“Pacific Rim National Park Reserve - Total GHG Emissions” datasets consist of estimates of GHG emissions (carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)) in carbon dioxide equivalents (CO2e) from forested ecosystems in 1990 to 2020 (tonnes carbon dioxide equivalent per hectare). Total GHG emissions for 31 national parks were estimated using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change (IPCC)-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires). Total GHG emissions include those from natural processes like respiration and decomposition and those due to natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These were calculated as the sum of CO2, CH4, and N2O emission estimates in tonnes carbon (tonnes C) generated by the GCBM. Emissions estimates were then converted to carbon dioxide equivalents (CO2e) using the 100-year Global Warming Potential (IPCC Fourth Assessment Report) factors for CH4 (25) and N2O (298). These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

Description: In the coming decades, warming and deoxygenation of marine waters are anticipated to result in shifts in the distribution and abundance of fishes, with consequences for the diversity and composition of fish communities. Here, we combine fisheries-independent trawl survey data spanning the west coast of the USA and Canada with high-resolution regional ocean models to make projections of how 34 groundfish species will be impacted by changes in temperature and oxygen in British Columbia (BC) and Washington. In this region, species that are projected to decrease in occurrence are roughly balanced by those that are projected to increase, resulting in considerable compositional turnover. Many, but not all, species are projected to shift to deeper depths as conditions warm, but low oxygen will limit how deep they can go. Thus, biodiversity will likely decrease in the shallowest waters (less than 100 m), where warming will be greatest, increase at mid-depths (100–600 m) as shallow species shift deeper, and decrease at depths where oxygen is limited (greater than 600 m). These results highlight the critical importance of accounting for the joint role of temperature, oxygen and depth when projecting the impacts of climate change on marine biodiversity. The rasters available in this dataset project the occurrence of each of the 34 groundfish species in a 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models (BCCM and NEP36). Each projection layer is provided as the mean projected occurrence as well as the lower and upper 95% confidence interval of projected occurrence. Methods: Estimated species response curves: We estimated how the observed distribution of groundfish species is determined by temperature, dissolved oxygen and seafloor depth using data from fisheries-independent scientific research trawls spanning the entire American and Canadian west coast. We included data from 4 surveys (NOAA West Coast, NOAA Alaska, NOAA Bering or DFO Pacific) from 2000 to 2019. For each species, we modelled occurrences in the coastwide trawl dataset using a generalized linear model (GLM) using the sdmTMB package in R v. 4.0.2. The predictors were temperature, log dissolved oxygen, log depth and survey. We included quadratic terms for temperature and log depth to allow species occurrences to peak at intermediate values. We fitted a breakpoint function for log dissolved oxygen to reflect the fact oxygen is a limiting factor. We assessed the forecasting accuracy of the SDM by comparing how well a model fitted to only data from 2000 to 2010 could forecast species’ occurrences in trawls within our focal region for the period of 2011–2019. We assessed all 77 groundfish species that were present in the overall trawl dataset, however the final analysis included only the 34 species for which the models had adequate forecasting ability. Projecting groundfish biodiversity changes: We based our groundfish biodiversity change projections on two regional models that downscale climate projections: the British Columbia Continental Margin model (BCCM) and the North-Eastern Pacific Canadian Ocean Ecosystem model (NEP36-CanOE). We used a historical baseline of 1986–2005 and future projected values for 2046–2065 based on RCP 4.5 and 8.5 emissions scenarios. Using the models that we validated in our forecasting accuracy assessment, we projected the occurrence of each species in each 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models. Uncertainties: Source survey data was collected by consistent methods with survey-grade GPS for all years included. Data quality is expected to be high. Modeled data are at 3 km resolution. Outputs are as accurate as source input models and are deemed to be of high quality and accurate based upon the precision of model inputs. Projecting biodiversity responses to climate change involves considerable uncertainty and our approach allows us to quantify some aspects of this. Of the uncertainty that we could quantify, roughly half was due to uncertainty in our SDMs and the remainder was due to regional ocean model uncertainty or scenario uncertainty. This amount of uncertainty in the SDMs is typical, stemming from the fact that contemporary species distributions are also influenced by other factors that we have not included in our model. In addition, although oxygen demand is understood to vary with temperature, limitations in the implementation of breakpoint models prevented us from estimating a temperature-dependent oxygen breakpoint. However, although somewhat unrealistic, this limitation is unlikely to have greatly increased the uncertainty in our SDMs because low oxygen concentrations occurred almost exclusively at depths where temperature variation and projected change was small. To reduce uncertainty due to year-to-year variation in climate, our model projections are based on 20-year climatologies with a future period that is far enough ahead to ensure that changes are unambiguously due to greenhouse gases. We have made projections based on two different emissions scenarios, and two different regional ocean models that are both downscaled from the same global model, the second generation Canadian Earth System Model (CanESM2), using different downscaling techniques. While the BCCM model was run inter-annually and then averaged to produce the climatologies, the NEP36 model used atmospheric climatologies with augmented winds to force the ocean model and produce representative climatologies. Comparing these regional projections provides an estimate of the uncertainty across different regional downscaling models and methods. We find that the projected impacts of climate change on the groundfish community are more sensitive to the differences in the regional ocean models than they are to the emissions scenarios used. However, these differences are in magnitude (changes tend to be larger based on NEP36 compared with the BCCM) rather than in direction, with both models resulting in similar overall patterns of biodiversity change and turnover for the groundfish community. Over the 60-year time period (1986–2005 versus 2046–2065) used in our study, our projections suggest that groundfish community changes are similar regardless of the scenario used.

“Thousand Islands National Park - Total GHG Emissions” datasets consist of estimates of GHG emissions (carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)) in carbon dioxide equivalents (CO2e) from forested ecosystems in Thousand Islands National Park from 1990 to 2020 (tonnes carbon dioxide equivalent per hectare). Total GHG emissions for 31 national parks were estimated using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change (IPCC)-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires). Total GHG emissions include those from natural processes like respiration and decomposition and those due to natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These were calculated as the sum of CO2, CH4, and N2O emission estimates in tonnes carbon (tonnes C) generated by the GCBM. Emissions estimates were then converted to carbon dioxide equivalents (CO2e) using the 100-year Global Warming Potential (IPCC Fourth Assessment Report) factors for CH4 (25) and N2O (298). These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

Percent of average precipitation represents the total amount of precipitation for a particular location over a specific time period, divided by the long-term average precipitation (1991-2020) for the same period and location, expressed as a percentage. Percent of average precipitation is to provide a clear and standardized way to compare how much precipitation has fallen during a specific time period relative to the long-term average for the same location and time period. Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Long-term average is 1991-2020. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

Crop development stage in a numerical scale. All living organisms move from one stage of development to the next over time. For annual crops, it life cycle (growing season) completed within a year. Crop water use differs from one stage to another mostly due to the differences in the amount of green leaves, thus crop stage is closely related to its water consumption and water stress condition. Crop stages are mostly controlled by growing season heat accumulation and regulated by day-length crop some crops. The crop stages provided here are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) , and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

This data publication contains a set of 30m resolution raster files representing Canadian wall-to-wall maps, at 5-year intervals from 1985 to 2025, of broad landcover type, forest canopy height, degree of crown closure and live aboveground dry tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours (k-NN) and random forest imputation method. SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a wide range of studies in a number of areas, such as forest economics, fire science and ecology. SCANFI v2 is described here. The main methodological and performance differences between SCANFI v1 (https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc) and SCANFI v2 are briefly outlined below. For a more detailed description of these changes, please consult the accompanying report. For the scientific publication about SCANFI v1, see Guindon et al. (2024). **SCANFI version 2 main updates** 1. Transition to Landsat Collection 2 Updated all Landsat time series inputs from Collection 1 to Collection 2, providing improved radiometric calibration and geometric consistency across sensors and years. 2. SCANFI time series with LandTrendr-smoothed winter imagery Introduced LandTrendr-smoothed winter imagery, enabling a full 5-year interval time series from 1985 to 2025 with dual-season inputs. 3. Updated climate normals Replaced previous climate layers with smoother 1991–2020 normals (MacDonald et al. 2024), eliminating tiling artifacts. 4. Increased sample selection density of NFI training data Shifted from a photoplot-level 250m systematic grid to a denser sampling strategy: 4 pixels/hectare per polygon (max 100 points, excluding border pixels). This sampling strategy greatly increased the number of training points available, improving representation of inter- and intra-polygon variability and strengthening model performance. 5. More accurate water prediction and delineation of urban areas and croplands Water and cropland prediction is now done using a soon-to-be-published landcover time series layer in order to: (i) prevent the misclassification of recently disturbed areas as water in SCANFI v1; and (ii) convert urban areas and croplands to forested land before 2020 when the Agriculture and Agri-Food Canada (AAFC) land use map used in the original study is not available (Agriculture and Agri-Food Canada, 2022). 6. Forest age map SCANFI v2 introduces new national forest age maps by combining recent disturbance history data with spatialized NFI age estimates for undisturbed stands, producing consistent stand age attribution across Canada from 1985 to 2025, at 5-year intervals. The resulting approach reduces noise relative to single-year estimates and yields spatially complete, temporally stable forest age maps. 7. Modified response and target variable sets Water is no longer used as a target variable, whereas land position (alpine, wetland or upland) was added to the target variable list and is now an output layer. The selection of Landsat bands and indices used in modeling was also refined, improving sensitivity to forest structure, composition, and disturbance. Finally, SCANFI now relies on the recently published Canadian Medium Resolution Digital Elevation Model (Government of Canada, Natural Resources Canada, Strategic Policy and Innovation Sector, 2025). See update report for more details. 8. Improved extrapolation of arctic ecozones The tile-level approach used to extrapolate arctic ecozones outside the sampling range of the NFI photoplots was replaced by the use of two training sets to predict two distinct geographical northern areas: (1) northern Quebec; and (2) northern Northwest Territories and Nunavut. This approach considerably improved model reliability and consistency in the arctic ecozones. 9. Updated time-since-disturbance layers Integrated complete 1965-2025 harvest and fire records (Correia et al. 2024; Perbet et al. 2025), improving disturbance history initialization and enhancing stand-structure attribution. 10. Modified tree species cover layers In order to avoid interpretation errors and facilitate user analyses, SCANFI v2 tree species layers now represent the corresponding tree species crown closure, instead of the proportion of the overall crown closure that is represented by that species. Unlike SCANFI v1, users no longer need to multiply overall crown closure by tree species proportion (and divide by 100) to obtain tree species crown closure. Examples include: - A pixel with 30% total crown closure composed of 50% black spruce and 50% jack pine will have 15% black spruce crown closure and 15% jack pine crown closure in SCANFI v2. - A pixel with 40% total crown closure entirely dominated by balsam fir will have 40% balsam fir crown closure in SCANFI v2. - A pixel with 100% total crown closure composed of 60% black spruce and 40% balsam fir will have 60% black spruce and 40% balsam fir crown closure in both SCANFI v1 and SCANFI v2. Tree species live aboveground tree biomass can still be obtained by estimating the proportion of the corresponding tree species relative to pixel-level crown closure and multiplying that value by pixel-level biomass. **Validation summary and comparison with version 1** External validation results show that SCANFI v2 consistently outperforms SCANFI v1 across all independent datasets used in Guindon et al. (2024). Structural attributes validated with Global Ecosystem Dynamics Investigation (GEDI), Potapov et al. (2021), and airborne lidar exhibit R² gains of approximately 2-3 points and modest reductions in RMSE and MAE, indicating improved estimates of height and crown closure. Species composition shows the largest improvements, with R² increases of roughly 4 to more than 15 points for key species such as black spruce, jack pine, tamarack, and the broadleaf grouping (see the update report for more details). Biomass estimates also improve, with smaller but consistent gains in explained variance and reduced error across ground-plot datasets. In contrast, internal cross-validation metrics are slightly lower for v2, reflecting the effect of a complex training dataset with much more forested samples rather than reduced real-world predictive performance. **SCANFI time series** SCANFI v2 provides a publicly available 5-year interval national time series (1985–2025) based on LandTrendr-smoothed summer and winter Landsat imagery with cross-sensor harmonization. Pixel-level temporal inconsistencies remain by design to preserve traceability and consistency across attributes, making the time series unsuitable for plot-level analyses but reliable for large-scale regional and national assessments where noise averages out. Although not intended for detailed biomass trend analysis, the estimated national biomass increase of approximately 4.8% between 1985 and 2025 suggests ecologically plausible values, pending further evaluation. **Limitations** 1. The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St. Lawrence River. These forests are misrepresented in our training data; therefore, our estimates in these areas are imprecise. 2. Attributes of open stand classes, namely shrub, herb, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates. 3. As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. 4. Our validation indicates that the areas in Yukon exhibit a notably lower R² value. Consequently, estimates within these regions are less dependable. 5. Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map (Agriculture and Agri-Food Canada, 2022). Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions. **Data download** The data can be downloaded from the FTP server (ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/SCANFI/v2/), referenced in the “Data and Resources” section, using a browser download manager or an external client such as FileZilla. **Dataset citation** - Guindon L., Correia D.L.P, Manka F. and Smiley B. 2026. SCANFI v2: Spatialized CAnadian National Forest Inventory data product v2. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbf **References** - Agriculture and Agri-Food Canada. (2022). 2020 AAFC Land Use [Map]. Retrieved from https://open.canada.ca/data/en/dataset/7a098ea9-cc31-4d79-b326-89f6cd1fbb7d. - Correia, D.L., Guindon, L. and Parisien, M.A., 2024. Extending Canadian forest disturbance history maps prior to 1985. Ecosphere, 15(8), p.e4956. - Guindon, L., Manka, F., Correia, D.L., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J. and Boulanger, Y., 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Canadian Journal of Forest Research, 54(7), pp.793-815. - MacDonald, H., McKenney, D.W., Pedlar, J., Lawrence, K., de Boer, K. and Hutchinson, M.F., 2024. Spatial datasets of 30-year (1991–2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States. Data in Brief, 55, p.110561. - Government of Canada, Natural Resources Canada, Strategic Policy and Innovation Sector. (2025, August 28). Medium Resolution Digital Elevation Model (MRDEM) - CanElevation Series. Natural Resources Canada, Federal Geospatial Platform. https://osdp-psdo.canada.ca/dp/en/search/metadata/NRCAN-FGP-1-18752265-bda3-498c-a4ba-9dfe68cb98da - Perbet, P., Guindon, L., Correia, D.L., Gahrouei, O.R., Côté, J.F. and Béland, M., 2025. Historical insect disturbance maps from 1985 onwards for Canadian forests derived using earth observation data. Scientific Data. - Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E. and Armston, J., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, p.112165.

“Gwaii Haanas National Park Reserve and Haida Historic Site - Total Ecosystem Forest Carbon Density” is the annual carbon density (tonnes carbon per hectare) within Gwaii Haanas’ forested ecosystems over a 31-year period from 1990 to 2020. Total Ecosystem Forest Carbon Density includes aboveground and belowground biomass, soil carbon, and dead organic matter. Total Ecosystem Forest Carbon Density was estimated for 31 national parks using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. Ecozones were classified according to Canada Ecological Land Classification Level 1. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires, insect outbreaks). Total Ecosystem Forest Carbon Density accounts for the effects of natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) Mud The categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.

Difference from Average Precipitation represents the accumulated precipitation value for a location, subtracted by the long-term average value. A negative value indicates that the location has received less than the normal amount of precipitation (mm) for that timeframe. A positive value indicates that the location has received more than the normal amount of precipitation (mm). Time periods calculated for difference from average precipitation are 1, 2, 3, 6, 9, 12, 18, 24 months. Long-term average is 1991-2020. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

The Standardized Precipitation Index (SPI) is widely regarded as one of the most accessible tools for quantifying and reporting meteorological drought. At shorter timescales, SPI values are highly correlated with soil moisture, while at extended timescales, they reflect variations in groundwater and reservoir storage. The SPI model derives probability distributions from historical precipitation records, which are then normalized using an incomplete gamma function across multiple timescales. SPI values indicate the number of standard deviations the observed anomaly departs from the long-term period of record, with positive values corresponding to wetter-than-average conditions. Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980 Indices values were calculated using the xclim python package The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.