26

CHAPTER 4

KRISHNAGIRI RESERVOIR

4.1 PONNAIYAR RIVER

The River Ponnaiyar takes its source near Nandidurg in Karnataka

state South India at an altitude of 1000 m above MSL draining through

Southeastern slope of Chennakesava Hills. In Karnataka it is known as

‘Dhakshina Pinakini’. After traversing through the Devanahalli and Hoskote

taluks of Karnataka, it enters the Tamil Nadu state at a place near Bagalur

village of Hosur taluk. The River is called Ponnaiyar from this point in Tamil

Nadu.

The Krishnagiri reservoir was constructed across the Ponnaiyar

River near Periyamuthur village about 10 km from Krishnagiri town in

Krishnagiri district, Tamil Nadu. It is located at the latitude of 12º 28' North

and the longitude of 78º 11' East (Figure 4.1). Krishnagiri district is in the

Northwestern part of Tamil Nadu, bordering Karnataka and Andhra Pradesh

states. The execution of the KRP dam was started on Mar 1955, completed

and opened for irrigation on Nov 1957, in less than 3 years. The reservoir is

now more than 50 years old and continues to serve this region successfully.

This reservoir is one of the earliest projects constructed in post

independence India in the dry and barren areas of the state. Since then, the

reservoir serves as the life line for the region serving multiple uses of water

from irrigation to fish culture (Mohanakrishnan 1988). Krishnagiri Reservoir

27

Figure 4.1 Krishnagiri Reservoir as Seen through Google Earth and Its location in Tamil Nadu, South India

TAMIL NADU

28

is a medium size storage and distribution structure with an initial capacity of

68.2×106 m

3 and irrigates 3642 ha of wet crop area supplying water through

left and right main canals. The length of the Dam is 1000 m and at FRL, the

reservoir has a water spread area of 12.32 km2 and height of 22.8 m from the

river bed. The dam has 8 spillways, 3 river sluices and 2 canal sluices. The

hydraulic details are given in Table 4.1.

Table 4.1 Hydraulic Details of Krishnagiri Reservoir, Krishnagiri

District, Tamil Nadu

S. No. Description Detail

1 Full Reservoir Level Elevation 483.23m

2 Full Reservoir Level Capacity 68.2×106

m3

3 Full Reservoir Level Watershed area 12.32 Km2

4 Maximum Water Level (Elevation) 484.75 m

5 Maximum Water Level (Capacity) 86.7×106 m

3

6 Maximum Water Level (Waterspread area) 15.04 Km2

7 Height of the Dam 22.86 m

8 Length of the Dam 1.003 Km

9 Design Maximum Flood 4250.8 m3/s

10 River bed Level 464.63 m

11 Spillway 118.9×97.56 m

12 Spillway Capacity 4250 m3/s

There are 16 villages that directly benefit from this reservoir for

irrigation and other purposes. In addition, the LMC and RMC supply water to

the already existing tanks in these villages, where culture of fishes is also

taken up. There is water supply from the reservoir for a period of 10 months

in a year, and this ensures that culture operations completed successfully.

29

There are two spring channels that supply water to ayacut even

earlier to the construction of the reservoir, and the farmers have acquired

riparian rights and water supply is ensured for these original command areas

through spring channels throughout the year.

The LMC (Left Main Canal) take off near the spillway, while the

RMC (Right Main Canal) off take is on the western part of the water spread

on the earthen bund part of the Dam structure. During its span of half a

century of useful life, the reservoir encountered environmental problems in

later years (IHH 2007) probably due to industrial and agricultural

developments in the catchment area (Ravichandran and Kaarmegam 2004).

Several soil and water conservation programs have been implemented in the

catchment area of the reservoir by the Agricultural Engineering Department

and Forest Department of the Government of Tamil Nadu, since 1990’s. This

includes contour bunding, afforestation, bio fencing, construction of check

dams and percolation ponds.

