Statistical Analysis of Precipitation over Seonath River ... · PDF fileChhattisgarh, which is...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 4 (2016) pp 2417-2423

© Research India Publications. http://www.ripublication.com

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Statistical Analysis of Precipitation over Seonath River Basin,

Chhattisgarh, India

Mani Kant Verma

Assistant Professor, Department of Civil Engineering, NIT Raipur, Chhattisgarh, India.

Dr. M. K. Verma

Professor, Department of Civil Engineering, NIT Raipur, Chhattisgarh, India.

Sabyasachi Swain

Research Scholar, IDP in Climate Studies, IIT Bombay, Maharashtra, India.

Abstract

The impact of climate change in terms of anomalies in

precipitation has risen up as a major challenge in the world, as

it may lead to havoc by intense floods or severe droughts.

This study presents trends in annual and monthly precipitation

data collected from 39 stations for 32 years (1981-2012) for

Seonath river basin, Chhattisgarh, India, using Mann-Kendall

(MK) test and Sen’s slope estimator test. Based on the

analysis, at 5% significance level, only few stations show a

significant change and the analysis for overall Seonath river

basin shows increasing trend of rainfall in monsoon season

and decreasing trend of rainfall in post-monsoon season. It

was also observed that the annual rainfall shows increasing

trend for the Seonath river basin. In addition, a comparison

has been performed between the observed and its pre-

whitened rainfall data and the result reflects similar pattern for

both the datasets.

Keywords Precipitation, Trend, Pre-whitening, Mann-Kendall

test, Sen’s slope estimator test.

Introduction Water resource is the prime concern for any project planning,

development and management. Indian agriculture primarily

depends on rainfall and its distribution. Distribution of rainfall

is the major factor in the planning and management of

projects related to water resources like agricultural

production, water requirement changes, irrigation and

reservoir operation (Corte-Real et al., 1998; Chakraborty et

al., 2013). Climate change indicates a different behavior of the

hydro-meteorological parameters comparing between two

different periods. The climatic variability is not a very short

span process. It takes years or decades to have a noticeable

change in climate. Whenever the word ‘Climate change’ is

coined, the changes in temperature and erratic rainfall come

first into picture. The variation in rainfall and temperature has

been arising as a challenging issue for the present generation

and it will also remarkably affect the future (Ventura et al.,

2002).From the point of view of India, this may lead to severe

detrimental conditions due to poor adaptation strategies and a

very high population. Intense flooding and severe drought

conditions may prevail in various parts of the country

simultaneously (Gosain et al., 2006, 2011; Swain, 2014). This

will be further accelerated by the rampant human

interventions. But the matter to look into is that, be it a

drought or a flood, the variation in amount of precipitation

will certainly govern these aspects to a significant extent

(Katz et al., 1992). In India, it matters for rainfall due to

South-West monsoon i.e. rainfall during June-September.

Study area and Data used Seonath (also called Shivnath) river basin is situated in the

fertile plains of Chhattisgarh Region. This basin is situated

between 20° 16’ N to 22° 41’ N Latitude and 80° 25’ E to 82°

35’ E Longitude. The basin occupies a large portion of the

upper Mahanadi valley. Seonath is the longest tributary of

Mahanadi river and it traverses a length of 380 km. It

originates near Panabaras village in Rajnandgaon district,

Chhattisgarh, which is at 624 m above the sea level. Tandula,

Arpa, Kharun, Agar, Hamp and Maniyari are its major

tributaries (Chakraborty et al., 2013). The area of the basin is

30560 km2. The topography of the watershed is almost flat.

The slope ranges from 1% to 2% and the weighted average

slope of the watershed is 1.6%. Seonath basin has a tropical

wet and dry climate; temperatures can be extremely hot from

March to June, although it remains moderate throughout the

year. The basin receives about 1150 mm of mean annual

rainfall and a vast majority of it is contributed by monsoon

season i.e. from June to early October, followed by post-

monsoon season (October to December).



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The daily rainfall data of 39 Meteorological Stations over

Seonath river basin for a period of 32 years(from 1981 to

2012) were collected from State Data Centre, Water

Resources Department, Raipur (Chhattisgarh) and Central

Water Commission (CWC), Bhubaneswar, to test the

variability with respect to space and time. Out of these 39

stations, only 5 stations are from Central Water Commission

(CWC) and the rest are the Chhattisgarh Water Resources

Department (WRD) stations. All the stations along with their

latitude (degree North), longitude (degree East) and area

(obtained from Thiessen polygon) are presented in Table 1.

