a simple surface energy balance to derive evapotranspiration from remote sensing and conventional...

Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 31, No. 2, 2003

A Simple Surface Energy Balance to Derive Evapotranspiration from Remote Sensing and

Conventional Meteorological Observations

C.V. SRINIVAS t, K.P.R. VITTAL MURTY z and Y.V.N. KRISHNA MURTY 3 ~Atmospheric Studies Section, Safety Physics Division,

Indira Gandhi Centre for Atomic Research, Kalpakkam - 603102, India 2Department of Meteorology and Oceanography, Andhra University, Visakhapatnam - 530 003, India

3Regional Remote Sensing Service Centre (RRSSC), Deparment of Space, Nagpur - 440 010, India

ABSTRACT

Monthly mean measurements of surface temperature, albedo and normalized difference vegetation index (NDVI) of NOAA AVHRR are processed for Maharashtra. These data are used in combination with monthly average surface meteorological observations in a surface energy balance model to estimate monthly mean actual evapotranspiration (AET) from different climatic zones of Maharasthra,. India. AET is estimated between April and December months for two contrasting monsoons in 1992 and 1995. Estimates reasonably agree with pan evaporation data during growing season and with AET estimated from water balance procedure. AET is low in semi-arid dry land areas of central Maharashtra and significantly high in the humid-perhumid western ghat region and subhumid eastern Maharashtra region. The modeled evapotranspiration show the influence of seasonal vegetation in different climatic zones from the region. The method can be used to obtain large-scale evapotranspiration with minimum ground observations.

Introduction

Evapotranspiration (ET) is one of the key variables that regulate energy and mass exchange in the Land-Biosphere-Atmosphere continuum. Local rate of evapotranspiration (ET) is a

Recd. 11 Aug., 2002; in final form Feb., 2003

complex phenomenon and less understood aspect of hydrological cycle at large scale (Rasool and Bolle, 1984; Avissar and Pielke, 1989; R a m a k r i s h n a and R u n n i n g , 1989) . Evapotranspiration fluxes are important for the determination of water use by plants at local level as well as in the large-scale circulation of the planetary atmosphere (Jarvis and Mc

130 C.V. Srinivas et al.

Naughton, 1986). Land surface is covered significantly with regions characterized by partial or sparse plant canopy (Dickinson, 1983). A seasonal character of such a phenomenon occurs in all agricultural areas and they occur naturally throughout the year in arid and semi- arid regions of the globe (Wilson and Handerson-Sellers, 1985). The relative contributions to the total ET from the soil and plant components can vary throughout a season. Tropical forests consume considerable amount of net radiation for evapotranspiration and reliable estimates of ET are important for assessment of hydrological cycle, for studying global climatic change and for effective management of water resources and productivity of a region.

Local estimates of potential ET (PET) are obtained through empirical methods with the available meteorological data using physical principles (Penman, 1948). However, ET is a complex biophysical process and the type and structure of vegetation can limit the rate of evaporation. Actual ET (AET) is related to PET through soil moisture which in turn by precipitation, soil properties and vegetation conditions. Physiographic, micrometeorological and biophysical conditions govern the AET within a given ecosystem. Estimation of land surface fluxes, particularly evaporation rates becomes complex with decreasing scale as the surface features and processes tend to become more complex. Surface energy balance (SEB) models contain more variables than are easily specified or observed (Tarpley, 1994). The insitu measurements over homogeneous areas are not applicable to large areas having diverse climatic and land cover scenario. In this respect, the remote sensing based spectral data coupled energy balance models provide a viable means for mapping of spatial distribution and quantitative assessment of ET from local to regional scales.

Evaluation of regional scale ET using Remote sensing data gained importance as the observations provide synoptic coverage and are

multi-temporal in nature (Hall e t al. , 1991). Observations collected in visible, near infrared and thermal infrared wave bands provide essential data on vegetation cover, albedo, land surface temperature which are required for energy balance modeling and evapotranspiration estimation (Kant and Badarinath, 1998). The combination of surface meteorological data and remotely sensed observations enables to the direct evaluation of net radiation, sensible heat flux, latent heat flux and soil/ground heat flux.

