Dive into HydroForecast

Why weather forecasts aren’t the (only) important forecast for climate change

NOAA Awards Upstream Tech Small Business Innovation Research Grant

Celebrating hydropower's role in a 100% renewable grid

The latest with HydroForecast: August 2023

How AI can be a useful tool for climate and the sciences: Part I

New joint research with WWF and Rhodes University colleagues accepted into Journal of Hydrology

Department of Energy awards Upstream Tech Small Business Innovation Research grant

The latest with HydroForecast: July 2023

Celebrating World Environment Day 2023

The latest with HydroForecast: June 2023

Leveraging remote sensing and forecasting to better understand an unpredictable climate

Preparing for Europe's drought with the best in forecasting

Hackweek round up: Spring 2023

Integrate state-of-the-art AI into your long-term investment decisions

The latest with HydroForecast: May 2023

A guide for new machine learning and hydrology enthusiasts

A changing climate requires new methods of forecasting

Join us for "Addressing Climate Change Using a Hybrid Approach to Forecasting"

Spatial visualizations of weather inputs and remote sensing observations

The latest with HydroForecast: February 2023

HydroForecast can improve hydropower earnings by 3–6%

2022 HydroForecast wrapped

Working in the water utility space?

Partnering with Sonoma Water to improve seasonal reservoir management

Two days on the Colorado River

HydroForecast wins 2022 Outstanding Stewards of America’s Waters Award

Fall at Upstream Tech

Delivering water intelligence to key regions with The World Wildlife Fund

HydroForecast featured in Water Power Magazine

Not all AI has the same IQ

Celebrating 2021 and looking ahead to 2022

Peter Stein joins Upstream Tech as an advisor on natural resource investment

Understanding the ebbs and flows of California's water markets

Developing tools to model impaired streamflow in streams throughout California

Upstream Tech adds new advisors to support our growth

Little Tallapoosa River near Newell, Alabama

Taylor River at Taylor Park, Colorado

Upstream Tech awarded Phase 2 SBIR grant from Department of Energy

North Fork Shoshone River at Wapiti, Wyoming

Reflections of an Upstream Tech intern: national hydropower assessment project

HydroForecast’s new machine learning-enabled seasonal streamflow forecasts

James River above Arrowwood Lake, North Dakota

CEATI April read

CEATI March read

CEATI February read

ENGIE selects HydroForecast to inform 1.7 GW of renewable generation

Hydro's Digital Generation

Preprint: How we are advancing uncertainty estimation with deep learning

Upstream Tech and Near Space Labs announce partnership

Trees, temperature, and trout: modeling impacts on freshwater species

Correcting a streamflow model with deep learning

Justice, solidarity, and action

Upstream Tech awarded Phase 1 SBIR grant from Department of Energy

Peer-reviewed paper on linking hydrology and theory-guided machine learning

Peer-reviewed milestone publication on our approach to predictions in ungauged basins

Forecasting water quality

South Payette case study

Diving into the science behind our model, the latest R&D, and feature updates

Integrating AI into long-term water investment decisions

Addressing climate change with a hybrid approach to forecasting

Improving water supply management for utilities

Improving seasonal water supply forecasts in California and the West

Could streamflow forecasts have predicted Lake Oroville's extreme conditions?

Freshet season in review: how HydroForecast predicts snowmelt using machine learning

Snowmelt, storms, and droughts: forecasting streamflow in the Pacific Northwest using machine learning

Unlocking the value of your data with machine learning

Innovations in wetland monitoring and evaluation

Bridging physical hydrology and neural networks for reliable streamflow forecasts

Satellites 101

The past, present, and future of hydrology forecasts

Insights from a comparison of two hydrological modeling approaches in the Kwando River and the western tributaries of the Zambezi River basin

Learned Unit Hydrograph Routing in an End-to-End Trainable, Distributed, Deep Learning Hydrologic Model

Challenges and Successes in Building a Global, Operational, Theory-Guided Machine Learning Model

Idaho National Lab study finds HydroForecast can improve hydropower earnings by 3-6%

Data Assimilation and Autoregression for Using Near-Real-Time Streamflow Observations in Long Short-Term Memory Networks

HydroForecast Accurately Predicts Rain on Snow Event in California

Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modeling

Hurricane Ida Hits Tennessee: How HydroForecast Helps Prepare for Extreme Weather

A Note on Leveraging Synergy in Multiple Meteorological Data Sets with Deep Learning for Rainfall–Runoff Modeling

Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning