*3.2. Sap Flow Measurements*

The four instrumented trees were continuously monitored at one-minute intervals and the data averaged every 15 min for 618 consecutive days from 18 August 2017 through 26 April 2019. The diurnal sap flow data for selected weekly periods in the growing seasons of each study year are shown in Figure 6. Several instances of morning peaks were observed in the data. They were particularly evident during the growing seasons of the study period for red alders 2 and 3 (Figure 6a), and attributable to direct incident solar radiation on the reflective shield wrapped around the probes, which served to minimize the occurrence of morning peaks. It may also indicate problems with installation of the probes. Another possibility is that they may be as a result of water release in the morning from tree trunk storage before tree roots uptake water to refill the storage according to [37,38] who found that tree trunk internal water storage can contribute as much as 28% of the daily water budget in some tree species. The arroyo willow and red alder 1 also show morning peaks, but had much lower amplitudes later in the morning. The time lag time between sunrise and initial sap flow for the arroyo willow and red alder may be due to partial shading by other trees. All instrumented trees showed some activity during the winter period of dormancy, with the peak amplitudes of greater than an order of magnitude smaller than those observed during periods of active growth.

**Figure 6.** Example weekly sap flow data of the four instrumented trees collected over the two-year monitoring period. The graphs show sap flow measured in (**a**) Fall 2017, (**b**) Spring 2018, (**c**) Summer 2018, (**d**) Fall 2018, (**e**) Spring 2019, and (**f**) Summer 2019.

The time series of the sap flow data for the entire two-year monitoring period of the four instrumented trees are shown in Figure 7 for (a) arroyo willow, (b) red alder 1, (c) red alder 2, and (d) red alder 3. The daily maximum air temperatures and daily mean solar radiation over the same monitoring period are included in Figure 7e to highlight the seasonality of the observed behavior. Seasonality is clearly evident in the sap flow data with periods of high sap flow generally coinciding with spring, summer and fall seasons, interspersed with periods of minimal flow in winter seasons. The spring-fall period is the period of active growth, with leafage increasing to summer-fall maxima. The instrumented phreatophytes were deciduous, losing leaves in late fall, with complete leaf loss deep in the winter months of dormancy. Fall, winter, spring, and summer seasons are marked clearly on the figures to highlight their correlation to periods of significant sap flow change. Specifically, the active and dormancy periods of all four trees clearly follow the spring equinoxes and winter solstices (Figure 7).

**Figure 7.** Daily total sap flow measured in (**a**) arroyo willow, (**b**) red alder 1, (**c**) red alder 2, (**d**) red alder 3, and (**e**) the CIMIS daily maximum air temperature and solar radiation over the monitoring period.

Sap flow peaked in the arroyo willow in early July 2018 and early June 2019. It was dormant from early January 2018 to early March 2018, and mid-December 2018 to mid-March 2019. It also showed a similar sap flow pattern to red alder 1 by mid-December 2018. Its sap flow pattern returned to normal by mid-March 2019. Mean peak sap flow in red alders occurred in early July 2018 and early June 2019. In general, the red alders were dormant from December to mid-March. Red alder 1 was dormant from early November 2017 to mid-March 2018 and mid-November 2018 to mid-March 2019. Red alder 2 was dormant from December 2017 to late March 2018, and late November 2018 to late March 2019. Red alder 3 was dormant from late December 2017 to late March 2018 and from mid-December 2018 to mid-March 2019. These results generally agree with those of [17], whose red alders were dormant during the winter period, though the period of dormancy is appreciably longer in Oregon, extending from October through March.

#### *3.3. Evapotranspiration of Riparian Forest*

Sap flow data were first used to estimate the ET of the individual instrumented trees. The results are shown in Figure 8. The seasonal variation in ET of the individual trees is clearly evident as one would expect from the sap flow data shown in Figure 7. The ET data among the four trees appear to show moderate to strong behavioral correlations, with red alders 2 and 3 consistently showing greater ET than the other two trees during the peak flow periods. The results obtained here did not show a general decrease in ET over the period of the study as has been observed by other workers. In fact, two trees (arroyo willow and red alder 3) appear to show increased ET in the final year (2019) of the study. The seasonal averages of the computed ET for the four trees are summarized in Table 3.

