Impact Of Climate On Water Quality Of Little Yarra River At Yarra Junction

Background

There has been increase water demand on Little Yarra River at Yarra Junction due to increased agricultural and industrial activity and demand due to population growth. Climatic changes over the years have had an impact on the rivers’ water quality (Barua et al. 2013). The water quality statistics have been documented in excel and correlation done for analysis besides prediction has been done to determine the expected water quality changes in the future using the current result. The results lead to a prediction that global warming will lead to a decrease in nutrients in the water (Crutzen  et al. 2016)

Climate change is known to have a significant effect on water quality of water bodies. The central effect of climate change on water quality is credited to hydrology and changing air temperature (Schewe et al. 2014)

Persistent drought in the Victoria state has affected the surrounding water bodies including the Little Yarra river (Daly et al. 2013 ). This has necessitated the monitoring of the water bodies and a keen study on the water qualities of the area rivers.  

Water demand in Little Yarra River has increased over the years and is probable to increase in the subsequent years because of increased demand (Cole and Stewart 2013 ). Climate is the weather statistics documented after a long period of time, it’s a result of observing the variation in metrological variables such as humidity, temperature, rainfall and wind (Pindyck 2013). In this project we will analyze the Little Yarra river located at Yarra junction. The sampling will be in the duration between 1963 and 2007. Water quality is sampled weekly by Melbourne water in the Little Yarra River at Yarra junction. We will analyze the water quality parameters comprising Turbidity, COD, dissolved oxygen, toxic substances, TN, TP, PH, EC, BOD and correlate them with temperature, evaporation and rainfall. The Little Yarra river in our analysis flows in from the southeast, it goes through the Yarra junction township joining the Yarra river (Koster, Dawson and Crook 2013) 

The map shows the location of the Yarra river at the Yarra junction, its inflow and outflow.   

Pictorial representation of Little Yarra River at Yarra junction information from the bureau of meteorology website. 

This project is documented based on the climate impact on water quality in the Little Yarra river located at Yarra junction in the state of Victoria in Australia (Shenton Hart and Chan 2014 ). The data used is owned by Melbourne water. Analysis is done on the statistical data on the climate of the area and the water quality parameters. A correlation is executed using excel and the results discussed in relation to the impact climate changes has had on the water quality of the above water body (Fowler et al. 2007).

Objectives

The statistical data used in this study has been documented over a period of fifty-four years from 1963 to 2017. The climate conditions and water quality parameters to be evaluated are given below:

Period (time scope)	Climatic Factors to be evaluated	Water quality factors to be evaluated
1963-2017	Rainfall, temperature, evaporation and precipitation.	TP, TN, PH, EC, BOD, COD, dissolved oxygen, toxic substances, turbidity and water course level.

 The data above has been obtained from the Bureau of meteorology and CSIRO.   

• Rainfall

This is the amount of rain falling within a given time and in a given area, in this project our area is the Little Yarra River and the period is 1963 to 2017.

• Temperature

This is the heat intensity of a given area, in this project our area is the Little Yarra River.

• Evaporation

This is the turning of water from liquid to vapor, focus is given to the Little Yarrra river.

• Precipitation

Condensing of rain, water.

• Water quality TP (Total Phosphorous)

This is the total amount of phosphorous present in Little Yarra River.  High concentration of Nitrogen leads to a high growth of aquatic plants e.g filamentous algae, phytoplankton, macrophytes and cyanobacteria (Moss et al.  2013) 

• Water quality PH

Water PH is an indicator of the alkalinity or acidity of the water. Acidic water has a PH of 0-6, neutral water 7 and alkaline water a value of 8-14.

• Water quality TN (Total Nitrogen)

This is the total amount of Nitrogen present in the Little Yarra River. High concentration of Nitrogen leads to a high growth of aquatic plants e.g. filamentous algae, phytoplankton, macrophytes and cyanobacteria (Bellinger and Sigee 2015).

• Water quality EC (electrical conductivity)

Electrical conductivity is used to measure the salinity of water (Chhabra 2017).

• Water quality BOD (Biological Oxygen Demand)

This is the level of oxygen required by organisms in the water to break down organic materials in it.

• Water quality COD (Chemical Oxygen Demand)

This is the level of water chemical Oxygen demand.

• Water quality Dissolved oxygen

This is the amount of oxygen in a water body..

• Water quality toxic substances

This is the level of impurities in a given water body.

