Trend and concentration characteristics of precipitation and related climatic teleconnections from 1982 to 2010 in the Beas River basin, India

Global and Planetary Change 145 (2016) 116–129

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Trend and concentration characteristics of precipitation and related climatic teleconnections from 1982 to 2010 in the Beas River basin, India Yixing Yin a,c,d, Chong-Yu Xu b,c,⁎, Haishan Chen a, Lu Li e,f, Hongliang Xu g, Hong Li c, Sharad K. Jain h a

Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China c Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway d College of Hydrometeorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China e Bjerknes Centre for Climate Research, Bergen, Norway f Uni Climate, Uni Research, Bergen, Norway g School of Environment and Energy, Shenzhen Graduate School, Peking University, Shenzhen, China h National Institute of Hydrology, Roorkee 247 667, India b

a r t i c l e

i n f o

Article history: Received 1 October 2015 Received in revised form 21 August 2016 Accepted 27 August 2016 Available online 01 September 2016 Keywords: Precipitation Trend Concentration Climatic teleconnections Beas River basin

a b s t r a c t The Beas River, located in the Western Himalayan mountainous regions in India, is one of the major tributaries of the Indus River. However, recent changes of precipitation and related climatic teleconnections in this river basin have rarely been investigated yet. In this study, the trend and concentration characteristics of precipitation during1982–2010 are investigated by using Mann–Kendall trend test and two kinds of concentration indices. The climatic teleconnections are explored with the help of cross correlation, wavelet transform and composite analysis, revealing the relationship of precipitation with climatic indices of Indian summer monsoon (ISM), El Nino/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO). The results indicate that: (1) Precipitation of most of the stations increased in the monsoon season while precipitation of all the stations decreased in the non-monsoon seasons. As a result, the annual precipitation of the majority of the stations was on the decrease. (2) A general increase in the precipitation Gini coefficient and precipitation concentration degree (PCD) was detected. Moreover, the precipitation concentration period (PCP) is mainly within the period from May to August, and more PCP occurred in the monsoon months recently. (3) The relationship between monsoon precipitation and ISM is not significant in the Beas River basin. The relationship between precipitation and ENSO in winter is less significant than in the monsoon season, and the relationship of monsoon/winter precipitation with IOD is not as evident as that with ENSO. Besides, ENSO and NAO play important roles in the changes of monsoon and winter precipitation in the Beas River basin. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Precipitation is one of the essential elements of the water cycle. It is also one of the main indicators in the studies of climate change impacts that influences water resources system and agriculture of the region. Precipitation changes occur over a wide range of temporal and spatial scales. The global average rainfall is projected to increase, but both increase and decrease can be expected on the regional and continental scales (Dore, 2005). Many studies have detected rainfall changes both on global (Trenberth, 2011; Sun et al., 2012) and regional scales (Karl and Knight, 1998; Kumar et al., 2010; Biabanaki et al., 2014; Onyutha, 2016) associated with climate changes during recent years. The

⁎ Corresponding author at: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China. E-mail address: [email protected] (C.-Y. Xu).

http://dx.doi.org/10.1016/j.gloplacha.2016.08.011 0921-8181/© 2016 Elsevier B.V. All rights reserved.

implications of these changes are significant especially in areas such as India where food security and the economy are highly dependent upon timely precipitation. India is the second most populous country in the world and the population growth rate is still high. Agriculture is an important sector of its economy which accounts for 14% of the nation's GDP, about 11% of its exports, and provides employment to nearly 60% of the population. Therefore, the failure of the monsoon rains and the resultant water scarcity is always a cause of great concern. A large percent of the annual rainfall (75% to 80%) over the country occurs during the summer monsoon season (from June to September). The Indian summer monsoon (ISM) annual cycle exhibits variability over intra-seasonal to decadal time scales (Webster et al., 1998). Hence, a deficit or excess in AllIndia Summer Monsoon Rainfall in a year will lead to drought or flood disasters causing great impacts on the agriculture and economic activities of the country. What's more, winter precipitation is of great importance for winter crops, especially for wheat in north India.

Y. Yin et al. / Global and Planetary Change 145 (2016) 116–129

Flood is one of the most frequent natural disasters in India. It causes more damages of loss of lives and property than all the other natural disasters (FEMA, 2000). Floods in India mainly occur during the southwest monsoon season in most part of the country. For example, severe floods occurred over large parts of Gujarat and Rajasthan in the monsoon seasons in 2006 and 2007 (India Meteorological Department, 2006, 2007). India also has a long drought history. Droughts affected almost 1061 million people and killed about 4.25 million people during the period 1900–2006 in the country (Samui and Kamble, 2011). The climate in India is controlled by the southwest monsoon in the summer season. The Indian summer monsoon rainfall (ISMR) during June to September plays an important role in the agriculture production in India. The study and prediction of ISMR variability has been a matter of great importance to both society and scientific community and therefore, has received widespread attention (Webster et al., 1998). With respect to teleconnections, significant links have been detected between Indian monsoon rainfall and a wide array of large scale characteristics of the global climate, such as El Nino/Southern Oscillation (ENSO) (Torrence and Webster, 1999, Kumar et al., 1999), North Atlantic Oscillation (NAO) (Dugam et al., 1997) and Indian Ocean Dipole (IOD) (Ashok et al., 2001; Pokhrel et al., 2012; Cherchi and Navarra, 2013). The Himalayan mountains are the sources of one of the world's largest natural supplies of fresh water. All the major rivers in south Asia take their sources in the Himalayas. However, the hydrology of Himalayan river basins has not been well understood because of the complexity in the climatic and geographic characteristics, and the scarcity of observations. The Beas River, located in the Western Himalayan mountainous

