Using highly resolved maximum gust speed as predictor for forest storm damage caused by the high‐impact winter storm Lothar in Southwest Germany

Schindler, Dirk; Jung, Christopher; Buchholz, Alexander

doi:10.1002/asl.679

Cited by 25 publications

(16 citation statements)

References 22 publications

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“…A total of 37 PVs that are known to influence GS [22,23,25] were developed to model the spatial GS pattern at 25 m × 25 m resolution. One PV was the measuring height of GS (h), which is often (47%), but not always, 10 m above ground level as recommended by the World Meteorological Organization.…”

Section: Predictor Variables (Pv)mentioning

confidence: 99%

“…Among the approaches used to model storm characteristics including GS, mechanistic models [7,8] can be differentiated from statistical (empirical) models [20][21][22][23]. Mechanistic models are useful tools for characterizing and investigating physical processes that determine storm formation, storm life cycle and storm-related GS dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…In another study, 69 GS time series were used to model GS distributions on a 50 m × 50 m resolution grid in Southwestern Germany [22]. Using terrain and roughness-related information as predictor variables (PV), storm event-related GS was modeled on a 50 m × 50 m grid in Southwest Germany [23].…”

Section: Introductionmentioning

confidence: 99%

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Historical Winter Storm Atlas for Germany (GeWiSA)

Jung

Schindler

2019

Atmosphere

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Long-term gust speed (GS) measurements were used to develop a winter storm atlas of the 98 most severe winter storms in Germany in the period 1981–2018 (GeWiSa). The 25 m × 25 m storm-related GS fields were reconstructed in a two-step procedure: Firstly, the median gust speed ( G S ˜ ) of all winter storms was modeled by a least-squares boosting (LSBoost) approach. Orographic features and surface roughness were used as predictor variables. Secondly, the quotient of GS related to each winter storm to G S ˜ , which was defined as storm field factor (STF), was calculated and mapped by a thin plate spline interpolation (TPS). It was found that the mean study area-wide GS associated with the 2007 storm Kyrill is highest (29.7 m/s). In Southern Germany, the 1999 storm Lothar, with STF being up to 2.2, was the most extreme winter storm in terms of STF and GS. The results demonstrate that the variability of STF has a considerable impact on the simulated GS fields. Event-related model validation yielded a coefficient of determination (R2) of 0.786 for the test dataset. The developed GS fields can be used as input to storm damage models representing storm hazard. With the knowledge of the storm hazard, factors describing the vulnerability of storm exposed objects and structures can be better estimated, resulting in improved risk management.

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Section: Predictor Variables (Pv)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Historical Winter Storm Atlas for Germany (GeWiSA)

Jung

Schindler

2019

Atmosphere

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“…The cumulative probabilities related to return periods (T) of 30, 50 or 100 yr were computed by [37]:…”

Section: Determination Of Gust Characteristicsmentioning

confidence: 99%

Global Gust Climate Evaluation and Its Influence on Wind Turbines

et al. 2017

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Strong gusts negatively affect wind turbines in many ways. They (1) harm their structural safety; (2) reduce their wind energy output; and (3) lead to a shorter wind turbine rotor blade fatigue life. Therefore, the goal of this study was to provide a global assessment of the gust climate, considering its influence on wind turbines. The gust characteristics analyzed were: (1) the gust speed return values for 30, 50 and 100 years; (2) the share of gust speed exceedances of cut-out speed; and (3) the gust factor. In order to consider the seasonal variation of gust speed, gust characteristics were evaluated on a monthly basis. The global monthly wind power density was simulated and geographical restrictions were applied to highlight gust characteristics in areas that are generally suitable for wind turbine installation. Gust characteristics were computed based on ERA-interim data on a 1 • × 1 • spatial resolution grid. After comprehensive goodness-of-fit evaluation of 12 theoretical distributions, Wakeby distribution was used to compute gust speed return values. Finally, the gust characteristics were integrated into the newly developed wind turbine gust index. It was found that the Northeastern United States and Southeast Canada, Newfoundland, the southern tip of South America, and Northwestern Europe are most negatively affected by the impacts of gusts. In regions where trade winds dominate, such as eastern Brazil, the Sahara, southern parts of Somalia, and southeastern parts of the Arabian Peninsula, the gust climate is well suitable for wind turbine installation.

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“…In addition, different approaches allowing more flexible model behaviour 81 than fully parametric GLMs have been used, such as generalized additive models (GAM; 82 Schmidt et al, 2010) that use non-parametric smooth functions to allow more flexibility in the 83 relationship of response variable and predictors (Hastie et al, 2009). Machine learning 84 approaches have also been successfully applied to wind disturbance modeling (see 85 Hanewinkel et al 2004 for an early example) and recently especially tree-based ensemble 86 models, such as random forests, have been shown to perform well in predicting wind 87 damage (Albrecht et al, 2019;Hart et al, 2019;Kabir et al, 2018;Schindler et al, 2016). 88…”

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confidence: 99%

High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry and climate change adaptation

Suvanto

Peltoniemi

Tuominen

et al. 2019

Preprint

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10Windstorms cause major disturbances in European forests and forest management can play 11 a key role in making forests more persistent to disturbances. However, better information is 12 needed to support decision making that effectively accounts for wind disturbances. Here we 13show how empirical probability models of wind damage, combined with existing spatial 14 datasets, can be used to provide fine-scale spatial information about disturbance probability 15 over large areas. First, we created stand-level damage probability models with predictors 16 describing forest characteristics, recent forest management history and local wind, soil, site 17and climate conditions. We tested three different methods for creating the damage 18 probability models -generalized linear models (GLM), generalized additive models (GAM) 19 and boosted regression trees (BRT). Then, the damage probability maps were calculated by 20 combining the models (GLM, GAM and BRT) with GIS data sets representing the model 21predictors. Finally, we demonstrated the predictive performance of the maps with a large, 22 high spatial resolution (16 x 16 m 2 raster resolution), making it useful in assessing the 28 vulnerability of individual forest stands when making management decisions. The map is 29 also a powerful tool for communicating disturbance risks to forest owners and managers and 30 it has the potential to steer forest management practices to a more disturbance aware 31 direction. Our study showed that in spite of the inherent stochasticity of the wind and 32 damage phenomena at all spatial scales, it can be modelled with good accuracy across 33 large spatial scales when existing ground and earth observation data sources are combined 34 smartly. With improving data quality and availability, map-based risk assessments can be 35 extended to other regions and other disturbance types. 36

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Using highly resolved maximum gust speed as predictor for forest storm damage caused by the high‐impact winter storm Lothar in Southwest Germany

Cited by 25 publications

References 22 publications

Historical Winter Storm Atlas for Germany (GeWiSA)

Historical Winter Storm Atlas for Germany (GeWiSA)

Global Gust Climate Evaluation and Its Influence on Wind Turbines

High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry and climate change adaptation

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