HYDROLOGICAL PROCESSES Hydrol. Process. (2011) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.8057 River water quality response under hypothetical climate change scenarios in Tunga-Bhadra river, India S. Rehana 1 and P. P. Mujumdar 1,2 * 1 Department of Civil Engineering, Indian Institute of Science, Bangalore, India 2 Also at Divecha Center for Climate Change, Indian Institute of Science, Bangalore, India Abstract: Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low-flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga-Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright 2011 John Wiley & Sons, Ltd. KEY WORDS river water quality; climate change impacts; hypothetical scenarios; fuzzy waste load allocation model Received 4 June 2010; Accepted 9 February 2011 INTRODUCTION Investigations of regional and global climatic changes and variabilities and their impacts on water resources have received considerable attention in recent years. Poten- tial impacts of climate change on water availability have received much attention, but relatively fewer studies focus on changes in water quality. From a global perspec- tive, climate change is usually perceived as an increase in average air temperature. Consequent immediate reac- tion to climate change is expected to be in lake and river temperatures (Hassan et al., 1998; Hammond and Pryce, 2007). Temperature rise of 1–3 ° C is found over the past 100 years in large European rivers such as the river Rhine and River Danube (European Environment Agency, 2007). Small streams have shown an increase in winter temperature maxima in Scotland (Langan et al., 2001) and there have been large increases in temperature reported for water courses in Switzerland at all altitudes (Hari et al., 2006). Most of the chemical and bacterio- logical processes are dependent on temperature and they run faster at high temperatures, increasing the growth * Correspondence to: P. P. Mujumdar, Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India. E-mail: [email protected]rates. The direct impact of an increased temperature will hence be on water quality parameters such as dissolved oxygen (DO) and biochemical oxygen demand (BOD). Cox and Whitehead (2009) show that, under a range of United Kingdom Climate Impacts Programme (UKCIP) scenarios, DO in the River Thames will be affected in the 2080s by enhanced BOD and by the direct effects of temperature that reduces the saturation concentration for DO. Climate change results in deterioration of water quality in terms of reduction in DO concentration, high levels of which are needed to sustain aquatic life. These adverse effects will worsen with changes in river flow and increased pollutants. Changes in air temperature and rainfall can affect river flow and river water tempera- ture, the primary variables that influence water quality. Therefore, in this article, an attempt is made to assess the impacts of climate change on water quality param- eters due to the changes in river flow and river water temperature for climate change scenarios. The three basic methods for estimating the impacts of climate change on hydrological behaviour, as imple- mented in a number of earlier studies are (1) using high- resolution regional climate models (e.g. Hostetler and Giorgi, 1993; Nash and Gleick, 1993; Malmaeus et al., 2006); (2) using general circulation models (GCMs) Copyright 2011 John Wiley & Sons, Ltd.
River water quality response under hypothetical climatechange scenarios in Tunga-Bhadra river, India
S. Rehana1 and P. P. Mujumdar1,2*1 Department of Civil Engineering, Indian Institute of Science, Bangalore, India
2 Also at Divecha Center for Climate Change, Indian Institute of Science, Bangalore, India
Abstract:
Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authorsin the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change inclimate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary inputvariables that can be considered for such an analysis are streamflow and water temperature, which are affected by changesin precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming andstreamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flowand river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flowduration exceedance percentage (e.g. Q90), single low-flow indices (e.g. 7Q10, 30Q10) and relationships between climaticvariables and surface variables. For the study region of Tunga-Bhadra river in India, low flows are found to be decreasingand water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found inrecent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model,QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for riverwater temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in waterquality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature andflows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity toimprove the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examinedthrough a fuzzy waste load allocation model developed earlier. Copyright 2011 John Wiley & Sons, Ltd.
KEY WORDS river water quality; climate change impacts; hypothetical scenarios; fuzzy waste load allocation model
Received 4 June 2010; Accepted 9 February 2011
INTRODUCTION
Investigations of regional and global climatic changes andvariabilities and their impacts on water resources havereceived considerable attention in recent years. Poten-tial impacts of climate change on water availability havereceived much attention, but relatively fewer studiesfocus on changes in water quality. From a global perspec-tive, climate change is usually perceived as an increasein average air temperature. Consequent immediate reac-tion to climate change is expected to be in lake andriver temperatures (Hassan et al., 1998; Hammond andPryce, 2007). Temperature rise of 1–3 °C is found overthe past 100 years in large European rivers such as theriver Rhine and River Danube (European EnvironmentAgency, 2007). Small streams have shown an increase inwinter temperature maxima in Scotland (Langan et al.,2001) and there have been large increases in temperaturereported for water courses in Switzerland at all altitudes(Hari et al., 2006). Most of the chemical and bacterio-logical processes are dependent on temperature and theyrun faster at high temperatures, increasing the growth
* Correspondence to: P. P. Mujumdar, Department of Civil Engineering,Indian Institute of Science, Bangalore 560 012, India.E-mail: [email protected]
rates. The direct impact of an increased temperature willhence be on water quality parameters such as dissolvedoxygen (DO) and biochemical oxygen demand (BOD).Cox and Whitehead (2009) show that, under a range ofUnited Kingdom Climate Impacts Programme (UKCIP)scenarios, DO in the River Thames will be affected inthe 2080s by enhanced BOD and by the direct effectsof temperature that reduces the saturation concentrationfor DO. Climate change results in deterioration of waterquality in terms of reduction in DO concentration, highlevels of which are needed to sustain aquatic life. Theseadverse effects will worsen with changes in river flowand increased pollutants. Changes in air temperature andrainfall can affect river flow and river water tempera-ture, the primary variables that influence water quality.Therefore, in this article, an attempt is made to assessthe impacts of climate change on water quality param-eters due to the changes in river flow and river watertemperature for climate change scenarios.
