dataesr/scientific-paragraphs-categorization huggingface dataset:
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 largescale land surface models, and hydrogeological risks following wind damage.
In 2009 and 2010, windstorms Klaus and Xynthia hit forests in France and Spain and caused timber losses totalling approximately 45 million cubic metres. In 2018, windstorm Vaia hit the northeastern regions of Italy causing a damaged growing stock volume of about 8. 5 million cubic metres. The socio-economic consequences of wind disturbances can be especially critical for local economies highly dependent on the forest sector. Countries in Northern Europe, Central Europe and Eastern Europe, where the forest sector may cover up to 6 % of the national GDP  (Forest Europe, 2015) , are, therefore, potentially more vulnerable to wind-related impacts. Despite the risks they pose, spatially explicit databases of wind disturbances across Europe currently do not exist. Recent assessments of current and future forest damage due to windstorms at a European scale are based on catalogues of disturbances collected at country level  (Gregow et al.
, 2017; Schelhaas et al. , 2003; Seidl et al. , 2014). Such databases (e. g. FORESTORM) are subject to multiple sources of bias and uncertainty associated with the diversity of the underlying inventories. Furthermore, estimates of forest damage aggregated at a national scale may only partially represent the spatial variability of the phenomenon. In fact, the coarse spatial resolution of such data hampers inferential analysis of potential drivers of forest vulnerability and their use in spatially explicit models to monitor or forecast wind-related im-pacts  (Masek et al. , 2015; Phiri and Morgenroth, 2017). Despite the lack of systematic mapping of wind disturbances in European forests, a multitude of local, national and transnational initiatives have accurately mapped forest areas affected by wind over the last decades.
These data represent highly informative observational records for characterizing spatial patterns of forest damage. However, they are collected by different institutes and are often difficult to retrieve or poorly documented. Since 2012, the Copernicus Emergency Management Service (https://emergency. copernicus. eu/, last access: 20 December 2019) has produced maps of natural disasters throughout the world based on the analysis of satellite images and other geospatial data. While this important initiative can help map wind-affected areas, it only covers recent years and, being an on-demand service, it is not comprehensive, as it depends on the interests of individual authorized users of the service to map a given forest disturbance. In this study, we try to fill the above-mentioned gap. To achieve this, we collected and harmonized 89 743 forest areas damaged by wind into a consistent geospatial dataset.
The work was carried out through a unique joint effort of 27 research institutes and forestry services across Europe. This collaboration led to the first spatially explicit database of wind disturbances in European forests over the period 2000-2018, hereafter referred to as the FORWIND database. We believe that it provides essential spatial information for improving our understanding of forest damage from wind and can assist in large-scale systematic monitoring and modelling of forest disturbances and their effects on the landatmosphere system. In the following sections, we describe the data collection, the harmonization process and the crosscomparison performed against satellite retrievals of changes in vegetation cover and data from national inventories of forest disturbances. We conclude the data description with some examples of the possible usage of the FORWIND database.
Methods We collected wind disturbance events caused by windstorms or tornadoes that occurred in Europe between 2000 and 2018. A wind disturbance event is represented by a geo-referenced polygon that delineates the damaged forest stand, regardless of the degree of damage. The original acquisition of the polygons was made by aerial and satellite photointerpretation or field survey (Table  1 ). Therefore, the polygons are delineated when a reasonably homogeneous patch of damaged forest is detected from the ground or remotely. The data were managed mostly on the Google Earth Engine platform  (Gorelick et al. , 2017)  to efficiently quantify the extent of disturbances over large scales and extract additional informative attributes (e. g. Hansen et al. , 2013; McDowell et al. , 2015). We structured the data collection process in four main phases, described below.
-Literature review and data gathering. We searched PubMed and Scopus for articles published up to January 2019, with no language restrictions, using the search terms "wind disturbance", "windthrow", "forest damage", "wind damage", or "forest disturbance" and "Europe" or a single country name in the publication title or abstract. The identified studies had mainly mapped the effects of wind on forests for single events and/or for a limited areal extent. We then retrieved the spatial delineation of the observed wind damage from the corresponding authors or contact persons responsible for the data acquisition. The collected data were originally recorded by different research institutes and international initiatives across Europe using diverse methodologies. Table  1  lists the data providers and the acquisition methods.