4.2 ENVIRONMENTAL INVESTIGATIONS

Table 4.2 shows the reports available on different aspects of the

Krishnagiri Reservoir and its catchment area investigated by earlier workers

from the Centre for Water Resources, Anna University. An environmental

investigation in the study of the catchment area of the Krishnagiri Reservoir

(Ravichandran 2002) suggests that soil erosion and sediment bound nutrient

transport may be the main processes affecting the sedimentation and

eutrophication of the reservoir. Investigation of the soil erosion in the

catchment area using spatial data modeling in MapInfo environment

(Ravichandran and Kaarmegam 2004) indicated that some parts of the

watershed are having erodible soils and are contributing significant soil losses

from the catchment. In a UGC funded project (Ravichandran 2006), the

investigations in the reservoir was continued which mainly consisted of the

monitoring of water quality and the nutrient profiles systematically.

30

Table 4.2 Reports of Environmental Investigations Conducted in Krishnagiri Reservoir and its Catchment Area

Author Period of

study

Title of Report Feature Source

Ravichandran 2002 – 2003 A pilot study on nutrient export and

eutrophication of Krishnagiri Reservoir,

Krishnagiri, Tamil Nadu

Catchment erosion,

Water Quality study

Technical Report submitted to Institute for

Water Studies, PWD, Chennai 600 113

Karunakaran 2002 - 2004 Eutrophication of Krishnagiri Reservoir:

Causes and Environmental Impacts

USLE application, EIA,

Water quality of

Reservoir

Ph.D. thesis, Anna University, Chennai

600 025, 2005

Karunakaran

and

Ravichandran

2006 Estimation of Soil Erosion in

Krishnagiri Reservoir catchment of

Ponnaiyar basin in Tamil Nadu

Soil erosion and

impacts

Indian Journal of Soil Conservation 34(2):

110 – 113

Ravichandran

and Ramanibai

2005 – 2006 A hydrobiological and water quality

investigation in Krishnagiri Reservoir

Reservoir water quality,

algae

Technical Report, Anna University,

Chennai 600 025, 2006

Ravichandran, 2006 – 2008 Erosion based watershed modeling

approach for NPS pollution assessment

in Krishnagiri Reservoir catchment area

Catchment erosion

Hydrobiology

Reservoir water quality

Technical Report submitted to UGC, New

Delhi 110 002, 2008

Dhrissia 2007 – 2008 Studies on sedimentation in Krishnagiri

Reservoir

Sediment yield and

water quality

M E thesis, Anna University, Chennai 600

025, 2008

Ravichandran 2007 – 2008 Where have fishes gone? Reservoir water quality

fisheries and

community impacts

Illustrative cases for teaching IWRM,

Course manual published by SaciWaters,

Hyderabad, 2010

31

Karunakaran and Ravichandran (2006) in a study of 10 years of

data on rainfall in the catchment area have found that the rate as well as the

loss of total load of top soil from the catchment was influenced by total

rainfall received in a year and the number of severe storms during each of the

season. Two further studies, one on developing sediment graph for the

Krishnagiri Reservoir (Dhrissia 2008) and another study (Ravichandran 2010)

on the culture of fishes in the reservoir by the Fisheries Department of

Government of Tamil Nadu and the problems of decline in the yield faced

during the last decade in the reservoir were completed recently.

The Institute of Hydraulics and Hydrology at Poondi made an

investigation on the rate of sedimentation in the reservoir and estimated as

41.79% of loss of capacity as on 2006 (IHH 2007).

4.3 SECONDARY DATA

The hydrological data of the Krishnagiri Reservoir was provided by

the Water Resources Organisation of the Public Works Department of

Government of Tamil Nadu. The office of the Sub Divisional Officer,

Krishnagiri in the Krishnagiri Dam site collects daily data on water level,

inflow into the reservoir and regulate outflow through the irrigation supply

canals and river sluices. A fully functional automatic weather station is also

operated at the dam site by the Department. The basic climate data including

daily rainfall for the period March 2008 to June 2009 was collected from their

records and used in calculation of the weekly, monthly and seasonal profiles

of hydrological conditions of the reservoir. The water quality data was

collected as part of an environmental monitoring program in Krishnagiri

Reservoir Project being conducted since January 2008.

4.4 SAMPLING PROGRAM

A systematic collection of water samples was done at weekly

intervals from different locations in Krishnagiri Reservoir. Onsite

32

measurements for temperature, pH and Electrical Conductivity were done

using Eutech field meters and water samples are collected for analysis of

water chemistry and nutrients at the laboratory during Mar 2008 to Jun 2009

in the reservoir.

