European Water 50: 3-23, 2015. © 2015 E.W. Publications

Impact of dam and barrage on flood trend of lower catchment of basin, eastern

K.G. Ghosh1* and S. Pal2 1 Postdoctoral Independent Scholar (Geography), Santiniketan, West , India 2 Assistant Professor, Department of Geography, University of Gour Bango, Malda, , India * e-mail: [email protected]

Abstract: In the present paper flood trend and frequency of the lower Mayurakshi river basin in relation to Masanjore dam and Tilpara barrage has been focused. For the analysis discharge, inflow-outflow, water level and rainfall data (1945- 2013) have been incorporated. Results show that, there is a sequence of high and low flow in the lower Mayurakshi river reach and in the last decade flow character was quite intensive. In the post-dam period average flood peaks have been declined but frequency of medium-small scale subnormal floods have been increased. The chances of floods have become more common in the months of September and October as the annual peak flow reallocates towards these months. Rainfall above 500 mm. within 3 or 4 days used to intensify flood incidence downstream due to the combined discharge from Masanjore and Tilpara. The observed peak flow is better fitted with Log-Pearson Type III (LP3) distribution. Recurrence interval of high flood above 200000 cusec discharge is 28 years. The estimated discharges as per LP3 for the return period of 2, 5, 10, 25, 50, 100 and 200 years are 19241.35, 59202.45, 109328.29, 214359.44, 334201.82, 502387.10 and 733880.19 cusec respectively. The study may help to offer decision toward sustainable storm water management.

Key words: Lower Mayurakshi River Basin, Flood Frequency, Probability Distribution, Log-Pearson Type III (LP3), Gumbel’s Extreme Value Type 1 (EV1), Return Period and Goodness of Fit.

1. INTRODUCTION

Dam and barrage have a long documented history, particularly in the Mesopotamia and in the Middle East, dated back to about 3000 B.C. (Viollet, 2005). The magnitude and extent has however increased with the growing demand of water, hydro-power, flood control, irrigation and aquaculture etc. in the 20th century. In 1900 there were only 427 large dams (>15 m. high) around the world, in 1950 the figure jumps to 5268 (ICOLD, 1988). About 45000 large dams (>15 m high) with a total reservoir surface of about 500,000 Km2 and an additional 800000 smaller dams have come up by the end of 20th century (Gleick, 1998; WCD, 2000). Up to 1900 A.D. there were only 81 dams in India, while in 1950 it becomes 302 and in 2012 the figure jumps into 5193 (NRLD, 2014). At present, India’s share on world total dam is 15% (Graf, 1999). Such hydro-engineering activities have played a key role in economic development, serving a variety of purposes particularly in the developing and rural countries. However, flow regulation by dams and barrages has caused some significant changes in the fluvial hydro-systems. Consequently, the issue has become a subject of growing international discourse. Effects of dams and barrages on river flow character have been documented in many studies. Alteration of dam downstream rivers flow regime (Williams and Wolman, 1984; Batalla et. al, 2004; Magilligan and Nislow, 2005) and upstream flow-form functions (Leopold et al., 1964; Klimek et al., 1990; Evans et al., 2007; Florek et al., 2008; Liro, 2014) are two basic dimensions assessed in most of the works. However, study regarding the impact of dam and barrage on flood hydrology particularly in Eastern India is very few (e.g. Pal, 2010, 2015; Ghosh and Guchait, 2014; Ghosh and Mukhopadhyay, 2015). Flood is the most common natural phenomenon and to a large extent unpredictable and entirely uncontrollable too (Dhar and Nandargi, 2003). Floods are inevitable in a fluvial system of tropical 4 K.G. Ghosh & S. Pal climate and they are expected to be repetitive (Sinha, 2011). More than 1/3rd of the world’s land area is flood prone affecting some 82% of the world’s population (Dilley et al., 2005). As per UNDP (2004) approximately 170000 deaths were associated with floods worldwide between 1980 and 2000 (Mosquera and Ahmad, 2010). Statisticians agree that floods are strictly random variables and should be treated as elements of statistics (see Al-Mashidani et al., 1978; Kidson and Richards, 2005; Bernardara et al., 2008; Tung et al., 2011). Flood frequency analysis (FFA) is a viable method of flood flow estimation and critical design discharge in most situations (Haktan, 1992). Distributional analysis of discharge time series is an important task in many areas of hydrological engineering, including optimal design of water storage and drainage networks, management of extreme events, risk assessment for water supply, environmental flow management and many others (e.g. Prasuhn, 1992; Morrison and Smith, 2002; Asawa, 2005; De Domenico and Latora, 2011). Under the assumption that flow of a river remains fairly constant, when one wants to estimate the flood probability and assesses risks associated with, one needs to summarize known knowledge to determine a threshold value and calculate the probability that the river discharge exceed the chosen threshold value (Fernandez and Salas, 1999a &1999b; Fernandes et al., 2010; Um et al., 2010). The probability is called complimentary cumulative distribution function (CDF or CCDF) or also known as the survival function in statistics and flow duration curve (FDC) in hydrology (Smakhtin et al., 1997; Castellarin et al., 2004; Iacobellis, 2008). Distributional analysis is usually carried out using the entire available record, called period of record (Vogel and Fennessey, 1994). Phenomenologically, river flows with time series often exhibit spikes that rise far above the typical values in the series (Anderson and Meershaert, 1998; Villarini et al., 2011). In order to adequate captururing of this behaviour in the distributional analysis, distributions which allocate sufficient probability must be employed. Among such classes of distributions having diverging moments two multi-fractal model Log Pearson Type 3 (LP3) (Pearson, 1930; Chow et al., 1988) and Gumble’s Extreme Value Type 1(EV1) (Gumbel, 1941; Reddy et al., 2006) distributions have attracted widespread attention, especially for modelling of the likelihood of extreme events such as floods. However, straightforward distributional analysis always not connect well with the complicated dynamics of river flows, including fractal and multi-fractal behaviour, chaos-like dynamics, and seasonality (Wang et al., 2006; Bernardara et al., 2008; Zhang et al., 2008; Tung et al., 2011). Moreover, floods designs estimated by fitted distributions are prone to modelling and sampling errors (Alila and Mtiraoui, 2002). To reflect river flow dynamics, it is therefore necessary to carry out goodness of fit test in order to find out which is the best model for FFA. The result of the analysis is likely to be useful for forecasting of the events with large recurrence interval (Pagram and Parak, 2004). Such estimates are essential for flood plain management, future engineering design and modification, protection of public infrastructure, cost reduction and assessing hazards. Flood in lower Mayurakshi river basin (MRB) is an age old event and the documented history of ruinous extreme flood is more than 200 years old (Bhattacharya, 2013) in the River basin. Plenty of flood incidents in the catchment have occurred within the span of summer monsoon (June to September) owing to invasion of cyclonic rain bearing cloud from the Bay of Bengal and the antecedent saturated conditions (AMC) caused by unexpected rain spell occurring within the monsoon period itself (Bhattacharya, 2013; Ghosh and Mukhopadhyay, 2015; Ghosh, 2016). Initial reviewing of the flood incidents of the MRB has revealed that flood frequency has amplified significantly after the barrage construction in the river basin (Ghosh and Mukhopadhyay, 2015; Pal, 2015). It seems to be strange and mysterious. Saha (2011) was concerned about the obnoxious effect of dams while explaining the causes of the flood problems of Bengal. Jha and Bairaghya (2012) argued that Masanjore dam located on the river highly influence the discharge of Tilpara Barrage and mainly Tilpara barrage controls the flood condition of the lower catchment of the MRB. Bhattacharya (2013) and Let (2012) in their doctoral thesis have appositely shown that there is a positive relationship with flood and water release from the dam and barrage. Hence, it is imperative to investigate flood trend and its frequency of the catchment in relation to the flow records available.

