Machine learning for water quality forecasting and intervention in managed reservoirs

Temperature and clarity are critical indicators of reservoir ecosystem function, and they are often regulated at the local, state, and federal levels. Water resource managers face the challenge of balancing reservoir operations with these regulations, often working with sparse datasets and few, if any, tools for forward-looking operation management. As the occurrence of extreme climatic events, natural disasters and interannual variability increases, some water resource managers are turning to forecast models to assist with decision making. Understanding stakeholder needs and the available mitigation actions allows us to create applications guiding day-to-day water management within regulatory and delivery requirements. In this study, we utilize a dataset comprised of water temperature, local meteorology, stream discharge, and reservoir level in an autoregressive neural network to make estimations of water temperature at a managed reservoir in Northern Colorado. This model consistently outperforms the baseline of "yesterday-is-today" at a one-day time horizon, with a mean absolute error of 0.37°C for the top 1 meter and 0.29°C for 0-5 meters during preliminary hindcasting application. We are currently testing the model's sensitivity to changes in pumping operations, which is the water quality mitigation action used within this system. By leveraging machine learning techniques, we aim to provide stakeholders with valuable insights and tools to navigate the complexities of reservoir management while adhering to regulatory requirements.

Primary Presenter: Bethel Steele, Colorado State University (b.steele@colostate.edu)

Authors:

Bethel Steele, Colorado State Unviersity  (b.steele@colostate.edu)

Daniel Dominguez, Colorado State University  (daniel.dominguez@colostate.edu)

Matthew Ross, Colorado State University  (matt.ross@colostate.edu)