resilience, reliability and vulnerability analysis of a ...€¦ · titas ganguly and dhyan singh...

Roorkee Water Conclave 2020

Organized by Indian Institute of Technology Roorkee and National Institute of Hydrology,

Roorkee during February 26-28, 2020

Resilience, Reliability and Vulnerability Analysis of a Multipurpose Reservoir

under Projected Climate

Titas Ganguly and Dhyan Singh Arya

Department of Hydrology, IIT Roorkee

Abstract: Reservoirs are constructed for a time span of 50-100 years with the underlying assumption

of stationarity of inflows. However, this assumption is challenged as anthropogenic climate change are

affecting both the timing and volume of flows due to the changes in precipitation pattern. Thus, it is

important to revisit the design parameters and reservoir operations under the changing climate

scenarios. In this study of Tehri dam, located below the confluence of the rivers Bhagirathi and

Bhilangana Rivers, we have used a weighted average ensemble approach using CMIP5 models’ data

to project the future precipitation trends for RCP 4.5 and 8.5 scenarios during 2016-2099; SWAT

model to assess inflows to the reservoirs; and lastly resilience, reliability and vulnerability analysis of

reservoir operation was performed for the years it failed to meet the requirements.

SWAT model was calibrated using SUFI-2 algorithm for the duration of 2006-2008 and validated for

the period of 2009-2010. During the calibration period, NSE value at a daily time step was found to

be 0.53 and during validation period it was 0.79. To evaluate the performance of the ensemble data in

comparison to individual GCMs their respective NSE values were also computed for the validation

period. The NSE value for the weighted average was found to be 0.41 while for the individual GCM

i.e. CSIRO it was 0.22. Daily simulation of reservoir operation resulted that the reservoir was able to

meet all the demands of irrigation, power generation and flood control without any risk of dam failure

under RCP 4.5 scenario. In the RCP 8.5 scenario, it was found that the reservoir fails to meet the target

power generation in seven years while fulfilling all other obligations like irrigation etc. The resilience,

reliability and vulnerability were computed for these years as 0.916, 0.857 and 0.265. The high values

of the resilience, reliability and low value of vulnerability show that that inability to generate required

power is not chronic and may be addressed with short term reservoir planning and management.

1. Introduction

Understanding the uncertainty and reliability associated with surface water reservoirs are

central for planning purposes (Kuria and Vogel, 2014). This needs to be seen in the context of

the fact that climate change is affecting water resources planning from the level of cities to

countries (Huong, et al., 2013; Vairavamoorthy et al., 2008). The issue of climate change

affecting reservoir operations assumes significance in the context that more than 45,000 large

dams have been constructed globally (WCD, 2000) with a total storage capacity of 7000 km3

(ICOLD, 1998). Currently, the number of dams worldwide exceeds 6800 (Lehner et al., 2011),

which retain around 20 % of the annual runoff and 10% of the total volume of the world’s

freshwater lakes (Gleick, 2000; Meybeck, 2003; Wood et al., 2011).

Reservoirs are constructed for a time span of 50-100 years with the underlying assumption of

stationarity of volume and timing of flows. Vicuna et al., (2010) reports that the assumption of

stationarity is not valid due to the climate change projections. Owing to the lack of stationarity

in climate related variables, Gersonius et al., (2009) argued that focus of water planning should

be on the study of resilience of the systems. Sankarasubramanian et al., (2001) highlighted the




sensitivity of streamflow to climate which is itself dynamic and changes with climate change

thus necessitating the hydrologic model based prediction of flows under climate change

conditions.

The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998; Arnold and Fohrer,

2005) has been developed as an effective tool for water resource assessment for watersheds of

varying sizes and conditions (Tripathi et al., 2005; Das, 2011). The SWAT model has been

widely (Muttiah and Wurbs 2002 ;Van Liew and Garbrecht 2003; Gosain et al., 2006 ) used to

study the impact of climate change on hydrologic regime of rivers. Various literature

(Vilaysane et al., 2015; Tuo et al., 2016 Shivahre et al., 2018;) reported that the sequential

uncertainty fitting version 2 (SUFI-2) algorithm for calibration of the model performs better

than others. The future streamflow is modelled in SWAT by incorporating the projected

hydroclimatic variables, temperature and precipitation. Thus the efficacy of the modelling

exercise and in turn the assessment of reservoir operation is dependent on the accuracy of the

projected input data.

