Earth and environmental science data encompass temporal scales of seconds to hundreds of years, and spatial scales of microns to tens of thousands of kilometers. Because of rapid technological advances in sensor development, computational capacity, and data storage density, the volume, velocity, complexity, and resolution of these data are rapidly increasing. Machine learning, data mining, and other artificial intelligence approaches offer the promise for improved prediction and mechanistic understanding, and the path for fusing data from multiple sources into data-driven and hybrid models comprised of both process-based and deep learning elements. At the watershed scale, streamflow gauges and in situ measurements must be combined with near-surface, airborne, and satellite remote sensing data to understand the structure and function ecosystems in heterogeneous landscapes; their interactions with nutrients, water, and energy; and ecohydrological responses to environmental change. However, sampling in remote, dangerous, or topographically complex watersheds is often prohibitive, necessitating use of sensor optimization and scaling techniques for characterization of landscape properties, vegetation distributions, and responses to climate and weather events. A sampling of characterization studies and prediction approaches will be described, and strategies for applying a new generation of machine learning methods on high performance computing platforms to climate and environmental system science will be presented.