Select 7 Douglas-fir and 7 red alder in a ~70 m transect perpendicular to the stream at the base of WS1. Select 5 Douglas-fir and 3 western hemlock in a ~70 m transect perpendicular to the stream at the base of WS2. Install 2 sensors per tree in WS1 in the outer xylem at approximately 1 m above the ground at a depth of 0-20 mm. Install 1 sensor per tree in WS1 in the inner xylem at approximately 1 m above the ground at a depth of 20-40 mm. Install up to 3 sensors per tree in WS2 in the outer xylem at approximately 5 m above the ground at a depth of 0-20 mm. Install up to 2 sensor per tree in WS2 in the inner xylem at approximately 5 m above the ground at a depth of 20-40 mm
For replacement series plots, select 8 trees (4 Douglas-fir and 4 red alder in mixed plots) in each of 8 plots (4 at each site) for a total of 64 trees (and 64 sensors).
Sapflow
Transpiration is measured at 30-second intervals and averaged every 20 minutes with constant heat sapflow sensors (Granier 1996) installed in 5 to 7 trees of each species/age-class, with at least three sensors per tree (more details on sensor positions is provided below). These trees were cored with an increment borer to determine sapwood depth. It is critical to note that the power requirements for sapflow measurements preclude a good random sample of trees throughout the watersheds. Instead, the sample trees lie in a cluster near the base of the watersheds and thus the data to date are unavoidably biased due to the sampling design. In WS1 trees lie along two "transects", one each of alder and Doug-fir, just above the weir. These run normal to the stream through a pocket of each vegetation type up the southern (north facing) slope. The transects are 50m long, and 7 trees were selected along the transect at roughly equal intervals. Due to limitations of power and equipment, we measured red alder only in 1999; in subsequent years we estimated sap flux in red alder based on relationships between red alder and Douglas-fir in 1999. We began measurements in WS2 in 2000. In WS2 we selected 5 Douglas-fir and 3 western hemlock (all overstory trees) in a transect on the N side of the stream about 50m below the weir.
The maximum potential effect on sap flow estimates due to background temperature fluctuations (insert citation) was evaluated and found to be detectable yet minimal. Based on measurements taken of background temperature fluctuations during six warm, sunny days in July, we found that sap flow may be underestimated by 3.7+/-0.5% and 0.2+/-0.5% per day in young and old Douglas-fir respectively, and overestimated by 6.0+/-1.1% in hemlock during the month of July.
Sap flux density for each individual sensor over each 20 minute period was determined from temperature differentials using equations in Granier (1987). These measurements were scaled to the whole-tree and species level generally using the procedures described in Phillips et al. 2002. In red alder we installed sensors at three depths (0-20 cm, 20-40 cm and 40-60 cm) in five trees and we determined the average gradient in sapflow from the outer to inner sapwood. Using this gradient we "scaled" outer flux measurements in the other trees to a whole tree basis, and then divided by the total sapwood area of that tree to come up with the average sap flux density. In Douglas-fir and western hemlock we installed most sensors at a depth of 0-20 cm, but we also installed sensors at 20-40 cm in 4 of the old-growth trees. We combined information from these four trees with sap flux measurements from Douglas-fir at Wind River to analyze how radial gradients in sap flow are affected by site, tree age and seasonal variation. From this analysis we developed a predictive relationship to estimate radial variation based on measurements in the outer 2 cm of the sapwood, and we then used these relationships to estimate whole-tree sap flow over 20 min intervals for each measurement tree. For hemlock we took advantage of radial measurements of sapflow by F.R. Meinzer at Wind River. Meinzer's data show that sapflow declines linearly from the outer edge of sapwood to the sapwood/heartwood boundary. We used this relationship to estimate whole tree sapflow from measurements in the outer 2 cm in hemlock. We found no difference in whole-tree sap flux density for any species or size/age class as a function of distance from the stream, so we averaged the data (for each time increment) over the total number of sample trees to develop the mean sapflux density for measurement period for each species/size class. We multiplied this value for red alder by the sapwood basal area of hardwoods in WS1 to estimate hardwood transpiration. We multiplied this value by the sapwood basal area of all conifers (which is >95% Douglas-fir) to estimate conifer transpiration in WS1. We multiplied the valued for old hemlock and Douglas-fir, respectively, by the sapwood basal areas of these species in WS2 (which account for >95% of the sapwood basal area of all trees in this watershed) to estimate transpiration in WS2. The sap fluxes over 20 min intervals were summed to obtain daily sap fluxes.
