CHAPTER - VII

STUDIES ON WATER QUALITY OF PAlMBA RIVER

1.0 INTRODUCTION

Rivers have always been the lifelines of development and with the

course of time have borne the impacts of development and industrialization

in the form of abstractions of water besides other wastewater releases.

Industrial and domestic discharges produce diverse impacts on the quality

of the receiving body of water. Even though the flowing water bodies, or

lotic systems, have the capacity to recharge and self-cleanse themselves in a

given time, they rarely can do as fast as they are being used or polluted.

Water quality deterioration is imminent, of course, in such riverine systems.

Water quality management strategies to arrest the degradation of

aquatic systems include a combination of technological, social and financial

aspects. Mathematical modeling forms an integral part of the process for

predicting future conditions under various loading scenarios or management

action alternatives (Schooner, 1996). The latter enables selection of a

feasible treatment option to maintain the water quality.

1.1 ' River water quality modelling

Water quality models take recourse to the behavioural

characteristics of receiving bodies of water such as channel and flow

characteristics, which influence the mixing processes and dispersion of

waste into surface waters. The three types of mixing processes themselves,

viz, vertical, transverse and longitudinal, govern the extent of stratification,

the rate at which pollution spreads from one bank to the o w , and the

movement of wastewater in the downstream, respectively. The thermal and

hydraulic conditions of the river have a significant influence on both the

biochemical and chemical processes (Gandolfi et al, 1996).

Models for river water quality that are based on the one or two-

dimensional mass transport equation, are widely used to determine the

extent of pollution in rivers which are not very wide or deep. These models

consist of a set of dynamic equations governing the evolution of the

relevant hydrologic, thermal and biochemical phenomena. The equations

are all based on the conservation principles of mass, momentum and

energy, regardless of which phenomena is considered.

To assess the Impact of discharges on river water quality, the

widely used comprehensive and versatile stream water quality simulation

model QUAL 2E (Brown and Barnwell, 1987) devised by the US

Environmental Protection Agency has been employed. QUAL 2E is a

modeling program applicable to all dendritic streams that are well mixed. It

assumes that the major transport mechanisms, advection and dispersion are

significant only along the main direction of flow (longitudinal axis of the

river). The model can be operated either as a steady state or as a dynamic

one. In contrast to dynamic models, steady state models provide predictions

for only a single set of environmental conditions and can be applied for

critical or worst case conditions, that represent extremely .low assimilative

capacity (David and James, 2000). The basic equation solved by QUAL 2E

is the dimensional advection-dispersion mass transport equation, which is

numerically integrated over space and time for each water quality

constituent. For any constituent C, the equation may be written as:

6M/6t = 6 [A, DL(6C/6x )I 6x.dx-6 ( A,uC)/Gx.dx+ ( A, dx) dC/dt+ S

Where, M-mass ( M), x-distance ( L), t- time ( T), C-concentration

( ML-'), A,- cross section area ( L* ), DL- dispersion coefficient ( L~ TI), U-

mean velocity ( LT'), S-external sources or sinks ( MT -I).

Field data required to reproduce the hydrodynamic and water

quality aspects of a river by means of the mathematical model QUAL 2E

include the following:

rn Flow measurement and characterization

Hydraulics of the river

Cross section profiles of the main river and its branches

Point sources- their flows and characteristics

Water quality management is not simply the elimination of wastes

or technological removal of wastes, but economics and prevailing political

situation also need to be recognized as facts of life when dealing with the

problems of pollution control (Thomann, 1974). The approach should be

such that the problem of pollution control is placed in a frame work that

allows for mekingful decision making, depending on the appropriate

mathematical model that could be used to attain the desired quality of

water.

The reduction in the quantity of water in rivers can result in the

decrease of flow and in turn will affect the quality of water due to

diminishing levels of flushing within the system (de Mars and Ganitsen,

1997). Wherever there is the additional abstraction, water quality problem

has gained more attention (Chessman and Robinson 1987; Welsh and

Stewart, 1989; Barors et a1 1995; Muchrnore and Dziegielewiski, 1983)

A study on the influence of physico-chemical variables on the

quality of Thames estuary, London during drought condition (Martin and

Michael, 2000) has reported that most water quality parameters are

influenced by the freshwater flow. Reductions in freshwater flow towards

the estuary due to natural lowering of water levels in river and increased

abstraction, have a major influence on water quality of estuarine water

which could spread to many kilometers beyond the point of entry. QUAL

2E simulation analysis was successfblly validated for predicting the

concentrations of biochemical oxygen demand, dissolved oxygen, total

phosphorous and ammonia-nitrogen for the entire Kao-Ping River Basin,

Taiwan (Ning et al, 2000)

QUAL 2E was also applied for the modelling of one of the River

Kali in India(Kudesia,l998) The results indicated that, better the input data

base, stronger would be the accuracy of prediction.

