Mathematical modelling of the formation and evolution of surface ice

During the fall season in cold regions when the air temperature drops below freezing levels, it removes heat from the water surface and creates a supercooled surface layer. In weak turbulent flows, the supercooled surface layer initiates the formation of ice particles on the water surface, which could evolve into various types of surface ice runs. In this paper a mathematical model of the formation and evolution of surface ice is presented. A heat balance model at the water surface is applied to calculate the heat loss from the water. The turbulent kinetic energy and the energy dissipation rates are modelled to find the eddy viscosity that affects the mixing rate. The mathematical model is then calibrated and verified using experimental data collected at the Hydraulics Research and Testing Laboratory at the University of Manitoba. The model simulates the supercooling process reasonably well for all surface ice conditions.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Identification of hydrological models for enhanced ensemble reservoir inflow forecasting in a large complex prairie watershed

Summarization: Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times.Παρουσιάστηκε στο: Water (Switzerland