Here, a simple surface energy balance model (Tarpley, 1994) is used with satellite and conventional meteorological data to calculate monthly average evapotranspiration from different climatic zones of Maharashtra. The model analysis is done for two contrasting monsoon years 1992 and 1995. The model is modified and includes the remotely sensed data on land surface temperature, albedo in addition to the vegetation index and roughness parameter. The method has the advantage that no ancillary soil moisture model is required and calculates ET directly. The estimated ET is compared with available ground based observations and those obtained by conventional empirical methods.

Surface Energy Balance Model

The evapotranspiration process is governed by energy exchange at the vegetation surface and is limited by the amount of surface energy available. At high resolution, variability in soils, vegetation, terrain and local meteorology have significant effects. It is intended to use satellite observations at a spatial resolution of 1.1 km and monthly time scale to evaluate the surface energy fluxes. At these spatial and time scales the variability of surface features and day to day weather will average out considerably and a simple model may be adequate. The conservation of energy across the land-atmosphere interface is expressed as

R n + H + L E + G = 0 (1)

where R~ is the Net Radiation at the surface, H is

A Simple Surface Energy Balance to Derive... 13 !

the Sensible Heat Flux, LE is the Latent Heat Flux and G is the Soil Heat flux. The fluxes are considered to be positive when energy is transferred toward the surface and negative in case of transfer away from the surface. The net radiation term in equation (1) is given by

Rn = Qs(1-or + ~ a O" T~ 4 - ~ O T s f c 4 (2)

where Qs is insolation at the surface, o, is the surface albedo, a is the Stefan-Boltzman constant, ~ a i s the atmospheric emissivity, ~ is the surface emissivity, T, is the air temperature and Tsf r is the surface temperature. Here Qs is given by

Qs = [as+bs (n/N)] Qa (3)

where n is actual duration of sunshine [hours], N is maximum possible duration of sunshine [hours], n/N is relative sunshine duration and Qa is extra-terrestrial radiation, as is regression constant expressing the fraction of extraterrestrial radiation reaching the earth on overcast days (n=0), (as + bs) is fraction of extraterrestrial radiation reaching the earth on clear days (n=N). Depending on atmospheric conditions (humidity, dust) and solar radiation (latitude and month), the Angstrom values as and bs will vary. For Indian conditions based on regression analysis (IMD, 1982) the values for as = 0.32 and bs = 0.43 are used. The extraterrestrial radiation, Qa, for each day of the year and for different latitudes can be estimated from the solar constant, the solar declination and the time of the year by

Qa = [24 x 60/r] Qo dr [cos sin(qS) sin(/i) + cos(~b)

cos(6) sin (co s)] (4)

where Qo is solar constant (=1367 Wm2), dr is inverse relative Earth-Sun distance, co s is sunset hour angle, j latitude in radians, d is solar declination in radians. The inverse relative Earth- Sun distance dr, and the solar declination d vary with the number of the day 'J' of the year as given by

dr = 1 + 0.033 x cos [(2r/365) J = 0.409 x sin [ (2r/365) J-1.39]

An empirical equation (Idso, 1981) is used to estimate atmospheric emissivity,

ea = 0.70+5.95 • 10 -5 ea exp(1500/T,) (5)

where ea is vapour pressure in millibars. The sensible heat flux is derived from the aerodynamic theory of turbulent transfer. In finite difference form the flux equation for neutral stability is

H = -Cp K (Ts f r - I ' z - T,) (6)

where, Cp is specific heat of air at constant pressure and F is Dry-adiabatic lapse rate, Tsfc is the surface temperature and T, is the air temperature. The turbulent transfer coefficient K is given by

K = [p k 2 Ul/[ln (Z/Co)] z (7)

where k is yon Karman's constant = 0.4, r is air density, U is mean wind speed, Z is elevation above the surface at which U is measured, s is Stefan-Boltzmann constant -- 4.903 x 1 0 - 9

[MJK "4 m "2 dayt]. The roughness length (Zo) is spatially and seasonally variable. Zo is scaled by monthly average NDVI (Tarpley, 1994).

