Flow Regime Alteration Due to Anthropogenic and Climatic

Ecohydrology & Hydrobiology 14 (2014) 182–191

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Ecohydrology & Hydrobiology

jo urnal homepage: www.elsevier.com/locate/ecohyd

Original Research Article

Flow regime alteration due to anthropogenic

and climatic changes in the Kangsabati River, India

a, a a a

Neha Mittal *, Ashok Mishra , Rajendra Singh , Ajay Gajanan Bhave ,

Michael van der Valk

Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, India

The Netherlands National Committee IHP-HWRP, The Netherlands

A R T I C L E I N F O A B S T R A C T

Article history: According to the ‘natural ﬂow paradigm’, any departure from the natural ﬂow condition

Received 15 September 2013 will alter the river ecosystem. Flow regimes have been modiﬁed by anthropogenic

Received in revised form 27 May 2014 interventions and climate change is expected to cause additional impacts by altering

Accepted 2 June 2014 precipitation extremes. This study aims to evaluate the observed hydrologic alteration

Available online 17 June 2014

caused by dam construction and simulate alteration due to expected climatic changes in a

monsoon dominated mesoscale river basin in India. To analyze the natural ﬂow regime, 15

Keywords:

years of observed streamﬂow (1950–1965) prior to dam construction is used. Future ﬂow

Indicators of Hydrologic Alteration

regime is simulated by a validated hydrological model Soil and Water Assessment Tool

Climate change

(SWAT), using four high resolution (25 km) Regional Climate Model (RCM) outputs for

Anthropogenic intervention

SWAT the near future (2021–2050) based on the SRES A1B scenario. Finally, to quantify the

hydrological alterations of different ﬂow characteristics, the Indicators of Hydrologic

Alteration (IHA) method which is based on the Range of Variability approach is used. This

approach enables the assessment of ecologically sensitive streamﬂow parameters for the

pre- and post-impact periods. Results of our analysis indicate that ﬂow variability in the

river has been signiﬁcantly reduced due to dam construction with high ﬂows being

reduced and low ﬂows during non-monsoon months considerably enhanced. Streamﬂow

simulated based on projected climatic changes reveals reduced monsoonal ﬂows with

marginal changes in non-monsoon streamﬂow. The combined effect will reduce ﬂow

variability, potentially affecting the ecosystem. We conclude that in such modiﬁed basins,

adaptive river basin management will be necessary to maintain such an extreme river ﬂow

regime for the long term viability of riverine ecosystems.

ß 2014 European Regional Centre for Ecohydrology of Polish Academy of

1. Introduction

This paper in its early version was presented at the International

Symposium Ecohydrology, Biotechnology and Engineering: Towards

Amidst a growing understanding that sufﬁcient water is

Harmony between the Biogoeosphere and Society on the basis of Long-

essential for the sustainability of riverine ecosystems,

Term Ecosystem Research held in Ło´ dz´, 17–19th September 2013. The

special issue of the symposium papers was published in Ecohydrology & signiﬁcant stress has been placed on assessing important

Hydrobiology, Vol. 14, No. 1. 2014. characteristics of the river ﬂow regime needed to sustain

* Corresponding author. Tel.: +91 9775040454.

ecosystem functions. Poff et al. (1997) have discussed how

E-mail addresses: [email protected] (N. Mittal),

the ecological integrity of riverine ecosystems is a function

[email protected] (A. Mishra), [email protected]

(R. Singh), [email protected] (A.G. Bhave), [email protected] of ﬂow characteristics and their interactions with water

(M. van der Valk). quality, energy sources, physical habitat and biota. The

http://dx.doi.org/10.1016/j.ecohyd.2014.06.002

N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191 183

natural dynamism of a river is demonstrated by the ﬂow 2. Methodology

variability across hourly, daily, seasonal, annual and longer

2.1. Study area

time scales. Primarily, ﬁve ﬂow components; magnitude,

frequency, duration, timing and rate of change, which have

The Kangsabati river basin is located mostly within the

a signiﬁcant inﬂuence on the ecological dynamics of river

Indian state of West Bengal, bounded by the 868 E and

systems, have to be ‘mimicked’ in order to maintain 0 0 0

87830 E longitudes and the 22820 N and 23830 N

ecosystem diversity and the diversity of organisms therein 2

latitudes and having an approximate area of 5796 km .

