Blooms Like It Hot: Combating Cyanobacterial Blooms in a World Experiencing Climate Change
Dr. Hans Paerl
Friday, March 30, 2018
Borlaug Hall, Room 335
Harmful (toxic, hypoxia-generating, food web altering) blue-green algal or cyanobacterial blooms (CyanoHABs) are proliferating worldwide in freshwater ecosystems, where they represent a serious threat to drinking water, recreational and fishing use and overall sustainability. Nutrient (both phosphorus and nitrogen) input reductions have been prescribed to control CyanoHABs. However, climatic changes, specifically warming, increased vertical stratification, salinization, and intensification of storms and droughts, favor CyanoHABs and thus play synergistic roles in promoting CyanoHAB frequency, intensity, geographic distribution, and duration. In particular, rising temperatures cause shifts in critical nutrient thresholds at which cyanobacterial blooms can develop. From a management perspective, nutrient input reductions aimed at controlling CyanoHABs may need to be more aggressively pursued in a warmer, hydrologically more extreme world. Additional control steps that have been taken include 1) altering the hydrology to enhance vertical mixing and/or flushing and 2) decreasing nutrient fluxes from organic-rich sediments by physically oxygenating or removing the sediments or capping sediments with clay. These efforts, however, have met with mixed results and can disrupt benthic and planktonic habitats. In most instances, long-term effective eutrophication and CyanoHAB control must consider adaptive nutrient control strategies within the context of altered thermal and hydrologic regimes associated with climate change.
Hans W. Paerl is the Kenan Professor of Marine and Environmental Sciences at the UNC-Chapel Hill Institute of Marine Sciences.
Big Data in Water: Opportunities and Challenges for Machine Learning
Dr. Vipin Kumar
Friday, January 19, 2018
St. Paul Student Center, Northstar Ballroom
View recording | 
dr._kumars_presentation_slides.pdfpresentation_bigdatainwater_vipinkumar_pdf.pdf
Abstract:
Water resources worldwide are coming under stress due to increasing demand from a growing population, increasing pollution, and depleting or uncertain supplies due to changing climate in which drought and floods have both become more frequent. As domains associated with Water continue to experience tremendous data growth from models, sensors, and satellites, there is an unprecedented opportunity for machine learning to help address urgent water challenges facing the humanity. This talk will examine the role of big data and machine learning can play in advancing water science, challenges faced by traditional Machine learning methods in addressing the domain of water, and some early successes.