Z o = Zm[| +(NDVI-Vmm)/(Vm~x-Vmm)] (8)

where Zm, Vn~x and Vmi~ are the minimum roughness, the maximum monthly NDVI and minimum monthly NDVI respectively. The scaling assumes that normally the vegetation growth changes with season and vegetation height is proportional to NDVI, therefore Zo also varies proportionally between minimum and maximum according to the value of NDVI. A minimum roughness is used to tune the model according to the major land cover. The soil heat flux is given by

G =-(Ks/d) x (Tsfc-T~) (9)

where Ks is soil thermal conductivity which varies with the type of the soil (sandy, loamy, clayey, peat). The macroscopic thermal conductivity of soil depends upon its mineral composition and organic matter content, as well as on the volume fractions of water and air. An average value of Ks --- 0.3 (W m "t K) (van Wijk


and de Vries, 1963) is assumed for the soils of the study area. d is the distance to the bottom of the first soil layer (= 5 cm), TI is the temperature at depth (d) and Tsfc is the land surface temperature. The model is a fairly simple treatment of the SEB. The wind speed is specified at 10 m and temperatures at 2 m above the surface. The wind speed is defined in the turbulent transfer theory to be the mean value without turbulent ~'luctuations.

Study Area

The study area, Maharashtra lies between 15 ~ 44'and 21 ~ 40' N latitudes and 73 ~ 15' and

80 ~ 3Y E longitudes and forms a major part of peninsular India with an area of about 3,04,391 square kilometers (Figure 1). The study area has four climatic zones i.e., Sub-humid to Semi-arid in the Vidarbha region of Eastern Maharashtra (Region R1), Semi-arid in the Central Maharashtra (Region R2) and Perhumid along the Konkan coast plains, Humid to Perhumid transition zone along the Western Ghats (Region R3) (IMD, 1986). There is a strong east-west gradient of rainfall across Maharashtra. The rainy season is confined to southwest monsoon, 80 percent of the rainfall is received during June to September (Raman, 1974). The rainfall ranges from 450 mm in rain shadow areas to 2500 mm

c~

N a l ~ u r

1500

Parbhani

500

Latu r

, . . /

I"1

District boundary Annual rainfall (ram) Sampling sites

tO

Fig. 1. Location map of Maharashtra with Isohytes and sampling sites

A Simple Surface Energy Balance to Derive... 133

in Western Ghats. The rainfall pattem gradually increases from central towards eastern parts of the state ranging from 700 to 1500 mm. Studies of ET in this state gain significance in view of wide variation in physiography, climate, soils and land cover conditions.

Data and Methodology

Climatological analysis requires sufficient data to make averages of the observed quantities. In the present study AVHRR global land data set of 1.1 km resolution is used (USGS, 1993). The data consists of 10 day arrays of mapped AVHRR data that is originally preprocessed for cloud screening by maximum vegetation index and atmospheric corrections. Monthly averages were calculated from the above data sets for perceivable changes in biophysical quantifies. The data set consists of channels 1 and 2 in the visible and near infra-red converted to reflectance values and channels 4 and 5 in thermal infrared converted to brightness temperatures following inverse Plank's relation.

Using the visible and near-infrared bands a surface vegetation index (NDVI) was derived as the normalized difference of the two reflectances R1 and R2 in bands 1 (0.58-0.68 pm) and 2 (0.725-1.10~tm).

