Trend analysis of rainfall in Satluj River Basin, Himachal ...€¦ · Indian region have shown that there is no clear trend of increase or decrease in average annual rainfall over

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WSN 8 (2015) 1-55 EISSN 2392-2192

Trend analysis of rainfall in Satluj River Basin, Himachal Pradesh, India

Sandeep Kumar1,a, Santosh2,b

1Department of Environment Studies, Panjab University, Chandigarh - 160014, India

Phone: +918901288772

2Department of Environmental Sciences, MDU, Rohtak (Haryana), India

a,bE-mail address: [email protected] , [email protected]

ABSTRACT

Testing the significance of observed trends in hydro-meteorological time series has received a

great attention recently, especially in connection with climate change. The changing pattern of rainfall

deserves urgent and systematic attention for planning, development, utilisation and management of

water resources. The daily data on variable were converted to monthly and then computed to seasonal

and annual series. Annual rainfall (mm/yr) was calculated as the sum of monthly values. The missing

values in the data were computed by using average method. The records of rainfall were subjected to

trend analysis by using both non-parametric (Mann-Kendall test) and parametric (linear regression

analysis) procedures. For better understanding of the observed trends, data were computed into

standardised precipitation indices (SPI). These standardised data series were plotted against time and

the linear trends observed were represented graphically. Trend analysis results of rainfall show that out

of 15 annual trends 6 (40%) are increasing and 9 (60%) are decreasing in nature where 1 (6.6%) is

statistically significant (increasing) and 2 (13.3%) are statistically significant (decreasing) at 95%

confidence level. Similarly, the changes were investigated for the four seasons: winter (December-

March), pre-monsoon (April-June), monsoon (July-September) and post-monsoon (October-

November). The analysis of rainfall, annual as well as seasonal, of different gauge stations in Satluj

River Basin showed a large variability in the trends and magnitudes from 1984 to 2010. The rainfall

shows great temporal and spatial variations, unequal seasonal distribution with frequent departures

from normal. Majority of gauge stations have experienced decreasing trends, both on seasonal and

World Scientific News 8 (2015) 1-55

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annual scales. Some were statistically significant at 95% confidence level. The sensitivity of rainfall

variations provides important insight regarding the responses and vulnerability of different areas to

climate change. It will further strengthen the formulation of future strategy for management of water

resources.

Keywords: Mann-Kendall test; Rainfall; Regression; Satluj River Basin; Trend analysis

Reviewer: Dr. Piotr Daniszewski,

Faculty of Natural Sciences, Szczecin University, Szczecin, Poland

1. INTRODUCTION

Climate change arising from anthropogenic driven emissions of greenhouse gases has

emerged as one of the most important environmental issues in the last two decades.

Information about impacts of climate change is required at global, regional and basin scales

for a variety of purposes. The consequences of climate change on Indian sub-continent,

especially the mountainous regions are poorly understood. The Himalayan region has large

and intricate network of river systems which is reinforced by the snowmelt, glaciermelt and

rainfall. Many of major rivers originating in this region have their upper catchments covered

by snow/glacier. The altitude and climatic change induced temperature variability are

expected to play major role on rainfall pattern.

An understanding of rainfall response in a river basin under changing climatic

conditions will help to resolve potential issues associated with hydro-meteorological disasters

and availability of water for agriculture, industry, hydropower, domestic use etc. The

changing pattern of regional precipitation deserves urgent and systematic attention over a

basin which provides an insight view of historical trends. Therefore, information on trend

analysis over a basin scale is of utmost importance for planning, development and utilisation

of water resources.

The assessment of hydro-climatic trends of river basin can provide a perspective

assessment on catchment dynamics. Detection of temporal trends is one of the important

objectives of environmental monitoring. Trend analysis has proved to be useful tool for

effective water resources planning, design and management since trend detection of

hydrological variables provides a useful information on the possibility of change in tendencies

of the variables in future (Hamilton et al., 2001; Yue and Wang, 2004).

One of the most significant potential consequences of climate change may be alteration

in regional hydrological cycle. For this reason, it becomes very important to investigate the

changes in the spatial and temporal pattern of rainfall in order to improve water management

strategies.

Necessity of the hour is to concentrate on the studies, how the possible climate change

will affect the intensity, temporal and spatial variability of rainfall patterns in river basins. It

became utmost important to study the impacts of climate change and its variability on

changing rainfall pattern in river for designing regional and national long term developmental

strategies which are supposed to be benign in nature i.e. sustainable. Therefore, one large

catchment (Satluj) is selected for the present study.


-3-

Climatic variability and change in precipitation pattern will affect the runoff in the river

basin. The changes in the amount and seasonal distribution of precipitation have potentially

serious implications for the hydrological regimes of catchment area.

The nature of climate change and its impact on rainfall of the Satluj River Basin have

been studied inadequately because of inaccessibility, terrain ruggedness and sparse network of

gauge stations. The present study tries to fill this vacuum by studying the trend analysis of

rainfall.

Testing the significance of observed trends in hydrological and meteorological time

series has received a great attention recently, especially in connection with the assessment of

observed changes in the natural and human environment due to global warming. This is

reflected by a huge number of studies carried out over the last three decades, dealing with the

assessment of significance of trends in a variety of natural time series.

The global average precipitation is projected to increase, but both increases and

decreases are expected at regional and continental scales (Dore, 2005). The annual and

seasonal precipitation trends over Iran for the period 1966-2005 have been analysed using the

Mann-Kendall test, the Sen’s slope estimator and the linear regression. The results indicated a

decreasing trend in annual precipitation at about 60% of the stations.

The decreasing trends were significant at seven stations at the 95% and 99% confidence

levels. On the seasonal scale, the trends in the spring and winter precipitations time series

were mostly negative. The highest numbers of stations with significant trends occurred in

winter while no significant positive or negative trends were detected by the trend tests in

autumn precipitation (Tabari and Talaee, 2011). Buffoni et al. (1999) studied series of annual

and seasonal precipitation over Italy for the period 1833-1996. Their results showed a

decreasing trend in annual series, but it was statistically significant only in the central-

southern parts. On a seasonal basis, a decreasing trend was significant only for spring in

central-southern and for autumn in the North. Liu et al. (2008) investigated the spatial and

temporal patterns of the precipitation trends in the Yellow River Basin, China.

The results showed a decreasing trend in most of stations. Kampata et al. (2008)

investigated the trends of precipitation data from the head stream regions of the Zambezi

River Basin in Zambia and showed marginal downward insignificant trends. Zhang et al.

(2008) analysed annual, winter and summer precipitation records and found an increasing

trend in annual, summer and winter precipitation in the middle and lower sections of the

Yangtze River. A statistical analysis of annual and seasonal precipitation has been performed

over cumulated rainfall series in a region of southern Italy (Calabria). The results had shown a

decreasing trend for annual and winter-autumn rainfall and an increasing trend for summer

precipitation (Caloiero et al., 2011). In Germany, a positive linear tendency in heavy

precipitation for the winter, spring and autumn seasons was found. In the summer season,

however, heavy precipitation exhibits mostly negative trends (Zolina et al., 2008).

In India, various attempts have been made to determine the trends in the rainfall over

the country as a whole and also on regional scales. Studies related to changes in rainfall over

Indian region have shown that there is no clear trend of increase or decrease in average annual

rainfall over the country (Mooley and Parthasarathy, 1984; Thapliyal and Kulshreshtha,

1991). In the Himalayan region, Sharma et al. (2000) showed an increase in the annual

precipitation for the period 1943-1993.

The analysis of long datasets for studying the spatial and temporal variations in

precipitation in the Upper Indus Basin showed no significant trend from 1895 to 1960 but


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from 1961 to 1999, there was statistically significant increase in winter, summer and annual

precipitation (Archer and Fowler, 2004).

The frequency of heavy rain events during the southwest monsoon has shown an

increasing trend over certain parts of the country (Sinha Ray and Srivastava, 1999). On the

other hand, a decreasing trend has been observed during winter, pre-monsoon and post-

monsoon season.

Most of the rainfall studies are confined to the analysis of annual and seasonal series of

trends for some individual gauge stations in the river basins. Singh et al. (2008) while

studying the seasonal and annual trends of changes in rainfall, rainy days, heaviest rain and

relative humidity over the last century for nine different river basins in northwest and central

India have shown increasing trends both in annual rainfall and relative humidity. Seasonal

analysis showed maximum increase in rainfall during post-monsoon, followed by pre-

monsoon season. There were least variations in the monsoon rainfall during the last century

and winter rainfall has shown a decreasing trend.

2. STUDY AREA

The Satluj River (Vedic name - Satudri and Sanskrit name - Shatadru), also known as

the Langqên (Chinese) and Sutlej (Indian), is the principal and easternmost tributary of the

Indus River system. The basin area falls in Lahaul & Spiti, Kinnaur, Shimla, Kullu, Mandi,

Solan and Bilaspur districts of Himachal Pradesh. The geographical limits of area lie between

30°45′ N to 33°00′ N latitudes and 76°15′ E to 79°00′ E longitudes in the western Himalayas

(Figure 1). The total catchment area of Satluj River, from origin to Bhakra dam, is about

56,875 km2

(21,960 Sq. miles). The upper part of river basin is considerably wider than the

lower one.

In Himachal Pradesh, Satluj Basin has catchment area of 20,398 Km2

which is 30.7% of

the total catchment area of river systems (SCST & E, 2006). Indian part of river up to Bhakra

Dam is elongated in shape and covers the part of outer (Shiwalik range), middle (Dhauladhar

range) and greater Himalayas (Zaskar range).

Satluj River originates from the southern slopes of Kailash Mountains i.e. from Rakas

Lake, near the Mansarovar Lake as Longcchen Khabab River at an elevation of about 4,572 m

(15,000 ft), above msl. Total length of river is approximately 1,448 km (320 Km in China,

758 Km in India and 370 Km in Pakistan). It enters India from East of Shipki La (altitude –

3,048 m, above msl) after traversing a length of about 320 km (200 miles) in the Tibetan

province of Nari Khorsam, through a narrow gorge in the Kinnaur district of Himachal

Pradesh and flows in southwesterly direction.

The river is supported by a number of mighty tributaries on either side. Main tributaries

are Spiti, Baspa and Gambhar at Khab, Karchham and Kangri at an elevation of 2,600, 1,750

and 450 m above msl respectively. Near Rampur, it crosses the Dhauladhar range and then

traverses through a series of successive Shiwalik ranges. Before leaving the Himachal

Pradesh, it cuts a gorge in Naina Devi Dhar and mingles with the water of Govind Sagar

Lake. It enters the plains of Punjab near Bhakra where Asia’s one of the highest gravity

multipurpose dam (Capacity to generate electricity -1,325 MW and height - 740 ft/225.55 m)

has been constructed. It finally drains into the main Indus River in Pakistan.


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Figure 1. Schematic showing the study area map of Satluj River Basin upto Bhakra Dam,

Himachal Pradesh.


