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European Journal of Agronomy 13 (2000) 125 – 153 www.elsevier.com/locate/eja

Review

Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review G. Rana a,*, N. Katerji b a

Istituto Sperimentale Agronomico, 6ia C. Ulpiani, 5, 70125 Bari, Italy b INRA-Unite´ de Bioclimatologie, 78850 Thi6er6al-Grignon, France

Received 19 February 1999; received in revised form 13 August 1999; accepted 13 May 2000

Abstract The Mediterranean regions are submitted to a large variety of climates. In general, the environments are arid and semi-arid with summers characterised by high temperatures and small precipitation. Due to the scarcity of water resources, the correct evaluation of water losses by the crops as evapotranspiration (ET) is very important in these regions. In this paper, we initially present the most known ET measurement methods classified according to the used approach: hydrological, micrometeorological and plant physiological. In the following, we describe the methods to estimate ET, distinguishing the methods based on analytical approaches from the methods based on empirical approaches. Ten methods are reviewed: soil water balance, weighing lysimeter, energy balance/Bowen ratio, aerodynamic method, eddy covariance, sap flow method, chambers system, Penman – Monteith model, crop coefficient approach and soil water balance modelling approach. In the presentation of each method, we have recalled the basic principles, underlined the time and space scale of its application and analysed its accuracy and suitability for use in arid and semi-arid environments. A specific section is dedicated to advection. Finally, the specific problems of each method for correct use in the Mediterranean region are underlined. In conclusion, we focus attention on the most interesting new guidelines for research on the measurement and estimation of actual crop evapotranspiration. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Actual evapotranspiration; Direct ET measurement methods; Indirect ET measurement methods; ET modelling; Mediterranean climate

1. Introduction The Mediterranean climate is characterised by a hot and dry season in summer and a mild tem* Corresponding author. Tel.: + 39-080-5475026; fax: +39080-5475023. E-mail addresses: [email protected] (G. Rana), [email protected] (N. Katerji).

perature associated to annual rainfall in winter. Despite the apparent uniformity of the Mediterranean climate, a more detailed analysis shows great differences. The dry season duration (Fig. 1) clearly illustrates that, while the South is characterised by a long dry season, averaging \ 7 months without any precipitation, in the Northern part, the dry season is relatively limited and

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G. Rana, N. Katerji / Europ. J. Agronomy 13 (2000) 125–153

Fig. 1. Duration of the dry season in the Mediterranean basin (Hamdy and Lacirignola, 1999).

does not exceed 2– 3 months. In addition, the rainfall and temperature diagrams (Fig. 2) show great differences between the North (autumn rainfall) and the South (winter rainfall) of the basin. During summer, the simultaneous occurrences of high temperatures and small precipitation causes

high evapotranspiration (Hamdy and Lacirignola, 1999). For these reasons, 75% of the available water in the Mediterranean area is used for agricultural purposes. Furthermore, the Mediterranean region is characterised by:

Fig. 2. Annual pattern of air temperature and precipitation in several localities of the Mediterranean basin (Hamdy and Lacirignola, 1999).

G. Rana, N. Katerji / Europ. J. Agronomy 13 (2000) 125–153 Table 1 Estimation of the percentage of irrigated soils affected by salinity in several Mediterranean countries (Hamdy et al., 1995) Country

Irrigated surface (%)

Algeria Cyprus Egypt Spain Israel Greece Jordan Morocco Portugal Syria

10–15 25 30–40 10–15 13 7 16 10–15 10–15 30–35

Water scarcity in the South: The water availability in the Southern and Eastern countries of the Mediterranean basin is below the first needs (1000 m3 per capita per year) and prospects for the future indicate more severe difficulties (Hamdy and Lacirignola, 1999). The need to increase the irrigated surfaces because of the population increasing: The Mediterranean population increased by 67% between 1950 and 1985. Soil pollution: Nowadays, the problem of salinity affects 7– 40% of the irrigated surface of several Mediterranean countries (Table 1). Therefore, accurate determination of irrigation water supply is necessary for sustainable development and environmentally sound water management. This goal is far from achievement, in fact the irrigation efficiency is now :45% of the water supply and one half of this inefficiency could be attributed to bad estimates of crops water requirements (FAO, 1994). About 99% of water used in agriculture is lost by crops as evapotranspiration (ET) — it is defined as the water loss by a vegetative unsaturated surface, under vapour form, by evaporation from soil and transpiration from plants. Depending on the soil water conditions, a more or less important biological control is exerted on the water lost. In addition to this a resistance is opposed by the canopy structure to the water vapour transfer toward the atmosphere (Perrier, 1984; Lhomme, 1997).

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Moreover, semi-arid and, mostly, arid climates have a great impact on crop growth in conditioning yield and product quality. Under these weather conditions, rainfed crops and agricultural fields with limited water resources are often submitted to water stress. Thus, it becomes fundamental to know the exact losses of water by evapotranspiration (i.e. actual evapotranspiration) and the crop water status and its influence on production. Actual crop ET can be measured (directly or indirectly) or estimated. For example, for research purposes in plant eco-physiology, ET must be precisely measured, while for farm irrigation management it can be estimated. The lower the degree of accuracy in the estimation, the greater will be the water waste by incorrect management of irrigation. In this paper, we review the most important methods of measuring and estimating actual ET, at plot scale (e.g. at farm level), giving, for each method, the problems, limits and advantages for their use in a field under the Mediterranean climate. This summary cannot be, of course, an exhaustive and complete review of all the existing ET methods; in fact, we will focus attention on the most known and diffuse methods in the international research community, paying particular attention to those used in agricultural research. The scaling up of ET from plot scale to regional scale requires specific methods and techniques and, therefore, will not be treated in this paper.

2. Measurement and estimation There is a great variety of methods for measuring ET; some methods are more suitable than others for accuracy or cost or are particularly suitable for given space and time scales. For several applications, ET needs to be predicted, so it must be estimated by model. It is convenient to discuss the methods of determining ET in considering separately the measurement and modelling aspects.

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In general, a ‘measurement’ of a physical parameter is a quantification of an attribute of the material under investigation, directed to the answering of a specific question in an experiment (Kempthorne and Allmaras, 1986). The quantification implies a sequence of operations or steps that yields the resultant measurement. Conventionally, if the value of the parameter is quantified by the use of an instrument, it is ‘directly’ measured and when it is found by means of a relationship among parameters, it is ‘indirectly’ measured (Sette, 1977). The methods of measuring ET should be divided into different categories, since they have been developed to fulfil very different objectives. One set of methods are primarily intended to quantify the evaporation over a long period, from weeks to months and growth season. Another set of methods has been developed to understand the process governing the transfer of energy and matter between the surface and atmosphere. The last set of methods is used to study the water relations of individual plants or part of plants. Therefore, following Rose and Sharma (1984), it is convenient, when discussing the ET measurement, to place the variety of methods in groups, where the main approach or method depends on concepts from hydrology, micrometeorology and plant physiology, as follow: ET measurement Hydrological approaches (1) Soil water balance (2) Weighing lysimeters Micrometeorological approaches (3) Energy balance and Bowen ratio (4) Aerodynamic method (5) Eddy covariance Plant physiology approaches (6) Sap flow method (7) Chambers system A physical parameter can be considered as ‘estimable’ if it is expressed by a model. The objective of ET modelling can vary from the provision of a management tool for irrigation design, to the provision of a framework for either detailed understanding of a system or to interpret experimental results. To meet these requirements, it is more convenient to use methods with a sound physical

basis, but often the available data only allow the use of either empirical or statistical approaches. Thus, to discuss the ET modelling, it is convenient to divide the ET models as follows: ET estimation Analytical approach (8) Penman–Monteith model Empirical approach (9) Methods based on crop coefficient approach (10) Methods based on soil water balance modelling. This categorisation is, of course, far from completion, but can be considered a good trace in a review of ET determination.

3. Actual crop evapotranspiration measurement The different methods for directly and indirectly measuring ET are based on the measurement of two classes of factors: 1. The soil water content and the physical characteristics of the evapotranspirative surface (height, plant density, canopy roughness, albedo); 2. Climatic variables: solar radiation, wind speed and thermodynamic characteristics of the atmosphere above the canopy. In the description of the measurement methods, we will consider as true the hypothesis of ‘conservative flux’, i.e. the flux density does not change with the height above the canopy. So that, in a first approximation, we will consider the different factors (belonging to both (a) and (b) classes) as constant in the space considered (the field). To say that the factors (a) are constant in the space is the same as saying that evapotranspirative surfaces are perfectly homogeneous along the horizontal scale. In Mediterranean regions, this hypothesis is usually far from being verified. In fact, the fields present crop types, phenological stages and water conditions very changeable in the space. This induces a net discontinuity of surface moisture among fields and, consequently, a non-negligible effect of advection due to energy side exchange between contiguous plots.


The role of the advective regime on the measurement and estimation of ET will be analysed in a separate chapter at the end of the methods presentation.

3.1. Hydrological approaches 3.1.1. Soil water balance Soil water balance is an indirect method, in fact ET is obtained as a residual term in the water balance equation. This equation is based on the principle of conservation of mass in one dimension applied to the soil, its complete expression is: P + I + W− ET− R− D = 9 [DS]r0

(1)

where P is precipitation, I is irrigation, W is contribution from water table upward, R is surface runoff, D is drainage and DS is soil water storage in the soil layer, where the roots are active to supply water to the plant (r in m). All the terms are in milimeters per unit time. Soil water storage between two dates (i and i −1) is: [DS]r0 = [Si −Si − 1]r0

(2)

with S, the soil water content. Since it is often very difficult to accurately measure all the terms of Eq. (1), a number of simplifications makes this method unsuitable for precise ET measurements. In fact, irrigation water supply is, in principle, known and precipitation can be measured by rain gauges, but all the other terms need to be measured or, at least, estimated. The soil water balance method is applicable to small plots (:10 m2) or to a large catchment ( : 10 km2); it may cover periods ranging from a week to a year. Often, for operational application, the soil water balance Eq. (1) is expressed in its simplified form: P +I = ET9 [DS]r0

(3)

The simplifications introduced in Eq. (3) could be critical if applied in dry environments. In arid and semi-arid areas with very small slopes, runoff term R could be neglected (e.g. Holmes, 1984) but, actually, it depends on the occurrence and characteristics of precipitation (amount, duration

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and intensity) and can only be neglected for a particular type of soil (Jensen et al., 1990), i.e. coarse (sand and loamy sand) and moderately coarse (sandy loam). Drainage (D) is the most unknown of Eq. (1). Some researchers suggest that it can be neglected in dry regions (e.g. Holmes, 1984), but actually it depends on the soil depth, slope, permeability and surface storage (Jensen et al., 1990; Parkes and Li Yuanhua, 1996) and needs to be checked in each particular case (Brutsaert, 1982), depending also on the climate and weather (Katerji et al., 1984). In some situations it is so important that its direct measurement can be used to estimate evapotranspiration on a weekly or greater scale (for an extensive review see Allen et al. (1991)). Moreover, Katerji et al. (1984) demonstrated that it is not simple to establish if the water deep flow can be neglected, in fact it can be a non-negligible fraction of the water balance both in dry and humid seasons and at different time scale. In general, at daily scale it can be neglected if the water supply (P and/or I) does not exceed the soil water capacity (Holmes, 1984; Lhomme and Katerji, 1991). In arid regions, the terms W and DS could show problems of correct evaluation. In fact, if the soil system is closed (i.e. shallow soils or soils with a very deep water table), W can be considered negligible and DS can be easily determined. In this case, the simplified soil water balance works well, as shown in Fig. 3, where ET measured by Eq. (3) is compared with a reference method (Bowen ratio). The data are relative to a maize crop grown in a Mediterranean region (Southern Italy) and soil water storage is measured by TDR and the gravimetric method. Vice versa, if the system is open (deep soils or soils with a surfacial water table), W cannot be neglected and DS is difficult to measure exactly, so that the simplified form of the soil water balance does not work well. This inaccuracy was well demonstrated by Katerji et al. (1984). In fact, they showed that during humid seasons, W and DS are of the same order, while during dry seasons, W can reach 30% of cumulated seasonal actual ET (Fig. 4).