2001-2002: "Select 7 Douglas-fir and 7 red alder in a ~70 m transect perpendicular to the stream at the base of WS1. Select 5 Douglas-fir and 3 western hemlock in a ~70 m transect perpendicular to the stream at the base of WS2.
For replacement series plots, select 8 trees (4 Douglas-fir and 4 red alder in mixed plots) in each of 8 plots (4 at each site) for a total of 64 trees (and 64 sensors)." Install 2 sensors per tree in WS1 in the outer xylem at approximately 1 m above the ground at a depth of 0-20 mm. Install 1 sensor per tree in WS1 in the inner xylem at approximately 1 m above the ground at a depth of 20-40 mm. Install up to 3 sensors per tree in WS2 in the outer xylem at approximately 5 m above the ground at a depth of 0-20 mm. Install up to 2 sensor per tree in WS2 in the inner xylem at approximately 5 m above the ground at a depth of 20-40 mm. For replacement series plots, install 1 sensor per tree at approximately 0.5 m above the ground (below the first live branch) to a depth of 0-20 mm.
Follow methods for calculating sap flux from A. Granier (1987). For radial profiles, use the methods of N. Phillips (2002) in Douglas-fir. Use radial profile from 4 red alders in WS1 for alder. Assume all red alders are 100% sapwood (no heartwood).
Measure basal areas on every tree over 1 cm diameter in transects perpendicular to the stream on both sides of the stream in WS1 and WS2. In WS1, transects composed of five 10x10 meter plots alternating sides of stream every 200 meters for a total of 7 transects. In WS2, transects composed of 3 20x20 meter plots on both sides of the main stream every 200 meters for a total of 3 transects. Measure sapwood depth, bark depth, and heights on 5 of each species in each plot. Basal areas were measured using diameter tapes at breast height. Sapwood depths and bark depths were measured at breast height with and increment borer and by visually inspecting the oore for lenth of "wet" xylem. Heights were measured with a laser altimeter. Additional measurements were taken in each plot of slope aspect and angle, using a compass and laser altimeter, respectively. Species too small to be cored (~less than 5 cm DBH) were assumed to be 100% sapwood at breast height.
In many cases, individual sensors were not functional over periods of several days. Because of the small sample sizes, dropping these individuals from the overall mean could result in large artifacts in the time-series data. Therefore, we interpolated to fill "missing" data based on relationships among the sensors when all functioned properly.
Vegetation surveys were conducted in 1999 in WS1 and 2000 in WS2 to quantify the species composition and basal sapwood area of all woody vegetation >1 cm diameter in the riparian zones (arbitrarily defined as 100 m swath centered on the stream bed) of the two watersheds. In each WS, we established transects normal to the stream every 200 m upstream from the weir. The transects alternated from one side of the stream to the other. Along each transect we established contiguous square plots - in WS1 there were five 10m-square plots and in WS2 there were three 20m-square plots on each transect (plot dimensions were determined for the horizontal plane - i.e., they were slope-corrected). Within each plot we measured the diameter and species of every tree greater than 1 cm diameter as well as height and sapwood depth of 5 trees of each species in the plot, systematically selected to represent the size distribution in that plot. From the sample of trees used for measurements of height and sapwood depth, we developed species-specific regression equations to predict sapwood area from DBH outside the bark. Cover (by percent area) estimates of shrubs and herbaceous species were made using the line intercept technique from a diagonal transect running from the SW to the NE corners of the plots, with species identified when possible.
Integrated volumetric water content of the top 0.30 m of the soil was measured hourly using 4 water content reflectometers (Campbell Scientific, Logan, UT) distributed evenly within the sap flow transect of WS2.
Time domain reflectometry measurements (Tektronix 1502C, Gray and Spies 1995) integrated over the top 0.45 m were taken approximately once every 2 weeks at 16 locations throughout the sap flow transects of WS1 and WS2.