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Based on the amount of annual precipitation and the variation in temperature, the study

area, from North to South, has been divided into three broad climatic zones (Figure 2). Each

zone is characterised by its own peculiarities of climatic factors, geomorphic and topographic

features (Gupta et al., 1994; Bartarya et al., 1996):

1. Semi-arid to arid temperate zone (Cold desert) - This zone lies in the upper Satluj

Valley, upstream from Morang. Towards North of Morang, the cold desert conditions

prevail which are characterised by very low monsoonal precipitation, high speed of

cold winds and the precipitation generally occurs in the form of snowfall during

winter season.

2. Sub-humid to humid temperate zone - This zone covers the middle Satluj Valley

between the Wangtu and Morang. It is the transitional zone which receives low

rainfall during the monsoon period and moderate to heavy snowfall in the higher

reaches during winter.

3. Wet temperate or Monsoonal zone - It lies in the lower Satluj Valley downstream of

Wangtu. This zone is under the great influence of monsoonal winds and receives

heavy rainfall during rainy season from mid June to mid September.

Figure 2. Longitudinal profile of Satluj River from Shipki La to Bhakra Dam. Three climatic zones

are demarcated along with the major thrusts.

The fall of Satluj from its source to the plain areas is very uniform. A gross fall of 2,180

m is available in the river bed from Shipki La to Bhakra in a length of about 320 Km (Figure

2). The altitude in the study area increases from West to East and South to North. Based on

Shipki La

Khab (confluence with Spiti)

Morang

Wangtu

Rampur

Bilaspur Bhakra

0

500

1000

1500

2000

2500

3000

0 40 80 120 160 200 240 280 320

Ap

pro

x.

river

bed

hei

ght

(m,

abo

ve

msl

)

Distance (Km)

Tethyan Himalaya

Higher Himalaya

Lesser Himalaya

Semi arid-arid temperate

Sub humid-humid temperate

Wet temperate

Tethyan Thrust Vaikrita Thrust

Karchham Thrust

Main Central Thrust

Jutogh Thrust

Chail Thrust


-7-

broad climatic conditions, the Satluj River Basin has following four seasons: Winter

(December to March), Pre-monsoon (April to June), Monsoon (July to September), Post-

monsoon (October, November).

3. MATERIALS AND METHODS

For the considered study area, records of rainfall were subjected to trend analysis by

Mann-Kendall and regression coefficient test. In order to determine the degree and rate of

change in rainfall trends, long-term data sets are required. Burn and Elnur (2002) stated that a

minimum record length of 25 years ensures validity of the trend results statistically. The

present analytical study on the spatial and temporal trends is based on the available

meteorological data from Bhakra Beas Management Board (BBMB), Nangal. The trends were

identified by investigating the time series of different observation stations distributed over the

Indian part of Satluj River Basin. Paucity of long term data restricts the study as length

available was 27 years. Data was scarce especially in the upper catchment area because of

inadequate hydro-meteorological networks in the high altitude regions with rugged terrain and

poor accessibility.

In order to investigate trends in time series, observational records were prepared. The

daily data were converted to monthly and then computed to seasonal and annual series.

Annual precipitation (mm/yr) was calculated as sum of monthly values. The missing values in

the data were computed by using average method. To bring uniformity and facilitate

comparison between the annual and seasonal responses, yearly standardised precipitation

indices (SPI) can be computed by subtracting the mean and dividing by the standard deviation

of the rainfall data series. The SPI data series are then subjected to trend analysis by statistical

techniques (Pant and Rupakumar, 1997; Shreshtha et al., 2000; Bhutiyani et al., 2008). These

standardised time series data were plotted against time and the linear trends observed were

represented graphically. The linear trend values, represented by the slope of a simple least

square regression line with time as independent variable gave the magnitude of rise or fall.

Apart from annual, the changes were investigated for the four seasons: winter (December-

March), pre-monsoon (April-June), monsoon (July-September) and post-monsoon (October-

November).

The location (longitude and latitude) and altitude of rainfall gauge stations are shown in

Table 1 and figure 3. The altitude of the meteorological stations varied from 481 to 3,756 m.

Table 1. Rainfall data availability in Satluj River Basin.

Station Latitude and longitude Elevation (m)

Rampur 3127'15" & 7738'40" 1,302

Suni 3114'15" & 7706'30" 843

Kasol 3121'25" & 7652'42" 809

Bhakra 3124'53" & 7625'59" 588

Namgia 3148'11" & 7838'34" 3,083


-8-

Rakchham 3123'30" & 7821'20" 3,282

Kalpa 3132'25" & 7815'30" 2,633

Kaza 3213'30" & 7804'20" 3,756

Swarghat 3142'23" & 7644'58" 1,314

Bilaspur 3120'00" & 7645'00" 481

Kahu 3112'13" & 7647'15" 651

Berthin 3128'15" & 7637'20" 656

Naina Devi 3119'10" & 7632'16" 739

Kuddi 3123'23" & 7647'25" 659

Daslehra 3124'00" & 7633'00" 801

Figure 3. Location of rainfall gauge stations in Satluj River Basin.


-9-

In order to investigate the trends, several statistical techniques are currently available. In

the present study, trend analysis have been made by using both non-parametric (Mann-

Kendall test) and parametric (linear regression analysis) procedures. Parametric tests assume

that the random variable is normally distributed and homo-sedastic (homogeneous variance).

Non-parametric tests make no assumption for probability distribution. The purpose of trend

analysis is to determine if the values of a random variable generally increase (or decrease)

over some period of time in statistical terms (Helsel and Hirsch, 1992).

