Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling

被引:30
|
作者
Locatelli, Tommaso [1 ,2 ,4 ]
Tarantola, Stefano [3 ]
Gardiner, Barry [2 ]
Patenaude, Genevieve [1 ]
机构
[1] Univ Edinburgh, Sch Geosci, Inst Geog, Drummond St, Edinburgh EH8 9XP, Midlothian, Scotland
[2] INRA Unite Ephyse, F-33882 Villenave Dornon, France
[3] Joint Res Ctr, Via Enrico Fermi 2749, I-21027 Ispra, VA, Italy
[4] Northern Res Stn, Forest Res, Roslin EH25 9SY, Midlothian, Scotland
基金
英国自然环境研究理事会;
关键词
Method of Sobol; Assessment of model performance; Copula method; Correlated variables; WINDTHROW RISK; STORM DAMAGE; TREE; STANDS; UNCERTAINTY; STABILITY; CLIMATE; INDEXES; GROWTH; ASSESSMENTS;
D O I
10.1016/j.envsoft.2016.10.010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Sobol for correlated variables proposed by Kucherenko et al. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the model's predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure. Crown Copyright (C) 2016 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:84 / 109
页数:26
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