DETERMINATION OF NET FLOWS INTO ALMATTI RESERVOIR FROM CWC GAUGE DATA AND RESERVOIR DATA

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 8, Issue 2, February 2017, pp. 01–07 Article ID: IJCIET_08_02_001
Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=2
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
DETERMINATION OF NET FLOWS INTO ALMATTI
RESERVOIR FROM CWC GAUGE DATA AND
RESERVOIR DATA
M. Visweswararao
Professor, Civil Engineering Department, Malla Reddy Institute of Technology,
Mysammaguda, Kompalli, Secundrabad, India
Dr. G. K. Viswandh
Professor, Civil Engineering Department, OSD to Vice Chancellor,
JNTUH, Hyderabad, India
ABSTRACT
This paper presents the determination of net flows into Almatti reservoir from CWC data
and reservoir data. From the study it can be concluded that the average flow in to Almatti will
be 574.86 TMC, the maximum inflow will be 1196.8 TMC and the minimum flow will be
166.99 TMC. The flows in annual in deficit years may reduce by about 50 TMC but there is no
variation in the good years in the good years as the storage effects will take care of this aspect
during good years. It can be concluded that there will be reduction of flows in the June and
July flows in the ultimate scenario except in very good years.
Key words: Almatti, Netflows, CWC gauge data, working tables, Catchment
Cite this Article: M. Visweswararao and Dr. G. K. Viswandh, Determination of Net Flows
into Almatti Reservoir from CWC Gauge Data and Reservoir Data. International Journal of
Civil Engineering and Technology, 8(2), 2017, pp. 01–07.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=2
1. INTRODUCTION
The River Krishna rises in the Mahadev range of the Western Ghats near Mahabaleshwar at an altitude
of 1337m above sea level and flows through Maharashtra, Karnataka and Andhra Pradesh gathering
water on its way from innumerable rivers, streams or tributaries and drops into the Bay of Bengal.
River Bhima and Tungabhadra are major tributaries of river Krishna. Main Krishna, Bhima and
Tungabhadra constitute the stems of the river Krishna. Jurala, Srisailam, Nagarjunasagar, Krishna
delta are the major projects on main Krishna. Almatti reservoir was constructed across main Krishna
in the state of Karnataka up stream of this reservoir s. This reservoir is very important as it has to cater
to the needs of UKP project in addition to supplying water to downstream projects. The operation of
this reservoir is very important for the system downstream.
Therefore it is proposed to estimate the inflows by considering the CWC gauge data available at
various sites upstream of Almatti. This gauged data is available at the end of sub basin as well as at

Determination of Net Flows into Almatti Reservoir from CWC Gauge Data and Reservoir Data
some other sites from 1972 onwards. Therefore a monthly inflow series for the period 1972-73 to
2007-08 at Almatti is developed using these gauged flows.
The salient features of the Almatti reservoir are given below in Table 1.
Table 1 Salient features of the Almatti reservoir
S.No. Name of the project Unit Almatti
1 Sub-basin K-2
2 Catchment area (Sq.Km) 35926
3 Gross storage (TMC) 223.52
4 Live storage (TMC) 205.98
5 Dead storage (TMC) 17.57
6 F.R.L m 524.26
7 M.D.D.L m
8 D.S.L m 506.87
9 Length of spillway dam m 486.50
10 Length of non-spillway dam m 676.33
11 Length of earthen dam m 402.00
12 Total length of dam m 1564.83
13 Crest level m 509.02
14 Number of gates 26
15 Gate size mxm 15x10.584
16 Type of Gate Radial
17 Design flood Cumecs 31,007
2. LITERATURE SURVEY
An analytical method (AM) and a method using the ensemble Kalman filter (EnKF) are proposed by
Chao Dung et.all to determine nonfluctuating reservoir inflow based on the concept of inflow
continuity. The AM is developed based on the simultaneous minimization of both the estimated
reservoir water level error and the inflow variation. The EnKF, which is built on state equations
(inflow continuity and water balance equations) and an observation equation (the reservoir stage-
storage relationship), is used to update inflow states by assimilating water level observations. The two
proposed methods are evaluated using a synthetic experiment with various conditions including water
level observation error, reservoir stage-storage relationship error, and the influence of water surface
slope. The AM outperforms the EnKF under all conditions. Case studies of the Gaobazhou and
Danjiangkou Reservoirs in China demonstrate that both of the proposed methods can derive an hourly
inflow without fluctuations. The results indicate that the AM and the EnKF method can improve
reservoir inflow estimation compared with conventional methods.
In this study Samuel Dixton and Robert Willy explores the feasibility of applying remotely sensed
precipitation estimates (in this case from the Tropical Rainfall Measuring Mission [TRMM]) for
forecasting inflows to the strategically important Toktogul reservoir in the Naryn basin, Kyrgyzstan.
Correlations between observed precipitation at Naryn and 0.5° TRMM totals is weaker for daily
(r = 0.25) than monthly (r = 0.93) totals, but the Naryn gauge is representative of monthly TRMM
precipitation estimates across ~60% of the basin. The precipitations are estimated by remote sensing
techniques and transferred to the reservoir. Over 80% of the variance in monthly inflows is explained
with three-month lead, and up to 65% for summer half-year average. The analysis also reveals zones
that are delivering highest predictability and hence candidate areas for surface network expansion.
In this study ASG range et al have developed a complementary modelling framework for
improving real-time forecasting without needing to modify the pre-existing forecasting model, but
instead formulating an independent additive or complementary model that captures the structure the
existing operational model.. The application of this principle for issuing improved hourly inflow

