DOI: 10.5061/dryad.tb2rbp0dv
title: Data from: Climate change will increase forest disturbances in Europe throughout the 21<sup>st</sup> century

description: Wildfires, insect outbreaks, and storms cause large pulses of tree
 mortality. Climate change amplifies these forest disturbances, yet their
 future magnitude and extent remain uncertain. Here, we simulate future
 forest disturbance regimes at 100m resolution across Europe, using a deep
 learning-based simulation framework. Our results show that forest
 disturbances will continue to increase throughout the 21st century,
 withthe  disturbed area more than doubling relative to the recent
 past under an unabated continuation of climate change. Wildfires are the
 main agent driving future disturbance change. Changing disturbances result
 in an increase in young forests, substantially altering Europe’s forest
 demography. Because of their profound implications for forest carbon
 storage and the habitat value of forest ecosystems, disturbances should be
 a priority of forest policy and management.
description: # Data from: Climate change will increase forest disturbances in Europe
 throughout the 21^st^ century Dataset DOI:
 [10.5061/dryad.tb2rbp0dv](https://doi.org/10.5061/dryad.tb2rbp0dv) ###
 Corresponding author information Name: Marc Grünig Affiliation: Swiss TPH,
 Switzerland email: marc.gruenig@swisstph.ch ### Alternative contact
 information Name: Werner Rammer Affiliation: TUM, Germany email:
 werner.rammer@tum.de ## Description of the data and file structure ###
 Files and variables This folder contains data for the publication `Climate
 change will increase forest disturbances in Europe throughout the 21st
 century`. The data provided here allows to run code that is stored in the
 folder `00_code`. We provide several folders with data. Each folder
 contains its own README file with more details about the files within the
 folder. Additional details and description about the data can be found in
 the R and Python scripts. The data is organized in the following folder
 structure that roughly follows the order of the steps taken: ### Directory
 tree ``` ├── 00_code ├── 01_simulation_data_pipeline ├── 02_dnn ├──
 03_initial_forest_states ├── 04_disturbance_modules ├── 05_clim_data ├──
 06_gis ├── 07_reference_grids ├── 08_svd ├── 10_results └── 11_figures ```
 Below, the detailed description of all folders is listed. *00_code.zip*
 ``` 00_code ├── .gitignore ├── .Rhistory ├── 00_functions │ ├──
 check_attribute_cols_function.R │ ├── check_height_cols_function.R │ ├──
 check_species_cols_function.R │ ├── check_species_condition.R │ ├──
 create_aug_examples_function.R │ ├── create_examples_function.R │ ├──
 get_point_id.R │ ├── height_max_mean_function.R │ ├── height_transformer.R
 │ ├── kahn_factors_function.R │ ├── reorder_strings.R │ ├──
 states_mapping_function.R │ └── yield_table.R ├── 01_data_pipeline │ ├──
 00_examples.R │ ├── 01_collect_soildata.R │ ├──
 02_climate_data_timeseries.R │ ├── 03_collect_climatedata.R │ └──
 04_examples_complete.R ├── 02_fire_module │ ├── 01_wind_directions.R │ ├──
 02_summer_vpd_calc.R │ ├── 03_fire_freq_size_models.R │ ├──
 04_fire_occurrence_model.R │ ├── 05_create_fire_events.R │ └──
 06_fire_states.R ├── 03_wind_module │ ├── 01_wind_simulations.R │ └──
 02_wind_susceptibility.R ├── 04_bbtl_module │ ├── 01_background_proba.R │
 ├── 02_bbtl_susceptibility.R │ ├── 03_bbtl_kernels.R │ └── phenips │ ├──
 bbgenerations.cpp │ ├── bbgenerations.h │ ├── bbgenerations.o │ ├──
 bb_kernels.csv │ └── phenips.cpp ├── 05_mgmt_module │ ├── 01_mgmt_proba.R
 │ └── 02_mgmt_spatial_cap.R ├── 06_climate_data │ ├──
 01_cordex_biascorrection.R │ ├── 02_svd_climate_data.R │ └──
 03_svd_climate_compr.py ├── 07_svd_initial_landscape │ ├──
 01_prepare_canopyheight_layer.R │ ├── 02_vegetation_stacks.R │ ├──
 03_initial_soilconditions.R │ ├── 04_initial_states_landscape.R │ ├──
 05_initial_vegetation_evaluation.R │ └── 06_forest_state_lookups.R ├──
 08_outputs_analysis │ ├── 01_svd_grid_processing.R │ ├──
 02_extract_distrate_25km.R │ ├── 03_annual_outputs_processing.R │ ├──
 04_extreme_events.R │ ├── 05_old_young_forests.R │ ├──
 06_Fig1a_disturbance_rates.R │ ├── 06_Fig1b_heatmap.R │ ├──
 07_Fig2a_biomes_dist_change.R │ ├── 07_Fig2b_disturbancemaps.R │ ├──
 08_Fig3a_stacked_bars.R │ ├── 08_Fig3bcd_extreme_events.R │ ├──
 09_Fig4_structure_trends.R │ ├── 21_module_evaluation.R │ ├──
 22_uncertainties.R │ └── 23_feedbacks_interactions.R ├──
 disturbance_scenarios_europe.Rproj └── README.md ``` Contains the code and
 resources to create and analyze future disturbance scenarios for Europe.
