.03
0.01
0.01
0.03
0.03
0.01
0.03
0.04
0.02
0.03
0.01
0.05
0.02
0.00
0.03
0.01
0.02
0.01
0.01
0.02
0.01
0.02
0.02
0.03
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.05
0.06
0.06
0.11
0.07
0.05
0.08
0.09
0.05
0.07
0.05
0.06
0.06
0.02
0.07
0.05
0.08
0.04
0.02
0.05
0.04
0.06
0.05
0.05
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.02
0.09
0.06
0.08
0.03
0.05
0.10
0.09
0.04
0.11
0.01
0.07
0.09
0.00
0.07
0.06
0.08
0.08
0.04
0.04
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.08
0.07
0.11
0.07
0.05
0.07
0.10
0.12
0.21
0.15
0.11
0.05
0.11
0.08
0.17
0.20
0.13
0.11
0.06
0.05
0.13
0.07
0.06
0.09
22.28
22.06
.
.
.
.
.
.
20.04
16.13
12.92
11.80
11.55
11.53
11.50
11.22
11.06
10.97
10.82
.
10.48
.
.
.
.
44.41
33.27
21.18
22.54
26.24
22.96
22.28
22.06
.
.
.
.
.
.
20.04
16.13
12.92
11.80
11.55
11.53
.
.
.
315.30
215.40
212.20
302.50
209.00
221.60
303.00
313.80
303.30
434.70
601.60
618.30
543.20
366.30
301.70
231.70
.
.
.
.
.
.
996.00
762.30
435.50
477.20
588.40
491.00
469.80
463.10
.
.
.
.
.
.
399.40
315.70
198.70
147.10
139.00
137.80
50.00
0.00
0.00
0.00
15.00
35.00
2.00
17.00
10.00
12.00
12.00
0.00
5.00
8.00
0.00
0.00
0.00
0.00
0.00
0.00
5.00
7.00
48.00
0.00
0.00
0.00
0.00
0.00
3.00
8.00
0.00
0.00
12.00
0.00
13.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
101
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
5.93
6.14
6.18
6.06
6.12
6.02
6.04
6.21
6.10
6.11
6.07
6.19
5.80
5.87
5.91
5.83
6.11
6.38
6.19
6.09
6.29
6.20
5.86
6.24
5.65
6.01
6.45
6.29
6.18
6.16
6.53
6.54
6.24
6.10
5.98
6.26
6.13
6.11
6.22
6.24
6.21
.
6.24
6.24
5.96
839.33
762.33
663.93
634.70
649.17
619.17
648.17
825.33
831.83
801.00
637.67
1066.33
2669.50
2328.50
1913.17
811.83
1196.33
1196.83
1023.83
1574.67
1452.50
1394.83
1985.83
3002.67
3010.50
1307.00
1687.50
1791.67
1914.67
1134.00
1026.83
1075.00
1293.33
916.67
906.50
1100.67
1934.67
817.17
1134.83
811.83
1378.50
.
1834.33
1503.83
724.00
84.28
74.81
77.96
67.81
66.18
69.58
62.41
75.33
79.64
70.94
64.10
120.44
272.17
292.58
164.17
86.22
130.36
123.08
143.43
166.25
142.11
131.58
275.75
502.67
290.25
107.97
138.23
151.58
221.83
140.17
122.85
115.02
132.21
71.50
97.68
154.04
487.08
268.17
154.50
87.11
168.74
.
356.67
271.08
115.73
0.01
0.02
0.04
0.01
0.01
0.05
0.01
0.05
0.02
0.03
0.04
0.04
0.00
0.09
0.04
0.07
0.02
0.02
0.03
0.06
0.03
0.02
0.08
0.02
0.12
0.02
0.00
0.02
0.04
0.01
0.01
0.05
0.05
0.04
0.03
0.03
0.09
0.03
0.01
0.05
0.05
.
0.11
0.04
0.05
0.08
0.04
0.08
0.04
0.05
0.04
0.04
0.05
0.09
0.05
0.04
0.17
0.05
0.06
0.03
0.08
0.07
0.08
0.07
0.12
0.05
0.09
0.08
0.14
0.07
0.06
0.03
0.04
0.04
0.05
0.03
0.09
0.07
0.05
0.08
0.06
0.06
0.07
0.05
0.08
0.07
.
0.11
0.09
0.08
0.09
0.04
0.09
0.01
0.04
0.05
0.00
0.07
0.09
0.09
0.03
0.16
0.08
0.07
0.00
0.10
0.09
0.08
0.09
0.14
0.06
0.06
0.09
0.08
0.07
0.04
0.05
0.07
0.08
0.04
0.03
0.06
0.08
0.06
0.14
0.07
0.13
0.05
0.05
0.11
0.05
.
0.05
0.02
0.04
0.17
0.10
0.12
0.05
0.08
0.07
0.12
0.14
0.16
0.11
0.07
0.15
0.25
0.08
0.17
0.11
0.15
0.10
0.21
0.23
0.09
0.20
0.11
0.16
0.10
0.06
0.12
0.11
0.06
0.13
0.23
0.16
0.15
0.11
0.15
0.29
0.11
0.24
0.23
0.14
0.08
.
0.46
0.27
0.09
11.50
11.22
11.06
10.97
10.82
10.48
.
.
.
.
.
.
.
.
23.30
26.58
31.34
35.99
41.95
49.86
59.84
67.54
76.93
86.41
94.97
70.38
80.88
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
136.50
126.80
121.00
118.10
112.70
101.10
.
.
.
.
.
.
.
.
501.40
597.60
720.00
826.80
946.60
1111.10
1302.70
1432.20
1577.40
1713.00
1825.70
1631.30
1477.30
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.00
10.00
38.00
0.00
0.00
0.00
0.00
25.00
0.00
0.00
0.00
45.00
0.00
0.00
10.50
14.00
0.00
0.00
0.00
0.00
0.00
0.00
5.00
25.00
0.00
5.00
0.00
0.00
94.00
0.00
5.00
0.00
3.00
0.00
102
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
6.20
6.18
5.92
6.17
5.95
.