3. 1. Mann-Kendall test

The non-parametric tests are more suitable for non-normally distributed, censored data,

including missing values which are frequently encountered in hydro-meteorological time

series (Hirsch and Slack, 1984; Yue and Pilon, 2004). The Mann-Kendall trend test has

therefore been widely used for testing trends in many natural time series that deviate

significantly from the Normal distribution, such as temperature. The MK test was found to be

an effective tool for identifying trends in hydrologic and other related variables, resistant to

the effect of extreme values (Hirsch et al., 1982; Burn, 1994). This test has been applied to

temperature, precipitation and stream flow data series (Yu et al., 1993; Douglas et al., 2000;

Yue et al., 2003; Burn et al., 2004). The MK test applied in this study is a rank based method

for evaluating the presence of trends in time series data, without specifying whether the trend

is linear or nonlinear.

To identify the trends in the climatic variables with reference to climate change, the

non-parametric Mann-Kendall test (Mann, 1945; Kendall, 1975) has been applied in hydro-

meteorological data. Mann originally used this test and Kendall subsequently derived the

test for statistical distribution. The co-variances between Mann-Kendall statistics were

proposed by Dietz and Kileen (1981) and the test was extended in order to include seasonality

(Hirsch and Slack, 1984). The test compares the relative magnitudes of sample data rather

than the data values themselves (Gilbert, 1987). In the present study, test was applied to mean

temperature for determining monotonic trends.

The MK test has two parameters i.e. significant level, indicates the trend’s strength and

the slope magnitude, indicates the direction as well as magnitude of the trend. The data values

are evaluated as an ordered time series. Each data value is compared to all subsequent data

values. The initial value of the Mann-Kendall statistic, S, is assumed to be 0 (e.g., no trend). If

a data value from a later time period is higher than a data value from an earlier time period, S

is incremented by 1. On the other hand, if the data value from a later time period is lower than

a data value sampled earlier, S is decremented by 1. The net result of all such increments and

decrements yields the final value of S. A very high positive value of S is an indicator of an

increasing trend and a very low negative value indicates a decreasing trend. However, it is

necessary to compute the probability associated with S and the sample size, n, to statistically

quantify the significance of the trend. So, the MK test considers only the relative values of all

terms in the series X = {x1, x2,….,xn} to be analysed. The Mann-Kendall statistic S which is

the sum of the differences between the data points is defined as (Salas, 1992)


-10-

where: xi and xj are the sequential data values and n is the number of data points. Let xj - xi =

θ.

The value of sign (θ) is computed as

For large samples (n > 10), the test is conducted using a normal distribution (Helsel and

Hirsch, 1992) with mean E(S) and variance Var (S). Kendall (1975) obtained the theoretical

mean and variance of S under the assumption of no trend as:

where tk is the extent of any given tie (number of x’s involved in a given tie), and Σtk denotes

the sum of the terms tk(tk – 1)(2tk + 5) which are evaluated and summed for each tie of the tk

number in the data. The standard normal variable Z is computed by:

Compute the probability associated with this normalised test statistic. The trend is said

to be decreasing if Z is negative and the computed probability is greater than the level of

significance. If the q-value is less than or equal to the significance level then it is correct to

reject the null hypothesis that a trend does not exist in the data set. The trend is said to be

increasing if the Z is positive and the computed probability is greater than the level of

significance. If the computed probability is less than the level of significance, there is no

trend. If the computed value of |Z| > z α/2, the null hypothesis Ho of having no trend in the

data series is rejected at α level of significance in a two-sided test. Thus, in a two-tailed test

for trend, the null hypothesis, that there is no trend in the dataset, is either rejected or accepted

depending on whether the calculated Z statistics is more than or less than the critical value of


-11-

Z-statistics obtained from the normal distribution table at 5% significance level. Significance

levels (p-values) for each trend test can be obtained as:

p = 2[1-Φ|Z|]

where Φ denotes the cumulative distribution function (cdf) of a standard normal variant.

3. 2. Pre-Whitney

However, a basic assumption for the original Mann-Kendall test is that the data should

be random and identically distributed which is seldom the case in natural time series. It has

been long known (Cox and Stuart, 1955) that positive serial correlation among the

observations would increase the chance of a significant answer even in the absence of a trend.

The presence of serial correlation can complicate the identification of trends that a positive

serial correlation can increase the expected number of false positive outcomes for MK test

(Von Storch and Navarra, 1995). So, before applying the MK test, the data series was tested

for serial correlation. In order to limit the influence of serial correlation, pre-whitening was

proposed by Von Storch (1995). Bayazit and Önöz (2007) suggested that pre-whitening

should be avoided when the test has a high power, the slope of trend is high, and the sample

size is large (i.e., n ≥ 50).

In present study, Mann-Kendall test was used in conjunction with the widely used

method of pre-whitening. If the lag -1 auto-correlation r1 was found to be non-significant at

95% confidence level, then the MK test was applied directly to the original data series x1, x2, .

. . ,xt. Otherwise, the test was applied to the pre-processed data series x2 - r1x1, x3 - r1x2, . . . ,xt

- r1xt-1 referred to as ‘pre-whitened’ (Von Storch and Navarra, 1995; Partal and Kahya, 2006).

The pre-whitening removes serial correlation from the data by means of the following

formula:

t = xt - r1xt-1

where xt is the original time series value for time interval t, t is the pre-whitened time series

value and r1 is the lag -1 autocorrelation coefficient that can be expressed as:

where E(xt) is the mean of the sample data. Von Storch and Navarra (1995) also demonstrated

that pre-whitening operation is not necessary for r1 ≤ 0.1.

