Simulating wind disturbance impacts on forest landscapes: Tree-level heterogeneity matters

Wind is the most detrimental disturbance agent in Europe's forest ecosystems. In recent years, disturbance frequency and severity have been increasing at continental scale, a trend that is expected to continue under future anthropogenic climate change. Disturbance management is thus increasingly important for sustainable stewardship of forests, and requires tools to evaluate the effects of management alternatives and climatic changes on disturbance risk and ecosystem services. We here present a process-based model of wind disturbance impacts on forest ecosystems, integrated into the dynamic landscape simulation model iLand. The model operates at the level of individual trees and simulates wind disturbance events iteratively, i.e., dynamically accounting for changes in forest structure and newly created edges during the course of a storm. Both upwind gap size and local shelter from neighboring trees are considered in this regard, and critical wind speeds for uprooting and stem breakage are distinguished. The simulated disturbance size, pattern, and severity are thus emergent properties of the model. We evaluated the new simulation tool against satellite-derived data on the impact of the storm Gudrun (January 2005) on a 1391?ha forest landscape in south central Sweden. Both the overall damage percentage (observation: 21.7%, simulation: 21.4%) as well as the comparison of spatial damage patterns showed good correspondence between observations and predictions (prediction accuracy: 60.4%) if the full satellite-derived structural and spatial heterogeneity of the landscape was taken into account. Neglecting within-stand heterogeneity in forest conditions, i.e., the state-of-the-art in many stand-level risk models, resulted in a considerable underestimation of simulated damage, supporting the notion that tree-level complexity matters for assessing and modeling large-scale disturbances. A sensitivity analysis further showed that changes in wind speed and soil freezing could have potentially large impacts on disturbed area and patch size. The model presented here is available as open source. It can be used to study the effects of different silvicultural systems and future climates on wind risk, as well as to quantify the impacts of wind disturbance on ecosystem services such as carbon sequestration. It thus contributes to improving our capacity to address changing disturbance regimes in ecosystem management. We present a process-based model of wind disturbance impacts on forest landscapes.Stand structure is iteratively updated during a wind event.Disturbance size, pattern, and severity emerge dynamically from tree-level processes.We successfully evaluated the model against observations from the storm Gudrun.Neglecting structural and spatial heterogeneity resulted in underestimated damage.

[1]  K. Byrne.  Mechanistic modelling of windthrow in spatially complex mixed species stands in British Columbia , 2011 .

[2]  Barry Gardiner,et al.  A review of mechanistic modelling of wind damage risk to forests , 2008 .

[3]  R. Birdsey,et al.  The effects of forest harvest intensity in combination with wind disturbance on carbon dynamics in Lake States Mesic Forests , 2011 .

[4]  Manfred J. Lexer,et al.  Unraveling the drivers of intensifying forest disturbance regimes in Europe , 2011 .

[5]  J. Innes,et al.  A tree and climate assessment tool for modelling ecosystem response to climate change , 2008 .

[6]  Monica G. Turner,et al.  Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research , 2012, Landscape Ecology.

[7]  M. Raupach.  Drag and drag partition on rough surfaces , 1992 .

[8]  M. Schelhaas.  The wind stability of different silvicultural systems for Douglas-fir in the Netherlands: a model-based approach , 2008 .

[9]  Ulf Hansson,et al.  Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations , 2011 .

[10]  Andrew P. Robinson,et al.  Description and test of a simple process-based model of forest growth for mixed-species stands , 2007 .

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  U. Ulbrich,et al.  A model for the estimation of storm losses and the identification of severe winter storms in Germany , 2003 .

[13]  T. Fourcaud,et al.  Root architecture and wind-firmness of mature Pinus pinaster. , 2005, The New phytologist.

[14]  Jürgen Bauhus,et al.  How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations , 2010, European Journal of Forest Research.

[15]  Manfred J. Lexer,et al.  Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change , 2008 .

[16]  B. Muys,et al.  The effect of mechanical stimulation on root and shoot development of young containerised Quercus robur and Robinia pseudoacacia trees , 2009, Trees.

[17]  Alexis Achim,et al.  Wind loading of trees: influence of tree size and competition , 2010, European Journal of Forest Research.

[18]  Guangqing Chi,et al.  Applied Spatial Data Analysis with R , 2015 .

[19]  Robert J. Pabst,et al.  Multi-scale Drivers of Spatial Variation in Old-Growth Forest Carbon Density Disentangled with Lidar and an Individual-Based Landscape Model , 2012, Ecosystems.

[20]  D. C. van der Werf,et al.  Introducing tree interactions in wind damage simulation , 2007 .

[21]  T. Spies,et al.  An Individual-Based Process Model to Simulate Landscape-Scale Forest Ecosystem Dynamics , 2012 .

[22]  K. Blennow,et al.  Pervasive Growth Reduction in Norway Spruce Forests following Wind Disturbance , 2012, PloS one.

[23]  Fredrik Lagergren,et al.  Storms can cause Europe‐wide reduction in forest carbon sink , 2009 .

[24]  Barry Gardiner,et al.  Improvements in anchorage provided by the acclimation of forest trees to wind stress , 2008 .

[25]  Christopher P. Quine,et al.  The potential of distance-limited topex in the prediction of site windiness , 1998 .

[26]  L. Bärring,et al.  Relating forest damage data to the wind field from high-resolution RCM simulations: Case study of Anatol striking Sweden in December 1999 , 2007 .

[27]  Manfred J. Lexer,et al.  Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps , 2011 .

[28]  Barry Gardiner,et al.  Destructive storms in European forests: past and forthcoming impacts. , 2010 .

[29]  Ari Venäläinen,et al.  Influence of clear-cutting on the risk of wind damage at forest edges , 2004 .

[30]  C. Pfister,et al.  Winter storms in Switzerland North of the Alps 1864/1865–1993/1994 , 1997 .

[31]  C. Messier,et al.  A Critique of Silviculture , 2008 .

[32]  Ola Sallnäs,et al.  Climate change and the probability of wind damage in two Swedish forests. , 2010 .

[33]  P. Hannah,et al.  Wind and Trees: Predicting windspeeds for forest areas in complex terrain , 1995 .

[34]  Ola Sallnäs,et al.  WINDA—a system of models for assessing the probability of wind damage to forest stands within a landscape , 2004 .

[35]  Hsin-I Wu,et al.  Ecological field theory: A spatial analysis of resource interference among plants , 1985 .

[36]  Benjamin Smith,et al.  Implementing storm damage in a dynamic vegetation model for regional applications in Sweden , 2012 .

[37]  Peter C. Jordan,et al.  Reviewing the Science and Implementation of Climate Change Adaptation Measures in European Forestry , 2011 .

[38]  Klaus J. Puettmann,et al.  A Critique of Silviculture: Managing for Complexity , 2008 .

[39]  Alexander Arpaci,et al.  Modelling natural disturbances in forest ecosystems: a review , 2011 .

[40]  D. Stephenson,et al.  Future extreme events in European climate: an exploration of regional climate model projections , 2007 .

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

[42]  Marco Bindi,et al.  Assessing risk and adaptation options to fires and windstorms in European forestry , 2010 .

[43]  H. Peltola,et al.  Mechanical stability of Scots pine, Norway spruce and birch: an analysis of tree-pulling experiments in Finland , 2000 .

[44]  R. Waring,et al.  A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning , 1997 .

[45]  Uldis Silins,et al.  The effect of temperature on mechanical properties of standing lodgepole pine trees , 2000, Trees.

[46]  B. Gardiner,et al.  Field and wind tunnel assessments of the implications of respacing and thinning for tree stability , 1997 .

[47]  H. Peltola,et al.  A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce, and birch , 1999 .

[48]  Jonas Fridman,et al.  Factors affecting the probability of windthrow at stand level as a result of Gudrun winter storm in southern Sweden , 2011 .

[49]  Abolghasem Akbari,et al.  Slope adjustment of runoff curve number (CN) using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) for Kuantan River Basin , 2015, SPIE Remote Sensing.

[50]  Keryn I. Paul,et al.  Soil temperature under forests: a simple model for predicting soil temperature under a range of forest types , 2004 .

[51]  Alain Denis,et al.  Root anchorage of inner and edge trees in stands of Maritime pine (Pinus pinaster Ait.) growing in different podzolic soil conditions , 2004, Trees.

[52]  Thomas Kätterer,et al.  The ICBM family of analytically solved models of soil carbon, nitrogen and microbial biomass dynamics — descriptions and application examples , 2001 .

[53]  Satoshi N. Suzuki,et al.  Variability of local spatial structure in a wave-regenerated Abies forest , 2012, Ecological Research.

[54]  R. Barthelmie,et al.  Analyses of possible changes in intense and extreme wind speeds over northern Europe under climate change scenarios , 2010, Climate Dynamics.

[55]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[56]  Kenneth E. Byrne,et al.  Testing of WindFIRM/ForestGALES_BC: A hybrid-mechanistic model for predicting windthrow in partially harvested stands , 2013 .

[57]  Gert-Jan Nabuurs,et al.  Natural disturbances in the European forests in the 19th and 20th centuries , 2003 .

[58]  Thomas Wohlgemuth,et al.  Increasing storm damage to forests in Switzerland from 1858 to 2007 , 2010 .

[59]  Michael R. Raupach,et al.  Simplified expressions for vegetation roughness length and zero-plane displacement as functions of canopy height and area index , 1994 .

[60]  B. Gardiner,et al.  Anchorage of coniferous trees in relation to species, soil type, and rooting depth , 2006 .

[61]  Michael J. Papaik,et al.  Species resistance and community response to wind disturbance regimes in northern temperate forests , 2006 .

[62]  Jakob Mann,et al.  The effects of disjunct sampling and averaging time on maximum mean wind speeds , 2006 .

[63]  Gregory A. Ruark,et al.  PRINCIPAL COMPONENTS REGRESSION TO MITIGATE THE EFFECTS OF MULTICOLLINEARITY , 1991 .

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

[65]  M. Nilsson,et al.  Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory , 2003, Ambio.