Hydropower Decision-Support with Machine Learning and Satellite Driven Forecasts

Existing approaches for predicting streamflow on shortterm 110 day and seasonal 1090 day horizons are not sufficiently accurate, flexible, or scalable to inform smart and flexible water decisions across the planet. This problem is clear today in the hydropower industry, for water allocation managers, and is becoming more acute for conservation organizations as ecosystem protection grows in complexity. These groups rely on forecasts for planning and operational decisionmaking, but shortcomings in current forecasts hamper decision makers’ ability to plan operations, prepare for extreme weather events and climatic shifts, and protect ecological services. The overall objective of the Phase I and proposed Phase II project is to advance the technical and commercialization of a novel hydrologicallyguided machine learning approach to predict streamflow. Through integration of near realtime satellite basin data, weather forecasts, and ontheground observed data, the model delivers forecasts with demonstrated accuracy using a cloudbased system that is scalable, resilient, and secure. In Phase I, an initial prototype was transformed into a fully operational system and commercialized for a cohort of initial customers. The three main accomplishments from Phase I were: 1 built, tested, and implemented a distributed neural network model that creates accurate forecasts in large> 20,000 mi2 basins; 2 demonstrated the added value in accuracy that results from including multiple weather forecast sources as parallel model inputs; and 3 operationalized an evaluation system such that experimental model versions can be easily compared with competitors’ models and alternative methods. During Phase I, operational forecasts were deployed at 36 points. The Phase II objectives build on findings from Phase I, and broadly aim to: 1 increase accuracy by including additional weather forecast sources that span the shortterm and seasonal products, 2 quantify the model’s financial benefits over alternative approaches to communicate the return on investment with target customers, and 3 build out commercial scalability of the model system and delivery methods. Achievement of these objectives will enable and accompany a concerted business effort to scale from the initial cohort of customers by integrating results from 2 and 3 into the product marketing strategy. In the shortterm, three user groups will benefit from the technology: hydropower entities will optimize generation and operate safely, helping transition the grid to renewable sources while reducing their environmental impact and increasing revenues; water regulators will make datainformed allocation decisions; and conservation organizations will prioritize restoration projects more effectively.