Floods, droughts, and heat waves are projected to increase over the next few decades due to climate change. These disturbances can cause declining stream flows and worsen water quality by increasing salt, nutrient, and contaminant concentrations, which will have direct consequences for human and ecosystem health. There is a pressing need to understand how the  flows and water quality in rivers will respond and be resilient to new  disturbance regimes.  This project uses a novel, data synthesis and analysis framework to investigate the impacts to stream flows and water quality caused by climate-driven disturbances. The  goal is to determine how the intensity, duration, and frequency of disturbance events will affect rivers over time at impacted locations and further downstream, across watersheds with different characteristics of geomorphology, soil properties, climate, land use, and land cover. This will be achieved using a data-driven framework comprising  statistical analyses and machine learning methods to identify patterns and explanatory variables for changes following a disturbance event. This will result in better predictions of  river flows and water quality, characterize watershed resistance and resilience to disturbance, and lay the foundation for capability to generate and analyze integrated watershed data.