Water samples were collected from five locations in the reservoir

(Figure 4.2); location 1 is at Madhepatti village which is 500 m above the

water spread area in the Ponnaiyar River (Inflow), location 2 is near the boat

yard in the reservoir, location 3 is 100 m downstream of the head sluice of

LMC, location 4 is 100 m downstream of the head sluice of RMC and

location 5 is 200 m below the gates of spring channel. The first location

represents the inflows into the reservoir, the second location represents the

reservoir storage and the 3rd to 5th locations represent outflows from the

reservoir. Water samples were also collected from river sluice whenever

discharge took place during the period of study.

Figure 4.2 Krishnagiri Reservoir and the Sampling Locations for the

Study

33

The sampling program could not be conducted due to logistical

reasons during August 2008 and therefore no data is available for this period.

Similarly nitrates could not be analysed during March and April 2008 and no

data is available for this period also.

The pH and Electrical conductivity of the samples were measured

in the sampling location itself using portable meters (Eutech Inc). The

remaining parameters were analyzed in the laboratory of the Centre for Water

Resources at Chennai for the water samples collected in 1 liter clean

polythene containers and transported in an ice box. On most of the occasions

the samples were transported the same day and analysis started the next day.

4.5 ANALYTICAL METHODS

Standard Methods (APHA 2004) were followed for the estimation

of chemical parameters. Carbonates and bicarbonates were analysed by

titration in the unfiltered samples against standard acid and the alkalinity of

the sample was calculated. Chlorides were estimated by argentometric method

and total hardness was measured by EDTA complexometric method. The

nutrients Total Phosphate and Nitrate nitrogen were estimated by colorimetric

methods. The Ascorbic acid method was adopted for the estimation of

phosphates after digestion of the samples and the Cadmium reduction column

method was used for the analysis of nitrates.

Simple analytical tools in MS Excel were used for data preparations

and SPSS 10 was used for statistical analyses and preparation of graphical

illustrations.

4.6 RESERVOIR RELEASE AND TRAP EFFICIENCY

The sedimentation study took advantage of the earlier investigations

made in the Krishnagiri Reservoir and its catchment area. The rainfall,

inflows, sediment and other hydrological data for the period 1957 to 1985

34

except 1958 to 1965 was collected from a report on water resources

evaluation of Ponnaiyar basin by the Institute for Water Resources

Organisation PWD Government of Tamil Nadu (IWS, 1985) and for the

remaining period from the office of Water Resources Organisation in

Krishnagiri. The available hydrological data and other information is

compiled to test various models for Reservoir trap efficiency estimation and

compare their relative efficiency and utility in assessing the sediment

retention capacity of Krishnagiri Reservoir. The useful lifespan of the

reservoir and the possible environmental problems are also calculated and

discussed.

The sediment release efficiency of a reservoir is the mass ratio of

the released sediment (o

V ) to the total sediment inflow (i

V ) over a specified

time period. It is the complement of trap efficiency (TE):

100V

VVTE

i

oi ´-= (4.1)

Release efficiency = 100 – TE (4.2)

4.6.1 Brune Method

Brune (1953) developed an empirical relationship for estimating

long-term trap efficiency in normally impounded reservoirs based on the

correlation between the capacity to inflow ratio (C/I) and applied this method

to calculate trap efficiency observed in Tennessee Valley Authority reservoirs

in the southeastern United States. This is probably the most widely used

method for estimating the sediment retention in reservoirs, and gives

reasonable results from very limited data: storage volume and average annual

inflow. As a limitation, the method is applicable only to long-term average

conditions. Brune noted that significant departures can occur as a result of

changes in the operating rule. Brune has used the following equation:

35

)I

C(Log19.0970.0[TE = ]×100 (4.3)

Normally dry reservoirs tend to be less efficient at trapping

sediment, and shallow sediment-retention basins designed for the express

purpose of trapping sediment can operate much more efficiently than

indicated by the curve (Figure 4.3). For instance, the All-American Canal

desilting basins in Arizona would have negligible sediment trapping

efficiency based on their C/I ratio, but the basins operate at a trapping

efficiency of 91.7 percent. Trapping efficiency also depends on the actual

storage level at which the reservoir is held during flood periods (as opposed to

its nominal storage capacity), and the placement of outlets.