 The code is also available at
 [https://github.com/magrueni/disturbance_scenarios_europe](https://github.com/magrueni/disturbance_scenarios_europe) *01_simulation_data_pipeline.zip* ``` 01_simulation_data_pipeline ├── climate_expl.csv ├── climate_slopes.csv ├── clim_bias_expl.csv ├── clim_slopes │ ├── prec_ICHEC_EC_EARTH_historical.csv │ ├── prec_ICHEC_EC_EARTH_rcp_2_6.csv │ ├── prec_ICHEC_EC_EARTH_rcp_4_5.csv │ ├── prec_ICHEC_EC_EARTH_rcp_8_5.csv │ ├── prec_MPI_M_MPI_ESM_LR_historical.csv │ ├── prec_MPI_M_MPI_ESM_LR_rcp_2_6.csv │ ├── prec_MPI_M_MPI_ESM_LR_rcp_4_5.csv │ ├── prec_MPI_M_MPI_ESM_LR_rcp_8_5.csv │ ├── prec_NCC_NorESM1_M_historical.csv │ ├── prec_NCC_NorESM1_M_rcp_2_6.csv │ ├── prec_NCC_NorESM1_M_rcp_4_5.csv │ ├── prec_NCC_NorESM1_M_rcp_8_5.csv │ ├── temp_ICHEC_EC_EARTH_historical.csv │ ├── temp_ICHEC_EC_EARTH_rcp_2_6.csv │ ├── temp_ICHEC_EC_EARTH_rcp_4_5.csv │ ├── temp_ICHEC_EC_EARTH_rcp_8_5.csv │ ├── temp_MPI_M_MPI_ESM_LR_historical.csv │ ├── temp_MPI_M_MPI_ESM_LR_rcp_2_6.csv │ ├── temp_MPI_M_MPI_ESM_LR_rcp_4_5.csv │ ├── temp_MPI_M_MPI_ESM_LR_rcp_8_5.csv │ ├── temp_NCC_NorESM1_M_historical.csv │ ├── temp_NCC_NorESM1_M_rcp_2_6.csv │ ├── temp_NCC_NorESM1_M_rcp_4_5.csv │ └── temp_NCC_NorESM1_M_rcp_8_5.csv ├── examples_expl.csv ├── metadata_expl.csv ├── metadata_processed_expl.csv ├── readme.txt ├── simdata_expl.csv ├── states_lookup_pruned.csv ├── states_lookup_pruned_init_ls.csv └── time_series.csv ``` This folder contains data which can be used to have a look at the data pipeline that converts local forest simulations from the harmonized simulation database ([https://zenodo.org/records/12750180](https://zenodo.org/records/12750180)) to deep neural network training data. Values in the example have been modified for demonstration purposes. While we cannot provide the full dataset here, the corresponding code allows to create the full training dataset from the underlying database. More information and code is currently under review (Grünig, M. et al. Loss of competitive strength in European conifer species under climate change. Under review) The four main variables that are contained in the .csv files with climate information are: temperature [°C], precipitation [mm], VPD [kPa] and radiation [W/m²]. The file names represent one of the four climate variables (tas, prec, vpd, rad). All files containing climate data, start with `clim_`. The folder `clim_slopes` contains .csv tables with annual mean temperatures which are used to translate climate data from the forest simulations to the climate change scenarios used in SVD. File naming follows the convection of `climatevariable_GCM_RCP.csv` where climate variable can be `prec` or `temp`, GCM is one of `ICHEC_EC_EARTH`, `MPI_M_MPI_ESM_LR`, `NCC_NorESM1_M`. RCP is `historical`,  `rcp_2_6`, `rcp_4_5` or `rcp_8_5`. Further, the folder contains .csv files with examples of simulation data and related metadata. Those files contain predominantly the three dimensions of forest states: species proportions [%], LAI [m²/m²], Dominant height [m], as well as stand soil information on WHC [mm], texture (sand, silt, clay [%]), depth [mm], plant available nitrogen [kg/ha/year]. The file `examples_expl.csv` contains training data examples with forest state transitions and residence times. *02_dnn.zip* ``` 02_dnn ├── dnn_states_lookup.csv ├── models │ ├── climcompressor_v4.h5 │ └── simple_frozen_graph_new_dnn_v17_7.pb ├── readme.txt ├── states.csv └── states_europe_meta.csv ``` This folder contains information that is relevant for the DNN part of SVD. It includes .csv files with forest state information (e.g. PIAB_20_22_2) and corresponding state IDs which are needed as SVD inputs. The trained models for climate compression and the model driving SVD are stored in the `models` subfolder. *03_initial_forest_states.zip* ``` 03_initial_forest_states ├── evaluation ├── initial_soil.csv ├── init_restime.tif ├── init_soil_rast.tif ├── init_veg_v2.tif ├── readme.txt ├── sampler │ ├── vegetationSampler.html │ └── vegetation_sampler.cpp ├── states_lookup_pruned.csv └── vegetation ``` This folder contains underlying data and results for the initial forest landscape and soil condittions that are used as starting point in SVD. * .tif & .csv files are the final layers and data for vegetation and soil that are used in SVD. The values represent forest state ids, residence time (1-10) or soil conditions (soil depth [mm], sand content [%], water holding capacity [mm], plant available nitrogen [kg/ha/year]) for each grid cell. * `evaluation` contains the results from the corresponding script as figures (.png) and tables (.csv). These files are not relevant to recreate the pipeline. * `sampler` contains C++ code for the tree species sampling performed for the initial vegetation * `vegetation` is currently empty. We cannot share the data aggregate from different sources that do not underly CC0 licensing. The relevant links to the sources are provided in the methods, suppl. material and code. *04_disturbance_modules.zip* ``` 04_disturbance_modules ├── bbtl_module │ ├── age_estimator.rds │ ├── background_equalproba_100km.tif │ ├── bb_kernels.csv │ ├── bb_patches_vpd.csv │ ├── patches │ ├── phenips │ ├── readme.txt │ ├── spruce_share.tif │ ├── states_bb.csv │ └── transition_matrix_bb.csv ├── fire_module │ ├── climate │ ├── complexes │ ├── figures │ ├── firestates.csv │ ├── fire_event_series │ ├── fire_transitions.csv │ ├── gis │ ├── models │ ├── readme.txt │ └── wind_table.csv ├── mgmt_module │ ├── management.csv │ ├── mean_harvest_100km.tif │ ├── mgmt_transitions.csv │ ├── patches │ └── readme.txt └── wind_module ├── copernicus_storms ├── patches ├── readme.txt ├── results ├── wind_event_series └── wind_transitions.csv ``` This folders contain all relevant data to the SVD external part of the disturbance modules (i.e., creating disturbance events). Readme files are included in each subfolder. We provide predominantly the calculated disturbance events along their position for wind and fire modules. The wind event series files contain information on the location of the event (cell_position, x, y), the number of 10km grid cells affected (number), proportion of 100m grid cells affected (proportion) and timepoint in the simulation (year). The wind event series tables follow the naming convention `simulated_storms_drawX.csv`, where `X` corresponds to the draw 1 to 90. The fire event series contains .csv tables with a unique fire id (fire_id), the timepoint in the simulation (year), coordinates of ignition (x, y). The allowed maximum size (max_size), the wind direction and speed (winddirection, windspeed). The table follow the naming convention `future_fire_simulation_GCM_RCP_simulation_REP.csv`. GCM is one of `ncc`, `ichec`, `mpi`, RCP is one of `historical`, `rcp26`, `rcp45`, `rcp85` and REP is the replicate 1 to 10. For bark beetle and management modules no such event series exist, however all relevant input for SVD is provided in the folders. The underlying data of the disturbance patches can be downloaded from [https://zenodo.org/records/7080016](https://zenodo.org/records/7080016), [https://zenodo.org/records/8202241](https://zenodo.org/records/8202241) and [https://zenodo.org/records/11070255](https://zenodo.org/records/11070255). Additionally, we provide VPD data [kPa] for fire and bbtl, as well as .tif and .csv files with results. *05_clim_data.zip* ``` 05_clim_data ├── annual_climate │ ├── svd_clim_ICHEC-EC-EARTH_historical_v3_aux_biascorr.csv │ └── svd_clim_ICHEC-EC-EARTH_rcp_8_5_v3_aux_biascorr.csv ├── biascorr_ICHEC-EC-EARTH_v3.csv ├── daily_climate └── readme.txt ``` This folder contains the examples of the climate data pipeline and the resulting annual climate that drives SVD. the example annual climate is also used for the SVD example landscape and has been modified. Due to limited data storage capacities, we can only provide one example for historical and future climate dataset here. The full datasets