There is no infallible methodology to ascertain the accuracy of projected data, however Glecker

et al., (2008) suggested that it is not likely that a model with lower efficiency in simulating

present day climate will do a better job in the long term. With the objective of improving the

efficiency of GCM ensembles in terms of replicating the statistical properties of the current

state of the hydroclimatic variables (maximum and minimum temperature and, precipitation),

a framework was developed and validated (Ganguly, 2019). The same framework has been

used in this study to generate the current and future temperature and precipitation inputs.

The evaluation of reservoir operation comprises the analysis of ability of reservoir to fulfil all

its committed flows and power generation targets (in case of multipurpose reservoirs). Various

forms of the Resilience Reliability Vulnerability (RRV) analysis, initially proposed by

Hashimoto et al., (1982a, 1982b) has been widely used (Fowler et al., (2003), Li et al., (2010),

Raje and Mujumdar, 2010; Okkan and Kirdemir, 2018) for assessing the performance of

reservoirs. The assessment of projected reservoir operation, using RRV analysis, encompasses

modelling of inflows, demands and daily reservoir operation based on available observed series

and reservoir operation rules.

2. Study Area, Data and Methodology

2.1 Study area

The Tehri reservoir was selected for this study as it is important for power generation (capacity

of 1000 MW), irrigation and flood control. The dam is also a major source of drinking water

for about 7 million people. Tehri dam, a multipurpose project, is situated in the district Tehri

of Uttarakhand state of India (Figure 1). The catchment area up to the dam axis is 7293 km2.

Bhagirathi and Bhilangana are the major rivers which contribute to Tehri reservoir. Tehri dam

has been designed for Probable Maximum Flood of 15540 Cumecs. The routed flood discharge

corresponding to maximum water level at 835 m above mean sea level (amsl) is 13025 Cumecs.




There is a yearly fluctuation of 90 m in reservoir water level. A flood space of 4.8 m from full

reservoir level to maximum flood level has been provided to accommodate the floods. The

gross and live storages of the reservoir are 3540 and 2615 Million Cubic Meters (MCM)

respectively.

Figure 1: Tehri reservoir catchment map along with main contributing streams.

2.2 Data for SWAT and simulation of reservoir operation

The input data required for SWAT model are precipitation, temperature, landuse (Figure 2),

soil cover (Figure 3) and elevation (Figure 4). The stream network delineated from the SRTM

DEM, along with the outlet points are shown in Figure 5. A total of 420 sub-basins based on a

threshold of 2500 hectare were delineated which were further classified into 4455 Hydrologic

Response Unit (HRUs). Observed streamflow at Tehri are used for the calibration and

validation of the model. The source, resolution and duration of these data are presented in Table

1. The observed streamflow from 2005-2010, the reservoir operating curve, the elevation




capacity curve and power generation for different heads for Tehri reservoir were provided by

Tehri Hydropower Development Corporation (THDC).

Table 1: Source, resolution and duration of different inputs to SWAT model

Variable Source Duration

Precipitation

(0.250 x 0.250)

Past: IMD Gridded 1980-2010

Future: RCP 4.5 and RCP 8.5 ensemble data 2016-2099

Temperature

(0.250 x 0.250)

Past: Global Meteorological Forcing Dataset 1980-2010

Future: RCP 4.5 and RCP 8.5 ensemble data 2016-2099

Streamflow Observed (THDC) 11/2005-12/2010

Soil NBSS & LUP

Landuse

(56m x56 m)

Bhuvan (AWiFS sensor) 2011-2012

Elevation (DEM)

SRTM 90m data

USGS --------------

Figure 2: Landuse/Landcover map of

Tehri reservoir catchment

Figure 3: Soil map of Tehri reservoir

catchment




2.3 Operation of Tehri Reservoir

The annual cycle of operation of Tehri reservoir can be segregated into three timeframes based

on the constraints on the reservoir operation. These are summer, monsoon and Rabi irrigation

(winter) seasons. The definition of timeframe and the respective constraints are given in Table

2.

Table 2: Timeframe, duration and constraints of reservoir operation for Tehri reservoir.

