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Potential evapotranspiration is expressed in terms of a depth of water or soil moisture percentage. If the actual evapotranspiration is considered the net result of atmospheric demand for moisture from a surface and the ability of the surface to supply moisture, then PET is a measure of the demand side (also called evaporative demand).
Inputs to SPEI datasets can include high-resolution potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM) and hourly Potential Evapotranspiration (hPET). GLEAM is a set of algorithms designed to calculate actual evaporation, PET, evaporative stress, and root-zone soil moisture.
The Penman-Monteith equation approximates net evapotranspiration (ET) from meteorological data as a replacement for direct measurement of evapotranspiration. The equation is widely used, and was derived by the United Nations Food and Agriculture Organization for modeling reference evapotranspiration ET 0 .
Crop coefficients are properties of plants used in predicting evapotranspiration (ET). The most basic crop coefficient, K c, is simply the ratio of ET observed for the crop studied over that observed for the well calibrated reference crop under the same conditions.
Given the limited data input to the equation, the calculated evapotranspiration should be regarded as only broadly accurate. Rather than a precise measure of evapotranspiration, the output of the equation is better thought of as providing an order of magnitude. [2] The inaccuracy of the equation is exacerbated by extreme variants of weather.
The Penman equation describes evaporation (E) from an open water surface, and was developed by Howard Penman in 1948. Penman's equation requires daily mean temperature, wind speed, air pressure, and solar radiation to predict E. Simpler Hydrometeorological equations continue to be used where obtaining such data is impractical, to give comparable results within specific contexts, e.g. humid vs ...
Class A evaporation pan. Pan evaporation is a measurement that combines or integrates the effects of several climate elements: temperature, humidity, rain fall, drought dispersion, solar radiation, and wind.
It uses meteorological data from the moment of the recording of the satellite data to solve the 'instantaneous' energy balance, and uses extrapolation to calculate daily evapotranspiration. Using a time series of satellite and meteorological data, periodic cumulative (e.g. weekly, monthly, yearly) evapotranspiration data can be calculated.