**Sensitivity of Evapotranspiration Models to Onsite and Offsite Meteorological Data for a Ponderosa Pine Forest**

Wonsook Ha, Abraham E. Springer, Frances C. O'Donnell and Thomas E. Kolb

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.68435

#### **Abstract**

[32] Falamarzi Y, Palizdan N, Feng Huang Y, Lee T S: Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural network (WNNs),

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Agricultural Water Management. 2014; 140:26–36

tural Water Management. 2016; 163:110–124

46 Current Perspective to Predict Actual Evapotranspiration

Evapotranspiration (ET) is a major component of the water budget in most forests, in many cases exceeding 70% of annual precipitation. Due to limitations in time and resources, input data necessary to model ET are not always available for a study site, but offsite data from meteorological networks may be a suitable substitute. In this study, we evaluated three models for estimating ET, Priestly-Taylor (P-T), Shuttleworth-Wallace (S-W), and Penman-Monteith with dynamic stomatal resistance (P-M-d), in a ponderosa pine (*Pinus ponderosa*) forest in northern Arizona where eddy covariance data exist for comparison. We tested the sensitivity of the models to the use of offsite meteorological data from a weather station and offsite soil moisture data from two snow monitoring sites in the SNOTEL network. Onsite data are required for accurate ET estimation with the P-M-d model because of its complexity. Acceptable accuracy in ET estimation required onsite net radiation data for the P-T model and onsite vapor pressure deficit data for the S-W model; other input data can be obtained from nearby offsite weather stations. Errors in ET estimation produced by the use of offsite soil moisture data varied between two nearby SNOTEL sites. Recommendations about the use of offsite data are presented.

**Keywords:** air temperature, evapotranspiration, net radiation, ponderosa pine forest, vapor pressure deficit
