Making Effective Use Of Rainfall

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An inadequate and variable water supply and extremes of temperatures are the two universal environmental risks in agricultural production. High temperatures in tropical climates limit the production of crops native to temperate latitudes, and low winter temperatures in high latitudes are a check on growing crops native to tropical areas. Inadequate and variable water supply, however, has a negative impact on crop production in every climatic region. The problem is more pronounced in tropical and sutropical semiarid and arid climates in which the water losses in evaporation and evapotranspiration are very high throughout the year. Management of water resources is a much greater and more universal problem than any other factor of the environment.

Not all rainfall that falls in a field is effectively used in crop growing, as part of it is lost by runoff, seepage, and evaporation. Only a portion of heavy and high-intensity rains can enter and be stored in the root zone, and therefore effectiveness of this type of rainfall is low. With a dry soil surface with no vegetation cover, rainfall up to 8 mm/day may all be lost by evaporation. A rainfall of 25 to 30 mm may be only 60 percent effective with a low percentage of vegetative cover. Frequent light rains intercepted by a plant canopy with full ground cover are close to 100 percent effective (FAO, 1977).

In most parts of the world crop production depends on rainfall. Knowledge of the probable dates of commencement and end of the rainy season and the duration of intermittent dry and wet spells can be very useful for planning various agronomic operations such as preparing a seedbed, manuring, sowing, weeding, harvesting, threshing, and drying. This results in minimizing risk to crops and in optimum utilization of limited resources including water, labor, fertilizer, herbicides, and insecticides. There are critical periods in the life history of each crop, from sowing to harvesting. With knowledge of frequency of occurrence of wet and dry spells, a farmer can adjust sowing periods in such a way that moisture-sensitive stages do not fall during dry spells. Under irrigated farming, irrigation can be planned using data regarding consecutive periods of rainfall to satisfy the demands for critical periods. Knowledge of wet and dry spells can also help a great deal in improving the efficiency of irrigation-water utilization.

Measurement of Effective Rainfall

Numerous studies have been done in many countries to identify rainfall patterns and characteristics which can be used for planning agricultural operations such as sowing dates, harvesting dates, and periods and frequency of irrigation. Many of these studies are based on statistical analysis of the historical rainfall records. To study these characteristics of rainfall, it is assumed that each year provides one observation for an event of characteristic interest, and the total observations are then analyzed, assuming that they are a simple random sample from a single distribution. An effective rain event has been defined in various ways for varied purposes.

1. The start of the rains in northern Nigeria is defined as the first ten-day period with more than 25 mm precipitation, provided that rainfall in the next ten days exceeded half the potential evapotranspiration (Kowal and Krabe, 1972).

2. Raman (1974), deciding on a criterion of rainfall favorable for commencement of sowing operations, considered two basic requirements that must be satisfied. First, a sustained rainspell, which more or less represents the transition from premonsoon to monsoon conditions, should be identified. Second, in the spell so chosen, the rain that falls should percolate into the soil down to a reasonable depth and also build a moisture profile after loss through evaporation. Keeping in view these requirements, Raman (1974) selected a criterion for rainfall occurrence favorable for the commencement of sowing operations as a spell of at least 25 mm of rain in a period of seven days, with 1 mm or more on any five of these seven days, assuming an evaporative loss of 18 mm at the end of five days in the spell. The weekly spell taken was compatible with the average life cycle of monsoon depression in the area. Based on this criterion, the dates of commencement of the first spell were chosen for each year, their mean, median, standard deviation, and quartile range were calculated, and these were mapped. These values were used to study the spatial distribution of the dates of commencement of sowing rains in the black cotton soils of Maharashtra in India.

3. Dastane (1974) recommended two methods for estimation of effective rainfall. In the first method, a percentage of rainfall varying from 50 to 80 percent was assumed to be effective. In the second method, rainfall less than 6.25 mm or in excess of 75 mm on any day, or a rainfall in excess of 125 mm in 10 days, is considered to be ineffective.

4. The U.S. Department of Agriculture (USDA) Soil Conservation Service (SCS) method estimates the effective rainfall by the evaporation/precipitation ratio method (FAO, 1977). Tables are given in which relationships are shown between average monthly effective rainfall and mean monthly rainfall for different values of average monthly crop evapotranspiration values. For use in irrigation, a net depth of irrigation water that can be stored effectively in the root zone is assumed to be 75 mm. Correction factors are given for different depths that can be stored.