• Water quality turbidity

Expressed in Nephelometric Turbidity Units (NTU), it’s the level of haziness or cloudiness in water as a result of suspended solids such as algae and sediment (Ji 2017)

• Water course level

This is the level of water in a given body in this case the Little Yarra River.  

• Salinity

Is the measurement done on the level of dissolved salts in the water. Fresh water does not conduct electricity readily while saline water conducts electricity readily.

• Alkalinity

This is the measure of the buffering strength of the water to resist PH change and neutralize acids ( Hu et al. 2015)

The objectives of our project are given below:

• To correlate rainfall, temperature and evaporation with water quality parameters.
• Document longtime database to determine climatic and water quality trends.
• Determine variation in water quality between years.
• Determine the causes of water quality problems.  
• Come up with remedies to the water quality problems.
• Determine Little Yarra River water quality physical, chemical and biological properties.

The report has six chapters which are arranged as follows:

Chapter 1: Introduction

Chapter 2: Literature Review

Data Used in the Study

Chapter 3: Methodology

Chapter 4: Results

Chapter 5: Discussions

Chapter 6: Concluding remarks

Chapter 7: References 

Additionally, there is the top page, executive summary, student contributions and table of contents at the beginning of the project and Appendices at the bottom of the project paper.

Delpla et al undertook a study on rivers in Victoria state Melbourne to asses water demands and climate impact on water quality and the future of water demand. He found out that an increase in water supply to the rivers including the Little Yarra River is required, this will increase water quality, I agree with the findings of Delpla climate changes have a major effect on water quantities thus affect supply(Haddelend 2014). A study by Canadian experts on climate change due to hydrological effects shows that low flow events due to climate change will be of less concern as compared to high flow events. The study further shows that lower flow rates and higher temperatures in water during winter may result to impairment to water quality in rivers (Taylor et al. 2013).

A study by Fowler outlines that increase in biological oxygen demand (BOD), increase in total phosphorous (TP), increase in ammonium concentration and a decrease in DO concentration in a given river may be as a result of lower flow in summer (Mosley 2015). This may result in a faster algae growth. Parris indicates in his research that water quality parameters are more sensitive to changes in flow as compared to changes in the climate (van Vliet et al. 2013). He gave an example of Dissolved Oxygen (DO) concentrations being up during the ice cover periods, this is most probably as a result of reaeration of the river. In late fall and early spring Dissolve Oxygen (DO) declines. Chen further specifies that there is a decrease in phosphate levels in summer (Chen et al. 2015). In the study on climatic changes it has been stated that increase in oxygen and water temperature enhance nutrients release from sediments this leads to the water column having more nutrients (Valiela 2013)

Fowler in his journal writes that due to consistent global warming in the universe there is a change in the climate and water quality parameters. There is a notable decline in stream flows and the dissolved oxygen (DO) concentrations. The decreasing levels of DO are a major threat to ecological health in the river (Postel 2014). There is a specific worry on the effects on national endangered species such as the Macquarie perch, Australian grayling and the Murray cod.

Literature Review

There was a focus catchment project undertaken by the eWater CRC and other organisations in partnership. This organizations had an interest in the Little Yarra river and the Yarra river both in Victoria state. The objective of this study is to examine the relationships between water temperature, dissolved oxygen (DO), the response in behavior of native fish and stream flow. The organizations purposed to use the findings to protect the rivers’ environmental health. Other objectives were to utilize the water quality stream flow relationships in establishing trigger levels that would be used to guide stream flow management in the two rivers, use the filed measurements to determine the correlation between temperature, flow and dissolved oxygen and how the native fish react to changing environmental factors and a change in DO conditions and to produce  hydrological time series for dissolved oxygen modeling in the two rivers with emphasis on the Little Yarra river using the eWater source modeling software (Lagne et al.  2015). The Little Yarra river and Yarra river source modeling allowed testing of situations to explore future threats and effects of management interferences. The future threats were such as climate change, water extraction regimes and urban expansion. The outcome of the tests is shown in the graph for the Yarra river: 

The pictorial representation of the Yarra river Focus Catchment Report Summary

A thorough analysis on water quality data and historical hydrologic data was done to determine the relationship between water quality, stream flow and native fish conduct.  During the project new data was collected such as the consistent tracking of the native fish movements and the measurement of water depth (water course level). The project findings indicated that the runoff  routing through the main stretch of the rivers has a flow rag as shown below: 