3. 2. 1. Regression

The changes in annual and seasonal rainfall were plotted against time and the trend was

examined by fitting the linear regression line. Linear regression analysis indicates the


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tendency rate (slope) using least squares at the 95% confidence level, indicates the mean

temporal change of the studied variable. Positive values of the slope show increasing trends,

while negative values indicate decreasing trends. The total change during the period under

observation is obtained by multiplying the slope with the number of years (Tabari and Marofi,

2010; Tabari et al., 2010a, b). The parametric test considers the linear regression of

the random variable Y on X, expressed as:

Y = β0 + β1X + ε

4. RESULTS AND DISCUSSION

The analysis of rainfall, annual as well as seasonal, of different observation sites in the

basin showed a large variability in the trends and magnitudes. Figures (4-18) and Tables (2-3)

show the results of trend analysis of rainfall in the winter, pre-monsoon, monsoon and post-

monsoon seasons along with mean annual rainfall in the river basin, relatively for shorter

time-span of about 27 years (1984-2010).

The Figure 4 indicates that the annual rainfall has shown decreasing but statistically

insignificant trend with episodic fluctuations. Seasonal analysis shows that the increasing and

decreasing trends were observed during pre-monsoon and monsoon respectively, but

statistically insignificant.

As there was no rainfall during winter and negligible during post-monsoon, so no trend

has been detected. The analysis shows decreasing trend with the exception of some periodic

behaviour.

y = -0,0149x + 0,2083

R² = 0,0139

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual

SPI(Annual) Linear trend


-13-

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1S

PI

Year

B). Winter

SPI(Winter) Linear trend

y = 0,0141x - 0,1976

R² = 0,0125

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

SP

I

Year

C). Pre-monsoon

SPI(Pre-monsoon) Linear trend


-14-

Figure 4. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Kaza

gauge station of Satluj River Basin.

y = -0,0182x + 0,2542 R² = 0,0208

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50S

PI

Year

D). Monsoon

SPI(Monsoon) Linear trend

y = 0,0032x - 0,0442

R² = 0,0006

-1,00

0,00

1,00

2,00

3,00

4,00

5,00

6,00

SP

I

Year

E). Post-monsoon

SPI(Post-monsoon) Linear trend


-15-

The Figure 5 indicates that the annual rainfall has shown increasing trend which is

statistically significant, with episodic fluctuations. Seasonal analysis shows that maximum

increasing trend was observed during pre-monsoon, followed by winter and monsoon

respectively which are statistically significant, while the post-monsoon has insignificantly

decreasing trend. The analysis shows overall increasing trend in the rainfall pattern.

y = 0.072x - 1.014 R² = 0.330*

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual


y = 0.055x - 0.780 R² = 0.196*

-1,00

0,00

1,00

2,00

3,00

4,00

5,00

SP

I

Year

B). Winter



-16-

y = 0.047x - 0.661

R² = 0.140*

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

D). Monsoon


y = 0.058x - 0.823 R² = 0.218*

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00

SP

I

Year

C). Pre-monsoon



-17-

Figure 5. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at

Rakchham gauge station of Satluj River Basin (*Significant at 95% confidence level).

y = -0,0067x + 0,093 R² = 0,0028

-1,00

0,00

1,00

2,00

3,00

4,00

5,00S

PI

Year

E). Post-monsoon


y = -0,023x + 0,3223 R² = 0,0334

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual



-18-

y = -0,039x + 0,5457 R² = 0,0957

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

B). Winter


y = -0,0323x + 0,4528 R² = 0,0659

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

C). Pre-monsoon



-19-

Figure 6. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Namgia


y = 0,0201x - 0,2817 R² = 0,0255

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon


y = -0,0107x + 0,1499 R² = 0,0072

-1,00

0,00

1,00

2,00

3,00

4,00

5,00

SP

I

Year

E). Post-monsoon



-20-

The Figure 6 indicates that the annual rainfall has shown decreasing trend but

statistically insignificant, with episodic fluctuations. Seasonal analysis shows that maximum

decreasing trend was observed during winter, followed by pre-monsoon and post-monsoon

but all are statistically insignificant, while the monsoon has shown increasing trend which is

statistically insignificant.

y = 0,0184x - 0,2573

R² = 0,0213

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual


y = -0,0297x + 0,4162

R² = 0,0556

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

B). Winter



-21-

y = -0,0012x + 0,0169 R² = 9E-05

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

C). Pre-monsoon


y = 0.059x - 0.835 R² = 0.224*

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon



-22-

Figure 7. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Kalpa

gauge station of Satluj River (*Significant at 95% confidence level).


statistically insignificant with episodic fluctuations. The slightly decreasing trend was

observed during winter and post-monsoon but statistically insignificant, while the pre-

monsoon has shown no trend. The monsoon season has shown increasing trend which is

statistically significant, having very high r2

value.

y = -0,0199x + 0,2782 R² = 0,0249

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00S

PI

Year

E). Post-monsoon


y = -0.059x + 0.741 R² = 0.175*

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

A). Annual



-23-

y = -0,0325x + 0,4065 R² = 0,0529

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50S

PI

Year

B). Winter


y = -0,0089x + 0,1111 R² = 0,0039

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

C). Pre-monsoon



-24-


Swarghat gauge station of Satluj River Basin (*Significant at 95% confidence level).

y = -0,0544x + 0,6798 R² = 0,1479

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00S

PI

Year

D). Monsoon


y = -0,0242x + 0,3021 R² = 0,0292

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

E). Post-monsoon



-25-

The Figure 8 indicates that the annual rainfall has shown decreasing trend which is

statistically significant. Seasonal analysis shows that maximum decreasing trend was

observed during monsoon, followed by winter, post-monsoon and pre-monsoon but all are

statistically insignificant. The overall analysis shows decreasing trend with the exception of

some periodic behaviour.