Follow methods for calculating sap flux from A. Granier (1987)
Methodology Instrumentation: constant heat sapflow sensors Methodology Algorithm/Statistics:,,, name, sapflow
Description, converts millivolt sapflow sensor output to sapflux per unit sapwood area (millimeters per second) by:
K=(dT(m)-dT)/(dt)
u=0.119 * K^1.231
F=u*S
dT(m) and dT are the temperature differences between the two probes
S = the cross sectional area of the sapwood at the location of the heated probe (in square meters)
F = the total sap flow in millimeters per second
The vegetation cover within watersheds has an important influence on stream flow, but the specific details of this influence are not well understood. In the Oregon Cascade Range, up to 25% of national forest land has been altered by logging activities since the 1930's, resulting in patches of 20 to 40 ha with vegetation of varying ages and species composition (Jones and Grant 1996). It is important to understand how these changes in vegetation affect the hydrologic cycle in order to manage watersheds for multiple uses that include water supply and slope stability.
Following a vegetation-removing disturbance to a watershed, such as fire or harvesting, peak flows as well as total flows of water generally increase for a period of a few to several years (Amthor 1998, Watson et al. in press). Jones and Grant (1996) determined that forest harvesting increased peak discharges in experimental basins in the H.J. Andrews by as much as 50% in small basins and 100% in large basins. After this initial increases in flow, water discharge decreases over a period of time in many watersheds. In at least three long-term datasets (Hubbard Brook, Coweeta, and Eucalyptus regnans forests of southeast Australia), total discharge from watersheds with the vigorous, recovering forests 10-30 years after a major disturbance was actually less than the initial condition with old-growth vegetation.
Some of these changes have good explanations. The initial increase in flow following disturbance is generally attributed to reduced transpiration due to the low leaf area after the disturbance, resulting in a greater amount of surface flow. In a few cases where flow decreased after harvest, it appears that the finely articulated conifer needles of the original forest intercepted significant fog and cloud water, so water input was reduced when trees were removed (Harr 1982, 1986). In most systems, as vegetation recovers following disturbance, the use of water by vegetation for transpiration reduces outflow. However, some of the interactions between vegetation and watershed hydrology are more elusive. Why is there less discharge from watersheds with recovering vegetation than in the original condition? Amthor (1998) proposed that change in atmospheric CO2 levels are affecting forest water use at Hubbard Brook; in Coweeta, it is possible that a shift in species composition in the recovering vegetation has resulted in increased transpiration. In the E. regnans forests of Australia, the overstory trees of the young, recovering forests had a higher leaf area compared with the original old forest, resulting in higher total transpiration (Watson et al. in press). Also in the old forest there was a better-developed understory which used less water per unit leaf area than the overstory (Watson et al. in press). Still, the transpiration per unit leaf area was much higher for young E. regnans trees compared with older trees. This could result from changes in the hydraulic resistance in the trees themselves as they age (Ryan and Yoder 1997). The changes in water discharge in all of these systems apparently result from combination of changes to vegetation structure, composition and age and possibly climate interactions.
H.J. Andrews (HJA) Experimental Forest has maintained records of water discharge from small basins (60-101 ha) for over 35 years. These are part of a paired-basin experiment. Experimental harvests were conducted in the 1960s in half of the basins, with clear pre-treatment and post-treatment measurement periods. The HJA is also fortunate to have an outstanding team of hydrology researchers who are on the forefront of new concepts and analysis techniques. For example, Jones and Grant (1996) introduced a new level of statistical rigor to watershed research and revealed surprisingly strong influences of vegetation succession and roads on peak discharges. Tracer studies by Wondzell (unpublished) have revealed strong diurnal trends in the flow from Watershed 1; flow increases over night and decreases during the day. Newer, high-precision weirs are now able to measure this change in flow directly; these instruments indicate that some of the small watersheds in the H.J. Andrews show strong diurnal variation and others do not (Post and Jones 2001). It is likely that the diurnal variation is influenced by water use by streamside vegetation, but there are no data to support this hypothesis. Missing from the current research effort at the H.J. Andrews are studies that relate vegetation processes to change in hydrological cycle.