The pictorial representation of the Yarra river focus catchment report summary 

The result further indicated that due to climatic changes leading to reduced rainfall the total annual run off of the two rivers was reduced. Dissolved Oxygen models for the years are presented as shown below: 

The models in all years indicate that during the cool months dissolved oxygen is higher and its lower during the warmer months (Raymont 2014). The report additionally shows that the flow of the stream has a great influence on the seasonal influences and the dissolved oxygen. Climatic changes such as wet and dry conditions were found to significantly affected dissolve oxygen levels. Extremely dry conditions in the analysis such as 2002, 2006, 2007, and 2008 led to significant reduction in DO levels. The organizations suggested that increasing the least flow to the two rivers will increase the dissolved oxygen levels this will in turn ensure the well being of ecological assets. The graph below indicates the relationship between the minimum daily DO and the average daily flow:

Findings of the Study

Yarra river summary report.

The report further stated that an increased number of aquatic animals assembled below oxygenated riffle habitants during low dissolved oxygen seasons. 

Correlation is testing whether two variables have a relationship in which one variable depends on the other (Gravetter and Wallnau 2016). There are different correlation coefficient outcomes when analyzing data. A correlation coefficient of +1 designates a positive correlation that is perfect such that as a given variable X increases variable Y increases too while a decrease in variable X makes variable Y to decrease too. A -1 Correlation coefficient indicates a negative correlation that is perfect in nature, in this case when a given variable X increases variable Y decreases and when variable X decreases variable Y increases. The last outcome is a correlation coefficient of zero, this indicates no correlation between the two variables X and Y (Cohen et al. 2013)

In this project on the Little Yarra river the correlation formula of: 

= CORREL(A2:A6,B2:B6)

Is used to find the correlation of different variables between the climatic conditions and the water quality parameters.

In this project comparison is used to compare the various parameters from water quality parameters to the given climatic conditions. Inferences are made from a keen observation of the results over the period of 1963 to 2017.

Turbidity 

Is measured by using a relationship of light reflected on the sample that is given (Jiang et al. 2014)

This is the method used to measure the salinity in the Little River Yarra.

In water phosphorous exists primarily in organic compounds or as orthophosphate (PO₄^3-). Total phosphorous is the sum of all phosphorous that exist in different forms. Phosphorous levels are determined using the vanadate method or the molybdenum blue method (Nagul et al. 2015).

This is the level of water pollution by nitrogen compounds. Nitrogen levels are determined by the measurement of the quantity of all inorganic nitrogen such as ammonia, nitrite and nitrate (Seinfeld and Pandis 2016).

The climate conditions used for this analysis are rainfall, temperature and evaporation while the water quality parameters used were water PH, Total nitrogen (TN), Electrical conductivity (EC), Biological oxygen demand BOD, chemical oxygen demand (COD), Dissolved Oxygen (DO), toxic substances and turbidity. The software used to compare data given on the span between 1963-2017 is excel sheet. A correlation is taken to compare the various years, parameters and the climatic conditions.

Since this project relies on already documented data and simulation in Microsoft excel, there is no cost on its implementation. However, there may be a purchase incurred on Microsoft excel add-ons which is subject to review.

Graphical Representation of the Yarra River

The project Gantt chart is as shown below:

Project stage	April 20	April 21	April 22	April 23	April 24	April 25	April 26
Project Abstract
Project introduction
Literature review
Methodology
Data analysis in excel
Results
Discussions
Concluding remarks
References
Appendices
Executive Summary
Table of Contents

The project was solely executed by me, comprising all the data collection, correlations and analysis.

Graph showing Yearly Statistical Analysis Graph of Little Yarra River at Yarra junction 

Data is fed on the excel sheet from the years 1963 to 2017, the data is on climatic conditions and water quality parameters as shown below: 

Pictorial representation of the raw data 

The raw data is contained in the excel sheet and the correlation is conducted in excel. In the table below, we sample some of the data contained in the excel sheet to show the variations of the temperature and water quality properties over the years:

Time	Temperature	TN	TP	PH	EC	BOD	COD	DO	TOXIC substances	TURBIDITY
1963	24.6	1.2	0.15	13.2	12.98	0.92	13.03	5	242.08	12.28
1965	19.7	1.6	1.3	7.07	13.48	0.88	14.01	3.45	219.06	11.42
1970	21.7	1.1	0.23	14	13.98	1.84	12.24	4.05	230.67	12.01
1975	32.2	1.1	0.56	5.45	13.03	0.85	13.54	8.35	264.07	13.45
1980	34.5	1.3	0.54	3.23	13.1	1.84	12.07	3.24	204.65	11.46
1985	26.1	1.3	1.5	13.22	12.65	0.79	13.05	5.25	254.92	11.23
1990	23.3	1.2	0.13	2.23	13	1.86	14.01	3.02	245.92	12.13
1995	34.2	1	0.53	12.34	11.01	0.86	12.79	7.34	252.93	12.54
2000	19.2	1.4	0.46	14.01	12.78	0.87	12.12	6.74	255.21	12.01
2005	17.2	1.6	0.54	5.67	12.07	1.56	13.19	2.89	234.13	12.91
2008	34.2	0.95	0.17	3.67	11.24	0.82	13.98	7.12	258.15	11.96
2009	33.2	1.15	0.4	12.45	13.4	0.85	13.54	8.45	245.23	12.67
2010	33.5	0.67	0.12	11.23	11.25	1.81	13.99	3.98	202.23	13.88
2011	24.5	1.15	0.75	1.55	13.22	1.92	14.24	2.67	256.73	13.79
2012	29.6	1.9	1.2	9.45	11.23	1.84	14.45	3.45	256.13	13.85
2013	25.4	1.8	0.14	8.87	12.25	1.86	14.01	3.23	260.13	13.76
2014	34.0	0.97	0.27	7.55	13.34	1.94	14.65	4.09	259.12	13.01
2015	34.6	1.13	0.17	8.45	12.34	1.91	14.85	4.5	248.59	13.92
2016	36.2	0.87	0.12	6.76	13.01	1.85	14.76	3.02	259.13	13.79
2017	33.5	1.14	0.43	8.76	11.32	1.95	14.87	3.42	258.13	13.69

The correlation between temperature and dissolved oxygen between the years 1963 to 2017 is as shown in the picture below: 

The correlation between temperature and Total Nitrogen between the years 1963 to 2017 is as shown in the picture below:

The correlation between temperature and Total phosphates between the years 1963 to 2017 is as shown in the picture below: 

The raw data is contained in the excel sheet and the correlation is conducted in excel. In the table below, we sample some of the data contained in the excel sheet to show the variations of the rainfall and water quality properties over the years: 

Time	Rainfall	TN	TP	PH	EC	BOD	COD	DO	TOXIC substances	TURBIDITY
1963	33.4	1.2	0.15	13.2	12.98	0.92	13.03	5	242.08	12.28
1965	19.1	1.6	1.3	7.07	13.48	0.88	14.01	3.45	219.06	11.42
1970	53.6	1.1	0.23	14.0	13.98	1.84	12.24	4.05	230.67	12.01
1975	17.4	1.1	0.56	5.45	13.03	0.85	13.54	8.35	264.07	13.45
1980	10.4	1.3	0.54	11.4	13.1	1.84	12.07	3.24	204.65	11.46
1985	24.6	1.3	1.5	13.22	12.65	0.79	13.05	5.25	254.92	11.23
1990	9.2	1.2	0.13	2.23	13	1.86	14.01	3.02	245.92	12.13
1995	42.3	1	0.53	12.34	11.01	0.86	12.79	7.34	252.93	12.54
2000	10.34	1.4	0.46	14.01	12.78	0.87	12.12	6.74	255.21	12.01
2005	19.2	1.6	0.54	5.67	12.07	1.56	13.19	2.89	234.13	12.91
2008	14.8	0.95	0.17	3.67	11.24	0.82	13.98	7.12	258.15	11.96
2009	25	1.15	0.4	12.45	13.4	0.85	13.54	8.45	245.23	12.67
2010	33.6	0.67	0.12	11.23	11.25	1.81	13.99	3.98	202.23	13.88
2011	37.2	1.15	0.75	1.55	13.22	1.92	14.24	2.67	256.73	13.79
2012	24.2	1.9	1.2	9.45	11.23	1.84	14.45	3.45	256.13	13.85
2013	28.2	1.8	0.14	8.87	12.25	1.86	14.01	3.23	260.13	13.76
2014	23.6	0.97	0.27	7.55	13.34	1.94	14.65	4.09	259.12	13.01
2015	36.2	1.13	0.17	8.45	12.34	1.91	14.85	4.5	248.59	13.92
2016	20.4	0.87	0.12	6.76	13.01	1.85	14.76	3.02	259.13	13.79
2017	26.2	1.14	0.43	8.76	11.32	1.95	14.87	3.42	258.13	13.69