6.36
6.03
6.11
.
5.84
5.73
5.61
5.47
5.83
5.97
5.86
5.79
5.82
5.66
5.66
5.95
5.99
6.04
5.96
6.06
5.89
5.79
5.50
5.42
5.42
5.68
5.17
5.14
5.45
5.55
5.63
5.82
5.64
5.89
5.84
5.67
6.00
5.87
5.90
824.83
1310.50
1266.17
2124.00
1800.50
.
1241.17
723.50
825.50
.
2365.50
944.00
924.50
760.83
636.83
941.67
994.00
1143.83
755.73
1088.67
1088.67
822.17
615.33
746.33
588.33
580.17
2239.00
3440.83
2903.00
1930.00
792.83
1281.50
0.00
0.00
1534.83
449.17
616.67
602.00
896.00
2754.17
1275.17
789.17
884.50
953.67
559.83
93.21
127.82
135.68
233.42
198.33
.
132.58
76.47
71.87
.
228.18
105.12
88.95
81.66
73.64
97.02
111.26
114.88
106.66
92.39
92.39
80.72
60.54
76.66
61.10
62.23
228.41
349.00
309.75
200.42
118.63
158.18
255.42
199.03
113.50
73.13
71.68
68.87
100.53
288.83
139.21
78.18
111.63
117.22
61.33
0.05
0.03
0.05
0.00
0.02
.
0.03
0.01
0.02
.
0.03
0.03
0.01
0.01
0.00
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.00
0.05
0.02
0.03
0.01
0.01
0.04
0.01
0.03
0.04
0.00
0.00
0.03
0.01
0.03
0.05
0.04
0.02
0.03
0.02
0.02
0.02
0.02
0.07
0.06
0.09
0.03
0.04
.
0.05
0.04
0.06
.
0.05
0.08
0.03
0.04
0.07
0.07
0.04
0.04
0.04
0.03
0.03
0.05
0.05
0.17
0.16
0.08
0.05
0.05
0.07
0.10
0.07
0.05
0.00
0.00
0.05
0.04
0.06
0.08
0.06
0.06
0.06
0.04
0.09
0.09
0.05
0.06
0.05
0.07
0.00
0.04
.
0.06
0.03
0.10
.
0.06
0.05
0.02
0.03
0.04
0.06
0.07
0.07
0.03
0.05
0.05
0.03
0.02
0.18
0.10
0.07
0.03
0.02
0.05
0.06
0.03
0.05
0.00
0.00
0.04
0.05
0.08
0.04
0.06
0.05
0.08
0.03
0.09
0.12
0.09
0.14
0.14
0.20
0.08
0.03
.
0.04
0.09
0.13
.
0.10
0.12
0.04
0.10
0.08
0.12
0.11
0.06
0.07
0.11
0.11
0.06
0.10
0.10
0.13
0.14
0.06
0.22
0.31
0.04
0.13
0.12
0.00
0.00
0.07
0.06
0.09
0.03
0.14
0.26
0.09
0.05
0.19
0.13
0.11
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
22.65
19.73
17.86
16.80
16.14
15.51
17.32
55.23
95.56
111.38
98.74
88.06
111.75
130.78
119.30
97.33
80.50
68.62
60.25
73.30
79.51
66.27
62.26
64.94
62.55
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
481.40
389.60
341.00
321.10
311.50
304.80
355.50
1192.00
1827.10
2004.70
1869.90
1734.30
2001.20
2165.20
2072.40
1851.90
1628.80
1448.70
1310.10
1521.20
1615.20
1411.20
1344.80
1389.90
1349.30
5.00
0.00
25.00
0.00
18.00
0.00
2.00
0.00
65.00
6.00
1.00
0.00
0.50
0.00
0.00
0.00
0.00
0.10
13.00
0.00
0.00
0.00
0.00
0.00
0.00
134.00
55.00
0.00
17.00
25.00
84.00
0.00
22.00
0.00
0.00
0.00
0.00
32.00
10.00
0.00
0.00
42.00
0.00
3.00
0.00
103
358
359
360
361
362
363
364
365
5.78
5.53
5.80
5.93
5.88
6.03
5.92
5.46
613.50
485.00
676.00
521.50
374.17
477.50
568.62
0.00
58.96
55.77
112.88
53.99
41.69
49.86
53.41
63.58
0.03
0.04
0.03
0.02
0.02
0.01
0.05
0.00
0.07
0.13
0.09
0.06
0.05
0.03
0.09
0.00
0.08
0.08
0.10
0.07
0.05
0.02
0.13
0.00
0.09
0.12
0.14
0.09
0.07
0.07
0.17
0.00
52.17
38.78
25.47
21.43
19.19
18.09
18.60
16.89
1159.10
880.20
565.30
443.30
373.40
345.60
357.70
.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
104
APPENDIX C1 - DESCRIPTIVE STATISTIC FOR SERIES 2004
PH
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
5.937337
0.015064
5.92
5.9
0.204333
0.041752
1.274442
-0.06142
1.39
5.27
6.66
1092.47
184
0.029721
Colour
Mean
891.3921
Standard
Error
39.78122
Median
706.41
Mode
690.46
Standard
Deviation
539.6188
Sample
Variance
291188.4
Kurtosis
10.85423
Skewness
2.659279
Range
4308.05
Minimum
5.87
Maximum
4313.92
Sum
164016.2
Count
184
Confidence
Level(95.0%) 78.48883
Turbidity
Mean
118.4759
Standard
Error
4.981494
Median
85.69
Mode
76.29
Standard
Deviation
67.57227
Sample
Variance
4566.012
Kurtosis
2.523233
Skewness
1.689449
Range
316.12
Minimum
39.09
Maximum
355.21
Sum
21799.57
Count
184
Confidence
Level(95.0%) 9.828547
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
AL
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
NH4
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.038533
0.001785
0.04
0.04
0.024215
0.000586
1.43568
1.111998
0.12
0
0.12
7.09
184
0.003522
FE
0.108804
0.006234
0.09
0.08
0.08456
0.00715
9.884475
2.672763
0.59
0
0.59
20.02
184
0.0123
0.078043
0.004245
0.07
0.07
0.057588
0.003316
8.234527
2.247256
0.39
0
0.39
14.36
184
0.008376
105
MN
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.122337
0.005444
0.11
0.06
0.073844
0.005453
5.66902
2.035956
0.42
0.03
0.45
22.51
184
0.010741
Rainfall
Mean
9.013587
Standard
Error
1.223698
Median
0
Mode
0
Standard
Deviation
16.59905
Sample
Variance
275.5285
Kurtosis
6.685971
Skewness
2.526333
Range
89
Minimum
0
Maximum
89
Sum
1658.5
Count
184
Confidence
Level(95.0%) 2.414371
Flow
30.89429348
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
2.168440157
19.215
3.33
29.41415338
865.1924192
-0.87225271
0.720439827
98.08
-0.9
97.18
5684.55
184
4.278358175
106
APPENDIX C2 - DESCRIPTIVE STATISTIC FOR SERIES 2005
PH
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
6.036957
0.012791
6.05
6.04
0.173509
0.030105
0.068437
-0.45166
0.9
5.58
6.48
1110.8
184
0.025237
Turbidity