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Soil water content needs to be measured accurately and over an adequate depth. It can be measured by a wide number of methods (an extensive review can be found in Stafford (1988) and Phene et al. (1990)). Three methods are the most important: gravimetric sampling, neutron scattering and electrical resistance. The gravimetric method remains the most widely used, particularly by technicians and consultants; it is well developed at research level and also for irrigation management purposes. It is simple to apply but it can be used only for large time scale (weekly or greater). Neutron probe remains a useful tool at research level only and its performance has not been well-

established in arid environments. In fact, Payne and Bruck (1996), for example, stated that neutron probe tends to lose accuracy under the arid climate of Sahelian Africa, mostly near the surface, wetting fronts and textural discontinuities; conversely, Evett and Steiner (1995) found very good results in a wide range of situations. Furthermore, neutron probe poses particularly difficult and regulatory problems in developing countries. Electrical resistance matrix type sensors are the least expensive (gravimetry is cheaper) and can be readily used for automatic irrigation control systems (Phene and Howell, 1984; Phene et al., 1989, among many others), but they are not very accu-

Fig. 3. Comparison between ET measured by Bowen ratio method, soil water balance with TDR and soil water balance by gravimetric method, at daily scale, on corn grown in Southern Italy (Mastrorilli et al., 1998).


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and hourly scale. Problems can be encountered in clay soils when the soil moisture is measured by means of probes (neutron scattering, electrical resistance or TDR). In fact, clay soils submitted to an arid climate (i.e. at low moisture content) crack where they are not adequately irrigated (e.g. Diestel, 1993); the deep cracks do not allow suitable contact between soil and probes (Haverkamp et al., 1984a,b) and the reading of the soil moisture can be affected by large errors (Bronswijk et al., 1995).

Fig. 4. Water balance component for a lucerne crop grown in a deep silt soil; (1) cumulative difference between rainfall and actual evapotranspiration since an initial date; and (2) variation of the total water content in the 0–170 cm upper layer of the soil since the same initial date (Katerji et al., 1984).

rate in estimating actual ET in soil water depletion conditions (see, e.g. Bausch and Bernard, 1996). The accuracy of soil water balance strongly depends on time and space scales of actual soil moisture measurements (Burrough, 1989) and on the representativeness of the soil sampling (Bertuzzi et al., 1994; Leenhard et al., 1994). In order to meet all these requirements for soil moisture measurement, the technique of time domain reflectometry (TDR) has been introduced quite recently by Topp and Davis (1985) and Topp et al. (1994) among others. It seems that TDR is able to correctly estimate soil moisture both under temperate (Ledieu et al., 1986; Werkhoven, 1993; Plauborg, 1995) and semi-arid climates (Mastrorilli et al., 1998). When TDR probes are used it is possible to have a soil water balance at plot scale

3.1.2. Weighing lysimeters Weighing lysimeters have been developed to give a direct measurement of the term ET in Eq. (1). Historically, they have always been considered as a suitable tool to correctly measure evapotranspiration (Tanner, 1967; Aboukhaled et al., 1982). In general, it is a device, a tank or container, to define the water movement across a boundary; actually, only a ‘weighing lysimeter’ can determine ET directly by the mass balance of the water as contrasted to non-weighing lysimeter, which indirectly determines ET by volume balance (Howell et al., 1991). Weighing lysimeters placed in the field contains soil cultivated as the field around it, the sensor is a balance able to measure the weight variation due to ET, often this variation is measured by means of an electronic sensor (i.e. load cell). Under temperate climate, they are able to measure ET at a daily scale with an accuracy of \10% (Perrier et al., 1974; Klocke et al., 1985b among others) and at an hourly scale with an accuracy of 10–20% (Pruitt and Lourence, 1985; Allen et al., 1991). However, the weighing lysimeter data are not always representative of conditions of the whole field but, in several situations, they represent only the ET of just one point in the field (Grebet and Cuenca, 1991). Besides the soil, height and vegetation density differences between the lysimeter and outside vegetation can severely affect ET measurements. In this case, both aerodynamic and radiative transfers to the lysimeter canopy are increasing. If the lysimeter surface and its close area are surrounded

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by drier vegetation or bare soil, an oasis effect occurs. Net radiation in excess of latent heat is converted in sensible heat that is advected toward the lysimeter, resulting in net supply of energy to the lysimeter vegetation. All these previously described problems cause an increase of ET relative to the surrounding crop. This overestimation of ET could be particularly important under a high radiative climate, as in the Mediterranean region. Furthermore, environmental problems can affect measurements of ET with lysimeters (for a review see Allen et al. 1991). One of the most common errors made is the incorrect estimation of an evaporating area of a lysimeter. In fact, this area is calculated by the inner dimensions of the container rather than the true vegetative area, which can be greater than the lysimeter surface when the vegetation from both inside and outside the lysimeter reaches across the rim. This error could be : 20%. The lysimeter rim can also influence ET measurements. One of the most important effects, mainly in arid environments, is the heating of the metallic rim by radiation resulting in microadvection of sensible heat into the lysimeter canopy. Moreover, if the lysimeter rims are too tall relative to crop, the wind is shielded and the radiative energy balance is modified due to the reflection of solar radiation by the inner wall of the rim toward the crop. Under arid and semi-arid climates, problems linked to the atmospheric evaporation demand can worsen the performances. In fact, if the soil

inside lysimeters has deep cracks (often along the border in contact with the soil), the water evaporation continues from the deepest layers, so that lysimeter can overestimate ET in these periods and underestimate ET in the following periods, when the water depletion inside is greater than that in the field, due to water stress condition of inner plants (Jensen et al., 1990). Conversely, irrigation and rainfall infiltrate into these cracks; this, along with lack of root extraction of water, causes the soil within the lysimeters to be much wetter than the surrounding field soil (Klocke et al., 1991). Furthermore, the weighing lysimeter is limited in depth in order to make possible the weighing of the soil–plants system; this limitation adds new problems for its use in Mediterranean regions. In fact, it does not take into account the effects of the deeper water fluxes (capillary rising) on crop, which can be very important in water stress periods. Moreover, in areas with deep soil, the weighing lysimeter does not take into account the effects of the total soil available on the plant growth. In this case, i.e. very deep soils, the representativeness of the lysimeter becomes a very difficult technical problem, due to the excessive weight of the system soil–tank–plants. In Fig. 5, three kinds of lysimeter are shown: since the total available water of nearby soil is : 260 mm, only the first one is well representative of the actual field, but in this case the weight of the system soil–tank is very high (12 t).

3.2. Micrometeorological approaches

Fig. 5. Three kinds of weighing lysimeter: S is surface, V is volume, P is weight, AW is total available water (Perrier et al., 1974).

From the energetic point of view, evapotranspiration can be considered as the energy employed for transporting water from inner leaves and plant organs to the atmosphere as vapour. In this case it is called ‘latent heat ’ (lE, with l, latent heat of vaporisation) and it is measured as energy flux density (W m − 2). We shall describe in the following sections the main methods based on physical or meteorological principles, by which the latent heat flux can be measured. Three techniques will be analysed: the energy balance and Bowen ratio, the aerodynamic method and the eddy covariance. In general, all


these techniques require accurate measurements of meteorological parameters on small temporal scale (1 h or less). Due to the conservative hypothesis of all the flux density above the crop, they can be applied only in large flat areas with uniform vegetation. There are many situations of practical interest where they cannot be used, mostly in the Mediterranean regions. For example, mixed plant communities, hilly terrain and small plots. Much care must be taken to analyse local and regional advection under arid conditions (Prueger et al., 1996) and, eventually, to introduce corrections to the measured fluxes (see the section on advection).

3.2.1. The energy balance and Bowen ratio method The latent heat flux can be obtained from measurements of the energy budget of the surface covered with an active growing crop. In fact, lE represents the major used part of available energy due to the radiation balance. All the energy involved in the evapotranspiration phenomenon must satisfy the closure of the energy balance: Rn − G= H+ lE

(4)

where we neglect advective processes and where Rn (W m − 2), net radiation and G (W m − 2), soil heat flux, are directly measurable by net-radiometers and soil heat flux plates, respectively (for an extensive review of environmental instrumentation see Fritschen and Gay (1979)); and H is the sensible heat flux density (W m − 2). We define the Bowen ratio b =H/lE, so Eq. (4) can be rearranged to give lE=

Rn − G 1 +b

DT De

Fuchs and Tanner, 1970; Sinclair et al., 1975; Revheim and Jordan, 1976) and can be estimated to be within 10% of the measured value. The Bowen ratio method has been widely studied in a variety of field conditions and it has been proven a standard very accurate method in semi-arid environments (e.g. Dugas et al., 1991; Frangi et al., 1996; Zhao et al., 1996) and for tall crops also (Cellier and Brunet, 1992; Rana and Katerji, 1996). As previously stated, under arid climates the crops can experience water stress. In this case DT can be quite high but De is very low; so that it is very important to have highly accurate measurements of air vapour pressure (Angus and Watts, 1984). The usual and simplest way is to use differential psychrometry; to meet the requirements of accuracy, for continuous recording, one has to keep the bulbs wet and clean (Fritschen and Gay, 1979). Furthermore, the used thermometers have to be well calibrated, in order to detect temperature differences of 0.05–0.2°C; accuracy can be sensibly improved by fluctuating the psycrometric system (Webb, 1960; Gay, 1988; Fritschen and Simpson, 1989). Recently, improvements have been obtained by using: (i) just one hygrometer that measures alternatively the humidity of air pumped from two levels (Cellier and Olioso, 1993); (ii) a single cooled mirror dew point hygrometer (some such instruments are briefly described in Dugas et al., (1991)); and (iii) spatial averaging systems (Bausch and Bernard, 1992). In any case, technical problems remain in the hygrometers for the measurement of air humidity in arid regions, especially during water stress periods, when its value is usually very low.

(5)

b can be measured by the ratio of the air temperature difference between two levels (DT) and the vapour pressure difference (De), with e (kPa) air vapour pressure, measured at the same two levels: b= g

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(6)

The Bowen ratio is an indirect method, its accuracy has been analysed by many authors (e.g.

3.2.2. The aerodynamic method If we assume that a flux density can be related to the gradient of the concentration in the atmospheric surface layer (ASL), the latent heat flux by the aerodynamic technique can be determined directly by means of the scaling factors u* and q*, with q specific air humidity (kg kg − 1), (see e.g. Grant, 1975; Saugier and Ripley, 1978): lE= − lru*q*

(7)


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here r is density of air (kg m − 3) and the friction velocity u* (m s − 1) is derived from the wind profile measurement:

ku (8) z−d ln −Cm z0 where k= 0.41 is the von Karman constant, d (m) is the zero plane displacement height, z0 (m) is the roughness length of the surface and Cm is the stability correction function for momentum transport. q* is determined similarly from the humidity profile measurement: u*=

k(q−q0) (9) z−d ln −C6 z0 where q0 is the air humidity extrapolated at z = d + z0 and C6 is the correction function for latent heat transport. The calculation of stability functions is made by iterative processes (e.g. Pieri and Fuchs, 1990); users who did not take stability corrections into account have been criticised (Tanner, 1963; Perrier et al., 1974). The problem of taking into account the atmospheric stability is particularly important in arid environments. In fact, the coupling of dry soil and rapid warming or cooling up of air at contact with the earth surface (vegetation or bare soil) leads to extreme conditions of strong stability and instability, with sudden passage from a regime to the opposite one. The major difficulty with this technique is the correct measurement of the vapour pressure at different heights above the crop. For this reason lE can be derived indirectly by the energy balance Eq. (4) if the sensible heat flux is determined by the flux-gradient relation:

q* =

H = −rcpu*T*

(10)

where T* is deduced by the air temperature profile: k(T −T0) (11) z− d ln − Ch z0 where T0 is the temperature extrapolated at z= d + z0 and Ch is the correction function for the heat transport. T* =

Under this form, the main advantage of the aerodynamic technique consists in avoiding humidity measurements. Nevertheless, the accuracy depends on the number of measurement levels of wind speed and temperature profiles. In fact, Eq. (8) and Eq. (11) require at least three or four levels (Webb, 1965), but accuracy is improved when several levels are used (Legg et al., 1981; Wieringa, 1993). When the stability correction functions of Dyer and Hicks (1970) and Paulson (1970) are used, this method gives good results (Grant, 1975; Pieri and Fuchs, 1990). A simplified version of the method has been proposed by Itier (1980, 1981) and Riou (1982), based on the measurement of Du and DT, i.e. wind speed and temperature at two levels only. This method was successfully used to measure actual evapotranspiration of soybean grown in a region of Southern Italy (Rana et al., 1990). The aerodynamic method does not work with enough accuracy on tall crops, neither in its complete form (Garratt, 1978; Thom et al., 1975) nor in its simplified form (Rana and Katerji, 1996). Correction for making this method applicable for tall crops was attempted by Cellier and Brunet (1992).

3.2.3. The eddy co6ariance The transport of scalar (vapour, heat, CO2) and vectorial amounts (i.e. momentum) in the low atmosphere in contact with the canopies is mostly governed by air turbulence. The first complete scientific contributions to this topic were given by Dyer (1961) and Hicks (1970); for extensive details of the theory see, for example, Stull (1988). When certain assumptions are valid, theory predicts that fluxes from the surface can be measured correlating the vertical wind fluctuations from the mean (w%) with the fluctuations from the mean in concentration of the transported admixture. So that for latent heat, we can write the following covariance of vertical wind speed (m s − 1) and vapour density (q% in g m − 3): lE= lw%q%

(12)

By making measurements of the instantaneous fluctuations of vertical wind speed w% and of humidity q% at sufficient frequency to obtain the


contribution from all the significant sizes of eddy and summing their product over an hourly time scale (from 15 min to 1 h), Eq. (12) gives directly the actual crop evapotranspiration. A representative fetch is required; fetch to height ratios of 100 are usually considered adequate but longer fetches are desirable (Wieringa, 1993). The distribution of eddy size contributing to vertical transport creates a range of frequencies important to eddy correlation measurements (for a brief review of the method requirements, see Tanner et al. (1985)). The sensors must sufficiently respond to measure the frequencies at the high end of the range, while covariance averaging time must be long enough to include frequencies at the low end (Kaimal et al., 1972; McBean, 1972). To measure ET directly by this method, vertical wind fluctuations have to be measured and acquired contemporary to the vapour density. The first one can be measured by sonic anemometer, the second by fast response hygrometer; both have to be acquired at a typical frequency of 10 – 20 Hz. The Krypton-type fast hygrometers perform well in the field, but they are expensive and very delicate, so they need particular maintenance. In fact, the commercial fast hygrometers can be severely damaged if moistened, therefore they can only be installed during the day period, which makes it difficult to use this instrument continuously for a period longer than a few days. Furthermore, errors in eddy covariance method can be due not only to possible deviations from the theoretical assumptions, but also to problems of the sensors configuration and meteorological characteristics (Foken and Wichura, 1996). One important hypothesis to be verified is that time series must be stationary at the scale of the averaging period, which requires some kind of detrending of the original turbulent signals, like linear detrending or more complex high-pass filtering (Kaimal and Finningan, 1994). Other known problems are due to the geometrical configuration of the sensors. A distortion of the flow can be caused by the sensor arrangement of the anemometer itself and other sensors. The spatial separation between the sonic anemometer and the hygrometer can cause lack of covariance

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between the wind speed and the humidity fluctuations. In fact, the typical distance between the measuring path of the vertical wind fluctuation and the hygrometer is 30–40 cm, this spacing acts like a lower-pass filtering process on the measured signals and must be corrected (Foken and Wichura, 1996). An important aspect to be considered is the density correction (Webb et al., 1980), this correction is particularly important in semi-arid environments, in fact it is proportional to the sensible heat flux and may be quite large for high Bowen ratio (Villalobos, 1997). To avoid some of the above problems linked to the humidity fluctuations measurements, lE can be obtained indirectly as residue of the energy budget Eq. (4) if the sensible heat flux is expressed by: H= rcpw%T%

(13)

where cp (J kg − 1 C − 1) is the specific heat and density of air. The wind speed and temperature fluctuations are measured by means of sonic anemometer and fast response thermometer, respectively. Despite problems linked to the correct management of the sensors and data remain, this method has very good performances both at hourly and daily scale, also in semi-arid environments. In Fig. 6, the comparison between ET measured by this method and Bowen ratio method, at daily scale, is shown for a tall crop (sweet sorghum) grown in Southern Italy. In conclusion, the use of eddy covariance for latent or sensible heat flux is still a useful tool only at research level, even if the recent development of robust sensors could permit in the future its practical application in arid regions.

3.3. Plant physiology approaches Plant physiology methods either measure water loss from a whole plant or a group of plants. These may include methods such as tracer technique and porometry but here, only the most diffuse methods will be analysed: the sap flow method and the chambers system.

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stem from the heater element. The difference between the heat input and these losses is assumed to be dissipated by convection with the sap flow up the stem and may be directly related to water flow (Kjelgaard et al., 1997). The mass flow rate F (g t − 1) is expressed by the relationship: F=

Fig. 6. Comparison between ET measured by eddy covariance for sensible heat flux and ET measured by Bowen ratio method, at daily scale, on sweet sorghum grown in Southern Italy (Rana and Katerji, 1996).

3.3.1. Sap flow method Sap flow is closely linked to plant transpiration by means of simple accurate models; sap flow can be measured by two basic methods: (i) heat pulse and (ii) heat balance. In heat pulse method, applied by Cohen et al. (1988) in herbaceous plants, sap flow is estimated by measuring heat velocity, stem area and xylem conductive area. This method seems to be inaccurate at a low transpiration rate (Cohen et al., 1993); moreover, it needs calibration for every crop species. Therefore, the most popular sap flow method is based on the heat balance method. It is based on the concepts proposed by Cermak et al. (1973), Sakuratani (1981), Granier (1985) and Steinberg et al., (1990). The plant transpiration can be estimated by determining the sap mass flow; this is done using gauges that are attached to or inserted in the plant stem. For the heat balance method, a heater element is placed around the plant stem to provide energy to the system. Thermocouples are used to determine how much heat is lost by conduction up, down and radially in the

Qh − Qv − Qr cw·dT

(14)

where Qh is input heat, Qv is vertical conductive heat, Qr is radial heat loss to environment, cw (J g − 1 K − 1) is specific heat of water and dT is the temperature difference between the upstream and downstream thermocouples. With the commercial instrumentation, the mass flow rate per plant can be measured at hourly scale. The micro-engineering problems of the heat emitters and detectors discussed by Cohen et al. (1981) were recently overcome by Kjelgaard et al. (1997), by using constant heat input instead of variable heat input and using surface-mounted thermocouples instead of the inserted ones. These gauges do not disturb the plant root medium but there is still biometric difficulty in extrapolating from one or more plants to the canopy. Furthermore, in spite of the fact that the heat balance method is more dependent upon the sap flow rate than upon stem anatomy (Zhang and Kirkham, 1995), at low flow rates (often the case in a semi-arid and arid environment when the crops are under water stress) important differences were observed between the sap flow determined by the gauges and plant water losses observed by a reference method (Kjelgaard et al., 1997). The best results are obtained at a daily scale, mostly on trees. For the sap flow method it is necessary do a scaling up of the transpiration measurement from plant to field scale. This is possible only if the canopy structure and the spatial variability of the plants characteristics (density, height, LAI) are precisely known. Recently, Grime and Sinclair (1999) reported sources of error for the constant power stem heat balance method when commercial gauges are used. Their analysis demonstrated that measurement errors in determining the diurnal pattern of transpiration could be due to the heat storage in


the stem and the pattern of ambient temperature. Moreover, the determining of the sheath conductance by the night-time sap flow measurement can be affected by error due to the stem variation during the season. These errors are highly dependent on operating conditions and can be minimised by following appropriate recommendations. Problems linked to the death of the fresh tissues around the heated area, due to silicone application, can be minimised by reducing the period of continuous measurement to 1 – 2 weeks (Wiltshire et al., 1995). The sap flow method approaches only transpiration measurements, neglecting soil evaporation. Nevertheless, under the Mediterranean climate evaporation from soil can be a very important fraction (up to 20% of total evapotranspiration) of the soil–plant–atmosphere water budget (Brutsaert, 1982; Klocke et al., 1985a,b).

3.3.2. Chambers system method The chambers to rapidly measure ET were described for the first time by Reicosky and Peters (1977). They consist of aluminium conduits covered with Mylar, polyetilene or other plastic or glass films; the air within the chamber was mixed continuously with strategically located fans. The first version was portable (by means of a tractor, for example) and ET rate was calculated by a psychrometer before the chamber was lowered on the plot and 1 min later, as latent heat storage. The volume of the chamber can be easily adapted to a herbaceous field crop and the accuracy was : 10%, compared with weighing lysimeters (Reicosky et al., 1983). This system was used by Reicosky (1985) to measure, on a daily scale, ET on several different treatments more easily than the weighing lysimeter, but it is not suitable for long term ET measurements. Furthermore, more serious errors may be introduced if chamber results were extrapolated over time as well as spatially (Livingston and Hutchinson, 1994). Under a semi-arid climate, Dugas et al. (1991) demonstrated that portable chambers, equipped with an infrared analyser (BINOS, Inficon Leybold-Heraeus, NY) for measuring vapour density

137

differences in differential mode, are not representative of the field, particularly on its margins, where ET measurements were affected by surrounding conditions. Furthermore, the last improved version of portable chambers can be very expensive, due to the high costs for key components (data acquisition system and infra-red analyser) and accurate chamber manufacturing that may require some heavy engineering. Recent developments of portable chamber systems can be found in Wagner and Reicosky (1996); here the equipment also provides CO2 measurements, but its complexity and costs limit its use for research to a small temporal scale. The chambers are also suitable for research studies on orchard crops (Katerji et al., 1994). The most serious problems of almost all chambers are related to the modification of the microclimate during the measurement period. First, the solar radiation balance, since the chambers were designed for simultaneous measurement of both CO2 and water vapour exchanges (Denmead, 1984). These dual requirements are not often compatible because wall materials are usually selected for high transmission in the short wavelengths, neglecting the long-wave exchange (: 20% of the incoming short-wave radiation). Second, the air temperature: its rapid increase inside the chambers could alter the biological control of leaves transpiration process. Finally, the wind speed: inside the chamber it could be strongly reduced with direct consequences on ET measurement accuracy.