The correlation between rainfall and PH between the years 1963 to 2017 is as shown in the picture below: 

The correlation between rainfall and Toxic substances between the years 1963 to 2017 is as shown in the picture below: 

The raw data is contained in the excel sheet and the correlation is conducted in excel. In the table below, we sample some of the data contained in the excel sheet to show the variations of the evaporation and water quality properties over the years: 

Time	Evaporation	TN	TP	PH	EC	BOD	COD	DO	TOXIC substances	TURBIDITY
1963	2800	1.2	0.15	13.2	12.98	0.92	13.03	5	242.08	12.28
1965	2900	1.6	1.3	7.07	13.48	0.88	14.01	3.45	219.06	11.42
1970	2200	1.1	0.23	14.0	13.98	1.84	12.24	4.05	230.67	12.01
1975	1500	1.1	0.56	5.45	13.03	0.85	13.54	8.35	264.07	13.45
1980	2200	1.3	0.54	11.4	3.23	1.84	12.07	3.24	204.65	11.46
1985	1600	1.3	1.5	13.22	12.65	0.79	13.05	5.25	254.92	11.23
1990	1050	1.2	0.13	2.23	13	1.86	14.01	3.02	245.92	12.13
1995	1100	1	0.53	12.34	11.01	0.86	12.79	7.34	252.93	12.54
2000	1400	1.4	0.46	14.01	12.78	0.87	12.12	6.74	255.21	12.01
2005	2400	1.6	0.54	5.67	12.07	1.56	13.19	2.89	234.13	12.91
2008	1600	0.95	0.17	3.67	11.24	0.82	13.98	7.12	258.15	11.96
2009	1800	1.15	0.4	12.45	13.4	0.85	13.54	8.45	245.23	12.67
2010	2300	0.67	0.12	11.23	11.25	1.81	13.99	3.98	202.23	13.88
2011	2800	1.15	0.75	1.55	13.22	1.92	14.24	2.67	256.73	13.79
2012	3100	1.9	1.2	9.45	11.23	1.84	14.45	3.45	256.13	13.85
2013	3200	1.8	0.14	8.87	12.25	1.86	14.01	3.23	260.13	13.76
2014	3500	0.97	0.27	7.55	13.34	1.94	14.65	4.09	259.12	13.01
2015	3400	1.13	0.17	8.45	12.34	1.91	14.85	4.5	248.59	13.92
2016	2900	0.87	0.12	6.76	13.01	1.85	14.76	3.02	259.13	13.79
2017	3600	1.14	0.43	8.76	11.32	1.95	14.87	3.42	258.13	13.69

The correlation between evaporation and Electric Conductivity between the years 1963 to 2017 is as shown in the picture below:

The raw data is contained in the excel sheet and the correlation is conducted in excel. In the table below, we sample some of the data contained in the excel sheet to show the variations of the precipitation and water quality properties over the years: 

Time	Precipitation	TN	TP	PH	EC	BOD	COD	DO	TOXIC substances	TURBIDITY
1963	265.9	1.2	0.15	13.2	12.98	0.92	13.03	5	242.08	12.28
1965	300	1.6	1.3	7.07	13.48	0.88	14.01	3.45	219.06	11.42
1970	280.2	1.1	0.23	14.0	13.98	1.84	12.24	4.05	230.67	12.01
1975	90.6	1.1	0.56	5.45	13.03	0.85	13.54	8.35	264.07	13.45
1980	93	1.3	0.54	3.23	13.1	1.84	12.07	3.24	204.65	11.46
1985	252.6	1.3	1.5	13.22	12.65	0.79	13.05	5.25	254.92	11.23
1990	266.9	1.2	0.13	2.23	13	1.86	14.01	3.02	245.92	12.13
1995	93.2	1	0.53	12.34	11.01	0.86	12.79	7.34	252.93	12.54
2000	292.1	1.4	0.46	14.01	12.78	0.87	12.12	6.74	255.21	12.01
2005	284.2	1.6	0.54	5.67	12.07	1.56	13.19	2.89	234.13	12.91
2008	92.4	0.95	0.17	3.67	11.24	0.82	13.98	7.12	258.15	11.96
2009	92.5	1.15	0.4	12.45	13.4	0.85	13.54	8.45	245.23	12.67
2010	92.1	0.67	0.12	11.23	11.25	1.81	13.99	3.98	202.23	13.88
2011	256.31	1.15	0.75	1.55	13.22	1.92	14.24	2.67	256.73	13.79
2012	245.19	1.9	1.2	9.45	11.23	1.84	14.45	3.45	256.13	13.85
2013	234.2	1.8	0.14	8.87	12.25	1.86	14.01	3.23	260.13	13.76
2014	90.1	0.97	0.27	7.55	13.34	1.94	14.65	4.09	259.12	13.01
2015	92.2	1.13	0.17	8.45	12.34	1.91	14.85	4.5	248.59	13.92
2016	96.4	0.87	0.12	6.76	13.01	1.85	14.76	3.02	259.13	13.79
2017	91.2	1.14	0.43	8.76	11.32	1.95	14.87	3.42	258.13	13.69