5. Benoit (1977) defined the start of the growing season in northern Nigeria as the date when rainfall exceeded evaporation and remained greater than zero for the remainder of the growing season, provided that a dry spell of five days or more did not begin in the week after this date. Based on this criterion, he determined the start of the growing season in northern Nigeria. The planting dates of millet in Nigeria are observed to coincide with the first occurrence of 20 mm of rain over a two-day period.

6. The India Meteorological Department uses a chart showing normal dates for the onset of the southwest monsoon over India, taking long-term averages of five-day accumulated rainfall at 180 stations (Ashok Raj, 1979). The period characterizing an abrupt rise in the normal rainfall curve was taken to define the onset of the monsoon. This chart assists in overall indication of the arrival and progress of the monsoon over the entire country. However, for agricultural planning over small areas, this chart has serious limitations. This criterion has no relationship to the buildup of a moisture reserve in the soil, which alone is vital for commencement of the sowing operation.

7. Ashok Raj (1979) proposed a method for forecasting rainfall characteristics, such as the onset of an effective monsoon, based on the following criteria:

a. The first day's rain in the seven-day spell, signifying the onset of an effective monsoon, should not be less than e mm, where e is the average daily evaporation.

b. The total rain during the seven-day spell should not be less than 5e + 10 mm.

c. At least four of these seven days should have rainfall, with not less than 2.5 mm of rain on each day.

Using these criteria, Ashok Raj determined the onset of an effective monsoon at various probability levels for several states of India.

8. Stern and Coe (1982) used a general definition for the start of rains with these criteria:

a. The event making the start of the season was not considered until after a stated date D.

b. An event E then indicates a potential start date, defined as the first occurrence of at least x mm rainfall totalled over t consecutive days.

c. The potential start could be a false start if an event F occurs afterward, where F was defined as a dry spell of n or more days in the next m days.

For determining the start of rains at Kano, Nigeria, D was taken as May 1, x as 20 mm in two consecutive days, and F as a ten-day spell in the next 30 days. By using frequency distribution, they determined the potential start and false starts at different probability levels.

In all the aforementioned models, workers defined the event signifying the start of rains as a particular amount of rainfall received over a period of days. However, they neglected the soil moisture characteristics, which decide the availability of water and workable condition of the soil. A potential start of rains must make the soil sufficiently moist to support the germination of seeds. Thus, while deciding the start of rains or the onset of monsoon, it is important to consider the soil's moisture characteristics.

9. Patwardhan and Nieber (1987) proposed a soil-water balance model based on the equation of conservation of water in the soil profile. The water balance of the entire soil profile is considered in terms of individual processes:

where P is rainfall, I is irrigation, R is runoff, RN is rainfall interception, ET is evapotranspiration, D is deep drainage, and AS is the change in water content in the soil profile. All the measurements are in mm of water. Effective rainfall is defined in the model as being that portion of the rainfall that infiltrates into the soil and does not contribute to deep percolation. This is expressed as

where EP is the effective rainfall. The time scale on which the effective rainfall can be defined can be as small as one day; however, there is no upper limit.

10. In Taiwan, Chin, Komamura, and Takasu (1987) developed a model for the estimation of effective rainfall in order to use rainfall more effectively. The basis of the model is the equilibrium equation of the water balance in a paddy field. An irrigation area of a farm pond in northwest Taiwan was chosen to test the model's accuracy because of its simple cropping and single-rotation irrigation block, where inflow and outflow could be easily measured. The average measured and computed values were in close agreement, and the effective rainfall rate for this area was 40 to 65 percent.

11. A modified water balance model was used to estimate effective rainfall for lowland paddy in Thailand (Mizutani et al., 1991). An interception component was included in the model. The relationship of interception to rainfall at three growth stages was established from field experiments and utilized in the model. Eight stations with records for 30 years were selected for analysis. Simulations were run with computed crop water requirement and various values of percolation rate, ponding depth, and irrigation interval to study their effects on effective rainfall, irrigation requirements, and types of irrigation practiced. A 150 mm ponding depth and a five-to-six-day irrigation interval provide the most efficient irrigation and effective use of rainfall for lowland rice.

12. Drainage lysimeters were used by Kanber and colleagues (1991) to determine the effective rainfall in the Cukurova region of Turkey. They concluded that the relationship between total and effective rainfall increased linearly. An equation was derived to estimate the total monthly effective rainfall. The study showed that 16 percent of the rainfall was lost by deep percolation and 84 percent was retained on plant surfaces or stored in the soil.

13. In Japan, Komamura (1992)assessed the lower limit of effective rainfall for a small rainfall event. It was concluded from the study that (a) the degree of interception varies with crop type and (b) the useful lower rainfall limit for increasing soil water content is a minimum of 2 to 3 mm.

14. Alqarawi, Aldoss, and Assaeed (1997,1998) carried out studies to investigate the effect of amount of rainfall (100, 200, and 400 mm) and rainfall distribution (7 and 14 days between two rains) on seedling survival, establishment, and growth characteristics of three populations of Hammada elegans in different areas in Saudi Arabia. Water equivalent to the specified amounts of rainfall was evenly distributed every 7 or 14 days over a period of three months. Seedlings were then left to grow for another two months without irrigation. The results showed that survival and establishment under 400 mm rainfall were significantly higher than the other two rainfall averages (47 percent and 11 percent, respectively). Survival percentage increased as the period between two rains was extended to 14 days, although not significantly. Establishment increased from 3 to 9 percent with extension of the period between two rains.

15. Mohan, Simhadrirao, and Arumugam (1996) proposed a model for determining effective rainfall for use in estimating irrigation requirements for lowland rice. The method assumes that a paddy field can store additional rainfall up to the paddy spillway. The water balance equation reflecting the storage at the end of a time period t is given as

where St is storage at the end of period t; St-1 is storage at the beginning of the period t; It is irrigation applied during the period t; ETt is actual evapotranspiration during the period t; ERt is effective rainfall during the period t; and Pt is percolation loss during the period t.

Free board is the rainfall storage capacity in the field. It is the difference between the spillway height and the depth of water in the paddy field. The depth of water use by ET and percolation losses during the period are added with free board to obtain the available storage capacity. If the rainfall amount is greater than this capacity, the rainfall excess is taken as runoff. A field spillway height of 100 mm was adopted. The percolation losses were taken as 2 mm/day, according to the local data. The depth of the water layer was 50 mm throughout. Mohan, Simhadrirao, and Arumungam (1996) compared this method to a number of other methods, including the USDA (SCS) method, and found this to be more appropriate than all the other methods.

16. A numerical simulation model (E-RAIN) was used to estimate long-term average and extreme values of monthly and annual effective rainfall for both seepage (seep) and fully enclosed seepage (FES) irrigation systems in Florida (Smajstrla, Stanley, and Clark, 1997). The model calculates effective rainfall as the difference between rainfall and runoff.

where E-Rain = effective rainfall (mm), Rainfall = rain depth (mm), and Runoff = runoff volume per unit land area (mm). Runoff is calculated as

where S = a watershed storage coefficient (mm). The model was used with 41 years of daily rainfall data at Bradenton, Florida, demonstrating that the average annual effective rainfall is 775 mm with FES and 577 mm with seep irrigation. The model also simulates probabilities of occurrence of effective rainfall extreme values. The researchers claimed that this model should be useful to water management districts that issue water use permits on a probability basis and to irrigation system designers and managers who require estimates of effective rainfall as a component of crop water use.


The change of the state of water from solid and liquid to vapor and its diffusion into the atmosphere is referred to as evaporation. It plays a major role in the redistribution of thermal energy between the earth and the atmosphere and is an essential part of the hydrological cycle.

The process of evaporation involves the supply of energy for the latent heat of vaporization and the transfer process. The transfer process is governed by turbulence. Evaporation is a continuous process as long as there is a supply of energy, availability of moisture, and vapor pressure gradient between the water surface and the atmosphere.

Water vapor diffuses into the atmosphere from different surfaces such as lakes, rivers, ponds, cloud droplets, rain drops, moist soil, animals, and plants, but there is no fundamental difference in the physics of the process. Evaporation also occurs directly from the solid state, that is, from snow and ice, provided an appropriate vapor pressure gradient exists.


Most of the water absorbed by plants is lost to the atmosphere. This loss of water from living plants is called transpiration. It can be stomatal, cuticular, or lenticular. Transpiration that takes place through stomata is called stomatal transpiration. The maximum stomatal transpiration takes place through leaves. Outside the epidermal cells of a leaf is a thin layer called the cuticle. Sometimes gaps or pores in the cuticle are present. Water loss through these gaps is called cuticular transpiration. Pores or gaps in roots or stems are called lenticules, and loss of water through lenticules is called lenticular transpiration. The rate of transpiration depends on both meteorological factors and crop characteristics.