4. Actual crop evapotranspiration modelling Actual ET can be estimated by means of more or less complex models: the accuracy of ET estimation is proportional to the degree of empiricism in the used model or sub-models. Thus, we can categorise the ET estimation into three groups of methods: 1. Methods based on analytical modelling of evapotranspiration; 2. Methods in which actual crop ET is deducted from the evapotranspiration of a reference surface;


138

3. Methods based on a soil water balance modelling.

4.1. ET analytical models One-dimensional equations based on aerodynamic theory and energy balance — for this reason called combination models (Penman, 1948; Monteith, 1965, 1973) — have proved very useful in the actual crop ET estimation, because they take into account both the canopy properties and meteorological conditions (Szeicz and Long, 1969; Black et al., 1970; Szeicz et al., 1973). The most widely used form of the combination equation, called Penman-Monteith equation, can be expressed under the form (Allen et al., 1989): lE=

DA + rcpVPD/ra D+ g(1 +rc/ra)

(15)

In Eq. (15) we can distinguish weather non-parametric and parametric variables: weather non-parametric variables: A = Rn −G (W m − 2) is available energy, D (kPa C − 1), is the slope of the saturation vapour pressure versus temperature function, VPD (kPa) is air vapour pressure deficit and g (kPa C − 1) is the psychrometric constant; parametric variables: ra (s m − 1) is the aerodynamic resistance and rc (s m − 1) is the bulk canopy resistance. All the terms have to be accurately evaluated (Allen, 1996). While the non-parametric data are standard measurable climatic variables, the parametric data are not directly measurable and they need to be modelled. Indeed, the first one (ra) is the aerial boundary layer resistance and describes the role of the interface between canopy and atmosphere in the water vapour transfer; the second one (rc) is the resistance that the canopy opposes to the diffusion of water vapour from inner leaves toward the atmosphere and it is influenced by biological, climatological and agronomical variables. This model is applied to the whole plant community as if it were a single ‘big leaf’ located at the height of virtual momentum absorption (Thom, 1975). The degree of empiricism of the Penman – Monteith equation (and consequently its success)

mainly depends on the accuracy of the estimation of the canopy resistance (Beven, 1979). Rana and Katerji (1998) demonstrated that, under a semi-arid climate, the canopy resistance plays the major role, so that its modelling is the most critical point of the Penman–Monteith model to estimate actual crop ET under the Mediterranean climate. In particular, their sensitivity analysis on Penman–Monteith model showed that: (i) in well-watered conditions, rc modelling is sensible to Rn variations in the case of low crops (i.e. reference grass) and it is sensible to vapour pressure deficit variations, both in the case of medium (Fig. 7) and tall crops (Fig. 8); (ii) in water stress conditions, rc modelling is sensible to the actual crop water status (Figs. 7 and 8).

4.1.1. Methods of determining canopy resistance One of the most diffuse methods of estimating rc is that proposed by Szeicz and Long (1969), in which canopy resistance can be determined as a function of daily mean stomatal resistance of the single leaves (rs) and leaf area index of the leaves effective in transpiration (LAIeff). These authors assumed that only the surfacial layer of the canopy participates effectively to the transpiration. Usually, such a canopy resistance is assumed to be half the full crop LAI: rc =

rs LAIeff

(16)

This equation is suggested to be used for reference crop ET (Allen et al., 1989) and it is also used, under light different form, for field crop ET (Steiner et al., 1991; Stannard, 1993; Howell et al., 1995). Since the spatial and temporal variability of the soil water status (and, consequently, of the plant water status) is very high and the microclimate to which they are exposed is very different (Denmead, 1984), to obtain sufficiently accurate canopy resistance values by means of this method, it is necessary to realise a great number of measurements of stomatal resistance in a brief interval time. Furthermore, from the theoretical and practical point of view, the problems of scaling-up the canopy resistance from leaf to canopy is not


completely solved (Jarvis and McNaughton, 1986; Baldocchi, 1989; Baldocchi et al., 1991; Raupach, 1995); particularly in semi-arid environments (Steduto et al., 1997). An improvement of this estimating method has been proposed by Monteith (1965) and tested and applied by Katerji and Perrier (1985). In this method, the canopy was divided into layers and canopy resistance was estimated starting from stomatal resistance measured layer by layer and weighted with the LAI of each layer. The expression of the canopy conductance (gc =1/rc) is given, in this case, by the relationship: gc = %gci ·LAIi

(17)

i

where gci is the stomatal conductance measured on the leaves belonging to the layer characterised by LAIi. Usually i is equal to 2 or 3, following the

139

crop growth: an example is given in Table 2 for an alfalfa crop divided into three layers. This approach is difficult and hard to carry out, giving rc values not always accurate. Nevertheless, the estimation of field crop ET can be acceptable, at least for well-watered crops (Katerji and Perrier, 1985). The most complete model of rc should have the following general form (Stewart, 1988, 1989; Itier, 1996): rc = f(LAI, Rg, VPD, T, crop water status)

(18)

where Rg is global radiation. Actually, the better way to evaluate the crop water status is by means of measurements of leaf water potential (Cf) or root-sources abscisic acid (ABA) (Zhang et al. 1987; Zhang and Davies, 1989). As previously stated, the plant water status varies almost instantaneously, so that to have

Fig. 7. The sensitivity coefficients as function of predawn leaf water potential (PLWP, MPa) for grain sorghum; A is available energy, ra is aerodynamic resistance, rc is canopy resistance and VPD is vapour pressure deficit (Rana and Katerji, 1998).


140

Fig. 8. The sensitivity coefficients as function of predawn leaf water potential (PLWP, MPa) for sweet sorghum; for symbols see Fig. 7 (Rana and Katerji, 1998).

acceptable accuracy of the actual crop water status, a great number of Cf or ABA measurements has to be carried out during the entire day. Hence, this method is not suitable for practical application in a field. In order to avoid the problem of a large num-

ber of punctual measurements, a number of models as expressed by the general relationship Eq. (18), have been developed for dry conditions. For example, Hatfield (1985) modelled rc in the function of global radiation and available soil water by means of an experimental function. Fuchs et

Table 2 Variability of stomatal resistance on the two sides of the leaf in an alfalfa canopya I

Canopy layer

1 2 3

Top Medium Bottom

Canopy a

Stomatal resistance and S.E. (s m−1) Upper side

Lower side

1179 29 1999 70 1044 9365

1159 29 5599 196 1200 9 420

LAI and S.E.

Canopy conductance (mm s−1)

1.75 90.35 2.1 9 0.42 0.85 9 0.17

30.2 14.3 1.5

4.7

46.0

The resulting canopy resistance is 22 s m−1 (Saugier and Katerji, 1991).


141

al. (1987) stated that rc can be modelled by means of photosynthetic active radiation (PAR) and soil moisture deficit, but this model is only valid for mild to medium stress. Again, regressive experimental functions were used by Jarvis (1976) to model rc in the function of LAI, Rg, VPD, T and soil water potential. Unfortunately, all these functions need to be locally calibrated per crop and, often, they need also a time calibration, i.e. they are not valid for the whole growth season (Stewart, 1988). An original approach to take into account the crop water status to estimate ET by means of Penman–Monteith model was presented by Rana et al. (1997b), in which rc is modelled as a function of ‘predawn leaf water potential’, Cb. This parameter represents the crop water status and it does not need to be measured instantaneously, but just once a day. The application of such a model in semi-arid environments gives quite good results. Such rc model is valid for crops growing under semi-arid climate (i.e. also for crops under water stress) and needs to be calibrated only ‘per crop’ so that it can be generalised (Rana et al., 1997c). It is possible to use the transpirable soil water as input of the model instead of Cb, but in this case the model looses its generality and must be locally calibrated (Rana et al., 1997a).

Moreover, the data necessary for the determination of reference evapotranspiration ET0 are usually collected in standard agrometeorological stations. These stations are usually installed to be representative of the catchment, i.e. an area of several squared kilometres in extension.

4.2. ET empirical models

and

By these methods, the water consumption of crops is estimated as a fraction of the reference evapotranspiration (ET0): ET=Kc ·ET0

(19)

where Kc is the experimentally derived crop coefficient and ET0 is the maximum evapotranspiration; the latter can be evaluated (i) on a reference crop or (ii) on free water in a pan. The accuracy of such an estimation depends on: the reference chosen (grass meadow or free water in a standard pan); the method used to evaluate reference ET (measurement or modelling); the method used to evaluate the crop coefficient Kc.

4.2.1. ET0 reference crop determination In this case, ET0 is the water consumed by a standard crop. In order to make procedures and results comparable world-wide, a well adapted variety of clipped grass has been chosen. It must be 8–16 cm high, actively growing and in well-watered conditions, subjected to the same weather as the crop whose water consumption is to be estimated. ET0 can be again measured (e.g. by means of a weighing lysimeter) or estimated. Therefore the comments and observations made for actual crop ET are still valid for ET0 determination. In the following, we analysed the more used models of ET0. A semi-empirical approach to estimate ET0 was proposed by Penman (1956) as: DRn + gEA D+ g

(20)

EA=f(u)(e −e*)

(21)

ET0 = where

f(u)=1 +0.54 u2

(22)

with u2, wind speed measured 2 m above the soil surface. The empiricism of this formula is into the wind function f(u). Actually, this function is just a linear regression adjustment to take into account the differences between estimated and observed ET0 for grass grown in the UK. Therefore, it is a pseudo exchange coefficient, which is supposed to take into account the ET grass regulation. This observation explains why it is necessary to adapt the function f(u) to each site, in order to correctly apply the Penman formula in the Mediterranean regions. Furthermore, it is clear that the wind function decreases going from North toward South (Fig. 9) and these results point out the dependence of canopy resistance on the vapour

142


pressure deficit, which increases going from North to South Europe (Choisnel, 1988). Today the most used and suggested method to evaluate reference ET is based on the Penman – Monteith model, therefore, having the same problems encountered in actual crop ET estimation regarding rc modelling. Rana et al. (1994) proposed an ET0 estimated by means of the Penman – Monteith model, in which canopy resistance is analytically modelled. This model was tested in Mediterranean regions, but it is valid in humid environments too. They demonstrated that rc varies with the climate and it is sensitive to any agronomic practice (irrigation date, grass cutting, phytopathological situation); in Fig. 10 hourly rc values are shown before and after grass cutting and irrigation.

Recently, Todorovic (1997) proposed a variable canopy resistance, both at hourly and daily scale, modelled in the function of standard meteorological parameters. It was shown that ET0 calculated by the Penman–Monteith model with such a resistance works well under different climates. Several authors proposed an ET0 Penman– Monteith formula using constant value of rc (Allen et al., 1989). Steduto et al. (1996) tested such ET0 estimates with constant rc in several Mediterranean regions. Their results demonstrated that this approach does not have good performances in all the experimental sites. Other simpler methods to estimate reference crop ET are based on statistical–empirical formulas (an exhaustive review of these methods can be found in Jensen et al. (1990)). These methods can

Fig. 9. Comparison among four f(u) wind functions in the Penman equation: Penman is f(u) =0.26(1 +0.54 u); Businger is f(u) = 0.37 u; Doorenbos and Pruitt is f(u)= 0.27(1+ 0.86 u); Seguin is f(u) =0.37 u 0.88; Losavio et al. is f(u) =0.12 u 0.67 (Losavio et al., 1992).


143

4.2.2. The reference ET0 calculated by the pan e6aporation Pan evaporation data can be used to estimate reference ET, using a simple proportional relationship: ET0 = Kp·Epan

Fig. 10. Canopy resistance during a day for a reference grass grown in Southern Italy, (a) before and after grass cutting and (b) before and after irrigation (Rana et al., 1994).

be used very easily from a practical point of view, above all in rural lands. However, their empiricism may lead to very inaccurate estimations, as clearly shown by Ibrahim (1996), in arid environments (Table 3).