117

regions in India, is one of the major tributaries of the Indus River. The Beas basin is frequently influenced by flash floods, which lead to loss of life and property almost every year (Kumar et al., 2007). Considerable research on the hydrology of the Beas Basin has been conducted (Prasad and Roy, 2005; Kumar et al., 2007; Negi et al., 2009; Shah et al., 2013; Li et al., 2015, 2016; Hegdahl et al., 2016). However, recent changes of precipitation and related climatic teleconnections in the Beas River basin have rarely been investigated. In this paper, the annual and seasonal trend and concentration characteristics of precipitation for the recent years are first investigated. And climate mechanisms behind the changes, i.e. the climatic teleconnections between changes of precipitation and climatic indices of ISM, ENSO, NAO, and IOD, were then explored. The results will be helpful for the hydro-climatic understanding and issue of early warning of flood and drought disasters in the region. 2. Study area and data The Beas basin up to the Pandoh dam is the study area in this work (Fig. 1). The Beas River is an important tributary of the Indus River system. The length of the Beas River is about 460 km and the catchment area is around 20,303 km2. The river length up to the Pandoh dam is 116 km, and the catchment area up to Pandoh is about 5000 km2. Floods have created much loss and anxiety in this area during the last decades (Sah and Mazari, 1998). Flood events occurred in Beas River due to cloud burst and large landslides in 1902, 1945, 1988, 1993 and 1995 (Sah and Mazari, 1998).

Fig. 1. DEM of the Beas River basin and location of the rain stations.

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Daily rainfall data for the period 1982 to 2010 from seven stations in the Beas River basin were used. The DEM of the study area and seven rain gauge stations are shown in Fig. 1. More detailed information about the rain stations can be found in Table 1. Manali Station is not used in our analysis due to long period of missing data. The ISM index (ISMI) used in this paper was proposed by Wang et al. (2001). The ISMI (also known as Dynamic Indian monsoon index) is defined as the difference of the 850-hPa latitudinal winds between the southern region of 5°–15°N, 40°–80°E and the northern region of 20°– 30°N, 70°–90°E. The ISMI describes well the precipitation anomalies averaged over the region from the Bay of Bengal, India to the eastern Arabian Sea, and it has a high correlation with the all-India summer precipitation (Parthasarathy et al., 1992). The ISMI data were derived from the website http://apdrc.soest.hawaii.edu/projects/monsoon/ seasonal-monidx.html. All of the indices are normalized between 1948 and 2013. The El Nino/Southern Oscillation (ENSO) represents the dominant mode of the tropical Pacific ocean-atmosphere system (Cane, 1992). ENSO has been identified as one of the most important forcings of the variability of Indian summer monsoon. In general, occurrence of the El Nino events (warm phase of ENSO) causes deficient monsoon rainfall while the opposite is true for the La Nina events (cool phase of ENSO) (Panda and Kumar, 2014). However, since the late 1970s the strong relationship between ENSO and the Indian monsoon has weakened (Kumar et al., 1999). The NINO 3.4 SST anomaly index, an indicator of the averaged anomalous SST over the equatorial Pacific box (5°S–5°N, 120°W–170°W), is adopted in the current research. The monthly NINO 3.4 SST data were derived from the International Research Institute for Climate prediction (IRI) data library (available at http://iridl. ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO34). The North Atlantic Oscillation (NAO) is defined as a meridional seesaw in atmospheric pressure with atmospheric action centers near Iceland and the Azores. A positive NAO index is associated with above normal westerlies in the mid-latitudes of North Atlantic, and vice versa for negative NAO indices (Moore et al., 2013). It is a leading mode of climate variability in the North Hemisphere, mainly in winter months. The relationship between NAO and Northwest India rainfall is stronger than that for the other parts of India rainfall (Dugam et al., 1997). On the other hand, according to a recent study by Yadav et al. (2009), the impacts of ENSO on northwest India winter precipitation have increased in comparison to NAO/AO for the recent decades. The NAO index (NAOI) is computed by utilizing rotated principal component analysis, and the data for the NAOI in this study were obtained from the Climate Prediction Center of NOAA (http://www.cpc.ncep. noaa.gov/products/precip/CWlink/pna/nao.shtml). The Indian Ocean Dipole (IOD) is an irregular oscillation of sea surface temperatures where the western Indian Ocean turns alternately warmer and cooler than the eastern Indian Ocean. Intensity of the IOD is defined as anomalous SST gradient between the tropical western Indian Ocean (50°–70°E, 10°S–10°N) and the tropical southeastern Indian Ocean (90°–110°E, 10°S–0°N), known as Dipole Mode Index (DMI) (Saji et al., 1999). ENSO and IOD events take up 30%and 12% of the variability of tropical Indian Ocean SST respectively. However, according to Saji et al. (1999) the linkage between the DMI and the changes of ISMR Table 1 Detailed information of the rain stations in the Beas River basin. Stations Series length

Lat (E°)

Long (N°)

Elevation (m)

Mean annual precipitation (mm)

Banjar Bhuntar Janjehli Larji Manali Pandoh Sainj

31.64 31.88 31.52 31.73 32.25 31.67 31.73

77.31 77.15 77.22 77.22 77.18 77.05 77.31

1000 1080 1784 995 1926 899 1384

1304 1418.3 1551.9 1426.7 2133.4 1416.1 1647.3

1982–2010 1982–2010 1990–2010 1982–2010 1990–2010 1982–2010 1982–2010

is not clear. Ashok et al. (2001) demonstrated that the IOD plays a part in the ENSO-ISMR relationship: When the ENSO-ISMR correlation is low (high), the IOD-ISMR correlation will be high (low). The monthly DMI data were extracted from Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset (http://www.jamstec.go.jp/frsgc/research/ d1/iod/iod/dipole_mode_index.htm). Fig. 2 shows the ISMI, NINO 3.4 SST, NAOI and DMI climatic indices during the monsoon and winter seasons over 1982–2010. 3. Methods 3.1. Mann–Kendall test Of the methods to detect trend in hydrometeorological series, the non-parametric Mann–Kendall trend test (Mann, 1945; Kendall, 1975; Khadr, 2016; Caloiero et al., 2015) is one of the most commonly used methods. It has been recommended by WMO and has several advantages. Some of them are: it is free from the assumptions of normality and variance homogeneity; it compares medians rather than means and, as a result, it is resistant to the effects of outliers (Suryavanshi et al., 2014). The monotonic trend can be estimated by the statistic S. The S can be computed as: S¼

n−1 X

n X

  Sgn x j −xk

ð1Þ

k¼1 j¼kþ1

where xj and xk stand for the sequential data in years j and k respectively, and Sgn(d) = 1, if d N 0; Sgn(d) = 0, if d = 0; Sgn(d) = −1, if d b 0. The variance of S can be calculated according to the following equation: "

# n X nðn−1Þð2n þ 5Þ− t i iði−1Þð2i þ 5Þ i¼1

VarðSÞ ¼

18

ð2Þ

where ti stands for the number of ties of extent i. When n N 10, the test will be conducted using an approximation of normal distribution and the standardized statistic Z will then be calculated as follows: 8 S−1 > > pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; > > < Var ðSÞ Z¼ 0 > > Sþ1 > > : pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; Var ðSÞ

if SN0 ; if S ¼ 0

ð3Þ

if Sb0

A positive (negative) value of Z implies an increasing (decreasing) trend. The null hypothesis H0 of no trend is rejected if |Z| N Z1−α/2. Another useful index related to the Mann–Kendall trend test is the magnitude of the trend slope β, a robust estimate. It implies an upward or downward trend by a positive or negative value. The estimation of the trend slope can be determined as: 

x j −xi β ¼ median ð j−iÞ

 for all ibj

ð4Þ

3.2. Precipitation concentration Two kinds of precipitation concentration indices have been studied in this paper as described in the following. 3.2.1. Gini coefficient The Gini coefficient has been widely used in economics to measure income or wealth inequality, and is also utilized in other fields like hydrology and climatology (Masaki et al., 2014; Sun et al., 2015). The Gini coefficient can be used to measure how unevenly rainfall is

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Fig. 2. Climatic indices of ISMI, NINO 3.4 SST, DMI and NAOI in the monsoon and winter seasons during 1982–2010.

distributed over the year. A high precipitation Gini coefficient indicates that the precipitation concentrates on a few rainy days within the year. To estimate the Gini coefficient, daily precipitation is sorted in ascending order, summed cumulatively, and then transformed into the fraction of total precipitation, and the Lorenz curve is obtained in the end. The Gini coefficient is computed by doubling the area between the line of perfect equality (straight line y = x) and the observed Lorenz curve according to the following equation (Rajah et al., 2014): 11 0 n X B B ðn þ 1−iÞyi CC CC B i¼1 1B CC n þ 1−2B G¼ B CC B n X nB @ AA @ yi 0

ð5Þ

i¼1

where G stands for the Gini coefficient, yi (i = 1 to n, yi ≤ yi + 1) stands for the precipitation of rainy days in a year. The Gini coefficient ranges from 0 to 1. The Lorenz curve shape determines the Gini coefficient values and describes the uneven temporal distribution of precipitation in the year. Gini coefficient = 0 indicates a uniform distribution (perfect

equality) of precipitation throughout the year and occurs only when the Lorenz curve and the line of perfect equality overlap. Gini coefficient = 1 implies that all the precipitation of the year occurs on a single day (highest inequality), and the corresponding Lorenz curve would overlap with the curve which is at y = 0 for all x b 1, and y = 1 when x = 1 (known as line of perfect inequality). We have not chosen the wetday Gini coefficient (Rajah et al., 2014) but use the original index since we feel that the number of dry-days is also an indication of precipitation concentration. 3.2.2. PCD and PCP A seasonal index was first proposed to calculate the degree and timing of concentration making use of vector composition, considering monthly precipitation as a vector (Markham, 1970). Precipitation concentration degree (PCD) and precipitation concentration period (PCP) were proposed by Zhang and Qian (2003) to analyze the concentration characteristics of precipitation. The two indices have been successfully utilized to detect precipitation concentrations recently (Xie et al., 2005; Li et al., 2011). PCD can describe to what degree the annual rainfall is distributed in the year and PCP describes the period on which the

Table 2 Relationship between the angles of PCP and the months in a year. Months PCP

Range of angles Middle of angles

Months PCP

Range of angles Middle of angles

January

February

March

April

May

June

0°–30° 15°

30°–60° 45°

60°–90° 75°

90°–120° 105°

120°–150° 135°

150°–180° 165°

July

August

September

October

November

December

180°–210° 195°

210°–240° 225°

240°–270° 255°

270°–300° 285°

300°–330° 315°

330°–360° 345°

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total rainfall concentrates in the year. The principle for the calculation of the PCP and the PCD is based on the vector of monthly total rainfall in this study. It is then assumed that monthly rainfall is a vector quantity with both magnitude and direction. The magnitude of the vector is the degree of precipitation concentration, and the vector direction is the period of precipitation concentration. The whole year is considered as a

circle (360°), and a month corresponds to 360°/12 = 30°. Table 2 shows the relation between the angles of PCP and the months in a year. The yearly PCD and PCP in a station can be calculated as: PCDi j ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R2xi þ R2yi Ri

Fig. 3. Box plots of monthly precipitation of the six stations in the Beas River basin.