The three basic methods for estimating the impactsof climate change on hydrological behaviour, as imple-mented in a number of earlier studies are (1) using high-resolution regional climate models (e.g. Hostetler andGiorgi, 1993; Nash and Gleick, 1993; Malmaeus et al.,2006); (2) using general circulation models (GCMs)
Copyright 2011 John Wiley & Sons, Ltd.
S. REHANA AND P. P. MUJUMDAR
through statistical downscaling techniques (e.g. Wilbyand Wigley, 1997); and (3) using hypothetical scenariosas input to hydrologic models (e.g. Nemec and Schaake,1982; Gleick, 1986; Arnell, 1992; Xu, 2000; Jiang et al.,2007).
The preferred climate scenarios are usually thosederived from the GCMs. Given the deficiencies ofGCM predictions and downscaling techniques, the useof hypothesized scenarios as input to catchment-scalehydrological models is widely used. In addition, for theinitial stage of impact studies, hypothetical scenariosare preferred by several researchers (Bobba et al., 1999;Mimikou et al., 2000; Xu, 2000; Yu et al., 2002). Sim-ple alteration of the present conditions or hypotheticalscenarios may be chosen for key climatic parameters,covering a reasonable range of possibilities. Changes intemperature and precipitation, independently or simulta-neously, are considered to perform a sensitivity analysisof regional water resources under varying climatic con-ditions. For the evaluation of effects of climate changeon flow regime, the climate inputs to a rainfall–runoffmodel are perturbed and the effect on a statistic of themodelled flows is examined. Generally, hypothetical sce-narios assume that air temperature will rise by 0Ð5–4 °Cand precipitation will change by š10–25% (Mimikouet al., 1991; Xu, 2000; Jiang et al., 2007); some alsospecify changes in evaporation (Arnell, 1992).
Following this perspective, an attempt is made toevaluate the regional climate change impacts on waterquality issues using hypothetical scenarios by analysingtheir sensitivity to plausible climatic change scenarios.Plausible hypothetical scenarios of river temperature andstreamflow were assumed to represent potential climaticchange and were used as inputs to the water qualitymodel. The derivation of those scenarios was made bysuperimposing uniform �20, �10 and 0% changes in thehistorical annual time series of streamflow and uniformC1 and C2 °C increases in the respective historicalair temperature time series. The motivation behind theselection of decreasing streamflow scenarios is only dueto the observation of decreasing trends from historicaldata analysis. For each of the six pairs of temperatureand streamflow scenarios, climatically affected river flowand air temperature time series were produced.
In water quality, water temperature is a key elementthat affects the health of a fresh water ecosystem. Twotypes of models have been developed in the past to pre-dict stream temperatures: empirical and regression mod-els (Stefan and Preud’homme, 1993; Webb and Walling,1993; Webb and Nobilis, 1997; Mohseni et al., 1998; Pil-grim et al., 1998; Erickson and Stefan, 2000) and phys-ical process models (Edinger et al., 1968; Cooter andCooter, 1990; Stefan and Sinokrot, 1993). Traditionally,water temperature has been related to air temperature as asurrogate for net heat exchange and as an approximationto equilibrium temperature (e.g. Smith, 1972). Althougha mechanistic temperature model could give very accu-rate results, this type of model requires large amounts ofdetailed input data and is also computationally intensive.
Due to their computational feasibility and ease of imple-mentation, regression models can be used to obtain therelation between air and water temperature. In some ear-lier studies (e.g. Stefan and Preud’homme, 1993; Pilgrimet al., 1998; Erickson and Stefan, 2000; Neumann et al.,2003), a linear regression is used to relate air temperatureand water temperature. Therefore, in this article, a simplelinear regression is used for the prediction of river watertemperature, for the modified air temperature scenarios,to develop hypothetical water temperature scenarios.
The main objective of this study is to examine waterquality parameters that are affected by plausible climatechange scenarios, based on the historical data assessmentof hydroclimatic variables. To start with, historical datais assessed to see whether there is a change in thehydroclimatic parameters in recent years with low flowvalues of 7Q10, 30Q10 and flow duration curves (FDCs).7Q10 is the minimum average 7-day flow once every10 years (Thomann and Mueller, 1987) or otherwise itcan be interpreted as the 7-day low flow with a 10-yearreturn period using daily flow data. It is an adequatestreamflow for maintaining a healthy ecosystem. Suchtype of low-flow analysis is also done with 30Q10. 30Q10is defined in a similar way as 7Q10, as 30-day averagelow flows with a 10-year return period.
Trends in river temperature and air temperature wereanalysed. For the selected scenarios of streamflow andwater temperature, the responses of water quality param-eters are obtained using a water quality simulation model,QUAL2K. The resulting water quality parameters [DO,DO saturation, BOD, total organic carbon (TOC), pH,alkalinity, conductivity and river water temperature] forsuch scenarios were compared with the present con-ditions. The water quality indicators are also obtainedwhen the dischargers are at safe permissible levels usingQUAL2K for each hypothetical climate change scenario.The optimal fractional removal levels are evaluated usinga Fuzzy waste load allocation model (FWLAM) devel-oped by Sasikumar and Mujumdar (1998) for each sce-nario and compared with the present conditions.