Mean
78.96685
Standard
Error
4.44983
Median
63.58
Mode
38.98
Standard
Deviation
60.36044
Sample
Variance
3643.382
Kurtosis
42.89098
Skewness
5.200464
Range
622.2
Minimum
24.37
Maximum
646.57
Sum
14529.9
Count
184
Confidence
Level(95.0%)
8.779568
Colour
571.5275543
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
22.65161413
484.33
370.67
307.2614434
94409.5946
12.48525432
2.703393877
2590.61
32.14
2622.75
105161.07
184
44.69190363
FE
Mean
0.118423913
Standard
Error
0.019682563
Median
0.08
Mode
0.07
Standard
Deviation
0.266987269
Sample
Variance
0.071282202
Kurtosis
102.9381072
Skewness
9.532775206
Range
3.21
Minimum
0
Maximum
3.21
Sum
21.79
Count
184
Confidence
Level(95.0%) 0.03883393
AL
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
MN
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.045543
0.002922
0.03
0.03
0.039639
0.001571
10.46294
2.594816
0.27
0
0.27
8.38
184
0.005766
0.114511
0.012978
0.1
0.1
0.176043
0.030991
165.9619
12.56701
2.43
0
2.43
21.07
184
0.025606
107
NH4
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
SS
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Flow
0.075109
0.005743
0.06
0.04
0.077903
0.006069
62.45777
6.416356
0.88
0
0.88
13.82
184
0.011331
66.43641
17.00927
4
2
230.7249
53233.98
21.94531
4.618791
1459.3
0
1459.3
12224.3
184
33.55948
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Rainfall
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
13.14304
2.670551
2.08
0.79
36.22512
1312.259
18.40065
4.189472
222.91
0.43
223.34
2418.32
184
5.269029
4.855978
0.916944
0
0
12.43803
154.7046
11.70162
3.302461
73
0
73
893.5
184
1.809141
108
APPENDIX C3 - DESCRIPTIVE STATISTIC FOR SERIES 2006
PH
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
6.255163
0.016891
6.315
6.4
0.229123
0.052497
1.11421
-0.98282
1.33
5.39
6.72
1150.95
184
0.033326
Colour
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Turbidity
Mean
97.68353
Standard
Error
5.092511
Median
85.715
Mode
44.28
Standard
Deviation
69.07817
Sample
Variance
4771.794
Kurtosis
6.833139
Skewness
2.016659
Range
497.28
Minimum
0.05
Maximum
497.33
Sum
17973.77
Count
184
Confidence
Level(95.0%)
10.04758
AL
776.4055
33.06224
742.655
883.67
448.4781
201132.6
1.497503
1.053271
2560.96
18.71
2579.67
142858.6
184
65.2322
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
FE
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.129511
0.007444
0.1
0.07
0.100975
0.010196
6.206547
2.247908
0.6
0.02
0.62
23.83
184
0.014687
NH4
0.039457
0.002601
0.03
0.02
0.035278
0.001245
13.24282
3.086618
0.26
0
0.26
7.26
184
0.005131
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.142554
0.00752
0.11
0.08
0.102004
0.010405
5.778571
2.089788
0.69
0
0.69
26.23
184
0.014837
109
MN
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Flow
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
SS
0.108207
0.004635
0.1
0.13
0.062878
0.003954
3.282175
1.295854
0.38
0
0.38
19.91
184
0.009146
31.35745
2.483012
23.14
21.5
33.68121
1134.424
23.08307
4.69058
231.26
14.2
245.46
5769.77
184
4.899012
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Rainfall
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
201.9578
28.63962
100.5
46.6
388.4867
150921.9
22.01071
4.585519
2619
0
2619
37160.23
184
56.50631
6.86413
1.099966
0
0
14.92067
222.6262
13.3303
3.336701
100
0
100
1263
184
2.170246
110
APPENDIX C4 - DESCRIPTIVE STATISTIC FOR SERIES 2007
PH
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
5.891196
0.014983
5.9
6.01
0.203233
0.041303
1.375501
0.232924
1.42
5.29
6.71
1083.98
184
0.029561
Colour
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Turbidity
Mean
117.5756
Standard
Error
5.470317
Median
99.83
Mode
52.99
Standard
Deviation
74.20299
Sample
Variance
5506.084
Kurtosis
5.462413
Skewness
1.999891
Range
450.47
Minimum
35.2
Maximum
485.67
Sum
21633.91
Count
184
Confidence
Level(95.0%)
10.793
AL
1060.475
42.82744
914
798.33
580.9397
337490.9
1.762885
1.339611
2842.83
321.17
3164
195127.3
184
84.49905
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
FE
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.080272
0.002872
0.08
0.08
0.038954
0.001517
2.709499
1.161991
0.25
0
0.25
14.77
184
0.005666
NH4
0.032011
0.001601
0.03
0.03
0.021723
0.000472
3.722074
1.468346
0.13
0
0.13
5.89
184
0.00316
Mean
Standard
Error
Median
Mode
Standard
Deviation
Sample
Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
0.080924
0.002819
0.08
0.07
0.038236
0.001462
-0.30606
0.259256
0.2
0
0.2
14.89
184
0.005562
111
MN
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Flow
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
SS
0.141413
0.030115
0.11
0.12
0.408503
0.166874
179.6278
13.32451
5.62
0
5.62
26.02
184
0.059418
36.93984
4.839028
19.84
16.81
65.63978
4308.58
38.17022
5.822475
529.96
14.8
544.76
6796.93
184
9.547461
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
Rainfall