(23)

where Kp is dependent on the type of pan involved, the pan environment in relation to nearby surfaces and the climate. Doorenbos and Pruitt (1977) provided detailed guidelines for using pan data to estimate reference ET0. In the case of a pan surrounded by short green grass, Kp ranges between 0.4 and 0.85. In semi-arid environments its mean value is : 0.7 (Jensen et al., 1990). The value of Kp to be adopted is strongly dependent on the upwind fetch and on the local advection. An attempt to model the pan coefficient, Kp has been made by Perrier and Hallaire (1979a,b), on the basis of experimental pan data measured in a humid–tropical area (Baldy, 1978). They expressed Kp in the function of climatic variables and a coefficient b, the ratio between the exchange coefficient of the pan and the wind function f(u) of the Penman’s formula, as Kp =

1+a(1− RH) 1+ ab(1− RH)

(24)

with RH relative air humidity and a a coefficient, function of air temperature, net radiation and wind speed. This latter coefficient is dependent on

Table 3 Seasonal reference evapotranspiration (ET0) in 3 years and average percent deviation from the mean value in the north central area of the Nile delta region (Egypt) (Ibrahim, 1996). Method

Seasonal ET0

Average seasonal ET0 (cm)

Percent deviation from mean value

−4.37 +1.66 −5.13 +27.03 −22.59 +27.20 −17.28 −6.59

1979

1980

1981

(a) Blaney–Criddle (b) Radiation (c) Modified Penman (d) Rijtema (e) Thornthwaite (f) Jensen and Haise (g) Evaporation pan (h) Turc

63.2 66.78 62.82 81.64 52.16 86.75 56.75 63.47

61.81 63.20 60.60 81.35 49.73 80.50 54.14 58.93

60.31 66.82 60.24 82.92 48.00 79.41 49.24 58.45

61.71 65.60 61.22 81.97 49.96 82.08 53.38 60.28

Average

66.62

63.78

63.17

64.53

144


4.2.3. The crop coefficient Kc The crop coefficient represents an integration of the effects that distinguish the crop from the reference ET; many such Kc are reported in the literature (e.g. Doorenbos and Pruitt, 1977; Allen et al., 1998) usually derived from soil water balance experiments. Crop coefficient can be improved for estimating the effects of evaporation from wet soil on Kc on a daily basis (Wright, 1982). In this case, the crop coefficient can be expressed by the equation: Kc = Ks·Kcb + Ke

Fig. 11. Theoretical curves of pan coefficient Kp in function of relative air humidity (a= 0.8), b =2 is for humid seasons, b = 2.5 is for medium seasons and b =3 is for dry seasons (Perrier and Hallaire, 1979b).

the site climate and can be considered constant and = 0.8 in arid environment. Fig. 11 shows the coefficient Kp in the function of RH for three values of b (b = 2, humid seasons; b =2.5, intermediate seasons; b =3, dry seasons).

Fig. 12. Relative evapotranspiration (actual ET/maximum ET) in function of available soil water for a corn crop grown in a closed soil system (pots) and in open soil system (a deep field with favourable environmental conditions) (Tardieu and Katerji, 1991).

(25)

where Ks is a stress reduction coefficient (0} 1). Kcb is the basal crop coefficient (0} 1.4) and represents the ratio of ET and ET0 under conditions when the soil surface is dry, but where the soil water content of the root zone is adequate to sustain full plant transpiration; Ke is a soil water evaporation coefficient (0} 1.4). It can be experimentally calculated in the function of soil water storage, DS and depends on the amount of soil available to the plants’ roots. In fact, in open soil systems, when the conditions are favourable for root system development, Ks is almost constant, also when soil humidity decreases considerably, because of an appreciable contribution of the non-rooted soil layer to the water balance. In closed soil systems (pots, for example) Ks is variable following the soil humidity and it begins to decrease appreciably for values of soil water reserve :60–70% of available soil water to transpiration. This Ks behaviour is reported in Fig. 12, in which the ratio of actual to potential ET of a well-irrigated corn crop is reported in the function of the available soil water, for the above mentioned two conditions (open and closed soils). In order to avoid the difficulties linked to the available soil water conditions, it is possible to evaluate Ks by means of the predawn leaf water potential, Cb. Itier et al. (1992) demonstrated that this relationship (and Ks as a consequence) has general validity, since it does not depend on the soil type and site. The methods to estimate crop coefficients and reference ET have been recently modified by Allen et al. (1994a), Allen et al. (1994b) and Allen et al. (1996).


The crop coefficient plays an essential role in practice (Pereira et al., 1999) and it has been widely used to estimate actual ET for irrigation scheduling purposes. However, it can be subject to serious criticism regarding the meaning and the use of crop coefficient. Besides obvious variations among different crops, empirical crop coefficients were shown to be affected by crop development and weather conditions (De Bruin, 1987). Furthermore, Stanghellini et al. (1990) demonstrated that no coefficients should be expected to vary according to the conditions of both climate and crop stage under which they are derived. Moreover, these researchers stated that the crop requirements based on a same value of the crop coefficient do not have the same accuracy for different months, different seasons, and, above all at different sites. This inaccuracy of the Kc approach can be found in the discrepancies between local calculated Kc and crop coefficients reported in literature (e.g. Rana et al., 1990; Vasic et al., 1996).

Fig. 13. Comparison between ET estimated by a water reservoir model as proposed by Lhomme and Katerji (1991) and ET estimated by water balance with DS measured by TDR on a daily scale (Ben Nouna, 1995).

145

4.2.4. Soil water balance modelling An exhaustive survey of the most diffuse models for soil water balance can be found in de Jong and Bootsma (1995) and Leenhardt et al. (1995). Two classes of models are generally retained for the simulation of soil water balance: (i) mechanistic models and (ii) analogue (or water reservoirs) models. In the mechanistic approach, the water flux in the soil is controlled by the existence of soil water potential gradients, by means of Darcy’s law and continuity principle. The equations are usually solved by different methods, all involving the splitting of the soil in more or less small layers (de Jong, 1981; Feddes et al., 1988). The difficulties in the application of these models are linked to (i) the accuracy of the used pedo-transfer functions for estimating the water transfer and (ii) the procedures used for estimating the boundary conditions of the soil–plant–atmosphere system. In the analogue approach, the soil is treated as a collection of water reservoirs, filled by rainfall or irrigation and emptied by evapotranspiration and drainage. They can be based on the two following principles (Lhomme and Katerji, 1991): 1. determination of soil water storage DS as function of the soil and roots depth (e.g. Lhomme and Eldin, 1985; Brisson et al., 1992) 2. the split of soil water in readily transpirable soil water (RTSW) and total TSW (e.g. Fre´teaud et al., 1987). The stress coefficient Ks is supposed to be approximately = 1 in the RTSW range, then it decreases as the water reserve is in the TSW range. In a previous paragraph we have analysed the difficulties in determining the soil water storage and the evolution of Ks in function of DS, particularly under a Mediterranean climate. Effectively, Ben Nouna (1995) showed that, in semi-arid regions with closed system soils, the water reservoir approach can be very useful in estimating actual ET only if very accurate measurements of DS can be realised (Fig. 13). 5. ET evaluation in advection regime All the previously presented methods to measure and/or estimate actual crop ET are based on

146


a fundamental critical hypothesis that the considered systems were conservative, i.e. the profiles of air temperature, water vapour concentration and wind speed are constant along the horizontal axe X (e.g. Itier and Perrier, 1976; Stull, 1988). In the Mediterranean regions, the scarcity of available water resources leads to irrigated lands in a selective way, with the creation of areas characterised by strong discontinuity of water vapour concentration at surface level, also for similar crops. The air thermodynamic characteristics are strongly influenced by the surface conditions, so that the air passing from a dry field toward an irrigated field is cooled as well as its humidity increases. Hence, near the boundary of the irrigated field, ET rapidly increases, then slowly decreases along the wind direction following the thermodynamic characteristics of the air coupled with the vegetation surface. In general, the latent heat flux density measured or estimated in one point of the plot is, actually, the sum of two terms: (i) a term translating the equilibrium between the evaporative demand of the air determined by the Penman model and crop water conditions; (ii) an additional flux, more or less important, depending on the lateral transfer of energy by advection. From an energetic point of view, latent best flux depends on the modifications of the vapour and thermal characteristics of the air passing through the considered surface. The theoretical studies on local advection (Philip 1959; Taylor, 1970; Itier and Perrier, 1976; Itier et al., 1994) showed that the advective flux (Fa) depends on: (i) the fetch F (m); (ii) the roughness length of the crop, z0 (m); (iii) the wind speed through the friction velocity u* (m s − 1); and (iv) the temperature difference between the dry and irrigated fields, DT (°C). From the operational point of view such a situation in which an advection regime is verified leads to two main consequences: 1. In order to have a correct representativeness of ET, it is necessary to correct a posteriori the evapotranspiration measured by means of weighing lysimeters, soil water balance, sap flow method and chambers system method when the fetch is not large enough to avoid

advection effects. A possible simple correction was proposed by Itier et al. (1978) as: 6 Fa = 360·u*·DT z 0/F

(26)

This correction can be of 1–2 mm per day for temperature differences between dry and irrigated field of 5–10°C (Itier et al., 1978). 2. The micrometeorological methods must be applied in order to make the measurements inside the equilibrium fully adapted boundary layer. The fetch value assuring an acceptable adapted layer for micrometeorological is reported by Wieringa, (1993) as:

F$ 2z0

10z 10z ln −1 z0 z0

n

(27)

where z is the height of the top sensor. Regarding the Bowen ratio method, it is well known that it can underestimate ET under strong regional advective conditions (Blad and Rosenberg, 1974). This implies an extensive fetch in the upwind direction for the air flowing over the surface of at least 100 times the maximum height of measurements, but Heilman et al. (1989) demonstrated that the Bowen ratio method can be successfully applied up to fetch-to-height ratios as low as 20:1.

6. Conclusions In Mediterranean areas submitted to arid and semi-arid climates, ET ranges over a large interval depending on water regimes (irrigated or non-irrigated fields). Among the other weather variables, air temperature and humidity experience a large gap between night and day and between winter and summer; the wind is very variable moving from the coast to internal areas. So that, in general, we can state that all the weather variables range in a very large interval of values. Moreover, the variation in one parameter immediately influences all the other variables that are mutually linked. These facts make difficult to correctly evaluate the crop actual evapotranspiration. In this review, we have presented the most important methods of measuring and modelling actual evapotranspiration in arid and semi-arid


147

Table 4 Classification by space and time of ET methods to measure or model actual evapotranspiration, adapted by Stewart (1984) Minute

Hour

Day

Month

Catchment Uniform area

Growth season

Year

-----------Crop coefficient method----------—Micrometeorological method--

------------Crop coefficient method------------------Soil water balance--------------------Soil water balance with TDR-----------------Penman–Monteith model---------------

Group of plants

-------Weighting lysimeter; chambers system---------------Sap flow--------

Plant

--------Sap flow-------

environments. Some methods are more suitable than others in terms of convenience, accuracy or cost for the measurement of ET at a particular spatial and over a particular time scale. In Table 4 the classification of the presented ET methods by space and time scale is shown.