CASE STUDY AND METHODOLOGY
The Tunga-Bhadra river stretch is used to study theimpact of climate change on water quality. Tunga-Bhadrais a perennial river formed by the confluence of twotributaries of river Krishna, Tunga and Bhadra. TheBhadra reservoir project intercepts the Bhadra river flowand provides water for irrigation and also to generatehydropower to a minor extent. The Bhadra river flowwhich is downstream of the reservoir is considered in thisstudy and is governed by release from Bhadra reservoir.As the reservoirs cause negative effects on the regulatedreaches of rivers, sufficient water should be releasedfrom the reservoirs to ensure a healthy downstreamenvironment. The available records show that a constantmonthly downstream release of 9 M.cu.m exclusive ofother releases has been made throughout the data period
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
Tunga
Bhadra
1 2
3
4
MPML VISL
Shimoga City Sewage
Bhadravathi City
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3
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7
Harihar City Sewage8
9 8 9 10
10
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HP
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Tunga-Bhadra River
Head Water Flow
Point Load
Reach
Reach End point
Check pointHaridra
12
13
11
7
Honnali City Sewage
16
MPM Mysore Paper Mill
VISL Vishveshwaraya Iron and Steel Limited
HPF Harihara Poly Fibers
BhadraReservoir
Lakavalli
Harlahalli
Kuppelur
Byladahalli
Figure 1. Schematic diagram of Tunga-Bhadra river
for maintaining the minimum downstream river flow.Babu and Kumara (2009) compared actual annual averagereleases with environmental flow rates computed fromTenant Law for 32 years and found that for only 10 years,the environmental flow rate met the requirement inBhadra Reservoir. In this study, the streamflow of Bhadrariver considered is at the gauge station Lakkavalli, whichis a village near Bhadra reservoir. The schematic diagramof the Tunga-Bhadra river is shown in Figure 1. The riverhas two other tributaries, Kumadavati and Haridra. Theyjoin Tunga-Bhadra river from west and east directions ata distance of 84 and 124 km downstream of the junction,respectively.
To study the impact assessment for river water qualityusing hypothetical climate change scenarios, initiallythe historical data have to be analysed to assess thechanges in the hydroclimatic variables as discussed inSection on Historical Data Analysis. Then the climatevariables and the corresponding surface variables affecteddue to climate change have to be selected. It is wellknown that the primary climate variables affected byglobal warming are precipitation and air temperature.The surface variables input to a water quality modelare streamflow and water temperature. Therefore, inthis study, precipitation, streamflow, air temperature andwater temperature are selected for the data analysis.
The climate variables and water quality input variableshave to be related to find the future climate scenarios.Therefore, a simple linear regression is used to relateair temperature with water temperature as given inSection on Relating Climate Variables to Water QualityVariables. After selecting the hydroclimatic variables,climate change scenarios have to be selected for thosevariables, hypothetically or based on GCM output. Inthis study, hypothetical scenarios are developed based ondata analysis of streamflow, air and water temperatures.
A water quality simulation model has to be used for asensitivity analysis of the water quality variables for theselected scenarios. In this study, QUAL2K is used tosimulate the water quality parameters. Finally, the resultsare compared with the present base line conditions.
HISTORICAL DATA ANALYSIS
Hypothetical scenarios are often based on long-termobservations or historical data. To select a reasonablerange for the scenarios of streamflow, the historical dataare analysed. To have an overview of the changes insteamflow, historical data are analysed and a significantdecrease in annual mean values is found for the past fewyears for the Tunga-Bhadra river, as given in Table I. Themean streamflow decrease is from 3Ð1% (reduction forShimoga) to 24Ð16% (reduction for Byladahalli) for thelast 10–15 years along Tunga-Bhadra river. The reducedannual mean flows produce similarly large reductions inlow flows, as illustrated in Figure 2, where 7Q10 valuesover the two periods, 1971–1991 and 1992–2006, werecompared.
The 7Q10 values computed at Shimoga along Tungariver are found to reduce from 0Ð052 to 0Ð002 cumecs forthe period from 1971–1991 to 1992–2006. Similarly, atHonnali along Tunga-Bhadra river, the reduction in lowflow value of 7Q10 is from 2Ð65 to 0Ð009 cumecs for
Table I. Reduction in annual average flows
Station Period Percentage reductionin annual mean flow
Shimoga 1971–1991 to 1992–2006 3Ð1Honnali 1980–1990 to 1991–2006 12Ð26Kuppelur 1991–1999 to 2000–2006 16Ð8Byladahalli 1985–1995 to 1996–2005 24Ð16
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
S. REHANA AND P. P. MUJUMDAR
10 20 30 40 50 60 70 80 90
0
0.5
1
1.5
2
Percent of time flow is equal to or less than
10 20 30 40 50 60 70 80 90 100
Percent of time flow is equal to or less than
10 20 30 40 50 60 70 80 90
Percent of time flow is equal to or less than
100 20 30 40 50 60 70 80 90 100
Percent of time flow is equal to or less than
0 10 20 30 40 50 60 70 80 90
Percent of time flow is equal to or less than
10 10020 30 40 50 60 70 80 90
Percent of time flow is equal to or less than
Flo
w in
cum
ecs
0
0.5
1
1.5
2
Flo
w in
cum
ecs
7Q10 Value for the Period of 1971-1991
7Q100.052
7Q10 Value for the Period of 1992-2006
7Q100.00
(a)
(b)
(c)
05
10152025
20
25
30354045
Flo
w in
cum
ecs
0
5
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15
Flo
w in
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ecs
20
0
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10
15
Flo
w in
cum
ecs
05
10152025303540
Flo
w in
cum
ecs
7Q10 Value for the Period of 1980-1991
7Q102.65
7Q10 Value for the Period of 1992-2006
7Q100.009
7Q10 Value for the Period of 1971-1991
7Q100.164
7Q10 Value for the Period of 1992-2005
7Q100.15
10 20 30 40 50 60 70 80 90 100
Percent of time flow is equal to or less than
0
0.2
0.4
0.6
0.8
1
1.2
Flo
w in
cum
ecs
7Q10 Value for the Period of 1985 to 1995
7Q100.027
(d)
Figure 2. Low-flow analysis (7Q10) values: (a) Shimoga along Tunga river, (b) Harlahalli along Tunga-Bhadra, (c) Honnali along Tunga-Bhadra and(d) Byladahalli along Haridra river
the period from 1980–1991 to 1992–2006 cumecs. AtByladahalli along Haridra river, the reduction in 7Q10value from historical to recent years is from 0Ð027 to0Ð00 cumecs. Such type of low-flow analysis is also donewith 30Q10 for the same stations along Tung-Bhadrariver. 30Q10 is defined in a similar way as 7Q10, as30-day average low flows with a 10-year return period.It is found that there is significant reduction in the 30Q10flow values also.