NDVI = (R2-R1)/(R2+R1) (10) t

Surface albedo was obtained from the spectral reflectance in visible (A1) and near- infrared bands (A2) according to Saunders (1990),

A = 0.5(AI+ A2) (1 I)

Using the thermal infrared data mean monthly surface temperature was derived by a split window algorithm (Becker and Li, 1990) which accounts the surface emissivity variation (d r from NDVI (Van de Griend and Owe, 1993)

TS = T4 +tSw + ~E (12)

r -- 1.009 + 0.047 ln(NDVI) (13)

where 6w and ~ are water vapour and emissivity correction respectively, calculated as suggested by Gutman (1994)

/~w = 2.63 (T4-Ts) + 1.274 (14)

6~ = [0.078(T4 + %) + 1.69(T4-Ts)] (1-~)/~ (15)

Here T4 and Ts are the brightness temperatures in the thermal infra-red channels 4 (10.3-11.3 ~tm) and 5 (11.5-12.5 lam). The weekly meteorological data for maximum, minimum temperatures, wind speed, sunshine hours, soil temperature at 5 cm depth of 32 locations spread in the 4 meteorological sub- divisions of Maharashtra were averaged at monthly intervals and spatially interpolated for integrating with satellite data. The AVHRR data were co-registered from topographical sheets at 1:1 million scale using Lambert's Conformal Conic (LCC) earth projection.

Results and discussion

The surface temperatures for 10 sites ranging from the Perhumid to dry semi-arid in Maharashtra are shown in Fig. 2a. The surface temperatures show a distinctive seasonal variation that was similar at all sites with high temperatures during the summer months and less during the winter months. A regular variation from west to east in the surface temperature was observed according to the aridity of the site. The semi-arid locations have higher temperatures than the sub-humid and humid/perhumid locations. Fig. 2c shows the monthly mean NDVI for the same locations in Maharashtra. The NDVI was larger in the west Coast plains and in the eastern region where the rainfall is higher, it decreased toward the central semi-arid region. The NDVI reached a maximum in September-October and a secondary maximum in December at all locations, probably due to the growth of summer and winter crops. The albedo was high in the pre-monsoon season (April- June), decreased to low values in the rainy season (July to October) and increased again in November, the higher albedo coincided with the


Fig. 2a. Seasonal variation of surface temperture in Maharashtra

Fig. 2b. Seasonal variation of Albedo in Maharashtra


harvesting time of summer and winter crops (Fig. 2b), Albedo was higher in the semi-arid central Maharashtra having 10w rainfall and decreased toward the humid west coast plains and the sub- humid eastern region. A steady progression in the quantities was noticed from the central region to the eastern and western regions, the surface temperature and albedo decreased and the NDVI increased.

The evapotranspiration varied significantly

in different climatic zones of Maharashtra (Fig. 3). The mean evapotranspiration was low in the central semi-arid dry land zone compared to the eastern sub-humid and western humid zones in both 1992 and 1995. The AET was higher in the humid western ghats and Konkan coast plains than the sub-humid eastern Maharasthra in 1995 where it was vice versa in 1992 (Fig. 4). The estimated AET was lower in 1992 than in 1995 (Table 1) which may be due to the low annual rainfall in 1992. AET followed the NDVI pattern

Table 1. Mean monthly evapotranspiration (SEB) in different climatic zones in Maharashtra in 1992 and 1995.

Evapotranspiration (mm/day) Region

Year Apr May Jun Jul Aug Sep Oct Nov Dec

R1 1992 5.27 4.17 5.89 9.35 7.21 5.02 6.06 3.75 2.84

R2 1992 3.80 3.90 4.89 7.56 5.89 4.89 4.38 5.01 2.86

R3 1992 3.86 4.88 6.41 11.13 8.41 7.19 6.79 6.24 3.64

RI 1995 4.62 3,89 5.92 9.07 12.25 7.05 6.84 3.04 2.98

R2 1995 3.59 4.02 4.95 5.77 5.89 6.63 3.72 2.90 2.86

R3 1995 4.67 6.18 6.75 8.21 9.26 8.25 4.32 3.05 2.82

Fig. 2e. Seasonal variation of NDVI in Maharashtra


r O~ O~

t'~ o

o ~o

._=

0 E

aa ga O0

0

0

0J "d 2

gO r4

r~


1 2 -

~ 10 "