The correlation between precipitation and Turbidity between the years 1963 to 2017 is as shown in the picture below: 

There is an increase in water course level as evident in the raw data below:

Sampled years	Water course level
1963	0.885
1973	1.5
1983	1.05
1993	2.068
2009	2.721
2010	2.842
2011	1.542
2012	1.432
2013	1.552
2014	2.556
2015	2.523
2016	2.532
2017	2.558

Implications of the Study

The raw data is contained in the excel sheet and the correlation is conducted in excel. In the table below, we sample some of the data contained in the excel sheet to show the variations of BOD (Biological Oxygen Demand) and DO (Dissolved Oxygen) over the years:

Time	BOD	DO
1963	0.92	5
1965	0.88	3.45
1970	1.84	4.05
1975	0.85	8.35
1980	1.84	3.24
1985	0.79	5.25
1990	1.86	3.02
1995	0.86	7.34
2000	0.87	6.74
2005	1.56	2.89
2008	0.82	7.12
2009	0.85	8.45
2010	1.81	3.98
2011	1.92	2.67
2012	1.84	3.45
2013	1.86	3.23
2014	1.94	4.09
2015	1.91	4.5
2016	1.85	3.02
2017	1.95	3.42

The correlation between BOD (Biological Oxygen Demand) and DO (Dissolved Oxygen) between the years 1963 to 2017 is as shown in the picture below:

The sample below shows some of the years with the levels of EC:

Time	Electric Conductivity
1963	12.98
1965	13.48
1970	13.98
1975	13.03
1980	13.1
1985	12.65
1990	13
1995	11.01
2000	12.78
2005	12.07
2008	11.24
2009	13.4
2010	11.25
2011	13.22
2012	11.23
2013	12.25
2014	13.34
2015	12.34
2016	13.01
2017	11.32

The results indicate increased phosphorous which may result to eutrophication of the aquatic setting in Little Yarra River due to oxygen deficiency (Das 2016). When total phosphorous increases algae levels increase, too this significantly reduces the water clarity and river depth. The various amounts of algae in the water can be classified as follows:

TSI SCORE (Trophic State Index)	CLASSIFICATION OF SCORE	DESCRIPTION
0-40	Oligotrophic	This is an indicator of very clear water with no algae or just some little which blooms nutrients (Williams et al. 2013 ).
40-50	Mesotrophic	This indicates moderately clear water with algae blooming nutrients occasionally (Dittrich et al.  2013).
50-70	Eutrophic	This designates decreased clarity with algae blooming high nutrient levels (Zamparas and Zacharias 2014)
70-100	Hypereutrophic	This is an indicator of heavy algae blooming excessive nutrients with poor water clarity (Barrington,  Reichwaldt and Ghadouani 2013)

There are various causes of the variation in the water quality within the years. This causes vary from human to natural causes. Such as industrial emissions, agricultural emissions and increase in rain amounts. Water temperature and the general environmental temperature increases as a result of global warming, the water temperature increase lead to oxygen solubility reduction esulting in less dissolved oxygen (DO) concentrations (Urry 2015 ). Reduction in dissolved oxygen affect the intensity and duration of algae blooms (Paerl 2016). The decrease in DO is significant during ice cover as compared to during summer. There is an increase in DO over the years as evident in the given data.

Increase in air temperature decreased nutrient concentrations, the magnitude of the changes depended on the seasons resulting to the differences over the years (Keenan 2013). The greatest deviation is observed on nitrogen over the years, followed by phosphorous.   