The 5 stations namely Andhiyakore, Jondhra, Kotni,

Patharidih and Simga with suffix CWC represent that these

are the CWC stations. The location of the stations in the basin

and their respective area is presented through Thiessen

polygon map (Figure 1). Thiessen polygon map was prepared

in order to determine the rainfall over the basin as a whole, so

that the trend can be obtained for the overall study area.

Table 1: Location and Area (Thiessen Polygon) for Stations

Station Name Latitude Longitude Area (km2)

Ambagarh Chowki 20.778 80.749 1298.78

Andhiyakore CWC 21.780 81.610 364.887

Balod 20.733 81.233 934.391

Bemetera 21.729 81.549 573.711

Bilaspur 22.083 82.150 1185.89

Bodla 22.182 81.223 114.639

Chilhaki 21.792 82.308 1030.25

Chirapani 22.208 81.196 547.109

Chuikhadan 21.533 81.017 1167.67

Dhamtari 20.822 81.552 1158.84

Dongargaon 20.975 80.863 622.855

Dongargarh 21.183 80.767 976.891

Doundi 20.485 81.096 789.881

Durg 21.217 81.283 1574.88

Gandai 21.667 81.117 684.035

Ghonga 22.300 81.967 1562.44

Gondly 20.750 81.133 641.397

Jondhra CWC 21.720 82.340 490.286

Kawardha 22.017 81.233 630.165

Kendiri 21.100 81.733 563.022

Kharkhara 20.967 81.033 924.559

Khuria 22.388 81.599 1519.24

Khutaghat 22.300 82.208 1523.81

Kota 22.267 82.033 382.005

Kotni CWC 22.130 81.240 229.803

Madiyan 21.990 83.200 386.266

Mungeli 21.133 80.617 1114.24

Nawagarh 22.067 81.683 689.732

Newara 21.906 81.606 1050.78

Pandariya 21.550 81.833 881.239

Patharidih CWC 22.217 81.417 552.498

Pindrawan 21.340 81.600 610.785

Raipur 21.400 81.850 394.213

Semartal 21.250 81.633 432.258

Shahspur 20.970 81.870 667.415

Simga CWC 21.900 81.117 291.545

Simga WRD 22.183 82.167 590.477

Sond 21.620 81.705 931.621

Surhi (Palemeta) 21.220 81.690 472.045

Figure 1: Location of rainfall stations in Seonath river basin

(Thiessen Polygon map)

Methodology In the present study, daily rainfall data is collected and

arranged in 4 different parts of a year (winter, pre-monsoon,

monsoon and post-monsoon seasons), and thereafter, non-

parametric Mann-Kendall test and Sen’s slope estimator test

has been used for trend analysis. For individual stations, only

monsoon and post-monsoon season data are considered as the

randomness of the data will be very high for winter and pre-

monsoon seasons as these phases of a year receive almost no

rainfall over the study area. Then, rainfall over the basin as a

whole is estimated by Thiessen polygon method and is

statistically analyzed.

Pre-whitening of Data:

The detection of trend is significantly affected for

autocorrelation among dataset (Hamed and Rao, 1998;

Serrano et al., 1999; Yue et al., 2002; Blain, 2013). For a

discrete time series, the coefficient of autocorrelation ρk of a

discrete time series for lag k is given by,

(1)

A positive autocorrelation among data may lead to a clear

presence of trend by the non-parametric tests while it may not



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actually exist. Similarly, presence of a negative

autocorrelation may ignore the presence of an actual trend

(Hameed and Rao, 1998). Hence, the removal of remarkable

autocorrelation is necessary before carrying on statistical tests.

This method of removal of autocorrelation is referred to as

pre-whitening (Blain, 2013). In this case, pre-whitening has

been done using lag-1 autocorrelation.

(2)

Here, represents the pre-whitened dataset i.e. after

removing the positive autocorrelation among the data points.

Then the results of the statistical methods applied to actual

data and pre-whitened data are analyzed and a comparison is

presented.

Mann-Kendall Test

Mann Kendall test is used for identifying the monotonicity in

a time series (Yue et al., 2002; Kumar et al., 2010; Caloiero et

al., 2011; Jain and Kumar, 2012; Jain et al. 2013; Adarsh and

Reddy, 2015). Being a non-parametric test, the problem due to

data skew can be evaded easily (Swain et al., 2015).

The Mann-Kendall statistic S is given as

The trend test is applied to a time series xi that is ranked from

i = 1, 2 …n-1, and xj, which is ranked from j = i+1, 2 ….n.

Every data point xi is taken as a reference point for comparing

with the all other data points, xj so that,

= (4)

It is observed that when n ≥ 8, the statistic S is approximately

normally distributed i.e. with zero mean and variance given

by,

(5)

Where, ti represents the number of ties up to sample i (Zhu et

al., 2010).

