el niño 1997–98 and the hydrometeorological variability of chapala, a shallow tropical lake in...

El Nino 1997–98 and the hydrometeorological variability of

Chapala, a shallow tropical lake in Mexico

Irina Tereshchenkoa,*, Anatoliy Filonova, Artemio Gallegosb, Cesar Monzona,Ranulfo Rodrıguezb

aDepartment of Physics , University of Guadalajara, Apdo. Postal 4-040, Guadalajara 44421, Jal., MexicobInstituto de Ciencias del Mar y Limnologia, UNAM, Mexico, Mexico

Received 18 July 2000; revised 7 December 2001; accepted 22 March 2002

Abstract

One hundred years chronological water level fluctuations for Chapala Lake, a shallow tropical lake in west Mexico, as well as

their relationships with air temperature fluctuations, precipitation and Lerma River discharge are examined in this study. It is

shown, that these relationships were strongly revealed in the last 1997–98 El Nino, which caused anomalous air temperature

and evaporation increase throughout Western Mexico territory and, as a consequence, 1 m downturn of the Chapala lake level

since January till June, 1998. To investigate 1997–1998 El Nino-Southern Oscillation phenomenon (ENSO 1997–98)

AVHRR-NOAA satellite image analysis of the Chapala Lake surface water temperature (LCST) was performed. These images

provide a temperature database for 1996–99 (38 months). Analysis of annual and seasonal LCST fluctuations was carried out

using monthly averages for this period. Time series analysis on the LCST data suggest that seasonal surface temperature

variations may be almost completely obscured by the annual harmonic. In 1998 the temperature pattern was altered due to the El

Nino 1997–98 episode. In winter the water surface temperature was on average almost 1 8C lower than the temperature values

registered in 1996. However, for the summer and autumn of 1998, such temperatures were 1.5 8? higher in comparison with the

reference year of 1996. q 2002 Elsevier Science B.V. All rights reserved.

Keywords: Chapala Lake; Surface water temperature; El Nino 1997–98

1. Introduction

Lake Chapala, the largest in Mexico and third

largest in Latin America, has an average length-width

of 75 £ 22 km and an average depth of only 6 m, with

a maximum of 11 m (Fig. 1). Among shallow lakes,

Lake Chapala is the largest in the world (Sandoval,

1994; 1996). It plays an important role in the economy

of the region. Approximately 14 million people live in

large and small settlements around, including import-

ant industrial and cultural sites. The lake provides for

a mild climate and establishes a land-lake breeze

circulation throughout its costal area. Atmospheric

humidity in this area is also moderate which together

with the pleasant landscape, makes the lake a great

tourist attraction.

The lake’s tributary and out-flow rivers are the

Lerma and Santiago, respectively. Both rivers and the

lake form a unique reservoir system covering an area

of approximately 47 000 km2. The annual rainfall is

750 mm, and evaporation from the lake surface ranges

0022-1694/02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved.

PII: S0 02 2 -1 69 4 (0 2) 00 0 66 -5

Journal of Hydrology 264 (2002) 133–146

www.elsevier.com/locate/jhydrol

* Corresponding author.

E-mail address: [email protected] (I. Tereshchenko).

http://www.elsevier.com/locate/jhydrol

from 1000 to 1400 mm per year, resulting in a

negative balance. The deficit is offset by inflow from

the Lerma river (Mosino and Garcıa, 1973; Jauregui,

1995; Filonov and Tereshchenko, 1997, Filonov et al.,

1998). In dry years annual rainfall can drop to

500 mm and the lake does not contribute any water

to the Santiago river (Riehl, 1979). However, in the

high-rainfall years the annual precipitation can reach

1000 mm and an important volume of water exits

through this same river.

According to data from the national water

commission (Comision Nacional del Agua, Mexico;

CNA), approximately 1.28 £ 106 m3 of suspended

particles enter the lake via its tributary from the lake’s

watershed. The size of the particles can reach up to

0.5 mm and are mainly deposited in the eastern part of

the lake. The result is a decreasing depth by the

accumulation of particles in the bottom of the lake.

Suspended particles modify the water transparency.

Secchi disk depth measures 0.2 m close to the Lerma

entrance, whereas at the eastern end a value of 0.5 m

(Sandoval, 1994) has been recorded.

Suspended particles of anthropogenic origin heav-

ily contaminate the water entering the lake via the

Lerma River. Large investments in water treatment

are needed in order to clean this water for human

consumption. In addition, large quantities of nutrients

reach the lake in the form of water weeds such as lilies

(Eichchornia crassipes ) and tule species (Tupha

latifolia, T. dominguensis, T. angustifolia ), and

build up huge floating islands (Filonov et al., 1998).

The lake water level has dropped dramatically

during this century (Fig. 2a). According to the

Comision Nacional del Agua, Mexico from 1945 to

1955 the drop was nearly 4 m. Lake water level

records for several years indicate that the mean value

has remained about 96.4 m (CNA determines the lake

water level relative to a fixed reference mark of 87 m).

Fig. 1. (a) Batimetrical map of Chapala Lake. Depth is given in meters in relation to the 87 m depthmark. (b) Diagram showing superficial

temperature sections obtained from the AVHRR images. The dot represents the SBE-19 location. The instrument measured superficial

temperature fluctuation on October 18–19, 1996.

I. Tereshchenko et al. / Journal of Hydrology 264 (2002) 133–146134

After the 1955 drop, 2 years later the water level rose

5 m and kept a mean value of 97.1 for almost 20 years.

However, since 1977 the level started to drop again

and nowadays the level is only 1 m above the 1955

historical low of 91.9 m (Filonov, 1998).

Sandoval (1994) mentions that the lake has reached

its current status due to water usage of the Lerma for

irrigation and industrial needs, and to cover the

domestic demand of Guadalajara city. Also, some

water is withdrawn upland from the Lerma River and

some is pumped out of the lake to cover the needs of

the local villages. In sum, these volumes of water

exceed the target limit for water preservation. There-

fore, Sandoval (1994) states that the lake will dry out

by the year 2007.

However, the principal loss of water from the lake

is through evaporation. During the spring months,

from March to May, evaporation brings down the lake

water level, on average, 10 mm a day. The shallow

depths (1–2 m) and low transparency of water in the

east part of the lake create less thermal inertia and

allow for stronger heating in comparison with deeper

and more transparent portions of the lake, resulting in

higher evaporation rates (Filatov, 1983). In this

connection, Sandoval (1994) has proposed the con-

struction of a dam to cut off the eastern 20% of the

lake to lower these losses. Detailed measurements

have shown (Filonov and Tereshchenko, 1997) that

the shallow east sector has a surface water tempera-

ture of as much as 3 8C above that in the central area of

the lake. However, on the basis of these measurements

it is difficult to advance any conclusion about the east

sector as being the main evaporation area of the lake.

Resolution of this controversy would require a surface

water temperature monitoring program (weekly

measurements) of a year duration, at least.

Not all authors agree that the lake will dry up in the

next years. Filonov (1998) puts forward different

arguments based on numerical modeling. He states

that the main cause of the long period level

fluctuations is unfavorable climatic factors and not

just anthropogenic influence. But he fails to defini-

tively establish the causes of Chapala Lake water

level fluctuations, as well as of rainfall fluctuations

over its watershed.

Filonov (1998) also shows that the

Fig. 2. (?) Average fluctuations of the lake Chapala level monthly mean (1), air temperature in Guadalajara (2), precipitation on the Mexican

territory (3) and the mean annual Lerma River discharge (4). (b) Frequency spectrums of lake level fluctuations, temperature, precipitation and

the Lerma River discharge. Here 4a is the traditional spectrum estimation, and 4b is an estimation from the method of the maximum likelihood.

The vertical line indicates the 80 % confidence limit.

I. Tereshchenko et al. / Journal of Hydrology 264 (2002) 133–146 135

hydrometeorological regimen of Chapala Lake is

influenced by El Nino episodes. The El Nino 1997–98

event was the strongest of this century (Magana et al.,

1999; Strub and James, 2002). This event registered

unusually high air temperatures and consequently a

very intense dry season in the west-central Mexico. In

Guadalajara, Jalisco the monthly average air tem-

perature from March to May 1998 was 3–4 8C higher

than the climatological mean for this same season.

The atmospheric relative afternoon humidity

decreased 6–8% (Tereshchenko and Filonov, 1998).

Due to the intense dry season, the lake lost more than

1 m of its water level due to evaporation and the

excess of water pumped to satisfy water demand from

the local industry and population (Filonov, 1998).

Measurements for water quality assessment and for

pollution dispersion in the Chapala Lake are con-

ducted irregularly and only at several discrete points.

But its thermodynamic condition is not monitored at

all. This complicates the development of scientific

concepts for the Chapala Lake rescue. The accessible

modern way of periodic monitoring of the lake’s

thermal mode can be achieved through the analysis of

surface water temperature satellite images, received

from the Advanced Very High Resolution Radio-

meters (AVHRR). Such analyses have found wide

application in physical oceanography (Scambos et al.,

1997; Soto-Mardones et al., 1999).

This work investigated long period fluctuations, as

well as seasonal variations in the lake’s hydrometeor-

ological regime and their relationship with the El

Nino 1997–98 event. In this analysis we have used

the monthly averages of AVHRR-NOAA satellite

images that were registered for more than three years.

2. Data and methods

2.1. Data

For the analysis of seasonal and interannual

variability of the hydrometeorological characteristics

of the lake and its surrounding area, we used the

following synchronous time series:

1. The 1900 to 1999 monthly average air temperature

series from the Guadalajara station located 50 km

from the lake.

2. The Lake Chapala level: (a) annual mean values

from 1900 to 1931, and (b) monthly means from

1932 to 1999.

3. The total precipitation in the Mexican territory

from 1941 to 1999.

4. For 1996 to 1999, the following series were used:

(a) the lake monthly average evaporation and lake

level, (b) the monthly average precipitation from

Chapala station, and (c) the Lerma River discharge.

Data were obtained from the Comision Nacional del

Agua, Mexico. In addition, Chapala Lake surface

temperature (CLST) data was received from the

AVHRR carried aboard the NOAA-12 and NOAA-14

polar orbiting satellites. These satellites operate in near

polar sun-synchronous orbits at a distance of about

850 km from the center of the Earth and provide global

coverage of the earth surface roughly twice a day. The

AVHRR has a 1.1 km2 resolution at nadir and is a five

channel device, measuring the Earth’s surface radiation

at visible, near infrared, and infrared wavelengths

(Monaldo, 1996; Schmugge et al., 1997; Barbosa,

1999). The CLST database was obtained from daily

images. This was done for the following local mean time

(LMT): 2:30, 6:30, 15:00 and 19:00 h (GTM þ 6 h).

The registered images of the 38 month period that

covered the Chapala Lake area (i.e. from April 1996

to May 1999) were used to prepare monthly CLST

composites (more than 4500 images). In the present

study four linear sections of the CLST were analyzed

(Fig. 1b): One with a west–east direction (section I)

and the remaining three running perpendicular to it,

with north–south direction (sections II, III and IV).

The initial daily CLST data at each section

represent a random sample of N data. On the time

coordinate there were N ¼ 4624 values, and from

1320 up to 8280 on the spatial one, depending on the

length of the section. Statistical analysis was applied

after data validation and their mean values, mean

square deviation and the 95% confidence limits over

space and time were computed. These were then used

to calculate more specific average values �T and root

mean square deviations sT (Walpole and Myers,

1993). The 95% confidence limits of the average daily

values of CLST were calculated with �T 2 d , �T0 ,�T þ d; where d ¼ t0:05sT=

ffiffiffiN

pis the 95% confidence

level and t0.05 is the deviation of the average of the

partial set �T with respect to the average of the general


normal set �T0; determinated as: t0:05 ¼ ð �T 2�T0Þ=ðR=

ffiffiffiN

pÞ: Here R is the unbiased estimate of the

dispersion sT2; calculated as:

R ¼1

N 2 1

XNi¼1

ðTi 2 T0Þ

!1=2

:

2.2. Energy spectrum analysis

The estimation of the energy spectrum for each of

the time series was obtained by smoothing the

periodogram (Jenkins and Watts, 1969; Bendat and

Piersol, 1967, 1986; Konyaev, 1990):

SxxðvÞ ¼ð1

21Sxxðv

0ÞZðv2 v0Þdv0: ð1Þ

where Z(v ) is a smoothing function, and Sxx(v ) is

defined as the auto-periodogram:

SxxðvÞ ¼1

TCxðvÞCx

pðvÞ; ð2Þ

where Cx(v ) is the amplitude spectrum:

CxðvÞ ¼ðT

0xðtÞ expð2i2pvtÞdt

of the time series x(t ); T is the total length of the

series; (p) denotes the complex conjugate of the

amplitude spectrum. The standard algorithms

described in the literature (Jenkins and Watts, 1969;

Bendat and Piersol, 1967, 1986; Konyaev, 1990) were

used to calculate the confidence intervals for all

spectral estimates. The number of degrees of freedom

n, was found as n ¼ 2ð2F þ 1Þ; where F is the half-

width of the filter that is used to smooth the

periodograms. The mean square amplitudes of the

harmonics of the dominant peaks in the spectra were

found from the values of the spectral density.

The mean annual data of the Lerma River

discharge was used to find the energy peaks of the

spectra, by nonlinear methods of maximum like-

lihood, together with the traditional method of

spectral analysis (Burg, 1967, 1972; Akaike, 1969;

Konyaev, 1990). The spectral estimates from the

maximum likelihood method were calculated with

SðvÞ ¼ Dt=lAMLðvÞl2; ð3Þ

where the averaging filter was found by

AMLðvÞ ¼XNk; j

ak; j expð2i2pvðk 2 jÞDtÞ; ð4Þ

and the coefficients ak,j were obtained from the

inverse of the initial correlation matrix of the time

series:

Bð0Þ Bð1Þ · · · BðN 2 1Þ

Bð1Þ Bð0Þ · · · BðN 2 2Þ

· · · · · · · · · · · ·

BðN 2 1Þ BðN 2 2Þ · · · Bð0Þ

26666664

37777775

21

¼

a11 a12 · · · a1N

a21 a22 · · · a2N

· · · · · · · · · · · ·

aN1 aN2 · · · aNN

26666664

37777775 ð5Þ

Here M # N; M is the order of the white filter; N the

number of data points in the time series; and B(0),

Bð1Þ;…;BðM 2 1Þ are the data of the correlation

function of the time series analyzed.

2.3. Harmonic modelling

The seasonal fluctuations of the analyzed hydro-

meteorological characteristics, for example, tempera-

ture, represent the sum of annual and semi-annual

harmonics:

TðtÞ ¼ T0 þ A1 cos½2pðv1t þ w1Þ�

þ A2 cos½2pðv2t þ w2Þ� þ gðtÞ; ð6Þ

where T0 is the mean multi-annual air temperature in

the lake region; A1, A2, v1, v2, w1, w2 are the

amplitudes, frequencies, and initial phases of the

annual and semi-annual harmonic; g(t ) is the low and

high-frequency random component. All unknown

parameters in these equations were estimated by

discrete Fourier transformation using the initial time

series (Jenkins and Watts, 1969; Bendat and Piersol,

1967, 1986):

an ¼1

N

XNi¼1

Ti cosð2pn Dt=NÞ; ð7Þ


bn ¼1

N

XNi¼1

Ti sinð2pn Dt=NÞ; ð8Þ

and further were defined

T0 ¼1

N

XNi¼1

Ti; An ¼

ffiffiffiffiffiffiffiffiffiffiffiffian

2 þ bn2

q;

wn ¼ arctgðbn=anÞ:

ð9Þ

n ¼ 1; 2 are the annual and semi-annual harmonic; N

is the time series length ðN ¼ T=DtÞ; and Dt is the

time interval. Mean square deviations of the model

from the initial time series were calculated by the next

equation:

s ¼1

N 2 1

XNi¼1

ðTi 2 TmiÞ

!1=2

: ð10Þ

3. Results and discussion

3.1. Interannual and seasonal variability

Interannual and seasonal variability of the princi-

pal meteorological variables will allow us to under-

stand climatic features of the study area. Fig. 2a shows

monthly means of Chapala Lake water level fluctu-

ations, air temperature in Guadalajara, precipitation

over the Mexican territory and the mean annual

Lerma river discharge. Fig. 2b shows the spectra of

the analyzed time series.

