evaluation of the temporal variability of the evaporative fraction in a tropical watershed.pdf

8/11/2019 Evaluation of the temporal variability of the evaporative fraction in a tropical watershed.pdf

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International Journal of Applied Earth Observation

and Geoinformation 5 (2004) 129140

Evaluation of the temporal variability of the evaporativefraction in a tropical watershed

H.O. Farah a,, W.G.M. Bastiaanssen b, R.A. Feddes c

a Department of Civil and Structural Engineering, Moi University, P.O. Box 3900, Eldoret, Kenyab Garstsraat 23, 4021 AB Maurik, The Netherlands

c Sub-Department of Water Resources, Department of Environmental Sciences, Wageningen Agricultural University,

Nieuwe Kanaal 11, 6700 PA Wageningen, The Netherlands

Received 4 September 2003; accepted 26 January 2004

Abstract

Evaporation exhibits diurnal variation in response to the changes in the available energy at the land surface. This requires

continuous measurements of evaporation to determine daily total evaporation. This is not feasible without sophisticated field

equipment, which at the end, only provides field scale evaporation rates. Remote sensing methods are a good alternative but

these give snapshot measurements. If the partitioning of available energy into the different surface fluxes can be assumed to

be diurnally constant, then instantaneous remotely sensed measurements could be used to derive daily total evaporation. In

situ evaporation measurements were obtained for about a year at a grassland and woodland site in the Lake Naivasha basin,

Kenya. These measurements were used to test the validity of the diurnal constancy of the partitioning of the available energy,expressed as evaporative fraction, and the extrapolation of evaporation from instantaneous to daily totals. A good relationship

between midday and average day evaporative fraction was obtained at the two sites. Estimated daily evaporation from midday

evaporative fraction was within 10% of measured evaporation for both sites. The deviation reduced if evaporation is further

integrated in time. The seasonal progression of evaporative fraction is gradual at both sites although grassland evaporative

fraction responds faster to changes in rainfall and moisture availability. The results provide a basis for the determination of

regional evaporation across a season in tropical watersheds if evaporative fraction is determined instantaneously at intermittent

intervals of 510 days.

2004 Elsevier B.V. All rights reserved.

Keywords: Evaporation; Evaporative fraction; Remote sensing; Lake Naivasha basin

1. Introduction

Evaporation is required on a daily as well as longer

time scales for applications in hydrology, agriculture,

forestry and environmental studies in general. How-

ever in practice, continuous daily evaporation mea-

surements are rarely available. Daily reference or po-

Corresponding author.

E-mail address: [email protected] (H.O. Farah).

tential evaporation can be estimated from mean daily

values of available meteorological variables such as

temperature, solar radiation, humidity and wind speed

(Allen et al., 1998). More recently, one or more in-

stantaneous measurements of evaporation have been

used to estimate daily total evaporation (Brutsaert

and Suigita, 1992). There has been a growing inter-

est in this approach because of its attractiveness for

remote sensing applications. Remote sensing offers

a means of estimating actual evaporation at a large

0303-2434/$ see front matter 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jag.2004.01.003


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130 H.O. Farah et al. / International Journal of Applied Earth Observation and Geoinformation 5 (2004) 129140

spatial scale, which is not possible with the tradi-

tional point methods. Many techniques have been

proposed to solve the surface energy balance from re-

motely sensed surface temperature, surface reflectanceand vegetation indices (Moran and Jackson, 1991;

Kustas and Norman, 1996; Bastiaanssen et al., 1999).

Remote sensing data are however instantaneous mea-

surements and a method is required to temporally

integrate instantaneous estimates of evaporation.

