A Reynolds-stress transport equation model for turbulent drag-reducing viscoelastic flows, such as that which occurs for dilute polymer solutions, is presented. The approach relies on an extended set of Reynolds-Averaged Navier-Stokes equations which incorporate additional polymer stresses. The polymer stresses are specified in terms of the mean polymer conformation tensor using the FENE-P dumbbell model. The mean conformation tensor equation is solved in a coupled manner along with the Navier-Stokes equations. The presence of the polymer stresses in the equations of motion results in additional explicit polymer terms in the Reynolds-stress transport equations, as well as implicit polymer effects in the pressure-strain redistribution term. Models for both the explicit and implicit effects have been developed and implemented in a code suitable for boundary layer, rectangular channel and pipe-flow geometries. Calibration and validation is has been carried out using results from recent direct numerical simulation of viscoelastic turbulent flow.

Water content reflectometry is a method used by many commercial manufacturers of affordable sensors to electronically estimate soil moisture content. Field‐deployable and handheld water content reflectometry probes were used in a variety of organic soil‐profile types in Alaska. These probes were calibrated using 65 organic soil samples harvested from these burned and unburned, primarily moss‐dominated sites in the boreal forest. Probe output was compared with gravimetrically measured volumetric moisture content, to produce calibration algorithms for surface‐down‐inserted handheld probes in specific soil‐profile types, as well as field‐deployable horizontally inserted probes in specific organic soil horizons. General organic algorithms for each probe type were also developed. Calibrations are statistically compared to determine their suitability. The resulting calibrations showed good agreement with in situ validation and varied from the default mineral‐soil‐based calibrations by 20% or more. These results are of particular interest to researchers measuring soil moisture content with water content reflectometry probes in soils with high organic content.

Alaska currently relies on the Canadian Fire Weather Index (FWI) System for the assessment of the potential for wildfire and although it provides invaluable information it is designed as a single system that does not account for the varied fuel types and drying conditions (day length, permafrost, decomposition rate, and soil type) that occur across the North American boreal forest. The FWI System is completely weather-based using noontime measurements of precipitation, relative humidity, temperature and wind speed. The most common problem observed with the FWI system is in the initialisation and need for calibration of one of the moisture codes that make up the FWI system, the Drought Code (DC), which is representative of the deeper organic soil layers and has a 53 day lag period. SAR data represent an innovative tool to improve the current weather-based fire danger system of interior Alaska by initialising the spring values of DC, calibrating the codes throughout the season and providing additional point-source data. Using radar backscatter values from several recently burned boreal forests, an algorithm was developed that related backscatter to DC. The authors then demonstrated the application and validation of this algorithm at independent test sites with good correlation toin situsoil moisture and rainfall variations.

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