ABSTRACT:

Seasonal snowpack is the largest areal component of the global cryosphere and is a major source of summer water supply in regions such as western North America and High Mountain Asia. The amount of water stored in winter snowpack (snow water equivalent, SWE) can vary significantly in space and time due to heterogeneous climate and landscape processes that influence snow accumulation and melt processes. This critical water resource is under monitored due to sparse ground-based observational networks, and a lack of satellite remote sensing system that can measure SWE across all global snow types and conditions. However, emerging remote sensing techniques and new capabilities with model-data fusion offer the potential to improve our understanding and prediction of snow water resources. In this seminar, I will highlight how we can improve representation of SWE and related snowpack processes through the integration of numerical snowpack models and observations using data assimilation techniques and machine learning.

BIO:

Dr. Mark Raleigh is an Assistant Professor in Geography and Geospatial Science (CEOAS) and is the director of the CryoSphere Interactions & Geospatial Hydrology Team (CryoSIGHT) at OSU. CryoSIGHT research focuses on the integrated application of remote sensing, numerical modeling, and innovative ground-based observations with the goal of improving fundamental understanding of seasonal snow in watersheds and its role in physical and human systems.