Another informative means for characterization of riverdischarge is the FDC. FDC relates flow to the percentageof the time that it is exceeded in the record. A flowduration curve is plotted using flow on a logarithmic scaleas the ordinate and percentage of time discharge exceededon a probability scale as the abscissa. To determine themagnitudes of the change in low flows, FDC for theentire historical data at various locations of river arecompared with the recent years as shown in Figure 3.
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
0 20 40 60 80 100 0 20 40 60 80 10010-2
100
102
104
% Time flow equaled or exceeded
Flo
w in
Cum
ecs
FDC for period of 1971 to 2005FDC for period of 1971 to 1991FDC for period of 1992 to 2005
90 95 1000
5
10
15Low Flows
% Time flow equaled or exceeded
FDC for period of 1971 to 1991FDC for period of 1992 to 2006FDC for period of 1971 to 2006
94 96 98 1000
5
10
15
20Low Flows
(a)
0 20 40 60 80 10010-2
100
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% Time flow equaled or exceeded
0 20 40 60 80 100
% Time flow equaled or exceeded
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w in
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ecs
(c)
10-4
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Flo
w in
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ecs
(d)
10-2
100
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Flo
w in
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ecs
(b)
FDC for period of 1972 to 1991
FDC for period of 1992 to 2006
FDC for period of 1972 to 2006
90 95 1000
0.5
1
1.5Low Flows
FDC from Period of 1985 to 1995
FDC from Period of 1995 to 2005
FDC from Period of 1985 to 2005
60 80 1000
2
4
6Low Flows
Figure 3. Flow duration curves (FDCs) at (a) Harlahalli along Tunga-Bhadra, (b) Honnali along Tunga-Bhadra, (c) Shimoga along Tunga and(d) Byladahalli along Haridra
From Figure 3(a), it is seen that the Q90 value reducesfrom 13Ð5 cumecs (for the period from 1971 to 1999, blueline) to 5Ð5 cumecs (for the period from 1992 to 2005,red line) at Harlahalli along Tunga-Bhadra river.
The black line shows the FDC curve for the entireperiod of historical data. Similarly, the reduction in Q90values from historical to recent years for Honnali isfrom 25Ð2 to 23Ð4 cumecs (Figure 3(b)), at Shimoga,the reduction in Q90 value is from 1Ð2 to 0Ð45 cumecs(Figure 3(c)), for Haridra river along Byladahalli, thereduction in Q60 values are from 3Ð7 to 0Ð00 cumecs(Figure 3(d)). Q60 is considered for Haridra, as all theQ90 values are almost zero.
From the flow analysis performed by dividing theavailable data into two sets of historical and recent yearsand then comparing the flows, it is evident that low flowsare reducing significantly.
To study the trends (increasing or decreasing) ofthe streamflow, air temperature and water temperature,Mann–Kendall (MK test) nonparametric trend analysisis carried out for each station. Decreasing trends areobserved for streamflow at all stations at the 5% sig-nificance level. Air temperature and water temperatureshowed increasing trends at the 5% significance level.The trend significance level along with the MK valuesfor the station Honnali and Shimoga are shown in theFigure 4. A negative MK value indicates decreasing trendand a positive MK value indicates increasing trend.
1 2-10-8-6-4-202468
10
Station
MK
Val
ue
Air TemperatureWater TemperatureStreamflow5% Significance level
Figure 4. Z values and significance level of Mann–Kendall trend analysisfor streamflow, air temperature and water temperature for stations
(1) Honnali and (2) Shimoga
Furthermore, the changes in air temperature and watertemperature in recent years with respect to historicaldata are compared for two periods: 1988–1999 and2000–2006, as given in Table II. A significant increasein air temperature and consequent increase in watertemperature can be observed in recent years. The increasein air temperature is about 0Ð215 °C (Shimoga) to 1Ð39 °C(Kuppelur) and the increase in water temperature is about0Ð6 °C (Shimoga) to 3Ð34 °C (Honnali) along Tunga-Bhadra river.
As a result of decrease in annual average and low flowsand increase in water and air temperatures, the waterquality is reduced in recent years. For the reduction in
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
S. REHANA AND P. P. MUJUMDAR
Table II. Change in hydroclimatic variables in recent years
Station Variable Period Annual mean change
Shimoga Air temperature 1988–1999 to 2000–2006 Increase by 0Ð215 °CWater temperature 1988–1999 to 2000–2006 Increase by 0Ð599 °C
Honnali Air temperature 1988–1999 to 2000–2006 Increase by 0Ð315 °CWater temperature 1988–1999 to 2000–2006 Increase by 3Ð34 °C
Kuppelur Air temperature 1991–2001 to 2002–2006 Increase by 1Ð39 °CWater temperature 1991–2000 to 2002–2006 Increase by 1Ð79 °C
0.50
1.50
2.50
3.50
4.50
5.50
6.50
7.50
1 3 6 9 10 11 12 13 14
Dis
solv
ed O
xyg
en (
mg
/L)
Check Point
1988-1999
2000-2006
20.00
25.00
30.00
35.00
40.00
45.00
50.00
Riv
er W
ater
Tem
par
atu
re (
°C)
1988-1999
2000-2006
2 4 5 7 8 1 3 6 9 10 11 12 13 14
Check Point
2 4 5 7 8
Figure 5. Annual average water quality indicators
low flows as given in Table I and increase in temperaturesas given in Table II, the reduction in water qualityis estimated and compared for the periods 1988–1999and 2000–2006. The effluent data of the pollutants areassumed the same for both the periods (i.e. 1988–1999and 2000–2006). Details of the historical effluent dataalong with their concentrations are given in Sectionon Model Application Under Future Scenarios. Thereduction in DO from 1988–1999 to 2000–2006 iscomputed using the water quality simulation model,QUAL2K, developed by the United States EnvironmentalProtection Agency (USEPA). The resulting water qualitylevels for the periods of 1988–1999 and 2000–2006 havebeen compared as shown in Figure 5. The maximumreduction in DO level is about 2Ð1 mg/l at check point3, and a significant reduction is found for the remainingcheck points also. The decreases are more pronouncedwhen the river is subjected to effluent discharges. Theaverage reduction in the DO levels of all check points isabout 0Ð41 mg/l and this is the average decrease in DOlevel for the considered river stretch. Therefore, it can beconcluded that there is a reduction in water quality alsoin recent years. The decline in DO predicted is due toincrease in river temperature and reduction in flows. Forthe same periods, the simulated river water temperatureswere also calculated (Figure 5) and the increase in watertemperature is found to be 3Ð37 °C at check point 14.
It is clear from data analysis that flows are decreasingand water temperature is increasing due to the increasein air temperature. Consequent adverse effect on waterquality is also observed. Efforts are made in the following
section to relate climate variables with surface variablesto find their relationships.
Relating climate variables to water quality variables
The relationship between hydroclimatic variables ofthe river was studied by computation of correlation coef-ficients between them. The climatic and hydrologicalvariables considered are precipitation, average air tem-perature, streamflow and water temperature. Table IIIreveals the correlations of hydroclimatic variables ofTunga-Bhadra river, which are significant at 90% con-fidence level. The correlation results show that there isa significant negative correlation of air temperature withprecipitation and a positive correlation of precipitationwith streamflow. With a positive correlation between airtemperature and water temperature, it is clear that therewill be an increasing effect on river water temperaturesdue to increase in air temperatures. Correlation of watertemperature with streamflow shows a significant nega-tive correlation, but with rainfall shows a negative poorcorrelation. Furthermore, to establish a relation betweenair temperature and water temperature, a linear regres-sion relationship is fitted using daily timescale data atShimoga along Tunga river and at Honnali along Tunga-Bhadra with the regression equations as follows:
WT D 3Ð03 C 0Ð79 AT with R2 D 0Ð53 for Shimoga
WT D 1Ð75 C 0Ð86 AT with R2 D 0Ð57 for Honnali
where WT is the water temperature and AT the airtemperature.
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
Table III. Correlations of hydroclimatic variables
Shimoga Harlahalli Honnali
Parameter Airtemperature
Rainfall Streamflow Airtemperature
Rainfall Streamflow Airtemperature
Rainfall Streamflow
Air temperature 1Ð0 �0Ð97 �0Ð36 1Ð0 �0Ð11 �0Ð33 1Ð0 �0Ð13 �0Ð50Rainfall �0Ð97 1Ð0 C0Ð33 �0Ð11 1Ð0 C0Ð14 �0Ð13 1Ð0 C0Ð32Streamflow �0Ð36 C0Ð33 1Ð0 �0Ð33 C0Ð14 �0Ð50 �0Ð50 C0Ð32 1Ð0Water temperature C0Ð54 �0Ð040 �0Ð389 a a a C0Ð66 �0Ð038 �0Ð318
AT D change in air temperature; F D change in streamflow.
CLIMATE CHANGE SCENARIOS
In this study, the water quality response was simulated forthe present climate conditions as well as six hypotheticalclimate change scenarios. The six scenarios, as givenin Table IV, are considered based on the literature (e.g.Arnell, 1992; Xu, 2000; Jiang et al., 2007) and fromhistorical data analysis done in this study.
From the scenarios of air temperature, scenarios ofwater temperature are estimated based on regressionequations fitted between air and water temperature asdescribed in Section on Relating Climate Variables toWater Quality Variables. The next section describesthe water quality responses to the scenarios of climatechange.
MODEL APPLICATION UNDER FUTURESCENARIOS
Configuration of the river
Based on the river morphology, a 200-km-long stretchof the Tunga-Bhadra river is divided into 16 reaches ofvarying lengths, each one of which is further discretizedinto computational elements of 1 km in length. Theriver receives the waste loads from eight major effluentpoints, which include five industrial effluents and threemunicipal effluents. The original sources of the dataare Central Water Commission (CWC), Karnataka StateWater Resources Development Organization (KSWRDO)and Karnataka State Pollution Control Board (KSPCB).The details of river parameters and configuration aregiven in Table V. For the industries and for municipaleffluents, the KSPCB limits the effluent BOD as 15 mg/land the details of the effluents are given in Table VI. Theannual average flows of Tunga, Bhadra, Kumudavathiand Haridra are given as 162Ð93, 17Ð76, 10Ð19 and11Ð54 m3/s, respectively.
The water quality simulation model, QUAL2K, usedin this study to simulate the water quality, is a river
Table V. Hydraulic variable values used in water quality simula-tions
Source of data: Ł Karnataka State Pollution Control Board (KSPCB).ŁŁ Sumithra and Narayana (2003).
and stream water quality model and is a modernizedversion of the QUAL2E (Brown and Barnwell, 1987).QUAL2K has been applied worldwide for the evaluation
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
F D streamflow (m3/s); AT D air temperature (°C); WT D water temperature (°C); IF D incremental flow (m3/s/km) used for the entire stretch foraccounting for nonpoint source pollution.
of surface water quality and to estimate the impactsof pollutants on water quality indicators, such as DO.Detailed information about QUAL2K can be obtainedfrom the study by Chapra and Pelletier (2003). There area number of publications on the application of QUAL2K(e.g. Park and Lee, 2002; Fang et al., 2008; Fan et al.,2009).
The following features characterize QUAL2K:
1. One dimensional (the channel is well-mixed verticallyand laterally)
3. Diurnal heat budget (the heat budget and temperatureare simulated as a function of meteorology on a diurnaltime scale)
4. Diurnal water quality kinetics (all water quality vari-ables are simulated on a diurnal time scale)
5. Heat and mass inputs (point and nonpoint loads andabstractions are simulated).
In the QUAL2K model, the river is divided intoreaches and each reach can be further divided into a seriesof equally spaced elements. A steady-state flow balanceis implemented for each model reach as follows:
Qi D Qi�1 C Qin,i � Qout,i
where Qi is the outflow from element i into the down-stream element i C 1 (m3/s), Qi�1 the inflow from theupstream element i � 1 (m3/s), Qin,i is the total inflowinto the element from point and nonpoint sources (m3/s)and Qout,i is the total outflow from the element due topoint and nonpoint withdrawals (m3/s).