The Enhanced Stream Water quality Model QUAL 2E permits

simulation of several water quality constituents in a branching stream

system using a finite difference solution to the one dimensional advective-

dispersive mass transport and reaction equation.

In a QUAL 2E Model, the main stream is divided into many sub

reaches or computational elements equivalent to finite differences. The

model is formulated based on flow (Q) for the hydrology balance,

temperature (T) for heat balance and concentration(C) for material balance,

which include both advective and dispersive transport. National Council of

the Paper Industry for Air and Stream Improvement (NCASI), India and US

Geological Survey tested the revised program of QUAL 2E on several

intensively sampled rivers across the United States. The model was applied

in many European countries, England, Greece, Belgium and Spain and also

widely used in South America, Korea and China.

In QUAL 2E Model, a particular stream is divided into a network

of "Head Waters", "Reaches" and "Junctions". QUAL 2E assumes that 26

physical, chemical and biological properties are constant along a particular

"Reach". The QUAL 2E computer program includes the major interactions

of the nutrient cycles, algal production, benthic carbonaceous oxygen

demand, atmospheric re-aeration and their effect on the dissolved oxygen

balance. In addition, the program includes a heat balance for the

computation of temperature and mass balance for conservative minerals,

coliform bacteria and non-conservative constituents such as radioactive I

substances (NCPI, 1985)

In this study QUAL 2E Model was used to find out the discharge

required to dilute the colifom generated in the Pamba river during a

pilgrimage season.

2.0 HYDROGEOLOGICAL DETAILS OF PAMBA RIVER

Pamba river (Fig. 41 and 42) the third largest river in Kerala,

originates from the Pandalarn Hills, the second segment of the forest in the

southern Western Ghats, adjacent to the north of Agasthyamalai range. The

main tributary Kakki river join with the Pamba river in the highlands, the

river Manimala join with it in the plains. After meandering through

Pathanamthitta, Idukki and Alleppey districts in Kerala, over 176 km, the

river ultimately falls into the Vembanad lake. The Sabarigiri Hydro-

Electric project, the second major hydel scheme in Kerala is situated on this

river. The famous forest Shrine of Swami Ayyappa (Sabarimala Pilgrim

Centre) is situated amidst the north western foot hills of Pamba plateau and

the mount plateau.

Many streams originating from the hills flow through Sabarimala

and join together to form Njunangar. Marakkoottam, Sabaripeedom,

Appachimedu, Chelikkuzhi and Cheriyanavattom flow into the Njunangar

before it merges with the river Pamba about 50 m downstream of

Arattukadavu near the Pamba temple. Over the years, the Sabarimala has

become one of the most popular pilgrim centres in South India and it is

estimated that around 50 million people visit Sabarimala, during October to

January every year. The pilgrims use the Pamba river sides for responding to

nature3s call due to the lack of alternate facilities leading to contamination of

the river water. Also the sewage tank near the parking ground at Pamba is

always found leaking into the Pamba.

According to the Kerala Pollution Control Board (KPCB), though

there are about 46 sewage collection tanks at the Sannidhanam alone, it is

observed that the present facility is grossly inadequate to treat the sewage

generated during the peak days of the pilgrimage season. So the untreated

sewage finally reaches the river through Njunangar. Besides, waste water

from various hotels and restaurants also reaches Njunangar. A huge volume

of wastewater and solid wastes fiom the Devaswom mess and the Appam

and Aravana units directly flow into the Njunangar stream, leading to the

contamination of the Pamba river water.

River water pollution at Pamba- Triveni, the confluence of the

streams of Kakkiar and Kochupamba is a major environmental problem. In

this chapter, the water quality of river Pamba during the pilgrimage season

and the application of QUAL 2E Model to estimate the assimilative

capacity of the river are presented.

