The current and most detailed version of the approved corporate provincial digital Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021). Use this version when performing GIS analysis regardless of scale. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

The current and most detailed version of the approved corporate provincial digital Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021). Use this version when performing GIS analysis regardless of scale. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

A list of the various 'regional' (zone/subzone/variant/phase) ecological units of the current biogeoclimatic ecosystem classification. At this 'regional' level, vegetation, soils and topography are used to infer the climate and to identify geographic areas that have relatively uniform climate. These geographic areas are termed biogeoclimatic units. The basic biogeoclimatic unit is the Subzone. These units are grouped into Zones and may be further subdivided into variants based on further refinements of climate (e.g., wetter, drier, snowier). The map units of the Biogeoclimatic map are mapped to the highest possible thematic resolution - subzone or variant. In some cases, where further sampling is required to define the unit climatically, polygons are labelled as an undifferentiated unit (e.g. CWH un)

Superseded versions of the provincial biogeoclimatic zone/subzone/variant (BGC) maps replaced by finer resolution - excludes the current version. Each subsequent version of the biogeoclimatic subzone variant map of British Columbia is a result of refinements to either the mapping or definitions of the BGC units. The various versions of the BGC map are NOT a result of the BGC units having changed their spatial location over time.

Biogeoclimatic Ecosystem Classification (BEC) system is the ecosystem classification adopted in the forest management within British Columbia based on vegetation, soil, and climate characteristics whereas Site Series is the smallest unit of the system. The Ministry of Forests, Lands, Natural Resource Operations and Rural Development held under the Government of British Columbia (“the Ministry”) developed a web-based tool known as BEC Map for maintaining and sharing the information of the BEC system, but the Site Series information was not included in the tool due to its quantity and complexity. In order to allow users to explore and interact with the information, this project aimed to develop a web-based tool with high data quality and flexibility to users for the Site Series classes using the “Shiny” and “Leaflet” packages in R. The project started with data classification and pre-processing of the raster images and attribute tables through identification of client requirements, spatial database design and data cleaning. After data transformation was conducted, spatial relationships among these data were developed for code development. The code development included the setting-up of web map and interactive tools for facilitating user friendliness and flexibility. The codes were further tested and enhanced to meet the requirements of the Ministry. The web-based tool provided an efficient and effective platform to present the complicated Site Series features with the use of Web Mapping System (WMS) in map rendering. Four interactive tools were developed to allow users to examine and interact with the information. The study also found that the mode filter performed well in data preservation and noise minimization but suffered from long processing time and creation of tiny sliver polygons.

The current and most detailed version of the approved corporate provincial digital Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021). Use this version when performing GIS analysis regardless of scale. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

The Natural Disturbance Type map is based on the Provincial Biodiversity Guidebook (1995) and the current and most detailed version of the approved corporate provincial Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021) (Data Catalog record: https://catalogue.data.gov.bc.ca/dataset/bec-map). The natural disturbance type classification code is used to designate a period process or event such as insect outbreaks, fire, disease, flooding, windstorms and avalanches that cause ecosystem change and renewal. Natural disturbance type classification and mapping is used for a wide variety of applications in British Columbia. A few examples include: delineation of Natural Disturbance Types for Landscape Unit Planning; delineation of Seed Planning Zones; as an input for Predictive Ecosystem Mapping; reporting on the ecological representation of the Protected Areas Strategy; and as a level in the classification hierarchy for Broad Ecosystem Units. Note that this mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

Biogeoclimatic Ecosystem Classification (BEC) has been applied extensively in characterizing forested ecosystems in British Columbia. With a lack of qualified vectorization method used for BEC data transformation, the main goal of this research is to polygonize discontinuous BEC raster classes into vector map with better overall effectiveness and efficiency especially regarding the linear areas. The original data input for analysis is a machine-learning BEC zone raster map of Deception Study Area located in middle BC near Telkwa, with a resolution of 5m*5m. A comprehensive comparison between vectorization algorithms in GIS applications was conducted, including different filtering, simplifying and smoothing algorithms. Since we have the original predicted BEC raster map as the performance measurement, accuracy was directly measured as the percentage of correctly classified pixels when rasterizing the polygons. The evaluation criteria include visual effect, number of polygons, linear patches accuracy processing time. We found an appropriate vectorization routine to polygonize the classification raster maps. The polygonal map using Scenario D has overall satisfactory effectiveness and efficiency with a 46% linear patch accuracy and 62,014 polygons. The method also provides good approximations of the areas with moderate processing time. This is partly because we allow vertices to be located anywhere and not just exactly on the boundary of the original raster zones. We can promote this polygonization method in future predicted ecosystem mapping (PEM) product with similar linear and discontinuous areas. Priority of several key BEC zone classification with importance level regarding to the ecosystem condition related to endangered species can be further explored and added to the algorithms to better polygonize those areas in future studies.

