**1. Introduction**

Evapotranspiration (ET) consists of evaporation from the soil surface and transpiration from the plant canopy [1]. It is one of the major components of the hydrologic cycle, accounting for up to 85% of annual precipitation in some forests [2, 3]. Forest ET affects the frequency

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and timing of water saturation in soils [4]. Therefore, accurate estimation of ET is needed to predict stream flow emanating from forestland, to investigate hydrological processes, and to manage water resources [4, 5]. Forest ET is known to be hard to quantify [6]. It is a complex hydrological process [7, 8], which is influenced by interactions between the atmosphere, soil, and plant canopy [9, 10].

ET can be directly measured by lysimeters, water balance methods, and eddy covariance systems [6, 11–13]. Direct measurement of ET is difficult and requires large amounts of time, labor, and funding [14, 15]. The eddy covariance method is considered the most reliable, but frequently is deficient from a lack of surface energy balance closure [16]. In the absence of direct measurements, ET can be modeled using climatic and ecosystem data [17–20]. Depending on the model used, estimating ET requires input data such as net radiation, air temperature, vapor pressure deficit, wind speed, and soil moisture. Net radiation is the difference between incoming and outgoing radiation [21], and a key variable for calculating potential ET. Air temperature, wind speed, and vapor pressure deficit control atmospheric conditions that impact ET [22]. Vapor pressure deficit also regulates stomatal resistance, which directly affects ET [23]. Soil moisture controls the availability of water for ET and is especially important in arid and semi-arid ecosystems, where ET is primarily water limited [24, 25]. The use of ET equations with fewer input variables is recommended when complete climatological data cannot be obtained [26]. Meteorological models that use onsite input data can produce estimates of ET similar to measurements by eddy covariance and are less expensive [27].

Installing equipment to measure onsite climatological and soil moisture data may still require more resources than are available to forest and water managers. Publicly available climatological data from weather stations may provide a reasonable substitute. Not all weather stations include the full set of measurements necessary to model ET. The U.S. Surface Climate Reference Network provides data from 114 stations in the USA that record all necessary meteorological data. Other networks, such as Ameriflux (ameriflux.ornl.gov), provide additional coverage. Soil moisture data needed to convert potential ET to actual ET are more limited, distributed across a range of government and academic networks, and summarized in the Texas A&M University North American Soil Moisture Database (http://soilmoisture.tamu.edu/).

In the absence of a nearby weather station with a complete suite of measurements, certain inputs, such as net radiation and vapor pressure deficit, can be calculated using empirical models based on basic weather data [26, 28]. For example, Irmak et al. [28] developed equations to calculate net radiation from input variables such as minimum and maximum air temperatures, measured or predicted solar radiation, inverse relative distance from earth to sun, and mean relative humidity. Tabari et al. [26] derived regression equations to estimate potential ET from air temperature and solar radiation.

The impact on ET estimation of using offsite meteorological data rather than onsite data is unknown for most forest regions. In this study, we estimated ET for a ponderosa pine (*Pinus ponderosa*) forest from three meteorological models (Priestly-Taylor (P-T), Shuttleworth-Wallace (S-W), and Penman-Monteith with dynamic stomatal resistance (P-M-d)) using onsite and offsite data for net radiation, air temperature, vapor pressure deficit, wind speed, and soil moisture content (θ or SMC in this chapter), and compared these estimates to measurements of ET at the same site using the eddy covariance approach as a standard [3, 27]. The objectives of this study are to (1) compare the sensitivity of the P-T, S-W, and P-M-d meteorological ET models to the use of offsite meteorological data, (2) determine if the models can produce reliable results with offsite data, and (3) identify the input data that would result in the largest improvement in ET estimates if measured onsite. Results from the study are valuable to land and resource managers to better understand and predict the forest hydrological cycle using commonly available meteorological data and to prioritize the installation of monitoring equipment with limited resources.