Fig. 42 A View of Pamba River

2.1 Methodology

A network of sampllng stations along the Pamba river especially

near the Sabarimala pilgrimage centre were selected for the water quality

study (Fig.43). Water samples from five stations were collected for

physico-chemical and bacteriological studies. Sampling and analysis were

done as per APHA (1998). The various water quality parameters analysed

for the samples included temperature, pH, EC, DO, BOD, chloride,

sulphate, total hardness, total alkalinity, nitrate, phosphate, sodium,

potassium, calcium, magnesium, iron, copper and coliforms.

Primary data on the water quality of Pamba river has been collected

from stations of Kochupamba, Kakkiar, Triveni, Nadappalam and

Ganapathi temple. These stations are very close to the Sabarimala

Pilgrimage centre, which is one of the important places in the river basin.

Secondary data is collected from Kerala Pollution Control Board.

3.0 RESULTS AND DISCUSSION

Of all the 18 parameters analysed, the major water quality problem

of Pamba river is associated with bacteriological contamination and the

BOD fluctuation. The faecal coliform values ranged from 1100

MPNl100ml to 46000 MPN/lOOml. Kakkiar and Nadappalam are the

grossly polluted areas. Physico-chemical parameters like pH, temperature,

total dissolved solids, chlorides, nitrates, phosphates etc were observed to

be within the limits of Pollution Control Baard.

The major findings of the water quality status of river Pamba are

the following:

BOD fluctuation

The BOD value ranged from 0.90 mg/l to 4.53 mg/l and the

maximum value of 4.53mgA was obsenred at Kakkiar, Pamba upstream

during the month of December, 2000. BOD value is found to be within the

permissible limit at all other stations. According to Central Pollution

Control Board, BOD values for drinking and bathing water should not

exceed 3.0 mgll. The BOD values were high at Kakkair and Triveni during

the month of December. This is attributed to the increased demand of

oxygen for the degradation of the organic wastes dumped into the Pamba

river. This observation is in agreement with that of Singh and Singh

( 1995).

*:* Bacteriological contamination

All the samples from all the five stations were c~ntaminated with

coliform bacteria. The value ranged from 240 MPN/lOOml to 46000

MPN/100 rnl. The pollution in Pamba near Sabarimala is due to defecation

of the pilgrims besides bathing activities. The tanks overflow quite often

and hence pollute the river. In addition wastes are dumped in the river at

many locations through sewage collecting earthen tanks on the banks of

Pamba. The total coliform number per 100 rnl is reported to be from 4000

to b 0 0 0 MPN. The maximum permissible limit of coliform content in

river waters is 500MPNl100 ml according to Central Pollution Control

Board (Table.40).

Table: 40 Criteria for designated best use (CPCB)

Source:- CPCB

DO- Dissolved Oxygen, BOD- Bio-chemical Oxygen demand

EC- Electrical Conduct~vity, SAR- Sodium Adsorption Ratio

3.1 Tolerance limit for various uses

As per BIS-IS 2296-1982, the tolerance limits of various

parameters had been specified based on the classified use of water. The

classification as given in table 1 has been adopted in India. River water

quality usage is classified as follows:-

Class A is the drinking water source without conventional

treatment, Class B allows water to be used for outdoor bathing, Class C for

drinking water with conventional treatment, Class D is fit for wildlife and

fisheries, and Class E means water is fit for recreation, horticulture,

irrigation and industrial cooling.

AS a water quality planning tool, the Enhanced Stream Water Quality

Model (QUAL 2E) which is a steady state model for conventional pollutants is

used for simulating the flow requirements needed for diluting coliform contents to

the desirable limit. The details of the model used and results are given below.

3.2 Water Quality Simulation of Pamba river

A 20 km stretch of Pamba river is segmented into 6 reaches each of

which is sub-divided into elements of equal length of 1.0 km. A layout of

Pamba river is given in Fig.43. Stream network with computational

elements and reaches is given in Fig.44. Only coliforms are generated since

the other parameters are not of much significance in the considered system.

Physico-chemical and bacteriological analyses were camed out and the

results are furnished in Tables 41-43. This data is utilized for applying

QUAL 2E model. Water samples collected from various stretches are tested

in-situ for dissolved oxygen. Velocity and cross section are measured for

calculating the river flow.

Table: 41 Water Quality Characteristics of Pamba River during November, 2000

; SI.No I

I

2

3

4

, 5

6 ' 7

I 8 ~ 1 9

Note:- All the parameters are in mgA except Temperature, pH, EC and colifonns.

Parameter

Temperature

PH

I

I I4 I

I I5 1 ' 16

17

, 18 L

Temperature is expressed in degree centigrade, EC in pS /cm k d colifonns in MPNII 00ml.

EC

DO

BOD

NO3-P Pod-P

C1

TH

BDL- Below detectable level

Kochu Pamba

23 (k0.50)

7.12

Na

K

Fe

Cu

Colifonns

The values represents the mean monthly averages and in paranthesis represents the standard deviation to the respective data

(d.20) 36.1

(10.50) 7 8

(i0.70) 0.13

(*O. 10) BDL BDL

2 0 (k0.50)

12

Kakklar

24 (kO.50)

7.18

(iO.50) 2.0

(k0.40) 0.1

(kO.01) 0.06

(iO.01) 0.03

(d.10) 1100

(*2.5)

(+0.20) 32.1

(*O 60) 7.67

(10.50) 1.07

(k0.50) BDL BDL

2.0 (20.20)

14

Tnver~~

25 (i0.50)

7.25

(k0.50) 1.2

(k0.40) 0 1

(kO.01) 0.02

(kO.01) 0.05

(k0.02) 46000 (k5.0)

(k0.40) 34.4

(kO.50) 7.6

(10.30) 1.13

(i0.50) BDL BDL

2.0 (iO.10)

10

Nadappalam

25 (*O.SO)

6.86

(k0.99) 1.6

(iO.50) 0.1

( i0 01) BDL

0.03 (iO.01) 24000 (k3.0)

Ganapath~ Temple

25 (iO.50)

7.02 (*0.20)

36.5 (kO.50)

7.53 (k0.20)

1.40 (10.20) BDL 0.004

(kO.001) 2.0

(iO.50) 16

(a.50) 36.2

(*0.80) 7.93

(k0.50) - 1.73

(a. 10) BDL 0.004

(i0.00 1) 2.0

(iO.10) 12

(i0.80) 2.0

(h0.50) 0.1

(iO.01) 0.01

(iO.00 1) 0.01

(*0.001) 24000 (k3.0)

(M.50) 2.4

(i0.30) 0.1

(kO.0 1) 0.01

(iO.011) 0.03

(i0.01) 9300 (k4.0) ,

Table: 42 Water Quality Characteristics of Pamba River during December, 2000

Note:- All the parameters are in mg/l except Temperature, pH, EC, and coliforms.

I

) 15

1 1 6 , 1 1 7

; 18

Temperature is expressed in degree centigrade, EC in $3 Icm and coliforms in MPNI 100rnl.

BDL. Below Detectable Level

K

Fe

Cu

~ o l i f o m

The values represents the mean monthly averages and in paranthesis n p m t s the standard deviation to the reap&% drte

186

(io.60) 0.6 (a. 10) 0.21 (M.01) 0.01

(M.001) 1 100 (h2.0)

(M.30) 0.3

(M.lO) 0.05 (49.01) 0.09

(M.002) 9300 (14.0)

(M.20) 0.4

(io.10) 0.09 (io.01) 0.01

(M.001) 460

(* 1 .O)

(iO.50) 0.7

(io.20) 0.1 1 (M.05) 0.02

(iO.001) 1 500 (h2.0)

(M. 10) 0.8

(M. 10) 0.1

(io.05) 0.02

(M.OO 1) 2400 (Q.0)

Table: 43 Water Quality Characteristics of Pamba River during March, 2001

Note:- All the parameters are in mgA except Temperature, pH, EC and coliforms.

Temperature is expressed in degree centigrade, EC in pS /cm and coliforms in MPN1100ml. BDL- Below detectable level .

The values represents the mean monthly averages and in paranthesis represents the standard deviation to the respective data

There are. no point loads in the form of drainagelsewer. The point

loads considered are points/elements where waste is accunt~lated and the

coliforms are generated within the system itself by the bathing of crowds of

pilgrims of Sabarimala (of the order of 50 million people during one season

starting from immediately after north-east monsoon and ending up

sometime near the middle of January). The six point loads considered in

this study are presented in Table 44. For running the model a flow of

0. lm3/sec is assumed for the point load. The colifom count is assumed as

zero for the headwaters of the initial stage. A plot between simulated

coliforms and discharge is furnished in Fig. 45 and the input and output

data of water quality modelling is presented in Table 45 and 46.