ð6Þ

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Table 3 Results of Mann-Kendall trend (Z) and Sen's slope estimate (b) for annual and seasonal precipitation of the six stations. Banjar

Annual Winter-1 (Jan–Feb) Winter (Dec–Mar) Pre-monsoon (Mar–May) Monsoon (Jun–Sep) Post-monsoon (Oct–Dec)

Bhuntar

Janjehli

Larji

Pandoh

Sainj

Z

b

Z

b

Z

b

Z

b

Z

b

Z

b

0.96 −0.66

4.94 −1.34

−1.14 −0.24

−6.74 −0.24

−3.14⁎ −2.3⁎

−31.4 −8.05

−0.66 −0.32

−3.12 −0.33

−0.24 −2.18⁎

−2.43 −2.76

−2.57⁎ −1.37

−14.9 −2.69

−2.19⁎

−7.23

−1.92

−6.56

−2.5⁎

−17.05

−1.82

−6.08

−3.62⁎

−5.86

−2.86⁎

−11.44

−1.33

−4.51

−3.17⁎

−8.72

−3.47⁎

−14.49

−2.66⁎

−7.98

−2.31⁎

−4.85

−3.83⁎

−12.23

2.57⁎

11.25

2.16⁎

5.8

−2.57⁎

−16.41

1.11

5.59

1.61

8.06

0.13

1.28

−1.35

−1.19

−1.37

−2.27

−0.03

−0.03

−1.78

−2.04

−1.91

−2.1

−2.59⁎

−2.87

⁎ Indicates statistically significant at 0.05 level.

  R PCP ij ¼ arctan xi Ryi

Rxi ¼

N X

ð7Þ

rij  sinθ j

ð8Þ

rij  cosθ j

ð9Þ

j¼1

Ryi ¼

N X j¼1

Rij ¼

N X

3.3. Wavelet transform The continuous wavelet transform (CWT) discussed by Torrence and Compo (1998) is suitable for hydrometeorological time series due to the wide range of possible dominant frequencies and it has been successfully applied recently (Jevrejeva et al., 2003; Zhang et al., 2007; Yin et al., 2009). Torrence and Compo (1998) also defined the cross wavelet of two series X and Y with wavelet transforms WX and WY for investigation of the covariance of the two series, which is W XY ðs; t Þ ¼ W X ðs; t ÞW Y ðs; t Þ

r ij

ð10Þ

j¼1

where i represents the year (i = 1982, 1983,⋯, 2010), and j stands for the month (j = 1, 2,⋯, 12) in a year. rij stands for the rainfall in the jth month of the ith year. Rij represents monthly total rainfall in the jth month of the ith year, and θj stands for the azimuth of the jth month. PCDij represents to what degree the total rainfall of the ith year concentrates on certain months and PCPij stands for the period (month) when the total rainfall concentrates for the ith year. The yearly PCD can indicate the degree to which the annual total rainfall distributes among 12 months, which ranges from 0 to 1. When the whole annual rainfall concentrates on a certain month, the PCD will reach its maximum of 1. When the rainfall of each month in a year is uniformly distributed, the PCD will reach the minimum of 0 (Zhang and Qian, 2003). The yearly PCP, the azimuth of composite vector, indicates the population effect of each monthly rainfall after it is composited. Thus, it can reveal the month when the biggest monthly precipitation occurs.

ð11Þ

where asterisk represents complex conjugation. |WXY(s,t)| was then defined as the cross-wavelet power. The WXY phase angle represents the phase correlation between X and Y in time and frequency space. Wavelet coherence resembles the cross-wavelet power while it reveals regions in both time and frequency space in which two series co-vary but might not have high common power (Jevrejeva et al., 2003; Grinsted et al., 2004). The coherence is defined as  −1  S s W XY ðs; t Þ 2 

 R2 ðs; t Þ ¼

S s−1 jW X ðs; t Þj2  S s−1 jW Y ðs; t Þj2

ð12Þ

where S stands for a smoothing operator. The scales in time and frequency where S is smoothing describe the scales where the coherence represents the covariance. It is helpful to regard the wavelet coherence as a localized correlation coefficient in time and frequency space. Cross-wavelet power can reveal areas with a high common power value, but Maraun and Kurths (2004) pointed out that it appears not appropriate for significance testing of the correlation between two series.

Fig. 4. The contribution of the monsoon precipitation (A) and winter precipitation (B) for the six stations during 1982–2010.

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Table 4 Mann-Kendall trend (Z) and Sen's slope estimate (b) for contribution of the monsoon and winter precipitation in the total annual precipitation. Banjar

Monsoon Winter

Bhuntar

Janjehli

Larji

Pandoh

Sainj

Z

b

Z

b

Z

b

Z

b

Z

b

Z

b

2.53⁎ −2.59⁎

0.007 −0.007

3.58⁎ −1.64

0.009 −0.006

1.24 −2.11⁎

0.006 −0.009

2.76⁎ −2.03⁎

0.008 −0.005

3.06⁎ −3.18⁎

0.006 −0.004

3.17⁎ −1.52

0.009 −0.004

⁎ Indicates statistically significant at 0.05 level.

The use of wavelet coherence was recommended which reveals the covariance intensity of the two series in the time-scale space. In this study, the wavelet coherence is calculated to describe the cross-correlation between precipitation and climatic indices series as a function of frequency. The phase correlation between the two series in time and frequency space is represented by the phase angle, and significance test was applied using Monte Carlo models with red noise based on the autoregressive process (Grinsted et al., 2004). Wavelet analysis was performed using the MATLAB code by Torrence and Compo at: http:// paos.colorado.edu/research/wavelets/.

