| Monitoring systems
operational contexts. Some customers use it for day- ahead energy market participation, others for seasonal reservoir planning or flood risk management. What unites these use cases is the need for timely, reliable streamflow information. “We work with large utilities that need forecasts for
dozens of sites,” Moutenot says. “They can’t afford to spend weeks calibrating models for each one. That’s where a machine learning approach really shines – it can generalise without sacrificing accuracy.” With Seasonal-3, Upstream Tech has also improved how the model communicates its predictions. Enhanced visualisations, clearer probabilistic outputs, and integration with existing data pipelines all help customers put forecasts into practice. “We often hear that our model helps teams have
better conversations,” Moutenot says. “When everyone’s looking at the same forecast with the same level of confidence, it makes coordination easier.”
Trust and transparency While machine learning has immense promise, it can
also be a black box – an opaque system that users struggle to understand. To address this, Upstream Tech has invested heavily in model interpretability and transparency. “We publish our methods, we validate our performance, and we make sure the model’s predictions make sense,” says Moutenot. “We’ve open-sourced components of our work and provided detailed documentation so users can see how the model works.” This has helped build trust with partners in both the public and private sectors. HydroForecast is currently used by utilities, energy traders, government agencies, and hydropower producers across 15 countries. The team has collaborated with industry leaders to evaluate and refine the tool. Key examples include grant awards from the US Department of Energy as well as leading results in a streamflow forecasting competition alongside hydropower organizations, like Hydro-Québec (H-Q), the Tennessee Valley Authority (TVA), the U.S. Bureau of Reclamation (USBR), and Southern Company. “We don’t expect people to take the forecasts on faith,” Moutenot says. “We want to earn that trust through rigorous validation and continuous improvement.” The need for accurate seasonal streamflow
forecasts is only growing. Climate change is disrupting historical patterns, leading to more frequent droughts and floods. Meanwhile, hydropower operators are under increasing pressure to balance energy
generation with environmental flows, recreation, and flood risk mitigation. “There’s a lot riding on how well we understand our
water systems,” says Moutenot. “That’s why we see this as more than just a technical challenge. It’s about making better decisions for people and the planet.” For Upstream Tech, the launch of Seasonal-3 is part of a broader mission to bring modern data science to water management. The company was founded in 2016 with the goal of applying cutting- edge tools to environmental challenges. In addition to HydroForecast, it offers Lens, a remote sensing platform for land and habitat monitoring. “Our team comes from a range of backgrounds –
NASA, Google, conservation NGOs – and we’re united by a shared mission,” Moutenot says. “We believe technology can help us steward natural resources more wisely.”
Beyond the model Looking ahead, Upstream Tech sees room for further
innovation. While Seasonal-3 focuses on streamflow, future versions could incorporate water temperature, sediment load, and other hydrologic variables. The team is also exploring how to integrate local observations and user feedback to continually refine the model. “We’re just scratching the surface of what’s possible,” says Moutenot. “There’s so much potential in combining global data with local expertise.” For now, the focus is on getting Seasonal-3 into the hands of more users and demonstrating its value across diverse applications. That includes helping dam operators plan for spring melt, utilities manage energy markets, and conservationists protect aquatic ecosystems. “We’re proud of the progress we’ve made, but we’re even more excited about what’s next,” Moutenot says. “This is a long-term effort. Water forecasting is never ‘solved,’ but we’re getting better with each season.”
Above: How HydroForecast compares to traditional modeling approaches
Below: HydroForecast incorporates the best data inputs, builds off a strong foundational model, and tunes the model to each basin
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