No abstract

Abstract. Wildfires in the United Kingdom (UK) pose a threat to people, infrastructure and the natural environment. During periods of particularly fire-prone weather, wildfires can occur simultaneously across large areas, placing considerable stress upon the resources of fire and rescue services. Fire danger rating systems (FDRSs) attempt to anticipate periods of heightened fire risk, primarily for earlywarning and preparedness purposes. The UK FDRS, termed the Met Office Fire Severity Index (MOFSI), is based on the Fire Weather Index (FWI) component of the Canadian Forest FWI System. The MOFSI currently provides daily operational mapping of landscape fire danger across England and Wales using a simple thresholding of the final FWI component of the Canadian FWI System. However, it is known that the system has scope for improvement. Here we explore a climatology of the six FWI System components across the UK (i.e. extending to Scotland and Northern Ireland), calculated from daily 2 km × 2 km gridded numerical weather prediction data and supplemented by long-term meteorological station observations. We used this climatology to develop a percentile-based calibration of the FWI System, optimised for UK conditions. We find this approach to be well justified, as the values of the "raw" uncalibrated FWI components corresponding to a very "extreme" (99th percentile) fire danger situation vary by more than an order of magnitude across the country. Therefore, a simple thresholding of the uncalibrated component values (as is currently applied in the MOFSI) may incur large errors of omission and commission with respect to the identification of periods of significantly elevated fire danger. We evaluate our approach to enhancing UK fire danger rating using records of wildfire occurrence and find that the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and FWI components of the FWI System generally have the greatest predictive skill for landscape fire activity across Great Britain, with performance varying seasonally and by land cover type. At the height of the most recent severe wildfire period in the UK (2 May 2011), 50 % of all wildfires occurred in areas where the FWI component exceeded the 99th percentile. When all wildfire events during the 2010-2012 period are considered, the 75th, 90th and 99th percentiles of at least one FWI component were exceeded during 85, 61 and 18 % of all wildfires respectively. Overall, we demonstrate the significant advantages of using a percentile-based calibration approach for classifying UK fire danger, and believe that our findings provide useful insights for future development of the current operational MOFSI UK FDRS.

Factors affecting repeated sprint ability (RSA) were evaluated in a mixed-longitudinal sample of 48 elite basketball players 14-19 years of age (16.1 ± 1.7 years). Players were observed on 6 occasions during the 2008-09 and 2009-10 seasons. Three following basketball-specific field tests were administered on each occasion: the shuttle sprint test for RSA, the vertical jump for lower body explosive strength (power), and the interval shuttle run test for interval endurance capacity. Height and weight were measured; body composition was estimated (percent fat, lean body mass). Multilevel modeling of RSA development curve was used with 32 players (16.0 ± 1.7 years) who had 2 or more observations. The 16 players (16.1 ± 1.8 years) measured on only 1 occasion were used as a control group to evaluate the appropriateness of the model. Age, lower body explosive strength, and interval endurance capacity significantly contributed to RSA (p ≤ 0.05). Repeated sprint ability improved with age from 14 to 17 years (p ≤ 0.05) and reached a plateau at 17-19 years. Predicted RSA did not significantly differ from measured RSA in the control group (p ≥ 0.05). The results suggest a potentially important role for the training of lower body explosive strength and interval endurance capacity in the development of RSA among youth basketball players. Age-specific reference values for RSA of youth players may assist basketball coaches in setting appropriate goals for individual players.

The Global Fire Emissions Database (GFED)-currently by far the most widely used global fire emissions inventory-is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a 'small fire boost' (SFB) methodology that estimates the 'small fire' burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and northwestern India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED's fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In northwestern India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate 'August' burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s' 'boosted' BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.