Stomata open in light and close in the dark, and the opening of stomata during day leads to transpiration. Lowered humidity results in higher transpiration. An increased difference between atmospheric and leaf humidity leads to increased transpiration. Humidity or vapor pressure is a function of temperature. A decrease in temperature increases vapor pressure in the environment, reducing the saturation deficit. The reverse is the case at higher temperatures. It follows that at higher temperatures there will be an increase in transpiration. In windy conditions, fresh dry air will replace the saturated air around the plant, leading to increased transpiration.

If the root/shoot ratio is high, there will be more absorption and less transpiration and vice versa. With greater availability of water to plants, transpiration will rise, while under a water stress condition, transpiration is restricted.

Leaf characteristics also influence transpiration. If the leaf area is large, transpiration will be high. A thicker cuticle will result in lowered cuticular transpiration. The presence of epidermal hair on leaves restricts the loss of water vapor to the atmosphere.

Evaporation versus Transpiration

The fundamental difference between evaporation from a free water surface and transpiration from plants is that in transpiration a diffusive resistance occurs due to the internal leaf geometry, including the stomata. No such resistance exists in evaporation from a free water surface. Because the stomata closes at night, the rate of transpiration drops to 5 to 10 percent of that occurring during the day, but the rate of evaporation remains relatively high because of the availability of energy stored at night.

Evapotranspiration and Potential Evapotranspiration

Over a land surface covered with vegetation, evaporation involves the following processes:

1. movement of water within the soil toward the soil surface or toward the active root system of the plants;

2. movement of water into the roots and then throughout the plant tissues to leaf surfaces;

3. change of water into vapor at the soil surface or at the stomata of plants;

4. change of rain water or snow from the outer surface of plants into vapor; and

5. the physical removal of water vapor from the boundary layer.

The overall process that involves these activities is termed evapotranspiration (ET).

Evapotranspiration is the combined loss of water from vegetation—both as evaporation from soil and transpiration from plants. Both processes are basically the same and involve a change of state from liquid to vapor. When water is adequately available at a site of transformation (i.e., soil or plant surfaces) the rate of evapotranspiration is primarily controlled by meteorological factors, including solar radiation, wind, temperature, and the evaporating power of the atmosphere.

The dependence of evapotranspiration on meteorological factors at a given place has led to the concept of potential evapotranspiration (PET). It is the upper limit of evapotranspiration. The concept assumes that there is an ample supply of water at the site of evaporation and that the rate is governed by the evaporating capacity of the atmosphere. However, the aerodynamic properties and stomatal behavior of the crop may modify the effect of meteorological factors on evapotranspiration. Potential evapotranspiration is therefore defined as the rate of evapotranspiration from an extensive surface of 8 to 15 cm green grass cover of uniform height, actively growing, completely shading the ground, and not short of water (Doorenbos and Pruitt, 1977; Smith, 2000).

When empirical methods of determining potential evapotranspiration are calibrated under conditions of unlimited water supply, they provide reasonably quantitative estimates. This is due to the conservativeness of potential evapotranspiration and because the variance from average values of potential evapotranspiration is correlated with variances of many climatic variables from their means. Empirical formulae, in general, correlate evapotranspiration with air temperatures, incident solar radiation, wind, atmospheric humidity, or a combination of these.

The measurement of evapotranspiration under normal conditions is of great importance in the estimation and management of present and future water resources and for solving many theoretical problems in the field of hydrology and meteorology. In planning irrigation, evapotranspiration data are used as a basis for estimating the acreage of various crops or combination of crops that can be irrigated with a given water supply or as a basis for estimating the amount of water that will be required to irrigate a given area. There has been a tremendous increase in the use of evapotranspiration data in scheduling irrigation. Evapotranspiration data are also used as a basis for evaluating the overall efficiency of irrigation in the field. As an agroclimatic index it has been widely used to assess the effect of the water supply on both the growth and yield of the crops.

Measurement of Evaporation and Evapotranspiration

There are several simple devices and empirical methods of estimating evaporation. Small containers of different kinds can measure evaporation quite accurately. However, for practical purposes, the measurement of evaporation from the surface of large water bodies, crop fields, bare soil, or catchment basins has greater significance. The relationship between the size of the evaporating surface and the rate of water loss is illustrated in Figure 4.1. The rate of evaporation is fairly independent of the size of the measuring pan under high humidity conditions. However, when the air is dry the size of the pan greatly influences the rate of evaporation. Therefore, to make use of measurements taken from these pans and the other bodies, a relationship between them needs to be established. There are five main types of evaporimeters or pans used for measuring evaporation. These are pans placed above the ground, pans sunk in the soil, floating pans, lysimeters, and Piche evaporimeters.

