A satellite-derived climatology of unreported tornadoes in forested regions of northeast Europe

Shikhov, Andrey; Chernokulsky, А. V.

doi:10.1016/j.rse.2017.10.002

Cited by 41 publications

(49 citation statements)

References 37 publications

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“…Information on forest areas affected by fires were retrieved from the European Forest Fire Information System (EFFIS, http://effis.jrc.ec.europa.eu/, last access: 20 December 2019). Insect outbreaks, which may be triggered by large numbers of dead trees following wind disturbances (Stadelmann et al, 2013), generally lead to a slow change in tree cover, which may only marginally affect the 1-year temporal lag used for our estimates of forest cover loss. Furthermore, forest logging following a wind event can be considered a secondary effect of the strong winds, as it is often employed to reduce the risk of other forest disturbances (specifically insect outbreaks and fires).…”

Section: Forwind Versus Landsat-based Forest Cover Lossmentioning

confidence: 99%

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Forzieri

Pecchi

Girardello

et al. 2020

Earth Syst. Sci. Data

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Abstract. Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

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Section: Forwind Versus Landsat-based Forest Cover Lossmentioning

confidence: 99%

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Forzieri

Pecchi

Girardello

et al. 2020

Earth Syst. Sci. Data

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“…Alternative approaches to widespread forest damaged areas are satellite image processing as recently reported by Shikhov and Chernokulsky (2018) and Shikhov et al (2019).…”

Section: Forest Damage Assessmentmentioning

confidence: 99%

A methodology to conduct wind damage field surveys for high impact weather events from convective origin

Rodríguez¹,

Bech²,

Soriano³

et al. 2019

Preprint

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Post-event damage assessments are essential to document the effects of high-impact weather events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extension and intensity can be helpful for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin, i.e. tornado, downburst and other types of convective winds. It is based on previous studies and also on 136 fieldworks performed by the authors in Spain from 2004 to 2018. The methodology 5 includes the systematic collection of pictures and records of damages on man-made structures and on vegetation, as well as collection of any available Automatic Weather Station data, witness reports and images of the phenomenon and their location and orientation. Three final deliverables are proposed to synthesize the data recorded: (i). A summary of the fieldwork; (ii). A table consisting of detailed geolocated information about each damage, and (iii). A map or a .kml file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, 10 sometimes only a few hours after the event occurrence and, in many occasions, when the type of convective phenomenon (e.g. tornado, downburst) was uncertain. In the latter case the information resulting from this methodology has proofed very useful to discern the phenomenon type, based on the damage patterns particularly if no witness reports were available. The application of systematic methodologies as the one presented here is necessary in order to build homogeneous databases of severe weather cases and high impact weather events. 15

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“…Дешифровочные признаки ветровалов по снимкам Landsat описаны в ряде работ [7,10]. Эти же признаки могут быть использованы для выделения ветровалов по продуктам обработки снимков Landsat -GFC и EEFCC.…”

Section: наполнение базы данных о ветровалахunclassified

“…Оценки, приведенные в работе [8], показывают высокую точность выделения нарушений лесного покрова в бореальной зоне по этим данным -свыше 99 %. Также высокая точность выделения ветровалов по данным GFC была обоснована в работе [10] на примере смерчевых ветровалов. Для полученных контуров ветровалов выполнена экспертная верификация, которая включала разделение перекрывающихся участков ветровалов и рубок.…”

Section: наполнение базы данных о ветровалахunclassified

“…Для каждого ветровала был определен тип вызвавшего его явления погоды -шквал, смерч, штормовой ветер или сильный снегопад. Ветровалы, вызванные смерчами, были выделены на основе методики [10] Поскольку крупные ветровалы являются редкими событиями, их межгодовое распределение крайне неравномерно (рис. 4).…”

Section: наполнение базы данных о ветровалахunclassified

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Spatio-Temporal Distribution of Windthrows in the Northern Part of the European Russia With Landsat Images

Shikhov¹,

Chernokulsky²,

Azhigov³

et al. 2019

EEIGIS

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A satellite-derived climatology of unreported tornadoes in forested regions of northeast Europe

Cited by 41 publications

References 37 publications

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

A methodology to conduct wind damage field surveys for high impact weather events from convective origin

Spatio-Temporal Distribution of Windthrows in the Northern Part of the European Russia With Landsat Images

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