el niño/southern oscillation (enso)-related variablity in mean-monthly streamflow in nepal

El Nino/Southern Oscillation (ENSO)-related variablity in

mean-monthly streamflow in Nepal

Archana Shrestha1, Ray Kostaschuk*

Department of Geography, University of Guelph, Guelph, Ont., Canada N1G 2W1

Received 22 December 2003; revised 14 September 2004; accepted 29 October 2004

Abstract

Harmonic analysis is used to examine the impact of the El Nino/Southern Oscillation (ENSO) on mean-monthly streamflow

variability in Nepal. El Nino causes below normal streamflows in two core regions: the Western Region and the Eastern Region.

La Nina produces above normal streamflows in only one core region: the Western Region. There is a stronger El Nino influence

on streamflows compared to La Nina. A stronger overall ENSO impact in western Nepal than in eastern Nepal suggests an

inverse relationship between El Nino streamflows and monsoon strength and a direct relationship between La Nina flows and

monsoon strength.

q 2004 Elsevier B.V. All rights reserved.

Keywords: Harmonic analysis; El Nino/Southern Oscillation; Streamflow variability; Nepal

1. Introduction

The El Nino/Southern Oscillation (ENSO), a

coupled atmospheric–oceanic phenomenon centered

in the equatorial Pacific, is the most significant factor

causing global hydroclimatic variability (Kahya and

Dracup, 1993; Allan, 2000; Terry et al., 2001).

The ENSO phenomena consist of two oceanic

phases—the warm El Nino phase and the cold La

0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jhydrol.2004.10.020

* Corresponding author. Fax: C1 519 837 2940.

E-mail addresses: [email protected] (A. Shrestha),

[email protected] (R. Kostaschuk).1 Present address: Climate Section, Department of Hydrology and

Meteorology, Ministry of Science and Technology, His Majesty’s

Government of Nepal, Kathmandu, Nepal.

Nina phase—that are connected to the atmosphere

through a sea-saw atmospheric pressure fluctuation in

the South Pacific called the Southern Oscillation (SO).

There is growing interest in ENSO–streamflow

relationships because ENSO can generally be

predicted 6–12 months in advance (Simpson et al.,

1993; Kahya and Dracup, 1994; Piechota et al., 1997;

Gutierrez and Dracup, 2001; Whitaker et al., 2001).

This relationship has been investigated in two ways: by

identifying the impact of ENSO on streamflow, and by

developing models to forecast streamflow fluctuations

using predicted ENSO indices. This paper focuses on

identification of ENSO–streamflow relationships in

Nepal. The rivers of Nepal are important in an

international sense because they supply most of the

flow in the Ganges River system, a densely-populated

Journal of Hydrology 308 (2005) 33–49

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A. Shrestha, R. Kostaschuk / Journal of Hydrology 308 (2005) 33–4934

region of major economic, cultural and environmental

significance (Subba, 2001).

Several studies have shown significant relation-

ships between ENSO events and streamflows at global

(Dettinger et al., 2000; Chiew and McMahon, 2002),

regional (Redmond and Koch, 1991; Cayan and

Webb, 1992; Kahya and Dracup, 1993, 1994;

Simpson et al., 1993; Chiew et al., 1998; Cayan

et al., 1999) and local scales (Waylen and Caviedes,

1986, 1990; Krasovskaia et al., 1999; Gutierrez and

Dracup, 2001; Terry et al., 2001; Zubair, 2003). Work

in the Ganges River in India by Whitaker et al. (2001)

and in Mahaweli catchment in Sri Lanka by Zubair

(2003) are among the few studies of ENSO impacts on

South Asian streamflows. An ENSO impact has

already been identified for precipitation in Nepal

(Shrestha, 2000), but the ENSO–streamflow relation-

ship remains unexplored.

Most studies on ENSO–streamflow relationships

are based on statistical analyses, such as comparisons

of streamflows between ENSO and non-ENSO years

(Cayan and Webb, 1992; Kahya and Dracup, 1994;

Cayan et al., 1999) and correlation analysis (Redmond

and Koch, 1991; Kahya and Dracup, 1994; Chiew

et al., 1998; Gutierrez and Dracup, 2001; Kostaschuk

et al., 2001; Chiew and McMahon, 2002). A more

comprehensive method is that of harmonic analysis.

Harmonic analysis not only identifies the type of

anomaly and the time of response but also allows for

spatial analysis of the ENSO impacts. Both of these

elements are crucial in the ENSO–streamflow

relationship because the timing and nature of ENSO

effects on both precipitation and streamflow have

spatial variability (e.g. Kahya and Dracup, 1993,

1994; Singh, 2001). Nepal is a country of diverse

topography and variable climate so we expect spatial

variation in ENSO–streamflow relationships—

harmonic analysis was therefore selected as the

main analytical technique in this study.

2. Study area

Nepal is a small country that covers 147,181 km2

in the center of the Hindu Kush Himalayas between

India and China (Fig. 1). There is a wide spectrum of

climatic regimes within this small geographical area,

ranging from a hot monsoon climate (sub-tropical) in

the southern plain to tundra in the northern mountains

(Shanker and Shrestha, 1984–1985). Nepal has four

distinct seasons: winter (December–February),

pre-monsoon (March–May), monsoon (June–

September) and post-monsoon (October–November).

The monsoon contributes about 80% of the annual

precipitation so the spatial distribution of annual

precipitation follows the pattern of monsoon precipi-

tation (Fig. 2). Central Nepal (at about 848E

longitude) receives the highest mean annual and

monsoon rainfall, and rainfall decreases from central

to eastern and western Nepal. A higher gradient in

rainfall occurs from central to western Nepal because

the monsoon enters Nepal from the southeast and the

eastern region receives higher annual precipitation

than the west. Annual snowfall accounts for 10% of

the total precipitation (Steinegger et al., 1993) and is

higher in the northern part of Nepal where most of the

region lies above 3000 m. Snowfall is maximum

during the monsoon season, accounting for more than

70% of the annual accumulation (Ueno et al., 1993).

Three major river systems were analyzed in this

study: the Koshi in the east (including the Bagmati

basin), the Narayani in the center and the Karnali in

the west (including West Rapti basin; Fig. 1). All

three basins originate in the dry region of Tibet and

contain snow-fed and rain-fed tributaries from the

mountains and hills of Nepal (Sharma, 1993).

Climatologically, the Karnali basin is least exposed

to the monsoon and the Narayani is most exposed

(Sharma, 1993). Representative average annual

hydrographs for each of the basins are presented in

Fig. 3. Mean monthly flow is highest in August in all

the basins. In western Nepal (Karnali), the lowest

mean flow occurs earlier (February) than in central

and eastern Nepal (Narayani and Koshi; March–

April), reflecting an earlier snowmelt in the west.

About 70–90% of the annual runoff occurs during

monsoon and post-monsoon seasons (June–November;

Khanal et al., 1998). Snowmelt is the primary source

of flow in pre-monsoon from April to mid June and is

not significant in other months (Sharma, 1993).

3. Methodology

Mean monthly streamflows from the three major

basins of Nepal were obtained from the Department of

Fig. 1. Three main basins and distribution of 19 hydrometric stations in Nepal.

A. Shrestha, R. Kostaschuk / Journal of Hydrology 308 (2005) 33–49 35

Hydrology and Meteorology (1998). Streamflow data

are not available after 1995 so it is not possible to

include recent ENSO events in this investigation.

Nineteen hydrometric stations (Fig. 1) with more than

Fig. 2. Mean monsoon precipitation (June–Sept

30 years of record (30–34 years) were selected for

study so that as many ENSO events as possible could

be incorporated into the analysis. Multivariate ENSO

Index (MEI), Southern Oscillation Index (SOI) and

ember) in cm (based on ICIMOD, 1996).

Fig. 3. Average annual hydrographs of major rivers basins in Nepal.


Sea Surface Temperature at Nino 3.4 (SST) ENSO

indices were acquired from the NOAA website (2002)

(www.cdc.noaa.gov).

3.1. ENSO index and ENSO years

Several ENSO indices and criteria have been used

to define ENSO years (Kiem and Franks, 2001) so it is

necessary to identify the ENSO index that best defines

the ENSO–streamflow response in Nepal. The ENSO

years were first defined using SOI, SST and MEI,

based on the criteria in Table 1. Next, mean flows for

each season (winter, pre-monsoon, monsoon and

post-monsoon) during the period 1962–1995 were

calculated for all the stations. For each season, these

data were categorized as El Nino, La Nina and

non-ENSO years, as defined by each of the three

ENSO indices. The Mann–Whitney U test was then

used to compare mean monthly flows during El Nino,

La Nina and non-ENSO seasons for each ENSO

index. The MEI resulted in significant differences in

means (at the 90% level) for the highest percentage of

stations so it was selected as the ENSO index for

subsequent analysis (Fig. 4). An El Nino (La Nina)

Table 1

Criteria in defining ENSO years for three indices

ENSO index Criteria

SST El Nino (La Nina): Maximum (Minimum) SST O1 (!and SSTO0.5 8C (!K0.5 8C) for at least 8 months

SOI El Nino (La Nina): 5-month running mean of SOI!K0

consecutive months between April of the year to March o

MEI El Nino (La Nina): 5-month running mean of MEI O0.5

consecutive months between April to March of the follo

peak MEI O1 (!K1).

year in this study is defined by a 5-month running

mean of MEIO0.5 (!K0.5) for 5 or more consecu-

tive months between April and March, and with the

peak MEIO1 (!K1) during this period.