Timeframe Dates Main Constraint

Summer 01st May-20th June Reservoir level > 740 m (above MSL)

Monsoon 21st June-31st October Reservoir operating curve

Rabi irrigation 1st November-30th April Dedicated outflows for irrigation

The irrigation flows dedicated by THDC to the state of Uttar Pradesh are 100 cumecs in

November, 150 cumecs in December and 200 cumecs from January to April. During summer

the inflows and storage are utilised to maximise power generation (by maximising outflows)

with the constraint that the reservoir level remains above 740 m. From 21st June, the filling up

of the reservoir is started following the reservoir operating curve (ROC). The daily inflows

were extracted from the hydrological model and the outflows/reservoir levels are known from

the requirements/ROC. Using this inflow and outflow data the daily volume at the reservoir

was calculated. Using tables provided by THDC this volume was converted to reservoir level

and vice versa. This head and the amount of outflow (which takes place through the turbines)

then allowed us to calculate the amount of power generated on each day. During design of the

simulation of the reservoir operation, the reservoir level at which the spillway opens was held

to be at 835m (which is the maximum flood level) while the overtopping height was set at

Figure 4: Elevation map of Tehri

reservoir catchment

Figure 5: Catchment boundary and

stream network upto Tehri reservoir




839.5m amsl. Using this procedure the reservoir levels, outflows and power generated for each

day, from 2017-2098, were computed for RCP 4.5 and RCP 8.5 scenario.

2.4 Reliability, Resilience and Vulnerability (RRV) analysis

Hashimoto et al., (1982a) described the Reliability Resilience and Vulnerability (RRV) as

indices to quantify the performance of any water resources system.

These indices were used in the present study to analyse the hydropower output (with respect to

annual hydropower target of 2.698x106 MWh) from Tehri under climate change conditions

while meeting the other constraints. The following sections describes the RRV indices (from

Hashimoto et al., 1982a):

i. Reliability: It is defined as the frequency or probability that a system is in a satisfactory

state. In other words, it is the number of times a system reaches satisfactory state out of

the total number of events. It is mathematically expressed by equation.

𝛼 = 𝑃𝑟𝑜𝑏{𝑋𝑡 ∈ 𝑆} (5.6)

Where, 𝛼 represents reliability and S represents a satisfactory state (targets are met).

ii. Resilience: This defines the ability of a system to bounce back once it has failed. It can

be mathematically expressed by

𝛾 =𝜌

1−𝛼 (5.7)

Where, 𝜌 = 𝑃𝑟𝑜𝑏{𝑋𝑡 ∈ 𝑆, 𝑋𝑡+1 ∈ 𝐹} (5.8)

Where, 𝛾 represents resilience and F denotes a failure state (targets are not met).

iii. Vulnerability: It is defined by the likely magnitude of the failure when one occurs and

can be mathematically expressed by

𝑣 = ∑ 𝑠𝑗𝑗∈𝐹 𝑒𝑗 (5.9)

Where, 𝑣 represents vulnerability, j denotes a failure state, 𝑠𝑗 denotes the magnitude of failure

(in this case it is the difference between generated and target power divided by target power),

𝑒𝑗 is the probability of the event 𝑠𝑗 in a particular sojourn into failure state.

3. Results and Discussion

3.1 Hydrological modelling

Data for three years (2006-2008) was used for calibration of the SWAT hydrologic model in

using the SUFI-2 algorithm while date for two years (2009-2010) were used as validation.




Model was run at daily time step. The model was calibrated for the parameters affecting the

snowmelt and baseflow component. The NSE at daily scale during calibration period was 0.53

which increased to 0.79 during validation period. These values increased to 0.6 and 0.92 for

monthly time steps. Moriasi et al., 2007, stated that NSE above 0.5, at daily time scale, is

considered good and the calibration may be accepted. To ascertain the efficiency of the

meteorological data used to model projected flows, the model was run with data from

individual GCMs and weighted average of all GCMs from 1981-1984. The NSE value for the

weighted average was found to be 0.41 while for the individual GCM (CSIRO) it was 0.22.

Hence the data from the weighted average was used for simulation of projected streamflows

from 2016-2099 for RCP 4.5 and RCP 8.5 and they are shown in Figure 6 and 7, respectively.

Figure 6: Daily streamflow hydrograph and hyetograph from 2016-2099 for RCP4.5.

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(m

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amfl

ow

(cu

mec

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RCP 4.5

streamflow

precipitation




Figure 7: Daily streamflow hydrograph and hyetograph from 2016-2099 for RCP8.5.

3.2 Reservoir operation

Following the methodology described in section 2.3, the operation of the reservoir was

simulated and compared with the observed reservoir levels for 2017 which is shown in Figure

8. The NSE of the simulated reservoir levels was found to be 0.89 and the model was

considered suitable for the simulation of future reservoir operations. The exceedance

probability curves of the projected reservoir levels for both RCP 4.5 and 8.5 scenarios, are

shown in Figure 9. It can be seen that the reservoir levels do not go below 740 m (maximum

drawdown level) and does not exceed 839.5 m (maximum flood level). This indicates that there

are no major challenges to dam operations in either of the two RCP scenarios.

