05Mobin-ud-Din- estimating...-Physics and Chemistry of the Earth

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ESTIMATING ACTUAL EVAPOTRANSPIRATION THROUGH REMOTE

SENSING TECHNIQUES TO IMPROVE AGRICULTURAL WATER

MANAGEMENT: A CASE STUDY IN THE TRANSBOUNDARY OLIFANTS

CATCHMENT IN THE LIMPOPO BASIN, SOUTH AFRICA

Mobin-ud-Din Ahmad1

, Thulani F. Magagula2

, David Love3,4,

, Victor Kongo5

, Marloes L. Mul6, 7

andJeniffer Kinoti2

1 International Water Management Institute (IWMI), PO Box 2075, Colombo, Sri Lanka2 International Water Management Institute, 41 Creswell St, Weavind Park, 0184, Pretoria, South

Africa3 WaterNet, PO Box MP600, Mount Pleasant, Harare, Zimbabwe

4 ICRISAT Bulawayo, Matopos Research Station, PO Box 776 Bulawayo, Zimbabwe5 School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal,

PB X01, Scottsville, 3209 Pietermaritzburg, South Africa6Department of Civil Engineering, University of Zimbabwe, PO Box MP167, Mount Pleasant,

Harare, Zimbabwe7UNESCO-IHE, Institute for Water Education, PO Box 3015, 2601 DA Delft, the Netherlands

ABSTRACT

This paper describes a case study that uses a remote sensing technique, the Surface Energy

Balance Algorithm for Land (SEBAL) to assess actual evapotranspiration across a range ofland uses in the middle part of the Olifants Basin in South Africa.. SEBAL enables the

estimation of pixel scale ETa using red, near infrared and thermal bands from satellitesensors supported by ground-based measurements of wind speed, humidity, solar radiation

and air temperature.

The Olifants River system, although supplying downstream users in Mpumalanga Province(South Africa) and Chkw District (Mozambique), is over-committed, principally forirrigation, in the upper reaches. Therefore, quantification of evapotranspiration from

irrigated lands is very useful to monitor respect of compliance in water allocations and

sharing of benefits among different users.

A Landsat7 ETM+ image, path 170 row 077, was acquired on 7 January 2002, during therainy season and was used for this analysis. The target area contains diverse land uses,

including rainfed agriculture, irrigated agriculture (centre pivot, sprinkler and drip

irrigation systems), orchards and rangelands. Commercial farming (rainfed and irrigated

agriculture) is one of the main economic activities in the area. SEBAL ETa estimates varyfrom 0 to 10 mm/day over the image. Lowest ETa was observed for barren/fallow fields andhighest for open water bodies. ETa for vegetative areas ranges 3 to 9 mm/day but irrigated

areas, using central pivot, drip and sprinkler systems, appear to evaporate with a higher rate:

6 and 9 mm/day. Penman-Monteith reference crop evapotranspiration ET0 on the same daywas found to be 7 mm/day. This indicates that these irrigated areas have no water stress and

potential yields can be achieved provided there is no nutrient deficiency. The major finding isthat SEBAL results showed that 24% of ETa is from agricultural use, compared to 75% from

non-agricultural land use classes(predominantly forest) and only 1% from water bodies.

Although irrigation accounts for roughly half of diverted streamflow in the basin, itcontributes only about 4% of basin-scale daily ETa at the time of assessment.

Keywords: agricultural water management, evapotranspiration, SEBAL, remote sensing

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INTRODUCTION

Actual Evapotranspiration ETa is one of the most useful indicators to explain whether the

water is used as intended or not. ETa variations, both in space and time, and from different

land use classes (particularly from irrigated lands) are thought to be highly indicative for the

adequacy, reliability and equity in water use; the knowledge of these terms is essential for

judicious water resources management. Unfortunately, ETa estimation under actual field

conditions is still a very challenging task for scientists and water managers. The complexity

associated with the estimation of ET has lead to the development of various methods for

estimating this parameter over time Doorenbos and Pruitt (1977); Allen et al. (1998).

The methods for estimating ET can generally be grouped into 4 categories i.e. the

hydrological methods (water balance), direct measurement (lysimeters), micrometeorological

(energy balance) and empirical or combination methods (Thornthwaite), based on energy

balance or climatic factors Thomthwaite and Mather (1955). Most of these methods can only

provide point estimates ofETwhich are not sufficient for system-level water management.

Distributed physically-based hydrological models can compute ET patterns but require

enormous amounts of field data which are often unavailable in many river basins in the world.