Provides a suitable label point for the current version of the biogeoclimatic subzone/variant map (version 12, September 2, 2021) layers in iMap and MapView. Intended as a cartographic layer for iMap and MapView. NOT FOR ANALYTICAL PURPOSES.

A simplification of the current version of the biogeoclimatic subzone/variant map (BEC_BIOGEOCLIMATIC_POLY, version 12, September 2, 2021), and intended as a cartographic layer for viewing at a scale of 1:20,000. NOT FOR ANALYTICAL PURPOSES as polygon boundaries have been generalized. Use WHSE_FOREST_VEGETATION.BEC_BIOGEOCLIMATIC_POLY when performing analysis.

A list of the site series ecological units of the current biogeoclimatic ecosystem classification. Site series are subdivisions of the biogeoclimatic subzone/variant, and describe sites capable of producing the same mature or climax vegetation unit (plant association or sometimes, subassociation). Site series are described in the Regional Field Guides to Site Identification. Site and soil conditions, and the vegetation community, are used to identify site series

These data tables describe biogeoclimatic units for Western North America. These data were assembled as inputs to the Climate Change Informed Species Selection (CCISS) framework. The CCISS framework is built on Biogeoclimatic Ecosystem Classification (BEC). CCISS uses spatial climatic analogs (BEC subzone/variants) to make inferences about future tree species suitability, known as biogeoclimatic projections. Creating species suitability projections for the future climates of British Columbia requires finding climate analogs in Alberta and the Western US. For Alberta, we adapted the Ecological Classification of Alberta (e.g., Archibald et al. 1996), with 21 natural subregions (Natural Regions Committee 2006) as the biogeoclimatic map units and 167 ecological sites as the site series units. For Washington, Idaho, Montana, Oregon, northern California, and northwestern Wyoming, we use a draft biogeoclimatic ecosystem classification for the Western US developed by Del Meidinger and Will MacKenzie. Biogeoclimatic units are detailed in the: Western North America Biogeoclimatic Units Attribute Table. The CCISS tool predicts climate change implications to tree species environmental suitability at a site series level. We have compiled sites series information for Western North America biogeoclimatic units, detailed in; Site Series Information Table and Edatopic Space Table.

A simplification of the current version of the Biogeoclimatic Map feature (version 12, September 2, 2021), and intended as a cartographic layer for viewing at a scale of 1:250,000. NOT FOR ANALYTICAL PURPOSES as polygon boundaries have been generalized. Use WHSE_FOREST_VEGETATION.BEC_BIOGEOCLIMATIC_POLY when performing analysis.