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Some of the works done on the flood aspects of this catchment (like, Mukhapadhyay and Bhattacharyya, 2010; Pal, 2010, 2015; Jha and Bairagya, 2012; Bhattacharya, 2013; Ghosh and Mukhopadhyay, 2015) mainly emphasized on flood characters, but present work focuses on the trend of flood flow in connections with the functions of Tilpara barrage and Masanjore dam. It is further attempted to show the best fitted probability distribution type against random peak flood discharges. This paper is organized as follows. In section 2 we described the regional settings of the study area. In section 3 and 4 data used in this study and procedures of analysis followed have been highlighted. In section 5 results of the present analysis and corresponding discussions have been outlined to offer a good understanding. General concluding remarks and recommendations have been given in the section 6.

2. REGIONAL SETTINGS OF THE STUDY AREA

River Mayurakshi (Catchment Area: 5325 sq. Km., Length: 288 Km., Extension: 23° 15' N to 24° 34'15'' N Lat. and 86° 58' E to 88° 20' 30'' E Long.) is a 5th order rain fed tributary of Bhagirathi (see Figure 1). The MRB constituting the transitional zone between two mega physiographic provinces of the Chottanagpur plateau fringe and the Bengal basin is a well known name in the flood scenario of West Bengal (Konar, 2008; Saha, 2011; Bhattacharya, 2013). Originating from a spring at the foothill of Trickut Pahar, the river forms winding movements towards South-East along with the tributaries of Motihari, Dhobi, Pusaro, Bhamri, Tepra, Kushkarani, Sidhweswari etc. at its upper course (O’Malley, 1914). Several tributaries, distributaries, anabranching loop and spill channels – the Manikornika, Gambhira, Kana Mayurakshi, Mor, Beli or Tengramari etc. forms the interwoven network in its lower catchment and flow into the lower pocket of Hizole wetland in the district of . From the Hizole wetland, the river Babla starts its journey finally draining into the river Bhagirathi (Mukhopadhyay and Pal, 2009). Geologically the catchment is having Dharwanian sedimentary deposition followed by Hercinian orogeny in the upper part, lateritic soil and hard clays deposition in the middle catchment and recent alluvial deposition of alternative layers of sand, silt and clay in the lower extensions. On considerations of relief (ranges between 12 m. to 400 m.), the river basin is primarily a part of the farthest eastern extension of the Chotonagpur Plateau in the West and Moribund Delta in the East, often referred to Rahr Bengal (Bagchi and Mukerjee, 1983; Biswas, 1987). Rolling uplands and lateritic badlands in the upper part followed by wide undulating planation surface and low lying flat and depressed land in the middle and lower part of the basin characterize the morphological features. Depressed part and flat slope of the lower catchment is highly vulnerable for long flood stagnation period and frequent flood incidents (Pal, 2010). This region bears the imprints of Late Pleistocene ferruginous formations and the remnants of tropical deciduous forest. The catchment is characterised by dry, mild, sub-humid and subtropical monsoonal type climate and rainfall varies with elevation between 829 mm and 2179 mm between the period 1901 and 2013. During South-West Monsoon (June to September) advective rainfall yields more than 80% out of the total annual average rainfall and this rainfall pattern consequences water crowd in the lower catchment of the basin. The area is covered mostly with the alluvial (younger and older), laterite, loamy (Red and Plateau Stulfs), clayey (Ustochrents and Huplustulfs) soil. Most part of the upper catchment is claded with sparse Sal forest having substantial agricultural invasion and high rate of soil erosion. It leads to aggravating rate of sedimentation in channel. Lower catchment of this area is densely populated (1100/sq.km.) for availing the facilities of fertile flood plain. Intense agriculture practice in this counterpart also aggravating the rate of sedimentation in channel and inspires spilling of flood water. To prevent flood, embankment is erected astride river bank but embank failure often invite flood in its command area.

6 K.G. Ghosh & S. Pal

GANGES RIVER BASIN PAKISTAN CHINA

New Delhi NEPAL BHUTAN

BANGLADESH

Kolkata INDIA MAYANMAR O 1000 ARABIAN SEA 500 km BAY OF BENGAL

Figure 1. Location map of Mayurakshi River Basin in the River Basin, India.