E - c 8

�9 ~. 6 t~

, , > ,2 -

1 9 9 2 _ _ ~ , . - . r ~ - . R1 Eas tem Maharashtra / --,,,. ---o--- R2 Central Maharashtra

~./ . /~""- . . .~zL-~ _ A _ R3 Westem ghats and Konkan

//,,. o . . . . " n.. ~ / ~ ; ~

. . " "0 . . . . " '- . . , . , O , , ' - ~

' I ' I ' I ' I ' I ' I ' I ' I ' I ' I

Apr May Jun Jul Aug Sep Oct Nov Dec

~12 CO

E - ,- 8 ._o

O.

~ 4

~E 2 "

1 9 9 5 / \

/ \

o . . . . . o . . . . . , ,

I ' I ' I ' I ' I ' I ' I ' I ' I

Apr May Jun Jul Aug Sep Oct Nov Dec ' I

Fig. 4. Actual evapotranspiration in different climatic zones in Maharashtra during 1992 and 1995

in different climatic zones of Maharashtra.

Comparison of SEB and conventional methods

Instrumental measurements of evapotranspiration are scarce and their coverage is limited over a large heterogeneous area of Maharashtra. So, AET deduced from SEB was compared with the mean monthly pan evaporation and with AET

obtained from water balance procedure. Pan evaporation accounts only evaporation component of potential evapotranspiration when raised to pan coefficient and represents the net environmental demand. Pan evaporation was higher than AET during summer season (April to June) (Table 2, Fig. 5). The transpiration component from vegetation as well as the evaporation from open or barn lands in the non- growing season is expected to be very low due to


sparse vegetation and poor soil moisture. During the m o n s o o n s e a s o n the a c t u a l evapotranspiration and pan evaporation were in close agreement. The satellite ET has a correlation of 0.68 with the pan evaporation. Obviously, the environmental demand is less in the humid phase as against the moist, moderately dry and completely dry periods. Peak growth of vege ta t ion gene ra l ly occurs dur ing September/October months in the tropical monsoon climatic conditions of India, as is also evident from the NDVI profile (Fig. 2b).

The pan evaporation was less in R3 and R1 regions having more area under forest vegetation (Challa et al., 1995) as compared to R2 region with more area under dry lands and cultivated lands. The estimated actual evapotranspiration in R3 and R1 regions was higher than in R2 region, the R3, R1 regions receive high annual rainfall. The transpiration component of forest vegetation contributes to relatively high evapotranpiration in R3 and R1 regions signifying the biospheric control on evapotranspiration.

The montly average ET was alternatively estimated from conventional meteorological and soil data by applying water balance procedure (Thomthwaite and Mather, 1955). The soil water holding capacity of different locations was obtained from analysed soil information (Challa e t a l . , 1995). M o n t h l y p o t e n t i a l evapotranspiration was estimated from montly

mean air temperature, surface wind speed, relative humidity and net radiation by Penman (1948) equation. This approach assumes that ET proceeds at the potential rate when the soil moisture is at the water holding capacity. Actual evapotranspiration (AET) is arrived as tSS+P when ~5S is positive and as PET when bS is negative, where ~5S is the monthly change in soil moisture and P is the rainfall. The monthly mean ET for each month between April and December by conventional method is shown with the satellite estimates of ET in Table 3. A correlation of 0.64 was found between the two estimates with a standard error of 1.34, mean percentage error of 35 and RMS error of 2.104. The AET by SEB is more compared to the one obtained from water balance procedure. At all locations the satellite ET peaks higher in the summer than the conventional ET but this effect decreases from humid/sub-humid stations to semi-arid stations. The conventional method gives the point estimates whereas the satellite based SEB method gives the aerial estimates of ET, thereby giving some disagreement. The deviation is also partly due to cloud contamination of satellite data, which appeared while averaging the data from 10-day time composites. Cloud screening by maximum vegetation index over calendar periods of 10 days could not eliminate cloud bands of cyclones in some data in the summer months from the large area of Maharashtra, which resulted in the imprecise calculation of ET in certain months. These are a few limitations in