M. Visweswararao and Dr. G. K. Viswandh
forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure
reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises
an error model added on top of an unalterable constant parameter conceptual model. This procedure is
applied in the 207 km2 Krinsvatn catchment in central Norway. Deterministic and probabilistic
evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h.
Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval
indicated that the degree of success in containing 95 % of the observations varies across seasons and
hydrologic years.
Hiroyukisuzuki et.al has estimated the amount of water inflow into a dam reservoir from the
increment of hydrostatic water level in the constant time intervals. The data of water inflow is
important for efficient dam operation. Therefore, the measurement of hydrostatic water level with high
accuracy is important work to know the exact amount of water inflow. However, the data of water
level is disturbed by the signals of wind wave, seiche and so on. The estimation method of hydrostatic
water level is demanded from a dam administrator. This paper proposes the new estimation method of
hydrostatic water level using IIR digital filter, and the effect of this filter is discussed. It is confirmed
that the water inflow can be estimated using the new method. And the accuracy of estimation is
discussed by investigating the variance of filter output.
The FYE(Firm yield estimator) methodology to estimate stream flow to the reservoir at an
ungaged site was tested by simulating streamflow at two streamflow-gaging stations in Massachusetts
and comparing the simulated streamflow to the observed streamflow. In general, the FYE-simulated
flows agreed well with observed flows. There were substantial deviations from the measured values
for extreme high and low flows. A sensitivity analysis determined that the model’s streamflow
estimates are most sensitive to input values for average annual precipitation, reservoir drainage area,
and the soil-retention number—a term that describes the amount of precipitation retained by the soil in
the basin. Firm yields for 25 (14 single-reservoir systems and 11 multiple-reservoir systems) reservoir
systems were determined by using the historical records of streamflow and precipitation. Current
water-use data indicate that, on average, 20 of the 25 reservoir systems in the study were operating
below their estimated firm yield; during months with peak demands, withdrawals exceeded the firm
yield for 8 reservoir systems. Introduction Growing demands on Massachusetts drinking-water
supplies have increased the likelihood that withdrawals could deplete available storage capacity and
result in supply shortfalls. A common way to evaluate reservoir behavior is by calculating a firm yield
for the reservoir, defined as the maximum yield that can be delivered by the reservoir, even through a
severe drought. Ideally, the firm yield would be determined from a period of record that included the
worst drought likely to affect the reservoir. Firm-yield estimates typically are not based on streamflow
and precipitation data that include the drought-of-record because these data are unavailable for most
drinking-water supply reservoirs. Furthermore, even at monitored locations where streamflow and
precipitation data are available, the length of the record often does not include the drought-of record.
Because of these limitations, Massachusetts drinking Firm Yield and the Estimation of Firm Yield for
Streamflow is to be properly evaluated during drought periods.
During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir
operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate
reservoirs could help maximize flood protection while saving water for drier times of the year. In this
study Shenichi Yang &Tsun hua yang combined ensemble quantitative precipitation forecasts and a
hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of
six historical typhoons were used for model calibration, validation, and application. An understanding
of cascaded uncertainties from the numerical weather model through the hydrological model is
necessary for a better use for forecasting. This study thus conducted an assessment of forecast
uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the
ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts

with shorter length of cumulative time had a higher uncertainty. The results showed that using the
ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead
time and serve as a valuable reference for operating reservoirs.
In the present study the CWC gauged data available at various gauge sites is transferred to Almatti
and the net flows are arrived at on monthly basis. The results are compared with the actual observed
data at Almatti for the period 2003-2007.
3. METHODOLOGY FOR THE PRESENT STUDY
Almatti reservoir receives flows from sub basins K1, K3, and part of K2 of Krishna basin. The gauge
data at Kurundwad and Sadalga was used to estimate the gross flows of K1 sub basin. Similarly
Bagalkot station and galgali is used for K3 and K2 respectively. For the period when the data at these
gauge sites is not available other nearby sites are used. The reservoir working table data was used to
develop the gross flows at the reservoir site. Whenever the reservoir data is not available the
catchment rain fall proportions are used to develop the gross flow series at that reservoir. For the first
reservoir in the sub basin the net flows are worked out as the gross flows minus the planned upstream
utilisations including minor irrigation and small projects upstream of reservoir. The intermediate flows
from 2nd
to first are worked out by taking the difference of gross flows of the two reservoirs. The flows
are routed through the first reservoir considering the planned utilisations of that project. The spills and
the intermediate gross flows less the minor irrigation and the small projects in the catchment of first to
second reservoir form the net flows in reservoir2. These flows are again routed considering the
planned utilisations from the second reservoir to the third ans so on till the gauge site. The net flows
that flow from the gauge site are then worked out at the end of sub basin at the gauge site. This process
is done for all the three sub basinsK1,K2, and K3. The monthly planned utilisation pattern is derived at
based on 10 year average utilisation of major projects. The utilisations are considered as provided in
the awards. The catchment contribution below the gauge sites is worked out with catchment and rain
fall proportions and minor irrigation deducted from the same. This is then added to the flows from K1,
K3 and K2 to get the net flows at Almatti.
The net flows at Almaati are shown in Table 2.
Table 2 Monthly net inflows in to Almatti reservoir
Monthly net inflows in to Almatti reservoir
Year june july Aug sep oct nov dec jan feb mar apr may total
1972 0.21 144.38 43.22 17.62 4.68 1.18 0.89 0.98 0.95 0.9 0.96 0.84 216.81
1973 6.38 226.29 163.21 96.62 37.73 6.881 1.82 1.91 1.76 1.98 1.19 4.08 549.85
1974 0.17 140.52 166.96 58.19 75.01 11.29 1.87 2.26 1.87 1.31 1.04 1.04 461.53
1975 38.42 227.82 219.4 117.18 109.67 38.45 4.21 3.49 2.6 2.16 1.6 1.36 766.36
1976 51.08 190.43 242.66 91.81 15.32 2.71 2.04 2.23 2.06 1.81 1.5 2.99 606.64
1977 32.15 200.8 121.1 112.31 32.14 12.3 9.22 3.38 3.12 2.16 2.66 3.02 534.36
1978 50.63 128.49 271.96 114.55 18.82 5.68 2.67 3.34 2.91 2.04 1.48 1.21 603.78
1979 7.08 75.2 311.55 72.76 32.33 7.9 3.12 3.1 2.8 1.82 2.37 7.94 527.97
1980 22.47 219.56 300.98 56.8 10.41 2.51 2.15 2.72 2.51 1.73 1.38 1.05 624.27
1981 0.17 166.2 250.48 88.48 23.91 3.54 2.99 3.51 3.08 2.41 1.43 1.97 548.17
1982 1.31 89.12 255.86 20.08 8.5 4.59 2.77 3.17 2.61 2.12 1.49 1.12 392.74
1983 35.94 165.86 266.38 63.26 14.15 1.95 2.25 2.54 2.25 2.11 1.9 1.45 560.04
1984 1.3 151.86 136.13 36.41 19.14 3.61 2.9 3.1 2.49 2.48 1.97 1.2 362.59
1985 6.85 78.82 188.5 13 26.62 1.99 2.7 3.11 2.25 2.09 1.77 1.8 329.5
1986 13.5 89.15 160.64 7.48 4.64 3.25 1.75 2.27 1.99 1.71 1.6 1.39 289.37
1987 0.17 67.35 57.33 19.48 10.81 0.85 1.59 1.3 1.27 1.42 1.44 1.02 164.03

