In flood forecasting and design for peak flows, understanding and characterizing the hydrologic response to rainfall events is vitally important. One of the key parameters utilized to characterize the catchment response time is the Time-to-Peak (Tp), which represents the rise time of a storm hydrograph. Previously, prediction methods for Tp have been static in nature, providing a single value for any storm within a catchment. Using ~1400 storm event observations and the corresponding catchment characteristics of 153 stream gauges across the United Kingdom (UK), the importance of different factors on estimating Tp are evaluated through literature, data and machine learning analyses. A prediction model is presented which introduces dynamic prediction by applying seasonal and average catchment wetness, as well as a storm specific variable to encompass the magnitude of the individual event. This model is then reconfigured for broad application and tested against ~200 events in North American study areas. Natural Sciences and Engineering Research Council of Canada