We tried to give, for each method, the advantages and disadvantages for their use in the Mediterranean regions; the summary of this analysis for the measurement methods is reported in Table 5. In summary, two major observations are possible:

Table 5 Summary of the advantages and disadvantages of the ET measurement methods Measurement method

Advantages

Disadvantages

Soil water balance

Soil moisture simple to be evaluated with gravimetric method Not expensive if the gravimetric method is used

Large spatial variability Difficult to be applied when the drainage and capillary rising are important Difficult to measure soil moisture in cracked soils Fixed Difficult maintenance It could be not representative of the plot area Expensive Difficult to have correct measurement of the wet temperature if psychrometers are used The sensors need to be inverted to reduce bias Difficult maintenance It needs to be corrected for the stability Not suitable for tall crops

Weighing lysimeter Direct method

Energy balance/ Bowen ratio

Aerodynamic

Eddy covariance

Simple sensors to be installed Suitable also for tall crops It can be also used when the fetch is 20:1 Not very expensive if psychrometers are used Simple sensors to be installed It does not need humidity measurements Not very expensive Direct method with fast hygrometer

Sap flow

Suitable for small plots It takes into account the variability among plants

Chambers method

Suitable for small plots It can be used also for detecting emissions of different gases

Delicate sensors Difficult software for data acquisition Hygrometer very delicate expensive Difficult scaling-up The gauges need to be deplaced every 1–2 weeks The soil evaporation is neglected It modifies the microclimate Difficult scaling-up

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1. The ET measurement methods are based on concepts which can be critical under semi-arid and arid environments for several reasons: (i) representativeness (the weighing lysimeter, for example); (ii) instrumentation (air humidity sensors for example); (iii) microclimate (advection regime); and (iv) hypothesis of applicability (the simplified aerodynamic method for example). Thus, in order to establish the degree of accuracy of the obtained ET measurement and the validity of a method, it is necessary to consider all these parameters. 2. The estimation methods based on an analytical approach could be very accurate but, usually, they are not practical enough. The more operational estimation methods (crop coefficient approach, ET0 calculation, water balance modelling) can be generally affected by very large errors. Nowadays, a new research trend is being developed in order to integrate these two approaches of actual evapotranspiration estimation. These new models tend to introduce into the analytical approach an acceptable degree of empiricism (Allen et al., 1989; Rana et al., 1994; Pereira et al., 1999). We think that such methods should be accepted and encouraged in the future, since these approaches can greatly improve the water management at farm level, one of the greatest problems of the Mediterranean countries.

References Aboukhaled, A., Alfaro, A., Smith, M., 1982. Lysimeters. FAO Irrigation and Drainage Paper No. 39, p.69. Allen, R.G., 1996. Assessing integrity of weather data for use in reference evapotranspiration estimation. J. Irrig. Drain. Eng. ASCE 122 (2), 97–106. Allen, R.G., Jensen, M.E., Wright, J.L., Burman, R.D., 1989. Operational estimate of reference evapotranspiration. Agron. J. 81, 650 – 662. Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E. (Eds.), 1991. Lysimeters for evapotranspiration and environmental measurements. Proceedings of the International Symposium on Lysimetry, July, 23–25, 1991, Honolulu, HI, ASCE Publication, p. 444. Allen, R.G., Smith, M., Perrier, A., Pereira, L.S., 1994a. An update for the definition of reference evapotranspiration. ICID Bull. 43 (2), 1 –34.

Allen, R.G., Smith, M., Pererea, L.S, Perrier, A., 1994b. An update for the calculation of reference evapotranspiration. ICID Bull. 43 (2), 35 – 92. Allen, R.G., Smith, M., Pruitt, W.O., Pereira, L.S., 1996. Modification of the FAO crop coefficient approach. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 124 – 132. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, p. 300. Angus, D.E., Watts, P.J., 1984. Evapotranspiration — how good is the Bowen ratio method? Agric. Water Manag. 8, 133 – 150. Baldocchi, D.D., 1989. Canopy – atmosphere water vapour exchange: can we scale from leaf to a canopy? In: Estimation of Areal Evapotranspiration. IAHS Publication No. 177, pp. 21 – 41 Baldocchi, D.D., Luxmoore, R.J., Hatfield, J.L., 1991. Discerning the forest from the trees: an essay on scaling canopy stomatal conductance. Agric. For. Meteorol. 54, 197 – 226. Baldy, C., 1978. Utilisation d’une relation simple entre le bac class A et la formule de Penman pour l’estimation de l’ETP en zone soudano-sahe´lienne. Ann. Agron. 29 (5), 439 – 452. Bausch, W.C., Bernard, T.M., 1992. Spatial averaging Bowen ratio system: description and lysimeter comparison. Trans. ASAE 35 (1), 121 – 128. Bausch, W.C., Bernard, T.M., 1996. Validity of the Watermark sensor as a soil moisture measuring device. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 933 – 938. Ben Nouna, B., 1995. Caracte´ristation et mode´lisation du bilan hydrique parcellaire pour une culture de sorgho sucrier. Master Thesis, CIHEAM-IAM Valenzano, Bari, Italy, 112 pp. Bertuzzi, P., Bruckler, L., Bay, D., Chanzy, A., 1994. Sampling strategies for soil water content to estimate evapotranspiration. Irrig. Sci. 14, 105 – 115. Beven, K., 1979. A sensitivity analysis of the Penman – Monteith actual evapotranspiration estimates. J. Hydrol. 44, 169 – 190. Black, T.A., Tanner, C.B., Gardner, W.R., 1970. Evapotranspiration from a snap bean crop. Agron. J. 62, 66 – 69. Blad, B.L., Rosenberg, N.J., 1974. Lysimetric calibration of the Bowen ratio-energy balance method for evapotranspiration estimation in the Central Great Plains. J. Appl. Meteorol. 13, 227 – 236. Brisson, N., Seguin, B., Bertuzzi, P., 1992. Agrometeorological soil water balance for crop simulation models. Agric. For. Meteorol. 59, 267 – 287. Bronswijk, J.J.B., Hamminga, W., Oostindie, K., 1995. Fieldscale solute transport in a heavy clay soil. Water Resources Res. 31 (3), 517 – 526.

G. Rana, N. Katerji / Europ. J. Agronomy 13 (2000) 125–153 Brutsaert, W.H., 1982. Evaporation into Atmosphere: Theory, History and Applications. D. Reidel, Dordrecht, p. 299. Burrough, P.A., 1989. Matching spatial databases and quantitative models in land resource assessment. Soil Manag. 5, 3 – 8. Cellier, P., Brunet, Y., 1992. Flux-gradient relationships above tall plant canopies. Agric. For. Meteorol. 58, 93–117. Cellier, P., Olioso, A., 1993. A simple system for automated long-term Bowen ratio measurement. Agric. For. Meteorol. 66, 81 – 92. Cermak, J., Deml, M., Penka, M., 1973. A new method of sap flow determination in trees. Biol. Plantar. 15, 171–178. Choisnel, E., 1988. Estimation de l’e´vapotranspiration potentielle a` partir des donneés me´teorologiques. Meteorologie 12, 19 – 27. Cohen, Y., Fuchs, M, Green, G.C., 1981. Improvement of the heat pulse method for determining sap flow in trees. Plant Cell Environ. 4, 391 –397. Cohen, Y., Fuchs, M., Falkenflug, V., Moreshet, S., 1988. Calibrated heat pulse method for measuring water uptake in cotton. Agron. J. 80, 398–402. Cohen, Y., Takeuchi, S., Nozaka, J., Yano, T., 1993. Accuracy of sap flow measurement using heat balance and heat pulse methods. Agron. J. 85, 1080–1086. De Bruin, H.A.R., 1987. From penman to makkink. In: Hooghart, J.C. (Ed.), Evaporation and Weather, vol. 39. TNO Communication On Hydrolic Research, The Hague Procedures and Information, pp. 5–31. de Jong, R., 1981. Soil water models: a review. Land Resource Research Institute. Contribution No. 123, Agriculture Canada, Research Branch, Ont., Canada, 39 pp. de Jong, R., Bootsma, A., 1995. Review of recent developments in soil water simulation models. Can. J. Soil Sci. 75, 263 – 273. Denmead, O.T., 1984. Plant physiological methods for studying evapotranspiration: problems of telling the forest from the trees. Agric. Water Manag. 8, 167–189. Diestel, H., 1993. Saturated Flow and Soil Structure; A Review on the Subject and Laboratory Experiments on the Basic Relationships. Springler-Verlag, New York, p. 123. Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements. FAO-ONU, Rome, Irrigation and Drainage, Paper No. 24 (review) 144 pp. Dugas, W.A., Fritschen, L.J., Gay, L.W., Held, A.A., Matthias, A.D., Reicosky, D.C., Steduto, P., Steiner, J.L., 1991. Bowen ratio, eddy correlation, and portable chamber measurements of sensible and laten flux over irrigated spring wheat. Agric. For. Meteorol. 56, 1–20. Dyer, A.J., 1961. Measurement of evaporation and heat transfer in the lower atmosphere by an automatic eddy-correlation technique. Q. J. Roy. Meteorol. Soc. 87, 401–412. Dyer, A.J., Hicks, B.B., 1970. Flux-gradient relationships in the constant flux layer. Q. J. Roy. Meteorol. Soc. 96, 715 – 721. Evett, S.R., Steiner, J.L., 1995. Precision of neutron probe scattering and capacitance type soil water content gauges from field calibration. Soil Sci. Soc. Am. J. 59, 961–968.

149

FAO, 1994. State of food and agriculture. Part III. Water policies and agriculture. Food and Agricultural Organisation, Rome, 126 pp. Feddes, R.A., De Graaf, M., Bourma, J., Van Loon, C.D., 1988. Simulation of water use and production of potatoes as affected by soil compaction. Potato Res. 31, 225 – 239. Foken, T., Wichura, B., 1996. Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol. 78, 83 – 105. Frangi, J.P., Garrigues, C., Haberstock, H., Forest, F., 1996. Evapotranspiration and stress indicator through Bowen ratio method. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 800 – 805. Fre´teaud, J.P., Poss, R., Saragoni, H., 1987. Ajustement d’un mode`le de bilan hydrique a` des mesures tensioneutroniques in situ sous culture de mais. Agron. Trop. 42 (2), 94 – 102. Fritschen, L.J., Gay, L.W., 1979. Environmental Instrumentation. Springer-Verlag, New York, p. 215. Fritschen, L.J., Simpson, J.R., 1989. Surface energy and radiation balance systems: general description and improvements. J. Appl. Meteorol. 28, 680 – 689. Fuchs, M, Tanner, C.B., 1970. Error analysis of Bowen ratios measured by differential psychrometry. Agric. Meteorol. 7, 329 – 334. Fuchs, M., Cohen, Y., Moreshet, S., 1987. Determining transpiration from meteorological data and crop characteristics for irrigation management. Irr. Sci. 8, 91 – 99. Garratt, J.R., 1978. Flux-profile relations above tall vegetation. Q. J. Roy. Meteorol. Soc. 104, 199 – 211. Gay, L.W., 1988. A portable Bowen ratio system for ET measurements. Proceedings of the National Conference on Irrigation, Drainage, American Society of Civil Engineers, New York, pp. 625 – 632. Granier, A., 1985. Une nouvelle me´thode pour la mesure du flux de se´ve brute dans le tronc des arbre. Ann. Sci. For. 42, 193 – 200. Grant, D.R., 1975. Comparison of evaporation measurements using different methods. Q. J. Roy. Meteorol. Soc. 101, 543 – 551. Grebet, P., Cuenca, R.H., 1991. History of lysimeter design and effects of environmental disturbances. In: Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E. (Eds.). Proceeding of the International Symposium on Lysimetry, July 23 – 25, Honolulu, HI, pp. 10 – 18. Grime, V.L., Sinclair, F.L., 1999. Sources of error in stem heat balance sap flow measurements. Agric. For. Meteorol. 94, 103 – 121. Hamdy, A., Lacirignola, C., 1999. Mediterranean water resources: major challenges towards the 21st Century. CIHEAM-IAM Bari, Italy, March, 1999, 570 pp. Hamdy, A., Lasram, M., Lacirignola, C., 1995. Les proble`mes de salinite´ dans la zone Mediterraneénne. C.R. Acad. Agric. 81, 47 – 60. Hatfield, J.L., 1985. Estimation of crop water requirements: present and future. In: Perrier, A., Riou, C. (Eds.), Les Besoins en Eau des Cultures. Paris, pp. 3 – 21.