(Poff et al., 1997; Gibson et al., 2005; Arthington et al.,

The Kangsabati river ﬂow in a south-easterly direction

2006; Laize´ et al., 2013).

before becoming the last contributing river to the Ganges

Several studies over the past decade have discussed

river in India. The river basin is traditionally considered

changes in river ﬂow regimes brought about by human

drought prone (Saxena, 2012), although it receives an

intervention (Gibson et al., 2005; Sun and Feng, 2012; Yang

average annual rainfall of 1200 mm. The high concentra-

et al., 2012; Poff and Matthews, 2013). Dams are known to

tion of rainfall during monsoon (JJAS) months results in a

alter natural ﬂow regimes of rivers when compared to the

skewed streamﬂow distribution with large differences in

pre-impoundment period and have been found to homog-

daily and monthly river ﬂows within the natural ﬂow

enize distinct river ﬂow regimes across broad geographic

regime. The Kangsabati river which originates in the

scales (Poff et al., 2007). Simplistic and static rules for

uplands of the Chhota Nagpur plateau has caused highly

delineating environmental ﬂow requirements may be

eroded and gullied topography in the upper reaches

detrimental to the critical ecological functions of river

where ephemeral 1st and 2nd order streams are domi-

ﬂow variability and sustenance of ecosystems (Arthington

nant. This eroded landscape turns into undulating

et al., 2006). Dampened seasonality and interannual ﬂow

uplands towards the middle reaches and ﬁnally alluvial

variability of rivers due to dams alters the habitat

plains in the lower reaches. The Kangsabati reservoir

dynamics which makes assessment of these impacts

project is located at the conﬂuence of the Kangsabati river

critical from the point of view of biodiversity conservation

and its major tributary; the Kumari. This reservoir, built in

(Poff et al., 2007). In their global analysis, (Do¨ll and Zhang,

two phases (1965 and 1973), receives inﬂow from both

2010) ﬁnd that along with ecological impacts of water

sub-basins. Along with the provision for irrigation and

diversion by dams, climate change induced ﬂow altera-

domestic water consumption needs the project also

tions can have a strong impact on freshwater ecosystems. 2

provides 246.7 million m storage for ﬂood relief. Along

However, a strong regional variation in dam impacts may

with these beneﬁts, drought mitigation, pisciculture and

be observed in such studies, and also might have been

recreational facilities are also a part of the project (Saxena,

underestimated. For an improved ability to gauge potential

2012). Some of the storage capacity has diminished over

implications for riverine ecosystems, which experience

the past 40 years due to siltation. As a result, the current

different types of ﬂow regimes across different regions,

reservoir storage capacity is about 1/3rd of the annual

observed changes at a basin scale need to be analyzed,

reservoir inﬂow (Bhave et al., 2013b). Previous studies in

along with ﬂow alterations caused by future climate

this basin (Bhave et al., 2013a,b; Mittal et al., 2013) have

change.

discussed stakeholder experiences of past climatic

In this study, we analyze long-term observed and

changes. Moreover, projected climatic changes in mid-

simulated streamﬂow at a location which lies downstream

21st century demonstrate an ampliﬁcation of the impacts

of a large earthen dam on the Kangsabati river to evaluate

due to changing precipitation extremes, particularly,

dam and future climate change induced ﬂow alterations.

consecutive dry days (CDD), highest ﬁve day precipitation

This river, located in the lower Ganges basin, has been

(RX5D) and number of days with precipitation greater

dammed to create the Kangsabati reservoir which provides

than 50 mm (PD50).

