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Seasonal inflow forecast

6-month forecasting services of discharge in small and big rivers.

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Water predictions for the next 6 months

Operating effectively in the market requires a thorough planning of water harvesting for the next months.

Will the reservoir capacity be sufficient to accommodate incoming water? Will I be subject to water stress?

Addressing these questions is necessary to optimize the reservoir level and anticipate possible drought conditions.

Seasonal inflow forecast is the right way to answer these questions, with consequent optimization of water resources in the long-range.

Current approach on long-range forecasts

Current prediction methods adopted by most water managers are based on a “past-to-future” approach, i.e. historical data are simply averaged and extrapolated to the future. Sometimes the snow water equivalent estimate is used to update the current prediction.

This approach is subject to several drawbacks:

  • Poor water/snow separation: it is not possible to quantify the rain from the snow melt effect;

  • Does not take into account the actual water condition in the catchment, with consequent model inefficiency;

  • Need for snow measurement campaigns, with consequent cost and safety issues;

  • Historical data are not representative of current climate condition: a pure historical approach can lead to poor accuracy and wrong decisions.

New technology for long-range forecasts

Waterjade is the answer to the new climatic and technological challenges, because it adopts an innovative multiple modeling – multiple data approach. It leverages on data sources available in the hydro-meteorological sector, like satellite images, numerical weather predictions and in-situ observations. The obtained big data are feeding multiple modeling architecture, comprising physical models and machine learning, capable to follow the physical processes occurring in the catchment and adapt to the local configuration.

The results are:

  • We can separate the snow and water contributions for a better understanding of the balances;

  • We can provide a dedicated tool to calculate the reservoir volume and thus improve the reservoir management;

  • The system can be internationally exported;

  • Compared with state-of-the-art methods, we can reduce the forecast errors by 50%;

  • The approach uses historical data just for the calibration of the model, and so the forecasts are climate change compliant.

Applications

Hydropower plants

Water Utilities

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