Webinar: Could Streamflow Forecasts Have Predicted Lake Oroville's Extreme Conditions?

In February 2017, historically high flows caused two of Oroville Dam’s spillways to collapse. Just four years later the reservoir is dealing with the opposite challenge, as its water levels are at their lowest ever. Could anyone have predicted this unprecedented swing in conditions? If so, what could we have done with that foresight?

As climate change intensifies and such extremes become more common, water managers need new tools that can provide them with better information, sooner. From acting with confidence earlier to prevent dam failures to managing scarce water supplies more effectively as droughts emerge, high-quality water inflow forecasts can provide leaders with the data they need to make smarter decisions.

In November 2021 we hosted a webinar discussing how Upstream Tech’s HydroForecast uses machine learning and remote sensing to predict streamflow with unprecedented accuracy and scalability. Watch the recording here.

We used Oroville as a case study to demonstrate how HydroForecast’s high quality forecasting tools could have better predicted these events and supported the decision making of Oroville’s operators. To do so, we dug into Oroville’s hydrology, recent observed inflows, and seasonal water supply forecast predictions from agencies and HydroForecast over the past couple of years.

HydroForecast informs water resources managers operating on both short and long-term time horizons. This includes supporting hydropower owners in improving their operations and safety through hourly forecasts and modeling how much water can be expected to flow through a stream over a season for environmental management. HydroForecast supports forecasting at individual stream points (whether or not they have gauges) and across entire regions, such as California’s Central Valley.

To learn more, reach out at team@hydroforecast.com.

Lake Oroville's historically low levels as of April 2021. (Photo credit: Justin Sullivan/Getty Images)

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