The analysis of the diagrams reveals clear

relationship between long time fluctuations of the

lake water level and the Lerma River discharge. The

periods of the level downturn were always preceded

by years of low Lerma River discharge and, on the

contrary, several years of steadily high discharge of

the river resulted in increase of the lake level. The

relationship between the level and both long-period-

ical temperature fluctuations and river discharge is not

so obvious and it is difficult to find it with the visual

analysis alone. However, the results of applying

traditional and maximum likelihood spectral analysis

methods to the time series have confirmed the

presence of the fluctuations with the periods typical

for El Nino, i.e. from 2 to 7 years. The earlier

researches (Florescano and Swan, 1995; Magana et al.,

1999) have shown, that winters are more cold, and

both springs and summer beginnings are more hot and

arid in the Mexico territory during El Nino time. With

El Nino arrival the rain season begins one month later

and the precipitation intensity increases.

The seasonal variability of the meteorological

characteristics in the lake’s region is completely

determined by two overlapping harmonics (i.e. annual

and semi-annual), as well as by the contribution of

low-frequency components and the high frequency

random noise. Contribution of seasonal and sub-

seasonal harmonics (with periods of 4, 3 months, etc.)

is almost two orders of magnitude smaller than the

main harmonic, though such oscillations are clearly

separated in all the spectra (Mosino and Garcıa, 1973;

Filonov et al., 1998).

Results of the seasonal variability model are

presented in Table 1, the amplitude of annual

harmonics was on average five orders of magnitude

greater than the semi-annual amplitude for the same

time series. The maximum amplitude of the annual

harmonic occurred in mid May, and the maximum

amplitude of precipitation in early June. Concerning

the seasonal fluctuations of the lake water level, only

Table 1

Characteristics of annual and semi-annual harmonics of temporary series, given in Fig. 2(a). The amplitudes of harmonics and their

corresponding average square deviations are given. Their dimensions correspond to initial sizes. Initial phases are given in months

Data Annual harmonic Semi-annual har-

monic

s

Start date Stop date A1 w1 A2 w2

Air temperature (8C) 1/1/1900 8/1/1999 4.4 5.4 0.9 5.6 1.12

Precipitation (mm) 1/1/1941 8/1/1999 53.2 7.2 10.1 7.7 15.3

Lake level (m) 1/1/1900 8/1/1999 0.41 10.3 – – 1.75


annual harmonics were seen with an amplitude about

40 cm with the maximum occurring in early October.

Fluctuations of the hydrometeorological parameters,

which are illustrated in Fig. 2a fitted satisfactorily to

the model, although the mean square deviations

between calculated values and the initial time series

are large.

3.2. Spatial–temporary variations in suface water

temperature

The CLST monthly mean values registered for

sections I, II, III and IV (Fig. 1b) were analyzed. They

are shown in Figs. 3–6. The letter a in these figures

represents a two-dimensional diagram where CLST is

a function of the spatial and temporary coordinates:

T(x,t ). The letter b refers to the average of all

analyzed time of measurements (38 months) of

temperature values on sections Tt(x ) and, finally, c

illustrates the time variation of the monthly average

temperature on sections Tx(t ).

The statistical characteristics of Tt(x ) and Tx(t ) for

all four sections calculated with algorithms described

in Section 2.1 are given in Table 2. It is clear that the

temperature averages over space and time for different

sections vary little. The changes of their mean square

amplitudes over space do not exceed 1 8C, but over

time they vary from 2.43 8C along section IV, up to

2.86 8C; along section I, and characterize the

amplitude of the CLST seasonal fluctuations.

The analysis of section I CLST (Fig. 3) suggests

that the water surface temperature in the central part

of the lake was on average 0.7 8C ðT ¼ 22:6 8CÞ

higher than in the western and the shallow eastern part

ðT ¼ 21:9 8CÞ: Such differences can be explained in

terms of the lake’s surface water circulation. During

the winter, water from the western and eastern parts of

the lake are strongly cooled and fully mixed with

shallow waters. However, in the central part where the

Fig. 3. Superficial temperature variability on section I. (a) The spatial–temporary diagram Tðx; tÞ: (b) Average spatial variability TtðxÞ; the

average was worked out from every pixel of temporary readout of all time period. (c) Average temporary variability of TxðtÞ; for all pixels of

section.


lake depth ranges from 6–10 m, the water surface

temperature was usually higher than in the western

and eastern parts.

Monthly means of the CLST time variation

(sections I–IV) fit closely to a ‘sine wave’ configur-

ation. It suggests the annual harmonic as the chief

contributor for the CLST seasonal fluctuations. The

contributions of the semi-annual and the high-

frequency harmonics are insignificant. CLST monthly

average fluctuations in 1996 and in the first half of

1997 had a ‘normal’ form (a ‘normal’ form means null

influence of El Nino on the CLST evolution).

However, modulation of the CLST time variation

became evident in November 1997. In 1998, the

surface temperatures were 0.2 8C (18.9 8C) lower in

January and 0.6 8C higher (25.4 8C) in August than

those for the corresponding months in 1996. Our

conjecture is that El Nino 1997–98 caused such

disturbance in the CLST evolution. On average in

winter 1997, all lake CLST were 0.9 8C lower than in

1996. In autumn 1998 such differences were even

more compared with 1996, thus reaching 1.4 8C.

The analysis of the spatial and temporal variability

of the CLST (sections II to IV, Figs. 3 and 4) indicates

that the northern part of the lake is about 1 8C higher

than the shallow eastern part. In the central part of the

lake, and in particular along section III, the CLST is

higher than along sections II and IV. A similar pattern

is seen along section I (Fig. 1). The period 1996–99

registered the lowest CLST, for example in winter

Fig. 4. Superficial temperature variability on section II. Figure explanation is similar to explanatory notes in Fig. 3.

Fig. 5. Superficial temperature variability on section III. Figure explanation is similar to explanatory notes in Fig. 3.


1997–98. The highest CLST was registered in

summer 1998.

The average CLST course was modeled by

algorithms (1)–(5). The results are shown in

Table 2 and in Fig. 7. The sum of the annual

and semi-annual harmonics fitted well to the real

evolution of surface water temperatures for the

first 18 months. However, for the second 18

months, the CLST evolution deviated from

normal due to El Nino 1997–98 influence. In

this way the maximum negative deviations were

recorded in winter 1997–98 (20.9 8C) and the

positive deviations were registered in winter

1998–99 (post El Nino). They reached þ1.5 8C

in December 1998.

3.3. Influence of El Nino 1997–98 on daily CLST

We obtained CLST data four times daily. The

average values for sections I to IV were obtained

(Table 3). Daily CLST variation in 1996 and the first

half of 1997 did not exceed 2 8C (Fig. 8a). In the

winter of 1997–98, it increased up to 3 8C, and in

summer of 1998 it reached a value of 3.3 8C. In the

winter periods of 1996 – 97 and 1997 – 98, the

maximum daytime temperatures of the surface water

were up to 21 8C. However, the morning temperatures

in the cold winter of 1997–98 were 1.2 8C lower than

those of the previous year. In the summer of 1997 and

1998, the morning temperatures were the same, but

during the daytime they were higher than in previous

Fig. 6. Superficial temperature variability on section IV. Figure explanation is similar to explanatory notes in Fig. 3.

Table 2

The statistical characteristics of the LCST on the sections I–IV

Section number Averaging method Data quantity Average temperature

(8C)

Mean square amplitude

(8C)

95% Confidence limits

(8C)

I Space averaging 4624 21.67 2.86 ^0.071

Time averaging 8280 22.30 0.41 ^0.008

II Space averaging 4624 21.96 2.70 ^0.067

Time averaging 1320 22.32 0.37 ^0.103

III Space averaging 4624 21.94 2.87 ^0.071

Time averaging 1920 22.43 0.89 ^0.034

IV Space averaging 4624 21.75 2.43 ^0.060

Time averaging 2040 22.07 1.01 ^0.037


years. In Fig. 8a, the increase of the daily evolution of

the average CLST amplitude in the period July–

October 1998 is evident.

A special experiment was conducted to assess

the CLST daily evolution obtained by the AVHRR

images. The expriment consisted in establishing a

floating buoy at the intersection of sections I and

IV. Here, a SBE-19 with a temperature sensor was

attached to the buoy; leaving the sensor 5 cm

below the water surface. The instrument took

continuous readings throughout 2 days in October

1996, at time intervals of 10 s. The curve from

this experiment is shown in Fig. 8b. The CLST

registration times were 2:30, 6:30, 15:00 and

19:00 LMT. Bold points represent the congruence

of satellite data with the curve of the SBE-19

sensor. CLST data coincide well with the exper-

imental surface water temperatures.

