| Spotlight


HydroForecast provided early warning with a lead time nine days ahead of the first peak at this customer site. Compared to the NWRFC forecasts, the prediction showed warning four days earlier and more accurately predicted peak flow rates by 35%, or approximately 815ft3


/


sec – a differential of 15,000 acre-feet of water volume throughout the first two peaks


could strategically refill while preserving the flood control space needed for storms still approaching. This forward-looking capability reduced the need for reactive, high-volume releases later in the event. This balancing act of maintaining public safety while optimising water storage required confidence in forecasts that extended well beyond the traditional short-term operational window. Having reliable insight into future inflows allowed operators to make controlled release decisions while safeguarding both downstream communities and long-term water supply.


Real-time decision support during


extreme events As conditions intensified beyond historical norms, machine learning–based forecasting approaches proved particularly valuable. HydroForecast’s models draw on global hydrological training data, enabling them to identify flood indicators even when local historical records offer little precedent. At one customer site, HydroForecast issued a warning nine days ahead of the first peak inflow – four days earlier than the NWRFC forecast – and predicted peak flow rates with 35% greater accuracy, approximately 815 ft3


/sec closer to


observed levels. Across the first two peaks, this represented a difference of around 15,000 acre- feet of water volume. At another site, the system predicted peak flows 10 days in advance, three days earlier than the NWRFC forecast, while improving peak-flow accuracy by around 75%. Managing a multi-wave storm is an iterative


process, requiring operators to adjust release schedules daily as conditions evolve. Continuous forecast updates allowed dam operators to refine operations and coordinate closely with downstream stakeholders. Reservoir managers reported that having a reliable forecast signal enabled them to communicate release plans confidently with flood control districts and emergency authorities, while making smaller, more controlled adjustments to spillway gates to minimise sudden impacts on river systems. Throughout the storms, HydroForecast’s


hydrologists monitored model outputs and remained in constant contact with partner utilities.


This combination of automated forecasting and human oversight ensured operators had the technical validation and real-time support needed to protect infrastructure and communities under extreme pressure. The December 2025 atmospheric rivers underscored a growing operational reality for water managers: extreme weather events are becoming more frequent and less predictable. Building resilience will increasingly depend on modern forecasting tools capable of delivering accurate insights even when conditions fall outside the historical record, supported by seamless coordination between agencies and operators.


About HydroForecast


HydroForecast, developed by Upstream Tech, is a data-driven hydrological forecasting platform designed to support operational decision-making in hydropower, water resources, and flood risk management. The system applies machine learning techniques trained on large, globally distributed hydrometeorological datasets to generate streamflow and inflow forecasts across diverse basin types, including those with limited or non-stationary historical records. The platform integrates multiple data inputs, including numerical weather


prediction (NWP) outputs, remote sensing observations, and in-situ measurements where available. These inputs are processed through ensemble-based modelling approaches to produce probabilistic forecasts, providing users with a range of possible hydrological outcomes rather than a single deterministic prediction. HydroForecast delivers forecasts across multiple temporal scales, from


short-term (hours to days) operational guidance to extended outlooks of up to approximately 10 days. Forecasts are updated continuously as new meteorological data becomes available, enabling near real-time situational awareness and iterative operational planning. Outputs are typically provided at user-defined points of interest, such as reservoir


inflows, river reaches, or ungauged catchments, and can be accessed via web-based dashboards or integrated into existing operational workflows via API. This flexibility allows utilities and water managers to incorporate forecast data directly into dispatch planning, reservoir optimisation, and flood mitigation strategies. In addition to automated model outputs, HydroForecast includes support


from a team of hydrologists who assist with forecast interpretation, uncertainty communication, and event-based analysis. This combination of machine learning– based modelling and expert oversight is intended to enhance forecast reliability and usability, particularly during extreme or rapidly evolving hydrological events. HydroForecast is used by utilities, independent power producers, and public agencies to improve forecast lead times, quantify uncertainty, and support more informed, risk-aware water management decisions.


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