Masanjore dam (661.4 m long and 47.24 m high), constructed over Mayurakshi river is one of the earliest post-independence multipurpose river valley project of Eastern India followed by the construction of Tilpara barrage to feed irrigation water in the agriculturally potential lower catchment. Total areal coverage of Masanjore reservoir is 67.4 square kilometres, water holding capacity is 620,000,000 cubic metres and average release during monsoon is 1.2 lakh cusec. Flood is an age old event in the vast stretches in this lower segment of the present watershed. However, construction of Masanjore dam in 1954 and Tilpara Barrage in 1976 (Figure 2) and consequent storage of water and diversion of water through 216.71 km long main canal and 147.07 km. long branch canal (india-wris.nrsc.gov.in) projects have led to intensive modifications of river flow as well as the dynamic nature of the flood character of the MRB.

Table 1. Location and hydrological appraisal of the Masanjore Dam and Tilpara Barrage.

Particulars Masanjore Dam/Canada Dam Tilpara Barrage Masanjore in Santal Parganga district of Near Suri, of West Bengal (W.B.) Location Jharkhand state on the river Mayurakshi state on the river Mayurakshi 148 km. downstream of the source and 49 km. Distance from Source 99.26 km. downstream of the source downstream from Masanjore dam. 23°06'2.60˝ N. 23°56'46.91˝N. Co-Ordinates 87°18'30.83˝ E. 87°31'30.73˝ E. Catchment area 1859 sq. km. 3,208 sq. km. Full Reservoir Level 398.09 ft. (121.34 m.) 212.04 ft. (64.63 m.) Design Discharge 157,032 Cusec (4,446 Cumec) 300,000 Cusec (8,495 Cumec) Design Flood Level 402.09 ft. (122.56 m.) 213.62 ft. (65.11 m.)

Source: Compiled from http://www.wbiwd.gov.in/index.php/applications/mayurakshi and Saha (2011)

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MASANJORE DAM

(a) (b)

TILPARA BARRAGE

(c) (d)

Figure 2. Satellite imagery of Masanjore Dam (a) Tilpara barrage (c) and respective field photographs (b) and (d).

3. DATABASES

Prediction of the trend of flood peaks, flood discharge, flood hydrograph and flood volume are some important aspects of flood appraisal and are useful for designing several hydraulic structures and forecasting flood flows (Garde, 1998; Kale, 1998; Goswami 1998). In this study annual peak discharge data of Mayurakshi River at Tilpara since 1954 to 2013 and monthly inflow and outflow data from 1990-2013 of the barrage and the dam have been used which we obtained from the measurements/gauging carried out by the Investigation and Planning Circle, Suri, Birbhum, Irrigation and Waterways Directorate, Govt. of W.B. and Masanjore dam authority, Jharkhand, India (2013). River gauge (i.e. water level) data of Mayurakshi river from 1945 to 2013 at Narayanpur gauge station (110 km downstream from Masanjore dam; Bench Mark: 23.71 m.) under the control of Investigation and Planning Circle-1 (Baharampore), Irrigation and Waterways Department, Govt. of W.B. have been used for flow regime assessment. Other information related to flood has been compiled from Sechpatra (Journal) and Annual Report of the Irrigation and Waterways Department, Govt. of West Bengal, India. The available temporal data have divided into three phase: i. un-regulated flow/Pre-Masanjore phase (1945-1953), ii. Dam regulated flow/post- Masanjore-Pre Tilpara Phase- (1954-1975) and Dam and Barrage regulated flow/Post Tilpara Phase (1976-2013). Annual peak rainfall data (1945-2013) has been obtained from http://www.indiawaterportal.org.

4. METHODOLOGIES

Descriptive statistics, curvilinear regression, probability analysis, moving average etc. were applied for hydrological interpretation of stream flow data. To understand flood frequency Gumbel’s Extreme Value Type 1 (EV1) distribution (Gumbel, 1941; Reddy et al., 2006) and Log Pearson Type III distribution (LP3) (Chow et al., 1988; Rao and Hamed, 2000) were used. Kolmogorov-Smirnov test, Anderson-Darling test and Chi-Squared test (Hembree, 2012) have been adapted to measure goodness of fit in between observed predicted discharge. Easy Fit 5.5 Professional software was used for necessary calculation and plotting. Simple Chi-square test has done to assess the significance of controlling power of Masanjore dam on flood occurrences. Detail

8 K.G. Ghosh & S. Pal methods of analysis have been integrated in respective sections.

4.1 Flow Duration Analysis (FDA)

The annual or monthly or daily discharge data is commonly used for drawing the Flow Duration Curve (FDC). In FDC mean flow is plotted against the exceedance probability, P, which is calculated (Raghunath, 2006) as follows: M P= ⎡⎤×100 (1) ⎣⎦⎢⎥n+1 where, P = the probability that a given flow will be equalled or exceeded (% of time), M = the ranked position on the listing (dimensionless) and n = the number of events for period of record (dimensionless)

4.2 Flood Frequency Analysis (FFA)

Annual flood series were found to be often skewed which led to the development and use of many skewed distributions with the most commonly applied distributions now being the Gumbel (EV1), the Generalized Extreme Value (GEV) and the Log Pearson Type III (LP3) (Pilon and Harvey, 1994). However, there is no theoretical basis for adoption of a single type of distribution (Benson, 1968). Actually EV1 and GEV is fitted well when a river is less regulated (Mujere, 2006) but the river Mayurakshi is to some extent regulated by Masanjore Dam and Tilpara Barrage (Bhattacharya, 2013) hence LP3 distribution model have also been used for the present assessment of flood frequency.

4.2.1 LP3 probability distribution model

Using LP3 distributions technique extrapolation can be made of the values for events with return periods well beyond the observed flood events and widely used by Federal Agencies in the United States (Bedient and Huber, 2008). In carrying out FFA using the LP3 distribution, the following steps suggested by Jagadesh and Jayaram (2009) have adopted.

Step I: Peak flow (Xi) during a water year (Shaw, 1983) was assembled to fairly satisfy the assumption of independence and identical distribution (Chow et al., 1988).