ﬂood/drought relief and diverts water for irrigation and

domestic purposes in the command area. Flow regime

changes due to dam construction could be exacerbated in 2.2. Analytical approach

the coming decades as future climate projections forecast a

general decrease in precipitation, increase in temperature Availability of long duration river discharge data is an

and an intensiﬁcation of climatic extremes in the basin essential requirement for assessing ecologically important

(Mittal et al., 2013). Use of hydrologic indicators for characteristics of the natural ﬂow regime. Kennard et al.

assessing environmental ﬂow requirements. From the (2010) suggest that hydrologic metrics should be based on

myriad hydrologic indicators available for assessing more than 15 years of discharge data to reduce bias and

environmental ﬂow requirements (Olden and Poff, 2003; increase accuracy. Mohanpur, a discharge gauging station

Suen, 2010), we employ the Range of Variability Approach of the Irrigation & Waterways Department, Government of

(RVA) (Richter et al., 1997) derived from aquatic ecology West Bengal, approximately 80 km downstream of the

theory which involves a detailed assessment of the Kangsabati reservoir has recorded and made available

hydrological variability of river. This method is useful daily discharge data from 1950 onwards. Natural river ﬂow

for detecting changes in the hydrological regime due to a conditions existed for a 16-year period between 1950 and

known perturbation and for establishing ﬂow-based river 1965. The period between 1965 and 1973 constitutes an

management targets (Chen, 2012) making it suitable for intermediate period between the completion of the Phase I

analyzing the impact of human intervention and climate and II of the Kangsabati reservoir project. The 37-year

change on the Kangsabati river basin. period from 1974–2010 marks the post-dam period where

184 N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191

Table 1

SWAT model performance of ﬁve calibrated subbasins in the Kangsabati basin.

River discharge station Calibration Validation

2 2

P factor R factor NSE R P factor R factor NSE R

Simulia 0.74 1.08 0.63 0.69 0.71 0.94 0.53 0.69

Tusuma 0.78 0.75 0.72 0.86 0.76 0.65 0.76 0.79

Rangagora 0.68 1.1 0.74 0.66 0.62 1.02 0.67 0.66

Kharidwar 0.72 0.84 0.64 0.75 0.79 0.9 0.64 0.75

Mohanpur 0.4 0.59 0.68 0.87 0.63 0.72 0.49 0.85

dam effects on ﬂow regime are studied using Mohanpur as period 2021–2050 represent future hydrologic conditions

the reference location. under a climate change scenario.

A calibrated SWAT model is used for simulating future Based on this understanding, observed and simulated

runoff (Section 2.3). Kangsabati reservoir is not considered ﬂows are then analyzed with the help of the Indicators of

during the model setup, so as to isolate the climate change Hydrologic Alteration (IHA) software of The Nature

signal and analyze only the impact of climate change. Conservancy (2009). Pre- and post-impact periods are

Taking due notice of the oft-discussed uncertainty issues, it compared in this approach to evaluate the degree of

has been proposed that climate-induced changes in river hydrologic alteration caused by the intervention. More-

ﬂow regime can be best analyzed using multiple climate over, recent addition of the Environmental Flow Compo-

models (Do¨ll and Schmied, 2012). Regionally relevant, nents (EFCs) in the IHA software provide greater support

comprehensive and state-of-the-art high resolution RCM for translating key ﬂow characteristics into environmental

climate simulations are therefore used here (Mathison ﬂow management (Mathews and Richter, 2007). The entire

et al., 2013; Mittal et al., 2013). SWAT simulations for the range of hydrologic metrics available are compared to

period 1970–1999 are based on historical simulations of determine key alterations in the natural ﬂow regime and

climate models which represent the natural climatic their potential implications for the riverine ecosystem are

variability in the basin, while SWAT simulations for the discussed.

Fig. 1. Map of the Kangsabati basin showing land cover land use classes, location of hydro-meteorological stations and Kangsabati reservoir.