3.2. Identification of ENSO impacts

The identification of ENSO impacts consists of two

parts. The first part, regionalization, consists of log–

normal transformations, composite analysis, harmo-

nic analysis, significance tests and delineation of a

candidate region. The second part, season detection,

involves aggregate composite analysis and temporal

consistency tests.

3.2.1. Part I: regionalization

3.2.1.1. Log–normal transformation. Mean monthly

flows were transformed into log–normal distribution

(LND) percentiles to remove the annual seasonality in

the data set. As a result, LND percentiles of each

month averaged for the whole data period are

approximately the same, allowing direct interpret-

ation of the ENSO effect. The LND percentile is

Source

K1) standard deviation Wang et al. (2000)

.5 (O0.5) for 5 or more

f the following year (C)

Kiem and Franks (2001)

(!K0.5) for 5 or more

wing year (C) and the

Modified after Kiem and Franks

(2001)

http://www.cdc.noaa.gov

Fig. 4. Variation of average percentage of stations with significant difference in mean streamflows averaged over all four seasons.

Table 2

List of MEI-based ENSO years and Year (0) of 24-month ENSO

events

El Nino years 1965, 1972, 1982–1983, 1986–1987,

1991–1994

La Nina years 1964, 1970, 1971, 1973, 1974, 1975,

1988

24-month El Nino events 1965, 1972, 1982, 1986, 1991, 1993

24-month La Nina events 1964, 1970, 1973, 1975, 1988


the probability that the streamflow is equal to or less

than a given value (Kahya and Dracup, 1993).

3.2.1.2. Month ENSO composite. The life span of El

Nino and La Nina ranges from 18 to 28 months

(Kahya and Dracup, 1993) but a 24-month period

extending from July of the year preceding the ENSO

year to June of the year following the ENSO year is

considered to be the life span of a typical El Nino or

La Nina event (Kahya and Dracup, 1993; Chiew and

McMahon, 2002). By convention, the symbol (0) is

assigned to the months of the ENSO year (El Nino or

La Nina), (K) for the preceding year and (C) for the

following year. The ENSO year (0) is the year

identified by MEI criteria.

There were some consecutive El Nino and La Nina

years during our study period—1986 and 1987 were

consecutive El Nino years and 1970 and 1971 were

consecutive La Nina years. In these cases, the first

year is considered to be year (0) (i.e. El Nino or La

Nina year) of the 24-month ENSO life cycle (Kahya

and Dracup, 1993, 1994; Ropelewski and Halpert,

1986; Ropelewski and Halpert, 1987). Moreover,

there were some cases when the 24-month period of

El Nino overlapped with 24-month period of La Nina.

For example, 1973 was a year (0) for La Nina year and

also year (C) of the 1972 El Nino year. For these

cases, El Nino and La Nina events are considered

to be independent events in the analysis, as

recommended by Halpert and Ropelewski (1992).

Year (0) of the 24-month ENSO periods for both El

Nino and La Nina are presented in Table 2.

The 24-month ENSO composite for each station

was calculated by averaging the monthly LND

percentiles of all 24-month ENSO events. However,

record lengths for the 19 stations are different and data

are missing at some stations so not all the 24-month

ENSO events in Table 2 can be used to calculate

composites. The number of El Nino events identified

at individual stations varies from 3 to 6 and from 3 to

5 for La Nina (Table 3).

3.2.1.3. First harmonic fit. Harmonic analysis

involves determination of a finite sum of sine and

cosine terms in a time series (Panofsky and Brier,

1968) and the first harmonic has a period equal to the

total period studied (24 months in the present study).

The harmonics are expressed in terms of amplitude

and phase angles. Amplitude is half the distance from

the maximum to the minimum (Davis, 1986) and

phase angle denotes the distance (in terms of angle) of

the maximum from the origin (Panofsky and Brier,

1968; Rayner, 1971). In a climatological time series,

long period harmonics (low frequency) represent

large-scale systems of atmospheric circulation while

short period harmonics (high frequency) refer to the

influences of local phenomena (Kahya and Dracup,

1993). Since ENSO is a large-scale phenomenon,

Kahya and Dracup (1993) suggested the use of the

first harmonic to represent it. This implies that the

streamflow anomaly during a 24-month ENSO period

can be approximated by the first harmonic curve

Table 3

Streamflow data during ENSO events

Basins Station number Data period 24-month El Nino events 24-month La Nina events

Incomplete events No. of complete

events

Incomplete events No. of complete

events

Karnali 240 1962–1995 X 6 1988 4

250 1963–1995 X 6 X 5

260 1963–1995 1993 5 X 5

270 1963–1995 1993 5 X 5

280 1962–1993 1993 5 X 5

West 330 1964–1993 1993 5 1964 4

Rapti 360 1964–1995 X 6 1964 4

Narayani 410 1964–1995 X 6 1964 4

415 1964–1993 1991; 1993 4 1964 4

420 1964–1995 1982; 1986; 1993 3 1964; 1988 3

440 1964–1993 1993 5 1964; 1988 3

445 1964–1995 X 6 1964 4

450 1963–1995 X 6 1975 4

465 1963–1993 1993 5 X 5

Koshi 505 1963–1995 1991 5 1973 4

620 1964–1995 1986 5 1964; 1988 3

630 1964–1994 1991 5 1964; 1988 3

660 1964–1995 1982 5 1964; 1988 3

690 1965–1995 1965 5 1964 4

X represents complete data.


corresponding to the ENSO forcing (Kahya and

Dracup, 1993). The amplitude and phase of the first

harmonic of a 24-month ENSO streamflow composite

represent the magnitude and phase of ENSO signal.

