Science needs for determining the effects of climate change on harmful algal blooms in the southeastern United States

Abstract

The Southeastern United States has many lakes, streams, and reservoirs that serve as important drinking water sources with recreational, agricultural, and ecological uses. However, harmful algal blooms (HABs) are becoming more common in these waters, causing health issues for humans and animals. HABs have been listed as a contaminant of emerging concern, and the magnitude, frequency, and duration of HABs appear to be increasing at the global scale. While it is well known that nutrients stimulate algae growth, it is not clear how climate change and other parameters stimulate the development of toxin production by HABs. The scientific literature describes parameters, such as storm occurrence, temperature, dissolved metals, erosion of soils, increasing length of growing season, discharge, and hydroperiod, that may affect algae growth and toxin production. Climate and hydrologic models address many of the physical and environmental parameters that influence HABs, but no climate models directly address HABs. This report compiles information from the existing literature pertaining to HABs and the modeling and forecasting of HABS. This compilation is done through the incorporation of climate change models. HAB research that involves climate change will require multiple disciplines that bring together ecologists, hydrologists, climatologists, engineers, economists, and new technology. Resource managers could use geographic data about the occurrence and distribution of HABs to develop models that identify waterbodies more vulnerable to HAB events. Development of such models will require teams capable of integrating biological, chemical, and physical factors. Model development will require additional research that can resolve anthropogenic and climate-related environmental factors to identify trends in freshwater HABs. The complexity and interconnectedness of the parameters that influence HAB occurrences will make model development challenging and require rigorous regional calibration.