N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191 185

2.3. SWAT model and data for P factor is 1 (100% values within the band) (Vagheﬁ

et al., 2013). Input data used for SWAT simulation includes

SWAT 2009 (Neitsch et al., 2009) is used to simulate a 90 m 90 m resolution Shuttle Radar Topography

river discharges for observed and future period. SWAT Mission (SRTM) DEM and soil data from survey toposheets

typically operates on a daily time step and accounts for of the National Bureau of Soil Survey and Land Use

spatial heterogeneities by subdividing the basin into Planning (NBSS&LUP), Govt. of India. Unsupervised land

multiple hydrological response units (HRUs). Each HRU use classiﬁcation of LandSat ETM and ETM+ images for the

represents a unique combination of soil, land cover and time periods 1990 and 2001 are used for the calibration

elevation. The rainfall-runoff model simulates the dis- (1991–2000) and validation (2001–2010) periods respec-

charge from each sub basin and routes the streamﬂow to tively, while 2011 land use information is used as reference

the watershed outlet (Neitsch et al., 2009). Preprocessing for the future (2021–2050) period.

and model setup are performed using the Arc-SWAT SWAT simulations are driven by four RCM simulations

extension for ArcGIS 9.3. The Sequential Uncertainty and their ensemble mean (refered as MME hereafter). RCM

Fitting algorithm (SUFI-2) (Abbaspour, 2007) is used to simulations are obtained by forcing two regional climate

calibrate SWAT and to perform uncertainty analysis at ﬁve models REMO and HadRM3 with two CMIP3 GCMs

streamﬂow gauge stations in the Kangsabati Basin. Two ECHAM5 (Roeckner, 2003) and HadCM3 (Gordon et al.,

optimization criteria are used to assess model perfor- 2000), under the SRES A1B scenario. The resulting four

mance: Nash-Sutcliffe efﬁciency (NSE) and the coefﬁcient simulations are REMO-ECHAM5, HadRM3-ECHAM5,

2 2

of determination (R ). Both NSE and R describe the HadRM3-HadCM3 and REMO-HadCM3. This is the only

goodness-of-ﬁt between simulated and observed ﬂow; and and the most comprehensive set of high resolution

the model simulation would be considered best when their (25 km) future climatic projections available for the

values approach one (Moriasi et al., 2007). SUFI-2 Kangsabati river basin developed under the EU project

quantiﬁes the uncertainty using P factor and R factor HighNoon (http://www.eu-highnoon.org/). The perfor-

statistics. The P factor, which varies from 0 to 1, represents mance of these RCMs for the Kangsabati basin has been

the fraction of observed discharge which falls within the validated by comparing 20 year model simulations for the

95PPU band, while the R factor is derived by taking the period 1989–2008, driven by lateral boundary forcings

ratio of the average width of the 95PPU and the standard from ERAInterim reanalysis data (Simmons et al., 2007),

deviation of the observed discharge. While a value of less with the observational datasets; Climate Research Unit

than 1 is considered desirable for R factor, the ideal value (CRU) for temperature and Asian Precipitation Highly

7-day minimum 30-day minimum 8 a) 7

/s) st I phase nd 3 6 II phase 5 4 3

Streamflow (m Streamflow 2 1

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

9000

1-day maximum 3-day maximum 7-day maximum 30-day maximum

8000 st I phase IInd phase b) 7000 /s)

3 6000 5000 4000 3000

Streamflow (m Streamflow 2000 1000

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Fig. 2. Changes in annual (a) minimum and (b) maximum streamﬂow from 1950 to 2010.

186 N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191

Resolved Observational Data (APHRODITE) for precipita- of hydrologic alteration in ecologically relevant statistics,

tion. Both the RCMs have demonstrated an adequate the IHA estimates 67 hydrologic parameters derived from

ability to capture the seasonal characteristics and interan- daily ﬂow statistics. These are subdivided into two

nual variability (IAV) of temperature and precipitation categories: 33 IHA parameters and 34 Environmental Flow

(Mittal et al., 2013). The study also evaluates the Components (EFC). Most widely used IHA parameters

applicability of these simulations for the Kangsabati basin, consist of ﬁve major categories: (i) magnitude of monthly

while the high spatial resolution of these simulations have streamﬂows; (ii) magnitude of and duration of annual

been found to be useful for hydrological assessments extreme ﬂows; (iii) timing of annual extreme ﬂows; (iv)

