Evapotranspiration (ET) is a combination of evaporation and plant transpiration processes into a total moisture flux from the ground to the atmosphere. It plays an important role in the water and energy balance on earth’s surface and is of particular interest to agricultural and irrigation practices.

ET observations are only available for a limited number of locations across the southeastern United States. Empirical models are usually used to estimate ET at local and regional scales. No estimation technique is universal, but a standard method is the Penman-Monteith equation as specified by the UN Food and Agriculture Organization (FAO) in paper number 56.

The FAO56 Penman-Monteith method estimates ET rates for a well-watered reference surface based on physical atmospheric observations of solar radiation, temperature, wind speed, and relative humidity. This estimate is commonly referred to as reference ET. The reference surface is a theoretical grass reference crop with a height of 0.12m, an albedo of 0.23, and a constant surface resistance of 70 s/m. While dependent on time of year and location, the equation is developed for the hypothetical grass reference crop and is thus independent of specific crop characteristics and soil factors. Crop coefficients can be applied to adjust the estimate for a particular crop. For more detailed information on the FAO56 Penman-Monteith method, please refer to the UN FAO56 Penman-Monteith documentation.

Please note that solar radiation measurements are not readily available at all stations across the Southeast US. In these cases, the Hargreaves and Samani (1985) solar radiation estimate is utilized as an input to the Penman-Monteith equation. The Hargreaves and Samani (1985) technique is based on the assumption that diurnal temperature range varies indirectly with cloud cover and thus, is related to incoming solar radiation.

To obtain raw data values of daily estimated reference crop ET, crop ET, and/or open water evaporation, please visit our CRONOS database, or contact our office for larger datasets.