Table: 44 Details of point load

Source:- Kerala State Pollution Control Board. u/s - Upstream dls-

Downstream

\ 4 hwma-m

Fig. 43 Schematic layout of Pmmba river at Triveni

Most Upstream point 1

Junction 1 1 l 8 1

--- 12

- Reach 3 I5

16

17

Reach S

Reacb 2

Reach 4

-

r-7 Reach 6 Computational 22 elemeat aomber

Fig44 St- metwork nith computrhd dements md ruches

0 20 60 80 100 Flow. rd/sec

Fig 45 Vanatmn of faecal coliforrns with flow

4.0 CONCLUSIONS

*:* To bring down the observed coliform count (46000 MPN/100 ml) to the

maximum permissible level of 500MPN/100ml (central Pollution

Control Board) the discharge in the main stream has to be increased

from an observed value of about 4 m3/sec to the order of about 35

m3/sec. This is possible only if water is released from the upstream

reservoirs or by impounding water upstream. Moreover water is to be

released every time it is required.

*:* Full-fledged treatment plant is to be set up at Sannidhanam and Pamba

for the treatment of waste water and garbage.

*:* A scientifically designed drainage system is to be developed with an

updated sewage disposal facility at the Sannidhanam, and Pamba.

T : ~ ~ E O 1 STREAM QUALITY MODEL--QUALZE WORKSHOP; METROPOLIS T : ~ L E 0 2 DATA SET 1 - PAMBA RIVER REACHES BOD ONLY T:TLED~ NO CONSERWITIVE MINERAL I TDS I N MG/L gTLE04 NO CONSERVATIVE MINF%AL I I ~ T L E O S NO CONSERVATIVE MINERAL 111 :!TLEO~ NO TEMPERATURE T ~ ~ ~ ~ ~ 7 n s 5-DAY BIOCHEMICAL OXYGZN DEMAND 7lTLEOe NO ALGAE AS CHL-A I N UG/L : ;TLEO~ NO PHOSPHORUS CYCLE AS P IN MG/L ::TLEIO (ORGANIC-P; DISSOLVE)-PI ::?LEI1 NO NITROGEN CYCLE AS N IE: MG/L ::?LEI2 (ORGANIC-N; AMMONIA-N; NITRITE-N; ' -NITRATE-N! T I T L E I 3 DISSOLVED OXYGEN IN MG/L '::LEI4 YES FECAL COLIFORM I N N 0 . / 1 0 0 ML :::LEIS t i0 ARBITRRRY N O N - C O N S E R V A T I ~ !':XITLE . . ? W ...,. DATA INPUT W R I T E OPTIONAL SVMMRRY N; FLOW AUGMENTATION c T t k D Y STATE C: TRAE CHANNELS 3INT LCD/SOLAR DATA rL3T DO AHD BOD :;XEL DNSTM CONC ( Y E S = l ) = 0 . 5D-ULT BOD C O W K COEF = 6 . 2 5 :K?UT METRIC - - - 1. OUTPUT METRIC 1 . T'IXBER OF REACHES - 6 . NUMBER OF JUNCTIONS = 1. ':JP, Of HEADWATERS = 1. NUMBER OF POINT LOADS = 6 ::ME STEP (HOURS) - LNTH. COMP. ELEMENT (KMl= 1. YGIMUM ROUTE TIME IHRS) = 3 0 . TIME INC. FOR RPTZ (FIRS)= LE..L.TITUDE OF BASIN IDEG) = LONGITUDE O F BASIN I D E G ) = 3TAJVDARD HERIDIAh IDEG) = DAY OF YEAR START TIME = - .. 7