1. Pans placed above the ground: The U.S. Weather Bureau Class A pan is widely used in most countries of the world. The Class A evaporation pan

01 01 a

Low RH

-Medium RH

High RH

Low RH

-Medium RH

High RH

Hand Brakes For Carts

Piche Small pan evaporimeter

Large pan Small irrigated Large irrigated field field

Piche Small pan evaporimeter

Large pan Small irrigated Large irrigated field field

FIGURE 4.1. Size of evaporating surface and rate of evaporation is circular, 121 cm in diameter, and 25.5 cm in depth. It is made of galvanized iron (22 gauge). The pan is mounted on a wooden frame platform with its bottom 15 cm above ground level. The pan must be level. It is filled with water below the rim, and water level should not drop to more than 7.5 cm below the rim (FAO, 1977). The major drawback of this pan is that the sensible heat flux from the sides and bottom results in increased evaporation, and it gives inflated values of evaporation.

2. Sunken pans: Many countries use sunken pans for measuring evaporation. The U.S. Bureau of Plant Industry and the British Institute of Water Engineers use pans of different dimensions in which the water surface is kept close to the earth's surface. The most common is the Sunken Colorado pan. It is 92 cm square and 46 cm deep. It is made of glavanized iron, set in the ground with the rim 5 cm above the ground (FAO,1977). The water inside the pan is maintained at or slightly below ground level. Sunken pans suffer from several operational difficulties including cleaning and heat leakage.

3. Floating pans: These pans are made to float in water bodies with suitable rafts. Water loss from these pans is similar to the water loss from the surrounding water surface. The installation and operation of these pans in water bodies are costly. Moreover, their operation becomes difficult when the wind is strong.

4. Lysimeters: Lysimetry is defined as the calculation of the vertical output fluxes using the volume and concentration of leached water over a period of time from a defined volume of soil (Muller, 1995). Lysimeters are tanks, filled with soil and buried in the ground, to measure the loss of water from the soil. They are commonly used for measuring evapotranspiration from a crop. However, they can also be used to measure the evaporation from a bare soil. Lysimeters are of the drainage and weighing types, with the latter the most commonly used.

The weighing lysimeter can measure evaporation and evapotranspiration for very short intervals of time. In addition to the measurement of evaporation and evapotranspiration, weighing lysimeters can give information such as the diurnal patterns of evaporation, variations in energy partitioning, and the relationships between transpiration and soil moisture tension. The biggest drawback of lysimeters is the high cost of their installation and their immobility.

5. Piche evaporimeters: A Piche evaporimeter consists of an inverted graduated tube filled with water and a filter paper clamped over its mouth. The instrument is kept in a Stevenson's screen. The Piche evaporimeter is not very reliable. It overestimates the effects of wind and underestimates the effects of solar radiation.

Empirical Methods

There is no end to the list of empirical methods that have been proposed for measuring evapotranspiration. The methods enumerated in this section are only a sample of that population. These are in common use within the irrigation profession. A brief description of each of these methods for computing reference crop evapotranspiration (in mm/day) is given.

1. Hargreaves method: This method (Hargreaves and Samani, 1985) estimates grass-related reference evapotranspiration. According to this method,

where ET0 is reference crop evapotranspiration in mm/day; Ra is extra terrestrial radiation in equivalent evaporation in mm/day; TR is temperature range in °C, and T is mean daily air temperature in °C.

Because this is basically a temperature-based method, it is less accurate. However, local calibration of this method gives reasonably accurate ET0 estimates. It requires only the measurements of maximum and minimum air temperatures. The method is recommended for ET estimates over ten days or longer periods (Smith, 1992).

2. Ritchie method: This method, as quoted in Meyer, Smith, and Shell (1995), is principally based on the radiant energy concept. It can be expressed as

where Eeq is equilibrium evapotranspiration (mm/day); a is albedo, equal to 0.23; Td is adjusted mean daily temperature, defined as (0.6 Tmax + 0.4 Tmin); and E0 is daily potential evaporation (mm/day).

3. Class A pan method (FAO-24 Pan): Doorenbos and Pruitt (1977) provided a simple proportional relationship to estimate the ET, from U.S. Class A pan evaporation as

where Kp is the pan coefficient, which depends on the pan environment in relation to nearby surfaces, obstructions, and the climate itself. Kp values can be obtained from FAO-24 Table-18 (Doorenbos and Pruitt, 1977).