Latent heat flux (L) and other components of the

energy balance display considerable diurnal variation

over land surfaces. However several ratios of the fluxes

have been shown to be relatively constant during day-

light hours (Jackson et al., 1983; Shuttleworth et al.,

1989; Bastiaanssen et al., 1996).The classical energy

partitioning indicator is the Bowen ratio (), which is

a ratio of the sensible heat flux (H) and L. The pit-

fall of applying for time integration is that it shows

distinct diurnal variation features. More recently the

evaporative fraction () has been found to have little

variations during daytime, although it is directly re-

lated to (Crago and Brutsaert, 1996). Evaporative

fraction is defined as:

=L

Rn G=

L

L + H=

1

1+ (1)

where, Rn is the net radiation and G the soil heatflux. Shuttleworth et al. (1989), were the first to no-

tice the constancy of during daylight hours. They

analyzed 4 clear sky days data from the first ISLSCP

field experiment (FIFE) over relatively homogeneous

grasslands and found that midday is nearly equal to

the average daylight . Nichols and Cuenca (1993),

used 72 days data from Hydrologic Atmospheric

Pilot Experiment-Modelisation du Bilan Hydrique

(HAPEX-MOBILHY) experiment and showed that

the midday was highly correlated with average

daytime but that the midday and daytime are

not statistically equal. Crago (1996a), evaluated 77days data from FIFE. He used the data irrespective of

weather conditions of a particular day and concluded

that midday is significantly different from the av-

erage daytime value, the reason being the concave-up

shape of the diurnal progression of.

The central question is whether an instantaneous

value of can be used to estimate daily actual evap-

oration (E) as:

Ed = ins (Rn G)d (2)

where, the subscripts d and ins indicate total daytime

and instantaneous values respectively. This way of ex-

pressing E is a simple approach to integrate E on a

daily basis and across a season, if at least the tem-poral variations of are known. However, Eq. (2)

may not be valid under non-clear sky conditions be-

cause the diurnal constancy of may not be satisfied

under cloudy conditions (Zhang and Lemeur, 1995).

For areas with persistent cloud cover, such as in the

humid tropics, it is important to test the validity of

Eq. (2). In order to assess the performance of the ap-

proach, long term data series of measurements are re-

quired so that a wide range of different conditions are

encountered. Most of the previously published stud-

ies have used data from relatively short time periods

as reported above. In this study, field data collected

over a period of about 1 year in Lake Naivasha basin

in Kenya is used to investigate the applicability of

the method to estimate E at daily scale and for

a season. Continuous daily E measurements at two

sites were compared with daily Eestimated by using

Eq. (2).

The objective of this paper is to demonstrate the ca-

pability of instantaneous measurements of to esti-

mate the average day and Ethroughout a season in

tropical watersheds with data scarcity problems. Al-

though only field data was used in this study, the re-sults are expected to establish a sound basis for the es-

timation ofEfrom instantaneous remote sensing data

and routine daily weather data. The theoretical back-

ground of and reasons for its stable diurnal behav-

ior are discussed inSection 2. The field measurements

carried out are detailed inSection 3. InSection 4, the

diurnal stability of is discussed. The results of the

comparison between instantaneous and average day are presented inSection 5,while the results of es-

timating time integrated E is presented inSection 6.

Finally the seasonal variations of are described inSection 7.

2. Theoretical background

2.1. Reasons for the diurnal stability of

The diurnal behavior of can be understood from

its relationship with atmospheric conditions and sur-

face characteristics. The PenmanMonteith equation


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H.O. Farah et al. / International Journal of Applied Earth Observation and Geoinformation 5 (2004) 129140 131

ofL combines these conditions and is expressed as:

L =(Rn G)+ Cp[e

(z) e(z)]/ra

+ (1 + rs/ra)(3)

where, is the slope of the saturation vapor pressure

curve, e*(z) ande(z) are the saturation vapor pressure

and actual vapor pressure at height z, Cp the specific

heat of air at constant pressure, the air density,

the psychrometric constant, rs the surface resistance

to water vapor transport and ra is the aerodynamic

resistance to vapor transport. can be obtained by

dividing both sides ofEq. (3) by Rn G giving the

following expression:

=1

+(1+ rs/ra) +

Cp(e(z) e(z))/ra

Rn G(4)

Eq. (4)shows that is a function of vapor pressure

deficit (VPD = e(z) e(z)),ra andrs, besides avail-

able energyRn G.