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence
Level(95.0%)
637.7016
47.69005
411.6
361.78
646.8993
418478.7
13.09032
3.380164
4256.5
52.6
4309.1
117337.1
184
94.09304
10.36685
2.203295
0
0
29.88694
893.2294
62.10285
6.902274
315
0
315
1907.5
184
4.347127
112
APPENDIX D1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (PH, 2006)
100
7.5
7.0
80
6.5
60
6.0
40
5.5
20
Std. Dev = .25
5.0
PH
Mean = 6.08
N = 316.00
0
4.5
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
5.00
343
324
362
5.25
5.13
5.38
5.75
5.63
6.00
5.88
6.25
6.13
6.50
6.38
6.75
6.63
7.00
6.88
PH
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
PH
PH
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
Confidence Limits
ACF
5.50
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
113
APPENDIX D2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (PH, 2006)
60
7.0
6.8
50
6.6
40
6.4
30
6.2
6.0
20
5.8
10
Std. Dev = .22
PH
5.6
Mean = 6.29
N = 308.00
0
5.4
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
5.56
343
324
362
5.69
5.63 5.75
(a) Plotting of Original Data
6.00 6.13
6.25
6.44
6.56 6.69
6.38 6.50
6.63
6.75
PH
PH
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
Confidence Limits
ACF
6.19 6.31
(b) Histogram of Original Data
1.0
-1.0
Coefficient
3
2
5.88
6.06
PH
Sequence number
1
5.81 5.94
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
114
APPENDIX D3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (PH, 2007)
6.8
50
6.6
40
6.4
6.2
30
6.0
5.8
20
5.6
10
PH
5.4
Std. Dev = .22
Mean = 5.89
5.2
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 326.00
0
343
362
5.38
5.50
5.63 5.75
5.44 5.56
5.69
5.88
6.00 6.13
5.81 5.94
6.06
6.25
6.38 6.50
6.19 6.31
6.44
6.56
Sequence number
PH
(a) Plotting of Original Data
(b) Histogram of Original Data
PH
PH
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) ACF Plot of the Original Data
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
115
APPENDIX E1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Colour, 2004)
5000
30
4000
20
3000
2000
COLOUR
10
1000
Std. Dev = 405.84
0
Mean = 865.7
1
39
115
58
96
153
134
191
172
229
210
267
248
305
286
343
324
362
0
N = 148.00
.0
00
20 0 .0
0
19 0 .0
0
18 .0
00
17 0 .0
0
16 0 .0
0
15 0 .0
0
14 0 .0
0
13 0 .0
0
12 0 .0
0
11 0 .0
0
10 0
0.
90 0
0.
80 .0
0
70 .0
0
60 .0
0
50 .0
0
40 .0
0
30
20
77
Sequence number
COLOUR
(a) Plotting of Original Data
(b) Histogram of Original Data
COLOUR
COLOUR
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
116
APPENDIX E2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Colour, 2005)
3000
60
50
2000
40
30
COLOUR
1000
20
10
Std. Dev = 395.16
Mean = 713.1
0
N = 316.00
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
0
0.
1
.0
00
26 0
.
00
24 0
.
00
22 0
.
00
20 0
.
00
18 0
.
00
16 0
.
00
14 0
.
00
12 0
.
00
10
0
0.
80
0
0.
60
0
0.
40
0
0.
20
0
362
Sequence number
COLOUR
(a) Plotting of Original Data
(b) Histogram of Original Data
COLOUR
COLOUR
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
ACF
Confidence Limits
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
117
APPENDIX E3
COLOUR
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Colour, 2006)
4000
40
3000
30
2000
20
1000
10
Std. Dev = 480.30
Mean = 869.5
N = 309.00
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
0
0.
1
343
324
.0
00
24
.0
00
22
.0
00
20
.0
00
18
.0
00
16
.0
00
14
.0
00
12
.0
00
10
0
0.
80
0
0.
60
0
0.
40
0
0.
20
0
0
362
COLOUR
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
COLOUR
COLOUR
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
118
APPENDIX E4
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Colour, 2007)
4000
40
3000
30
20
2000
10
1000
COLOUR
Std. Dev = 591.81
Mean = 1091.0
0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
N = 326.00
.0
00
31 .0
00
29 .0
00
27 .0
00
25 .0
00
23 .0
00
21 .0
00
19 .0
00
17 .0
00
15 .0
00
13 .0
00
11
0
0.
90
0
0.
70
0
0.
50
0
0.
30
0
343
324
362
COLOUR
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
COLOUR
COLOUR
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
ACF
Confidence Limits
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
119
APPENDIX F1
Sequence Plot, Histogram, ACF and PACF Plot of Original Data (Turbidity, 2004)
400
60
50
300
40
200
30
20
TURB
100
10
Std. Dev = 71.04
Mean = 126.2
0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 155.00
0
343
362
40.0
80.0
60.0
120.0
160.0 200.0 240.0
100.0 140.0
180.0 220.0
280.0 320.0
360.0
260.0 300.0 340.0
Sequence number
TURB
(a) Plotting of Original Data
(b) Histogram of Original Data
TURB
TURB
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
120
APPENDIX F2
Sequence Plot, Histogram, ACF and PACF Plot of Original Data (Turbidity, 2005)
700
100
600
80
500
60
400
300
40
TURB
200
20
100
Std. Dev = 67.81
Mean = 96.0
0
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 316.00
0
343
362
Sequence number
0
5.