4. Results and analysis 4.1. Precipitation trend We first explore the characteristics of monthly precipitation in the Beas River basin, which will be helpful for the understanding of the seasonal and annual precipitation trend. Boxplot charts show the monthly precipitation for each station (Fig. 3). The bottom and top of the boxes are the first and third quartiles, and the bands inside the boxes stand for the second quartiles (medians). Outliers are plotted as individual points. As we can see, July and August have the highest precipitation, followed by June and September for each of the station. In addition, the rainfall from February to May is also high for some of the stations. On the other hand, the ranges of the precipitation in July and August are also the biggest for most of the stations. October, November and December have the lowest precipitation, and they are also the months with the smallest ranges. Therefore, the higher is the precipitation in a particular month, the wider is the range of precipitation magnitude. The annual and seasonal precipitation trend detected by using Mann–Kendall trend test and Sen's slope is shown in Table 3. Generally, winter is from January to February, pre-monsoon is from March to May, monsoon is from June to September and post-monsoon is from October to December in India. However, Yadav et al. (2009) defined winter from December to March, and investigated the influences of ENSO and AO/ NAO on the winter precipitation over northwest India. Therefore, we also define winter from December to March for the purpose of comparison, and the other seasons change accordingly (Table 3). Besides, the months from January to February are referred to as winter-1. As can be seen from Table 3, the annual precipitation of the majority of the stations was on the decrease (negative Z and b), and the decrease passed the 0.05 significance level for Sainj and Janjehli stations. What's more, precipitation in the non-monsoon seasons showed a decreasing trend for all the stations, while precipitation in the monsoon season increased for most the stations. Increase of monsoon precipitation in Bhuntar, Larji, Pandoh and Sainj Stations was not strong enough to offset the decrease trend of non-monsoon seasons, so their annual series showed negative trend. In other words, although precipitation trend is mainly positive in the monsoon season, the negative trend of the nonmonsoon seasons leads to the negative trend of annual precipitation for most of the stations. The detailed reason for this will be given in this section later. Precipitation in the Janjehli Station has a declining tendency for all the four seasons. As a result, the downward trend of annual precipitation in Janjehli is the most significant for the six stations. Banjar Station has a weak upward trend for the annual precipitation, this is because

the monsoon season precipitation increased significantly, offsetting the negative trend in its non-monsoon seasons. Yadav et al. (2009) held that winter precipitation contributes 15– 20% of the annual rainfall over the northwest India. We also explored the contribution of the monsoon and winter precipitation in the total annual precipitation in the Beas River basin. As can be seen from Fig. 4(A), the contribution of the monsoon precipitation in the total annual precipitation is mainly within 20 to 80%, with Pandoh the biggest and Bhuntar the smallest. The changes of the six stations are similar, and they all show a positive trend, indicating that monsoon precipitation contributes more during recent years. On the other hand, the contribution of winter precipitation is generally within 10% to 60% (Fig. 4(B)), with Bhuntar the biggest and Pandoh the smallest. Besides, the contribution of winter precipitation shows a negative trend. Mann–Kendall trend test presents similar results (Table 4). The contribution of the monsoon precipitation has positive Z values and Sen's slope for all the stations, with 5 of them passed significance level of 0.05; while the contribution of winter precipitation has negative Z value and Sen's slope, with 4 of them passed the significance level of 0.05. The average contribution of non-monsoon seasons in the annual precipitation is up to 44% (Table 5) which is near half of the annual precipitation in the Beas River basin. Moreover, the downward trend in the non-monsoon seasons is more significant than the upward trend in the monsoon season: The number of the stations with significant downward trend in the non-monsoon seasons is bigger than that of the stations with significant upward trend in the monsoon season, and Z values in the former are also higher than those in the latter (Table 3). Consequently, the decrease of non-monsoon season precipitation can skew the annual rainfall toward decrease. Our results are not in agreement with those of Kumar et al. (2010) and Bhutiyani et al. (2010). According to Kumar et al. (2010), annual and monsoon rainfall was on the decrease, while the rainfall in premonsoon, post-monsoon and winter was on the increase at the national scale during 1871–2005. However, their data did not include the northwest India where the Beas basin is located. Bhutiyani et al. (2010) concluded that there was no trend for the winter rainfall but there was significant negative trend for the monsoon rainfall during the period of 1866–2006. And this is because their study period is quite different from the current study. 4.2. Precipitation concentration Gini coefficients of the total series for the six stations were first computed based on monthly and daily data, and the results are shown in Fig. 5(A). It can be seen that the Gini coefficients based on daily data are bigger than 0.8, while those based on monthly data are within 0.4 and 0.5, implying that the concentration of the daily precipitation is significantly higher than that of the monthly precipitation. The Lorenz curve can

Table 5 Contribution (%) of the precipitation from monsoon and non-monsoon seasons in the total annual precipitation. Banjar Bhuntar Janjehli Larji Pandoh Sainj Average Non-monsoon seasons Monsoon season

46.2

58.7

41.1

47.1 25.4

45.8

44

53.8

41.3

58.9

52.9 74.6

54.2

56

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Fig. 5. Gini coefficients of the total series (A) for the six stations based on daily and monthly data, and the decades of 1980s, 1990s and 2000s (B) based on daily data.

provide a graphical plot of the cumulative percentage of the annual precipitation. Fig. 6 shows the Lorenz curves of the monthly and daily precipitation in the Banjar Station over 1982–2010, which also confirms the differences of concentration between the daily and monthly data. The total time series were then divided into three periods, namely the 1980s (from 1982 to 1990), 1990s (from 1991 to 2000) and 2000s (from 2001 to 2010), and the Gini coefficients were also calculated for each period. As can be seen in Fig. 5(B), the Gini coefficients are mainly within 0.85 and 0.9. The Gini coefficients of the 2000s are significantly bigger than the first two decades, while those of the 1980s and 1990s are quite similar to each other. This shows that the precipitation concentration has increased from the 1980s to 2000s. The results of annual Gini coefficients during 1982–2010 for the six stations were also computed (based on daily data) and are presented in Fig. 7(A). Fig. 7(A) indicates that precipitation concentration was highly variable both temporally and spatially among stations. Most of the stations had high to very high concentrations. Some of the stations have Gini coefficients higher than 0.9, even some of them higher than 0.92. The minimum Gini coefficient was detected in Sainj Station (0.778) in 1997 (which is the only case that Gini coefficient is lower than 0.8), and the maximum was also noted in Sainj (0.935) in 2002. The second biggest Gini coefficient occurred in Bhuntar (0.932) in