During the last two to three decades, significant progress has been made to estimate actual

evapotranspiration (ETa) using satellite remote sensing Engman and Gurney (1992), Kustas

and Norman (1996), Bastiaanssen etal. (1998, 2002) and Kustas etal. (2003). These methods

provide a powerful means to compute ETa from the scale of an individual pixel right up to an

entire raster image.

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This study demonstrates the application of a remote sensing method, the Surface Energy

Balance Algorithm for Land (SEBAL) Bastiaanssen etal. (1998) & Bastiaanssen (2000) in a

catchment in the middle reach of Olifants basin in South Africa. The Olifants River system,

although supplying downstream users in Mpumalanga Province (South Africa) and Chkw

District (Mozambique), is over-committed in the middle reaches by 94 Mm3/year. The

commitments are mainly for irrigation, which accounts for 86% of the abstracted water

demand in the middle reaches, and 57% of the total water requirements in the South African

part of the basin Basson and Rossouw (2003). Therefore, quantification of evapotranspiration

from irrigated lands is very useful in cross-checking actual water use against water allocation

and in understanding its implications for the specification and management of water rights in

a basin.

MATERIALS AND METHOD

Description of the study area

Location

The Olifants River passes through three provinces of South Africa (Gauteng, Mpumalanga

and Limpopo Province), through the Kruger National Park, into Mozambique, where it joins

the Limpopo. It is a major tributary to the Limpopo River, located in the north east of South

Africa (see figure 1). Its catchment area spans over 54,672Km2. The topography of the basin

varies widely with altitudes ranging between 2,300m at highest point in the upper part of the

catchment and 300m at the Mozambique border.

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Figure 1: Location of the Olifants Basin (Source: DWAF, 2002)

Rainfall and Runoff

On average, the Olifants catchment receives an annual precipitation of 631 mm, which varies

spatially over the basin (see figure2). The mean annual runoff is estimated at 2,040 million m3

and the demand for human purposes is estimated at 965 million m3 including hydropower.

460 million m3 is estimated to be the annual reserve requirement. To honour international

commitments, about 1,137 million m3

annually still flows to Mozambique. This means that of

the annual runoff of 2040 million m3 the basin has to meet its demands from the 903 million

m3 left after meeting international commitments. It is in this light that the National Water

Resources Strategy NWRS (2004) recognizes that judicious assessment of the Reserve

together with careful implementation planning to minimise possible social disruption will be

required. The South African Water Act requires that a portion of the available water resources

be reserved for ecological purposes; this is what is termed the reserve. Estimated future

4

O

li f

a

n

t sR

i ve

r


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demand for human purposes by 2010 is projected at 1,356.5 million m3, without the reserve

and international commitments DWAF (2002). The river has however been known to have

zero flow during short periods as it enters Kruger Park, making it a water scarce catchment.

Figure 2: Spatial variation of Rainfall over the Olifants basin [Source: Schulze, (1997)]

Agriculture

There are about 1.2 million hectares of cultivated land in the Olifants catchment. Three

distinct forms of farming exist in South Africa: commercial irrigated, commercial dryland and

subsistence/semi-commercial farming. About 44% of the cultivated area in the catchment is

used for the growing of maize, which is South Africas staple crop. The area and estimated

production of maize are shown in table 1 below.

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Table 1: Area, production and yields of maize in the Olifants catchment

The total estimated value of production from all crops grown in the catchment based on 2002

market prices prepared by the Department of Agriculture Statistical Department is about R5

billion, van-Heerden and Magagula (2003). It is estimated that about 96% of the total value of

production is from commercial farming, split 59% and 37% between dryland/rain-fed and

irrigated farming respectively van-Heerden and Magagula (2003).

Irrigation water requirement is estimated 557 million m3 according to the National Water

Resources Strategy, 2004. This makes irrigation by far the largest user accounting for about

58% of the total demand for human purposes.

The cropping calendar (figure 5) follows the hydrological year, which begins with summer

rain around October and ends in September the following year. A dry season starts around

March/April.

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Figure 5: Cropping calendar for some of the crops grown in the Olifants basin [Source: van-Heerden and Magagula, (2003)]

Data collection

Satellite imagery

A LANDSAT ETM+ image (Path 170 Row 077), covering nearly the entire middle Olifants,

was acquired on 7th January 2002 and was downloaded from Global Land Cover Facility of

the University of Maryland website (http://glcf.umiacs.umd.edu/data/landsat/).

Weather data

Meteorological data for a representative station was obtained from the Weather Service

Department. Hourly and daily data were used in SEBAL processing. The weather conditions

prevailing on 7th January 2002 are shown in table 2.