62
0
5.
57
0
5.
52
0
5.
47
0
5.
42
0
5.
37
0
5.
32
0
5.
27
0
5.
22
0
5.
17
0
5.
12
.0
75
.0
25
1
TURB
(a) Plotting of Original Data
(b) Histogram of Original Data
TURB
TURB
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
121
APPENDIX F3
Sequence Plot, Histogram, ACF and PACF Plot of Original Data (Turbidity, 2006)
400
60
50
300
40
200
30
20
TURB
100
10
Std. Dev = 62.67
0
Mean = 105.4
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
362
N = 308.00
0
0.0
40.0
20.0
Sequence number
80.0
60.0
120.0 160.0 200.0 240.0 280.0 320.0
100.0 140.0 180.0 220.0 260.0 300.0 340.0
TURB
(a) Plotting of Original Data
(b) Histogram of Original Data
TURB
TURB
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) ACF Plot of the Original Data
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
122
APPENDIX F4
Sequence Plot, Histogram, ACF and PACF Plot of Original Data (Turbidity, 2007)
400
50
300
40
30
200
20
TURB
100
10
Std. Dev = 59.99
0
1
39
58
115
96
153
134
191
172
229
210
267
248
305
286
Mean = 114.0
343
324
362
N = 320.00
0
.0
80
.0
60
.0
40
0
0.
28
0
0.
26
0
0.
24
0
0.
22
0
0.
20
0
0.
18
0
0.
16
0
0.
14
0
0.
12
0
0.
10
20
77
Sequence number
TURB
(a) Plotting of Original Data
(b) Histogram of Original Data
TURB
TURB
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
ACF
Confidence Limits
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
123
APPENDIX G1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Al, 2004)
.14
70
.12
60
.10
50
.08
40
.06
30
.04
20
AL
.02
0.00
Std. Dev = .03
10
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
Mean = .038
362
N = 155.00
0
0.000
Sequence number
.025
.013
.050
.038
.075
.063
.100
.088
.125
.113
AL
(a) Plotting of Original Data
(b) Histogram of Original Data
AL
AL
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
124
APPENDIX G2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Al, 2005)
1.0
300
.8
200
.6
.4
100
.2
AL
Std. Dev = .06
Mean = .05
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 316.00
0
362
Sequence number
0.00
.13
.25
.38
.50
.63
.75
.88
AL
(a) Plotting of Original Data
(b) Histogram of Original Data
AL
AL
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
125
APPENDIX G3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Al, 2006)
.12
140
.10
120
100
.08
80
.06
60
.04
40
AL
.02
Std. Dev = .02
20
Mean = .039
0.00
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 299.00
0
362
Sequence number
.013
.038
.050
.063
.075
.088
.100
AL
(a) Plotting of Original Data
(b) Histogram of Original Data
AL
AL
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
Confidence Limits
ACF
.025
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
126
APPENDIX G4
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Al, 2007)
.10
160
140
.08
120
.06
100
80
.04
60
AL
.02
40
Std. Dev = .02
20
0.00
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
Mean = .028
343
324
362
N = 326.00
0
0.000
.013
.025
.038
.050
.063
.075
.088
Sequence number
AL
(a) Plotting of Original Data
(b) Histogram of Original Data
AL
AL
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
127
APPENDIX H1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Fe, 2004)
.4
40
.3
30
.2
20
10
.1
Std. Dev = .07
FE
Mean = .104
N = 152.00
0
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
0.000
343
324
.050
.025
.100
.075
.150
.125
.200
.175
.250
.225
.300
.275
.350
.325
362
FE
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
FE
FE
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) ACF Plot of the Original Data
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
128
APPENDIX H2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Fe, 2005)
3.5
140
3.0
120
2.5
100
2.0
80
1.5
60
1.0
40
FE
.5
Std. Dev = .03
20
0.0
Mean = .072
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
362
N = 330.00
0
0.000
Sequence number
.050
.075
.100
.125
.150
.175
.200
FE
(a) Plotting of Original Data
(b) Histogram of Original Data
FE
FE
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
Confidence Limits
ACF
.025
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
129
APPENDIX H3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Fe, 2006)
.4
80
.3
60
.2
40
.1
20
FE
Std. Dev = .06
Mean = .121
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 299.00
0
362
.025
.075
.050
.125
.100
.175
.150
.225
.200
.275
.250
.325
.300
Sequence number
FE
(a) Plotting of Original Data
(b) Histogram of Original Data
FE
FE
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
130
APPENDIX H4
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Fe, 2007)
.3
140
120
100
.2
80
60
.1
40
Std. Dev = .03
FE
20
Mean = .072
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 330.00
0
362
Sequence number
0.000
.100
.125
.150
.175
.200
FE
FE
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
Confidence Limits
ACF
.075
(b) Histogram of Original Data
1.0
-1.0
Coefficient
3
2
.050
FE
(a) Plotting of Original Data
1
.025
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
131
APPENDIX I1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (NH4, 2004)
.5
80
70
.4
60
.3
50
40
.2
30
NH4
.1
20
Std. Dev = .06
10
Mean = .08
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 155.00
0
362
Sequence number
0.00
.05
.25
.30
.35
.40
NH4
NH4
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
Confidence Limits
ACF
.20
(b) Histogram of Original Data
1.0
-1.0
Coefficient
3
2
.15
NH4
(a) Plotting of Original Data
1
.10
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
132
APPENDIX I2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (NH4, 2005)
1.0
160
140
.8
120
100
.6
80
.4
60
40
NH4
.2
Std. Dev = .07
20
Mean = .09
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 316.00
0
343
362
0.00
.13
.06
.25
.19
.38
.31
.50
.44
.63
.56
.75
.69
.88
.81