LIN C O E f . , (AE) = EXAP COEF. , ( B E ) EL-. OF W I N (METERS) DUST ATTENUATION COEF. = S!;5AThl S l : > h T h l ~ ?ETA OXY TRAN 1 . 0 1 5 9 '.IIDATAl B i T R C A RMCH 1. RCH-KAKKI UP FROM 2 0 . 0 TO ;TREJLII REACH 2 . R C H - W K I FROM 1 0 . 0 TO s T R M RERCH 3 . RCH-KOCHU PAMBA FROM 0 8 . 0 TO STREAM REACH 4 . RCH-THRIVEN1 FROM 0 8 .O TO STREAM W C H 5 . RCHsBRIDGE FROM 0 6 . 0 TO :TkERM REACH 6 . RCH=PAMBA DOWN FROM 0 3 . 0 TO E!:DAT~ ENDATA3 p s i FIELD RCH= 1. 1 0 . 1 . 2 . 2 . 2 . 6 . 2 . 2 . 2 . 2 . 2 . 7 ; k G FIELD RCH- 2 . , 2 . 6 . 3 . ?LAG FIELD RCH= 3 . 2 . 2 . 2 . F A G FIELD RCH= 4 . 2 . 4 . 6 . '&G FIELD RCH- 5 . 3 . 6 . 6 . 6 . ' G G FIELD RCH= 6 . 3 . 2 . 2 . 5 . ~NDATA~ ~YDRAULICS RCH= 1. 5.9 . 3 4 . 6 5 .12 . 5 5 , 0 4 0 p , Y D ~ U ~ ~ ~ ~ RCH- 2 . 5 . 9 . 3 4 . 6 5 . 5 1 . 61 . 0 4 0 H v D m ~ ~ ~ ~ ~ RCH- 3 . 5.9' . 4 9 . 5 9 . 4 8 . 5 8 . 0 4 0 E V ~ ~ ~ ~ ~ ~ ~ RCH* 4 . 5.9 . 1 7 . 6 3 1.1 0 . 0 5 , 0 4 0 H Y D ~ ~ ~ ~ ~ RCH- 5 . 5 .9 . 1 5 .67 .51 . 6 1 . 0 4 0