1988 0.17 172.11 182.75 106.96 26.74 1.31 2.31 2.57 2.31 1.85 1.56 1.15 501.79
1989 3.36 149.39 112.51 39.38 17.12 1.66 2.27 2.39 2.16 1.63 1.02 4.53 337.42
1990 0.78 173.5 268.12 81.04 28.46 8.11 1.97 2.35 1.75 1.47 1.8 1.2 570.55
1991 55.51 226.17 314.89 44.37 24.84 1.98 2.61 4.26 3.62 2.52 1.39 0.81 682.97
1992 0.17 80.34 190.08 66.14 16.42 2.71 2.57 2.85 2.04 1.69 0.96 0.65 366.62
1993 18.66 171.34 256.39 62.01 113.24 10.76 11.29 8.08 6.55 5.28 6.56 3 673.16
1994 49.76 611.76 218.36 262.98 30.05 4.03 2.42 10.03 2.74 1.8 1.7 1.2 1196.8
1995 0.17 77.87 58.3 77.66 56.65 4.04 2.76 2.87 2.26 1.42 0.95 0.98 285.93
1996 5.33 109.24 128.79 30.31 139.87 8.86 4.03 5.25 2.7 1.93 1.1 2.55 439.96
1997 12.84 170.96 488.9 54.11 13.3 11.3 7.53 3.38 2.82 2.78 1.94 1.21 771.07
1998 2.08 122.93 115.79 71.73 117 12.02 2.44 2.83 3.41 3.8 3.12 5.98 463.13
1999 40.48 248.08 138.76 29.34 101.96 10.27 2.29 3.08 2.48 1.58 2.56 2.69 583.57
2000 7.29 94.18 60.62 61.15 35.39 2.42 0.95 1.17 0.92 0.88 0.87 0.51 266.35
2001 3.85 87.07 85.27 19.5 23.78 0.57 0.77 0.87 0.48 0.44 0.49 0.42 223.51
2002 6.5 45.02 136.57 19.55 12.97 0.38 0.73 1.1 1.19 1.41 1.35 0.99 227.76
2003 10.38 69.04 59.35 21.93 1.56 0.21 0.19 0.67 1.19 1.18 0.87 0.42 166.99
2004 51.08 34.44 266.93 31.77 14.2 1.25 1.41 1.4 1 1.04 1.05 1.05 406.62
2005 7.38 261.27 578.68 223.85 26.4 1.42 1.58 1.76 1.7 1.59 1.41 1.22 1108.3
2006 26.32 275.67 639.49 95.16 31.9 1.14 1.78 1.79 1.78 1.63 1.51 1.38 1079.6
2007 26.83 292.65 237.84 136.57 21.89 1.81 2.02 2.34 2.45 2.05 1.64 2.18 730.27
The actual net flows observed during the period 2003-04 to 2007-08 at Almatti are given in Table
3.
Table 3 Actual observed flows in Almatti reservoir
Actual observed flows in Almatti reservoir
Month 2003 2004 2005 2006 2007
June 21.21 70.12 5.74 32.78 38.897
July 75.28 39.12 223.6 287.76 338.604
August 61.58 306.05 577.01 621.15 206.398
September 22.55 29.78 213.38 81.9 119.147
October 3.04 15.80 43.08 23.53 22.8
November 1.5 0 0
December 0 0
January 0 0
Febraury 0 0
March 0 0
April 0 0
May 0.67 0 0
Total 183.66 460.87 1064.98 1047.12 725.85
4. RESULTS AND DISCUSSIONS
It could be seen that the average inflows on annual basis in the ultimate scenario in to Almatti will be
486.71 TMC. The Maximum inflow in to the Almatti in ultimate condition will be 1033.91 TMC
whereas the minimum net flow in ultimate condition will be very low and will be 98.59 TMC. A
comparision of table 2 and table 3 shows that there will be a reduction of 16.67,54.27 TMC in the