The transfer equations for heat and water vapor be-

tween the surface of the earth and the atmosphere can

also be used to express without the explicit involve-

ment ofRn G:

0

0,2

0,4

0,6

0,8

1

0 200 400 600 800

r s (m s-1 )

Evaporativefraction(-)

0

0,2

0,4

0,6

0,8

1

0 200 400 600 800

Rn-G( W m-2 )


0

0,2

0,4

0,6

0,8

1

0 2 4 6 8 10 12

To-Ta(oC)


0

0,2

0,4

0,6

0,8

1

0 5 10 15 20 25 30 35

VPD(hP)

Evaporativefraction

(-)

(a) (b)

(d)(c)

Fig. 1. Evaporative fraction as a function of available energy, Rn G, surface resistance, rs, fromEq. (4)and surface and air temperature

difference, T0 Ta and vapour pressure deficit, VPD, from Eq. (7), with the following conditions prevailing on 28th October 1998

at a grassland site: (a) rs = 300s m1, ra = 70 s m

1, VPD = 15mb; (b) Rn G = 300wm2, ra = 7 0 s m

1, VPD = 15 mb; (c)

rs = 300sm1, ra = 7 0 s m

1, VPD = 15mb (d) rs = 300sm1, ra = 70 s

1, T0 Ta = 2C.

H=Cp(T0 Ta)

ra(5)

LE =

Cp(e(T0) e(Ta)

(rs + ra) (6)

where,T0 andTa are the surface temperature and air

temperature, respectively. By Further expressing asL/(L+H)(seeEq. (1)), an alternative expression forbecomes:

=L

L + H= 1

1

(1 + [rs((eT0) e(Ta))]/

(ra + rs)(T0 Ta)

(7)

For ideal conditions with no cloud obstructions and no

heat or moisture advection, Rn G,rs, and VPD fol-

low a regular diurnal cycle.Rowntree (1991),showed

that is more sensitive to Rn G when Rn G is

small.Fig. 1ashows as a function ofRnG. It can

be seen that up to a value of 200 Wm2, decreases

rapidly with increasing RnG. then remains almost

constant with further increase in Rn G. Available


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energy greater than 200 Wm2, usually occurs be-

tween 9.00 and 16.00 h. This means that variations in

is largest in the mornings and the evenings when

RnGis small (


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KenyaLake NaivashaBasin

woodland

grassland

200 km

Nairobi

Study area

20 km

Fig. 2. Location of study area showing the grassland and woodland sites where micrometeorological measurements were carried out.

Table 1

Measured meteorological variables which were used to determine evaporative fraction and evaporation

Measured variable Height above

surface (m)

Measurement

interval

Period grassland Period woodland

Air temperature, Ta 0.5, 2 20 m in 14th May 9814th April 1999 27th September 9814th April 1999

Air relative humidity, RH 0.5, 2 20min 14th May 9814th April 1999 27th September 9814th April 1999

Shortwave incoming

radiation, K

4 20 min 14th May 9814th April 1999 27th September 9814th April 1999

Shortwave reflected

radiation, K

2 1 h (once

a month)

14th May 9814th April 1999 27th September 9814th April 1999

Rainfall 0.3 20 min 27th September 9814th

April 1999

27th September 9814th April 1999

bucket rain gauge. These measurements were col-

lected by a data logger and recorded as twenty minute

averages. The surface reflectance, was measured 1

day in each month at 1 h intervals at both sites.Table 1

shows the details of the measurements. Malfunction-

ing instruments caused a period of 36 days in February

and March 1999 with missing data for the grassland

site.