Step II: Estimates of the recurrence interval, Tr are obtained using the Cunane plotting position formula (Cunnane, 1978) as recommended in Ojha et al. (2008). n+0.2 Tr = (2) m-0.4 Where, n is the number of years of record and m is the rank obtained by arranging the annual flood series in descending order of magnitude with the maximum being assigned the rank 1.

Step III: The logarithms (base 10) of the annual flood series are calculated as

Yi = Log Xi (3)

Step IV: The mean y ̅, the standard deviation σy and skew coefficient Csy or g of the yi (i.e. Log Xi) were calculated by the formulas-

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n (Log Xi) Y ==LogX ∑1 (4) n ½ ⎡⎤(LogXi− LogX)² σy ==σLogXi ⎢⎥∑ (5) n 1 ⎣⎦⎢⎥( − )

n(LogXi− LogX )³ Csy org = ∑ (6) (n1(n2)−−) σ³LogX where, n is the number of entries.

Step V: The logarithms of the flood discharge or log Q for each of the several chosen probability level P (or return period, Tr) are calculated using the following frequency formula (Arora, 2007; Das and Saikia, 2009).

Log Q=+ yK σ y (7)

or, Log Q=+ LogX K σLogXi (8) where, K is the frequency factor, a function of the probability, P and Skewness coefficient Cs (g) and can be read from published tables of Hann (1977) developed by integrating the appropriate probability density function (PDF).

Step VI: The flood discharge X for each probability level P is obtained by taking antilogarithms of the Log Q values. The design flood itself is given by:

XAntiLogQ= (9)

4.2.2 Gumbel’s EV1 distribution model

The steps are followed to estimate the design flood for any return period using Gumbel’s EV1 distribution as given by Chow et al. (1988) is presented below.

Step I: Annual peak flood data for the river was assembled from 1954 -2013.

Step II: From the maximum flood data for n years, the mean and standard deviation have computed.

Step III: From N, the value of yn (reduced mean, a function of sample size N) and σ n (reduced S.D., a function of sample size N) were obtained from Gumbel’s Distribution Reduced Extreme table of Garg (2007).

Step IV: From the return period, Tr the reduced variate YT (a function of Tr), is computed using equation

⎡⎤T YT = −In In( ) (10) ⎣⎢⎥T1− ⎦

⎡⎤T or, YT = − 0.834+ 2.303LL og og (11) ⎣⎦⎢⎥T1−

10 K.G. Ghosh & S. Pal

Step V: From yn, σ n and YT obtained, the frequency factor, K is computed

Yт − yn K = (12) σn Step VI: With the use of equation (13) the magnitude of flood is computed.

XT = X + Kσn1− (13) where, XT = Probable Peak Discharge, X = Average Flood discharge, σn1− = standard deviation of the sample size n-1, K= frequency factor.

4.4 Plotting positions

As the real frequency distribution of the observed data is unknown so, plotting positions (PP) for the data; i.e. estimates for the likely annual exceedance probability/return period of the observed flood magnitudes need to be found. A frequently used approach is to rank the flood events from largest to smallest and the largest observation is assigned plotting position 1/n and the smallest n/n = 1 for its annual exceedance probability (AEP). The return period of an event is then the inverse of the AEP (Robson, 1999). In practice, there is a range of PP methods available e.g. Hazen (1914), California (1923), Weibull (1939), Beard (1943), Blom (1958), Gringorten (1963), Cunnane (1978), Nguyen et al. (1989), In-Na and Nguyen (1989) etc. For the present FFA Cunnane PP which is approximately quantile-unbiased (Stedinger et al., 1993) have been used for LP3 as recommended by Ojha et al. (2008) and Weibull PP which provides unbiased exceedance probabilities for all distributions (Stedinger et al., 1993) have been used for EV1 as suggested by Lettenmaier and Burges (1982).

4.5 Goodness of fit measures

The goodness fit measure involves identifying a distribution that best fits the observed data. When computing the magnitudes of extreme events, such as flood flows, it is required that the probability distribution function be invertible, so that a given value of recurrence interval and the corresponding value of frequency factor can be determined. Three goodness of fit test namely Kolmogorov-Smirnov test, Anderson-Darling test and Chi-Squared test (Hembree, 2012) were used to select the best flood frequency distribution model for the present study.

5. RESULTS AND DISCUSSION

5.1 Trend of discharge

The highest measured flow of 317124.2 cusec is recorded in the year 2000 at Tilpara barrage, while the lowest peak discharge of 1909.39 cusec is recorded in 1966. A 5-year moving average of the yearly peak discharge data highlights a little bit rising trend of discharge (Figure 3). The flood discharge trend can be sub-categorized into following consecutive phases: i. High discharge phase (1953 to 1960), ii. Low discharge phase (1961 to 1977), iii. High discharge phase (1978 to 1991) iv. Low discharge phase (1992 to 1997) v. Very high discharge phase (1998 to 2007) vi. Low discharge phase (2008 onward). Notably, the high peak discharge level is concentrated between 1998 and 2007. At the same time it should be mentioned that reduced volume of discharge concerned in this river does not mean lessening of spilling magnitude for the reason that, substantial river bed

European Water 50 (2015) 11 aggradations as observed by Bhattacharya (2013) and Pal (2015) supply momentum for steady inundation in the lower reach of this basin. The calculated 60-year mean instantaneous flood discharge was 46505.96 cusec with a standard variation of 71311.08 cusec and a mean coefficient of variation (Cv) is 153.34% as calculated. The Cv value obtained indicates that the distribution of flood flow is highly variable and it does mean growing uncertainty in flood flow prediction. In Figure 3 peak discharge above threshold discharge limit (i.e. the critical discharge limit over which usually causes flood downstream) of 140,000 cusec (Dasgupta, 2002; Konar, 2008) from Tilpara reflects the flood discharges happened in the year 1956, 1959, 1978, 1987, 1999, 2000 and 2007. Notably, in the devastating deluge year 2000 peak discharge amount (317124.152 cusec) have even crossed the design discharge (i.e. the quantity of flow/discharges that a highway drainage structure is sized to handle) limit of 300,000 cusec (8495 cumec).

Figure 3. Time series (1954-2013) plot of the annual peak discharge and 5 year moving average discharges for Mayurakshi River from Tilpara Barrage. The blue ceiling line indicates threshold discharge and the red line indicates design discharge limit for the barrage.

The descriptive statistics of the peak discharge data as outlined in the methodology are presented in Table 2 which reflects that the internal variation of flood discharge is remarkably high.

Table 2. Descriptive statistics of 60 year peak discharge from Tilpara

Statistic Value Percentile Value Sample Size 60 Min 1909.4 Range 315210.00 5% 2100.4 Mean 46506 10% 3205.6 Variance 5085300000.00 25% (Q1) 8075 Std. Deviation 71311 50% (Median) 21028 Coefficient. of Variation 1.5334 75% (Q3) 41457 Std. Error 9206.2 90% 183320.00 Skewness 2.4567 95% 246290.00 Excess Kurtosis 5.3584 Max 317120.00

5.2 Reservoir water level (stage) - Discharge relations

The discharge measurements made at Tilpara gauging station for the period of 1976 to 2013 with simultaneous reservoir water level (stage) observation results have illustrated graphically as shown in Figure 4, by the average curve fitting (power function) the scatter plot between water level (as ordinate) and discharge (as abscissa) on a semi log-graph paper. The trend line derived from the scatter plot is usually referred to as rating curve. This curve further will help to calculate the discharge by simply putting the gauge height.

12 K.G. Ghosh & S. Pal

If Q and H are discharge and water level, then for Tilpara Gauge station the relationship (power function) can be expressed as:

H = 41.87Q0.031or 0.031LogQ = LogH − Log41.87 (14) The co-efficient of determination (R2) calculated was 0.916 indicating that the curve satisfied the statistical goodness of fit criteria. It can then be seen from the Figure 4 that all those points having greater changes of reservoir water level (above 62 metre) the magnitude of discharge has been increased substantially and also the changes are erratic. This again confirms that the unsteady and accelerated discharge during flood is caused by the reservoir.

Figure 4. Rating relationship between the observed stages (reservoir water levels) and the corresponding discharges in a semi log graph paper.

5.3 Flow duration curve assessment

The mean daily discharge data (1990-2013) is used for drawing the FDC. Two maximum and minimum discharges for the said period is 19792 Cusec (Feb, 2005), 16158 cusec (May, 2000) and 2.02 cusec (Nov, 2005), 4.069 cusec (Dec, 2001) respectively. The average annual, average monthly and average daily discharges for the said period is estimated as 411144.42 cusec, 34262.035 cusec and 1175.169305 cusec respectively. Inflow-outflow character signifies nature of storage and downstream flow stability in a reservoir (Thornton et al., 1996). In the present case, inflow and outflow of Masanjore reservoir in different month’s varies respectively from 21% to 53% and 32% to 94% (simple coefficient of variation, CV value). It means that in the dam downstream flow is less consistent than upstream. The annual inflow-outflow ratio ranges from 1.22 to 2.14 in 96% of the years considered and rest 4% years show some abnormal ratio (>8). These demonstrate that half of the inflow volume is stored in the reservoir in most of the years. Moreover, the annual rainfall is found positively and significantly correlated with outflow (r value ranges from 0.608 to 0.912). Therefore, heavy rainfall in monsoon month’s force to release a good amount of water from Masanjore dam to the Tilpara barrage as flood discharges. It is important to note that the estimated mean daily discharge available as inflow (i.e. 2602.74 cusec) to the Tilpara barrage is far more than the actual daily discharge (i.e. 1175.17 cusec). Actually, the extra inflow amount above storage capacity of the Tilpara barrage (i.e. 290,000 Cusecs) is regularly diverted to (i) barrage located on through Mayurakshi-

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Dwarka canal system (Let, 2012) and (ii) Kultore barrage located on Kopai river through Mayurakshi Main Canal and Bakreshwar-Kopai Main Canal (Das, 2014 and Ghosh, 2015). This diversion is high during non-monsoon periods and causes very dying flow during this period. On the other hand peak monsoonal accumulation of water in the storage space of barrage and released discharge from Masanjore dam located upstream promotes excessive discharge downstream what is caused sudden rise of discharge volume. As it is shown on the FDC in Figure 5, the two extreme ends have steep slopes bending upward. In the case of upper-flow region it indicates the type of flow regime that the basin is likely to have flooded, and bending downward in case of dry conditions characterizes the ability of the river to sustain low flows during waterless seasons. A steep slope at the end of low flow region suggests relatively small contributions from natural storage like groundwater and the down streams may cease to flow during extreme dry period. Considering the central portion of the graph, the median flow is almost equal to 500 cusec and low-medium slope in this part of the curve reflects continuous discharge to the stream.

Figure 5. A plot of mean daily discharge verses the flow exceedance percentile for FDC of Mayurakshi River gauged at Tilpara barrage.

5.4 Phases of flow regime

Hydrological data series of Narayanpur gauge station shows that, both during monsoon and pre monsoon, average water level has been declined significantly in both dam and barrage after periods (Table 3). Storage within the reservoirs and water diversion through the irrigation canals as stated earlier is mainly responsible for such declining trend of water level. Maximum duration of high flow period during monsoon has reduced up to 16.5%, coefficient of variation of diurnal fluctuation of water level during monsoon has increased from 31% to 47%, ratio between maximum and mean flow has increased from 1.108 to 1.21; mean monsoon and pre monsoon water level ratio has increased from 1.08 to 1.11 in dam after periods. Moreover, in the years having heavy monsoonal rainfall, the demand for irrigation water is less hence water diversion is also less. Eventually, in those years if short durated huge rain spell occurs, reservoir water level rise significantly. Maximum of such incidents occur during late monsoon month (September). For example, about 872.4 mm. rainfall within 3 days (19th to 21st Sept. 2000) in the catchment area was highly responsible for releasing 487980 cusec of water from the reservoir within 5 days (18th to 22nd Sept.) (Dasgupta, 2002).

