An analysis of enhanced tree trimming effectiveness on reducing power outages.

We evaluated the effectiveness of an enhanced tree trimming (ETT) program for its ability to reduce tree-related power outages, and thereby improve resilience, on an electric utility distribution system during storm events. Evaluations encompassed thirteen years of trimming (i.e. 2005-2017) data and were performed for both backbone and lateral utility lines. Backbones included all three phase lines between a substation and a faultable device whereas all other lines were considered laterals. The study site spanned the entire state of Connecticut, where the dominant vegetation is temperate deciduous forest. We controlled for variations in weather, tree cover, and wire type, by pairing ETT-treated zones with nearby untreated zones. ETT-treated conductors had storm outage rates that were 0.07-0.36 outages/km/year lower than untreated conductors or 35-180% lower than the service-area's average annual outage rate for untreated conductors. ETT-treatment was associated with lower outage rates for "minor" outage types (i.e., blown fuse, tripped recloser, etc.) but the treatment effect was not statistically significant for "major" outage types (damaged poles or wires). System-wide ETT application, for the approximately 27,000 km of conductors in the study area, was predicted to reduce annual storm-related outages by an average of 81-104 and 318-759 outages/year for backbone and lateral lines, respectively. Our study provided a robust empirical evaluation of ETT and also proposes a geospatial methodology that controls for variations in weather and environment.

Distribution models for Panicum virgatum (Poaceae) reveal an expanded range in present and future climate regimes in the northeastern United States.

PREMISE OF THE STUDY: Expanded area cultivated with the bioenergy crop Panicum virgatum (switchgrass) could alter the genetics of native populations through gene flow, so understanding current and future species distribution is a first step toward estimating ecological impacts. We surveyed switchgrass distribution in the northeastern United States and generated statistical models to address hypotheses about current distribution relative to historical records and responses to climate change. METHODS: Surveys were conducted on 1600 km of road verges along environmental gradients. Switchgrass abundance became the training data for two multivariate generalized linear models that generated maps representing the probability of switchgrass in road verges. Models were evaluated and the superior model was used with variables from three climate change scenarios for 2050 and 2099. KEY RESULTS: Switchgrass populations were found in 41% of roadside plots and up to 188 km from the coast. The environmental variables temperature, urban areas, and sandy soils were positively correlated with switchgrass presence, while elevation, soil pH, and distance to the coast were negatively correlated. The model without spatial autocorrelation performed better. Models and maps incorporating climate change predictions showed a sharp northward shift in suitable habitat. CONCLUSIONS: Switchgrass populations in the northeastern United States occur on inland road verges, supporting the idea that species distribution has expanded relative to historical descriptions of a restricted coastal habitat. The optimal model showed that mean temperature, elevation, and urban development were important in switchgrass distribution today, and climate change will increase suitable habitat for future bioenergy production and wild populations.

A comparison of Lagrangian model estimates to light detection and ranging (LIDAR) measurements of dust plumes from field tilling.

A Lagrangian particle model has been adapted to examine human exposures to particulate matter < or = 10 microm (PM10) in agricultural settings. This paper reports the performance of the model in comparison to extensive measurements by elastic LIDAR (light detection and ranging). For the first time, the LIDAR measurements allowed spatially distributed and time dynamic measurements to be used to test the predictions of a field-scale model. The model outputs, which are three-dimensional concentration distribution maps from an agricultural disking operation, were compared with the LIDAR-scanned images. The peak cross-correlation coefficient and the offset distance of the measured and simulated plumes were used to quantify both the intensity and location accuracy. The appropriate time averaging and changes in accuracy with height of the plume were examined. Inputs of friction velocity, Monin-Obukhov length, and wind direction (1 sec) were measured with a three-axis sonic anemometer at a single point in the field (at 1.5-m height). The Lagrangian model of Wang et al. predicted the near-field concentrations of dust plumes emitted from a field disking operation with an overall accuracy of approximately 0.67 at 3-m height. Its average offset distance when compared with LIDAR measurements was approximately 38 m, which was 6% of the average plume moving distance during the simulation periods. The model is driven by weather measurements, and its near-field accuracy is highest when input time averages approach the turbulent flow time scale (3-70 sec). The model accuracy decreases with height because of smoothing and errors in the input wind field, which is modeled rather than measured at heights greater than the measurement anemometer. The wind steadiness parameter (S) can be used to quantify the combined effects of wind speed and direction on model accuracy.