ESSIC Assistant Research Engineer Soni Yatheendradas recently published a paper titled “Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations” in Computational Geosciences.
The study simulated slope failures using a physically-base model with three different precipitation data products as input. All three products performed well, though at the highest elevations the TMPA and NLDAS-2 based results degraded, while the quality of the Stage IV precipitation based results remained high. This indicates the importance of precipitation accuracy at such elevations for landslide modeling.
Yatheendradas is affiliated with the Hydrological Sciences Lab at NASA GSFC. His current work include leading optimization/uncertainty components on NASA’s Land Information System (LIS) software especially for SUSMAP applications, Machine and Deep Learning applied to spatial downscaling and retrieval in snow remote sensing, and multivariate hydrologic data assimilation for model structural learning and process-diagnostics.
To read the article, click here: “Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations”.