Application of a degree-day snow depth model to a Swiss glacierised catchment to improve neural network discharge forecasts

Abstract

A trained soft artificial neural network (SANN) model was applied to the Gornera catchment (Valais Alps, Switzerland) over the melt season May to September 2001 to predict hourly discharge up to five days ahead A SANN discharge forecast for three days ahead has previously been performed on this catchment using only past discharge and past and forecast air temperature as model training inputs. In this study, present zonal snow depth was included as a model input, which was predicted for five altitudinal catchment zones using an empirical degree-day model. Hourly discharge values for up to five days ahead were reconstructed using SANN predicted daily discharge parameters along with a normalised long-term moving average model (MAHM). The efficiency criterion R2 gives a model performance of 0.927 for a 24-hour-ahead forecast and 0.824 for a 120-hour-ahead forecast. Compared to previous work, adding the snow model to the SANN model inputs considerably increases the forecast accuracy, in particular during days of progressive discharge increase and thunderstorms. The SANN model yields excellent results on days marked by stable weather conditions, with an R2 value between 0.913 and 0.995. However, the model is unable to reliably predict low frequency, high magnitude events, e.g. release of stored water from a glacial lake.