Sequence number
NH4
(a) Plotting of Original Data
(b) Histogram of Original Data
NH4
NH4
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) ACF Plot of the Original Data
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
133
APPENDIX I3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (NH4, 2006)
.4
80
70
.3
60
50
.2
40
30
NH4
.1
20
Std. Dev = .07
10
0.0
Mean = .136
N = 303.00
0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
.025
362
Sequence number
.075
.050
NH4
.200
.275
.250
.325
.300
.375
.350
NH4
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
Confidence Limits
ACF
.150
.225
(b) Histogram of Original Data
1.0
-1.0
Coefficient
3
2
.100
.175
NH4
(a) Plotting of Original Data
1
.125
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
134
APPENDIX I4
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (NH4, 2007)
100
.3
80
.2
60
40
.1
NH4
20
Std. Dev = .04
Mean = .073
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 331.00
0
343
362
0.000
.025
.050
.075
.100
.125
.150
.175
.200
NH4
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
NH4
NH4
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
135
APPENDIX J1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Mn, 2004)
70
.5
60
.4
50
.3
40
30
.2
20
.1
Std. Dev = .06
MN
10
Mean = .13
N = 152.00
0
0.0
1
39
77
20
58
115
96
153
134
191
172
229
210
267
248
305
286
.05
343
324
.10
.15
.20
.25
.30
.35
362
MN
Sequence number
(a) Plotting of Original Data
(b) Histogram of Original Data
MN
MN
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
136
APPENDIX J2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Mn, 2005)
.3
200
.2
100
MN
.1
Std. Dev = .19
0.0
Mean = .13
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 316.00
0
362
Sequence number
0.00
.25
.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50
MN
(a) Plotting of Original Data
(b) Histogram of Original Data
MN
MN
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
.50
-.5
Confidence Limits
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
16
Lag Number
Lag Number
Transforms: difference (1)
Transforms: difference (1)
(e) ACF Plot of the Original Data
(e) PACF Plot of the Original Data
137
APPENDIX J3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Mn, 2006)
.3
100
80
.2
60
40
.1
20
MN
Std. Dev = .05
Mean = .123
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
324
N = 293.00
0
343
362
0.000
.050
.025
.100
.075
.150
.125
.200
.175
.250
.225
.275
Sequence number
MN
(a) Plotting of Original Data
(b) Histogram of Original Data
MN
MN
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
138
APPENDIX J4
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (Mn, 2007)
.3
100
80
.2
60
40
.1
20
MN
Std. Dev = .05
Mean = .109
0.0
1
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 325.00
0
0.000
362
Sequence number
.050
.025
.100
.075
.150
.125
.200
.175
.250
.225
MN
(a) Plotting of Original Data
(b) Histogram of Original Data
MN
MN
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
139
APPENDIX K1
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (SS, 2005)
30
400
300
20
200
10
100
SS
Std. Dev = 187.01
Mean = 52.6
0
77
20
58
115
96
153
134
191
172
229
210
267
248
305
286
343
324
N = 363.00
362
Sequence number
0
0.
39
.0
00
14 .0
00
13 .0
00
12 .0
00
11 .0
00
10
0
0.
90
0
0.
80
0
0.
70
0
0.
60
0
0.
50
0
0.
40
0
0.
30
0
0.
20
0
0.
10
1
0
SS
(a) Plotting of Original Data
(b) Histogram of Original Data
SS
SS
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-.5
Confidence Limits
-1.0
Coefficient
1
3
2
Lag Number
(e) ACF Plot of the Original Data
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
140
APPENDIX K2
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (SS, 2006)
600
50
500
40
400
30
300
200
20
100
SS
10
Std. Dev = 110.99
0
1
39
58
115
96
153
134
191
172
229
210
267
248
305
286
Mean = 132.3
343
324
362
N = 312.00
0
.0
80
.0
40
0
0.
0
0.
48
0
0.
44
0
0.
40
0
0.
36
0
0.
32
0
0.
28
0
0.
24
0
0.
20
0
0.
16
0
0.
12
20
77
Sequence number
SS
(a) Plotting of Original Data
(b) Histogram of Original Data
SS
SS
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
14
-1.0
Coefficient
1
15
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
141
APPENDIX K3
Sequence Plot, Histogram, ACF and PACF Plot of the Original Data (SS, 2007)
3000
40
30
2000
20
1000
10
SS
Std. Dev = 555.38
Mean = 752.4
0
39
20
77
58
115
96
153
134
191
172
229
210
267
248
305
286
0
343
324
362
Sequence number
N = 164.00
.0
00
21
.0
00
19
.0
00
17
.0
00
15
.0
00
13
.0
00
11
0
0.
90
0
0.
70
0
0.
50
0
0.
30
0
0.
10
1
SS
(a) Plotting of Original Data
(b) Histogram of Original Data
SS
SS
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(e) ACF Plot of the Original Data
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(e) PACF Plot of the Original Data
142
APPENDIX L1 – Estimation of ARIMA Model Parameters
Model Description:
Variable:
PH
Regressors: NONE
Non-seasonal differencing: 1
No seasonal component in model.
Parameters:
AR1
________
< value originating from estimation >
MA1
________
< value originating from estimation >
CONSTANT ________
< value originating from estimation >
95.00 percent confidence intervals will be generated.
Split group number: 1
Series length: 184
Number of cases skipped at beginning because of missing values: 182
Number of cases containing missing values: 29
Kalman filtering will be used for estimation.
Termination criteria:
Parameter epsilon: .001
Maximum Marquardt constant: 1.00E+09
SSQ Percentage: .001
Maximum number of iterations: 10
Initial values:
AR1
.15164
MA1
.52955
CONSTANT
-.00028
Marquardt constant = .001
Adjusted sum of squares = 4.7046594
Iteration History:
Iteration
Adj. Sum of Squares
Marquardt Constant
1
4.4732461
.10000000
2
4.4679331
.01000000
Conclusion of estimation phase.
