**6. Conclusions**

The concept of using remotely sensed ET products as "observations" for watershed model calibration offers grea<sup>t</sup> potential for resolving spatial heterogeneity in landscape properties. However, the extent to which that numerically resolved information corresponds to actual conditions on the ground has ye<sup>t</sup> to be determined. That correspondence should depend on the relative accuracies of the two models involved: (1) the model behind the remote sensing product and (2) the watershed model not calibrated to the remote sensing product. We examined this by comparing the accuracy of ET from a remote sensing product, MODIS MOD16A2, to the accuracy of ET from a watershed model (SWAT) calibrated to streamflow. ET accuracies were evaluated relative to observations from three flux towers in a Mediterranean climate extending from rain-dominated Ponderosa pine at 1160-m elevation to snow-dominated Lodgepole pine at 2700-m elevation.

The accuracy of ET from the SWAT watershed model surpassed that from the MODIS model across time and space. SWAT explained 4–68% (36% overall) of the variance in monthly ET observations at the flux towers, while MODIS explained none of the observed variance as shown by negative values of Nash-Sutcliffe efficiency. Long-term ET observed across the towers decreased with elevation at a rate of −0.013 mm mo<sup>−</sup><sup>1</sup> m<sup>−</sup>1. This elevational trend in long-term ET was slightly underestimated by SWAT, 7.7%, and largely underestimated by MODIS, 81%. These findings show that if the watershed model had been calibrated to remotely sensed ET rather than to stream discharge observations, the resulting accuracy of watershed model ET-predictions would have been substantially degraded.

The relatively large ET-errors from the MODIS model are interpreted to stem at least in part from an unrealistic dependence of canopy conductance on vapor pressure deficit (VPD). This interpretation is based on an erroneous reversal in slope of MODIS ET versus air temperature that approximately coincides with the transition from temperature- to VPD-controlled limitation on canopy conductance in the MODIS algorithm. This would explain the large underestimates in MODIS ET during the warmest times of the year when VPD reaches peak values. The empirical correction used in the MODIS algorithm to account for water limitation on ET may not represent the actual dynamics of water availability in the study area, which may be more loosely coupled to VPD than is assumed in the MODIS model.

Errors in monthly MODIS ET were found to be well correlated to air temperature. We showed that this could be used to "correct" ET-values from MODIS using linear regression with inputs of MODIS ET error, MODIS potential ET, and air temperature. This correction procedure removed much of the error in ET from the MODIS model, and produced ET predictions more accurate than those from the SWAT model. The regressioncorrected MODIS ET may therefore serve as an improved source of "observations" for spatial calibration of a watershed model over the original MODIS ET data.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/rs13071258/s1: Section S1: Construction of base watershed model in ArcSWAT; Section S2: Watershed model initialization with LAI and biomass of mature forest; Section S3: Sensitivity analysis of influential watershed model parameters; Section S4: Watershed model calibration and validation; Table S1: Spatial datasets used in construction of watershed model in ArcSWAT 2012; Table S2: Parameter values of "base model" manually entered into tables of ArcSWAT project; Table S3: Modifications to plant database of SWAT model to more closely simulate biophysical parameters of mature Sierra Nevada forest; Table S4: SWAT model parameters varied in global sensitivity analysis using Sobol method; Table S5: Results of Sobol sensitivity analysis showing contribution of each SWAT parameter to total modeled variance in Kling-Gupta efficiency (KGE) of monthly streamflow; Table S6: Parameter ranges of calibrated SWAT model found using SWAT-CUP with the SUFI-2 (Sequential Uncertainty FItting Ver. 2) method; Figure S1: Annual precipitation versus elevation in upper Kings River watershed in 100-m elevation bins; Figure S2: Annual air temperature versus elevation in upper Kings River watershed in 100-m elevation bins; Figure S3: Range of MODIS 8-day ET-values within or touching a 500-m radius buffer around each flux tower location; Figure S4: SWAT model parameterization of snow areal depletion curve for upper Kings River watershed; Figure S5: Monthly leaf area index (LAI) and biomass from 30-year spin-up of SWAT base model to steady-state weather conditions; Figure S6: Monthly ET versus vapor pressure deficit (VPD) from different data sources at each of the three study sites; Figure S7: Weather correction to canopy conductance at the lower site based on MODIS model.

**Author Contributions:** Conceptualization, Methodology, Formal Analysis, Visualization, and Writing— Original Draft Preparation: S.M.J.; Investigation: S.M.J. and T.C.H.; Resources: S.M.J. and B.G.; Writing— Review and Editing: S.M.J., T.C.H. and B.G.; Supervision: T.C.H.; Project Administration: S.M.J. and T.C.H.; Funding Acquisition: T.C.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by internal funding from the University of California, Merced.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Flux tower ET observations and "Flexible Filler" processing script (Matlab) were publicly available from the Goulden Lab website, Department of Earth System Science, UC Irvine. These data could be found here: https://www.ess.uci.edu/~california/ (accessed on 24 March 2021). All other data sources were publicly available as described in the Supplementary Materials.

**Acknowledgments:** We thank the following individuals for their assistance with various technical matters: John T. Abatzoglou, Mike L. Goulden, Qin (Christine) Ma, Xiande Meng, Erin Mutch, and Amy Newsam. We also thank the reviewers for their helpful comments on the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
