Comparison of wind speeds obtained using numerical weather prediction models and topographic exposure indices for predicting windthrow in mountainous terrain

Abstract Windthrow prediction models require data concerning stand characteristics and wind exposure. Geographic information system databases typically contain elevation, forest cover, and logging history layers, therefore attributes can be extracted for points distributed across a given landscape. Climate stations in forested areas are rare, but the wind regime at regularly spaced points can be estimated using mesoscale numerical weather prediction models such as MC2, MM5, and RAMS. More traditionally, wind exposure is estimated using topographic exposure indices. Using gridded and cutblock edge segment databases for areas of mountainous terrain in central British Columbia (McGregor) and on southwestern Vancouver Island (WIT), we examined the spatial variability of simulated wind speeds and topographic exposure indices, simple correlations between variables, and the utility of these variables in predicting clearcut edge windthrow. Approximately half of the spatial variability in topographic and wind variables occurred for points spaced within 4 km. After restricting the dataset to one point from every 16 km 2 panel, mean wind speed was found to be correlated with elevation (0.48, 0.86), but less well with topographic exposure indices (0.17–0.72). Correlations between local winds predicted during strong wind events and topographic exposure indices varied depending on the model used, ranging from non-significant to moderate (0.58). Concordance values for logistic regression models for predicting cutblock edge windthrow improved from 65.0 and 63.8 for base models with height and stand variables, to 70.2 and 68.2 with the addition of topographic exposure indices and extreme wind measures, for McGregor and WIT, respectively.

[1]  Jonathan Ruel,et al.  Research note. Effect of topography on wind behaviour in a complex terrain , 1998 .

[2]  Stephen J. Mitchell,et al.  Portability of stand-level empirical windthrow risk models , 2005 .

[3]  J. Carr.  Numerical Analysis for the Geological Sciences , 1994 .

[4]  Using airflow modelling and spatial analysis for defining wind damage risk classification (WINDARC) , 2000 .

[5]  R. Pielke,et al.  A comprehensive meteorological modeling system—RAMS , 1992 .

[6]  Henryk Modzelewski,et al.  Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction , 2003 .

[7]  Jimy Dudhia,et al.  A Global Version of the PSU/NCAR Mesoscale Model , 2002 .

[8]  Kevin R. Cooper,et al.  The estimation of wind exposure for windthrow hazard rating: comparison between Strongblow, MC2, Topex and a wind tunnel study , 1997 .

[9]  B. Gardiner,et al.  Comparison of two models for predicting the critical wind speeds required to damage coniferous trees , 2000 .

[10]  Michael Sprenger,et al.  The Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP , 2002 .

[11]  B. Gardiner,et al.  A comparison of three methods for predicting wind speeds in complex forested terrain , 1999 .

[12]  J. Grace,et al.  Wind and Trees: Tree physiological responses , 1995 .

[13]  T. A. Black,et al.  Two-dimensional simulation of airflow and carbon dioxide transport over a forested mountain: Part I: Interactions between thermally-forced circulations , 2006 .

[14]  Wei Yu,et al.  Hydrometeorological aspects of the Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP * , 2003 .

[15]  Stephen J. Mitchell,et al.  Empirical modeling of cutblock edge windthrow risk on Vancouver Island, Canada, using stand level information , 2001 .

[16]  C. Quine,et al.  Revised windiness scores for the windthrow hazard classification: the revised scoring method , 1992 .

[17]  E. Scott Krayenhoff,et al.  Ensemble Experiments on Numerical Weather Prediction Error and Uncertainty for a North Pacific Forecast Failure , 2003 .

[18]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[19]  Heli Peltola,et al.  Integration of component models from the tree, stand and regional levels to assess the risk of wind damage at forest margins , 2000 .

[20]  Clifford F. Mass,et al.  Major Numerical Forecast Failures over the Northeast Pacific , 2004 .

[21]  M. Desgagné,et al.  The Canadian MC2: A Semi-Lagrangian, Semi-Implicit Wideband Atmospheric Model Suited for Finescale Process Studies and Simulation , 1997 .

[22]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[23]  Jean-Claude Ruel,et al.  Analyse du chablis du 7 novembre 1994 dans les régions de Charlevoix et de la Gaspésie, Québec, Canada , 1999 .

[24]  C. P. Quine,et al.  Using the Relationship between Rate of Tatter and Topographic Variables to Predict Site Windiness in Upland Britain , 1994 .

[25]  René Laprise,et al.  The Formulation of the Andr Robert MC (Mesoscale Compressible Community) Model , 1997 .

[26]  C. Quine.  Estimation of mean wind climate and probability of strong winds for wind risk assessment. , 2000 .

[27]  R. Stull,et al.  A Mesoscale Analysis Method for Surface Potential Temperature in Mountainous and Coastal Terrain , 2005 .

[28]  Richard A. Anthes,et al.  Development of Hydrodynamic Models Suitable for Air Pollution and Other Mesometerological Studies , 1978 .

[29]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[30]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 1. A new ensemble design , 2006 .