The phase of the 24-month first harmonic designates

the time, measured with reference to July (K) as the

origin, when the sine curve of the first harmonic of

the ENSO composite is a maximum (i.e. monthly

streamflows show a positive response; Ropelewski

and Halpert, 1986).

The first harmonic was fitted to the 24-month

ENSO composite at each of the 19 stations used in this

study and amplitudes and phase shifts of the first

harmonic were computed. The amplitudes and phase

shifts were converted into vectors for map represen-

tation, following Conrad and Pollak (1950), Brooks

and Carruthers (1953) and Kahya and Dracup (1993).

The amplitude and phase shift of the fitted first

harmonic represent the magnitude and direction of the

vector, respectively, in the vector dial. It should be

noted that July (K) is considered 08 (tZ0) and Jan

(0), Jul (0), and Jan (C) are 908 (tZ6), 1808 (tZ12)

and 2708 (tZ18), respectively, in a clockwise

direction.

3.2.1.4. Significance test and goodness of fit. Schus-

ter’s quantitative test of significance was used to test

the degree of significance (DOS) of the fitted first

harmonic. The degree of significance is the

probability that the amplitude of the first harmonic

of 24-month ENSO composite is produced by chance.

Low DOS values indicate a higher significance of the

first harmonic fit to the ENSO composite. Kahya and

Dracup (1993) have incorporated stations with DOS

values %0.3 for regionalization but Kahya and

Dracup (1994) consider stations with DOS%0.24.

The strength of the ENSO impact or the portion of

total variance (total streamflow variability) explained

by the first harmonic is the variance reduction (VR)

(Kahya and Dracup, 1993). Higher VR values imply

that the first harmonic explains a higher proportion of

total variance in the composite, indicating a better

goodness of fit.

3.2.1.5. Candidate region (CR) delineation. The

vectors of all stations were plotted on a map and a

candidate region (CR) was delineated by grouping the

stations based on vector direction (phase shift),

magnitude, DOS and VR values. The vectors in


similar directions (similar phase shifts) were grouped

and subsequently scrutinized on the basis of DOS, VR

and higher magnitude. The scrutinzed stations were

tested for coherency (Kahya and Karabork, 2001),

which indicates the spatial consistency of the vector

directions and magnitude in the group of stations.

A group of stations with a coherence R0.80 can be

considered to be a CR (Ropelewski and Halpert, 1986;

Kahya and Dracup, 1993; Kahya and Karabork,

2001).

3.2.2. Part II: signal season detection

The main purpose of Part II is to identify the

common anomaly sign of the ENSO impact on

streamflows and the signal season (the season of

streamflow response to ENSO) for the CR. The

anomaly sign and season are then confirmed by testing

the temporal consistency of the detected signal season

in the CR. The CR with a temporally consistent signal

season is considered to be a core region (Kahya and

Karabork, 2001).

3.2.2.1. Aggregate composite of the candidate region.

In the first step of Part II, the 24-month ENSO

composites were spatially-averaged for all stations in

the CR to get an aggregate 24-month composite.

Stations that lie inside the basin but were excluded

due to dissimilar vector directions, high values of

DOS or low VR were not used to compute the

aggregate composite. A season with 5 or more

consecutive months of the same anomaly sign in the

aggregate composite during the year (0) or year (C) is

considered the signal season (Kahya and Dracup,

1993; Chiew and McMahon, 2002). Positive

(negative) LND percentiles during the signal season

indicate above (below) normal streamflow in response

to ENSO.

3.2.2.2. Index time series (ITS). The main purpose of

index time series (ITS) analysis is to measure the

temporal consistency of the signal season. To

compute an ITS, the LND percentiles of all the

stations in the CR were averaged for the signal season

for each year (1962–1995 in this study). The seasonal

averages of LND percentiles were then averaged

spatially over all the stations in the CR to get a single

time series of the signal season, which is the ITS. The

ratio of number of ENSO years in the ITS with

an identified anomaly sign in the aggregate composite

to the total number of ENSO years measures the

temporal consistency of the signal season sign. Kahya

and Karabork (2001) suggested that this ratio be

R0.80 for the CR to be considered a core region.

A hypergeometric test was used to examine the

significance of the signal season in the core region.