14 K.G. Ghosh & S. Pal

Table 3. Phases of hydrological regime at Narayanpur gauge station

Average water level (m.) Average water level departure Phases of Pre- Post- Monsoon-pre- Monsoon to hydrological regime Monsoon monsoon monsoon monsoon post monsoon Before 1954 (Phase-I) 25.163 24.44 24.595 0.723 0.568 1954-1975 (Phase-II) 24.785 24.035 24.266 0.75 0.519 1976-2013(Phase-III) 24.293 23.928 24.172 0.365 0.121 Source: Calculated based on the water level data of Nayanpurra Gauge station, Investigation and Planning Circle-1 (Baharampore), Irrigation and Waterways Department, Govt. of West Bengal.

3500 c

e Pre-Dam Post-Dam

m 3000

u (1945-1953) (1954-1975) C

n 2500 i

Annual Hydrograph

e [Based on Tilpara Gauge Data] g

r 2000 a h

c Post-Dam s i 1500 (1976-2013) D

e

g 1000 a r e v 500 A

0 l t r r v n g p y c b n u c a p o u a e a e e u J O A J J A M F S N D M Months

Figure 6. Annual hydrograph showing shift of peak flow from August (pre-dam) to September (post-dam).

Annual Hydrograph (Figure 6) drawn based on average monthly discharge data (1945-2013) of the Tilpara barrage signifies that the river experiences the tropical single maximum with a long low water (AW) hydrological regime (Beckinsale, 1969). Further, it is noteworthy to mention that, in the pre-dam phase the peak flows was usually in August (avg. 3106.92 cumec); but during the subsequent (i.e. post-dam) phase peak flow shifts to September (2937.79 cumec and 2617.54 cumec). The main cause is that, the dams/barrage temporarily store or divert inflow runoff till late August. Continuation of unexpected heavy rainfall, consequent heavy runoff due to the antecedent soil moisture condition (saturated soil) and increase of water level near critical storage limit, compelled to release water in September. That is why, now the flood probabilities or chances are more common in between September and October.

5.5 Trend of flood frequency

Graf (2006) stated that due to attenuation of peak flow in dam regulated downstream course, the chance of high flood may be reduced. In present study, Masanjore dam and Tilpara barrage have reduced the average water level and thereby reduced the possibility of high flood occurrence but frequency of occurrence of medium-small flood have increased by about 73%. Moreover, in pre- dam condition the flood peaks were unusually high but the duration was small (late Aug to Sept). The dams have now moderated the peaks but increased duration of floods (Aug to Oct). Out of the total 37 micro to macro sized flood in response to spatial extent and flood height since 1900, 28 floods happened after the construction of Masanjore dam (1954) and only 9 were occurred before mentioned time. About 17 floods occurred after the construction of Tilpara barrage (1976) and 14 devastating floods have been recorded during 1950-2010 (Bhattacharya, 2013). Chi square (χ2) test at 5% significance level reveals the fact that calculated value (17.441) is much higher than the tabulated value (3.84) and hence the result shows that these human constructions enhanced the frequency of flood in the downstream catchment. It is true that skewed patterned of high rainfall

European Water 50 (2015) 15 concentration within short period of time is highly responsible for flood incidents but in post-dam period, the combined outflow from Masanjore and Tilpara dams and consequent massive discharge is mainly responsible for the acceleration of flood incidents. Flood affected areas after dam condition has increased by 32% than average flood cover area and worsening of drainage quality has increased 12.4% of flood water stagnation period in this basin (Bhattacharya, 2013). Florsheim and Mount (2002) have gravely raised the issues of complex formation in response to intentional levee breaches in Lower Cosumnes River, California. This kind of embankment breaching event is very frequent in the highly embanked and regulated stream in lower courses of Indian stream naturally. In lower MRB, after the construction of the said dam incidents of embankment breaching has raised to 41.2% (Bhattacharya, 2013) as a result of that heap of coarse sand deposited as sand splay at the outside of embankment breaching points covering a good amount of fertile agricultural land (Pal, 2010).

30 100 n y n o c

i Tilpara Barrage o t m n i e t c a e (1976) g

25 c Before the construction u u D r a 80 u t q r r y e r

of Masanjore Dam t s e r c r a s

n Masanjore Dam o n 20 j F b n o

e n

o (1954) c a d u

a c r o

q e 60 s a e e o h a l p r t h l

i F t M F 15 r

T f f r e

d t f o e o f

t o o

f 40

A o

% l A

y = 1.181x - 1.545

F 10

e 2

f v R = 0.910 i o t

a l

% 20

5 u m u 0 C 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 5 7 3 1 2 4 6 8 9 0 1 0 5 7 3 1 2 4 6 8 9 0 1 9 9 9 9 9 9 9 9 9 9 0 0 9 9 9 9 9 9 9 9 9 9 0 0 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 (a) 1 1 1 1 1 1 2 2 Year (b) Year

Figure 7. Trend of flood incidents at the lower catchment of MRB since 1900 to 2010 (a) trend of % flood frequency and (b) Trend of cumulative % of flood frequency. Source: Plotting of data is based on the study reported on/by Ray (2001); Ankur Patrika (2002); Dasgupta (2001, 2002); Saha (2011); Konar (2008) & annual report (1992; 2007; 2009; 2011; 2014) of the Irrigation and Waterways Department, Govt of West Bengal, Kolkata, India.