annual flows at Almatti in the years 2003-04 and 2004-05 from the present scenario to the ultimate
scenario when full development upstream takes place.
In good years there is no much variation in the annual flows. This is because of the increase in
storage at Almaati from 123 TMC to 220 TMC. The flows in June have reduced by 10.83,19.04,6.46 and
12.10 TMC in the years
2003,2004,2006 and 2007. This is due to the reason that these flows will be stored upstream and
flows will come only after the storages are filled up or the generation below gauge sites is
considerable. In 2005 there is no reduction in june but the variation is small. This small variation may
be due to storage effects of Almatti and other projects up stream of Almatti.
The flows in july have reduced by 6.24,4.68,12.09 and 45.954 TMC in the years 2003,2004,2006
and 2007. This is due to the reason that these flows will be stored upstream and flows will come only
after the storages are filled up or the generation below gauge sites is considerable. In 2005 there is no
reduction in july . This variation may be due to storage effects of Almatti and other projects up stream
of Almatti. IT could be seen that there are non mosoon flows in the ultimate scenario this is because
the releases ordered by Tribunal for minimum environmental flows and releases to Chennai water
supply to be supplied by upstream projects to Almaati.
5. CONCLUSIONS
From the above study it can be concluded that the average inflow into Almatti will be 574.86 TMC,
the maximum inflow will be 1196.8 TMC and the minimum flow will be 166.99 TMC. The flows in
Annual in deficit years may reduce by about 50 TMC but there is no variation in the good years as the
storage effects will take care of this aspect during good years. It can be concluded that there will be
reduction of flows in the June and July flows in the ultimate scenario except in very good years. There
will be some minimum flows in the non-monsoon in the ultimate scenario to take care of
environmental aspects and Chennai water supply.
REFERENCES
[1] Chaw Dung et.al-estimation of non-fluctuating reservoir inflow from water level observations
using methods based on continuity- Journal of Hydrology, Vol.529,Part3 oct 2015, p.1198-1210
[2] Samuel G. Dixton, Robert l. Wilby- Fore casting reservoir inflows using remotely sensed
precipitation estimates- A pilot study for thr river Naryn, Kyrgyzstan-Hydrological sciences- Vol
16,2016, Issue1
[3] AS Grange et.al-Improving real time inflow forecasting in to hydropower reservoirs through a
complimentary model frame work- Hydrology and earth sciences, 27 th aug,2015
[4] 4.Hiroyuki et.al- A new estimation of inflow in to dam reservoir using digital filter- Japan society
of civil engineers, Vol 2001,(2001) No. 677, p.1-19, released in 2010
[5] USGS-Factors effecting firm yield and the estimation of firm yield for selected stream flow
dominated drinking water supply reservoirs in Massachusetts- Usgs scientific report 2006,5094
[6] Shenin chi yang & TSun yua Yang- uncertaintity assessment in reservoir inflow forecasting with
eon senble precipitation forecasts and HMC, HMS-Advanced meterology, Volume 2014,11 pages,
A.I.9-581756
[7] CWC gauge data record of Krishna basin 1972- 2007
[8] Almatti inflow observations 2003-2007
[9] Almatti project report
[10] KWDT1 & KWDT2 awards

[11] Patil R.A. and Shetkar R.V., Prediction of Sediment Deposition in Reservoir using Artificial Neural
Networks. International Journal of Civil Engineering and Technology, 7 (4), 2016 , pp. 01–1 .
[12] Maharam Dakua, Asef Mohammad Redwan, Begum Nazia Jahan, Syed Mohammed Tareq,
Saifuddin Ahmed and Nowroz Farhan Noor, A Case Study on Management of Rainwater Reservoir
in Hilly Areas of Bangladesh. International Journal of Civil Engineering and Technology (IJCIET),
7(6), 2016, pp.193 – 201.
[13] Mohamed Ayman El Salawy, Tamer Ali Elgohary, Amir Mohamed Akl Mobasher and Mai
Mahmmoud Abd El Aziz, Impact of Climate Change on Resrevior Operation Policies–The Case of
Aswan High Dam Reservoir, International Journal of Civil Engineering and Technology, 7(2),
2016, pp. 107–116

DETERMINATION OF NET FLOWS INTO ALMATTI RESERVOIR FROM CWC GAUGE DATA AND RESERVOIR DATA

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