Lanham, Maryland August 26, 2009 – S M Resources Corporation (SMRC) has accepted a $129,204 Federal Grant (Award Number NA09OAR4310145) from NOAA/Climate Program Office for “Developing and Testing a Multi-Hazard Vulnerability Assessment Support System”. The grant dollars will be used to fund graduate studies for a student.
Under this National Oceanic and Atmospheric Administration (NOAA) grant, SMRC is collaborating with Pennsylvania State University and a scientist at the United States Geological Survey to test a Vulnerability Assessment Support System (VASS) on two sample sites. The first location is in Talbot County, Maryland and the second is in Sarasota, Florida.
The tests will:
- Characterize climate-related vulnerability by communities faced with making decisions about managing water resources in a changing climate
- Implement a decision support tools for community water planners that reduce vulnerability in the face of natural hazards, climate variability, and climate change, as well as integrate climate science into key decisions on water resources at the community level
- Create innovative and transferable methodologies and products for identifying vulnerabilities and coordinating strategies for reducing risks
SMRC project leaders note that “unlike other vulnerability assessments, VASS will allow for the first time, for regulators and legislators at the state levels to quantitatively compare the effects of climate change on infrastructures, exposed populations, and ecosystems.”
Since different groups measure and model local risks differently, it is generally very hard to compare vulnerability assessments conducted on different sites. VASS solves the problem by adopting a protocol developed at Penn State University that classifies data and models according to standard categories defined on line by a collaborative virtual environment. When hazards and risk are classified according to standardized technical parameters and well defined managerial descriptors, risks and adaptation drivers of different sites can be compared, even if significant differences may exist in the data collection and in the modeling methods.