This series of datasets has been created by AAFC’s National Agroclimate Information Service (NAIS) of the Agro-Climate, Geomatics and Earth Observations (ACGEO) Division of the Science and Technology Branch. The Canadian Drought Monitor (CDM) is a composite product developed from a wide assortment of information such as the Normalized Difference Vegetation Index (NDVI), streamflow values, Palmer Drought Index, and drought indicators used by the agriculture, forest and water management sectors. Drought prone regions are analyzed based on precipitation, temperature, drought model index maps, and climate data and are interpreted by federal, provincial and academic scientists. Once a consensus is reached, a monthly map showing drought designations for Canada is digitized. AAFC’s National Agroclimate Information Service (NAIS) updates this dataset on a monthly basis, usually by the 10th of every month to correspond to the end of the previous month, and subsequent Canadian input into the larger North American Drought Monitor (NA-DM). The drought areas are classified as follows: D0 (Abnormally Dry) – represents an event that occurs once every 3-5 years; D1 (Moderate Drought) – represents an event that occurs every 5-10 years; D2 (Severe Drought) – represents an event that occurs every 10-20 years; D3 (Extreme Drought) – represents an event that occurs every 20-25 years; and D4 (Exceptional Drought) – represents an event that occurs every 50 years. Impact lines highlight areas that have been physically impacted by drought. Impact labels specify the longitude and magnitude of impacts. The impact labels are classified as follows: S – Short-Term, typically less than 6 months (e.g. agriculture, grasslands). L – Long-Term, typically more than 6 months (e.g. hydrology, ecology).

The Drought Impact Lines dataset highlights areas that have been physically impacted by drought. All drought impact lines have a drought impact label inside of them to express the longevity of the impact. The impact lines are classified using impact labels as follows: S – Short-Term, typically less than 6 months. L – Long-Term, typically more than 6 months. SL – A combination of Short and Long-Term impacts.

The first version of this gridded dataset (on a 10-km EASE grid) of monthly total precipitation amounts was produced using a kriging-based gridding scheme to grid the Canadian homogenized monthly precipitation dataset CanHoPmlyV1 (Wang et al. 2023; available at https://open.canada.ca/data/dataset/1dd0c28e-2266-42e2-8985-2f47659e9d02). More specifically, ordinary kriging was used to grid the 1961-1990 climate normal values and the relative anomalies, separately; the resulting gridded datasets were used to produce this gridded dataset of monthly total precipitation amounts (Wang et al. 2023, Abbasnezhadi and Wang, 2024). As detailed in Wang et al. (2023), CanHomPmlyV1 is based on the quality-controlled version 2020 of the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset (Wang et al. 2017, available at https://open.canada.ca/data/en/dataset/d8616c52-a812-44ad-8754-7bcc0d8de305), and on daily total precipitation data from automated gauges (including Belfort, Fisher & Porter, Nipher, Geonor, and Pluvio), with some records from neighbouring stations being joined to form long-term data series. Version 1 of ANUSPLIN surfaces of the adjusted monthly precipitation (MacDonald et al. 2021) was used to infill temporal data gaps in the 425 data series. A comprehensive semi-automatic data homogenization procedure was used to homogenize the data series. The aforementioned ANUSPLIN data and the Twentieth Century Reanalysis 20CRv3 ensemble-mean series of monthly precipitation (Slivinski et al., 2019) were used as reference in the homogeneity tests (Wang et al., 2023). The homogenized dataset CanHoPmlyV1, and its gridded version CanGridP mlyV1, which was called CanKrig mlyPv1 in Wang et al. (2023), provide more realistic estimates of precipitation trends (Wang et al. 2023). Although the latter is much better than the pre-existing CanGRD precipitation relative anomalies data, both gridded datasets contain biases due to changes in data availability over time and space (i.e., inhomogeneous sampling), which are non-negligible in the early period. Such biases are being assessed and corrected to produce a sampling bias-corrected gridded dataset CanGridP mlyV2 (upcoming). References: Abbasnezhadi, K. and X. L. Wang, 2024: Comparison of gridding methods for precipitation over Canada and assessment of station/data density effects on gridding results. Atmos.-Ocean, 62:4, 320-346, https://doi.org/10.1080/07055900.2024.2394829. Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598.

The Drought Impact Label dataset is used on all drought polygons from D1 to D4 to specify the longitude and magnitude of impacts. Impact labels are often used in association with the Drought Impact Line dataset. The impact labels are classified as follows: S – Short-Term, typically less than 6 months. L – Long-Term, typically more than 6 months. SL – A combination of Short and Long-Term impacts.

This data represents the dryness of the land surface based on vegetation conditions. The data is created weekly and uses weekly information on precipitation anomalies (namely the Standardized Precipitation Index or SPI) and satellite vegetation condition derived from Normalized Difference Vegetation Index (NDVI) from the MODIS Satellite. These dynamic data sets along with static data sets on land cover, soil water holding capacity, irrigation, ecozones and land surface elevation are used to model the drought severity, based on the Palmer Drought Severity Index (PDSI). The mapcubist model was trained on historical data and applied in real time to the dynamic inputs to produce drought severity ratings. The model is run at a 1km resolution and was developed by the AAFC, the United States Geological Survey and the United States Drought Monitor at the University of Nebraska Lincoln.

30-year Average precipitation represents the average amount (mm) of precipitation received in a month across a 30 year period (1961-1991, 1971-2000, 1981-2010, 1991-2020). These values are calculated across Canada in 10x10 km cells.

30-year Average Number of Days with Temperature above 30 °C are defined as the count of the number of climate days during the time period where the maximum daily temperature was greater than 30 °C. These values are calculated across Canada in 10x10 km cells.

This dataset contains the monthly climate normals for Quebec as measured at the stations of the Ministry of the Environment, Fight against Climate Change, Wildlife and Parks. If you have questions about this data, contact the Info-Climat service: [https://www.environnement.gouv.qc.ca/climat/surveillance/produits.htm](https://www.environnement.gouv.qc.ca/climat/surveillance/produits.htm “Info-Climat”)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Standardized Precipitation Evapotranspiration Index (SPEI) is computed similarly to the SPI. The main difference is that SPI assesses precipitation variance, while SPEI also considers demand from evapotranspiration which is subtracted from any precipitation accumulation prior to assessment. Unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand 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.

Growing Season Frost Free Period (-2 °C) is defined as the count of the number of days from the day after the last spring frost (-2 °C) to the day before the first fall frost (-2 °C). These values are calculated across Canada in 10x10 km cells.