**5. Conclusions**

**4. Discussion**

line indicates a 1:1 line and the dashed line is a regression line.

58 Current Perspective to Predict Actual Evapotranspiration

The performance of three meteorological ET models (P-M-d, P-T, and S-W) in predicting ET measured by eddy covariance for the ponderosa pine forest in our study was affected by the

**Figure 7.** 1:1 relationship between onsite eddy tower versus offsite weather station input variables for (a) net radiation (W m−2), (b) air temperature (°C), (c) vapor pressure deficit (kPa), and (d) wind speed (m s−1). Soil moisture content data from eddy tower versus Happy Jack (HJ) station (e) and Mormon Mountain Summit (MMS) (f) are also shown. The solid We investigated sensitivity to mixtures of on and offsite data inputs of three widely used models for calculating ET (P-M-d, P-T, and S-W) for a ponderosa pine forest where ET was measured previously by eddy covariance. The complexity of the P-M-d model makes it highly prone to inaccurate ET predictions with offsite data. The P-T and S-W models can provide reliable estimates of ET with selected input variables measured onsite and combined with offsite data for other inputs. Because measurement of some of these input data is expensive and difficult at specific field sites, resources should be devoted to onsite measurement of variables that model are most sensitive to. We found that offsite data from a nearby weather station were sufficient for air temperature and wind speed, whereas model accuracy in predicting ET was improved by onsite measurements of Rn for the P-T model, and vpd for the S-W model. The feasibility of using offsite soil moisture data depends on the proximity and similarity of the offsite monitoring location to the study site. Although, we did not determine the sensitivity of the models to data sources in forest conditions beyond one unmanaged ponderosa pine stand, our methods can be applied to other situations, where baseline measurements of ET by eddy covariance or other approaches exist.
