Abstract. 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.
Abstract. Accurate simulation of snowmelt runoff (SMR) is critical for water resource management. However, despite the abundance of global hydrological models, little is known about their SMR performance. This study presents a comprehensive evaluation of SMR across 15 state-of-the-art large-scale models and runoff products by focusing on their biases in first-order indices, i.e., the total volume (Qsum), peak flow (Qmax), and centroid timing (CTQ) of runoff in the snowmelt period. Then by introducing 1455 snow-dominated basins with diverse topography and vegetation complexities, we further proposed a novel model robustness metric to test how different models perform under increasing basin complexity, thereby allowing for a quantification on how they adapt to complex environmental conditions. Our results reveal that (1) most models exhibit underestimated Qsum and Qmax and predict CTQ too early. These biases are particularly pronounced in regions such as the western United States, northern Europe, and northeastern China. (2) Model biases systematically increase with basin complexity, with CTQ exhibiting strong sensitivity to mean elevation and topographic variability, while Qsum and Qmax being shaped more by mean elevation and the diversity of vegetation types in the basin. (3) The robustness assessment further shows that observation-constrained runoff products exhibit the most outstanding performance (i.e., low biases and strong adaptability to stern conditions), followed by the hydrological and land surface models. Notably, while global hydrological models generally exhibit stronger robustness in simulating Qsum and Qmax, land surface models show a clear advantage in simulating CTQ, highlighting their structural strength in capturing melt timing rather than runoff magnitude. This study provides a large-sample benchmark for SMR evaluation and complements existing model assessment approaches by examining model performance across basin complexity gradients, offering useful insights for future model development and uncertainty reduction.
Mountain regions in Europe increasingly face compound runoff-related hazards under a changing climate. This article compares postfire surface runoff (PFSR) and frozen-soil surface runoff (FSSR), 2 processes typically studied separately but sharing key meteorological triggers, as well as initiation and propagation mechanisms. Both are triggered by short, high-intensity rainfall acting on infiltration barriers: hydrophobic layers in burned soil and pore ice in frozen soil. Despite distinct thermal and seasonal contexts, PFSR and FSSR exhibit convergent process chains characterized by reduced interception, limited infiltration, surface ponding, and efficient sediment entrainment. Their occurrence increasingly transcends expected seasonal regimes, reflecting climate-driven shifts in fire activity, snow dynamics, and freeze–thaw cycles. Building on these parallels, this study proposes a research framework and outlines a future research agenda that links mapping, monitoring, experimentation, modeling, and governance. It advocates integrated observation networks combining remote sensing and in situ techniques, controlled experiments to quantify thermohydraulic and erosional processes and changes in vegetation cover, and physical models capturing preferential flow and transient infiltration barriers. For risk management, the article stresses predictive early warning systems that provide sufficient lead time to manage evacuations and infrastructure protection. By identifying shared mechanisms and converging research needs, this contribution supports a future research agenda and cross-disciplinary strategies for mitigating emerging compound hazards in alpine environments.
Vegetation-impervious surface-soil (V-I-S) fractions offer a continuous sub-pixel representation of urban surface heterogeneity. In this study, the influence of urban surface characteristics represented through V-I-S fractions on hydrological processes is analyzed at decadal intervals, i.e., 2000, 2010, 2020, and the projected year 2030 for the Mula–Mutha river catchment, Maharashtra, India. Pune city, as a major urban centre in this region, is experiencing significant changes in land surface characteristics over time, which have direct implications for its hydrology. The analysis uses the Soil and Water Assessment Tool (SWAT) to model these changes and their effects on water resources. Results show that the urban area has increased from 14% (2000) to 25% (2020), with projections indicating a further rise to 34% (2030). Such transitions yielded an increase in surface runoff from 47% (2000) to 53% (2020) and projected to reach 54% (2030). Groundwater recharge has declined from 10% to 6% and is expected to fall to 4% by 2030. Model validation using discharge data at Mirawadi outlet yielded a coefficient of determination of 0.72 using land use/land cover (LULC) data and 0.79 for simulations based on runoff Curve Number (CN) derived from V-I-S fractions, indicating the improved model performance. This study presents a novel framework, which incorporates remote sensing-derived V-I-S fractions to assess the spatiotemporal impact of urban expansion on water balance components.