1 Princeton University, Atmospheric and Ocean Science Program, Princeton, NJ, United States
2 NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States
The distribution of water resources over land highly varies in space and time. From local to global scales, it plays a key role in modulating water, energy, and carbon interactions between the land and the atmosphere. Detailed spatial and temporal hydrologic information is essential to understand and monitor hydroclimate extremes (e.g., droughts and floods), natural hazards (e.g., wildfires and landslides), irrigation demands, biogeochemistry, and ecosystems dynamics. In-situ hydrologic observations can provide detailed information, but their representativeness is limited, and networks of sensors are often not widely available. Hyperspectral satellite observations provide global coverage, but measurements can be infrequent or too coarse to capture the local spatial variability. This observation data gap limits the use of hydrologic information for scientific and water resources applications. To address these challenges, my research develops novel and scalable satellite land data assimilation approaches that use high-resolution land surface modeling, machine learning, and in-situ observations to obtain hydrologic information at the local spatial scales. In this presentation, I will introduce SMAP-HydroBlocks – the first 30-m resolution satellite-based surface soil moisture dataset for the United States. This unique dataset reveals the high degree of soil moisture spatial variability at the local scale and its complex interplay with the diverse landscape and hydroclimate. This spatial variability, however, does not persist across large spatial scales – up to 80% of the spatial information is lost at 1-km resolution, with complete loss expected at the scale of current state-of-the-art hydrologic and drought monitoring systems (5–25-km). I will conclude by presenting pathways forward to leverage the increasing availability of satellite Earth observations, high-performance computing, as well as advances in machine learning and Earth system models to further understand the impact of local-scale hydrology on hydroclimate extremes, natural geohazards, ecological, and biogeochemical processes across scales.