Rinnovo di superficie: un metodo avanzato micrometeorologici per la misurazione e l'elaborazione del campo scala dell'energia densità di flusso di dati

Water scarcity threatens agricultural sustainability in dry growing regions. This situation will likely worsen with changing climatic conditions and increasing competition between agricultural, municipal, industrial, and conservation entities for limited water supplies. To deal with this ongoing dilemma, growers continually search for ways to improve irrigation efficiency, and technology that better estimates crop water use (i.e. evapotranspiration, ET) in real time, and better agrometeorological methods will certainly help these efforts. Current technologies used by growers to estimate ET depend on the use of reference evapotranspiration (ET₀) and an empirical crop coefficient (K_c), both of which are highly susceptible to estimation errors. Agrometeorologists also measure ET to evaluate experimental treatments for improving crop water use efficiency¹ and to parameterize regional water allocation strategies², but these methods are highly technical and expensive. Efforts are currently underway to translate existing research-based ET measurement methods into cost-effective and user-friendly technologies for growers.

Surface renewal (SR) is one agrometeorological method used to measure crop ET. SR is based on analyzing the energy budget of air parcels that reside ephemerally within the crop canopy during the turbulent exchange process³ as measured with the station shown in Figure 1 and illustrated theoretically in Figure 2 and Movie 1. The air parcels are manifested as ramp-like shapes in turbulent temperature time series data, and the amplitude and period of the ramps are used to calculate the flux density (Figures 2 and 3). With the SR method, ET for a given crop surface is determined by calculating latent heat flux density (LE) as the residual of the following energy balance equation
LE=R_n-G-H,

Here, LE is the energy flux density associated with the phase change of water from a crop surface, R_n is the net radiation, G is the soil heat flux density (i.e. energy conducted into or out of the ground), and H is the sensible heat flux density (i.e. energy flux density from the surface to the air or vice-versa that results in a temperature change). R_n is a positive number when the net flux is downward (energy added to the surface), LE and H are positive numbers when the flux is upwards (energy added to the air), and G is a positive number when the flux is downward (energy added to the soil). LE (MJ/m²sec) is then divided by the latent heat of evaporation (L=2.45 MJ/kg) to obtain the mass flux density of water vapor from the surface (i.e. ET).

Measurements of R_n and G are relatively straightforward and inexpensive. Direct measurements of H are more complex and require high frequency data acquisition. The most common method to determine H is with eddy covariance; however, the sonic anemometer required for this method is expensive, complex and, consequently, not widely used by agronomists, horticulturalists, or engineers to determine H and LE. On the other hand, H derived from the SR technique is obtained by a simpler and less expensive method, which uses fine wire thermocouples to measure high frequency air temperatures at the surface-atmosphere interface. Despite the simplicity of the SR, current measurements still require calibration against a sonic anemometer's eddy covariance estimate of H to obtain sensible heat flux density.

The successful deployment of an SR flux tower for ET measurements can be a daunting challenge, especially for many agricultural researchers without formal training in atmospheric science. SR and eddy covariance measurements require sophisticated technical skills in both programming data loggers to execute tasks with complex instrumentation and writing computer programs to post-process the raw turbulence data into meaningful fluxes. Here, we describe how to setup a field station, install sensors, and utilize our new turnkey data logger program for data collection and post-processing procedures.