4. Diurnal stability of

The standard deviation of measured (SD) be-

tween 8.00 and 17.00 h was calculated and used as an

indicator of the diurnal stability of. The mean SD

for the grassland site is 0.071 at an average of 0.40

yielding a coefficient of variation of 0.18. SDvaries

considerably during the study period. The months of

MarchJune, have the largest diurnal variations with

mean standard deviation of 0.082 with minimum 0.02

and maximum 0.17 values occurring on single days.

The remaining period had a mean standard deviation

of 0.060 with a minimum of 0.01 and maximum of

0.15. For the woodland site, the mean SD is 0.045

at an average of 0.33, hence a coefficient of vari-

ation of 0.14 arises. The months of March and April

had the highest SDof 0.060. At both sites the peri-

ods of largest SD coincide with rainy season. Dur-

ing the rainy days Rn G, Ta1 Ta2 and VPD are

small. It was shown on theoretical basis that is

most sensitive to variations in Rn G,Ta1 Ta2 and

VPD when these variables are small values. These af-fect the diurnal cycle of the surface energy fluxes and

the stability of . In comparison, the SD of the

woodland site is much lower than that of the grass-

land site. This indicates that the diurnal stability is site

dependent.

An analysis of the relationship between SD andTa, RH and the degree of cloudiness was undertaken

to see if routinely collected weather data could be used

to understand the diurnal stability of. The degree of

cloudiness is more accurately expressed as a shortwave


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Table 2

Relationship between the daytime standard deviation of evaporative fraction and meteorological variables used to explain the diurnal

stability of the evaporative fraction

Meteorological variables 1 day, r

2

10 day, r

2

Grassland n = 304 Woodland n = 204 Grassland Woodland

Shortwave transmittance, 0.05 0.07 0.27 0.33

Relative humidity, RH 0.11 0.10 0.31 0.21

Air temperature, Ta 0.10 0.09 0.34 0.18

transmittance ():

=K

K TOA(13)

whereKTOAis the solar radiation incident on the topof the atmosphere which can be calculated on the basis

of standard astronomical equations (e.g.Iqbal, 1983).

Table 2shows the coefficient of determination (r2) of

the relationships. The relationships were modeled by

polynomial curves having an order 2. The daily SD

has a very weak relationship with Ta, RH and . The

relationship between 10-day average SDand 10-day

average Ta, RH andwas also weak (Table 2).

To examine the effect of cloudiness on the stability

of , the days were stratified according to the daily

averagevalues and put into three groups. The groupswere defined as cloudy (

< 0.65) and clear ( > 0.65). Table 3 shows that

the average SD for the three groups is almost the

same indicating that cloudiness is not related to sta-

bility of . Hence, the stability of the diurnal cycle

of can not be adequately explained by micromete-

orological state variables only. There is no consensus

in the literature on the effects of clouds on the diur-

nal cycle of. WhileHall et al. (1992)conclude that

variations in Rn due to cloudiness should not affectsignificantly.Suigita and Brutsaert (1991), attribute

Table 3

Average daytime standard deviation of evaporative fraction grouped according to shortwave transmittance, , in order to understand the

relationship between cloudiness and diurnal stability of evaporative fraction

Number of days Mean standard deviation of evaporative fraction,

Grassland Woodland Grassland Woodland

114 40 0.65 0.068 0.041

daytime changes in to changes in cloudiness. They

attribute increase into decrease inRnas clouds pass

over.Crago (1996b),observes that cloud fields tend to

change RnG and surface temperature erratically and

thereby cause changes in . However, he concludesthat the effect on may not be observed in practice

as it may masked by coincident changes in RH and

wind speeds. This implies that diurnal variability of is a complex phenomenon and other factors influ-

encing the variations of in Eqs. (3) and (7) need

to be considered more carefully. The other variables

that control , are rs and ra (seeEq. (4)), of which

rs is the dominant surface variable, which regulates

. rs depends on micrometeorological variables, soil

moisture and plant physiology (Jarvis, 1976; Stewart,

1988). Surface resistance has a diurnal trend. Model-ing of surface resistance is therefore required in or-

der to understand better the diurnal dynamics of ,

but considered outside the scope of the present paper

where a large divergence of time scales is discussed.