7

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct NovAug Dec

MAIZE: 136 Dyas

MAIZE (Late crop): 136 Days

WHEAT: 140 Days

DRY BEANS: 90 Days

CABBAGE: 115 Days

POTATOE: 115 Days

TEMPORAL PASTURE / FULLOW: 150 Days

SWEET POTATOE: 120 Days

CITRUS

GROUNDNUTS: 150 Days

TOMATOES: 139 Days

Hydrologic Year

WET SEASON DRY SEASON
http://glcf.umiacs.umd.edu/africa/index.shtmlhttp://glcf.umiacs.umd.edu/africa/index.shtml


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Table 2: Weather conditions at the time of satellite overpass on the 7 January 2002

Satellite Overpas

Date Jan 7, 2002

Methodology: Surface Energy Balance Algorithm (SEBAL)

SEBAL computes a complete radiation and energy balance along with the resistances for

momentum, heat and water vapour transport for each pixel Bastiaanssen et al. (1998) &

Bastiaanssen (2000). The key input data for SEBAL consists of spectral radiance in the

visible, near-infrared and thermal infrared part of the spectrum. In addition to satellite images,

the SEBAL model requires the following routine weather data parameters (wind speed,

humidity, solar radiation, air temperature).

Evaporation is calculated from the instantaneous evaporative fraction , and the daily

averaged net radiation, Rn24. The evaporative fraction is computed from the instantaneous

surface energy balance at satellite overpass on a pixel-by-pixel basis:

( )HGRE += 0n (1)

Where: Eis the latent heat flux, Rn is the net radiation, G0 is the soil heat flux and His the

sensible heat flux (see Figure 6).

The latent heat flux describes the amount of energy consumed to maintain a certain crop

evaporation rate. The surface albedo, surface temperature and vegetation index are derived

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from satellite measurements, and are used together to solve Rn, G0 and H. The instantaneous

latent heat flux, E, is the calculated residual term of the energy budget, and it is then used

to compute the instantaneous evaporative fraction :

0n GR

E

HE

E

-

=

+

= (2)

The instantaneous evaporative fraction expresses the ratio of the actual to the crop

evaporative demand when the atmospheric moisture conditions are in equilibrium with the

soil moisture conditions. The instantaneous value can be used to calculate the daily value

because evaporative fraction tends to be constant during daytime hours, although the Hand

Efluxes vary considerably. The difference between the instantaneous evaporative fraction

at satellite overpass and the evaporative fraction derived from the 24-hour integrated energy

balance is marginal and may be neglected Brutsaert and Sugita (1992), Crago (1996), Farah

(2001 and 2004)). For time scales of 1 day or longer, G0 can be ignored and net available

energy (Rn - G0) reduces to net radiation (Rn). At daily timescales, ET24 (mm/day) can be

computed as:

n24

w

24RET

31086400

=

(3)

Where: Rn24 (W/m2) is the 24-h averaged net radiation, (J/kg) is the latent heat of

vaporization, and w (kg/m3) is the density of water.

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Figure 6: Various components of Energy Balance & main equations to compute latent heatflux

RESULTS AND DISCUSSION

Actual evapotranspiration (ETa) in mm/day for the 7January 2002 was computed by solving

the surface energy balance using equation 1, 2 and 3. The spatial variation ofETa is shown in

figure 7. It ranges from 0 mm/day for bare soil and fallow land to 8 mm/day or more for water

bodies. Non-agricultural land classes, particularly forest and woodlands (including degraded

forest and woodlands) make up about 56% of the study area and have average ETa values of

4.27 mm/day and 2.74 mm/day respectively and an average of 3.51 mm/day for the entire

land cover class.

10

Rn

Rn

Rn

G0

GG0

H

H

H

LE

EE

E

EHGR ++=0n

( )0n

-GRE =

HE

E

GR

E

+==

0n-

Rn

Rn

Rn

G0

GG0

H

H

H

LE

EE

E

Rn

Rn

Rn

G0

GG0

H

H

H

LE

EE

E

EHGR ++=0n

( )0n

-GRE =

HE

E

GR

E

+==

0n-


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Figure 7: Actual Evapotranspiration (ETa) estimates using SEBAL for Landsat7 ETM+

imagery for part of the middle Olifants water management area. 7 January 2002.

Actual Evapotranspiration and Land Cover

Statistics have been extracted from the ETa map using an overlay of land cover/use map

Thomson (1999) and are shown in table 3, as mean ETa for each land cover/use. Water bodies

have an average ETa of 7.92 mm/day, inclusive of large and small water bodies that can

consist of multiple mixed pixels falling both on land and inside water bodies as well as

averaging differences in the water surface temperature due to turbidity.

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Table 3: Mean ETa of different land cover types and the percentage ETa from each land covertype.

