Monitoring systems |
Smarter streamflow with AI
Upstream Tech’s HydroForecast Seasonal-3 is transforming seasonal streamflow prediction with machine learning, offering hydropower operators better accuracy, longer lead times, and greater confidence in decision-making.
Above: Marshall Moutenot
WHEN IT COMES TO hydropower, water is both the resource and the challenge. Predicting how much water will be available weeks or months in advance has historically been fraught with uncertainty. But a new generation of machine learning-based tools is beginning to change that. Among those involved in this shift is Upstream Tech, whose HydroForecast platform has gained recognition across the hydropower sector for its data-driven, real-time forecasting capabilities. With the launch of HydroForecast Seasonal-3 , the company is pushing the frontier of seasonal streamflow prediction, helping dam operators and water managers better plan for what lies ahead. “There’s a clear need for reliable seasonal forecasts,” says Marshall Moutenot, CEO and co-founder of Upstream Tech. “But it has always been one of the most difficult challenges in the field of hydrology.” HydroForecast Seasonal-3, launched earlier this year, builds on five years of work by the Upstream Tech team, whose interdisciplinary background spans remote sensing, software engineering, environmental science, hydrology, and applied machine learning. The latest release represents a step-change in predictive power, particularly over multi-week to seasonal timescales.
A fundamental challenge Forecasting river flows is fundamentally difficult
Below: HydroForecast visualises streamflow uncertainty as confidence intervals to help with risk management and decision making
because of the complex, nonlinear interplay of variables involved. Snowpack, soil moisture, precipitation patterns, temperature trends, vegetation cover, and upstream infrastructure all influence how much water will make its way to a given point in a river
system. While short-term forecasts can often rely on recent observations and weather predictions, seasonal forecasting requires integrating long-range climate data, hydrologic memory, and historical patterns. “In many ways, streamflow forecasting is harder than weather forecasting,” Moutenot says. “You’re dealing with chaos, noise, and uncertainty. But the stakes are high, especially for hydropower operators who need to make decisions about water storage, turbine dispatch, and flood control weeks or even months in advance.” Traditional models used for seasonal forecasting
tend to be either physically based, requiring detailed hydrologic and land surface modelling, or statistical, relying on historical analogues. Each approach has limitations. Physical models can be cumbersome and require detailed calibration. Statistical models may struggle under nonstationary climate conditions. HydroForecast aims to blend the strengths of both, using machine learning to learn hydrologic behaviour from vast datasets without being locked into a rigid physics-based framework.
Learning from the data At the core of HydroForecast Seasonal-3 is a machine
learning architecture that ingests terabytes of global data, including satellite imagery, weather forecasts, reanalysis products, and river gauge observations. This allows the model to learn spatial and temporal patterns in hydrology without needing manual calibration for each watershed. “We use global data sources so we can train a single model that works across diverse conditions,” explains Moutenot. “We want it to be scalable and transferrable – something that can give you a good forecast whether you’re in the Rockies or the Alps.” This is critical for hydropower operators with assets
spread across multiple sites. A single unified model reduces the burden of customization and supports consistent, portfolio-wide forecasting. According to Moutenot, Seasonal-3 represents a major step forward in this direction, improving predictive skill, extending the forecast horizon, and increasing trust among end users. “We’ve focused a lot on making the uncertainty quantification more useful,” he notes. “Operators need to understand not just the most likely outcome, but the range of possible scenarios. We’re helping them turn forecast uncertainty into risk-aware decisions.”
From forecast to action HydroForecast is designed to deliver value in multiple
24 | August 2025 |
www.waterpowermagazine.com
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