Predicting water discharge on alpine catchments with downscaled seasonal forecasts

The prediction of seasonal water availability is a key element for an effective water storage management and hydropower production optimization. Here we propose a machine learning model for monthly water discharge prediction, which is based on statistical relationships between time series of a target, i.e. monthly water discharge, and predictors. The considered predictors can be divided into two classes: the initial catchment state variables and the seasonal forecast variables. Snow plays a crucial role as water storage component in alpine catchments. Thus, snow water equivalent is the predictor employed to describe the initial state of the catchment. To ensure the scalability of the method, snow water equivalent is represented here by ERA-5 climate reanalysis data (0.25° x 0.25° resolution). Depending on the prediction season, seasonal forecast of temperature can drive snowmelt or evapotranspiration, while precipitation provides a natural contribution to the total water availability. To describe these prediction variables, we employed a downscaled and bias-correction version of the ECMWF’s seasonal forecasting system (SEAS5) for temperature and precipitation. More specifically, the seasonal forecast fields were bilinearly downscaled from the original 1° x 1° resolution to the target ERA-5 grid and statistically corrected for bias in respect with ERA-5 data by means of a quantile mapping procedure. ERA-5 reanalysis data were used as reference for the bias-correction in order to allow the approach to be easily applied over different areas.

We tested the proposed method over an alpine catchment in Ulten Valley, South Tyrol, Italy, which is managed by three artificial reservoirs for hydropower production. For this catchment, a time series from 1992 to 2017 of measured daily water discharge is available. The water discharge prediction performances of the proposed method are compared with the ones obtained by considering the water discharge monthly climatology.