4. Penman-Monteith method (as quoted by Chiew et al., 1995):

5. FAO-24 Penman method (as quoted by Chiew et al., 1995):

ET o = c [0.408 a^( Rn - G ) + 2.7 a^(1 + 0.864U )(ea - ed)] (4.11)

c depends on shortwave radiation, maximum relative humidity, daytime wind speed, and ratio of daytime to nighttime wind.

6. FAO-24 Radiation method (as quoted by Chiew et al., 1995):

W depends on temperature and altitude; c depends on mean relative humidity and daytime wind speed.

7. FAO-24 Blaney-Criddle method (as quoted by Chiew et al., 1995):

Explanations of the symbols used and where not explained along with methods 4 to 7 are as follows:

p is daily percentage of total annual daytime hours and depends only on the latitude and time of year. c is a correction factor and depends on minimum relative humidity, sunshine hours, and daytime wind speed. It can be calculated with the procedure oulined in FAO-24. Rn is net radiation at crop surface (MJ m-2/day). Rs is shortwave radiation (MJ m-2/day). G is soil heat flux (MJ m-2/day). T is average daily temperature (°C). U is wind speed at 2 m above ground surface (m-s1). ea is saturation vapor pressure at air temperature (kPa). ed is actual air vapor pressure (kPa).

A is slope of saturation vapor pressure/temperature curve (kPa/°C). Y is psychrometric constant (kPa/°C).

8. Computerized crop water use simulations: Computer programs have been developed for the estimation of reference crop evapotranspiration from climatic data and allow the development of standardized information and criteria for planning and management of rainfed and irrigated agriculture. The FAO CROPWAT program (Smith, 1992) incorporates procedures for reference crop evapotranspiration and crop water requirements and allows the simulation of crop water use under various climate, crop, and soil conditions.

9. ET estimates from National Oceanic and Atmospheric Administration (NOAA) imageries: Di Bella, Rebella, and Paruelo (2000) used multiple regression analysis to relate evapotranspiration computed from a water balance technique data obtained from NOAA satellite imagery. This approach, based on only remotely sensed data, provided a reliable estimate of ET over the Pampas region of Argentina. The approach is useful to estimate evapotranspiration on a regional scale and not at a particular point.

As stated at the beginning of this section, there is no dearth of methods available in the literature that are proposed to measure evapotranspiration. Numerous studies have been conducted at locations in different parts of the world with a wide range of climatic conditions to compare the relative performance of various methods of ET estimation (Jensen, Burman, and Allen, 1990; McKenny and Rosenberg, 1993; Chiew et al., 1995; Kashyap and Panda, 2001). There are some common conclusions from these studies.

• Combination methods (based on a number of parameters) generally provide more accurate ET estimates because they are based on physical laws and rational relationships.

• Depending on the climatological situation of a specific site, a locally calibrated, limited data input, simple ET estimation method may produce better results than a data extensive, complicated method.

• Availability of climatic data alone should not be the sole criterion in selecting a method since some of the data needed can be estimated from other variables with sufficient accuracy to permit using one of the better ET estimating methods.

• Penman estimates are consistently 20 to 40 percent higher than the Penman-Monteith estimates. Given that Penman-Monteith is the current standard method recommended by FAO, ET values calculated using FAO-24 Penman should therefore be used with caution.

• The FAO-24 radiation, FAO-24 Blaney-Criddle, and Penman-Monteith give similar monthly ET estimates. The Blaney-Criddle method, which uses only temperature data and some long-term average climate information, is adequate for applications in which only monthly estimates of ET are required. The radiation method gives daily ET estimates similar to Penman-Monteith. Unlike Penman-Monteith, that also requires wind data, the FAO-24 radiation method estimates ET from temperature and sunshine hours, climate variables that are rela tively conservative in spatial dimensions. The FAO-24 radiation method can thus be used as a surrogate for Penman-Monteith to estimate daily ET for areas where wind data are not available.

• The use of a pan method for estimating ET is controversial. Some researchers do not favor the use of this method, as extreme care is required in the operation of a pan as compared to any other climatic instrument. On the other hand, others favor the use of this method due to the availability of long-term evaporation records and the ease of use.

• There is a satisfactory correlation between Class A pan data and Pen-man-Monteith evaporation totals over three or more days. However, pan data are useful only if an accurate pan coefficient is used to relate the pan data to Penman-Monteith ET. The pan coefficient is very much dependent on local conditions and should be determined by comparing the pan data with the Penman-Monteith ET estimates.

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