We present a novel approach based on fibre-optic distributed temperature sensing (DTS) to measure the two-dimensional thermal structure of the surface layer at high resolution (0.25 m, ≈0.5 Hz). Air temperature observations obtained from a vertically-oriented fibre-optics array of approximate dimensions 8 m × 8 m and sonic anemometer data from two levels were collected over a short grass field located in the flat bottom of a wide valley with moderate surface heterogeneity. The objectives of the study were to evaluate the potential of the DTS technique to study small-scale processes in the surface layer over a wide range of atmospheric stability, and to analyze the space-time dynamics of transient cold-air pools in the calm boundary layer. The time response and precision of the fibre-based temperatures 123 178 C. K. Thomas et al. were adequate to resolve individual sub-metre sized turbulent and non-turbulent structures, of time scales of seconds, in the convective, neutral, and stable surface layer. Meaningful sensible heat fluxes were computed using the eddy-covariance technique when combined with vertical wind observations. We present a framework that determines the optimal environmental conditions for applying the fibre-optics technique in the surface layer and identifies areas for potentially significant improvements of the DTS performance. The top of the transient cold-air pool was highly non-stationary indicating a superposition of perturbations of different time and length scales. Vertical eddy scales in the strongly stratified transient cold-air pool derived from the DTS data agreed well with the buoyancy length scale computed using the vertical velocity variance and the Brunt-Vaisala frequency, while scales for weak stratification disagreed. The high-resolution DTS technique opens a new window into spatially sampling geophysical fluid flows including turbulent energy exchange.

[1] The characterization of temporal and spatial distribution of sunlight is essential for understanding energy transport in natural ecosystems. Fiber-optic distributed temperature sensing (DTS) allows meter resolution measurements of temperature at subminute resolution. The difference in temperature due to absorption and reflection of a pair of helically twisted black and white fiber-optic cables was measured with a DTS to document areas exposed to sunlight over the Walla Walla River. A high correlation (R 2 = 0.99) was found between DTS-based results and manual field observations of effective shade. These preliminary results provide proof of the concept that this method can be used for estimating the effective shade at fine spatial resolutions. Potential shortcomings and the need for a more quantitative physical model are suggested for further research.

In 2005 and 2006, air samples were collected at the base of a Douglas-fir watershed to monitor seasonal changes in the delta13CO2 of ecosystem respiration (delta13C(ER)). The goals of this study were to determine whether variations in delta13C(ER) correlated with environmental variables and could be used to predict expected variations in canopy-average stomatal conductance (Gs). Changes in delta13C(ER) correlated weakly with changes in vapor pressure deficit (VPD) measured 0 and 3-7 days earlier and significantly with soil matric potential (psi(m)) (P value <0.02) measured on the same day. Midday G (s) was estimated using sapflow measurements (heat-dissipation method) at four plots located at different elevations within the watershed. Values of midday Gs from 0 and 3-7 days earlier were correlated with delta13C(ER), with the 5-day lag being significant (P value <0.05). To examine direct relationships between delta13C(ER) and recent Gs, we used models relating isotope discrimination to stomatal conductance and photosynthetic capacity at the leaf level to estimate values of stomatal conductance ("Gs-I") that would be expected if respired CO2 were derived entirely from recent photosynthate. We compared these values with estimates of Gs using direct measurement of transpiration at multiple locations in the watershed. Considering that the approach based on isotopes considers only the effect of photosynthetic discrimination on delta13C(ER), the magnitude and range in the two values were surprisingly similar. We conclude that: (1) delta13C(ER) is sensitive to variations in weather, and (2) delta13C(ER) potentially could be used to directly monitor average, basin-wide variations in Gs in complex terrain if further research improves understanding of how delta13C(ER) is influenced by post-assimilation fractionation processes.

Abstract:This research investigates large-scale climate features affecting inter-annual hydrologic variability of streams flowing into Upper Klamath Lake (UKL), Oregon, USA. UKL is an arid, mountainous basin located in the rain shadow east of the crest of the Cascade Mountains in the northwestern United States. Developing accurate statistical models for predicting spring and summer seasonal streamflow volumes for UKL is difficult because the basin has complex hydrology and a high degree of topographic and climatologic variability. In an effort to reduce streamflow forecast uncertainty, six large-scale climate indices-the Pacific North American Pattern, Southern Oscillation Index, Pacific Decadal Oscillation (PDO), Multivariate El Niño-Southern Oscillation Index, Niño 3Ð4, and a revised Trans-Niño Index (TNI)-were evaluated for their ability to explain inter-annual variation of the major hydrologic inputs into UKL.The TNI is the only index to show significant correlations during the current warm phase of the PDO. During the warm PDO phase (1978-present), the averaged October through December TNI is strongly correlated with the subsequent April through September streamflow (r D 0Ð7) and 1 April snow water equivalent (r D 0Ð6). Regional analysis shows that this climate signal is not limited to UKL but is found throughout the northwestern United States.Incorporating the TNI variable into statistical streamflow prediction models results in standard errors of forecasts issued on the first of February and earlier that are 7-10% smaller than those for the models without the TNI. This, coupled with other enhancements to the statistical models, offers a significant increment of improvement in forecasts used by water managers.