Research Grants/Fellowships/SBIR

Forecasting and Evaluating Vulnerability of Watersheds to Climate Change, Extreme Events, and Algal Blooms

EPA Grant Number: R835203
Title: Forecasting and Evaluating Vulnerability of Watersheds to Climate Change, Extreme Events, and Algal Blooms
Investigators: Stevenson, R. Jan , Hyndman, David , Moore, Nathan , Qi, Jiaguo
Institution: Michigan State University
EPA Project Officer: Hiscock, Michael
Project Period: January 11, 2012 through December 31, 2014
Project Amount: $750,000
RFA: Extreme Event Impacts on Air Quality and Water Quality with a Changing Global Climate (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Global Climate Change , Water and Watersheds , Climate Change , Air , Water


Climate change is projected to increase the intensity of extreme weather events along with temperature. Increases in water temperature with greater frequency and intensity of floods and droughts are a perfect storm for exacerbating problems in water quality. Recent developments in satellite remote sensing and water quality modeling enable quantification of empirical relationships between changes in water quality and extreme events at scales that can inform long-term water quality management.


The goals of this project are to advance our knowledge of relationships between extreme events and water quality across the diversity of climatic and geologic conditions of the US, as well as to develop tools for advancing that knowledge and informing water quality management strategies under different climate change scenarios. We hypothesize that effects of extreme events will be greatest in hydrologically variable regions of the country, which are often most dependent on surface waters for drinking water supply.


First, we will use satellite images since 1972 from Landsat, as well as more recent MODIS imagery, to develop histories of watershed land use and lake water quality change. We will also collect stream flow, weather, and watershed soil and geologic information, which will then be used to develop and test processed based models relating precipitation, geology, groundwater and surface runoff, transport of nutrients, water temperature, and algal biomass accrual in lakes. Climate models will be downscaled for the regions in which process based water quality models have been developed and used to forecast future changes in extreme events, water quality changes, and management needed to mitigate future problems. Management of storm water and riparian zones, as well as other watershed practices, will be varied with coupled process-based models to relate changes in risk of water quality problems resulting from climate changes to different management strategies. Effects of natural regional variation in hydrologic stability and climate on risk of water quality problems will be evaluated with water quality models.

Expected Results:

Process-based modeling and forecasting of water quality will be conducted in four watersheds in two hydrologic regions of the US. Water quality, hydrology, and land use histories will be generated for more than 50 lakes and watersheds distributed strategically in hydrologic regions across the US for use with process-informed statistical models to characterize the natural variation in response of water quality to extreme events and identify hydrologic regions most vulnerable to climate change.

Progress and Final Reports:
2012 Progress Report