(Bhave et al., 2013a,b; Bhave et al., 2014). Two sets of RCM frequency and duration of high and low pulses and (v) rate

simulations are used for the analysis; (i) historical and frequency of flow changes. The five flow components

simulations (20c3 m) for the period 1970–1999 (ii) future which describe the ways in which an organism experiences

simulations for the period 2021–2050 based on the A1B river ﬂow variability include: (i) extreme low ﬂows; (ii)

SRES emission scenario. low flows; (iii) high-flow pulses; (iv) small floods and (v)

large ﬂoods (Mathews and Richter, 2007). The IHA covers

2.4. Indicators of Hydrologic Alteration most ﬂow components and is useful for analysing high

information and non-redundant indices. However, the

The IHA methodology, which is conceptually based on choice of relevant indices has to be assessed on a case-to-

the Range of Variability Approach (RVA), is used to assess case basis (Olden and Poff, 2003). In this study, only those

degree of departure from the natural flow regime due to results which depict significant changes in flow regime are

dam and projected climate change (Richter et al., 1996, presented and discussed to increase their relevance.

1997). IHA, originally designed to analyze hydrologic

effects of dams by comparing streamﬂow in pre- and post-

3. Results and discussion

impact periods, assumes that natural ﬂow alteration alters

the ecosystem. It is useful to compare ﬂow parameters

3.1. SWAT model calibration and validation

under different scenarios (observed and future period in

this study) and particularly for assessing key ﬂow regime Results of the model performance and degree to which

characteristics, such as magnitude, timing, frequency and it accounts for uncertainty are evaluated in terms of P

duration (Pradhanang et al., 2013). To analyze the degree factor, R factor, NSE and R for both calibration and

Pre-dam Post-dam

100 February May 100

10 10

1 1 /s) 3 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90

August 1,000 November

Flow rate (m 1,000

100 100

10 10

1 1

10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90

Exceedence probability Excee dence probability

Fig. 3. Flow duration curves of seasonal streamﬂow for reference months February, May, August and November for pre-dam (1950–1965) and post-dam

(1974–2010) periods.

N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191 187

validation periods (Table 1). The P factor indicates that data and riparian species (Poff et al., 1997). Alterations due to

for majority of the stations are well bracketed in predicting dam (Figs. 2, 3 and 4a) reveal a marked reduction in peak

the uncertainty of the model, whereas the R factors are ﬂows, increase in low ﬂows and because of water diversion

mostly below or around 1. The small values for P and larger for irrigation and a reduction in total annual discharge. In

values for R factor in case of Mohanpur gauge station Fig. 4(a), middle, low and high alterations correspond to

indicate higher prediction uncertainties. NSE and R values changes in the median, 25th percentile and 75th percentile

for all stations reveal that discharge simulation is satisfac- flows. Dam construction modifies the natural flow regime

tory based on the criteria given by Moriasi et al. (2007). NSE directly by storing high ﬂows and maintaining certain

values lie between 0.6 and 0.8 whereas R values vary from minimum ﬂows during the non-monsoon months (Fig. 4a).

0.6 to 0.9 for majority of the stations except for Mohanpur. In Absorbing high ﬂows, which cause downstream ﬂoods, is

general, in the downstream of Kangsabati dam, the model an important function of the dam, while providing water

prediction has larger uncertainties. The poor calibration and for domestic consumption and irrigation in downstream

validation results in case of managed downstream ﬂow has sections necessitates regular dam releases during non-

also been observed in other studies (Faramarzi et al., 2010; monsoon months. Such alterations have been known to

Vaghefi et al., 2013) (Fig. 1). cause geomorphic simplification, floodplain disconnection

and disruption of lateral and longitudinal connectivity,

3.2. Flow regime alterations due to dam thereby affecting habitat dynamics and making it difﬁcult

for native biota to adapt (Bunn and Arthington, 2002; Poff

Natural ﬂow variability is essential to maintain et al., 2007). From pre-dam to post-dam period, the 1-day

channel and ﬂoodplain habitats, which support aquatic through 7-day minimum ﬂows remain almost the same,

