22.4. Methods of Estimating PET

These are derived from observed data and statistical relationships.

Thornthwaite Method: Based on mean monthly temperature and day length. Suitable for temperate regions but often inaccurate in tropical climates like India.

PET = (L)(N)(10T)^a / 12I Where:

• L = Average day length
• T = Mean monthly temperature (°C)
• I = Annual heat index
• a = Empirical coefficient

Blaney–Criddle Method: Incorporates temperature and daylight hours.

PET = p(0.46T + 8) Where:

• p = Mean daily percentage of annual daylight hours
• T = Mean daily temperature

Empirical methods for estimating PET are based on real-world data and established statistical relationships. The Thornthwaite Method uses average day length and mean monthly temperatures to estimate PET, making it useful in places with a temperate climate. However, it's less accurate in tropical areas like India, where factors other than temperature can significantly affect evapotranspiration. The method involves a formula that combines these values along with an empirical coefficient. On the other hand, the Blaney-Criddle Method also considers temperature, but it focuses on the fraction of the year when sunlight is available, hence providing a slightly different approach to calculate PET.

Overall, both methods simplify the estimation by using readily available climatic data.

Think of the empirical methods like a recipe for baking. Just like a recipe uses specific ingredients in measured amounts to produce a cake, these methods use specific climate measurements (like temperature and daylight) to produce an estimate for PET. Just as not all cakes come out perfectly with every recipe, these formulas may not always yield accurate results in every climate, especially tropical climates where conditions can be more variable.

Penman Method: Combines energy balance and aerodynamic terms. Requires temperature, humidity, radiation, and wind data.

PET = (Rₙ + γ f(u)(eₛ - eₐ)) / (Δ + γ) Where:

• Rₙ = Net radiation
• Δ = Slope of vapor pressure curve
• γ = Psychrometric constant
• f(u) = Wind function
• (eₛ - eₐ) = Vapor pressure deficit

Penman–Monteith Method: Recommended by FAO (Food and Agriculture Organization) for global applications. Considered the most accurate.

PET = (0.408 Δ (R - G) + γ u (eₛ - eₐ)) / (Δ + γ (1 + 0.34u₂)) Where:

• G = Soil heat flux density
• u₂ = Wind speed at 2m

Physically based methods for estimating PET focus on the underlying processes that govern water evaporation. The Penman Method takes into account multiple environmental factors, such as net radiation (the energy available for evaporation), humidity, and wind conditions. The formula requires data input for each of these factors. The Penman-Monteith Method expands on this by adding more comprehensive terms for soil heat flux and is widely recommended as it provides a more accurate value for PET. Both methods rely on actual physical science principles rather than just statistical relationships.

Think of these methods like understanding a plant's growth. Just as a gardener considers sunlight, irrigation, soil quality, and air circulation to help plants thrive, the physically based methods account for various climatic and environmental components when calculating PET. It’s about getting a complete picture of what influences evaporation rates, much like ensuring all conditions are right for a plant to grow.

MODIS and Landsat data are used to estimate PET over large areas.

Remote sensing allows spatial mapping of PET for inaccessible or vast regions like deserts or forests.

Satellite-based and remote sensing approaches utilize images and data collected from satellites, such as MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat, to derive estimates of PET across extensive areas. This method is particularly advantageous for regions that are hard to reach or where ground data collection is limited, such as remote deserts or dense forests. The use of remote sensing enables the analysis of PET variations over large scales, which is crucial for managing water resources effectively in these diverse landscapes.

Imagine using a drone to capture images of a vast agricultural field. It’s like having an aerial view of a landscape, allowing you to see patterns and variations that you might miss from the ground. Similarly, satellites serve as eyes in the sky, gathering information about water vapor and climate conditions over large regions, enabling us to map and estimate PET in places that are otherwise difficult to monitor directly.

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