150


Haverkamp, R., Vauclin, M., Vachaud, G., 1984a. Error analysis in estimating soil water content from neutron probe. 1. Local standpoint. Soil Sci. 137, 78–90. Haverkamp, R., Vauclin, M., Vachaud, G., 1984b. Error analysis in estimating soil water content from neutron probe. 1. Spatial standpoint. Soil Sci. 137, 141–148. Heilman, J.L., Brittin, C.L., Neale, C.M.U., 1989. Fetch requirements for Bowen ratio measurements of latent and sensible heat fluxes. Agric. For. Meteorol. 44, 261–273. Hicks, B.B., 1970. The measurement of atmospheric fluxes near the surface: a generalized approach. J. Appl. Meteorol. 9, 386 – 388. Holmes, J.W., 1984. Measuring evapotranspiration by hydrological methods. Agric. Water Manag. 8, 29–40. Howell, T.A., Schneider, A.D., Jensen, M.E., 1991. History of lysimeter design and use for evapotranspiration measurements. In: Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E. (Eds.). Proceeding of the International Symposium on Lysimetry, July 23–25, Honolulu, HI, pp. 1 – 9. Howell, T.A., Steiner, J.L., Schneider, A.D., Evett, S.R., Tolk, J.A., 1995. Evaporation of irrigated winter wheat, sorghum and corn. ASAE Paper No. 94 2081, ASAE, St. Joseph, MI. Ibrahim, M.A.M., 1996. Assessment of different potential evapotranspiration (ETP) methods in the Nile delta. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3–6, San Antonio, TX, 387 – 393. Itier, B., 1980. Une me´thode simplifieé pour la mesure du flux de chaleur sensible. J. Rech. Atmos. 14, 17–34. Itier, B., 1981. Une me´thode simple pour la me´sure de l’evapotranspiration reélle a` l’ećhelle de la parcelle. Agronomie 1, 869 – 876. Itier, B., 1996. Measurement and estimation of evapotranspiration. In: Pereira, L.S. (Ed.), Sustainability of Irrigated Agriculture. Kluwer Academic, Dordrecht, pp. 171–191. Itier, B., Perrier, A., 1976. Pre´sentation d’une e´tude analytique de l’advection. Ann. Agron. 27 (2), 111–140. Itier, B., Perrier, A., Gosse, G., 1978. Pre´sentation d’une e´tude analytique de l’advection. III. Ve´rification expe´rimentale du mode´le. Ann. Agron. 29 (3), 209–222. Itier, B., Flura, D., Bellabes, K., Kosuth, P., Rana, G., Figueiredo, L., 1992. Relations between relative evapotranspiration and predawn leaf water potential in soybean grown in several locations. Irrig. Sci. 13, 109–114. Itier, B., Brunet, Y., McAneney, K.J., Lagouarde, J.P., 1994. Downwind evolution of scalar fluxes and surface resistance under conditions of local advection. Part I: a reappraisal of boundary conditions. Agric. For. Meteorol. 71, 211–225. Jarvis, P.G., 1976. The interpretation of the variation in leaf water potential and stomatal conductance found in canopies. Philos. Trans. R. Soc. London Ser. B. 273, 593 – 610. Jarvis, P.G., McNaughton, K.G., 1986. Stomatal control of transpiration: scaling up from leaf to region. Adv. Ecol. Res. 15, 1 – 49.

Jensen, M.E., Burman, R.D., Allen, R.G. (Eds), 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals No. 70, 332 pp. Kaimal, J.C., Finningan, J.J., 1994. Atmospheric Boundary Layer Flows. Their Structure and Measurements. Oxford University Press, Oxford, UK, p. 289. Kaimal, J.C., Wyngaard, J.C., Izumi, Y, Cote´, O.R., 1972. Spectral characteristics of surface layer turbulence. Q. J. Roy. Meteorol. Soc 98, 563 – 589. Katerji, N., Perrier, A., 1985. De´termination de la re´sistance globale d’un couvert ve´ge´tal a` la diffusion de vapeur d’eau et de ses differentes composantes. Approche theórique et ve´rification expe´rimentale sur une culture de luzerne. Agric. Meteorol. 34, 105 – 120. Katerji, N., Daudet, F.A., Valancogne, C., 1984. Contributions des re´serves profondes du sol au bilan hydrique des cultures. De´termination et importance. Agronomie 4, 779 – 787. Katerji, N., Dauded, F.A., Carbonneau, A., Ollat, N., 1994. Etude a` l’ećhelle de la plante entie`re du fonctionnement hydrique et photosynthe´tique de la vigne: comparaison des syste`me de conduite traditionnel et en Lyre. Vitis 33, 197 – 303. Kempthorne, O, Allmaras, R.R., 1986. Errors and variability of observations. In: Klute, A. (Ed.), Methods of Soil Analysis. ASA and SSSA, Madison, WI, pp. 1 – 31. Kjelgaard, J.F., Sto¨ckle, C.O., Black, R.A., Campbell, G.S., 1997. Measuring sap flow with the heat balance approach using constant and variable heat inputs. Agric. For. Meteorol. 85, 239 – 250. Klocke, N.L., Martin, D.L., Heermann, D.F., 1985a. Soil evaporation and plant transpiration from irrigated crops. In: Advances in Evapotranspiration. ASAE, Chicago, IL, pp. 335 – 341. Klocke, N.L., Heermann, D.F., Duke, H.R., 1985b. Measurement of evaporation and transpiration with lysimeters. Trans. ASAE 28 (1), 183 – 189. Klocke, N.L., Todd, R.W., Martin, D.L., 1991 Lysimetry for evaporation and transpiration measurement. In: Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E. (Eds.). Proceeding of the International Symposium on Lysimetry, July 23 – 25, Honolulu, HI, pp. 281 – 289. Ledieu, J., De Ridder, P., De Clerk, P., Dautreband, S., 1986. A method of measuring soil moisture by time-domain reflectometry. J. Hydrol. 88, 319 – 328. Leenhard, D., Voltz, M., Bornand, M., 1994. Propagation of the error of spatial prediction of soil properties in simulating crop evapotranspiration. Eur. J. Soil Sci. 45, 303 – 310. Leenhardt, D., Voltz, M., Rambal, S., 1995. A survey of several agroclimatic soil water balance models with the reference to their spatial application. Eur. J. Agron. 1, 1 – 14. Legg, B.J., Long, I.F., Zemroch, P.J., 1981. Aerodynamic properties of field bean and potato crops. Agric. Meteorol. 23 (43), 21. Lhomme, J.-P., 1997. Towards a definition of potential evaporation. Hydrol. Earth Sys. Sci. 1 (2), 257 – 267.

G. Rana, N. Katerji / Europ. J. Agronomy 13 (2000) 125–153 Lhomme, J.-P., Eldin, M., 1985. Une mode`le agroclimatologique de simulation du bilan hydrique des cultures. In: Perrier, A., Riou, C. (Eds.), Les Besoins en Eau des Cultures, Conference International, Paris, 11–14 September, pp. 841 – 852. Lhomme, J.P., Katerji, N., 1991. A simple modelling pf crop water balance for agrometeorological application. Ecol. Model. 57, 11 – 25. Livingston, G.F., Hutchinson, G.L., 1994. Enclosure-based measurement of trace gas exchange: application and source of error. In: Matson, P.A., Herriss, R.C. (Eds.), Methods in Ecology: Biogenic Trace Gas Emissions from Soil and Water, pp. 14 – 51. Losavio, N., Mastrorilli, N., Rana, G., 1992. Review of the convective term in the Penman formula. Proceedings of the Second ESA Congress, Norwick University, pp. 184–185. Mastrorilli, M., Katerji, N., Rana, G., Ben Nouna, B., 1998. Daily actual evapotranspiration measured with TDR technique in Mediterranean conditions. Agric. For. Meteorol. 90, 81 – 89. McBean, G.A., 1972. Instrument requirements for eddy correlation measurements. J. Appl. Meteorol. 11, 1078–1084. Monteith, J.L., 1965. Evaporation and environment. In: Fogg, G.E. (Ed.), The State and Movement of Water in Living Organism. Soc. Exp. Biol. Symp. 19, 205–234 Monteith, J.L., 1973. Principles of Environmental Physics. Edward Arnold, London, UK, p. 241. Parkes, M., Li Yuanhua, 1996. Modeling mobile water flow. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3–6, San Antonio, TX, pp. 509 – 515. Paulson, C.A., 1970. The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteorol. 9, 857–861. Payne, W.A., Bruck, H., 1996. Evapotranspiration and other soil water balance terms estimated from neutron and capacitance probe measurements. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 455–461. Penman, H.L., 1948. Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. A. 193, 120–146. Penman, H.L., 1956. Estimating evaporation. Trans. Am. Geoph. Union 37, 43–50. Pereira, L.S., Perrier, A., Allen, F.G., Alves, I., 1999. Evapotranspiration: concepts and future trends. J. Irrig. Drain. Eng. 125 (2), 45 – 51. Perrier, A., 1984. Updated evapotranspiration and crop water requirement definitions. In: Perrier, A., Riou, C., (Eds.), Les Besoin en Eau des Cultures, Conference International Paris, 11 – 14 September, pp. 885–887. Perrier, A., Hallaire, M., 1979a. Rapport de l’e´vapotranspiration potentielle calculeé a` l’e´vaporation mesureé sur bac. I. Justification d’une relation expe´rimentale obtenue en zone tropicale. Ann. Agron. 30 (4), 329–336.