5. Relationship between midday and morning

and daytime

The relationships between mid and average day-

timeare presented inFig. 3aand b. All days were


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(a)

(b)

R2= 0.75

0,1

0,2

0,3

0,4

0,5

0,6

0 0,1 0,2 0,3 0,4 0,5 0,6

Evaporative fraction(12-13hrs)

Daytimeevap

orativefraction(-)

R2= 0.74

0

0

0,2

0,4

0,6

0,8

1

0 0,2 0,4 0,6 0,8 1

Evaporative fraction(12-13hrs)

Daytimeevaporativefraction

Fig. 3. Relationship between midday and daytime evaporative

fraction at (a) grassland site for the period May 1998April

1999 and (b) woodland site for the period October 1998April

1999.

used irrespective of weather conditions. There is astrong relationship between mid and daily . The

r2 for the regression lines through the origin are 0.74

and 0.75 while the root mean square error (RMSE) are

0.095 and 0.070 for the grassland and woodland site

respectively. The 1:1 line (Fig. 3a) shows that midlarger than 0.65 are higher than corresponding day-

time values while mid values smaller than 0.30 are

less than the daytime values, which reveals a slight

concave type of relationship. values larger than 0.65

occur in the rainy months of May, June and April.

During these wet periods, when there is no moisturedeficit, evaporation is highest at midday when solar ra-

diation is highest.is therefore expected to be higher

at midday as compared to the rest of the day. In con-

trast values less than 0.3 mostly occur in the dry

months of January, February and December. Evapora-

tion is significantly reduced for the whole day, how-

ever available energy (Rn G) is highest at midday.values will therefore tend to be lower at midday as

compared to the rest of the day and under estimate the

daytime.

The relationships between average mor between

9.00 and 10.00 h and average daytime was deter-

mined to study the potential of using satellite remote

sensing based data acquired during the morning hours.Ther2 for the 9.0010.00 h period is lower with 0.64

and 0.65 for the grassland and woodland sites, respec-

tively as compared to the midday conditions. Poorer

RMSE of 0.112 and 0.106 were also obtained at the

grassland and woodland site, respectively. The impli-

cation of the results for remote sensing studies is that

midday satellite passes (e.g. NOAA AVHRR) will give

better average daily than the morning satellite passes

(e.g. Landsat).

6. Seasonal variations of actual evaporation

Daytime E estimated from mid and mor simu-

late the results ofEobtainable from the satellite data

with morning (e.g. Landsat) or afternoon (e.g. NOAA

AVHRR) over passes at the equator. Fig. 4shows the

comparison of measuredEand estimatedEfrommid

Fig. 4. Comparison of measured evaporation, E and estimated E

by midday evaporative fraction at (a) grassland site for the period

May 1998April 1999 and (b) woodland site for the period October

1998April 1999.


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Table 4

Root mean square error of E on daily, 10 day and monthly time

scales at the two sites for the whole study period

RMSE ET (mm) Grassland WoodlandDaily 0.17 0.14

10 day 0.12 0.06

20 day 0.05 0.04

for the two sites. The r2 and RMSE are also pre-

sented inFig. 4andTable 4respectively. The values

of measured and estimated E compare very well at

both sites. The RMSE for daily values are 0.17 and

0.14 mm at the grassland and woodland sites, respec-

tively. These results are for the whole study period,

however on individual monthly basis the largest RMSEfor daily values obtained are 0.21 and 0.18 mm for the

month of April for the grassland and woodland sites

respectively. With respect to r2, the lowest values are

0.77 for the month of January at the grassland site

and 0.66 for the month of February at the woodland

site. The months of January and February are the dri-

est months in the year and therefore E is very small

during this period. Although the comparison between

measured and estimated Emay appear poorer for the

drier months, the RMSE are comparable to the other

months.Table 4shows the RMSE of estimated Eondaily, 10 day and monthly scales. It can be seen that