To account for nonpoint source pollution, a high valueof 30 mg/l for BOD and a low value of 4 mg/l forDO are used for the incremental flow in the analy-sis. The value of incremental flow is calculated basedon the gauge stations located at Bhadra (Reach 1),Tunga (Reach 4) and Tunga-Bhadra (Reach 7) rivers.The difference between sum of the flows at the Bhadraand Tunga gauge stations and Tunga-Bhadra gauge sta-tions is used to obtain the distributed load per unitdistance, which is 0Ð89 m3/s/km in the present case.
This value is used as incremental flow throughoutthe river stretch to account for nonpoint source pol-lution due to runoff (Subbarao and Mujumdar, 2004;Rehana and Mujumdar, 2009). As the incremental flowcomputed in this study is based on the streamflow,scenarios for incremental flow are also computed forthe hypothetical scenarios of streamflow as given inTable VII.
In Qual2K, the reach rates can be prescribed or theycan be computed as a function of the river’s hydraulics.First, the coefficients of reaeration and deoxygenationshould be corrected for the changed scenarios of stream-flow and water temperature, which can be done usingO’Conner–Dobbin (1956) and Maidment (1993) formu-lae, respectively, for each reach.
Deoxygenation rate at 20 °C temperature is given by(Maidment, 1993)
�Ka�20 D 1Ð80∑
V�0Ð49 �1�
where∑
VUe is total flow including streamflow andeffluent flow in the reach Ue (m3/s). Deoxygenation rate,�kd�20 (1/ day), at any temperature in reach Ue is givenby
�Kd�T D �Kd�20υ�T�20� �2�
where υ D 1Ð024 is the temperature correction factor(Camp, 1963).
The reaeration rate at 20 °C temperature is given by(O’Conner and Dobbins, 1956),
�Ka�20 D D0Ð5r U0Ð5H�1Ð5 �3�
Reaeration rate at any temperature T °C is given by
�Ka�T D �Ka�20υ�T�20� �4�
where υ D 1Ð047 is the temperature correction factor(Camp, 1963), Dr the diffusivity coefficient of oxy-gen, for natural waters, it is approximately equal to2Ð09 ð 10�5 cm2 s�1 (Chapra, 1998); U is the streamvelocity (ms�1) and H is the average depth of flow, whichcan be obtained from Manning’s formula. If the geomet-ric cross section of the river is assumed to be rectangularand the depth of the flow is assumed to be small when
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
compared with the river width, this results in the follow-ing functional relationship:
�Ka�20 D 3Ð93 S3/5W7/10n�6/5∑
V�0Ð7 �5�
The functional relationships of deoxygenation (Equa-tion (1)) and reaeration (Equation (4)) are dependenton streamflow, water temperature and effluent flow, ifriver geometry is assumed to be unchanged. In thisstudy, the effluent flows are kept constant in the com-putation of scenarios. Therefore, the rates are com-puted for the changed scenarios of streamflow and watertemperature only. The computed rates of deoxygena-tion and reaeration in response to the scenarios, foreach reach, are input to the water quality simulationmodel.
Both reaeration and decay coefficients are significantlyaffected for all the scenarios, the reason for which isthe rising temperature. The rise in water temperatureincreases the algal growth rates, i.e. decay and respiration,whereas decrease in streamflow rate reduces the mixingas well as dilution and thereby decreases the reaerationrate. The water temperature affected drivers are not onlyreaeration and BOD decay; in general, the impact willalso be on photosynthesis and sediment oxygen demand(SOD) and so on. However, due to data limitations,this study is limited to BOD decay and reaerationcoefficients.
Using QUAL2K, the water quality variables are sim-ulated for the present climatic conditions and for thescenarios. Six sensitivity tests have been run which com-bine all the scenarios represented in Table IV. For thechanged scenarios of air temperature, streamflow, watertemperature, deoxygenation, reaeration and incrementalflow, the resulting water quality variables were com-puted and compared with the present water quality vari-ables.
The changing scenarios of climate will influence futurewater quality responses, as a consequence, the adaptationpolicies of the dischargers and pollution control agency(PCA) should be updated to account for future climatechange to save the rivers from deterioration. For thehypothetical climate change scenarios, the correspond-ing resulting fractional removal levels for each dischargerwere evaluated using a FWLAM developed by Sasiku-mar and Mujumdar (1998). A brief description about theFWLAM is given in the following section and the detailscan be found in the study by Sasikumar and Mujumdar(1998).
FUZZY WASTE LOAD ALLOCATION MODEL
Sasikumar and Mujumdar (1998) proposed FWLAMaddressing the issue of imprecision in a multiobjec-tive waste load allocation problem. Uncertainty due toimprecision associated with the objectives and waterquality standards of PCA and dischargers are modelledin a fuzzy framework. A fuzzy optimization problem
incorporating the fuzzy membership functions is for-mulated with an objective of maximizing the minimumsatisfaction level of the PCA and dischargers in thesystem.
Formulation of FWLAM
Maximize � �6�
cl � cLl
cDl � cL
l
½ � 8 l �7�
xMm � xm
xMm � xL
m
½ � 8 m �8�
cLl � cl � cD
l 8 l �9�
xLm � xm � xM
m 8 m �10�
0 � � � 1 �11�
where � is the minimum goal fulfilment level of PCAand dischargers (defined through constraint 13) and is tobe maximized in the optimization model; cl is the waterquality indicator, DO, at check point l. The value ofcL
l and cDl are the minimum permissible and desirable
levels, respectively, in the above optimization model(Equations (7) and (9)) are considered from Rehana andMujumdar (2009); xm is the fractional removal level forthe discharger m; xL
m and xMm are the minimum aspiration
level and maximum acceptable fractional removal levels,respectively, for the discharger, m, given as xL
m D 0Ð30and xM
m D 0Ð95.The other input variables for the optimization model
are as given in Table VII. The water quality simulationmodel along with the fuzzy optimization model are runsix times for the evaluation of optimal fractional removallevels for six scenarios as shown in Table IV. FWLAMis used to determine how the optimal fractional removalswill be affected by each of the scenario. The effluentloadings are kept constant (as given in Table VI) whilerunning the FWLAM for all six scenarios.