0

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RCP 8.5

streamflowprecipitation

735740745750755760765770775780785790795800805810815820825830835

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Simulated

Observed




Figure 8: Comparison of observed and simulated reservoir levels using reservoir operation

rules

Figure 9: Exceedance probability curve of reservoir elevation for RCP 4.5 and RCP 8.5 for

2017 to 2098.

3.3 RRV analysis

The annual target power is 2.698x106 MWh which was taken as the basis of RRV analysis,

figures 10 and 11 present the power generated, the target power and the streamflow for the year

2017-2098 for RCP 4.5 and RCP 8.5 scenarios respectively. In the RCP 4.5 scenario, where

the projected streamflows are high, the projected power generation is also higher than the target

power in all the years.




Figure 10: Power generated, target power and streamflow at Tehri reservoir from 2017 to

2098 for RCP 4.5 scenario.

Figure 11: Power generated, target power and streamflow at Tehri reservoir from 2017 to

2098 for RCP 8.5 scenario.

It can be seen from the figures 10 and 11 that the power generation varies according to the

streamflow. From the above figures, it is inferred that the target power is met for every year

under the RCP 4.5 scenario while it is not met for few instances in the RCP 8.5 scenario. This

is due to the low streamflow, as seen in figure 11, which can be traced back to low projected

rainfall in RCP 8.5 scenario (Fig 8).

Since the irrigation requirement and flood control requirement were included as constraints

into the reservoir operation simulation, it can be said that under both climate change scenario,




the reservoir is successful in fulfilling these objectives. However, it fails to fulfil its power

output obligation in a few instances in RCP 8.5 scenario. Consequently, RRV analysis was

carried out, to quantify the performance of the reservoir with respect to power generation, only

for RCP 8.5 scenario. The reliability, resilience and vulnerability values are 0.916, 0.857 and

0.265 respectively.

The results demonstrate that reservoir reliability is 0.916, resilience is 0.857 and vulnerability

is 0.265. The results of the RRV analysis indicate that although the reservoir is not able to fulfil

its power generation targets in all the years (7 years) in the RCP 8.5 scenario, it has high values

of reliability and resilience and a low value of vulnerability. The reliability value can tell us

that the probability of the reservoir failing to generate required power is less than 0.1 indicating

occurrence with low frequency and may be managed with short term reservoir planning. The

high value of resilience indicates that the reservoir does not stay in the state of failure for

consecutive events even in the case of failure. Thus, it may be said that inability to generate

required power is not chronic and is a consequence of faltering inflows.

Reliability and resilience is linked to each other and the improvement of reliability will improve

resilience. Vulnerability, which indicates the magnitude of failure, is an important aspect of

integrated planning since mitigation strategy needs to be designed in a way which is

proportionate to the magnitude of failure of the system. In case of Tehri, it can be seen that the

values of vulnerability are low indicating that on the occasions of failure to meet the generation

target, the difference between targeted and generated power is not large. The average (of 7

years) difference between the generated and targeted power is 18%. Thus, it may be concluded

that the cost of compensation for the failures will be limited. It can be summarised that a better

short-term reservoir operation planning and management can address these issues of reliability

resilience and vulnerability.

6. Conclusion

This study intended to analyse the operation of the Tehri reservoir under the projected climate

change scenario by modelling the future flows into the reservoir and modelling the operation

of the reservoir based on those flows. Gridded data was used to set up and calibrate the SWAT

model. NSE values for the calibration and validation periods were 0.53 and 0.79. It was also

seen that the model performs better with weighted average of GCMs in comparison to

individual models. Based on this, the projected flows for the reservoir were generated from

2016-2099 at Tehri for RCP 4.5 and 8.5 scenarios. The operations of the reservoir were also

modelled successfully (NSE 0.89) and on the basis of this model the future operations were

simulated. It was seen that while the reservoir was able to meet all the constraints in RCP 4.5

scenario, in the RCP 8.5 scenario the annual hydropower generation of the target of 2.698x106

MWh could not be met in seven years. This can be attributed to the lesser projected rainfall in

RCP 8.5 scenario. The RRV analysis show that for RCP 8.5 the values of the reliability,

resilience and vulnerability are 0.916, 0.857 and 0.265 respectively. This shows that though

the target power could not be met for seven years the reliability and resilience is still high




indicating no continuous failure. The low values of vulnerability show short term adjustments

can address the issues of underproduction of power in future.

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