This polygon layer shows the spatial distribution of forecasted accumulated precipitation across watershed sub‑basins using data derived from the Regional Ensemble Prediction System (REPS). In other words, it aggregates precipitation amounts—computed from processed REPS forecast output (converted from GRIB2 files into raster [TIF] format)—over defined watershed boundaries to provide a detailed view of expected rainfall over a typical 72‑hour forecast period. This information supports regional hydrological forecasting, flood risk analysis, and water resource management. REPS forecast data are first processed to extract the accumulated precipitation field (APCP) and converted into high‑resolution raster images. These “REPS APCP rasters” represent the spatial distribution of forecast precipitation (in millimeters) over the region. Next, using pre‑defined watershed or sub‑basin boundaries, zonal statistics are applied to compute the average precipitation for each sub‑basin. The final layer displays these averaged values as polygon features, highlighting variations in forecasted rainfall across different drainage areas. This approach helps users pinpoint regions that may receive higher or lower rainfall, thereby enhancing hydrological assessments and emergency planning.

This polygon layer showcases ultra-fine (2.5 km) short-range precipitation forecasts from the High Resolution Deterministic Prediction System (HRDPS), a convection-permitting model by Environment and Climate Change Canada. It identifies local-scale rainfall or snowfall patterns up to 48 hours, supporting urban flood forecasting, severe weather response, and detailed water resource planning. Convection-Permitting: The HRDPS can explicitly resolve thunderstorms and other small-scale weather events by running at ~2.5 km. Short-Range Focus: Typically provides forecasts out to 36–48 hours, updated several times daily. Local Impact: Valuable for pinpointing high-impact precipitation in complex terrain or urban environments, aiding emergency managers and hydrologists in short-lead-time decisions. Nested Model: Receives lateral boundary conditions from RDPS, maintaining consistency with regional forecasts while refining detail in local domains.

This polygon layer shows sub-basin averages of HRDPS (High Resolution Deterministic Prediction System) precipitation. Ideal for capturing short-range (0–48h) high-resolution precipitation forecasts aggregated at the watershed scale. The HRDPS is a 2.5 km resolution model used for short-range, convection-permitting forecasts in Canada. This layer takes HRDPS precipitation totals and aggregates them by each sub-basin polygon, revealing how localized rain or snow could impact individual watersheds. Useful for near-term flood or flash-flood risk, as well as local water management during intense weather.

This polygon layer represents accumulated precipitation forecasts from the Regional Ensemble Prediction System (REPS), a regional probabilistic model. It delivers ensemble‑based, short‑range precipitation forecasts—typically a 72‑hour accumulation—that aid in assessing the risk and spatial distribution of rainfall events, supporting hydrological analysis, flood forecasting, and water resource management. This polygon layer is produced by processing REPS GRIB2 files. The workflow involves extracting the precipitation field, converting it to a TIF raster, and then applying resampling, smoothing, and classification to create polygon features. These features represent forecasted rainfall totals over a 72‑hour period and are updated with each model run to maintain current predictive information. Source: Environment & Climate Change Canada

This polygon layer displays sub-basin-level average precipitation derived from the ECMWF (European Centre for Medium-Range Weather Forecasts) model. This layer helps hydrologists, forecasters, and planners see how much rainfall/snowfall is predicted or has occurred in each sub-basin, supporting medium-range water resource and flood management. We are intersested in the forecast period of 7 days. This layer aggregates ECMWF forecast precipitation over polygonal sub-basins. Each feature includes attributes for average accumulated precipitation, forecast run/valid times, and sub-basin identifiers. ECMWF is a leading global model offering medium-range (up to 10 days) forecasts at a high skill level. By focusing on sub-basins, this layer aids in local-scale decision-making—enabling more precise flood risk assessments, reservoir inflow estimates, and water resource planning across the region of interest.

This polygon layer displays 84-hour accumulated precipitation forecasts from the Regional Deterministic Prediction System (RDPS), aggregated at the sub-basin level. This layer helps hydrologists, water resource managers, and emergency responders identify watersheds with potentially higher rainfall or snowfall, facilitating short-term flood risk analysis and operational planning. Model & Domain: The RDPS is Environment and Climate Change Canada’s regional numerical weather prediction model, running at ~10 km resolution to capture mesoscale weather patterns over Canada and adjacent regions. Forecast Integration: It produces short-range forecasts (up to 84 hours), updated 4 times daily with boundary conditions from the global GEM model (GDPS). Sub-Basin Aggregation: This layer averages forecasted precipitation across each sub-basin polygon, providing a convenient snapshot of expected accumulations for hydrological modeling and water management. Key Applications:Flood Forecasting – Identifying basins at risk of heavy runoff. Resource Allocation – Positioning crews and equipment in vulnerable watersheds. Planning – Adapting reservoir release schedules, urban drainage controls, and agricultural activities

This polygon layer reflects short-range (up to 84 hours) accumulated precipitation forecasts from the Regional Deterministic Prediction System (RDPS), a high-resolution (~10 km) weather model developed by Environment and Climate Change Canada (ECCC). It supports flood forecasting, hydrological modeling, and operational planning by providing refined, near-real-time precipitation guidance for Canada and surrounding areas. Short-Range Forecasts: RDPS runs multiple times per day, offering precipitation outlooks for days 0–3.5 with updates every six hours. High Resolution: At ~10 km, RDPS captures critical mesoscale phenomena like localized downpours, lake-effect snow, and terrain-driven precipitation. Hydrological Utility: Especially valuable for sub-basin-level flood risk assessment and water resource management in near-term scenarios. Technical Basis: The RDPS is a limited-area configuration of the GEM model, using initial/boundary conditions from ECCC’s Global Deterministic Prediction System (GDPS).

This polygon layer presents 7‑day and 10‑day accumulated precipitation forecasts from the Global Deterministic Prediction System (GDPS), aggregated by sub-basin. It is designed to help hydrologists, water resource managers, and emergency planners pinpoint watersheds facing higher rainfall or snowfall totals in the medium-to-long range, enabling proactive flood risk assessment, drought monitoring, and resource allocation. Developed by Environment and Climate Change Canada (ECCC), the GDPS is a global numerical weather prediction model running at approximately 15km resolution, updated twice daily (00Z and 12Z). This layer integrates 168-hour (7‑day) and 240-hour (10‑day) precipitation forecasts into sub-basin polygons, offering a comprehensive view of expected cumulative precipitation. By focusing on watershed boundaries, decision-makers can quickly gauge regional vulnerabilities to prolonged rainfall or snowfall events.Key highlights: Global Model Insight: Captures large-scale, multi-day weather systems (e.g., atmospheric rivers, persistent low-pressure systems). Sub-Basin Aggregation: Delivers averaged precip values per basin, simplifying hydrological analysis for flood or drought outlooks. Extended Outlook: Spanning from day 0 to day 10, covers both medium- and longer-term forecast horizons, essential for strategic planning and mitigation efforts. Typical Uses:Flood Forecasting – Identifying basins prone to heavy or prolonged precipitation. Water Resource Management – Adjusting reservoir release schedules or irrigation planning based on expected accumulations. Emergency Preparedness – Deploying resources or issuing advisories in vulnerable watersheds.

This polygon layer represents accumulated precipitation forecasts from the Global Forecast System (GFS), a global numerical weather prediction model operated by NOAA/NCEP. It provides global medium‑range precipitation forecasts, as a 168‑hour (7‑day) accumulation, to support a wide range of weather and hydrological applications. This polygon layer is generated by extracting the accumulated precipitation field from Global Forecast System (GFS) GRIB2 files. The raw data are converted into a TIF raster, then resampled, smoothed, and classified into discrete precipitation ranges. The resulting polygon features depict forecasted precipitation accumulations over a 7‑day (168‑hour) period, allowing users to monitor expected rainfall and snowfall patterns on a global scale.

This polygon layer provides medium-range (up to 10 days) accumulated precipitation forecasts from the Global Deterministic Prediction System (GDPS), a worldwide numerical weather model run by Environment and Climate Change Canada. It addresses broad-scale weather systems and supplies boundary conditions for nested regional models. Global Scope: The GDPS covers the entire planet at ~15 km resolution, projecting large-scale atmospheric developments over a 240-hour window. Coupled Model: Integrates atmospheric and oceanic interactions, improving forecast accuracy for cyclones, frontal systems, and long-traveling storm patterns. Operational Backbone: Frequently used as a reference for regional or local models (e.g., RDPS) and for medium-range planning in water resource management or agriculture. Forecast Frequency: Runs twice daily, producing deterministic outputs that guide meteorologists, hydrologists, and emergency preparedness teams.