Table 2. Mean monthly pan evaporation in different climatic zones in Maharashtra in 1992 and 1995

Region

RI

R2

R3

RI

R2

R3

Pan evaporation (mm/day)

Year Apr May Jun Jul Aug Sep Oct Nov Dec

1992 12 .73 13 .88 14.13 8.53 4.32 5.02 5.86 5.19 4.06

1992 10 .98 12 .50 12.06 7.83 4.74 4.40 5.42 5.26 4.34

1992 10 .08 10.48 8.83 5.03 2.66 3.00 4.24 4.60 3.97

1995 8.42 10.44 13.48 5.68 4.19 4.89 4.78 5.26 4.03

1995 10 .20 11 .22 12.10 7.40 5.08 5.29 4.71 5.03 4.52

1995 8.04 8.37 8.89 4.10 2.95 4.10 4.23 4.54 4.03


Table 3. Evapotranspiration (in mm/day) from the conventional (Waterbalance) and from the SEB model in 1992

Month Nagpur

Conv SEB

Apr 1.8 5.00

May 2.9 4.05

Jun 1.5 5.83

Jul 5.3 9.60

Aug 6.2 8.12

Sep 3.3 5.10

Oct 3.4 4.91

Nov 2.4 3.33

Dec 1.1 3.58

Chandrapur

Conv SEB

2.69 4.22

3.63 4.08

4.38 6.36

6.6 9.73

6.7 7.35

5.8 6.10

5.9 6.40

5.04 6.90

1.1 2.34

Jalgaon

Conv SEB

0.33 3.19

1.20 3.20

1.78 4.35

6.90 5.33

3.60 4.90

3.70 4.70

4.71 4.50

3.73 2.77

1.92 2.74

Pune

Cony SEB

1.03 3.59

2.14 4.64

2.49 5.15

4.74 7.16

4.19 5.90

3.10 5.40

5.87 4.23

4.73 7.20

1.52 3.25

Sholapur

Conv SEB

1.80 3.89

1.13 4.04

2.64 3.87

3.52 7.49

5.30 5.40

6.26 3.90

6.41 4,47

5.73 5.80

1.50 2.74

the method. The accuracy in the method can be improved by following more effective cloud screening techniques.

Conclusion

Actual evapotranspiration is a crucial component in hydrological cycle and depends on the functional processes of biosphere. Operational estimates of evapotranspiration require information on vegetation, surface temperature, albedo and surface roughness in addition to soil types and micrometeorological parameters. This study shows that with routinely available satellite data and conventional meteorological averages, monthly ET can be estimated using a simple surface energy balance model. On the larger scale of 1-kin, spatial averaging of the meteorological and surface variables makes a simple model adequate to describe the processes. The method was chosen to be as simple as possible while still retaining the important physics. The estimates have

accounted for the variations of ET in different climatic zones/ecosystems of the study area. The accuracy in the estimation can be improved further by either using cloud free satellite data or following precise cloud screening techniques. The required meteorological data -temperature, specific humidy and windspeed have been available from operational weather forecast models which calculate these quantities at lowest pressure level above the surface and can be extrapolated down to the reference level at which energy flux is calculated. The method can be applied to prepare evapotranspiration maps for large areas from archived databases such as NOAA GVI with minimum surface observations.

Acknowledgements

The authors are thankful to the India Meteorological Department for providing the meteorological data used in the study. Thanks are also due to the US Geological Survey for facilitating NOAA AVHRR data for use in environmental studies.


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