2007. What's more, the six curves have similar changing characteristics, and all the curves of the six stations have an upward trend. The changes of PCD during 1982–2010 are shown in Fig. 7(B). As can be seen from Fig. 7(B), the values of PCD are more scattered than those of Gini coefficients. The values range from a minimum of 0.03 at Banjar Station to a maximum of 0.75 at Pandoh Station. The change process has similar characteristics for different stations. What's more, the annual series of PCD have an upward trend for all the six stations. The Mann–Kendall trend of Gini coefficients and PCD for the six stations can be found in Table 6. A general increase was detected in Gini coefficients and PCD values during 1982–2010. Thus, increases in precipitation concentration are found for the recent years, and these positive trends were quite common for the whole Beas River basin. Besides, the trend of PCD is more significant than that of Gini coefficients. Thus, increases in precipitation concentration are found for the recent years, i.e. annual precipitation is more concentrated on several months (mainly in the monsoon season) in a year for the recent years in the Beas River basin. PCP has important implications for water resources management and agriculture. If the PCP mismatches the timing of agriculture irrigation, precipitation of high concentration may cause difficulties in the water resources management. Therefore, it is also helpful to explore the PCP characteristics in the Beas River basin.

Fig. 6. Lorenz curve of the daily (A) and monthly (B) precipitation in Banjar Station during 1982–2010.

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Fig. 7. Annual Gini coefficients (A), PCD (B) and PCP (C) for the six stations during 1982–2010.

Fig. 7(C) shows the changes of PCP for the six stations during 1982– 2010. The results of PCP are presented in Julian date. As can be seen, the PCP is mainly within the period from May to August. It tended to occur later and more PCP occurred in the monsoon months (Julian date 121 to 273 for common years or 122 to 274 for leap years) recently. The Mann– Kendall trend of PCP for the six stations can also be found in Table 6. A general increase was detected in PCP during 1982–2010, with two of the stations passing significance level of 0.05. We have also counted the numbers that PCP occurs in each month, and the results show that PCP can occur from March to September in the basin. The total occurrence frequency of PCP in March, April, May, June, July, August and September are 4, 15, 22, 39, 63, 22 and 1 respectively. So that most of the PCP occurs during the monsoon season, especially June, July and August. Consequently, the characteristics of PCP are in agreement with the trend, contribution and concentration characteristics of seasonal precipitation. Besides, the changes of precipitation concentration (Gini coefficient, PCD and PCP) are consistent with the trend of seasonal precipitation. Generally speaking, the annual precipitation is on the decrease for the study area according to our study. However, higher precipitation concentration is seen in monsoon season. As a result, the risk of

droughts and deficit of water resources will increase which should be given due attention. 4.3. Climatic teleconnections 4.3.1. Relationship with ISM Fig. 8 shows the wavelet coherence between monsoon precipitation in the Beas River basin and ISMI (The average precipitation of the six stations is used). The thick black contour represents the 5% significance level against red noise and the cone of influence in which edge effects may distort the figure is displayed as a lighter shade. The relative phase relationship is given by arrows in the squared wavelet coherence with in-phase correlation pointing right, anti-phase correlation pointing left, and precipitation lagging ISMI by 90° pointing straight up. In Fig. 8, the phase relation indicated by the arrows shows that monsoon rainfall and ISMI are dominated by nearly in-phase relationship. More accurately, in-phase linkage was evident between them during the period before 1992 (at the scale of 2–3 years) and 1992–2000 (at the scale of 5– 6 years), while the period 2005–2010 (at the scale of 2–3 years) is outside the cone of influence. However, their relationship is not significant at 5% level.

Table 6 Mann-Kendall trend (Z) and Sen's slope estimate (b) of Gini coefficient, PCD and PCP for the six stations. Banjar

Gini coefficient PCD PCP

Bhuntar

Janjehli

Larji

Pandoh

Sainj

Z

b

Z

b

Z

b

Z

b

Z

b

Z

b

0.96 1.82 1.59

0.001 0.006 1.22

1.37 0.36 2.53⁎

0.001 0.001 2.90

2.63⁎ 1.24 1.78

0.003 0.005 1.49

1.14 1.78 1.86

0.001 0.004 1.56

2.83⁎ 3.43⁎ 1.59

0.001 0.008 0.60

2.76⁎ 1.93 2.57⁎

0.002 0.005 3.34

⁎ Indicates statistically significant at 0.05 level.

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Fig. 8. Wavelet coherence between monsoon precipitation in the Beas River basin and ISMI.

The correlation coefficient between ISMI and monsoon precipitation in the Beas River basin was 0.188, not passing the 5% significance level. The Beas River basin is located in the western Himalayan Mountains, and the mountains limit the intrusion of the monsoon whose influence weakens northwestward. Thus the monsoon climate is not very typical and the relationship of monsoon precipitation with ISMI is not significant. 4.3.2. Relationship with ENSO Fig. 9(A) shows the squared wavelet coherence between monsoon precipitation and ENSO. As can be seen, monsoon precipitation exhibits a global anti-phase correlation with ENSO at scales of 2–5 years from 1982 to 1990, 5–6 years from 1991 to 2005, and 2–3 years after 2005. However, it highlights no relationship with ENSO at 2–5 years from 1990 to 2005. As for Fig. 9(B), the relationship between precipitation and ENSO in winter is not as significant as in the monsoon season. However, they exhibit in-phase relationship in winter in comparison to antiphase relation in the monsoon season. Positive correlation is highlighted in the 2–3 year band before 1992 and in the 5–6 year band during