This test calculates a probability (p-value) that the

detected period for El Nino (La Nina) has been

detected by chance, so a lower probability indicates a

higher significance level of the ENSO response

(Kahya and Dracup, 1994; Kahya and Karabork,

2001).

4. Results

4.1. Harmonic analyses

Fig. 5 is an example of the first harmonic curves

fitted to El Nino and La Nina composites. The vector

maps for El Nino and La Nina displaying the

amplitudes as lengths and phase shifts as the

directions are shown in Fig. 6. It must be noted that

the amplitudes of the first harmonics are corrected

when there is a difference between mean and median

of 24-month ENSO composites and these corrected

amplitudes are used as the magnitudes (lengths) of the

vectors for the vector map. The correction is

necessary because the amplitudes of the first

harmonics of individual stations are calculated as

the deviation from the mean of the 24-month-ENSO

composite, whereas the magnitude of the ENSO

response is measured as the magnitude of anomalies

from the median within the selected season in the

ENSO aggregate composite (Kahya and Dracup,

1993). Thus the amplitude underestimates (over-

estimates) the real magnitude of the ENSO impact

when the mean is greater (smaller) than the median so

the initial amplitude must be corrected by adding

(subtracting) the mean–median difference to it. For

example, the mean of the first harmonic for El Nino at

the station 240 is 51.5% so the mean–median

difference is 1.5%. Thus the amplitude, 22.7%, is

corrected by adding 1.5% to give the correct

amplitude of 24.2% (Fig. 5a).

Fig. 5. First harmonic fit at station 240, Karnali basin. (a) El Nino composite; (b) La Nina composite.


4.2. Core regions for El Nino and La Nina

Application of the procedures outlined in Section

3.2 above resulted in identification of two core regions

for El Nino, the Western Region (WR-E) and the

Eastern Region (ER-E), but only one core region for

La Nina, the Western Region (WR-L). Summaries of

the analytical results for the three core regions are

presented in Table 4.

4.2.1. Western region for El Nino (WR-E)

4.2.1.1. Scrutiny of stations. The WR-E covers the

Karnali, West Rapti and Narayani basins with a total

of 14 stations. Two stations (440, 465) are excluded

from the region because their phase shifts do not agree

with the streamflow anomaly sign of the other

stations. Station 440 has a phase shift in Sep (0) and

station 465 is in May (0), indicating above normal

streamflow during the peak warming period of the El

Nino year, which is inconsistent with other stations in

this region where streamflows during this period are

below normal. For the remaining 12 stations, all the

stations in the Karnali and West Rapti basins have

DOS !0.25, indicating a greater than 75% signifi-

cance level of the first harmonic fit for the El Nino

impact. The VR of these basins indicates that the

amplitude of the first harmonic of El Nino explains

between 88% (high) and 48% (moderate) of the total

variance of the streamflows with an average 68% of

the total variance. In contrast, the DOS values in the

Narayani basin range from 0.23 to 0.85 and the VR is

between 0.48 and 0.03, indicating a weak to

insignificant El Nino impact. Only two stations (410

and 420) situated in the west of the Narayani basin

have relatively low DOS values (0.23 and 0.3) and

moderate VR values (0.30 and 0.50). Signal ampli-

tudes are between 13 and 24.2% in the Karnali and

Fig. 6. Vector displays and core regions. (a) El Nino; (b) La Nina.

Table 4

Summary of results for core regions

El Nino La Nina

1. Core region WR-E ER-E WR-L

2. Number of stations 9 4 12

3. Impact on mean streamflows Below normal Below normal Above normal

4. Signal season Jul (0)–Dec (0) Jun (0)–Dec (0) Jun (0)–Jan (C)

5. Amplitude (anomaly magnitude) 13–24% !14% 0.8–21%

6. Percentage of stations O20% amplitude 44 0 17

7. Percentage of stations !0.25 DOS 89 50 58

8. Percentage of stations O50% VR 78 0 42

9. Coherency 0.99 0.99 0.89

10. Temporal consistency (hypergeometric test) 75% 72% 92%



West Rapti basins. In the Narayani basin only two

stations (410, 420) have amplitudes O13%, while the

rest are less than 7%. Therefore, based on DOS, VR

values and amplitudes, a total of nine stations—all the

stations of the Karnali and West Rapti basin and two

stations (410, 420) of the Narayani basin—are

included in WR-E and used for further analysis

(Table 4 and Fig. 6a). The coherency of this region is

about 0.99, which is greater than the recommended

level of 0.80 so it can be considered a CR.

4.2.1.2. Aggregate composite and signal season. The

aggregate composite of nine stations for the WR-E is

shown in Fig. 7a. The below normal streamflow of the

aggregate composite for the period Jul (0)–Jun (C) is

identified as the El Nino response. However, it is

necessary to further define the signal season from this

below normal streamflow period because the negative

anomalies are not consistent.