This product integrates Sea Surface Salinity measurements provided by the Soil Moisture and Ocean Salinity (SMOS) European satellite mission. This product provides 9-day integrated satellite Sea Surface Salinity (SSS) measurements at global scale. The product has a spatial resolution of 0.25ºx0.25º and the temporal coverage is from 2011 til May 2021. Acknowledgement=A complete description of the algorithms and quality assessment of the SMOS SSS product are described in Olmedo, E., et al.: Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center, Earth Syst. Sci. Data, 13, 857–888. This product has been developed by the Instituto de Ciencias del Mar / Barcelona Expert Center (CSIC) as part of the European Marine Observation and Data Network Physics (EMODnet Physics) - EASME/EMFF/2020/3.1.11/Lot4/SI2.838612. The development was also supported by the Ministry of Economy and Competitiviness, Spain, through the National R+D Plan under Interact Project PID2020-114623RB-C31 and previous grants and by the European Space Agency. cdm_data_type=Grid comment=These data were produced at BEC as part of the Lambda project. Please, send your feedback to olmedo@icm.csic.es Conventions=CF-1.6, COARDS, ACDD-1.3 copyright=BEC research products are freely distributed. If this data is used for publication, the following ackowlegment should be included: These data were produced by the Barcelona Expert Centre (http://bec.icm.csic.es/). The Barcelona Expert Center is a joint initiative of the Spanish Research Council (CSIC) and Technical (University of Catalonia (UPC), mainly founded by the Spanish National Program on Space. Easternmost_Easting=179.875 funding=The development was also supported by the Ministry of Economy and Competitiviness, Spain, through the National R+D Plan under L-Band Project ESP2017-89463-C3-1-R and previous grants and by the European Space Agency by means of the contract CCI+ Salinity. geospatial_lat_max=89.875 geospatial_lat_min=-89.875 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=179.875 geospatial_lon_min=-179.875 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east grid_mapping_datum=WGS84 grid_mapping_inverse_flattening=298.25723 grid_mapping_longitude_of_prime_meridian=0.0 grid_mapping_name=latitude_longitude grid_mapping_proj4tex=+proj=latlong +ellps=WGS84 grid_mapping_semi_major_axis=6378137.0 history=Thu Jun 18 14:29:41 2020: ncap2 -O -s time[time]=1310601600; tmp1.nc tmp2.nc infoUrl=http://bec.icm.csic.es institution=BEC, ICM-CSIC, Barcelona, Spain license_url=https://creativecommons.org/licenses/by/4.0/ NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) nco_openmp_thread_number=1 Northernmost_Northing=89.875 platform=PROTEUS project=This product has been developed as part of the Copernicus Marine Environment Monitoring Service (CMEMS) Land-Marine Boundary Development and Analysis (Lambda) project. references=SSS have been retrieved following the algorithm described in Olmedo, E. et al., De-biased non-Bayesian Retrieval: a novel approach to SMOS Sea Surface Salinity, Remote Sensing of Environment 193 (2017) 103–126 and Olmedo, E. et al.,CHARACTERIZATION AND CORRECTION OF THE LATITUDINAL AND SEASONAL BIAS IN BEC SMOS SEA SURFACE SALINITY MAPS, 2019, Proceedings of IGARSS 2019 (Paper number WE4.R12.4). Specific publication describing the entire methodology and the performance of the product has been submitted in Earth System Science Data journal, Olmedo, E. et al., Nine years of SMOS Sea Surface Salinity global maps at the Barcelona Expert Center sensor=SMOS/MIRAS sourceUrl=(local files) Southernmost_Northing=-89.875 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2021-05-24T23:00:00Z time_coverage_start=2011-01-24T00:00:00Z url=http://bec.icm.csic.es Westernmost_Easting=-179.875 wms_getcapabilities=https://erddap.emodnet-physics.eu/ncWMS2/wms?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0&DATASET=SMOS_SSS_L3

The Natural Disturbance Type map is based on the Provincial Biodiversity Guidebook (1995) and the current and most detailed version of the approved corporate provincial Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021) (Data Catalog record: https://catalogue.data.gov.bc.ca/dataset/bec-map). The natural disturbance type classification code is used to designate a period process or event such as insect outbreaks, fire, disease, flooding, windstorms and avalanches that cause ecosystem change and renewal. Natural disturbance type classification and mapping is used for a wide variety of applications in British Columbia. A few examples include: delineation of Natural Disturbance Types for Landscape Unit Planning; delineation of Seed Planning Zones; as an input for Predictive Ecosystem Mapping; reporting on the ecological representation of the Protected Areas Strategy; and as a level in the classification hierarchy for Broad Ecosystem Units. Note that this mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

Low boron (B) seriously limits the growth of oilseed rape (Brassica napus L.), a high B demand species that is sensitive to low B conditions. Significant genotypic variations in response to B deficiency have been observed among B. napus cultivars. To reveal the genetic basis for B efficiency in B. napus, quantitative trait loci (QTLs) for the plant growth traits, B uptake traits and the B efficiency coefficient (BEC) were analyzed using a doubled haploid (DH) population derived from a cross between a B-efficient parent, Qingyou 10, and a B-inefficient parent, Westar 10. A high-density genetic map was constructed based on single nucleotide polymorphisms (SNPs) assayed using Brassica 60 K Infinium BeadChip Array, simple sequence repeats (SSRs) and amplified fragment length polymorphisms (AFLPs). The linkage map covered a total length of 2139.5 cM, with 19 linkage groups (LGs) and an average distance of 1.6 cM between adjacent markers. Based on hydroponic evaluation of six B efficiency traits measured in three separate repeated trials, a total of 52 QTLs were identified, accounting for 6.14–46.27% of the phenotypic variation. A major QTL for BEC, qBEC-A3a, was co-located on A3 with other QTLs for plant growth and B uptake traits under low B stress. Using a subset of substitution lines, qBEC-A3a was validated and narrowed down to the interval between CNU384 and BnGMS436. The results of this study provide a novel major locus located on A3 for B efficiency in B. napus that will be suitable for fine mapping and marker-assisted selection breeding for B efficiency in B. napus.