the RMSE reduces with longer time scales. This indi-

cates that accumulated Eis more accurate than daily

E if estimated from instantaneous evaporation. It can

also be seen that the relationship between measured

0

0.1

0.2

0.3

0.40.5

0.6

0.7

0.8

0.9

15-Apr-

98

4-Jun-98 24-Jul-

98

12-Sep-

98

1-Nov-98 21-Dec-

98

9-Feb-99 31-Mar-

99

20-May-

99

Date

EvaporativeF

raction(-)

woodland

grassland

Fig. 5. Seasonal progression of evaporative fraction at the grassland site for the period May 1998April 1999 and woodland site for the

period October 1998April 1999.

and estimated E is better than the relationship be-

tween average day and mid. This is because more

weight is given to the midday period in the calculation

of daytime E, when Rn G is large and is morestable.

The daytime Eestimated by mor gave poorer re-

sults than for mid. The RMSE values are 0.37 and

0.29 mm at the woodland and grassland sites, respec-

tively. These values are about two times larger than

those obtained when mid was used. The r2 obtained

are 0.33 and 0.65 for the grassland and woodland sites

respectively. This implies that in remote sensing stud-

ies, data from satellites with afternoon overpass will

give better estimate ofEcompared to those with morn-

ing overpass.

7. Seasonal variations of

The pattern of the seasonal variation of is pre-

sented in Fig. 5. Each of the points represents the

average value of between 8.00 and 17.00 h. The

seasonal variation of is a reflection of the climate

of the area, in particular of rainfall and soil moisture.

Superimposed on this trend are fluctuations offrom

day to day caused by variations in the micrometeoro-

logical conditions elucidated in the previous sections.It can be seen that for the grassland site, drops

quickly from 0.7 at the end of May to 0.3 in approxi-

mately 60 days. The reduction in may be attributed

to the reduction of soil moisture availability in the

root zone due to sharply reduced rainfall rates.


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fluctuates around 0.3 for about 100 days between the

end of July and beginning of November. This is fol-

lowed by a sharp decline in, reaching virtually zero

within 45 days. This indicates that responds to soilmoisture condition when a certain critical level of

moisture and soil water potential is reached and plant

stress is triggered. Periods when is zero imply that

all of the available energy is partitioned into sensible

heat flux. There is an increase of in the month of

January from 0 to 0.4 in response to a rainfall event

(see Fig. 5). However declines to zero in a few

days., finally increases from zero at the end of Jan-

uary to 0.8 by April in the response to the rain period

starting at the end of March. Although no data is

available in the month of February and the beginning

of March, rainfall data was available. During this

period there was only 0.5 mm of rainfall recorded.

It is therefore expected that remains in the range

between 0 and 0.1 between February and March.

For the woodland site, remains fairly constant

at about 0.4 from the end of September for about 80

days. then begins to decline steadily to reach zero

in about 70 days. The declination takes a longer pe-

riod as compared to the grassland site. This, may be

related to the differences at the two sites. These dif-

ferences are caused by differences in rooting depth of

the vegetation at the two sites besides that the forestreceives more rainfall annually. For the grassland site

vegetation, can only get moisture from the top soil

surface and as soon as the soil surface dries vegeta-

tion stress emerges. Furthermore, the grasses at this

site begin to senescence, just before the dry season.

Evaporation from soils is the dominant component of

evaporation at this time. Evaporation therefore stops a

day or two after a rainfall event. The woodland site has

vegetation with deeper roots, which can extract mois-

ture from deeper soil layers. The vegetation continues

to transpire even after the surface soils have dried uptwo months after the last rainfall event.

The value of, finally increases in response to rain-

fall and soil moisture replenishment in early March.

However,increases to a maximum of 0.5 by the end

of April as compared to 0.8 in the grassland site. This

could be ascribed to the lower VPD prevailing in the

woodland site which causes lower degrees of parti-

tioning ofRnGintoL and hence limits evaporation.