Periods considered for the signal season are: Jul

(0)–Dec (0) with a distinct and consistent below

normal streamflow, Jul (0)–Jan (C) which includes

Fig. 7. (a) Aggregate LND composite for WR-E for El Nino. (b

a relatively small negative anomaly in Jan (C), Jul

(0)–Apr (C) with a near normal flow in Feb (C), and

Jul (0)–Jun (C) which contains a positive anomaly in

May (C). The selection of the best signal season is

based on the ITS analysis and a hypergeometric test.

The ITS analysis showed that among these four

periods, the ITS for Jul (0)–Jun (C) has below normal

flows for 4 out of 6 El Nino years (i.e. 67%). The ITS

for the remaining three periods have below normal

flows for 5 out of 6 of El Nino years (i.e. 83%), which

satisfies the criteria of Kahya and Karabork (2001) for

temporal consistency (O80%) for a signal season and

hence consideration as a core region. The ITS for the

period Jul (0)–Dec (0) is shown in Fig. 7b. Selection

of the best of these three seasons is based on the

hypergeometric test.

For the hypergeometric test, Kahya and Dracup

(1994) used the LND percentile corresponding to a

probability !10% (O90%) as the threshold value to

indicate the driest (wettest) condition, but Ropelewski

and Halpert (1987, 1989)) used LND percentiles

!30% (O70%). In this analysis, because most of

) ITS of WR-E for El Nino signal season Jul (0)–Dec (0).


the amplitudes of the first harmonic are between 10

and 25%, the ITS value !40% (O60%), a 10%

deviation from the median, is considered as the dry

(wet) threshold condition for the hypergeometric test.

The lowest p-value of 0.25 is found for the period Jul

(0)–Dec (0), indicating that the ITS value (anomaly)

with !40% LND percentiles associated with El Nino

will occur in the Jul (0)–Dec (0) period at a

significance level of 75% (Table 4). The period Jul

(0)–Dec (0), with below normal streamflows, is

therefore chosen as the signal season for the core

region WR-E.

4.2.2. Eastern region for El Nino (ER-E)

4.2.2.1. Scrutiny of stations. A consistent phase shift

of Sep (K)/Oct (K) indicates an ER-E consisting of

four stations: one from the Bagmati basin and three

from the Koshi basin (Table 4 and Fig. 6a). Station

690 of the easternmost part of the Koshi basin is not

incorporated in this CR because its phase shift

(Dec (K)) is consistent with the phase shifts of

Fig. 8. (a) Aggregate LND composite for ER-E for El Nino. (b)

the stations in the WR-E rather than with the stations

of the ER-E.

The results for the ER-E are not as consistent as in

the WR-E. Firstly, there are only four stations, while

Ropelewski and Halpert (1987) recommend including

at least five stations to delineate a CR (Table 4).

Secondly, the anomalies of the signal season, DOS

values and VR results are not as strong as for the WR-

E. However, the ER-E can still be considered as a CR

because of the consistent phase shifts (high coherency

of 0.99).


aggregate composite of the four stations in the ER-E

(Fig. 8a) depicts below normal streamflows from May

(0)–Dec (0) as the anomaly associated with El Nino.

For signal season detection, the ITS of the periods

May (0)–Dec (0) and Jun (0)–Dec (0) (Fig. 8b) are

examined. The ITS showed 100% temporal consist-

ency for the former period (with below normal

streamflows in all 6 El Nino years) and 83% for the

latter period (with below normal streamflows in 5 out

ITS of ER-E for El Nino signal season Jun (0)–Dec (0).


of 6 El Nino years). Since both ITS ratios are greater

than 80% and the latter period had a stronger

aggregate anomaly than the former, the hypergeo-

metric test is performed for both the periods. The

hypergeometric test for ER-E (Table 4) showed a

lower p-value of 0.28 (72%) for the period Jun

(0)–Dec (0), so it is chosen as the signal season for

ER-E. The only El Nino year in which Jun (0)–Dec (0)

streamflow is above normal is 1986, during which the

deviation is very small. As a result, ER-E is confirmed

as an El Nino core region with below normal

streamflows in the signal season Jun (0)–Dec (0).