y = 0,0345x - 0,4829

R² = 0,0749

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual


y = -0,0313x + 0,4384

R² = 0,0618

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

B). Winter



-26-

y = 0,0437x - 0,612

R² = 0,1203

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50S

PI

Year

C). Pre-monsoon


y = 0,0431x - 0,6029

R² = 0,1168

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon



-27-

Figure 9. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Rampur



statistically insignificant with episodic fluctuations. The increasing trend was observed during

pre-monsoon, followed by monsoon but statistically insignificant. The winter season has

shown decreasing trend, followed by post-monsoon which are statistically insignificant.

y = -0,0142x + 0,1988

R² = 0,0127

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50S

PI

Year

E). Post-monsoon


y = 0,0142x - 0,1989

R² = 0,0127

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

A). Annual



-28-

y = -0,0356x + 0,4979

R² = 0,0797

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

B). Winter


y = 0,0281x - 0,3932

R² = 0,0497

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

C). Pre-monsoon



-29-

Figure 10. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Suni

gauge station of Satluj River.

y = 0,0193x - 0,2707

R² = 0,0236

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

D). Monsoon


y = -0,0117x + 0,1631

R² = 0,0086

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

E). Post-monsoon



-30-


statistically insignificant with episodic fluctuations. The increasing trend was observed during

pre-monsoon, followed by monsoon but statistically insignificant. The winter season displays

a decreasing trend, followed by post-monsoon which are statistically insignificant.

y = -0,0215x + 0,3006

R² = 0,029

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

A). Annual


y = -0,042x + 0,5884

R² = 0,1113

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00

5,00

SP

I

Year

B). Winter



-31-

y = 0,0162x - 0,2274 R² = 0,0166

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

SP

I

Year

C). Pre-monsoon


y = -0,0081x + 0,1135

R² = 0,0041

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

D). Monsoon)



-32-

Figure 11. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Kasol



statistically insignificant. Seasonal analysis shows that maximum decreasing trend was

observed during winter, followed by post-monsoon and monsoon but all are statistically

insignificant. Although increasing trend was observed during pre-monsoon but statistically

insignificant. The overall analysis shows decreasing trend with the exception of some periodic

behaviour.

y = -0,0303x + 0,4237

R² = 0,0577

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

E). Post-monsoon


y = -0,0302x + 0,3712

R² = 0,051

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

SP

I

Year

A). Annual



-33-

y = -0,0299x + 0,3729 R² = 0,0502

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00S

PI

Year

B). Winter


y = 0,0104x - 0,1488 R² = 0,0061

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

C). Pre-monsoon



-34-


Daslehra gauge station of Satluj River Basin.

y = -0,0256x + 0,3226 R² = 0,0368

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon


y = -0,0233x + 0,2736 R² = 0,0301

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

E). Post-monsoon



-35-



observed during winter, followed by monsoon and post-monsoon but all are statistically



behaviour.

y = 0,0409x - 0,5111 R² = 0,0836

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

A). Annual


y = 0.061x - 0.773 R² = 0.191*

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

B). Winter



-36-

y = 0,0084x - 0,1047 R² = 0,0035

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

C). Pre-monsoon

SPI(Pre-monsoon) Liniowy (SPI(Pre-monsoon))

y = 0,0258x - 0,3219

R² = 0,0332

-3,00

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon



-37-

Figure 13. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Naina

Devi gauge station of Satluj River Basin (*Significant at 95% confidence level).

The Figure 13 indicates that the annual rainfall has shown increasing trend but

statistically insignificant with episodic fluctuations. Seasonal analysis shows that maximum

increasing trend was observed during winter which is statistically significant, followed by

monsoon and pre-monsoon but statistically insignificant, while the post-monsoon season has

no trend. The analysis shows overall increasing trend in the rainfall pattern.

y = -0,0035x + 0,0434

R² = 0,0006

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50S

PI

Year

E). Post-monsoon


y = -0,0464x + 0,6493 R² = 0,1355

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual



-38-

y = -0.048x + 0.684 R² = 0.150*

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00S

PI

Year

B). Winter


y = -0,0013x + 0,0175 R² = 1E-04

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

C). Pre-monsoon



-39-


Berthin gauge station of Satluj River Basin (*Significant at 95% confidence level).

y = -0,0279x + 0,3906 R² = 0,049

-3,00

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00

SP

I

Year

D). Monsoon


y = -0,0291x + 0,408 R² = 0,0535

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

E). Post-monsoon



-40-



observed during winter which is statistically significant, followed by post-monsoon and

monsoon but statistically insignificant. Almost no trend was observed during pre-monsoon

season. The overall analysis shows decreasing trend with the exception of some periodic

behaviour.

y = -0.062x + 0.874 R² = 0.245*

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual


y = -0.048x + 0.679

R² = 0.148*

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

B). Winter



-41-

y = -0,0286x + 0,4007

R² = 0,0516

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

C). Pre-monsoon


y = -0,0448x + 0,6279

R² = 0,1267

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

D). Monsoon



-42-

Figure 15. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Kuddi

(Ali) gauge station of Satluj River (*Significant at 95% confidence level).


statistically significant with some periodic behaviour. Seasonal analysis shows that maximum

decreasing trend was observed during winter which is statistically significant, followed by

monsoon, post-monsoon and pre-monsoon but all are statistically insignificant.