Data records The FORWIND database is the final output of the data collection procedure and it is publicly available at https://doi. org/10. 6084/m9. figshare. 9555008 (Forzieri et al. , 2019). The FORWIND dataset contains records as polygon features in shapefile format (. shp). The geometry of a feature is stored as a shape comprising a set of vector coordinates corresponding to the boundaries of the area of a given wind disturbance. Records are geo-referenced in geographical coordinates, i. e. latitude and longitude, following the WGS84 standard (EPSG:4326). Basic attributes of each disturbance (Table  3 ) are provided in an associated table, stored in a. dbf file. Overall, FORWIND includes 89 743 records, corresponding to a million hectares of forest area affected by wind disturbances during the 2000-2018 period.
Each record should not be viewed as independent as a single storm may cause multiple, geographically disjunct disturbances. At a European level, the median forest disturbance patch caused by wind measures 1. 07 ha (Table  4 ). However, there is substantial variability across disturbances and countries likely driven by the high heterogeneity of forest and landscape characteristics. Figure  1  shows the spatial and temporal variations of records in the FORWIND database. In order to better visualize the data, we summed the areas affected by wind disturbances in 0. 5 • cells (Fig. 1a ). A similar aggregation was used to show the timing of the disturbances, here expressed as the year in which the most area was disturbed within a given cell (Fig. 1b ). The current release of FORWIND in-   2  for some key windstorms.
According to the institutions responsible for the data acquisition, the wind disturbances recorded in FORWIND exhaustively represent the damaged forest areas caused by those specific events. However, some known damaging wind events are currently missing in the database. In order to provide a more comprehensive assessment of the representativeness of FORWIND, for each country we derived the ratio between the number of wind events included and the number of all wind events that occurred and are known to have caused forest damage (Table  5 ). The number of known damaging events is derived by summing up the number of distinct events recorded in FORESTORM (http://www. iefc. net/ storm/, last access: 20 December 2019) and FORWIND during the 2000-2018 period. Therefore, the temporal representativeness ranges between 0 (all known wind disturbances are missing in FORWIND) and 1 (all known wind disturbances are included in FORWIND).
Comparison of FORWIND with satellite-based metrics and national inventories The lack of alternative datasets with the same spatially explicit mapping of wind disturbances as in FORWIND does not allow for a standard validation exercise. Therefore, we evaluated the validity of FORWIND based on the plausibility of the collected spatial delineations of wind disturbances with respect to two satellite-based proxies of forest disturbances and estimates of forest damage reported in national inventories. based on time series analysis of Landsat images. Forest cover loss is defined as an area that has changed from a state of forest to non-forest following a given disturbance event (natural or anthropogenic). The change detection is based on the variation in the spectral properties of the land surface.
Therefore, the resulting estimates of forest cover loss for the selected areas should reflect wind disturbances first and foremost. We emphasize that Landsat-derived estimates of forest cover loss are affected by the uncertainty in satellite retrievals and do not represent the true impacts. However, their suitability for detecting forest disturbances over large scale has been widely recognized  (Curtis et al. , 2018; Hansen et al. , 2013)  and, therefore, they are here considered a good proxy of forest loss. For each selected FORWIND record we computed the area of affected forest based on the spatial delineation of the polygon and the corresponding Landsat-derived forest cover loss and calculated the correlation between the two sets of estimates. In order to account for the spatial dependence structure of FORWIND data, correlation values were derived for 100 subsets of 1000 records randomly selected from the entire dataset.
The final estimate of correlation was then quantified as the average of the correlation values derived from the 100 subsets. Results for the whole dataset are shown in Fig. 3a. Overall, we found a modest but significant Spearman rank correlation coefficient (ρ k = 0. 48; p value < 10 -3 ), which supports the validity of FORWIND in mapping areas subject to changes of forest coverage due to wind disturbances. We point out that for this calculation we did not mask the data based on the degree of damage because such information is available only in some countries. However, a similar correlation analysis performed by rescaling the recorded areas based in their damage degree (for those records that report the information) led to higher correlation values up to 0. 54. We further tested the sensitivity of our results to the temporal lag used to quantify the forest cover loss.
To achieve this, we complemented the previous analysis (lag-01) using Landsatbased forest cover loss estimated for the year of the event only (lag-0) and the following year only (lag-1). In order to investigate possible scaling relations, the correlation analysis was performed accounting for the FORWIND records with a spatial extent above a given threshold derived from the percentiles 0, 0. 25, 0. 50 and 0. 75 of the full dataset (corresponding to about 0, 0. 5, 1 and 3. 5 ha, respectively). Results show that correlation values between FORWIND affected ar-eas and lag-0 forest cover loss tends to slightly decrease with an increasing size of the wind disturbance (Fig. 3b ). The opposite pattern is observed for correlation values with lag-1 forest cover loss. The forest cover loss accumulated over the 2 years considered (lag-01) appears dominated by the contribution of lag-1 forest cover loss.