4.2.3. Western region for La Nina (WR-L)

4.2.3.1. Scrutiny of stations. All of the stations in the

Karnali basin have vector directions with phase shifts

of Jun (0)–Sep (0) for La Nina and all stations in the

West Rapti basin plus three stations (410, 420 and

450) in the Narayani basin have vector directions of

Oct (0)–Dec (0). The remaining Narayani stations

(415, 440 and 445), except station 465 situated in

Fig. 9. (a) Aggregate LND composite for WR-E for La Nina. (b)

the east of the basin, have vector directions of Jun (0)–

Sep (0). A single CR for all the stations (excluding

465) with phase shifts of Jun (0) to Dec (0) is therefore

proposed, so that all the stations in this CR have a

consistent phase shift with the maximum of the first

harmonic occurring during the peak period of the La

Nina event. Therefore, all the stations of the Karnali

and West Rapti and 6 of 7 stations of Narayani are

considered for the WR-L (Fig. 6b). Station 440 is

excluded because of low DOS and high VR and the

remaining 12 stations are included in the WR-L. The

coherence of this CR is 0.89, which is O80%.


aggregate composite of WR-L (Fig. 9a) indicates that

the above normal streamflow during Jun (0)–Feb (C)

is the La Nina response. Two periods, Jun (0)–Jan (C)

(Fig. 9b) and Jun (0)–Feb (C) are examined for the

temporal consistency to identify the best signal

season. The ITS for both periods showed above

normal streamflows for all 5 La Nina years. The

hypergeometric test (Table 4) showed that Jun (0)–Jan

ITS of WR-L for La Nina signal season Jun (0)–Jan (C).


(C) has a lower p-value than Jun (0)–Feb (C) so

Jun (0)–Jan (C) is chosen as the signal season for

WR-L. Thus WR-L qualifies as a La Nina core region

with above normal streamflows during Jun (0)–Jan (C).

4.2.3.3. Eastern region for La Nina. The five stations

in the eastern Nepal did not show any consistency in

vector directions so they could not be considered for a

CR (Fig. 6b). The vector direction (phase shift) varies

from Apr (0) to May (C) and the resulting coherence

of these stations is only 0.52 (Table 4).

Fig. 10. Spatial variation of monsoon rainfall and the impact of El

Nino in Nepal. A similar impact likely occurs for streamflow.

5. Discussion

There are several limitations to this study that

affect the interpretation of ENSO impacts. The

relatively small number of stations limits the spatial

resolution in the interpretation of the ENSO signal and

the identification of core regions. The short data

length, in turn, limits the definition of inter-annual

variability caused by ENSO or any other mechanism.

In addition, several hydrometric stations are down-

stream of one another, which will bias the results and

potentially increase the apparent regional coherency

of the ENSO signal. There is no way to avoid these

problems, but their implications must be considered.

Even though the impact of La Nina appears to be

geographically broader (12 stations) than that of El

Nino (9 stations) in Nepal, phase shifts, amplitudes

and significance tests indicate a weaker impact of La

Nina on mean streamflows (Table 4). This result

supports the suggestion of Voituriez and Jacques

(2000) that El Nino tends to shift climatic zones of a

region, resulting in dramatic changes in mean

climate, while La Nina intensifies existing climatic

characteristics.

5.1. Spatial pattern in ENSO impacts on streamflows

Magnitude, DOS and VR showed that the ENSO

impact on streamflows is stronger in the WR than in

the ER and stations in the ER show a consistent

response to El Nino only in phase shifts (Table 4). For

La Nina, most of the stations showed significant

impacts only in WR (WR-L) and no impact is

identified in eastern Nepal. It is important to note,

however, that there are only a few stations in the ER

so the results may be biased towards the WR.

The lower significance of El Nino and La Nina

impacts in eastern Nepal could also be because of the

increase in the frequency of monsoon depressions in

the Bay of Bengal during El Nino years and the

decrease during La Nina years (Singh et al., 2001).

Singh et al. (2001) pointed out that in eastern and

northeastern India where monsoon depressions are

particularly effective, the negative (positive) depar-

ture in rainfall during El Nino (La Nina) is not as

significant as in the western part of the country where

the effect of the monsoon depressions is weaker.

Monsoon depressions cause intense rainstorms in

eastern and central Nepal (Nayava, 1974).

Harmonic analyses in this study showed a spatial

pattern of the ENSO impacts on streamflows that may

be related to monsoon intensity. Fig. 10 summarizes

the relationship between the spatial variation of

monsoon rainfall and of impact of El Nino on rainfall,

and, by extension, on streamflow. The weakest El

Nino impact on streamflows, in terms of amplitude

and significance, is in the Narayani basin (central

Nepal: highest rainfall zone), the highest impact is in

western Nepal (lowest rainfall zone) and a moderate

impact occurs in eastern Nepal. This implies that the

El Nino has a tendency to produce a significant

negative anomaly of rainfall and thus streamflows in

the weak monsoon regions and lower tendency in the

strong monsoon region. Higher (lower) amplitudes

and significance in the basins that lie in the low (high)

rainfall zones in the ER-E also support this interpret-

ation. The spatial relation between monsoon rainfall

and ENSO impacts on streamflows also seems to be

true for La Nina but the effect is opposite to that of El

Nino, producing a higher impact in the high monsoon


rainfall zone and lower impact in the low rainfall

zone. However, this pattern is not as distinct as in the

El Nino case, possibly because the La Nina impact is

weaker than that of El Nino. For example, the stations

of the eastern part of Narayani basin (the highest

rainfall zone) that were excluded from WR-E for El

Nino showed higher magnitude and significance for

La Nina. Also, the magnitude and significance in the

western part of the WR-L (low rainfall zone) for La

Nina is not as strong as in the WR-E for El Nino,

implying the La Nina impact is less effective

compared to the El Nino impact in the weak monsoon

region. This is consistent with the findings of

Krishnamurthy and Goswami (2000) on variations in

the strength of monsoon–ENSO relationships on the

temporal scale. They found that El Nino has a higher

tendency to produce dry spells during the weak

interdecadal mode of the monsoon than during the

strong decadal mode.