y = -0,0315x + 0,441

R² = 0,0625

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

E). Post-monsoon


y = -0,0158x + 0,2215

R² = 0,0158

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

A). Annual



-43-

y = -0,0335x + 0,4687 R² = 0,0706

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

B). Winter


y = 0,0347x - 0,4859 R² = 0,0759

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

C). Pre-monsoon



-44-

Figure 16. Temporal variations (annual as well as seasonal) and linear trends in total rainfall at Kahu


y = -0,0175x + 0,2445 R² = 0,0192

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50S

PI

Year

D). Monsoon


y = -0,0226x + 0,3159 R² = 0,0321

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

E). Post-monsoon



-45-



observed during winter, followed by post-monsoon and monsoon but all are statistically



behaviour.

y = 0,0024x - 0,1195 R² = 0,0004

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

A). Annual


y = -0,0011x - 0,0942 R² = 7E-05

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

B). Winter



-46-

y = -0,0001x - 0,0272 R² = 1E-06

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

C). Pre-monsoon


y = 0,0064x - 0,167 R² = 0,0026

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

SP

I

Year

D). Monsoon



-47-


Bilaspur gauge station of Satluj River Basin.

The Figure 17 indicates that the annual rainfall has shown almost no trend with the

exception of some episodic fluctuations. The figures also show slightly increasing and

decreasing trends during monsoon and post-monsoon respectively but statistically

insignificant. Almost no trend was observed during winter and pre-monsoon. The overall

analysis shows no trend with the exception of some periodic behaviour.

y = -0,0089x + 0,1323 R² = 0,0052

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50S

PI

Year

E). Post-monsoon


y = -0,0417x + 0,5841 R² = 0,1097

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SP

I

Year

A). Annual



-48-

y = -0,0248x + 0,347 R² = 0,0387

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00S

PI

Year

B). Winter


y = 0,0083x - 0,1161 R² = 0,0043

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

SP

I

Year

C). Pre-monsoon



-49-


Bhakra gauge station of Satluj River.

y = -0,0443x + 0,6197 R² = 0,1234

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50S

PI

Year

D). Monsoon


y = -0,0217x + 0,3042

R² = 0,0297

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

2,50

3,00

SP

I

Year

E). Post-monsoon



-50-

The Figure 18 indicates that the annual rainfall has followed a decreasing trend which is


observed during monsoon, followed by winter and post-monsoon but all are statistically

insignificant. Although slightly increasing but statistically insignificant trend was observed

during pre-monsoon. The overall analysis shows decreasing trend with the exception of some

episodic fluctuations.

Table 2. Results of trend analysis of rainfall in Satluj River Basin.

S. No. Station Trend analysis

Mann-Kendall Linear regression

A S1 S2 S3 S4 A S1 S2 S3 S4

1. Kaza -(.70) -- +(.62) -(.50) +(.95) -(.56) -- +(.57) -(.47) +(.90)

2. Rakchham +(.00)* +(.01)* +(.04)* +(.04)* -(.93) +(.00)* +(.02)* +(.01)* +(.05)* -(.79)

3. Namgia -(.51) -(.29) -(.50) +(.36) -(1.00) -(.36) -(.12) -(.20) +(.43) -(.67)

4. Kalpa +(.68) -(.57) -(.90) +(.03)* -(.80) +(.47) -(.24) -(.96) +(.01)* -(.43)

5. Swarghat -(.05)* -(.27) -(.64) -(.07) -(.88) -(.04)* -(.28) -(.77) -(.06) -(.42)

6. Rampur +(.56) -(.41) +(.06) +(.26) -(.87) +(.16) -(.21) +(.07) +(.08) -(.57)

7. Suni +(.80) -(.34) +(.28) +(.25) -(.68) +(.57) -(.15) +(.26) +(.44) -(.65)

8. Kasol -(.31) -(.07) +(.41) -(.87) -(.80) -(.39) -(.09) +(.52) -(.75) -(.23)

9.Daslehra -(.32) -(.30) +(.98) -(.39) -(.27) -(.28) -(.29) +(.71) -(.36) -(.41)

10. Naina Devi+(.12) +(.04)* +(.84) +(.71) -(.88) +(.17) +(.03)* +(.78) +(.39) -(.90)

11. Berthin -(.10) -(.05)* -(.98) -(.46) -(.60) -(.06) -(.04)* -(.96) -(.27) -(.25)

12. Kuddi -(.01)* -(.03)* -(.41) -(.17) -(.87) -(.01)* -(.05)* -(.25) -(.07) -(.21)

13. Kahu -(.46) -(.19) +(.26) -(.54) -(.98) -(.53) -(.18) +(.16) -(.49) -(.37)

14. Bilaspur +(1.00) -(.71) -(.97) +(.97) -(.34) +(.92) -(.97) -(.99) +(.80) -(.72)

15. Bhakra -(.10) -(.51) +(.56) -(.11) -(.55) -(.09) -(.32) +(.74) -(.07) -(.39)

*Significance at 95% confidence level. (+) increasing, (-) decreasing.

A: Annual, S1: Winter; S2: Pre-monsoon; S3: Monsoon; S4: Post-monsoon

The trend analysis results (Table 2) of rainfall through non-parametric Mann-Kendall

and parametric linear regression tests indicate that the annual rainfall at Rakchham, Kalpa,

Rampur, Suni, Naina Devi and Bilaspur shows increasing but Kaza, Namgia, Swarghat,

Kasol, Daslehra, Berthin, Kuddi, Kahu and Bhakra show decreasing trends. Out of them, only

Rakchham is statistically significant (increasing) and Swarghat and Kuddi are statistically

significant (decreasing) at 95% confidence level. During winter, Rakchham and Naina Devi

display increasing trends which are statistically significant.