Figure 4.3 Trap Efficiency Related to C / I Inflow Ratio (Brune 1953)

36

4.6.2 Churchill Method

Churchill (1948) developed a relationship between the sediment

release efficiency and the sedimentation index of the reservoir, defined as the

ratio of the retention period to the mean flow velocity through the reservoir.

The minimum data required to use this method are storage volume, annual

inflow, and reservoir length. In applying the Churchill method, the following

definitions are used:

Capacity - Reservoir capacity at the mean operation

pool level for the analysis period (m3),

Inflow - Average daily inflow rate during study

period (m3/sec),

Retention period - Capacity divided by inflow rate (sec),

Length - Reservoir length at mean operating pool

level (m),

Velocity - Mean velocity computed by dividing inflow

by average cross-sectional area (m/sec).

The average cross-sectional area can be determined by dividing

reservoir capacity by length (m2).

Sedimentation index – Retention period divided by velocity

(sec2/m).

The sedimentation index computed form the above data is applied

to the curve (Figure 4.4) to estimate the sediment release efficiency.

Churchill’s method can be used to estimate the release efficiency in settling

basins, small reservoirs, flood retarding structures, semidry reservoirs, or

reservoirs that are continuously sluiced.

37

Figure 4.4 Churchill's (1948) Curves for Local and Upstream Sediment,

Relating TE to a Sedimentation Index

4.6.3 Brown Method

Brown (1944) developed a curve relating the ratio of reservoir

capacity (C, in acre-ft) and watershed area (W, in square miles) to trap

efficiency (TE in percent) and represented by the following equation:

]

)W

C(K1

11[100TE

´+-´= (4.4)

The coefficient K ranges from 0.046 to 1.0 with a median value of 0.1.

K increases:

(1) For regions of smaller and varied retention time (calculated

using the capacity-inflow ratio),

38

(2) As the average grain size increases, and

(3) For reservoir operations.

That prevents release of sediment through sluicing or movement of

sediment toward the outlets by pool elevation regulation. Variations are

mainly due to the fact that reservoirs having the same C/W ratio can have

different capacity inflow ratios. Brown’s curve is useful if the watershed area

and reservoir capacity is the only parameters known.

4.6.4 Gill Method

Later, Gill (1979) developed empirical equations which provided a

better fit to the three curves proposed by Brune.

Primarily for highly flocculated and coarse grained sediments:

25

2

)I

C(994701.0)

I

C(006297.0103.0

)I

C(

TE

++´=

- (4.5)

Median curve (for medium sediments) Morris and Wiggert (1972):

)I

C(02.1012.0

I

C

TE

+= (4.6)

Primarily for colloidal and dispersed fine-grained sediments:

3235

3

)I

C(02655.1)

I

C(02621.0)

I

C(10133.0101.0

)I

C(

TE

++´-´=

- (4.7)

39

4.7 PHOSPHATE DYNAMICS

4.7.1 Sources of Data

Table 4.2 shows the investigations conducted in the Krishnagiri

Reservoir project area since 2001 by different authors from the CWR, Anna

University and University of Madras, both at Chennai. Information on water

quality including phosphate concentration in the reservoir has been part of

these investigations starting with the initial study on the nutrient load from the

watershed in 2002.

During the course of collection of data, only monthly values are

collected. The gaps in data especially during 2005 and 2007 with missing

values especially for inflow and outflow concentrations were noticed. The

missing data was estimated by the linear interpolation method and the data

series for each year was then completed. However, there were no gaps in

hydrological data for the above period and the daily values are converted into

monthly averages for further analysis.

4.7.2 Data Analysis

The data analysis was done in MS Excel spreadsheet to prepare

graphical illustrations. Figure 4.5 shows the general classification and

grouping of data, before the computation and estimation of the parameters

was done.