151

Perrier, A., Hallaire, M., 1979b. Rapport de l’e´vapotranspiration potentielle calculeé a` l’e´vaporation mesureé sur bac. II. Expression en fonction d’un facteur de de´se´quilibre hydrique entre les surfaces e´vaporantes et l’air. Ann. Agron. 30 (4), 337 – 346. Perrier, A., Archer, P., Blanco de Pablos, A., 1974. Etude de l’e´vapotranspiration reélle et maximale de diverses cultures: dispositifs et mesures. Ann. Agron. 25, 697 – 731. Phene, C.J., Allee, C.J., Pierro, J., 1989. Measurement of soil matric potential and real time irrigation scheduling. Agric. Water Manag. 15, 181 – 191. Phene, C.J., Howell, T.A., 1984. Soil sensor control of high frequency irrigation. Trans. ASAE 27 (2), 392 – 396. Phene, C.J., Reginato, R.J., Itier, B., Tanner, B.R., 1990. Sensing irrigation needs. In: Hoffmann, G.J., Howell, T.A., Solomon, K.H. (Eds.), Management of Farm Irrigation Systems, ASAE, St. Joseph, MI, pp. 207 – 261. Philip, J.R., 1959. The theory of local advection. Int. J. Meteorol. 16 (5), 535 – 547. Pieri, P, Fuchs, M., 1990. Comparison of Bowen ratio and aerodynamic estimates of evapotranspiration. Agric. For. Meteorol. 49, 243 – 256. Plauborg, F., 1995. Evaporation from bare soil in a temperate humid climate-measurement using microlysimeters and time domain reflectometry. Agric. For. Meteorol. 76, 1 – 17. Prueger, J.H., Hipps, L.E., Cooper, D.I., 1996. Evaporation and the development of the local boundary layer over an irrigated surface in an arid region. Agric. For. Meteorol. 78, 223 – 237. Pruitt, W.O., Lourence, F.J., 1985. Experiences in lysimetry for ET and surface drag measurement. In: Advances in Evapotranspiration. ASAE Publication No. 74 – 85, pp. 51 – 69. Rana, G., Katerji, N., 1996. Evapotranspiration measurement for tall plant canopies: the sweet sorghum case. Theor. Appl. Climatol. 54 (3 – 4), 187 – 200. Rana, G., Katerji, N., 1998. A measurement based sensitivity analysis of Penman – Monteith actual evapotranspiration model for crops of different height and in contrasting water status. Theor. Appl. Climatol. 60, 141 – 149. Rana, G., Losavio, N., Mastrorilli, M., Venezian Scarascia, M.E., 1990. Crop evapotranspiration measured by two energy balance methods under Mediterranean climate. Acta Hortic. 278, 517 – 524. Rana, G., Katerji, N., Mastrorilli, M., El Moujabber, M., 1994. Evapotranspiration and canopy resistance of grass in a Mediterranean region. Theor. Appl. Climatol. 50 (1 – 2), 61 – 71. Rana, G., Katerji, N., Mastrorilli, M., 1997a. Environmental and soil-plant parameters for modelling actual evapotranspiration under water stress conditions. Ecol. Modell. 101, 363 – 371. Rana, G., Katerji, N., Mastrorilli, M., El Moujabber, M., 1997b. A model for predicting actual evapotranspiration under water stress conditions in a Mediterranean region. Theor. Appl. Climatol. 56 (1 – 2), 45 – 55.

152


Rana, G., Katerji, N., Mastrorilli, M., ElMoujabber, M., Brisson, N., 1997c. Validation of a model of actual evapotranspiration for water stressed soybeans. Agric. For. Meteorol. 86, 215 – 224. Raupach, M.R., 1995. Vegetation-atmosphere interaction and surface conductance at leaf, canopy and regional scale. Agric. For. Meteorol. 73, 151–179. Reicosky, D.C., Peters, D.B., 1977. A portable chamber for rapid evapotranspiration measurements on field plots. Agron. J. 69, 729 – 732. Reicosky, D.C., Sharratt, B.S., Ljungkull, J.E., Baker, D.G., 1983. Comparison of alfalfa evapotranspiration measured by a weighing lysimeter and a portable chamber. Agric. Meteorol. 28, 205 – 211. Reicosky, D.C., 1985. Advances in evapotranspiration measured using portable field chambers. In: Advances in Evapotranspiration, December 16–17, ASAE, Chicago, IL, pp. 79– 86. Revheim, K.J.A., Jordan, R.B., 1976. Precision of evaporation measurements using the Bowen ratio. Boundary-Layer Meteorol. 10, 97 – 111. Riou, C., 1982. Une expression analytique du flux de chaleur sensible en conditions superadiabatiques a` partir de mesures du vent et de la te´mperature a` deux niveux. J. Rech. Atmos. 16, 15 –22. Rose, C.W., Sharma, M.L., 1984. Summary and recommendations of the workshop on evapotranspiration of plant communities. Agric. Wat. Manag. 8, 325–342. Sakuratani, T., 1981. A heat balance method for measuring water flux in the stem of intact plants. J. Agric. Met. 37, 9–17. Saugier, B., Ripley, E.A., 1978. Evaluation of the aerodynamic method of determining fluxes over natural grassland. Q. J. Roy. Meteorol. Soc. 104, 257–270. Saugier, B., Katerji, N., 1991. Some plant factors controlling evapotranspiration. Agric. For. Meteorol. 54, 263–277. Sette, D., 1977. Elementi di Fisica. Patron Ed., 277 pp. Sinclair, T.R., Allen, L.H., Lemon, E.R., 1975. An analysis of errors in the calculation of energy flux densities above vegetation by a Bowen-ratio profile method. BoundaryLayer Meteorol. 8, 129–139. Stafford, J.V., 1988. Remote, non-contact and in-situ measurement of soil moisture content: a review. J. Agricul. Engn. Res. 41, 151 – 172. Stanghellini, C., Bosma, A.H., Gabriels, P.C.J., Werkhoven, C., 1990. The water consumption of agricultural crops: how crop coefficients are affected by crop geometry and microclimate. Acta Hortic. 278, 509–515. Stannard, D.I., 1993. Comparison of Penman–Monteith, Shuttleworth – Wallace and modified Priestley–Taylor evapotranspiration models for widland vegetation in semiarid rangeland. Water Resources Res. 29 (5), 1379–1392. Steduto, P., Caliandro, A., Rubino, P., Ben Mechlia, N., Masmoudi, M., Martinez-Cob, A., Jose Faci, M., Rana, G., Mastrorilli, M., El Mourid, M., Karrou, M., Kanber, R., Kirda, C., El-Quosy, D., El-Askari, K., Ait Ali, M., Zareb, D., Snyder, R.L., 1996. Penman-Monteith reference

evapotranspiration estimates in the Mediterranean region. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 357 – 364. Steduto, P., Katerji, N., Puertos-Molina, H., Unlu, M., Mastrorilli, M., Rana, G., 1997. Water-use efficiency of sweet sorghum under water stress conditions, gas-exchange investigations at leaf and canopy scale. Field Crops Res. 54, 221 – 234. Steinberg, S.L., van Bavel, C.H.M., McFarland, M.J., 1990. Improved sap flow gauge for woody and herbaceous plants. Agron. J. 82, 851 – 854. Steiner, J.L., Howell, T.A., Schneider, A.D., 1991. Lysimetric evaluation of daily potential evapotranspiration models for grain sorghum. Agron. J. 83, 240 – 247. Stewart, J.B., 1984. Measurement and prediction of evaporation from forested and agricultural catchments. Agric. Water Manag. 8, 1 – 28. Stewart, J.B., 1988. Modelling surface conductance of pine forest. Agric. For. Meteorol. 43, 19 – 35. Stewart, J.B., 1989. On the use of Penman – Monteith equation for determining areal evapotranspiration. In: Estimation of Areal Evapotranspiration, Vancouver, Canada, August 1987. IAHS Publication No. 177, pp. 3 – 12 Stull, R.B., 1988. An introduction to boundary layer meteorology. In: Atmospheric Science Library. Kluwer, Dordrecht, The Netherlands, p. 666. Szeicz, G., Long, I.F., 1969. Surface resistance of crop canopies. Water Resources Res. 5, 622 – 633. Szeicz, G., Van Bavel, C.H.M., Takami, S., 1973. Stomatal factor in the water use and dry matter production by sorghum. Agric. Meteorol. 12, 361 – 389. Tanner, C.B., 1963. Energy relations in plant communities. In: Evans (Ed.), Environmental Control of Plant Growth. Academic Press, London, UK, pp. 141 – 148. Tanner, C.B., 1967. Measurement of evapotranspiration. In: Hagan, R.M., Haise, H.R., Edminister, T.W. (Eds.), Irrigation of Agricultural Lands, Agron. Mon. No. 11, Am. Soc. Agron. Madison, WI, pp. 534 – 574. Tanner, B.D., Tanner, M.S., Dugas, W.A., Campbell, E.C., Bland, B.L., 1985. Evaluation of an operational eddy correlation system for evapotranspiration measurements. In: Advances in Evapotranspiration, December 16 – 17, ASAE, Chicago, IL, pp. 87 – 99. Tardieu, F., Katerji, N., 1991. Plant response to the soil water reserve: consequences of the root system environment. Irrig. Sci. 12, 145 – 152. Taylor, P.A., 1970. A model of airflow above changes in surface heat flux temperature and roughness for neutral and unstable conditions. Boundary-Layer Meteorol. 1, 18 – 39. Thom, A.S., 1975. Momentum, mass and heat exchange of plant communities. In: Monteith, J.L. (Ed.), Vegetation and Atmosphere Principles, vol. 1. Academic Press, London, UK, pp. 57 – 109.

G. Rana, N. Katerji / Europ. J. Agronomy 13 (2000) 125–153 Thom, A.S., Stewart, J.B., Oliver, H.R., Gash, J.H.C., 1975. Comparison of aerodynamic and energy budget estimates of fluxes over a pine forest. Q. J. Roy. Meteorol. Soc. 101, 93– 105. Todorovic, M., 1997. A model to estimate hourly and daily evapotranspiration using variable canopy resistance. PhD Thesis, University of Sassari, Italy, 204 pp. Topp, G.C., Davis, J.L., 1985. Time-domain reflectometry (TDR) and its application to irrigation scheduling. In: Hillel, D. (Ed.), Advances in Irrigation, vol. 3. Academic Press, New York, pp. 107–127. Topp, G.C., Zegelin, S.J., White, J., 1994. Monitoring soil water content using TDR: an overview in progress. In: Time Domain Reflectometry in Environmental, Infrastructure and Mining Applications. Special Publication SP 19– 94, US Department on Interior, Bureau of Mines, pp. 67– 80. Vasic, G., Milivojevic, J., Videnovic, Z., Petrovic, G., 1996. Maize evapotranspiration coefficient in Chernozem type soil region, In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3–6, San Antonio, TX, pp. 418–423. Villalobos, F.J., 1997. Correction to eddy covariance water vapour flux using additional measurements of temperature. Agric. For. Meteorol. 84, 77–83. Wagner, S.W., Reicosky, D.C., 1996. Mobile research gas exchange machine-MRGEM instrumentation update. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3–6, San Antonio, TX, pp. 781 – 786. Webb, E.K., 1960. On estimating evaporation with fluctuating Bowen ratio. J. Geoph. Res. 65 (10), 3415–3417.

.

153

Webb, E.K., 1965. Aerial microclimate. Agriculture meteorology, meteorological monographs. Am. Meteorol. Soc. 6 (28), 27 – 58. Webb, E.K., Pearman, G.I., Leuning, R., 1980. Correction of flux measurements for density effects due to heat and water vapour transfer. Q. J. Roy. Meteorol. Soc. 106, 85 – 100. Werkhoven, C., 1993. Time-domain reflectometry and tensiometers for detecting soil moisture content. Acta Hort. 335, 491 – 496. Wieringa, J., 1993. Representative roughness parameters for homogeneous terrain. Boundary-Layer Meteorol. 63 (4), 323 – 363. Wiltshire, J.J., Wright, C.J., Colls, J.J., Unsworth, M.H., 1995. Effects of heat balance stem flow gauges and associated silicone compounds of ash trees. Agric. For. Meteorol. 73, 135 – 142. Wright, J.L., 1982. New evapotranspiration crop coefficients, American Society of Civil Engineering. J. Irrig. Drain. Div. 108 (IR1), 57 – 74. Zhang, J., Davies, W.J., 1989. Abscisic acid produced in dehydrating roots may enable the plant to measure the water status of the soil. Plant Cell Environ. 12, 73 – 81. Zhang, J., Kirkham, M.B., 1995. Sap flow in a dicotyledon (sunflower) and a monocotyledon (sorghum) by the heat balance method. Agron. J. 87, 1106 – 1114. Zhang, J., Schurr, U., Davies, W.J., 1987. Control of stomatal behaviour by abscisic acid which apparently originates in roots. J. Exp. Bot. 38, 1174 – 1181. Zhao, W.G., Berliner, P.R., Zangvil, A., 1996. Heat storage terms in evapotranspiration estimation. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 – 6, San Antonio, TX, pp. 34 – 41.

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