(6)

Zmk is assumed to follow a standard normal distribution.

Hence, its value being positive indicates a rising trend and that

of negative indicates a decreasing trend. A significance level α

is also utilized for testing monotonicity of trend (a two-tailed

test). If Zmk is greater than Zα/2 where α depicts the

significance level, then the trend is considered to be

significant (Babar and Ramesh, 2014).Generally, Zmk values

are 1.645, 1.960 and 2.576 for significance level of 10%, 5%

and 1% respectively (Swain et al., 2015).

Sen’s Slope Estimator Test

Sen’s slope estimator is the most commonly used test to detect

a linear trend (Yue and Hashino, 2003; Karpouzos et al.,

2010; Jain and Kumar, 2012; Swain et al., 2015; Adarsh and

Reddy, 2015). The slope (Ti) of all data pairs is given as as

(Sen, 1968),

For i = 1, 2, 3…..N (7)

Where and are considered as data values at time j and k

(j>k) respectively.

The Sen’s estimator of slope is given by the median of these

N values of Ti, which is projected as

(8)

Very similar to the Mann-Kendall test, the positive and

negative values of Qi represents a positive and negative trend

respectively.

Result and Discussion Mann-Kendall and Sen’s slope estimator test is applied on

both the datasets (actual and pre-whitened). In Figure 2, the

stations are marked with different colors according to the

results of the Mann-Kendall on annual rainfall recorded data.

It can be noticed that 20 stations are showing a positive value

of Zmk whereas 19 stations are showing negative values. The

Mann-Kendall co-efficient Zmk value for 5% significance level

is 1.96. Thus, the stations marked blue indicate for significant

increase in annual rainfall and that of red shows significant

decrease. All other stations show no significant (at 5%

significance level) trend for the study period. So, out of 39

stations, only 5 stations namely Dhamtari, Newara, Kota,

Pandariya and Simga CWC shows significant rise in rainfall,

whereas only 3 stations Bodla, Chirapani and Chuikhadan are

showing a decreasing trend for annual rainfall.

From Figure 3, it is clear that most of the stations are showing

a positive trend for monsoon season although only a few of

them can be regarded as significant. Out of 39 stations, a

decreasing trend of rainfall can be observed for 10 stations of

pre-whitened and 12 stations of actual observed data. The 3

stations showing decreasing trend in annual rainfall are also

showing a negative trend for monsoon season whereas, 7

stations are showing an increasing trend in case of pre-

whitened data and 8 stations for actual data. Similarly from

Figure 4, it can be observed that, most of the stations are

showing insignificant trend for post-monsoon seasons. Only 4

stations are having a significant decreasing trend for pre-

whitened data and that of 3 stations for actual observed data

whereas, no station is showing a significant positive trend for

rainfall during post-monsoon seasons.



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(a)

(b)

Figure 2: Mann-Kendall Test results for annual rainfall for (a)

pre-whitened data; (b) observed data

(a)

(b)

Figure 3: Mann-Kendall Test results for monsoon season for

(a) pre-whitened and (b) actual data



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(a)

(b)

Figure 4: Mann-Kendall Test results for post-monsoon season

for (a) pre-whitened and (b) actual data

From the results of Mann-Kendall test, it is evident that the

significant trend of rainfall in monsoon season and over whole

year is almost same, the reason being, rainfall during monsoon

phase contributes more than 85% of the annual rainfall over

Seonath river basin. One more thing is to be noticed is that

pre-whitening doesn’t have much effect on trend for this data

from Mann-Kendall test. The results obtained for actual

observed data and pre-whitened data are hardly different from

each other.

Very similar to the Mann-Kendall test, the results are almost

same for annual rainfall and that of monsoon season by

Sen’sslope estimator test. The regions marked with blue color

indicates increasing trend and yellow color indicates

decreasing trend. For other regions, Sen’s slope value is zero.

From Figure 5 (a), it can be observed that, out of 39 stations,

20 stations are showing increasing trend in annual rainfall for

pre-whitened data and that of 18 stations for actual data. Rests

are showing a decreasing trend in both cases. From Figure 5

(b), in monsoon season, 11 stations are having a decreasing

trend for both pre-whitened and actual data. Only one station

is showing absolutely no trend for actual data. For most of the

stations, a positive Sen’s slope is obtained i.e. an increasing

trend can be observed for the entire study period. It can be

seen from Figure 5 (c) that most of the stations are showing a

decreasing trend for both pre-whitened and actual rainfall data

in post-monsoon season.

