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Demonstrator for flexible SHP (SmallFlex): Real-time hydrological forecasts


The overall aim of this project is to show how small-hydropower plants (SHP) can provide winter peak energy and ancillary services, whilst remaining eco-compatible. The outcome of recent research by SCCER-SoE partners will be applied to a pilot facility provided by FMV with the goal of providing operational flexibility to the SHP owner and therefore harvest additional revenues. The addition of flexibility will be done by testing infrastructure and equipment or operational adaptation measures, assessing their impact in terms of outflows, electricity output and revenues. The lessons learned from this Demonstrator will be publicly presented and used as a benchmark for the SHP sector.
The WSL contribution is to demonstrate how FMV could benefit from (very) short (< 1 day to 5 days) water inflow forecasts at the water intake in Gletsch. This will be achieved by combining different numerical models recently developed at MeteoSwiss, SLF, WSL, amongst others with real-time data.

The implementation of a short-term forecast (nowcast) system will allow a maximum of freedom to act and manage the power plant according to the actual energy market given the potentially available inflow for the next couple of hours. Especially the very limited storage capacities for optimizing the production according to intra-daily electricity price fluctuations requires high precision forecasts.


The basis of the implemented forecast system is the INCA-CH system, which merges all the meteorological information available in real-time, like station, radar and satellite data, and extrapolates this information into the future. These observation data are interpolated to a 1 km resolution grid and is updated every 10 minutes and yields predictions for the next 6 hours for the main meteorological variables like precipitation, temperature, wind. The hydrological model PREVAH, which calculates the streamflow taking these meteorological observed and forecasted variables as input, is running operationally with a spatial resolution of 100m and with hourly time steps. Thus, the INCA-CH forecasts have to be downscaled first. In the figure below an example of a INCA-CH temperature forecast for the Gletsch catchment is shown highlighting the different spatial resolutions of the 1km grid of the meteorological (in grey) and the 100m resolution after applying downscaling procedures within the catchment (in colours).


At WSL the coupling of the INCA –CH nowcasts and the PREVAH model is done hourly and results in stream-flow, resp. inflow forecasts at Gletsch for the next 6 hours. Beyond the 6 hours other meteorological forecasts provided by MeteoSwiss are assimilated seamlessly and transferred to streamflow forecasts.

In November 2018 the forecast system has been tested within the field campaign at Gletsch and WSL provided the project partners with forecasts in real-time twice a day.


An example of a forecast during the week of the field campaign in November 2018 is shown above. Black dots show the observed streamflow at the gauging station and the blue line is the forecast. The dots in grey are the true values, which are not available in real-time, but have been included to see the difference between the forecast and the measurement. The red dots are observations of runoff, which are retrieved in the time gap between the initialization of the meteorological forecast and the completion of the hydrological forecasts.

Within the last project year different machine learning methods (e.g. Gradient Boosting and Random Forests) and forecast combination methods have been tested in order to reduce the errors in the forecast system. These post-processing methods show some significant improvements.

The second part of WSL’s contribution to the SmallFlex project consists of improving of a high-resolution energy-balance-type snow model enabling a realistic representation of small-scale processes in alpine terrain. Accounting for spatial variability is key to accurately assess runoff in small mountain catchments. Our chosen snow model solves the energy balance at the snowpack surface directly. It uses downscaled Numerical Weather Prediction (NWP) in 250 m resolution as input to forecast snow melt. To provide best states of the snowpack conditions before the forecast starts, measured data were assimilated in the model. Snow density was modelled at the snow stations and the snow depth signal was transferred to solid precipitation. An optimal interpolation scheme was used to interpolate point measurements spatially and also account for measurements errors in both the precipitation gauges and snow depth measurements.

Since the snowpack is very variably distributed in complex terrain, patchy snowcover is a typical feature in the ablation season, which directly influences the available snow melt for runoff. Thus, a subgrid-paramerization of snow covered fraction (SCF) based on surface roughness was used. The model accounts for the ability of snow to store rain or meltwater in the snowpack, which influences the timing when water leaves the snowpack.


Finally, modelled surface water input (SWI) was provided to the hydrological model PREVAH to simulate the intake to a hydropower plant.

The forecasts of the combined model chain (i.e. energy-balance snow model and hydrological model PREVAH) will be visualized on our common website and thus can be used to assess the intake of the power plant for the next couple of hours in order to allow a flexible management of the power plant.