Initially all the data were listed as monthly values from January

2001 to June 2009. However, when calculating the annual loads, the calendar

year may not be appropriate, as the seasonal climatic conditions are different

in this part of the sub tropical region. The water year starts with the onset of

40

the southwest monsoon season in June that lasts for four months and followed

by the north east monsoon for another three months from October to December.

Figure 4.5 Analysis Scheme Adopted in the Study for Hydrological and

Water Quality Data

The winter is brief (Jan - Feb) and then the summer follows (March

to May). Therefore, the water year can be divided into monsoon (Jun - Dec)

and post monsoon (Jan - May) seasons. Therefore, the water year was

considered as more rational to study and compare the effects and all the data

were grouped into four seasons (Figure 4.5) before further analysis.

4.7.3 Hydrological Analysis

The hydrological changes in the reservoir from 2001 to 2009 were

assessed on the basis of daily water level, inflow, and discharge and rainfall

data as monthly averages. The methods employed for the computation and

input data are explained below. This include total inflow, total outflow,

reservoir volume, surface area, evaporation, rainfall inflow, mean depth,

hydraulic flushing rate and the hydraulic residence time.

Data calendar (Jan – Dec)

Water year (Jun – May)

Monsoon (Jun – Dec) Post Monsoon (Jan – May)

SWM (Jun – Sep) NEM (Oct – Dec) WIN (Jan – Feb) SUM (Mar – May)

41

The total inflow and total outflow was computed by the summation

of the daily values of inflow and outflow data for the reservoir. The inflows

include discharge from the Ponnaiyar River and inflows due to rainfall over

the reservoir. The outflow includes the discharge through the left and right

main canals, the river sluices and the spring channel excluding evaporation

losses.

Figure 4.6 shows the plot of water level vs. capacity and surface

area based on the data provided by the office of the Water Resources

Organisation at Krishnagiri from their survey records.

662534106x102x10637.3x10739.2V ´-´+´-´= (4.8)

525100122900547003023 +´-´+´= xxxA (4.9)

where

x = Water level (m),

V = Volume of reservoir (m3),

A = Surface area of reservoir (m2),

z = Mean depth of reservoir (m).

42

Figure 4.6 Water Level vs. Capacity and Surface Area Relations in

Krishnagiri Reservoir

The reservoir volume and surface area were calculated based on the

changes in daily water levels by reference to the Figure 4.6.

4.7.4 Hydraulic Budget

The hydraulic budget was calculated for the reservoir based on the

hydrological data available.

Cap

acit

y (

x1

06m

3)

43

The hydraulic Flushing Rate (HFR) is the number of times a

volume of water equal to the volume of the reservoir flows through the

reservoir per that time. It was calculated by

V

Qout=r (4.10)

where

ρ = Hydraulic flushing rate (yr-1

),

outQ = Total hydraulic outflow (m

3/yr),

V = Volume of reservoir (m3).

The Hydraulic Residence Time (HRT) of a reservoir is the average

amount of time that water remains in the reservoir. It is the inverse of the

HFR and is computed as follows:

outQ

V=t (4.11)

r=t1

(4.12)

where is residence time of water in reservoir.

Areal Hydraulic Loading Rate was calculated as follows:

A

Qq 1

s = (4.13)

where

1Q = Hydraulic inflow rate (m

3/yr),

A = Surface area of the reservoir (m2),

44

sq = Areal hydraulic loading rate (m/yr).

The Apparent Settling Velocity (ASV) was computed according to

Reckhow (1979), as follows:

sq2.06.11v ´+= (4.14)

where

v = Apparent settling velocity (m/sec).

The response time of the reservoir is computed as follows and it

represents a measure of the time it would take for the reservoir to respond to a

change in its phosphorus loading. Response time is a function of the

reservoir’s flushing rate and is independent of either the reservoir’s

phosphorus load or content. Because the rate at which a substance is

accumulated or removed from a lake is a logarithmic function, response time

is usually expressed as the time it would take to increase or reduce the

concentration of a substance by one-half and can be estimated by the

following equation (Dillon and Rigler 1975):

z

10HFR

69.0)2/1(RT

+= (4.15)

where

HFR = Hydraulic flushing rate of reservoir (yr-1

),

z = Mean depth of reservoir (m).