RESULTS AND DISCUSSION
Water quality responses were compared with the presentconditions to evaluate the changes in water qualityparameters in response to the altered hypothetical climatescenarios. Even though it is quite obvious that thehypothetical scenarios of increase in water temperatureand decrease in river flow will lead to deteriorationof water quality, the aim of this study is to quantifysuch deteriorations using a water quality simulationmodel. Therefore, the present water quality levels arecompared with the resulting water quality levels inresponse to the hypothetical scenarios to elucidate thechanges in river water quality due to climate change. Themaximum reduction in DO levels from present conditionsfor the various scenarios is for scenario 6 (scenariowith increasing temperature of 2 °C and reduction in
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
S. REHANA AND P. P. MUJUMDAR
Figure 6. Comparison of changes in (a) dissolved oxygen level and (b) water temperature at some critical check points in response to change instreamflow for a given temperature change
streamflow of 20%). The maximum reduction in DO isabout 1Ð06 mg/l at a length of 171 km, from 5Ð31 to4Ð25 mg/l for scenario 6. It is the location downstream ofdischarger 3 with BOD of 279 mg/l. The impact is alsofound to be more at locations that are at the downstreamof the high BOD loadings. Therefore, the comparisonstudy is mainly focused at some critical check pointsonly. The percentage changes in water quality variablesof DO and water temperature at some critical checkpoints along the river in response to the increase inwater temperature of 1 and 2 °C for a given increase instreamflow of 0, 10 and 20% are shown in Figure 6(a)and (b), respectively. These critical check points (2, 4,9, 11 and 13) are the locations immediately downstreamof the effluents, where the quality levels may go downdrastically. The other critical check points considered forthe analysis are check points 5 (confluence point of Tungaand Bhadra rivers) and 8 (middle point of the river).The remaining check points are not very sensitive asthe dilution is prominent for the released pollutants. Theresults of the water quality scenarios for the historicaldata of effluents indicate that there is significant impact
on DO, BOD, DO saturation and TOC due to the climatechange, as these parameters are influenced by the changesin the water temperature.
A negative percentage change is observed for DOlevels, which is more for scenarios with 2 °C increasein temperature compared to an increase of 1 °C, with thegiven percentage changes in streamflow along variouscheck points as shown in Figure 6(a). For the remainingwater quality indicators, the difference between 1 and2 °C increased temperature scenarios is not very high asobserved for DO. Positive percentage changes can beobserved for water temperature from Figure 6(b), withmaximum increase in water temperature of about 4Ð8 °Cfor scenario 6. Unlike that obtained for DO, a significantincrease in water temperature (1Ð44 °C) is found forscenario 3, even though there is no change in streamflow.Significant negative percentage changes of 5Ð54 and 7Ð17are observed for DO saturation for 1 and 2 °C increase inwater temperature respectively at check point 2. FromFigure 6(a) and (b), it can be seen that water qualityparameters are more sensitive to the temperature thanto streamflow. The water quality parameters that are
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
more sensitive to the climate change scenarios are DO,saturation DO and water temperature.
In the application of hypothetical scenarios to theTunga-Bhadra river, the effluent data are kept constantwith the assumption that the industries and their effluentswill remain unchanged in future. However, future pollu-tion scenarios for point sources of the dischargers can beconsidered to account for the effect of changed effluentsof the industries in future.
The pollution control boards maintain river waterquality by setting some safe permissible limits for theindustries. For example, the effluent BOD should notexceed 15 mg/l. Even though the dischargers maintainthe safe permissible limits for the effluents, due to theimpacts of climate change on temperature and flows, asignificant reduction in water quality can be found. Toexamine the water quality responses affected by climatechange when the dischargers are at safe permissiblelimit, the hypothetical scenarios are computed for Tunga-Bhadra river while maintaining the maximum limit of15 mg/l of BOD for all the industries and municipaleffluents. Figure 7 shows the responses of DO, watertemperature and pH for various scenarios of climatechange when the industries and municipal effluents areat safe limit. The length 180 km corresponds to thebeginning of reach 1 and the length 0 km correspondsto end of reach 16. A sudden change in the water qualityindicators around 153 km can be observed (Figure 7), itis due to the junction of main stream with the tributaryTunga. QUAL2K generates individual plots for the mainstem as well as each of the tributaries. Figure 7 shows themain stem water quality indicators variation excludingthe tributary Tunga. Some water quality parametersare significant (for DO, river water temperature, BODand TOC) to moderate changes (for alkalinity, pH andconductivity) compared with present conditions for thehypothetical climate change scenarios.
The percentage changes in the water quality indicatorsin response to the scenarios at critical check pointsare shown in Figure 8. Significant positive percentagechanges are observed for the water quality indicatorssuch as BOD and water temperature and negative changesare observed for DO and saturation DO. From Figure 8,it is seen that the percentage changes in DO andwater temperature are significantly more for the checkpoints that are downstream of the effluents. For theremaining check points, the percentage changes are notvery significant for both the conditions of pollutants beingat present concentrations as given in Table VI (Figure 6)and when the pollutants are at permissible limits set byPCA (Figures 7 and 8).