1 0 . 0 o e . o 0 . 0 0 0 6 . 0 0 3 . 0 OC.0

R y ~ ~ ~ U L I C S RCH= 6 . 5 . 9 . 1 2 . 7 3 . 4 3 . 3 8 ;ljuATA5 EN~ATASA RSCT COEF RCH= 1. 0 . 2 0 0 0 . 0 0 0 0 . 5 0 0 5 . 0 . 0 0 0 0.OOOC 6 . 0 0 0 0 ?mCT COEF RCH= 2 . 0 . 2 0 0 0 . 0 0 0 0 . 0 0 0 3 . 0 . 0 0 0 0 . 0 0 0 0 0 . 0 0 0 0 RDCT COEF RCH* 3 . 0 . 2 0 0 0 . 0 0 0 0 . 0 0 0 3 . 0 . 0 0 0 0 . 0 0 0 0 0 . 0 0 0 0 REACT COEF RCH- 4 . 0 . 2 0 0 0 .100 1 . 0 0 0 3 . 0 . 0 0 0 0 . 0 0 0 0 0 . 0 0 0 0 RWCT COEF RCHc 5 . 0 . 2 0 0 0 . 0 0 0 0 . 0 0 0 3 . 0 . 0 0 0 0 . 0 0 0 0 0 . 0 0 0 0 R ~ C T COEF RCH= 6 . 0 . 2 0 0 0 . 0 0 0 0 . 5 0 0 5 . 0 . 0 0 0 0 .0000 0 . 0 0 0 0 EN DATA^ sDATA6A aGlOTHER COEF RCH= 1 . 0 0 . 0 0 . 0 0 0 . 0 0 1 . 5 0 - tiG/OTHER COEF RCH= 2 . 0 0 . 0 0 . 0 0 0 . 0 0 1 . 5 0 h;G/OTHER COEF RCH= 3 . 0 0 . 0 0 . 0 0 0 . 0 0 1 . 5 0 ;;:/OTHER COEF RCH= 4 . 0 0 . 0 0 . 0 0 0 . 0 0 1 . 5 0 tiG/OTHER COEF RCH= 5 . 0 0 . 0 0 . 0 0 0 . 0 0 1 . 5 0 .=G/OTHER COEF RCH= 6 . 0 0 . 0 0 . 0 0 0 . 0 0 i . 50 ENDATA6B :NITIAL COND-1 RCH- 1. 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 0 . 0 IEITIAL COND-I RCH= 2 . 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 0 . 0 :%iTIAL COND-I RCH= 3 . 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 0 . 0 :KITIAL COND-1 RCH= 4 . 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 O . O C 0 . 0 0 0 0 . 0 IKITIAL COND-1 RCH= 5 . 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 O.OOC 0 . 0 !t:ITIFL COND-1 RCH= 6 . 2 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 6 . 0 0 0 . 0 0 0 0 . 0 E::DAT.47 EN3ATA7A INCP INFLOW-1 RCH= 1. 0 . 2 6 1 1 8 . 0 0 1 . 0 1 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 :NCP INFLOW-1 RCH= 2 . 0 . 0 0 8 1 8 . 0 0 1 . 0 5 . 0 0 . 0 0 . 0 C.0 0 . 0 0 . 0 :NCR INFLOW-1 RCH= 3 . 0 . 0 0 3 1 8 . 0 0 1 . 0 3 2 . 0 0 . 0 0 . 0 C.0 0 . 0 O . G INCR INFLOW-I RCH= 4 . 0 . 0 1 5 1 8 . 0 0 1 . 0 2 5 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 IKCX INFLOW-] RCH= 5 . 0 . 0 1 5 1 8 . 0 0 1 . 0 5 0 . 0 0 . 0 0 . 0 C . C 0 . 0 0 . 0 INCP INFLOW-1 RCH= 6 . 0 . 1 0 8 1 8 . 0 0 1 . 0 6 0 . 0 0 . 0 0 . 0 0.G 0 . 0 0 . 0 ENDATA8 ZKDATABk STREAH JUNCTION 1 . TRIVENI JUNCTI: < 1 2 . 1 5 . 1 4 . ENDATA3 KWWTR-1 HDW- 1 .KAKKI UP 0 . 5 0 0 2 2 . 0 8 . 3 3 5 . 0 0 . 0 0 . 0 0 . 0 EN DATA10 HWWTR-2 HDW= 1. 0 . 0 0 1 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 WDATAl OA POINTLD-1 PTL= 1. LOAD1 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 4 5 . 0 0 . 0 0 . 0 0 . 0 POINTLD-1 PTL= 2 . LOAD2 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 4 5 . 0 0 . 0 0 . 0 0 . 0 FOINTLD- 1 PTL= 3 . LOAD3 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 4 5 . 0 0 . 0 0 . 0 0 . 0 POINTLD- 1 PTL- 4 . LOAD4 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 4 5 . 0 0 . 0 0 . 0 0 . 0 POINTLD- 1 PTL- 5 . LOAD5 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 45 .0 0 . 0 0 . 0 0 . 0 POINTLD-1 PTL- 6. LOAD6 0 . 0 0 0 . 1 0 0 2 5 . 0 2 . 0 4 5 . 0 0 . 0 0 . 0 0 . 0 ENDATAl 1 POINTLD-2 PTL- 1 . 0 . 0 0 8 0 0 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 POINTLD-2 PTL= 2 . 0 . 0 0 80000 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 ? O I N T L D - ~ P T P 3 . 0 . 0 3 E O O O O 0 .00 0 .00 0 . 0 7 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 9 POINTLD-2 PTL- 4 . 0 . 0 0 8 0 0 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 POINTLD-2PTL- 5 . 0 . 0 0 8 0 0 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 POINTLD-2 PTLL 6 . 0 . 0 0 0 0 0 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 EN DATA^ LA WDATAlZ ~ W N S T R E A M BOUNDARY- 1 2 3 . 0 6 . 0 2 . 0 0 0 0 . 0 0 . 0 0 . 0 0 0 . 0 45000 . EN DATA^ 3

S T R W WALITY SIHUIATION WhL-2E STREAH WALITY RWTING MODEL

ELE IM( LLC BEGIN ORD HUH HUH LOC

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19.00 18.00 11.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00

9.00 8.00

1.00 6.00

1.00 6.00

5.00 4.00 3.00

2.00 1.00 0.00

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0.53 0.55 0.58 0.60 0.73 0.16 0.78 0 . 8 1 0.83 0.86

0.96 0. 91

0.91 0 .91

1.95 2.06

2.16 2.27 2.37

2.41 2.14 2 18

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0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0 .00 0.00

0.10 0.00

0 .00 0.00

0 .00 0 .10

0.10 0.10 0.10

0.00 0 .00 0.00

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0.03 0 .03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

0.00 0.00

0.00 0.00

0.01 0 .01

0.00 0.00 0.00

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