**Figure 8.** Evapotranspiration (mm/d) of the four instrumented trees from 18 August 2017 through 24 August 2019.

The sap flow data collected from the four instrumented trees were upscaled to the entire riparian forest canopy using Equation (7), and the estimated values are summarized and included in Table 3. The season averaged values range from a low of 0.5 mm/d during the winters to a high of 4.1 mm/d over the summer. Daily values show peak values in excess of 6 mm/d. It should also be noted that the winter average values are within margins of instrument measurement uncertainty.

The ET of the riparian forest estimated from sap flow data was compared to ET estimates based on NDVI and meteorological data, and *ETo* in the general vicinity of the study area. The seasonal averages of the sap flow-based ET, *ETo*, and ETndvi are summarized in Table 4. Generally, there is strong correlation in the observed temporal behavior, as well as moderate agreement in estimates of ET over the active growing periods of spring, summer, and fall. This is particularly the case when comparing the sap flowbased ET to *ETo*. However, there are notable divergences in the data. In fall of 2017, the sap flow-based ET appeared to be similar to the *ETo* and ETndvi based on CIMIS and WU data (Figure 9a). In winter of 2018, the sap flow-based ET was substantially lower than the *ETo* and both ETndvi estimates. In spring of 2018, the sap flow-based ET was marginally lower than the *ETo*, but was substantially lower than both ETndvi,wu values. In summer of 2018,

sap flow-based ET was similar to ETndvi,wu, but it was marginally lower than the *ETo* and ETndvi,cimis. This pattern was repeated in the second year of the study period.


**Table 3.** Seasonal estimates of mean ET (mm/d), and corresponding mean square errors, of the four instrumented trees and the entire riparian forest over study period.

<sup>2</sup> Seasons with incomplete or missing data

**Table 4.** Estimates of seasonal mean ET (mm/d) and corresponding MSE from the different methods across the entire riparian forest over study period.


<sup>2</sup> Seasons with incomplete or missing data.

The residuals of the ET, defined as the differences between the sap flow-based ET and the other methods, are shown in Figure 9b. The dashed red and blue lines on the graph in the figure mark ±1.0 and ±2.0 mm/d residual bounds, respectively. The residuals are highest during winter and early spring, during which periods the exceed 2.0 mm/d. The ET predicted by the other methods largely exceeds that based on sap flow due to dormancy of the willows and red alders during the winter seasons. During the mid-summer to late fall periods, the residuals are mostly within ±1.0 mm/d, indicating relatively strong agreement between sap flow-based ET and the other methods.

Scatter plots of sap flow based ET estimates versus the other three methods mentioned above are shown in Figure 10. The data show positive correlations between sap flow-based ET estimates and *ETo* and the NDVI based estimates, with high variance and some bias as much of the data scatter is widely distributed above the 1:1 line (dashed red line). Data points above the 1:1 line indicate that sap flow-based ET was lower than the *ETo* and the NDVI based ET. Table 5 shows the slopes and coefficients of determination (*R*2) of the scatter plots with and without the winter data. Excluding winter data marginally improved the slopes and *R*<sup>2</sup> values. The fact that the slopes of the regression lines are higher than 1:1 is an indication of overall bias in the sap flow-based ET prediction of the *ETo* and NDVI/weather-based ET. The excluded winter data are marked in cyan in Figure 10b, where the data clearly plot above the 1:1 line, which confirms the observation made above that sap flow-based ET underestimates winter ET predicted by the other methods.

**Figure 9.** A plot comparing (**a**) the sap flow-predicted riparian forest ET to the other methods and (**b**) the corresponding residuals over study period. The dashed red and blue lines represent residual bounds of ±1.0 and ±2.0 mm/d, respectively.

**Figure 10.** Scatter plots of *ETo* and NDVI/weather-based ET versus sap flow-based ET with winter data in (**a**), and without winter data in (**b**) (the removed winter data are highlighted in cyan). The red line represents the 1:1 slope.


**Table 5.** Model parameters for the best fit through the origin (0, 0) to correlate sap flow-based ET with the *ETo* and NDVI/weather-based ET.