160 Middle alteration Low alteration High alteration Pre-dam Post-dam 1 0.8 140 (a) 0.6 120 0.4

/s 100 0.2 3 0 80 -0.2 60 -0.4 Discharge m Discharge

-0.6 Hydrologic Alteration 40 -0.8 20 -1

0 -1.2

J F M A M J J A S O N D

9 1.5 Middle alteration Low alteration High alteration Pre-impact Post-impact 8 (b) 1 7

6 0.5 /s 3 5 0 4

Discharge m Discharge 3 -0.5 Hydrologic Alteration 2 -1 1

0 -1.5

J F M A M J J A S O N D

Fig. 4. Monthly median river ﬂow rates and hydrologic alterations for (a) pre-dam (1950–1964) and post-dam (1974–2010) and (b) control (1970–1999)

and future climate change (2021–2050) periods at the Mohanpur gauging station.

188 N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191

Table 2

Changes in annual mean, minimum and maximum ﬂows between pre- and post-dam periods. p-values from Mann–Whitney U-test at 10% signiﬁcance level.

Streamﬂow Pre-dam Post-dam Relative change p-value

(m3/s) (1950–1965) (1974–2010) (%)

1-day minimum 0.0 0.0 0.0 0.117

3-day minimum 0.0 0.0 0.0 0.094

7-day minimum 0.0 0.6 0.0 0.777

30-day minimum 1.1 2.5 127.3 0.287

90-day minimum 4.3 4.5 4.7 0.642

1-day maximum 2240.0 1105.0 50.7 0.007

3-day maximum 1647.0 774.7 53.0 0.014

7-day maximum 1024.0 474.3 53.7 0.040

30-day maximum 476.1 253.2 46.8 0.051

90-day maximum 254.1 190.5 25.0 0.077

whereas there is a marked increase in 30-day minimum of completion of dam construction. Such an effect is part of

about 127% (Table 2). There is a statistically signiﬁcant the regularly observed dampening effect of dams (Poff

difference (Mann–Whitney U-test) in the maximum ﬂows et al., 1997; Bunn and Arthington, 2002; Poff et al., 2007).

during the post-dam period. As shown in Fig. 2, the 30 Conversely 1-day through 90-day maximum ﬂows have

and 90-day minimum ﬂows increase dramatically after reduced signiﬁcantly because the dam absorbs and stores

Extreme low flows High flow pulses 200 300 Timing Timing 180 280 260 160 240 140 220 120 200

100 180

80 160 16 35 14 Frequency Frequency 12 30

10 25 8 Frequency Julian days (1-366) 6 20 4 15 2

0 10 40 6 35 Duration Duration 5 30 25 4 20 3 Days 15 2 10 5 1 0 0

1986 2034 2038 2042 1970 1974 1978 1982 1990 1994 1998 2022 2026 2046 2050 2030 2038 1974 1978 1982 1986 2026 2042 2046 2050 1970 1990 2022 2030 2034 1994 1998

Control Future 25th percentile 75th percentile median

Fig. 5. Timing, frequency and duration of ecologically important extreme low ﬂows and high ﬂow pulses for the control (1970–1999) and future climate change (2021–2050) periods.

N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191 189

high ﬂows generated by isolated precipitation events along hatching, rearing, movement onto the ﬂoodplains for

with the monsoon high ﬂows (Fig. 3). feeding and reproduction or migration upstream and

Fig. 3 and Table 2 provide an estimate of ﬂow duration downstream (Poff et al., 1997). Channel bed sedimentation

curves, which depict the percentage of time a speciﬁed brought about by reduction in ﬂoods also causes degrada-

discharge has been equaled or exceeded during the pre- tion of their habitat (Lisle, 1989; Mishra et al., 2009). The

and post-dam period, for four months (February, May, observed decline in these two species since 1960 and the

August and November) representative of four seasons (DJF, consequent absence by 2002 near Mohanpur station

MAM, JJAS and ON) respectively. In the post-dam period, (Mishra et al., 2009) may be associated with the hydrologic

there is an overall increase in the ﬂow rate in February, changes brought about by Kangsabati dam. Overall, it is

while the zero-flow duration has significantly reduced. observed that while low flows are enhanced, the high flows

Summer rice (Boro rice) water demand in the reservoir are reduced by the dam leading to a dampening of the

command area during summer months may be associated extreme seasonality of river ﬂows.

with signiﬁcant reduction of high ﬂows during May. There

is also a reduction in ﬂow rate especially for low ﬂows in 3.3. Flow regime alterations due to climate change

August and November while high ﬂows in November

remain consistent. Due to dam construction, the frequency The isolated impact of climate change on runoff, based

of occurrence of small ﬂoods (deﬁned as having a return on MME climate projections (2021–2050), is simulated

interval between a 2 and 10 years) has reduced signiﬁ- without considering the effect of the Kangsabati dam using

cantly. The dam has eliminated the large ﬂoods (deﬁned as SWAT. The results show an increase in monthly median

having a return interval greater than 10 years). Siluroid streamﬂow for winter months, with the maximum

ﬁshes, especially species such as Bagarius bagarius and increase during December (Fig. 4b) as compared to control

Mystus gulio, which are found in the Kangsabati River, are period (1970–1999). Analysis of IHA and EFC parameters

highly sensitive to reduction in high ﬂows. Benthic siluroid shows that there is an overall reduction in median ﬂows

ﬁshes are very good indicators of environmental degrada- during monsoon season with maximum reduction ob-

tion (Wootton et al., 1996) and changes in the timing of served during August. As shown in Fig. 5, the median

extreme low flows and high flow pulses affects their life timing of the extreme low flows and high flow pulses

cycle by disrupting various stages such as spawning, egg comes later in the year. However, whereas there is an

Control Future

1,000 February May

100 100

1 0.1 /s) /s) 0.1 3 0.01 3 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90

1,000 August 1,000 November Flow rate (m Flow rate (m

100 100

10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90

Exceedence probability Excee dence probability

Fig. 6. Flow duration curves of seasonal streamﬂow for reference months February, May, August and November for the control (1970–1999) and future

climate change (2021–2050) periods.

190 N. Mittal et al. / Ecohydrology & Hydrobiology 14 (2014) 182–191

increase in variability of timing of extreme low ﬂows, for nutrient inﬂux and water quality parameters also need to

high ﬂows there is a decrease. There is a slight reduction in be monitored for better understanding of ecosystem

the frequency of both, extreme low ﬂows and high ﬂow impacts which will inform the method of river restora-

pulses, which affects the overall ﬂow variability. Compared tion. Integrated assessments in the future may aid much

to the control period, the duration of low ﬂows has needed adaptive river basin management, in such a

increased by approximately 11% while for high ﬂow pulses modiﬁed river basin, to maintain the naturally extreme

there is a change in the 25th–75th percentile range river ﬂow regime.

without any change in the median duration. There is a

marked difference between the ﬂow duration curves for Conﬂict of interest

control and future period for months May, August and

November (Fig. 6). For May and August high ﬂows are None declared.

intensiﬁed which may be associated with the increase in

Rx5D and PD50 precipitation extremes (Mittal et al., 2013). Financial disclosure

For February, results show a slight increase in streamﬂow

for the extreme low ﬂows, which always remain above the None declared.

1 m /s ﬂow rate. The medium ﬂows from approximately

3 3

80 m /s to about 1 m /s show a signiﬁcant decrease during Acknowledgement

May. On the other hand, high ﬂows during the post-

monsoon November month shows are reduced from

This work has been supported by the HighNoon project

3 3

approximately 1050 m /s to 850 m /s. An increase in the

(www.eu-highnoon.org), funded by the European Com-

duration of extreme low ﬂows has been known to decrease

mission Framework Programme 7 under Grant No. 227087.

species richness of macroinvertebrate assemblages and

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