will be shared upon request. The .csv files of annual climate contain data on temperature [°C], precipitation [mm], VPD [kPa] and radiation [W/m²]. *06_gis.zip* ``` 06_gis ├── countries │ └── readme.txt ├── dem5.tif ├── europe_shp │ ├── europe.dbf │ ├── europe.prj │ ├── europe.shp │ ├── europe.shx │ ├── europe_lowres.dbf │ ├── europe_lowres.prj │ ├── europe_lowres.shp │ └── europe_lowres.shx ├── forest_mask_new_laea_masked.tif ├── forest_mask_new_laea_masked_nas.tif ├── forest_mask_wgs.tif ├── LAI_annual_mean_2020_2021_projwgs.tif ├── predicted_N_available.tif ├── readme.txt └── texture_categories.tif ``` This folder contains various shapefiles (.shp) and raster layers (.tif) that are used for calculation steps, including masks of the study region. *07_reference_grids.zip* ``` 07_reference_grids ├── biomes_hex.gpkg ├── eu_mask.cpg ├── eu_mask.dbf ├── eu_mask.prj ├── eu_mask.shp ├── eu_mask.shx ├── eu_mask_hexagons_25km.cpg ├── eu_mask_hexagons_25km.dbf ├── eu_mask_hexagons_25km.prj ├── eu_mask_hexagons_25km.shp ├── eu_mask_hexagons_25km.shx ├── gridID_10k.tif ├── hex_forest_mask_25km.gpkg ├── readme.txt ├── reference_grid.tif ├── reference_grid_100km.tif ├── reference_grid_10km.tif ├── reference_grid_1km.tif └── reference_grid_tab.csv ``` This folder contains raster layers (.tif) and sf objects (.shp) that are needed as reference grids in the code. Additionally, we provide a .csv table with the reference grid showing grid Ids along latitude and longitude. *08_svd.zip* ``` 08_svd └── svd_example ├── climate ├── dnn ├── example_ls_small │ └── output_example_1 ├── fire ├── gis ├── log └── wind ``` This folder contains the data for a small landscape to explore SVD. All relevant files to run a simulation are stored in the `svd_example` subfolder. Visit [https://github.com/edfm-tum/SVD/tree/main/SVDModel](https://github.com/edfm-tum/SVD/tree/main/SVDModel) to download SVD and look at the documentation. Note that we provide data for the fire and wind module only here for the sake of simplicity. *09_svd_simulations.tar.gz* ``` 09_svd_simulations ├── annual_outputs ├── biodiv ├── processed_data ├── raw ├── readme.txt └── svd_simulations_ids.csv ``` This folder contains the output of 120 SVD simulations. The file `svd_simulation_ids.csv` shows the configuration of each simulation with columns for simulation ID, RCP, GCM, Fire scenario, wind scenario and Replication ID. The folder `raw` contains the raw spatial outputs at 100 x 100m resolution at 10 year timesteps. There are four subfolders for each module output containing .tif raster files. name coding for the 10 year outputs is `module_timestep_RCP_GCM_REP_10year.tif`. values in the files correspond to number of disturbances per grid cell within the focal 10 year timestep. Additionally, we provide summed layers at 40 and 80 years of simulation (i.e. 2060 and 2100). Subfolders exist for barkbeelte, fire, management and wind. All files in the subfolers follow the naming convention `AGENT_YEAR_RCP_GCM_REP_10year.tif` (e.g. *`fire_10_rcp26_ncc_1_10year.tif`)*, summed layers at 40 and 80 years simulation end with `_summed.tif`. AGENT is one of `bbtl`, `fire`, `wind`, `mgmt`; YEAR corresponds to simulation decade (10, 20, 30, 40, 50, 60, 70, 80); RCP is one of `historical`, `rcp26`, `rcp45`, `rcp85`; GCM is one of `ichec`, `ncc`, `mpi`; REP is the simulation replicate 1 to 90 (future) and 91 to 120 (historical). The folder `annual_outputs` contains the annual timeseries of disturbed area across Europe the folder is organised in subfolders for each simulation which contain .csv files for barkbeetle, fire and wind. Subfolders are named `output_sim_eu_X` where X corresponds to the simulation replicate. The file `svd_simulation_ids.csv` shows the configuration of each simulation with columns for simulation ID, RCP, GCM, Fire scenario, wind scenario and Replication ID. Within each output folder, three .csv tables are provided with information on barkbeetles, fire and wind: * The barkbeetle output files show yearly outputs with columns for simulation year (year), number of gridcells with background infestations (n_background_infestation), number of gridcells with wind infestation (n_wind_infestation), number of grid cells impacted (n_impact), number of active infestations at the end of the year (n_active_yearend).  * The fire output files show records for each fire with columns for simulation year (year), fire id (id), coordinates of ignitions (x, y), planned size from external event series (planned_size), realized size in SVD (realized_size), severity (share_high_severity).  * The wind output files show records of each storm event with columns for simulation year (year), wind event id (id), coordinates of ignitions (x, y), proportion of planned effect (proportion), planned affected regions (=10km grids) (regions_planned), affected regions (regions_affected), forested cells in region (cells_forested), average successibility of forest in regions (mean_susceptibility), 100m cells planned (cells_planned), 100m cells affected (cells_affected).  The folder `biodiv` contains the biodiversity data aggregated to 25km hexagons stored as .gpkg files. The files are named `undist_vs_recently_dist_forest_X.gpkg` where is corresponds to the simulation ID 1 to 120.  The folder `processed_data` contains the processed spatial data at 25km and 50km hexagons. 50km hexagons were only used for demstration purposes. In the subfolder `dist_rates_25km` all process simulations used for the analysis are stored. The .csv tables follow the naming `all_dist_rates_YEAR_RCP.csv`, where YEAR corresponds to the simulation decade (10, 20, 30, 40, 50, 60, 70, 80); RCP is one of `rcp26`, `rcp45`, `rcp85` *10_results.zip* ``` 10_results ├── all_agents_distarea_final.csv ├── all_agents_distarea_fut.csv ├── all_agents_distarea_hist.csv ├── bbtl_results_final.csv ├── bbtl_results_fut_final.csv ├── bbtl_results_hist_final.csv ├── disturbance_change_final.csv ├── fire_results_final.csv ├── fire_results_fut_final.csv ├── fire_results_hist_final.csv ├── fut_dist_annual_final.csv ├── hist_dist_annual_final.csv ├── wind_results_final.csv ├── wind_results_fut_final.csv └── wind_results_hist_final.csv ``` Contains the results of the statistical analysis of the simulation outputs. Files are provided for individual all agents combined (`all_agents_distarea_`), individual agents (`bbtl_`, `fire_`, `wind_`), and split by historical and future simulations (`_fut.csv`, `_hist.csv`). The .csv files show simulation number (sim), simulation year (year), climate change scenario (scen), disturbed area [ha] (dist_area), time period (period) and disturbance agent (agent) as columns. *11_figures.zip* ``` 11_figures ├── figure_data │ ├── Fig1a_FigS1_fut.csv │ ├── Fig1a_FigS1_hist.csv │ ├── Fig1b.csv │ ├── Fig2a_biomes.csv │ ├── Fig2b.csv │ ├── Fig3a.csv │ ├── Fig3b_fire_extremes.csv │ ├── Fig3c_bbtl_extremes.csv │ ├── Fig3d_wind_extremes.csv │ ├── Fig4a_FigS23a.csv │ ├── Fig4b_FigS23b.csv │ ├── FigS10_data_map.csv │ ├── FigS11_data_map.csv │ ├── FigS12_data_map.csv │ ├── FigS13_sd_map_fire.csv │ ├── FigS14_sd_map_bbtl.csv │ ├── FigS15_sd_map_wind.csv │ ├── FigS16_fut.csv │ ├── FigS16_hist.csv │ ├── FigS17_fut.csv │ ├── FigS17_hist.csv │ ├── FigS18_fut.csv │ ├── FigS18_hist.csv │ ├── FigS19.csv │ ├── FigS2.csv │ ├── FigS20.csv │ ├── FigS21.csv │ ├── FigS22.csv │ ├── FigS24a.csv │ ├── FigS24b.csv │ ├── FigS29.csv │ ├── FigS3.csv │ ├── FigS31.csv │ ├── FigS32.csv │ ├── FigS33.csv │ ├── FigS34.csv │ ├── FigS35.csv │ ├── FigS36.csv │ ├── FigS4.csv │ ├── FigS5_data_map.csv │ ├── FigS6_data_map.csv │ ├── FigS7_data_map.csv │ ├── FigS8_data_map.csv │ └── FigS9_data_map.csv └── figure_descriptions.txt ``` Contains the underlying data of all figures in the manuscript and supplementary material (.csv tables). Figure descriptions are provided in the figure_descriptions.txt file.  ## Code/software - **Python**: Version 3.9 or higher - **R**: Version 4.4.1 or higher