Wildfire is an ecological disturbance that disrupts the hydrological cycle (1, 2). In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States (3, 4). Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to "Water-in-the-West" through negative impacts to water supply and its quality (5) , and is a direct threat to

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... stream communities, groundwater, and drinking water supply infrastructure (6, 7). While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations)(8), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context (9): 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.