A snow and ice melt seasonal prediction modelling system for Alpine reservoirs

Abstract

Abstract. The timing and the volume of snow and ice melt in Alpine catchments are crucial for management operations of reservoirs and hydropower generation. Moreover, a sustainable reservoir operation through reservoir storage and flow control as part of flood risk management is important for downstream communities. Forecast systems typically provide predictions for a few days in advance. Reservoir operators would benefit if lead times could be extended in order to optimise the reservoir management. Current seasonal prediction products such as the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2) enable seasonal forecasts up to nine months in advance, with of course decreasing accuracy as lead-time increases. We present a coupled seasonal prediction modelling system that runs at monthly time steps for a small catchment in the Austrian Alps (Gepatschalm). Meteorological forecasts are obtained from the CFSv2 model. Subsequently, these data are downscaled to the Alpine Water balance And Runoff Estimation model AWARE running at monthly time step. Initial conditions are obtained using the physically based, hydro-climatological snow model AMUNDSEN that predicts hourly fields of snow water equivalent and snowmelt at a regular grid with 50 m spacing. Reservoir inflow is calculated taking into account various runs of the CFSv2 model. These simulations are compared with observed inflow volumes for the melting and accumulation period 2015.

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

“…Altitudinal gradients are applied in order to realistically redistribute temperature and distribution on the model grid resulting in a decrease in temperature with increasing elevation and an increase in precipitation on the mountains. Even though the model is also capable of reading shortwave radiation fields (Förster et al, 2016) in order to improve ice melt predictions, a simplified snow-and ice-melt simulation using air temperature only is possible. This simplification considers the fact that air temperature and precipitation are readily available and more predictable compared to some other meteorological fields.…”

Reply to anonymous reviewer #2

“…Uncertainties in the data of antecedent meteorological conditions influence the quality of process-based hydro-meteorological models at hourly resolution, e.g. in the case of 2-day flood forecasts (Achleitner et al, 2012) or 1-month sub-seasonal streamflow drought forecasts (Fundel et al, 2013). Statistical seasonal streamflow forecast models can be improved when initial conditions with respect to soil moisture and groundwater flow (Robertson et al, 2013) or snow water equivalent (Pagano et al, 2004) are considered.…”

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

“…Based on air temperature, the fraction of rain and snow is linearly interpolated between these two thresholds. Even though the model is also capable of reading shortwave radiation fields (Förster et al, 2016) in order to improve ice-melt prediction, only a simplified snow-and ice-melt simulation using air temperature is possible. This simplification considers the fact that air temperature and precipitation are readily available and more predictable compared to some other meteorological fields.…”

“…The Alpine Water balance and Runoff Estimation model (AWARE; Förster et al, 2016) is a deterministic hydrological model operating on a regular grid at 1-month time steps. The model has been designed to estimate anomalies in hydrological variables at the catchment scale from anomalies in meteorological fields predicted by climate models.…”

“…Uncertainties in the data of antecedent meteorological conditions influence the quality of process-based hydro-meteorological models at hourly resolution, e.g. in the case of 2-day flood forecasts or 1-month sub-seasonal streamflow drought forecasts (Fundel et al, 2013). Statistical seasonal streamflow forecast models can be improved when initial conditions with respect to soil moisture and groundwater flow (Robertson et al, 2013) or snow water equivalent (Pagano et al, 2004) are considered.…”