The high-frequency fluctuations, shown in Fig.

8b occurred in the time period of 10–15 h. Such

fluctuations were caused by the influence of

breeze circulation on the temperature field. The

breeze over Chapala Lake is usually well

expressed and causes spatial and temporal varia-

bility in the distribution of the surface water

temperature. This was observed in various parts of

the lake (Filonov and Tereshchenko, 1999).

3.4. El Nino 1997–98 and fluctuations of

hydrometeorological characteristics

The evolution of the CLST was compared with the

variability of air temperature (from the Chapala

climatic station) and the surface water temperature

anomalies in the Pacific Ocean (Fig. 9). The

temperature anomalies were taken form the Monthly

Ocean Report (1996–99), for square B (48N–48S,

908W–1508W). The maximum phase of the El Nino

1997–98 took place in December, 1997 (Fig. 9c). One

month later, in January 1998, maximum temperature

increase occurred at active ocean layer and for the sea

level rise along the Mexican coast near the 208N

(Filonov and Tereshchenko, 2000; Strub and James,

2002). The maximum air temperature at the Chapala

climate station was recorded in June 1998 (Fig. 9b).

The peak of positive temperature anomalies in the

CLST time series was seen until December 1998.

(Fig. 9a).

The influence of El Nino 1997–98 was noticed

in the lake one year after its onset. El Nino

disturbed the typical course of the large-scale

circulation and the meteorological processes

within the Mexican territory; particularly in the

western part of the country. Here, the autumn and

winter of 1997–98 were extremely cold due to the

Fig. 7. Average temporary course from all four sections of Lake Chapala superficial temperature (curve 1) and its approximation by the sum of

annual and semi-annual harmonics (curve 2).

Table 3

Characteristics of annual and semi-annual harmonics obtained from the average data of the LCST four sections

Data Annual harmonic Semi-annual harmonic s

Start date Stop date A1 w1 A2 w2

LCST-1 4/1/1996 10/31/1997 2.91 7.4 0.58 7.8 0.08

LCST-2 11/1/1997 5/31/1999 – – – – 0.47


Fig. 8. The course of superficial temperature in Lake Chapala. (a) Average superficial temperature of all four sections at 2:30 (curve 1), 6:30

(curve 2), 15:00 (curve 3) and 19:00 (curve 4) local mean time. (b) Superficial temperature recorded by the SBE-19 on October 18–19, 1996.

Dots represent AVHRR superficial temperature measurements occurring at the same time of the SBE-19 records.

Fig. 9. (a) Fluctuation difference of average temperature between curves 1 and 2, and its approximation. (b) The course of monthly average air

temperature at Chapala station. (c) The courses of monthly average anomalies of water superficial temperature in the Pacific Ocean (square B

(48N–48S; 908W–1508W)). (d) The course of monthly average precipitation (curve 1) and evaporation (curve 2) at Chapala station and the

Lerma River discharge (3). (e) The course of monthly average lake level.


advection of cold air from the north. In December

1997 the morning air temperature in the area of

Chapala Lake dropped 3 8C, with an occasional

snowfall (Informe Meteorologico Nacional, 1997–

98). Such low temperatures had not been regis-

tered for more than 100-years in the historical

record of the Instituto de Astronomıa y Meteor-

ologia (IAM, University of Guadalajara).

Conversely, the spring of 1998 in west-central

Mexico was extraordinarily hot. In June, the Chapala

climatic station recorded mean air temperatures of

28.2 8C. This was 5.4 8C higher than those registered

in June of the previous year. Such a dramatic rise in

air temperature caused a severe drought and multiple

forest fires. The latter were larger in number and

covered the entire country with a thick layer of smoke

that extended into the United States (Trasvina et al.,

1999; Filonov and Tereshchenko, 2000).

Fig. 9d and e give the temporal course of monthly

mean precipitation, evaporation (measured at climatic

station Chapala), Lerma River discharge and lake

Chapala level. They show that the high air tempera-

tures occurring in spring of 1998 caused an increase in

evaporation, and resulted in a sharp downturn of lake

level. In fact, the level decreased 1 m from January to

June 1998 (92.5 m), thus exceeding 0.6 m the minimal

1955 mark (91.9 m) in the historical hydrometric

record of the lake.