5.6 Rainfall – gauge water level and discharge relation

Precipitation is the most important meteorological factor, inducing run off processes on earth (Hirschboeck et al., 2000; Dhar and Nandargi, 2003). Excessive rainfall within very short period of time is the most triggering factor to influence flood events (Burn, 1990; Bloschl et al., 2013). Storm events rainfall associated with sudden dam discharge results in a potential acceleration of the hydrologic cycle leading to greater frequent increase in extreme events like floods. Bhattacharya (2013) in his doctoral thesis (pp.125-127) has vividly revealed that, short duration heavy downpour during late monsoonal months of September or October in most of cases have resulted flood surge in the present river basin. Here an attempt has been taken to correlate i. rainfall and gauge water level (Narayanpur) (Table 4) and ii. Annual peak rainfall and peak discharge from Tilpara Barrage (Figure 8). During pre-dam period (Phase-I) rainfall regulated the channel flow significantly (Table 4), but this natural regulation has declined in the subsequent period (phase-II and III). This indicates that the alterations of flow in the downstream are due to the dam and water diversion from Tilpara barrage. As per the result as shown in Figure 8 the correlation between rainfall and discharge is positive (y = 1454.e0.006x) however, the R2 value (0.316) demonstrates a poor fit. Hence, control of rainfall on discharge is somehow less. This in turn reflects potential responsibility of the barrage on high discharge escalating the potentiality of flood. This type of same inference is drawn by Franchini et al. (1999) in his work.

16 K.G. Ghosh & S. Pal

Table 4. Rainfall-water level (Product Moment) co-relation

Phases of hydrological regime Rainfall/Water Level Pre-Monsoon Monsoon Post-Monsoon Pre-Monsoon 0.717* ------Before 1954 Monsoon --- 0.884* --- (Phase-I) Post-Monsoon ------0.793* Pre-Monsoon 0.316 ------1954-1975 Monsoon --- 0.693* --- (Phase-II) Post-Monsoon ------0.411 Pre-Monsoon 0.294 ------1976-2013 Monsoon --- 0.557* --- (Phase-III) Post-Monsoon ------0.431 N.B. * The relationship is significant at the 5% significance level. Source: Calculation is based on the water level data (1945-2013) of the Nayanpurra Gauge station, Investigation and Planning Circle-1 (Baharampore), Irrigation and Waterways Department, Govt. of W.B. and monthly rainfall data (1945-2013) of the Indian Meteorological department.

Figure 8. Regression (exponential) relationship between rainfall and peak discharge in semi-log graph for period of record (1954-2013) on Mayurakshi River at Tilpara gauging station and Meteorological Station. The relationship is positive and significant at 95% confidence level.

Figure 9. Annual peak rainfall and peak discharges plotting against return period (a) and annual exceedance probability (b). Note the Dark Cyan line have used in Figure 9b to show the break of slope in discharge curve and Red circle to indicate the critical rainfall limit to step up the discharge.

In the subsequent section annual exceedance probabilities were estimated for the annual peak discharges (Qmax) and peak rainfalls (Rmax) using 60 years (1954-2013) discharge and rainfall records of Tilpara barrage and Suri, Birbhum respectively. The annual magnitude and probability of

European Water 50 (2015) 17 flood discharges and storm rainfalls are plotted to annual exceedance probability, AEP (Figure 9). AEP formerly were reported as flood recurrence intervals expressed in years and is the inverse of the recurrence interval multiplied by 100. In Figure 9b it is important to notice that the discharge curve shows clear break of slope when rainfall amount go beyond 500 mm. (mostly concentrated within 3-4 days) and corresponding to the activation of major flood events at around the annual exceedance probability 20%. So it can be estimated that the threshold rainfall is about 500 mm. which often promotes excess discharge from dam and barrage. As per the Sechpatra reports of the flood event 2000 (Dasgupta, 2002) the critical discharge for the barrage during storm rainfall period to cause flood in the MRC is about 180000 cusec. Indeed the curvature of discharge plot also displays almost the same. A very strong message obtained is that, rainfall above a critical range is the leading factor which controls barrage discharge and thereby to intensify flood discharge downstream.

5.7 Flood frequency analysis

5.7.1 Estimated peak flows

Flood peaks corresponding to return periods of 2, 5, 10, 25, 50, 100 and 200 years are estimated in this section to get a trend of flood in different magnitude in the lower MRB. In general, a distribution with a larger number of flexible parameters would be able to model the input data more accurately than a distribution with a lesser number of parameters (Cunnane, 1989). In this case, the LP3 and EV1 showed larger numbers of flexible parameters those have used for estimating flood risks in the catchment. The estimated discharges using LP3 and EV1 distributions are shown in Tables 5 and 6 respectively.

Table 5. Results of the application of the LP3 distribution to observe discharge data.

Return Period, Probability, P Frequency Factor, K X AntiLogQ Tr (Yrs) (%) (g = 0.2328) LogQ=+y Kσy = 2 50 -0.033 4.28423553 19241.34958 5 20 0.83 4.7723397 59202.45277 10 10 1.301 5.03873259 109328.2987 25 4 1.818 5.33114262 214359.4431 50 2 2.159 5.52400881 334201.8195 100 1 2.472 5.70103848 502387.1008 200 0.5 2.763 5.86562517 733880.1989

Table 6. Results of the application of the EV1 distribution to observe discharge data.

T Return Period, X σ yn σ YT K XT Tr (Yrs) n−1 n T −1 2 46505.96 71321.72 0.552 1.175 2 0.367 -0.158 35253.17 5 46505.96 71321.72 0.552 1.175 1.25 1.500 0.807 104081.4 10 46505.96 71321.72 0.552 1.175 1.111 2.251 1.446 149651.7 25 46505.96 71321.72 0.552 1.175 1.042 3.199 2.254 207229.9 50 46505.96 71321.72 0.552 1.175 1.020 3.903 2.852 249944.7 100 46505.96 71321.72 0.552 1.175 1.010 4.601 3.447 292344 200 46505.96 71321.72 0.552 1.175 1.005 5.297 4.039 334588.7

As can be seen from Tables 5 and 6 the estimated stream discharges for return periods of 2 yrs, 5 yrs, 10 yrs, 25 yrs, 50 yrs, 100 yrs and 200 yrs as per LP3 distribution are 19241.35, 59202.45, 109328.29, 214359.44, 334201.82, 502387.10, 733880.19 cusec and as per EV1 are 35253.17, 104081.4, 149651.7, 207229.9, 249944.7, 292344, 334588.7 cusec respectively. These values are

18 K.G. Ghosh & S. Pal useful in the engineering design of hydraulic structure in the catchment and predicting probable flood magnitudes over time.