4.7.5 Phosphate Profile

The Total Phosphate concentration profile of the reservoir during

the period of 9 years (2001-2009) was collected from several authors

45

(Table 4.2) and averaged for monthly values, and used in calculations. This

include Total phosphate concentration measured at a point above the water

spread area in the Ponnaiyar River (TPin), within the water spread area, mostly

near the boat yard (TPres) and in the outflow channels and/or river sluice

(TPout).

4.7.6 Phosphate Load

The total phosphate load was computed by multiplying the monthly

inflow and outflow values by respective average phosphate concentration data

for each month. The reservoir phosphate load was computed by multiplying

the average lake volume by total phosphate concentration measured in the

reservoir.

The areal phosphate loading rate (L) was computed by dividing the

monthly total phosphate input load by the average surface area of the

reservoir during this period.

A

ML = (4.16)

where

M = Total phosphate input load to reservoir (mg/yr),

L = Areal phosphate loading rate (mg/yr/m2),

A = surface area of reservoir (m2).

46

4.8 PHOSPHORUS RETENTION COEFFICIENT (R) AND

SEDIMENTATION COEFFICIENTS (σ)

The P retention and the sedimentation coefficient for the study

period were calculated by different method available in literature. The

Table 4.3 lists the authors, and the method of calculation of the retention

coefficients. Eight methods of calculating the phosphate retention (R) and 5

methods of sedimentation coefficient ( ) were calculated for the data

available. All these methods have been used in the mass balance model for

estimating the TPres concentration as follows (Brett and Benjamin 2008).

t´s´+´=b1

TPaTP in

res (4.17)

a is the coefficient representing constant loss of TP and b is the

coefficient representing first order rate constant for TP loss due to various

processes. The sedimentation coefficient ( ) is the key parameter in the mass

balance modelling and its prediction is important in the success of the model.

The relationship between the sigma and the input - output load of phosphate

was plotted to analyse the relationship during different seasons of the year

with the available data. Table 4.4 shows the different methods used for the

computation of sedimentation coefficient.

The evaluation of the different methods of computing R and was

done separately for different seasons, monsoon and post monsoon seasons by

two criteria; a table of RMS error and a plot of predicted vs. observed value of

TP concentration. The methods that produced less error and showed better fit

of data with the Krishnagiri Reservoir are used for further computation and

presentation.

47

Table 4.3 Mass Balance Models Applied for Prediction of Phosphate

Retention Coefficient (R) in Krishnagiri Reservoir

S. No. Author Formula

1 Chapra (1975)sqv

vR +=

2 Larsen and Mercier (1976)r+=

1

1R

551.0515.01

1R r´+=

3 Kirchner and Dillon (1975) ss q00949.0q271.0e574.0e426.0R-- ´+´=

4 Nurenberg (1984)sq18

15R +=

5 Prairie (1989) t´+t´+=

18.01

18.025.0R

6 Hejzlar et al (2006)t´+

t´=84.11

84.1R

88.0in

in

)1

P(

P

43.11R t+-=

Table 4.4 Sedimentation Models Applied for Prediction of TP in

Krishnagiri Reservoir

S.No. Author Formula Explanation

1 Vollenweider (1975)z

10=s2 Welch et al. (1986)

78.0r=s3

Larsen and Mercier (1976) 49.083.0 -t´=s4 472.0761.0 -t´=s5

Michael T. Brett and

Mark M. Benjamin ( 2008))R1(

R

-´t=s

R from Chapra (1975)

6R from Larsen and Mercier

(1976-a)

7 R from Larsen and Mercier (1976-b)

8 R from Kirchner and Dillon (1975)

9 R from Nurnberg (1984)

10 R from Prairie (1989)

11R from Hejzlar et al (2006)

1 for Reservoirs

2 for Reservoirs and Lakes12

48

4.9 PRESENT STUDY

The application of different methods of P retention and

sedimentation to Krishnagiri Reservoir data provided opportunity to explore,

analyse and propose a method for computing Pin (calculated) concentration,

by modifying the model proposed by Canfield and Bachmann (1981). This

method provided a better fit of the data for Krishnagiri Reservoir and is

described in chapter 7.