Short-term forecasting of spring freshet peak flow with the Generalized Additive model

Abstract

• Spring freshet peak flow can be estimated with few variables, easily available. • Generalized Additive Model (GAM) provided a reliable 24-h forecast of the peak flow value. • GAM model forecasts compared favorably to a more complex deterministic hydrological model. • The forecasted flow can be used as a decision support to launch emergency measures. In cold boreal regions, for rivers with small to medium-sized watersheds under natural hydrological regimes, the risk of spring flooding is determined by peak flow intensity rather than flood volume. Nonetheless, short-term forecasting of peak flow intensity is subject to a lot of uncertainty and depends largely on ongoing specific snowmelt conditions. This study proposes a simple operational model based on the Generalized Additive Model (GAM) to forecast short-term spring freshet peak flow. The model uses hydrological and meteorological data publicly available on a daily basis. The model was tested on five rivers in the Province of Québec (Canada) with drainage basins varying between 350 km 2 and 1707 km 2 . The model results (forecasted peak flows) were compared to those obtained using the Generalized Linear Model (GLM) and a distributed deterministic hydrological model (Hydrotel) currently used for flow forecasting of several rivers in the Province. The peak flow was forecasted accurately with relatively few variables, mainly a combination of river flow and rate of flow increase a few days before peak flow, previous air temperature, rain accumulation and snow accumulation. Nonetheless, the best combinations of predictive variables were river-specific. The GAM model, using an automatic fitting and easily accessible daily data, can be implemented by any stakeholder.