Kalpa, Namgia, Swarghat, Rampur, Suni, Kasol, Bilaspur, Daslehra, Berthin, Kuddi,

Kahu and Bhakra show decreasing trends where only Berthin and Kuddi are statistically

significant. During pre-monsoon, Kaza, Rakchham, Rampur, Suni, Kasol, Daslehra, Naina

Devi, Kahu and Bhakra show increasing where only Rakchham shows statistically significant

trend. But Namgia, Kalpa, Swarghat, Berthin, Kuddi and Bilaspur show statistically


-51-

insignificant decreasing trends. During monsoon, Rakchham, Namgia, Kalpa, Rampur, Suni,

Naina Devi and Bilaspur show increasing where only Rakchham and Kalpa show statistically

significant trends. But Kaza, Swarghat, Kasol, Daslehra, Berthin, Kuddi, Kahu and Bhakra

has shown statistically insignificant decreasing trends. During post-monsoon, only Kaza

shows increasing and statistically insignificant trend. Rakchham, Namgia, Kalpa, Rampur,

Suni, Swarghat, Kasol, Naina Devi, Bilaspur, Daslehra, Berthin, Kuddi, Kahu and Bhakra

show decreasing trends which are statistically insignificant.

Table 3. Trend test results of rainfall.

Seasons No. of No. of increasing % No. of decreasing %

trends trends (significant) (sign) trends (significant) (sign)

Annual 15 6(1) 40(6.6) 9(2) 60(13.3)

Winter 15 2(2) 13.3(13.3) 13(2) 86.6(13.3)

Pre-monsoon 15 9(1) 60(6.6) 6(0) 40(0)

Monsoon 15 7(2) 46.6(13.3) 8(0) 53.9(0)

Post-monsoon 15 1(0) 6.6(0) 14(0) 73.3(0)

# Significance at 95% confidence level.

Trend analysis results of rainfall (Table 3) show that out of 15 annual trends 6 (40%)

are increasing and 9 (60%) are decreasing in nature where 1 (6.6%) is statistically significant

(increasing) and 2 (13.3%) are statistically significant (decreasing) at 95% confidence level.

During winter, 2 (13.3%) are showing statistically significant increasing trend but 13 (86.6%)

are showing decreasing trends where 2 (13.3%) are statistically significant. During pre-

monsoon, 9 (60%) are increasing but 6 (40%) are decreasing in nature where only 1 (6.6%) is

statistically significant (increasing). During monsoon, 7 (46.6%) are showing increasing

where 2 (13.3%) are statistically significant. But 8 (53.9%) are showing decreasing trends

where all are statistically significant at 95% confidence level. During post-monsoon, only 1

(6.6%) is increasing but statistically significant. 14 (73.3%) are decreasing in nature where

none is statistically significant.

The results indicate remarkable differences among the observation stations with

negative and positive trends. The trends found by the linear regression were almost similar to

Mann-Kendall test. The analysis shows mixed patterns of change where only few data series

has significant trends. The rainfall at some gauge stations showed statistically significant

increasing trends at the 95% confidence levels. The analysis indicates episodic fluctuations in

rainfall pattern which may have been caused due to changing climatic conditions. The uneven

distribution of rain, its intensity and periodicity, shows the irregular pattern at various

locations. The analysis shows that the study area reveals significant increasing/decreasing

trends in rainfall, at 95% confidence level, appear to have a response to climate change. The

analysis of annual as well as seasonal rainfall for the Satluj River Basin indicates significant

changes from 1984 to 2010. There is a clear contrast in the rainfall pattern between the high

and low altitude mountainous region where the orographic effect plays a significant role.

Shifts in climatic regimes, particularly precipitation, in space and time, would impact

heavily on the river systems originating in mountain areas (Beniston et al., 1997) like Satluj


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River Basin. The sensitivity of rainfall variations provides important insight regarding the

responses and vulnerability of such mountainous areas to the vagaries of climate change. Due

to climate change, the rainfall patterns are changing. The monsoons penetrating deeper into

the cold deserts in Satluj River Basin have become a matter of concern. Shifts of precipitation

in the form of rainfall towards upstream could have major environmental consequences. The

influence of climate change on hydrologic processes, especially in mountain basins is

paramount for understanding, forecasting and mitigating water related hazards. The impacts

of climatic change in the form of irregular precipitation and increased intensity and frequency

of the flooding events, likely became obstacles to the sustainable development of a life-

sustaining system.

5. CONCLUSIONS

Trend analysis of historical data concluded that the spatial and temporal variations in

rainfall pattern are due to climate change. The rainfall in the basin shows great temporal and

spatial variations, unequal seasonal and geographical distribution with frequent departures

from normal. The monsoons penetrating deeper into the cold deserts in Satluj River Basin

have become a matter of concern. It is predicted that the glaciers will receive more

precipitation in the form of rainfall rather than snowfall. Shifts of precipitation in the form of

rainfall towards upstream could have major environmental consequences. Information from

trend analysis will be useful in the planning, development and management of water resources

in the study area. Densification of rainfall gauge stations will further strengthen the

formulation of future strategy for the management of river catchment area.

Acknowledgements

Authors are thankful to Indian Council of Medical Research (ICMR) and University Grant Commission (UGC),

New Delhi for providing financial assistance in the form of research fellowship.

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( Received 12 June 2015; accepted 25 June 2015 )

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Trend analysis of rainfall in Satluj River Basin, Himachal ...€¦ · Indian region have shown that there is no clear trend of increase or decrease in average annual rainfall over