The water quality of the river is found to change signif-icantly, even when the dischargers are at safe permissiblelevels as shown in Figure 7. The maximum reductionin DO level from present conditions is about 0Ð54 and0Ð67 mg/l for scenarios 5 and 6, respectively. Scenario3, which is the scenario of increase in temperature of1 °C and no change in streamflow also shows signifi-cant decrease in DO level of 0Ð2 mg/l. Hence, for a rise
Figure 7. Responses of water quality for hypothetical climate changescenarios. industrial effluents are at safe permissible limit. Length 0 kmcorresponds to the end of the river stretch (end of reach 16) and length180 km corresponds to the beginning of the river stretch (beginning of
reach 1)
in temperature of even 1 °C, with no change in stream-flow, DO levels may decrease in future. Therefore, evenwhen the reduction in discharge is negligible, the climatechange impact on DO will be entirely due to water warm-ing. The changes obtained in response to the hypotheticalscenarios indicate that the current stnadards may need tobe modified in accordance to the future degradation evenwhen the effluent discharges are at safe perimissible lev-els. The quantified changes in water quality indicators inresponse to hypothetical scenarios can be used for plan-ning the standards of the water quality.
The fractional removal levels for the present conditionsof effluents and for the six scenarios for each dischargercomputed from the FWLAM are shown in Figure 9.These are the resulting fractional removal levels for eachscenario of temperature increase and streamflow changeas given in Table IV. For all scenarios and for all dis-chargers, an increase in fractional removal levels can
Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. (2011)
S. REHANA AND P. P. MUJUMDAR
Figure 8. Comparison of change in (a) dissolved oxygen levels and(b) water temperature at some critical check points in response to changein streamflow for a given temperature change when the effluents are at
safe permissible limits
be observed compared with present required fractionalremoval levels. These are the fractional removal levelssatisfying both goals of the dischargers and PCA foreach climate scenario. Scenario 3 shows comparativelyless fractional removal levels as this scenario incorporatessmaller impacts (C1 °C temperature and 0% streamflowchange). The present strategy of fractional removal lev-els does not fulfill the limit of the required fractionalremoval levels for the hypothetical scenarios as shown
in Figure 9. The present pollution loading and climaticconditions show a smaller fractional removal level ofaround 35%. However, due to changes in the climaticconditions for the same loading pattern, river water qual-ity management through a FWLAM demands maximumfractional removal levels of 90% for the dischargers.Therefore, modifications in the fractional removal levelstrategy should be implemented to account for the futurechanges in hydroclimatic variables.
CONCLUDING REMARKS
In this article, the impacts of climate change on waterquality variables have been studied using hypotheticalclimate change scenarios. Quantified evaluation of waterquality parameters for the response to increasing temper-atures and decrease in flows has been carried out. Thesix scenarios of air temperature, water temperature andstreamflow changes could result in substantial decrease inthe DO levels and increase in BOD and river water tem-perature. The changes caused by scenario 6, which incor-porates a decrease in streamflow of 20% and increase intemperature of 2 °C, shows a decrease of 1Ð02 mg/l in DOlevel compared with present conditions. The DO declineis then likely to lead to major degradation in water qualityconditions due to increase in oxygen-demanding sourcedischargers. From the historical data assessment and theresults of the hypothetical scenarios, it can be concludedthat Tunga-Bhadra river is expected to have reductionin flows, increase in water temperature and consequentdecrease in water quality levels due to climate change.The reduced river flows from climate change will mostlikely increase concentrations of pollutants in the watercolumn. If pollutant loadings are lower, water qualitystandards are less likely to be violated.
This study has used a water quality model, QUAL2K,to show the effects of climate change scenarios on waterquality parameters. Due to the limitation of data availabil-ity for the river, in this article, the water quality param-eters considered to study the responses are DO, BOD,
Figure 9. Fractional removal strategies for the present conditions and for the hypothetical climate change scenarios
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RIVER WATER QUALITY RESPONSE TO CLIMATE CHANGE
TOC, alkalinity, pH and conductivity. The responses ofwater quality parameters to the climate change scenarioscan be computed for other parameters also, which can besimulated by QUAL2K. Furthermore, the linear regres-sion relationship considered between air temperature andwater temperature can be improved by considering theeffect of streamflow, as water temperature is also depen-dent on streamflow and other meteorological variables.
Scenarios for future pollution are not considered inthis study, by the inclusion of which a realistic impactassessment can be achieved for river water qualitymanagement. Furthermore, the climate change scenariosused in this study should not necessarily be consideredas the future climates for the river. They are primarilydesigned to show the sensitivity to change within areasonable interval. Clearly, these scenarios do not relateto any particular time in the future, but do help developan understanding of how sensitive a river is to aparticular change in hydroclimatic variable. The mainconclusion is that the results may vary depending onriver, climate scenario, water quality model and theparameters considered.
The water quality indicators have shown significantchange in response to the hypothetical scenarios whenthe dischargers are at safe permissible levels set byPCA. The impacts will be particularly pronounced withincreasing magnitudes of point source inputs. The resultssuggest the necessity of improving the adaptation policiesof PCA to consider the future deterioration of waterquality to account for global climate change. For thescenarios of water quality inputs, the resulting decisionpolicies in terms of fractional removal levels for thedischargers have been evaluated and it is found that thepresent fractional removal policies are lower than thelimits obtained for the six scenarios. Therefore, thereis a necessity to revise the standards of the PCAs aswell as the fractional removal level policies for thedischargers.
Overall, climate change impact on water flow amountsof rivers and the resulting effect on water quality haveto be addressed in a combined framework. Attentionmust be paid when using water quality simulation mod-els for simulating the water quality in response toclimate change, as the increase in water temperatureand decrease in flows due to climate change are notcontrollable factors. As a counterpart, efforts can bemade to implement long-term adaptation policies as astarting point and also to design new treatment sys-tems to include climate change impacts. The adaptationpolicies may include minimum instream flow require-ment at various locations of the river, i.e. by adopt-ing enough environmental flows at various check pointsto reduce the pollutant loadings as well as water tem-perature in the river systems. In addition, revision ofstandards of PCA for the dischargers is needed so thatrivers will be less polluted. An appropriate strategyfor the effective water quality management of rivers isneeded in perceiving persistent trends of climate in thefuture.
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