Evaporation rate depends on wind speed, air temperature and cloudiness (Jensen and Allen 2016)

Global warming increase water temperature, the increase in water temperature over the years from 1963 to 2017 show that there has been gradual global warming in the Victoria area (Dai 2013). Increase in water temperature results in a decrease in ice thickness. The warm temperatures lead to an increase in concentration in nitrogen and phosphate (Paerl and Otten 2013). Predictions on the future indicate that the cover of ice may take lesser days as compared to the current voyage duration in the Little Yarra River. The decrease in nutrient concentration in some years may be as a result of the increase in phytoplankton growth e.g. algae (Valiela 2013)

In some years when there was winter there was a general decrease of phosphate concentration in the Little Yarra River (Daly et al. 2013)

Due to a gradual increase in BOD (biological oxygen demand) over the years there is a decrease in dissolve oxygen (DO) (Halliwell and Gutteridge 2015 ) 

The increase in water course levels from the year 2009 to 2017 shows that there is a risk of flooding occurring in the Yarra Junction area fue to the Little Yarra River waters (Shenton, Hart and Chan 2014). The sampled data shows that there are level water course level values in the years 1963, 1973, 1983 and 1993 as compared to recent years. This is as a result of climatic changes such as increase in rainfall.

The results of the correlation between temperature and dissolved oxygen are -0.60115 this indicate that as temperature increases DO decreases and as temperature decrease DO increases representing a negative correlation (Aboobakar et al. 2013 ).

The results of the correlation between temperature and Total Nitrogen are -0.55374 this indicate that as temperature increases TN decreases and as temperature decrease TN increases representing a negative correlation (Shen and Fan 2013).

The results of the correlation between temperature and Total phosphate are -0.40697 this indicate that as temperature increases TP decreases and as temperature decrease TP increases representing a negative correlation (Zheng et al. 2013 ).

The results of the correlation between rainfall and PH are 0.24144 this indicate that as rainfall increases PH increases and as rainfall decreases PH decreases too representing a positive correlation. This is so because rain neutralizes acids in the Little Yarra River (Shen et al. 2013).

The results of the correlation between rainfall and Toxic substances are -0.05079 this indicate that as rainfall increases Toxic substances decrease and as rainfall decreases Toxic substances increases representing a negative correlation (Kumar and Pal 2013).

The results of the correlation between precipitation and Turbidity are 0.251152 this indicate that as precipitation increases Turbidity increases and as precipitation decreases Turbidity decreases representing a positive correlation (Fabricius et al. 2013 ) Since turbidity is the measure of the haziness or cloudiness of a water an increase in condensation will result in increased haziness of the Little Yarra River (Efrem 2016).

The results of the correlation between evaporation and Electric Conductivity are 0.473088 this indicates that as evaporation increases EC increases and as evaporation decreases EC decreases too representing a positive correlation. This is attributed to the fact that an increase in evaporation leads to an increase in salinity, this results to high electric conductivity since the water conductivity depends on salinity (Rhein et al. 2013).

The results of the correlation between BOD and DO are -0.16939 this indicate that as BOD increases DO decreases and as BOD decreases DO increases representing a negative correlation. This is so because as the level of oxygen required by organisms in the water to break down organic materials increases dissolved oxygen decreases since its utilized (Manahan 2017) 

From the sampled data high levels of Electric conductivity indicate high salinity levels while Water course level increases gradually as shown in the data. This is as a result of increase in rainfall levels from 1963 to 2017.

Conclusion 

Phosphorous (TP) and nitrogen (TN) should be constantly monitored to prevent severe damage to aquatic life. Since the TP and TN levels in Little Yarra River are high waste water discharges into the river and drainage of agriculture areas into the river should be monitored. The waste water must be treated to eliminate the two before being discharged. Climate change alters the variability of flow, seasonability and availability of the Little Yarra River. Water quality parameters are highly sensitive to changes in rainfall and temperature. High temperatures result in a reduction in water nutrients a. dissolved oxygen decreased over the years due to an increase in biological oxygen demand by aquatic life. The water quality parameters are more sensitive to rainfall than changes in temperature. The gradual changes in climate over the years in the Yarra area have resulted in the changes in water quality parameters in the area. The organization manning the Little Yarra river should do more control on the parameters to protect aquatic life. 

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