Canada must focus on sectors with real strengths to drive innovation and competitiveness: agriculture, forestry, energy and critical minerals.

Quarterly Climate Impacts and Outlook for the Canadian and U.S. Prairies and High Plains for June–August 2025, with an outlook for October–December 2025. Dated September 2025. Summer brought mostly ne...

This fact sheet presents gross domestic product (GDP) data at the sub-drainage area geography. It outlines the way that natural resources and biophysical features throughout Canada help determine whic...

Abstract Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation. Significance Statement This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.

The Earth Observation Data Management System (EODMS) is a fully integrated geospatial platform provided by NRCan to the general public to discover and access authoritative Earth Observation (EO) data.

North America is undergoing a significant shift in how geographical positions are defined. Both Canada and the United States are moving away from NAD83 (North American Datum of 1983), a system introdu...

Welcome to the official website of the 2026 ISPRS Congress hosted by CRSS-SCT! Join us at the Metro Toronto Convention Centre for this significant event in Remote Sensing & Geoinformation. Discover in...

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Real-Time In-Situ Soil Monitoring for Agriculture</i> (RISMA) dataset is a collection of publicly ...