(a) Sen’s Slope values for different stations for Annual rainfall

(b) Sen’s Slope values for different stations for rainfall in

Monsoon season



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(C) Sen’s Slope values for different stations for rainfall in

Post-Monsoon season

Figure 5: Sen’sslope estimator test results for pre-whitened

data and actual data for (a) Annual rainfall; (b) Rainfall in

monsoon season; (c) Rainfall in post-monsoon season

The trend of rainfall over different stations is determined

using the non-parametric tests. But it is essential to determine

the behavior of rainfall over the whole basin. The Thiessen

polygon method was used to determine the rainfall over the

whole Seonath river basin considering the area covered by

each station. The monthly and seasonal variation over the

whole basin was analyzed by Mann-Kendall and Sen’s slope

estimator test.

From Figure 6, it can be observed that, the monthly rainfall is

showing an insignificant trend for overall Seonath river basin,

taking into consideration the pre-whitened data. But, for the

month of July and September, an increasing trend can be

observed whereas decreasing trend for January, February,

May and October months. The months of July and October are

showing almost significant (5% significance level) increasing

and decreasing trend respectively. Talking about the seasonal

variation, an increasing trend can be marked for monsoon

season whereas it is decreasing for winter, pre-monsoon and

post-monsoon season, although they are not significant at 5%

significance level. As the rainfall in monsoon season is

showing an increasing trend, the Zmk value for annual rainfall

is also positive i.e. 0.7622. Hence, over the study period

(1980-2012), the rainfall over Seonath basin has a rising trend.

(a) Monthly variation of rainfall over Seonath river basin for

pre-whitened data

(b) Seasonal variation of rainfall over Seonath river basin

Figure 6: Mann-Kendall test results for pre-whitened data for

monthly and seasonal rainfall

From Table 2, it can be observed that the Sen’s slope is

positive for April, July and September, zero for November

and negative for all other months.

Table 2: Monthly Sen’s slope results for rainfall

Month Variance Mean Median Standard

Deviation

Sen’s

slope(β)

(%

change α)

Jan 314.34833 13.30411 3.67972 17.72987 -0.03946 -0.094904329

Feb 117.38473 9.446052 5.39712 10.83442 -0.22121 -0.749369926

Mar 88.979965 8.214486 5.336704 9.432919 -0.02805 -0.109279824

Apr 29.140871 6.213002 4.745383 5.398229 0.024628 0.126847691

May 131.97939 10.84519 7.09259 11.48823 -0.17392 -0.51316768

Jun 3481.5742 150.3759 140.6504 59.00487 -0.33211 -0.070672857

Jul 9137.973 303.7481 294.1403 95.59275 3.528383 0.371716677

Aug 4333.2955 298.6511 303.0616 65.82777 -0.14358 -0.015384195

Sep 3056.0606 165.8706 163.2684 55.28165 1.059016 0.204307029

Oct 751.28343 41.5091 38.57057 27.40955 -0.72155 -0.556257041

Nov 373.96891 9.93016 1.533419 19.33828 0 0

Dec 164.88507 5.32481 1.092111 12.84076 -0.00489 -0.029405063

Figure 7 shows seasonal rainfall over Seonath basin where, a

high positive slope is observed i.e. Sen’s slope value is 3.983

for monsoon season and negative for post-monsoon

season.The Sen’s slope value for annual rainfall over entire

Seonath river basin is also 2.832, showing a clear increase in

annual rainfall for the study area in these 32 years.

Figure 7: Sen’sslope results for Seasonal rainfall



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Conclusion Trend analysis of monthly and annual rainfall data for Seonath

river basin, Chhattisgarh, for 32 years (1981-2012) using

Mann-Kendall and Sen’s slope estimator test, has been

presented in this article.For monsoon season, the Zmk value of

Mann-Kendall Test was positive for 7 stations and negative

for only 3 stations at 5% significance level for pre-whitened

data, although most of the stations showed positive value of

Mann-Kendall co-efficient. The Sen’s slope values for most of

the stations were also found to be positive. For overall

Seonath river basin, an observable rising trend was there for

the months of July and September and decreasing trend for

January, February, May and October. For seasonal variation,

the trend is clearly positive for monsoon season, both from

Mann-Kendalltest and Sen’s slope estimator test. Since the

study area represents more of agricultural lands and

agriculture is primarily dependent on monsoon season, such

an increasing trend is desirable for existing conditions. Further

study in this regard may reveal other aspects which will be

helpful to understand the changes in hydrological process

along with agricultural development.

Acknowledgment The authors are thankful to ‘State Data Centre, Chhattisgarh’,

‘Central Water Commission Office, Bhubaneswar’ and ‘Water

Resources Department, Chhattisgarh’ for providing data for

this study.We are also grateful to all those individuals whose

suggestions have helped to improve the quality of this article.

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