Estimation terminated at iteration number 3 because:
Sum of squares decreased by less than .001 percent.
FINAL PARAMETERS:
143
Number of residuals
154
Standard error
.17038307
Log likelihood
54.066272
AIC
-102.13254
SBC
-93.021686
Analysis of Variance:
Residuals
DF
Adj. Sum of Squares
Residual Variance
151
4.4678890
.02903039
Variables in the Model:
B
SEB
T-RATIO
APPROX. PROB.
AR1
.30549557
MA1
.80398562
.11827801
2.582860
.01074808
.07336834
10.958209
.00000000
CONSTANT
.00076285
.00362813
.210259
.83374922
Covariance Matrix:
AR1
MA1
AR1
.01398969
.00648001
MA1
.00648001
.00538291
Correlation Matrix:
AR1
MA1
AR1
1.0000000
.7467292
MA1
.7467292
1.0000000
Regressor Covariance Matrix:
CONSTANT
.00001316
Regressor Correlation Matrix:
CONSTANT
1.0000000
The following new variables are being created:
Name
Label
FIT_1
Fit for PH from ARIMA, MOD_1 CON
ERR_1
Error for PH from ARIMA, MOD_1 CON
LCL_1
95% LCL for PH from ARIMA, MOD_1 CON
UCL_1
95% UCL for PH from ARIMA, MOD_1 CON
SEP_1
SE of fit for PH from ARIMA, MOD_1 CON
144
APPENDIX L2 – ACF and PACF Description
Variable:
PH
Missing cases:
211
Valid cases:
155
Some of the missing cases are imbedded within the series.
Autocorrelations:
PH
Auto- Stand.
Lag
Corr.
Err. -1
-.75
-.5 -.25
0
.25
.5
.75
1
Box-Ljung
Prob.
63.900
.000
ùòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòú
1
.630
.079
.
ó**.**********
2
.486
.078
.
ó**.*******
102.476
.000
3
.381
.078
.
ó**.*****
126.505
.000
4
.394
.077
.
ó**.*****
152.507
.000
5
.442
.077
.
ó**.******
185.698
.000
6
.345
.076
.
ó**.****
206.286
.000
7
.298
.076
.
ó**.***
221.878
.000
8
.264
.075
.
ó**.**
234.248
.000
9
.322
.074
.
ó**.***
252.903
.000
10
.262
.074
.
ó**.**
265.452
.000
11
.268
.074
.
ó**.**
278.653
.000
12
.214
.073
.
ó**.*
287.226
.000
13
.175
.073
.
ó**.*
293.063
.000
14
.124
.072
.
ó**.
296.032
.000
15
.111
.071
.
ó**.
298.429
.000
16
.086
.071
.
ó**.
299.917
.000
Plot Symbols:
Total cases:
Autocorrelations *
366
Two Standard Error Limits .
Computable first lags:
Partial Autocorrelations:
151
PH
Pr-Aut- Stand.
Lag
Corr.
Err. -1
-.75
-.5 -.25
0
.25
.5
1
.630
.080
.
ó**.**********
2
.148
.080
.
ó***
3
.046
.080
.
ó* .
4
.175
.080
.
ó***
5
.193
.080
.
ó**.*
6
-.084
.080
.**ó
.
7
.015
.080
.
*
.
8
.036
.080
.
ó* .
9
.126
.080
.
ó***
.75
1
ùòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòôòòòòú
145
10
-.097
.080
.**ó
11
.084
.080
.
ó**.
12
-.024
.080
.
*
13
-.052
.080
. *ó
.
14
-.105
.080
.**ó
.
15
.040
.080
.
16
-.062
.080
. *ó
Plot Symbols:
Total cases:
Autocorrelations *
366
.
.
ó* .
.
Two Standard Error Limits .
Computable first lags:
151
146
APPENDIX M - DIAGNOSTIC CHECKING
1. Diagnostic Checking For PH 2005
Error for PH from ARIMA, (1,1,1)
Error for PH from ARIMA, (1,1,1)
1.0
1.0
.5
.5
0.0
Partial ACF
0.0
-.5
ACF
Confidence Limits
-1.0
-.5
Confidence Limits
-1.0
Coefficient
1
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
3
5
2
15
14
16
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
2. Diagnostic Checking For PH 2006
Error for PH from ARIMA, (1,1,1)
Error for PH from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
3
5
2
16
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
3. Diagnostic Checking For PH 2007
Error for PH from ARIMA, (1,1,1)
Error for PH from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
Partial ACF
1.0
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
147
4. Diagnostic Checking For Colour 2004
Error for COLOUR from ARIMA, (1,1,1)
Error for COLOUR from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
3
5
2
16
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
5. Diagnostic Checking For Colour 2005
Error for COLOUR from ARIMA, (1,1,1)
Error for COLOUR from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
7
4
6
9
8
11
10
13
12
-1.0
Coefficient
1
15
14
3
2
16
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
6. Diagnostic Checking For Colour 2006
Error for COLOUR from ARIMA, (1,1,1)
Error for COLOUR from ARIMA, (1,1,1)
1.0
1.0
.5
.5
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Partial ACF
0.0
-.5
Confidence Limits
-1.0
Coefficient
1
3
2
Lag Number
(a) ACF Plot of Best Fitted Model Error
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
148
7. Diagnostic Checking For Colour 2007
Error for COLOUR from ARIMA, (1,1,2)
Error for COLOUR from ARIMA, (1,1,2)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
3
2
16
5
4
7
6
9
8
11
10
13
15
12
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
8. Diagnostic Checking For Turbidity 2004
Error for TURB from ARIMA, (1,1,1)
Error for TURB from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
Confidence Limits
-1.0
Coefficient
15
14
1
16
3
5
2
Lag Number
7
4
6
9
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
9. Diagnostic Checking For Turbidity 2005
Error for TURB from ARIMA, (1,1,2)
Error for TURB from ARIMA, (1,1,2)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
Partial ACF
1.0
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
149
10. Diagnostic Checking For Turbidity 2006
Error for TURB from ARIMA, (1,1,1)
Error for TURB from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
-1.0
Coefficient
1
3
2
5
7
4
6
9
8
11
10
13
12
Coefficient
1
15
14
3
5
2
16
7
4
9
6
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
11. Diagnostic Checking For Turbidity 2007