This product integrates Sea Surface Salinity measurements provided by the Soil Moisture and Ocean Salinity (SMOS) European satellite mission. This product provides daily integrated satellite Sea Surface Salinity (SSS) measurements at global scale. The SMOS SSS maps are merged with daily Sea Surface Temperature maps in order to increase the spatial resolution of the original Level 3 SSS maps. The product has a spatial resolution of 0.05ºx0.05º and the temporal coverage is from 2011 til May 2021. _NCProperties=version=2,netcdf=4.7.3,hdf5=1.10.6 Acknowledgement=A complete description of the algorithms and quality assessment of the SMOS SSS product are described in Olmedo, E., et al.: Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center, Earth Syst. Sci. Data, 13, 857–888. This product has been developed by the Instituto de Ciencias del Mar / Barcelona Expert Center (CSIC) as part of the European Marine Observation and Data Network Physics (EMODnet Physics) - EASME/EMFF/2020/3.1.11/Lot4/SI2.838612. The development was also supported by the Ministry of Economy and Competitiviness, Spain, through the National R+D Plan under under Interact Project PID2020-114623RB-C31 and previous grants and by the European Space Agency. cdm_data_type=Grid comment=These data were produced at BEC as part of the Lambda project. Please, send your feedback to olmedo@icm.csic.es Conventions=CF-1.6, COARDS, ACDD-1.3 copyright=BEC research products are freely distributed. If this data is used for publication, the following ackowlegment should be included: These data were produced by the Barcelona Expert Centre (http://bec.icm.csic.es/). The Barcelona Expert Center is a joint initiative of the Spanish Research Council (CSIC) and Technical (University of Catalonia (UPC), mainly founded by the Spanish National Program on Space. description=BEC L4 product resulting from singularity analysis BEC binned L3 product and sea surface temperature (SST) provided daily by Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Visit http://ghrsst-pp.metoffice.com/pages/latest_analysis/ostia.html for additional information about OSTIA system Easternmost_Easting=179.75 funding=The development was also supported by the Ministry of Economy and Competitiviness, Spain, through the National R+D Plan under L-Band Project ESP2017-89463-C3-1-R and previous grants and by the European Space Agency by means of the contract CCI+ Salinity. geospatial_lat_max=89.75 geospatial_lat_min=-90.0 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=179.75 geospatial_lon_min=-180.0 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east history=Fri Jun 19 00:48:00 2020: ncap2 -O -s time[time]=1546214400; tmp1.nc tmp2.nc infoUrl=http://bec.icm.csic.es institution=BEC, ICM-CSIC, Barcelona, Spain keywords_vocabulary=GCMD Science Keywords license_url=https://creativecommons.org/licenses/by/4.0/ NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) nco_openmp_thread_number=1 Northernmost_Northing=89.75 platform=PROTEUS project=This product has been developed as part of the Copernicus Marine Environment Monitoring Service (CMEMS) Land-Marine Boundary Development and Analysis (Lambda) project. references=SSS have been retrieved following the algorithm described in Olmedo, E. et al., De-biased non-Bayesian Retrieval: a novel approach to SMOS Sea Surface Salinity, Remote Sensing of Environment 193 (2017) 103–126, Olmedo, E. et al.,CHARACTERIZATION AND CORRECTION OF THE LATITUDINAL AND SEASONAL BIAS IN BEC SMOS SEA SURFACE SALINITY MAPS, 2019, Proceedings of IGARSS 2019 (Paper number WE4.R12.4). The multifractal fusion method is described in Olmedo, E. et al., Improving time and space resolution of SMOS salinitymaps using multifractal fusion, Remote Sensing of Environment 180 (2016) 246–263. Specific publication describing the entire methodology and the performance of the product has been submitted in Earth System Science Data journal, Olmedo, E. et al., Nine years of SMOS Sea Surface Salinity global maps at the Barcelona Expert Center sensor=SMOS/MIRAS sourceUrl=(local files) Southernmost_Northing=-90.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2021-05-24T00:00:00Z time_coverage_start=2011-01-25T00:00:00Z url=http://bec.icm.csic.es Westernmost_Easting=-180.0 wms_getcapabilities=https://erddap.emodnet-physics.eu/ncWMS2/wms?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0&DATASET=SMOS_SSS_L4

Article citation:

Kuntzemann, C. E., E. Whitman, D. Lewis, and D. Stralberg. (In Revision). Climate, topography, or fuels? Top-down versus bottom-up controls on fire refugia across British Columbia, Canada. Ecosphere.

