This week: revisiting Cadillac Desert, climate change and California, learning from Joplin, and vDatum.
The book, by Marc Reisner, challenged basic beliefs by which the West had been developed and governed, beliefs still widely held by some Western residents and leaders. It argued that water had been badly managed and that competition for water supplies between rapidly growing communities and agribusiness was coming, that cropland would be rendered useless because of salt accumulation and finally that dams throughout the West would be impaired by silting, reducing their capacity significantly and making water less available.
If you think California’s water problems look bad now, just wait.
That seems to be the message of a new study by a team from the USGS, Scripps, Berkeley and elsewhere who ran detailed simulations of climate change scenarios on the Sacramento-San Joaquin Delta/San Francisco Bay system.
In a new report from NOAA, the Joplin tornado offers important lessons for disaster preparedness. The report identifies best practices and makes recommendations to help save more lives during future violent tornadoes. Through interviews with more than 100 Joplin residents, the NOAA team found that societal response to warnings is highly complex and involves a number of factors, such as risk perception, overall credibility of warnings and warning communications.
A useful geo transformation tool for anyone that has a need to work with shoreline data and other hydrographic data products from NOAA and others. A useful transofrmation tool from NOAA – vDatum. VDatum is a free software tool being developed jointly by NOAA’s National Geodetic Survey (NGS), Office of Coast Survey (OCS), and Center for Operational Oceanographic Products and Services (CO-OPS). VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums – allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels.