Vincent Fortin

As part of a project aimed at improving the Canadian High‐Resolution Deterministic Prediction System, the parameterization for bare‐ground evaporation is revisited and a new method, improving upon the soil‐resistance method used at the European Centre for Medium‐range Weather Forecasts (ECMWF), is proposed. Higher values of soil evaporation resistance are obtained, particularly in dry conditions. The proposed change, tested over the fall of 2021 and the spring of 2022, improves surface weather forecasts significantly in regions and seasons with large bare‐ground fractions. To confirm that the change also leads to better predictions of the latent heat flux, a point‐scale evaluation is performed at AmeriFlux station US‐Ne1 in Nebraska for three contrasted years in terms of precipitation and post‐harvest soil moisture (2015, 2016, and 2021). While significant improvements are observed, evaporation starts earlier in the day than predicted and evaporation following rainfall events is underestimated.

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded total precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. In land surface models, frozen soil infiltration is difficult to represent because soil structure and hydropedological processes vary at scales finer than the model grid. This is particularly true in operational modeling, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a new configuration of the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC) that enhances frozen soil infiltration by reducing both surface runoff and sub-surface lateral flow. We assessed the effects of this new configuration (Fr-Inf) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. Fr-Inf significantly improves the Kling-Gupta Efficiency (KGE) compared to the default soil freezing configuration (Fr; ΔKGE = 0.28) but slightly underperforms the configuration of SVS without frozen soil (noFr; ΔKGE = −0.07). Strong degradations relative to the no freezing configuration (ΔKGE < −0.5) are observed only at 4 stations with Fr-Inf (< 1 %) as opposed to 172 stations under the Fr configuration (33 %), highlighting the robustness of the approach. To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of soil freezing at ECCC for numerical weather and streamflow prediction.

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. Ice within the soil matrix reduces the pore space available for water to infiltrate, while the presence of soil macropores in structured soils maintains rapid water percolation even in frozen conditions. Representing the complex effect of soil freezing on water infiltration in land surface models is a challenging task. This is particularly true for operational models, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a conceptual approach to represent the effects of macropores on frozen soil infiltration into the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC). We assessed the effects of this new configuration (Fr-MP) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. The conceptual representation of macropores improves the Kling-Gupta Efficiency (KGE) at 88 % of the assessed stations, resulting in an increase in the median KGE of 0.28 compared to the configuration without macropores and soil freezing. Detailed analysis of a decomposed hydrograph shows that the Fr-MP configuration increases SVS soil drainage (slow response) and reduces surface runoff and lateral flow (quick response). To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of the Fr-MP configuration at ECCC for numerical weather and streamflow prediction.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. Ice within the soil matrix reduces the pore space available for water to infiltrate, while the presence of soil macropores in structured soils maintains rapid water percolation even in frozen conditions. Representing the complex effect of soil freezing on water infiltration in land surface models is a challenging task. This is particularly true for operational models, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a conceptual approach to represent the effects of macropores on frozen soil infiltration into the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC). We assessed the effects of this new configuration (Fr-MP) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. The conceptual representation of macropores improves the Kling-Gupta Efficiency (KGE) at 88 % of the assessed stations, resulting in an increase in the median KGE of 0.28 compared to the configuration without macropores and soil freezing. Detailed analysis of a decomposed hydrograph shows that the Fr-MP configuration increases SVS soil drainage (slow response) and reduces surface runoff and lateral flow (quick response). To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of the Fr-MP configuration at ECCC for numerical weather and streamflow prediction.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. Ice within the soil matrix reduces the pore space available for water to infiltrate, while the presence of soil macropores in structured soils maintains rapid water percolation even in frozen conditions. Representing the complex effect of soil freezing on water infiltration in land surface models is a challenging task. This is particularly true for operational models, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a conceptual approach to represent the effects of macropores on frozen soil infiltration into the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC). We assessed the effects of this new configuration (Fr-MP) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. The conceptual representation of macropores improves the Kling-Gupta Efficiency (KGE) at 88 % of the assessed stations, resulting in an increase in the median KGE of 0.28 compared to the configuration without macropores and soil freezing. Detailed analysis of a decomposed hydrograph shows that the Fr-MP configuration increases SVS soil drainage (slow response) and reduces surface runoff and lateral flow (quick response). To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of the Fr-MP configuration at ECCC for numerical weather and streamflow prediction.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. Ice within the soil matrix reduces the pore space available for water to infiltrate, while the presence of soil macropores in structured soils maintains rapid water percolation even in frozen conditions. Representing the complex effect of soil freezing on water infiltration in land surface models is a challenging task. This is particularly true for operational models, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a conceptual approach to represent the effects of macropores on frozen soil infiltration into the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC). We assessed the effects of this new configuration (Fr-MP) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. The conceptual representation of macropores improves the Kling-Gupta Efficiency (KGE) at 88 % of the assessed stations, resulting in an increase in the median KGE of 0.28 compared to the configuration without macropores and soil freezing. Detailed analysis of a decomposed hydrograph shows that the Fr-MP configuration increases SVS soil drainage (slow response) and reduces surface runoff and lateral flow (quick response). To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of the Fr-MP configuration at ECCC for numerical weather and streamflow prediction.