5.7.2 Regional growth curve analysis

In Figure 10 (a and b) for the site Tilpara return periods are shown against annual observed peak discharges for each distribution (LP3 and LV1). It is noticed that the co-efficient of determination in case of LP3 (R2 = 0.919) is greater than EV1 (R2 = 0.886). Hence, the plot of the annual peak discharge against return period using LP3 presented in Figure 10a having the model relationship y = 70264ln(x) – 22888 which can more effectively be used to extrapolate values of estimated magnitude of peak discharges for different return periods in order to promote integrated water resources planning and management.

Figure 10. Plotting of return periods against annual observed peak discharges (Regional growth curve) for the distribution (a) LP3 and (b) EV1.

Further improvement of the curves might however, be possible by considering discharge data from several nearby sites and including a more detailed investigation of the accuracy of the discharge observations (rating curve analysis) and the uncertainties involved in the extreme value analysis. Also the flooding influences identified from the statistical analysis, need to be cross- checked with physical information on the flood threshold or any other information on historical floods.

5.7.3 Goodness of fit test

PDF for the two distributions shown in Figure 11(a) confirms that the LP3 distributions are most likely to best fit the data. The histogram of annual maximum flood data reveals a positive skewed distribution. The CDF plot in Figure 11(b) shows the non-exceedance probability for a given magnitude. The probability-probability plot in Figure 11(c) represents graph of the empirical CDF values plotted against the theoretical (fitted) CDF values to determine how well a specific distribution fitted to the observed data. From the probability difference plot (Figure 11d) it can be alleged that LP3 distribution can be considered well as the maximum absolute difference is less than 0.1 (or 10%). Actually EV1 is fitted well when a river is less regulated (Mujere, 2006) but the flow character of river Mayurakshi is to some extent regulated by Masanjore Dam and Tilpara Barrage as we have outlined in the previous sections hence EV1 distribution does not fitted well in this river. Table 7 clearly shows that the LP3 distributions provided good estimates for floods in the MRB than EV1. The coefficient of determinations at each distribution shows statistically significant relation between estimated flood peaks and selected return periods, estimated discharge and random

European Water 50 (2015) 19 observed data etc.

0.88 1

Probability Density Function (PDF) )

0.8 x ( ) 0.9 F x

( 0.72 , F n

, 0.8 o i n Histogram t Cumulative Distribution Function (CDF) o

0.64 c i t n 0.7 c Log Pearson 3 (LP3) u n

0.56 F

u 0.6 n F

Gumbles Max. (EV1) o i

y 0.48 t t

i 0.5 u s b i n 0.4 r t e 0.4 s i D 0.32 D y

t

i 0.3

l Sample e i v i b 0.24 t a

a 0.2 Log Pearson 3 (LP3) l b u o 0.16 r

m 0.1 P

u Gumbles Max. (EV1) 0.08 C 0 0 0 40000 80000 120000 160000 200000 240000 280000 320000 0 40000 80000 120000 160000 200000 240000 280000 320000 (a) Random Stream Flow Data (x) (b) Random Stream Flow Data (x)

1 P-P Plot 0.4 Probability Difference Plot

n 0.9 o i

t 0.3 Log Pearson 3 (LP3) u 0.8 Log Pearson 3 (LP3) b i r

t 0.2 Gumble's Max. (EV1) s

i 0.7 Gumble's Max. (EV1) e c D

n

e 0.1 0.6 e v r i t e f a f l 0.5 i 0 u D

m y t u i

0.4 l

i -0.1 C

b l a a b c 0.3 i -0.2 t o i r r P o 0.2 e -0.3 h

T 0.1 -0.4 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 40000 80000 120000 160000 200000 240000 280000 320000 Empirical Cumulative Distribution (d) Random Stream Flow Data (x) (c)

Figure 11. (a) Probability density functions (b) cumulative distribution functions (c) probability-probability plots and (d) probability difference for the two compared frequency distributions.

Table 7. Goodness of fit results

Kolmogorov-Smirnov Anderson- Darling Chi-Squared Distribution Statistic Rank Statistic Rank Statistic Rank Gumbel Max (LV1) 0.28589 2 6.4662 34 16.554 2 Log-Pearson 3 (LP3) 0.07667 1 0.30702 2 4.2329 1

6. CONCLUSIONS

The present study has brought some new insight into the flood trend and frequency in the lower Mayurakshi river concerning the Masanjore dam and Tilpara barrage. It is to be affirmed that, there was a regular sequence of high low peak flow in the lower Mayurakshi river basin. One of the very high flow phases was after construction of Masanjore dam and Tilpara barrage. These two major human constructions have positive role to reduce flood storing water in their reservoirs. But, sometimes excessive rainfall and brimful reservoirs have invited large scale deluge in the flood history of the river basin. Further, the hydro-engineering structures (i.e. dam and barrage) have been moderated the flood peaks but increased the frequency of medium-small scale subnormal floods. The chances floods have become more common in between September and October as the annual peak flow shifted towards these late or extended monsoonal months. Recurrence interval of high flood above 200000 cusec discharge is 28 years. Goodness of fit test shows that the LP3 distributions provided good estimates for floods in the present study area. The information provided can help to start work of the aspirant scholars and provide decision support toward concerned executions. Implementation of more number of river gauge stations and systematic recording of

20 K.G. Ghosh & S. Pal data will certainly help to construct more down to earth flood model. Above all, the altered fluvial system may have significant affect on downstream hydrology and geomorphology and transformation of the riparian ecosystems. These are likely to take into account by the river researchers and managers as a means of restoring at the best possible extent.

ACKNOWLEDGEMENTS

The authors are like to express their sincere thanks to Prof. Sutapa Mukhopadhyay and Dr. Abhishek Bhattacharya for their fair and priceless assistance in obtaining the data. The authors are also thankful to the anonymous reviewers for their kind suggestion in upgrading the present article. Additionally, authors would like to acknowledge all of the agencies and individuals for obtaining the data required for this study.

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