5.2. Temporal pattern in ENSO impacts on

streamflows

The La Nina response is temporally more consist-

ent than the El Nino response, based on ITS and

hypergeometric tests (Table 4). The temporal incon-

sistency of the El Nino response might be because not

all the El Nino events are of same type. Fu et al.

(1986), for example, classified El Nino into three

types, based on locations of the warmest ocean

temperature and initiation of warming in the Pacific.

It is possible that El Nino impacts may vary with type,

as indicated by Kane (2000) for El Nino impacts on

the monsoon. He found that an El Nino with the peak

Nino 3 SST occurring in May–August is best

associated with below normal monsoon rainfall in

different parts of India so a similar effect could occur

in Nepal, in turn affecting the streamflows. Studies in

other regions also showed better temporal consistency

in the La Nina response to streamflows than the El

Nino response. A study in Turkey showed only 81%

temporal consistency for the El Nino response while

La Nina showed 92% temporal consistency (Kahya

and Karabork, 2001). Similarly, in the southwest

United States, only the Type 1 El Nino showed 100%

consistency while the general type of La Nina showed

100% temporal consistency (Kahya and Dracup,

1993, 1994). These results suggest that for both

rainfall and streamflow, the El Nino response is not as

temporally consistent as La Nina.

The evolution of MEI during El Nino and La Nina

life cycles for different events is presented in Fig. 11.

The deviation of individual events from the composite

of these events, particularly during the peak, is greater

for El Nino than for La Nina, suggesting that El Nino

events are more variable than La Nina, which could

result in a variety of responses and might be the reason

for lower temporal consistency in El Nino impacts.

Further, Fig. 11 shows that for all of the El Nino

events, except 1986, the MEI is greater than C0.9 in

Jun (0) and following months. It is interesting to note

that 1986 is the only El Nino year during which the

signal season showed above normal streamflows in

both WR-E and ER-E. It is therefore likely that for

streamflows to be less than normal during the signal

season Jul (0)–Dec (0) in Nepal, the MEI should be

greater than C0.9 in Jun (0) and subsequent months.

No similar threshold is observed for La Nina events.

6. Summary

Harmonic analysis of impacts of ENSO on stream-

flows in Nepal identified two core regions, the Western

Region (WE-E) and the Eastern Region (ER-E) for El

Nino, and one core region, the Western Region (WR-L),

for La Nina. El Nino produces more than 10% below

normal streamflows during the signal seasons July to

December of El Nino years in the WR-E and June to

December of El Nino years in the ER-E. La Nina

produces more than 10% above normal streamflow in

WR-L during the signal season from June of La Nina

years to January of the following year.

The magnitude and significance of the El Nino

impact is stronger than that of La Nina, but the

temporal consistency of El Nino is less than that of La

Nina. The impact of both El Nino and La Nina is

stronger in western Nepal than in eastern Nepal and

there are strong El Nino impacts on streamflows in

weak monsoon regions and strong La Nina impacts on

streamflows in strong monsoon regions. The impacts

of weak ENSO events are the opposite of strong

events.

This study on the ENSO impact on streamflows

provides valuable information for water resources

management and planning in Nepal, particularly in

Fig. 11. Variation in 24-month ENSO life cycle. (a) El Nino; (b) La Nina.


the west where the ENSO impact is stronger. In the

west, special consideration should be given to low

precipitation zones during El Nino. Water projects

should focus more on El Nino than on La Nina

because the El Nino impact is stronger. Further

research should focus on the El Nino impact on low

flows, which would be useful for hydropower and

irrigation projects in western Nepal, and the El Nino–

flood relationships, mainly in eastern Nepal, which

would provide information for flood forecasting and

disaster reduction programs. More precise results

could be obtained for eastern Nepal by analyzing

longer records, and hence more hydrometric stations,

as they become available.

Acknowledgements

The authors are thankful to Professor Terry

Gillespie, Professor Robin Davidson-Arnott and

Meng Yang, of the University of Guelph, and Dhiraj

Pradhananga, Tribhuvan University, Nepal, for their

valuable comments. The authors also wish to thank

the staff of Department of Hydrology and Meteorol-

ogy, Nepal, for providing streamflow data and the

Latornell Foundation for a travel grant. The manu-

script benefited significantly from the comments of

two anonymous reviewers.

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