Eric Wood’s contributions and recent advances on hyper-resolution land surface modeling

One of Eric Wood’s latest contributions was to set forth the needs and challenges for developing hyper-resolution LSMs in the order of ~100-m to 1-km spatial resolution. This expanded the applicability of land surface models (LSMs), to address critical challenges in monitoring terrestrial water.  Particularly, by representing the spatial variability of physical processes and their interactions with water, energy, and carbon fluxes at the fine-scale that are critical to advance monitoring and understanding of processes linked to freshwater dynamics, hydrologic extremes (floods and droughts), food security, water quality, among others. Over the past 10 years, Eric’s visions on hyper-resolution along with the ever-increasing availability of high-resolution environmental datasets, satellite and in-situ observations, computing resources, and the development of novel modeling frameworks provided a fertile environment for hyper-resolution land surface models to flourish. This presentation will review the community’s efforts towards the development of models, processes representation, and supporting datasets. In particular, it will highlight recent advances on leveraging big environmental datasets and machine learning for developing hyper-resolution LSMs’ sub-grid tiling schemes; the role of data assimilation in hyper-resolution LSMs to bridge spatial scale mismatch between satellite and in-situ observations; and applications of hyper-resolution LSM for understanding soil moisture spatial scaling.