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1992–2002, while precipitation lags ENSO by 90° in the 2–3 year band later (not within the cone of influence). The cross correlation between NINO 3.4 SST and monsoon/winter precipitation is explored from SST leading precipitation for 4 seasons to SST lagging precipitation for 4 seasons, and the results are presented in Fig. 10. The value of x-axis is positive when SST lags precipitation, negative when SST leads precipitation. The dashed lines represent the Pearson's t-test at 5% significance level. The series length is 29 (1982– 2010) and 28 (1983–2010)for the monsoon and winter seasons respectively, and the correlation coefficients for 5% significance level are 0.374 and 0.367 respectively. As can be seen, the correlation between them is quite evident. The average of cross correlation coefficients for monsoon precipitation is −0.255, and is 0.237 for winter precipitation. The concurrent correlation coefficient between them is up to −0.563 for monsoon precipitation, which is also the biggest in the cross correlation coefficients; while the concurrent correlation coefficient is 0.463 for winter precipitation, and the biggest is 0.534 when SST lags precipitation for one season. Studies have shown that the relationship between winter precipitation and ENSO has been enhanced in the northwest of India (Yadav et al., 2009). But it can be seen from our results that the relationship between them in winter is less significant than in the monsoon season. Besides, results show that the phase relationship is almost opposite between the monsoon season and winter. The occurrence of the El Nino events (warm phase of ENSO) will lead to less rainfall and vice versa for the La Nina events (cool phase of ENSO) in the monsoon season, and this kind of relationship is opposite in winter in the Beas River basin. 4.3.3. Relationship with IOD As can be seen from the squared wavelet coherence, the relationship between monsoon rainfall and DMI is not significant (Fig. 11(A)), and some regions with high coherence are all located in or near the cone of influence. However, more significant relation is evident in winter (Fig. 11(B)). More regions are prominent and significant at 5% significance level as compared to Fig. 11(A). It is indicated that there is quite big covariance between winter precipitation and DMI at scales of 2– 4 years. But the phase relation is not stable, anti-phase before 1990, in-phase during 1995–2000, and precipitation lagging DMI after 2005. The lag correlation of the seasonal DMI with respect to the monsoon and winter precipitation was also calculated and the results are presented in Fig. 12. The concurrent correlation coefficients between DMI and monsoon/winter precipitation are −0.065/−0.184, while the biggest

Fig. 9. Wavelet coherence between precipitation in the Beas River basin and NINO 3.4 SST. A is for monsoon, B is for winter.

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Fig. 10. Cross correlation of the seasonal NINO3.4 SST with respect to the monsoon and winter precipitation in the Beas River basin. The dashed lines indicate the Pearson's t-test at 5% significance level.

correlation coefficients are 0.383/0.46 when IOD lags precipitation for 2/ 1 seasons respectively. The cross correlation coefficients are mainly positive for monsoon precipitation, and negative for winter precipitation; and the average is 0.097 for monsoon precipitation, and − 0.197 for winter precipitation. This also indicates that the relationship is more evident for winter precipitation than for monsoon precipitation. Moreover, the relationship of monsoon/winter precipitation with DMI is not as evident as that with NINO 3.4 SST. According to the above results, the relationship of precipitation with DMI is not significant and the relationship with NINO 3.4 SST is significant during the monsoon season; however, the former relationship is stronger and the latter is weaker in winter. This may also support the view of the influences of IOD on the monsoon precipitation: IOD and ENSO complementarily affect the ISMR; whenever the ENSO-ISMR correlation is low (high), the IOD-ISMR correlation is high (low) (Ashok et al., 2001). 4.3.4. Relationship with NAO The wavelet coherence analysis of precipitation-NAOI relationship (Fig. 13(A) and (B)) indicates that there are big significant sections with lag or lead behaviors between them at the scales of 2–3 years

and 5–8 years mainly during 1990–2005, especially in winter. The phase relationship is opposite for the two regions: Precipitation lags NAO at the scales of 2–3 years, and it leads NAO at the scales of 5– 8 years. What's more, the significant regions and phase relationship in the monsoon and winter seasons are similar, while significant areas are smaller in the monsoon season than in winter. The concurrent correlation coefficients between NAOI and precipitation are −0.121 and 0.349 for the monsoon and winter seasons, while the biggest cross correlation coefficients are 0.434/0.46 when NAOI leads monsoon/winter precipitation for 1/2 months (Fig. 14). The average of cross correlation coefficients for monsoon precipitation is −0.15, and is 0.23 for winter precipitation. There are positive correlation coefficients between NAOI and winter precipitation, and negative correlation coefficients between NAOI and monsoon precipitation (but this is not well indicated in the wavelet coherence analysis); thus above-normal winter precipitation is associated with the positive phase of NAO, and above-normal monsoon precipitation with the negative phase of NAO. Yadav et al. (2009) concluded that the relationship of winter precipitation with NAO in Northwest India has weakened, while the relationship with ENSO is strengthening. Our results indicate that the influences of NAO and ENSO on winter precipitation in the Beas basin may still be of similar importance recently according to the correlation coefficients of cross correlation and the significant regions of wavelet coherence analysis. 4.3.5. Composite analysis Composite analysis is utilized to explore the influences of climate anomalies (extreme phases of climate indices) on the precipitation in the Beas River basin. We choose 8 years of the high and low indices in the monsoon and winter seasons for ISMI, NINO3.4 SST, NAOI and DMI respectively. For example, the 8 highest ISMI cases are considered as high ISMI years, and the 8 lowest ISMI cases as low ISMI years. For the winter NAOI and DMI, there are only 6 and 7 cases when the indices are negative, so we just use the 6 and 7 cases as low NAOI and DMI years. The composite precipitation is the ratio of the average rainfall during the anomalous years to the long-term average rainfall over 1982–2010. For instance, the composite rainfall for high ISMI is computed as the ratio of the average rainfall of the 8 high ISMI years divided by the average rainfall of the period 1982–2010. Fig. 15 shows the composites for the monsoon and winter precipitation in the Beas River basin, where a composite value over 1 indicates that the climate anomaly is associated with positive rainfall anomaly,

Fig. 11. Wavelet coherence between precipitation in the Beas River basin and DMI. A is for monsoon, B is for winter.

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Fig. 12. Cross correlation of the seasonal DMI with respect to the monsoon and winter precipitation in the Beas River basin. The dashed lines indicate the Pearson's t-test at 5% significance level.

and vice versa. It is evident that, low ISMI (high ISMI), El Nino (La Nina), high NAOI (low NAOI) and low DMI (high DMI) are generally associated with composite precipitation bigger than 1 (smaller than 1) during the monsoon season, and vice versa during winter. It shows that high (low) ISMI are associated with a 5.3% increase (1.2% decrease), El Nino (La Nina) are associated with a 18.7% decrease (17.3% increase), high (low) NAOI are associated with a 0.9% decrease (18.8% increase), high (low) DMI are associated with a 33.9% increase (11.2% decrease) over the mean monsoon precipitation during the 1982–2010 period, respectively. The changes of increase and decrease are opposite to the monsoon season during the winter season. In general, the impacts of warm/high or cold/low phases of these climate indices on precipitation are opposite to each other, and the results agree with those of the correlation analysis. However, due to the relative small length of observed data series, the results of composite analysis can indicate the relationship but there is still some uncertainty in the climate indices' impacts on the precipitation. 5. Discussions Generally, the annual precipitation in the Beas River basin showed a decreasing trend. Precipitation for most of the stations increased in the

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monsoon season, while decreased during the other seasons; among the seasons, winter precipitation decreased most significantly. Here we focus on the linkage of precipitation changes with climate indices in the monsoon and winter seasons. The trend of ISMI for the period 1982–2010 is not significant based on Mann–Kendall trend test results (Z value is only 0.17). What's more, the correlation between monsoon precipitation and ISMI is not significant, thus the upward trend in monsoon precipitation may not relate to the changes of ISM. The trend of NINO 3.4 SST is negative in both the monsoon and winter seasons based on Mann–Kendall trend test (not significant, Z values are − 0.39 and − 0.43), which indicates a tendency toward more La Nina events recently; the trend of NAOI is also negative (Z value is −2.64 and − 1.58 for the monsoon and winter seasons respectively), and the decreasing trend in the monsoon season is significant at 0.05 level, indicating that NAO is in its negative phase (low NAOI) more frequently for the recent years. What's more, the negative correlation between NINO 3.4 SST and precipitation for the monsoon season and positive correlation between them for winter are both significant at 5% level, and the correlation coefficients of monsoon and winter precipitation with NAOI are also quite big. Therefore, the trends of monsoon and winter precipitation are mainly related to ENSO and NAO. According to the results of composite analysis, this kind of trend for ENSO and NAO will lead to more precipitation in the monsoon season and less precipitation in winter. The trend of DMI is positive (Z = 2.16 and 0.96 for the monsoon and winter seasons respectively), and the increase is significant at 0.05 level during the monsoon season. This kind of DMI trend also agrees with the precipitation trend in the monsoon and winter according to composite analysis. Nevertheless, the correlation coefficients between DMI and precipitation are not evident in the monsoon and winter seasons. Besides, the role of IOD is mainly as a modulator of the Indian monsoon rainfall and it exerts an influence on the linkage between the ISMR and ENSO (Ashok et al., 2001). Thus the linkage between IOD and recent trend of precipitation may be quite complex and needs further investigation. In other words, the trends of the climate indices are generally in consistent with the precipitation trend in the monsoon and winter seasons, while ENSO and NAO play important roles in the changes of precipitation. Here we just explore the linkage between the trend of climate indices and monsoon/winter precipitation in the Beas basin. Detailed discussion of the mechanisms how ISM, ENSO, NAO or IOD influence the precipitation changes for the recent years is beyond the scope of the current work.

Fig. 13. Wavelet coherence between precipitation in the Beas River basin and NAOI. A is for monsoon, B is for winter.

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(trend and concentration characteristics) of precipitation and hydroclimatic teleconnections between precipitation and some climate indices of ISMI, NINO 3.4 SST, NAOI and DMI are investigated. The main results and conclusions are as follows: (1) Precipitation of most of the stations increased in the monsoon season while precipitation of all the stations decreased in the non-monsoon seasons. As a result, the annual precipitation of the majority of the stations was on the decrease. What's more, monsoon precipitation contributes more during recent years, while the contribution of winter precipitation shows a negative trend, which is consistent with the trend of monsoon and winter precipitation. (2) A general increase was detected in the annual Gini coefficient and PCD during 1982–2010. Thus, significant increases in precipitation concentration occurred for the recent decades. PCP is mainly within the period from May to August in the basin, and it occurred more frequently in the monsoon months recently. The changes of precipitation concentration are consistent with the trend of monsoon and winter precipitation. (3) The Himalayan Mountains limit the intrusion of the monsoon, and the relationship of monsoon precipitation with ISMI is not significant in the Beas River basin. The relationship between precipitation and ENSO in the monsoon season is more significant than that in winter, and the phase relationship is almost opposite between the two seasons. The relationship of monsoon/winter precipitation with DMI is not as evident as that with ENSO. There is negative correlation between NAOI and monsoon

Fig. 14. Cross correlation of the seasonal NAOI with respect to the monsoon and winter precipitation in the Beas River basin. The dashed lines indicate the Pearson's t-test at 5% significance level.

6. Conclusions

A

B

1.2

1.4

1.1

1.2

Composite

Composite

The Beas River is one of the Western Himalayan rivers in India, where hydrological elements are very sensitive to climate changes. The hydrological variables like precipitation in the basin have changed a lot under current climate changes. In this paper, the recent changes

1 Monsoon

Monsoon

High ISMI

Low ISMI

El Nino

La Nina

0.6

0.8

D 1.4

1.2

1.2

1 Monsoon

Winter

0.8

Composite

C 1.4

1 Monsoon

Winter

0.8 High NAOI

0.6

Winter

0.8

0.9

Composite

1

High DMI

Low NAOI

Low DMI

0.6

Fig. 15. Composite monsoon and winter precipitation associated with (A) High and low ISMI years, (B) El Nino and La Nina years, (C) High and low NAOI years, (D) High and low DMI years in the Beas River basin.

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precipitation, and positive correlation for winter precipitation. Besides, ENSO and NAO play important roles in the changes of monsoon and winter precipitation in the Beas River basin.

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