Land Cover

Cultivated Comme

Forest and woodlands account for about 58% of the ETa in this particular day. About 24% is

beneficial/agricultural field ETa, but inferences based on these statistics are not accurate

unless the contributions of each land use classes to livelihoods and productive use such as

livestock feeding are known. January is usually a wet month and it is a month of lots of

activity across all farming types as farmers are planting or have planted summer crops. It also

means that forest ETa will be higher than at other times of the year, due to minimum water

stress. It is observed in the ETa map that a greater part of the commercial temporal dryland

farming area has low ETa, with values of 2mm/day or less. This could be an indication that

most of the land has just been prepared. Under dryland or rainfed conditions, planting

depends on rainfall events that provide sufficient moisture for land preparation and planting.

It is for that reason that most of the cultivated land would still be fallow, or just been prepared

hence the close to zero values ofETa at this time of the year (January).

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We now focus on the quaternary catchment B31J, shown in figure 8, where four main types

characterize land cover/use: natural vegetation (forest and woodlands), cultivated land, water

bodies and a bit of built up area. Water bodies had the highest ETa (see figure 9). Forest and

woodland, which dominate the upper part of the catchment have higher average ETa than

commercial dryland cropping. The upper part of B31J is an endoreic area Van Vuuren et al

(2003), described as the portion of a hydrological catchment that does not contribute towards

local river flow nor to river flow in downstream catchments. In such catchments, the water

generally drains to pans where much of the water is lost through evaporation. In other places,

concentrated surface run-off recharges groundwater. The WARMS database of registered

water users reveal that about 96% of irrigators in quaternary catchment B31J use boreholes

for irrigation and centre pivot is the predominant irrigation system.

Figure 8: Actual Evapotranspiration ETa, a focus on the quaternary catchment B31J.

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Max of ETa_MEANFigure 9: Max average ETa for the different land cover types in the quaternary catchmentB31J in the middle Olifants.The interpretation of ETa values depends on the knowledge of actual vegetation cover if

accurate determinations of water use by vegetation are to be made. The wide spread use of

centre pivot was observed in a field trip to the middle reaches of the Olifants, evident in the

image as circular areas with high ETa (see figure 8).

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CONCLUSION

The paper demonstrates one the first applications of the remote sensing method, SEBAL, to

determine spatial variation of actual evapotranspiration for the Olifants river basin in South

Africa. Data for SEBAL processing can be sourced from Landsat, NOAA AVHRR, MODIS

and ASTER at different scales but requires routine meteorological measurement of air

temperature, humidity, wind speed and sunshine duration.

In this study, 30 meter spatial resolution, Landsat7 ETM+ image of Jan. 7, 2002 was used to

delineate the spatial variation in ETa. The snapshot computed in this study demonstrates that

water bodies have highest ETa, forest and woodlands transpire at a higher rate than cultivated

land on Jan. 7, 2002. Volumetrically, forest and woodlands account for about 58% of the ETa

in this particular day, the highest of all land cover types. Agricultural field ETa is only 24% of

the overall ETa from the investigated area. However, in addition to ETa,, knowledge of the

contribution of the each land use to livelihoods and productive use is essential 1) to compute

beneficial vs. non-beneficial uses of water and 2) to devise strategies to improve water

management/productivity. We can see that, although irrigation requires over 50% of the

diverted streamflow and groundwater in the basin, it accounts for a much more modest

portion of basin evapotranspiration. In this study, we do not know the beneficial values of

forest in terms of timber produced and in terms of hydrological services in maintaining base-

flows and catchment yield. Therefore, it is not possible to make further comparisons, nor

assess the water productivity. Clearly, a snapshot indicates an overall annual trend in spatial

ETa in the basin, due to the relative magnitudes of the areas of each type of land use.

However, some form of seasonal and annual integration is also desirable to account for,

among other things, reduced forest ETa in the dry season and conversely relative increase in

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irrigated ETa. Temporal integration is currently only feasible using MODIS or AVHRR data

at 1km2 resolution, which then loses the ability to define ETa precisely by land use class.

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ACKNOWLEDGEMENTS

This paper contains research results from a workshop funded by the International Water

Management Institute (IWMI). The cooperation of the Departments of Water Affairs and

Forestry and Weather Service has been essential and is gratefully acknowledged. Authors are

also thankful to Dominique Rollin (IWMI-South Africa office), Steve Twomlow (Global

Theme Leader-Agro-Ecosystems Development at ICRISAT) and Hugh Turral (Theme Leader

Basin Water Management at IWMI) for useful discussions.

The opinions and results presented in this paper are those of the authors and do not

necessarily represent the donors or participating institutions

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