The seasonal progression of is gradual at both

sites. The implication of this for the monitoring of

is that it would be sufficient to measure say ev-

ery 510 days to capture the seasonal evolution of.

Interpolation between the measurements can be done

to estimate on days when there are no mea-surements. This means that for remote sensing pro-

grams processing of daily images is not necessary to

estimate the seasonal variations of for large water-

sheds, albeit daily acquisition might be required to se-

lect the qualitative best cloud free image for a given

period.

7.1. Estimation of by standard

meteorological data

Soil moisture dynamics and thus indirectly the rain-fall events, control the long term seasonal variations

of . The seasonal trends of micro-meteorological

variables such as Ta, RH and follow the annual

rainfall regime. These variables obtained from stan-

dard weather stations could be used to estimate the

seasonal variations of , rather than the daily pro-

cessing of satellite images. A regression analysis

between and Ta, RH and was performed on

the basis of 1 and 10-day average values. Multiple

linear regression between and all the three mi-

crometeorological variables was performed as well.

The relationships between and the variables atthe grassland site at the seasonal scale are presented

in Fig. 6, while the coefficient of determination, r2,

of the relationships at the two sites are shown in

Table 5.

The maximum value of coincides with Ta of

25 C (see Fig. 6). The optimum RH for both sites

is 50%. These agree with the optimum meteorolog-

ical condition for evaporation for vegetated surfaces

found by Stewart (1988). RH best explains the av-

Table 5Relationship between evaporative fraction and meteorological vari-

ables at the two sites for the whole study period

Meteorological

variables

1 day average, r2 10 day average, r2

Grassland Woodland Grassland Woodland

0.25 0.23 0.45 0.31

RH 0.62 0.63 0.74 0.83

Ta 0.57 0.49 0.74 0.81

RH Ta 0.64 0.62 0.82 0.83

RH Ta 0.67 0.64 0.87 0.86


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the study period. The daily standard deviation of

varies from as low as 0.01 to as high as 0.16 for in-

dividual days indicating that is not stable under all

vegetation, soil and atmospheric conditions. The re-sults also show that on the daily time scale, the vari-

ations of cannot be explained well by meteoro-

logical variables and cloudiness alone. The variations

could be due to other causes such as the diurnal vari-

ation of surface resistance and energy and moisture

advection. The evaporative fraction is more unstable

during the cloudy and rainy period (AprilJune) as

compared to the other months due to low Rn G,

VPD andTa1Ta2values. The evaporative fraction is

more temporally stable at the woodland site than at the

grassland site.

The data presented showed that there is a strong

relationship between mid and daytime with the

average r2 of the regression lines through the ori-

gin at the two study sites being 0.74 and 0.75. The

changes of over an annual period are gradual.

It can be concluded that for remote sensing pro-

grams, an acquisition of images say every 510

days may be able to capture the seasonal evolu-

tion of for large watersheds. Furthermore the

interpolation of , between remote sensing days,

can be accomplished by routinely collected weather

data.The estimated daytime Efrom mid compare very

well with measured daytime E(RMSE = 0.17 mm,

r2 = 0.88 for the grassland). For the whole study

period the average daily difference between the esti-

matedEand the observedEwas within 10%. The dif-

ferences reduced even further if 10 day and monthly

integrated Evalues are considered. PoorEresults were

obtained from mor (RMSE = 0.37 mm, r2 = 0.33

for the grassland). This indicates that the use of data

from satellites with morning overpasses will give less

accurate daily Evalues in the environmental condi-tions of Kenya. NOAA AVHRR satellite images with

afternoon over pass are preferred although a loss of

spatial scale accuracy should be accepted. The impor-

tant conclusion from this study is that the hypothe-

sis of quasi-constant to estimate seasonal variations

of evaporation is valid for tropical watersheds under

general weather conditions. This provides a basis for

the use of remote sensing methods in applied regional

hydrology in tropical watersheds with data scarcity

problems.

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