Data description

This data consists of 5 GEOTIFF rasters covering the extent of British Columbia, Canada, as well as 2 CSV files. All rasters have a 90 m resolution, datums of D North American (1983), and a latitude of origin of 45. Projections are Albers Conic Equal Area (predictions) and NAD (1983) BC Environment Albers (FRUs; EPSG: 3005). The final fire sample consists of all final points (truncated at 25K randomly selected points per FRU) and variables used in analyses, extracted at a 30 m scale in the NAD (1983) BC Environment Albers projection.

Data included:

1. Rasters of predicted fire refugia probabilities under wetter (2001), drier (2017), and average climate conditions, as well as those based on static terrain features (topography), for the province of British Columbia, Canada.

- Fire_Refugia_2001.tif

- Fire_Refugia_2017.tif

- Fire_Refugia_Average.tif

- Fire_Refugia_Topography.tif

2. Fire regime unit (FRU) boundaries used in analyses, as well as a lookup table with each FRU’s associated biogeoclimatic ecosystem classification (BEC) zones and natural disturbance types (NDT).

- FRUs.tif

- FRU_BEC_Lookup.csv

3. Final sample used in analyses.

- Fire_Sample.csv

A publicly available web application, created through the Google Earth Engine App program, can be found at: https://ee-cekfirerefugia.projects.earthengine.app/view/predicted-fire-refugia-probability-across-british-columbia This app includes visualizations of each of the predictive maps, as well as a map detailing the various fire regime units (FRUs) and their associated regions throughout the study area.

Abstract

Surviving pockets of vegetation within fire perimeters, termed fire refugia, are an important component of ecological recovery following disturbance. Understanding the relative influence of the drivers of fire refugia throughout diverse landscapes and climate conditions can help identify areas that are conducive to their formation. We investigated the role of various top-down (climate) and bottom-up (fuels, physical setting) controls on fire refugia creation throughout twenty-one unique fire regime units in the forests of British Columbia, Canada, over a 20-year (2000-2019) period. Boosted regression tree models were used to determine the relative influence of each of these controls and their associated variables on fire refugia, as well as to create predictive maps of fire refugia probabilities over a range of interannual climate conditions. We found that the bottom-up controls, particularly variables relating to physical setting, generally held the greatest influence on fire refugia creation, though those relating to fuels were of higher importance in the more disturbance-prone forests of the boreal and central interior regions. These bottom-up controls, however, can be overwhelmed by extreme climate conditions, which have variable effects on refugia depending on the region. There was an overall positive correspondence between locations of persistent (long-term) fire refugia and mapped old-growth, suggesting that strong, static terrain features may shelter some forests over the course of multiple fire events, allowing for the development of old-growth stands. We concluded that, while strong topographic features confer the strongest measure of protection in some regions of the province, there are many areas in which fuel mitigation tactics (e.g., fuel thinning, prescribed and cultural burning) may be particularly useful for protecting areas of high human or ecological value in the face of increasingly extreme climate conditions. Although our maps can help predict where and when fire refugia may form under provided climatic and environmental conditions, they do not reflect real-time conditions and are therefore not intended for risk assessment or for operational management.

Methods Summary

We fit a series of boosted regression tree models (Elith et al. 2008) to determine the relative importance of top-down and bottom-up controls on fire refugia probability for each of 21 fire regime units (FRU, Erni et al. 2020) in British Columbia. Fires were sampled via randomly generated points representing 1% of fire pixels (30-m resolution). We extracted point and landscape variables (Appendix S1: Table S1) at each sample point. Landscape variables were extracted using square-shaped moving windows of 300 m or 1200 m on a side. All processing and extraction of the covariates was conducted in Google Earth Engine (Gorelick et al. 2017). Fire sampling and model development was conducted using R version 4.4.1 (R Core Team 2024). Final models were used to create predictive maps of fire refugia probability in each FRU under a range of climatic conditions.

References

Elith J, Leathwick JR, Hastie T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77:802–813.

Erni S, Wang X, Taylor S, Boulanger Y, Swystun T, Flannigan M, Parisien M-A. 2020. Developing a two-level fire regime zonation system for Canada. Canadian Journal of Forest Research:259–273.

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27.

R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available from https://www.R-project.org/.

Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescale...

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.

All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).