Error for TURB from ARIMA, (1,1,2)
Error for TURB from ARIMA, (1,1,2)
1.0
1.0
.5
.5
0.0
Partial ACF
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
12. Diagnostic Checking For Al 2004
Error for AL from ARIMA, (1,1,1)
Error for AL from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
150
13. Diagnostic Checking For Al 2005
Error for AL from ARIMA, (1,1,1)
Error for AL from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
4
7
6
9
8
11
10
13
-1.0
Coefficient
1
15
12
14
3
2
16
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
14. Diagnostic Checking For Al 2006
Error for AL from ARIMA, (1,1,1)
Error for AL from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
7
4
6
9
8
11
10
13
12
-1.0
Coefficient
1
15
14
3
2
16
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
15. Diagnostic Checking For Al 2007
Error for AL from ARIMA, (1,1,1)
Error for AL from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
151
16. Diagnostic Checking For Fe 2004
Error for FE from ARIMA, (1,1,1)
Error for FE from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
4
7
6
9
8
11
10
13
Partial ACF
1.0
-.5
Confidence Limits
-1.0
12
14
Coefficient
1
15
3
2
16
5
7
4
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
17. Diagnostic Checking For Fe 2005
Error for FE from ARIMA, (1,1,1)
Error for FE from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
4
7
6
9
8
11
10
13
12
Partial ACF
1.0
-.5
Confidence Limits
-1.0
15
14
Coefficient
1
16
3
2
Lag Number
5
7
4
6
9
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
18. Diagnostic Checking For Fe 2006
Error for FE from ARIMA, (1,1,1)
Error for FE from ARIMA, (1,1,1)
1.0
1.0
.5
.5
0.0
0.0
Partial ACF
-.5
ACF
Confidence Limits
-1.0
-.5
Confidence Limits
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
-1.0
Coefficient
1
3
2
Lag Number
(a) ACF Plot of Best Fitted Model Error
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
152
19. Diagnostic Checking For Fe 2007
Error for FE from ARIMA, (1,1,1)
Error for FE from ARIMA, (1,1,1)
1.0
1.0
.5
.5
0.0
-.5
Partial ACF
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
4
7
6
9
8
11
10
13
12
15
14
Confidence Limits
-1.0
Coefficient
1
16
3
5
2
7
4
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
20. Diagnostic Checking For Nh4 2004
Error for NH4 from ARIMA, (1,1,1)
Error for NH4 from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
Coefficient
1
15
14
3
5
2
16
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
21. Diagnostic Checking For Nh4 2005
Error for NH4 from ARIMA, (1,1,2)
Error for NH4 from ARIMA, (1,1,2)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
153
22. Diagnostic Checking For Nh4 2006
Error for NH4 from ARIMA, (1,1,1)
Error for NH4 from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
Partial ACF
1.0
-.5
Confidence Limits
-1.0
15
14
Coefficient
1
16
3
2
Lag Number
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
23. Diagnostic Checking For Nh4 2007
Error for NH4 from ARIMA, (1,1,1)
Error for NH4 from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
1
16
3
2
Lag Number
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
24. Diagnostic Checking For Mn 2004
Error for MN from ARIMA, (1,1,1)
Error for MN from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
154
25. Diagnostic Checking For Mn 2005
Error for MN from ARIMA, (1,1,1)
Error for MN from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
Partial ACF
1.0
-.5
Confidence Limits
-1.0
Coefficient
15
14
1
3
16
5
2
Lag Number
7
4
9
6
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
26. Diagnostic Checking For Mn 2006
Error for MN from ARIMA, (1,1,2)
Error for MN from ARIMA, (1,1,2)
1.0
.5
.5
0.0
0.0
Partial ACF
1.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
-1.0
14
Coefficient
1
15
3
5
2
4
7
6
9
8
11
10
13
12
15
14
16
16
Lag Number
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
27. Diagnostic Checking For Mn 2007
Error for MN from ARIMA, (1,1,1)
Error for MN from ARIMA, (1,1,1)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-.5
Confidence Limits
-1.0
Coefficient
1
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
Lag Number
(b) PACF Plot of Best Fitted Model Error
155
28. Diagnostic Checking For SS 2005
Error for SS from ARIMA, (2,1,2)
Error for SS from ARIMA, (2,1,2)
1.0
.5
.5
0.0
0.0
-.5
Partial ACF
1.0
ACF
Confidence Limits
-1.0
Coefficient
1
3
5
2
7
4
6
9
8
11
10
13
12
-.5
Confidence Limits
-1.0
15
14
Coefficient
1
16
3
5
2
Lag Number
7
4
9
6
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
29. Diagnostic Checking For SS 2006
Error for SS from ARIMA, (2,1,1)
Error for SS from ARIMA, (2,1,1)
1.0
1.0
.5
.5
0.0
-.5
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
7
4
6
9
8
11
10
13
12
15
14
Partial ACF
0.0
-.5
Confidence Limits
-1.0
Coefficient
1
16
3
2
Lag Number
5
7
4
9
6
8
11
10
13
12
15
14
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
(b) PACF Plot of Best Fitted Model Error
30. Diagnostic Checking For SS 2007
Error for SS from ARIMA, (1,1,1)
Error for SS from ARIMA, (1,1,1)
1.0
1.0
.5
.5
0.0
Partial ACF
0.0
-.5
-.5
Confidence Limits
ACF
Confidence Limits
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
-1.0
Coefficient
1
3
2
5
4
7
6
9
8
11
10
13
12
15
14
16
16
Lag Number
(a) ACF Plot of Best Fitted Model Error
Lag Number
(b) PACF Plot of Best Fitted Model Error