Undergraduate Research & Mentoring Program
Advisor
Karen Karavanic
Document Type
Poster
Publication Date
2016
Subjects
Drought forecasting, Droughts, Precipitation (Meteorology)
Abstract
In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance.
We present a detailed performance study of several alternative implementations of the hybrid approach. We combined the Precipitation Runoff Modeling System (PRMS), a widely used hydrologic model published by the U.S. Geologic Survey, with satellite data. This problem is representative of many science efforts in that it incorporates software used across the scientific domain (PRMS) with novel functionality by integrating satellite data.
Persistent Identifier
http://archives.pdx.edu/ds/psu/17824
Citation Details
Cooney, Henry, "High-Performance Computing for Drought Prediction" (2016). Undergraduate Research & Mentoring Program. 9.
http://archives.pdx.edu/ds/psu/17824
Project Abstract
Description
The project abstract is located below in Additional Files