FORWIND versus MODIS Global Disturbance Index FORWIND was also compared with an independent dataset of satellite-based estimates of forest disturbance as expressed by the MODIS-based Global Disturbance Index  (Mildrexler et al. , 2009)  (MGDI, http://files. ntsg. umt. edu/data/NTSG_ Products/MGDI/, last access: 20 December 2019). MGDI maps quantify the overall annual forest disturbance globally for the period 2004-2012 at 500 m spatial resolution. The disturbance retrieval is based on the variations in the enhanced vegetation index and land surface temperature following a given sudden change in forest cover. Consistent with the previous Landsat-based analysis -the total change in MGDI potentially related to a given wind disturbance was computed as the accumulated net change in MGDI over the event year and the following year (lag-01).
The change was quantified with respect to the pre-event conditions (MGDI in the year before the event). The technique used to disentangle the fire signal, as well as the correlation and sensitivity analyses with respect to the temporal lags and wind disturbance size, were performed analogously to the previous validation exercise (Sect. 4. 1). Overall, we found a low but significant correlation coefficient (ρ k = 0. 27; p value < 10 -3 ) (Fig. 3c ). The lower correlation compared to the Landsat-based dataset is presumably due to the coarser spatial resolution of MGDI that probably does not fully capture the changes in land surface properties due to wind disturbances  (Mildrexler et al. , 2009). This seems to be supported by the generally increasing correlation values up to 0. 31 for wind disturbances of 1 ha consistently across the different temporal lags (Fig.
FORWIND versus FORESTORM FORWIND data were finally compared with estimates of damaged growing stock volume (GSV) that are recorded at country level in the FORESTORM database for five windstorm events: Slovakia in 2004, Sweden in 2005 (windstorm Gudrun), Germany in 2007 (windstorm Kyrill), the Czech Republic in 2007 (windstorm Kyrill) and France in 2009 (windstorm Klaus). We derived the damaged GSV by multiplying the estimated GSV by the percentage damaged, both of which are reported in FORESTORM. An analogous metric was derived from FORWIND data by first calculating, for each FORWIND record, the amount of GSV lost by multiplying the areal average GSV by the damage level reported for the record. As the damage level was only reported for windstorm Klaus, for the other events we assumed a damage level equal to the average level reported for windstorm Klaus weighted on the spatial extent of each record.
The GSV was retrieved from the GlobBiomass dataset  (Santoro et al. , 2018 ; https://doi. org/10. 1594/PANGAEA. 894711), which is based on multiple remote sensing products and is considered the state-of-the-art global biomass product. This satellitebased GSV estimate refers to the year 2010 and has a spatial resolution of 100 m. The damage to GSV were then summed by event and country. Event-scale FORWIND damaged GSVs were then compared with estimates derived from FORESTORM. Overall, results show that the magnitude of damage estimated from FORWIND and FORESTORM are largely different, except for windstorm Klaus in 2009 in France, for which we found a very good agreement (Fig. 3e ). For most of the events, however, FORESTORM tends to systematically give higher forest damage estimates than FORWIND, with differences exceeding 90 %.
We note that such differences persist when we derive FORWIND estimates of damaged GSV assuming a 100 % damage degree for all records (not shown). Therefore, the uncertainty in the damage degree in FORWIND does not substantially affect the difference between FORWIND and FORESTORM. We recognize that estimates of forest damage based on FORWIND are fully dependent on the GSV derived from GlobBiomass. Indeed, any deviations of the mapped GSV from the true forest state are inherently translated into our damaged GSV estimates. In particular, the GSV map refers to the year 2010; therefore, it is very likely that it largely reflects the biomass conditions following, rather than preceding, the windstorm events (all five events considered in this validation exercise occurred before 2010). In order to disentangle such source of bias, we derived country-scale estimates of average GSVs for the year 2000 (pre-event conditions) from the State of Europe's Forest dataset (Forest Europe, 2015) (https://www.
Tree height values where retrieved from 1 km spaceborne light detection and ranging (lidar) data acquired in 2005 by the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud and land Elevation Satellite), (https://webmap. ornl. gov/wcsdown/dataset. jsp?ds_id=10023, last access: 20 December 2019)  (Simard et al. , 2011). Similar to the previous results, we found higher values of damaged GSVs in FORESTORM than in our estimates based on the integration of FORWIND and country values of GSVs (Fig. 3f ), except for windstorm Klaus. We recognize that FORWIND could miss some wind damage occurrences, for instance due to incomplete detection of wind disturbance from aerial photointerpretation or difficulties in mapping inaccessible areas by ground surveys. However, according to the institutions responsible for the data acquisition, the forest areas affected by the windstorm events considered in this validation exercise were exhaustively mapped.