It is known that during El Nino events, the

precipitation regime in Northern America varies

strongly (Angel and Korshover, 1987; Philander,

1990). Hongbing and Furbish (1997) analyzed

long time series of precipitation (44 years) at

many points in the Florida Peninsula. Their

statistical analysis proved that El Nino and La

Nina are responsible for up to 40% variation in

annual precipitation, and up to 30% variation of

river discharges in Florida.

In the El Nino 1997–98, the mode of atmospheric

precipitation in Mexico changed drastically (Magana

et al., 1999). The seasonal rains in 1998 began at the

end of June (i.e. one month after its usual onset), and

precipitation was more intense than in the previous

(non El Nino) years. The plentiful precipitation in the

lake’s watershed increased the Lerma River dis-

charge. Thus the volume of water feeding the lake

increased the level by almost 1.5 m in November.

4. Summary and conclusion

The variability of the meteorological character-

istics in the Chapala Lake region is completely

determined by annual and semi-annual harmonics,

as well as by the contribution of lower (interannual

and decadal) frequency components. The contribution

of higher frequency harmonics (seasonal and sub-

seasonal) is almost two orders of magnitude smaller.

The amplitude of annual harmonics was on average

five orders of magnitude greater than the semi-annual

amplitude for the same time series. The analysis

shows that interannual fluctuations of Chapala Lake

water level are caused by changes in the cycles of

atmospheric circulation that occur in the lake’s

watershed area. The analysis of the diagrams reveals

clear relationship between long time fluctuations of

the lake water level and the Lerma River discharge.

The periods of the level downturn were always

preceded by years of low Lerma River discharge

and, on the contrary, several years of steadily high

discharge of the river resulted in increase of the lake

level. The results of applying traditional and maxi-

mum likelihood spectral analysis methods to the time

series have confirmed the presence of the fluctuations

with the periods typical for El Nino, i.e. from 2 to 7

years.

Our results also show that the lake’s thermodyn-

amics and its level are firstly determined by annual

variations, and secondly by lower frequency vari-

ations such as El Nino events and solar activity.

Analysis of the lake’s surface temperature obtained

from the AVHRR-NOAA images were on average

1 8C warmer in the north than in the south. The eastern

and western sectors are cooler than the central one.

The surface temperature evolution is fully determined

by annual harmonics. Our results do not confirm the

conventional view that the east shallow part of lake is

warmest and consequently evaporates more water

than other parts of lake. Thus, Sandoval’s (1994)

proposal to reduce water evaporation by cutting off

the east part of lake (supposedly the main area of

evaporation) has no scientific base. Apparently it is

necessary to rely on other alternatives to stabilize the

lake’s water level; for example, supply the Chapala

Lake with water from other river watersheds.

The El Nino 1997–98 event was the most severe of

this century, as many authors agree. The phenomenon


caused the mean global temperature in the atmosphere

to increase. (Magana et al., 1999; Karl et al., 2000).

Our research has shown that this El Nino drastically

changed the temperature patterns not only along the

Pacific coast of Mexico, but also within a significant

distance from it. In the area of Chapala Lake the

annual atmospheric temperature course was intensely

modified during this El Nino. In the winter of 1997–

98, the mean temperature showed a negative anomaly

of 1 8C. However, in the summer of 1998 this

figure was positive, with a value of 1.5 8C in

comparison to 1996. Data analysis shows a

relationship between surface temperature vari-

ations and hydrometeorological characteristics

(temperature, evaporation, Lerma discharge and

lake level). The model we propose makes possible

the forecasting of the Chapala Lake mean surface

water temperature for any month of the year, both

for ‘normal years’ and El Nino years.

Acknowledgments

We would like to thank our colleagues from the

University of Guadalajara for their help in data

collection and processing, and especially to MSc.

Arturo Figueroa Montano for his help with the

Spanish to English translation. We also thank Erik

Marquez for the technical operative support in image

processing at ICML/UNAM. Finally, our thanks go to

the CUCEI, University of Guadalajara and CON-

ACYT (Mexico) for the financial support (Project

N33667-T).

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