Therefore, possible residual omissions are expected to only marginally affect our results. We, therefore, argue that a possible source of error may be associated with the FORESTORM database. Estimates of forest damage from FORESTORM originate from different sources and are collected by multiple actors. Hence, the loss figures should be viewed in light of their potential biases, including a possible overestimation of the true impacts. www. earth-syst-sci-data. net/12/257/2020/ Earth Syst. Sci. Data, 12, 257-276, 2020 For demonstration purposes, we show a series of possible applications of the FORWIND database. We recognize that the examples described in the following sections are an oversimplification of the relationships observed in nature and of the biomechanical processes that may cause wind disturbances or that can be triggered by wind disturbances.
More sophisticated approaches could be employed to better explore and predict the forest response functions to wind disturbances. For example, multiple variables, susceptibility factors and drivers (e. g. tree species, tree dimension, management regimes, planting patterns, soil depth and snow cover) contribute concurrently to modulate the forest response to wind disturbances  (Hart et al. , 2019; Klaus et al. , 2011; Mitchell, 2013) , and their contribution should be analysed in a multidimensional space (e. g. Sect. 5. 1 and 5. 2). Therefore, the approaches described here should not be considered a reference methodology but only as informative applications for exploring the usefulness of the FORWIND database.
, 2017)  mostly due to the limited number of sampled wind-affected areas available for training and testing classification algorithms  (Schroeder et al. , 2017). In this respect, FORWIND data can be used to enhance our capability to detect and attribute forest damage due to windstorms from remote sensing data. Here, we tested different types of classification trees in combination with a Sentinel-2 imagery and the FORWIND database to automatically map wind disturbances that occurred following windstorm Vaia in October 2018 in the Dolomites in northern Italy  (Pirotti et al. , 2016). Google Earth Engine was used to create a single image composite from a stack of cloud-free pixels (11 and 28 images acquired before and after the windstorm event, respectively). The median was used as a reducer over the vector of pixel values derived from each image, after masking cloudy pixels using the cloud probability raster delivered from atmospheric, terrain and cirrus correction of the Sen2Cor processor  (Louis et al.
, 2018). Further masking was applied to process only pixels covered by forest, using the 2018 estimated forest cover map from the Global Forest Change 2000-2018 dataset  (Hansen et al. , 2013). Binary classification, i. e. damaged versus non-damaged, was applied over a set of 1000 completely damaged areas retrieved from FORWIND and 1000 non-damaged areas. Half of these were used for training and validation, the other half for unbiased testing of the model performance. The feature vector used for predictors included reflectance values recorded by Sentinel-2 after radiometric and atmospheric correction (i. e. bottom of atmosphere) and a tasselled cap (TC) transform of reflectance bands to the brightness, greenness and wetness domain. The TC was added as it is reasonable that wind-affected areas will provide a higher degree of brightness and lower degree of greenness with respect to undisturbed areas  (Baumann et al.
Representation of wind disturbances in Land Surface Models Land surface models (LSM) are key components of Earth System Models that are widely applied to support policyrelevant assessments on the impact of climate change on terrestrial ecosystems  (Quéré et al. , 2018). Recently, wind-storm effects have been incorporated in LSMs  (Chen et al. , 2018). However, these models are hampered by the lack of harmonized spatially explicit information on windstorms required as input for robust model parameterization and largescale representation of wind disturbance. In such contexts, the FORWIND database represents a valuable source of harmonized wind-affected forest areas for improving model calibration and/or evaluation. To illustrate such possible applications, FORWIND was used as an independent data source to evaluate the LSM ORCHIDEE (revision r4262), which simulates windthrow damage and was parameterized with observations prior to the FORWIND time frame.
At first sight these results strongly contrast with the 14 000 km 2 of storm-damaged area archived in the FORWIND database between 2000 and 2015, but it should be noted that FORWIND was estimated to represent just 30 % of the European storms since 2000 (see Table  5 ). Extrapolating FORWIND to the European domain suggests that based on the observations, the area affected by windstorms could exceed 38 000 km 2. Differences in spatial and temporal definitions between ORCHIDEE and FORWIND were partly accounted for by extracting storm damage estimates from ORCHIDEE only when the storm was included in FORWIND. Following this, the ORCHIDEE model appears to overestimate the damage rate in years with small storms but failed to estimate the damage rate of windstorm Klaus in 2009 (Fig. 7 ). This suggests that the tuned relaxation factor for the damage function (R f = 6), which allows for individual tree damage at actual wind speeds below the critical wind speed, is too high.