2016 Progress Report: 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: June 1, 2012 through May 31, 2017
Project Period Covered by this Report: June 1, 2015 through May 31,2016
Project Amount: $749,801
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 , Water Quality , Climate Change , Air , Water

Objective:

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 with algal blooms in lakes. 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.

Our project requires using historic satellite images to characterize algal abundance in lakes and to provide sufficient records of past conditions that we can relate changes in temperature and extreme hydrologic events that result from climate change to changes in algal abundance. Most algorithms for estimating lake chlorophyll a with satellite data are based on linear regression and have not been sufficiently accurate to provide measurements of chlorophyll in lakes that are useful for ecological analyses and lake management.  We have used a machine learning algorithm to develop an innovative tool for measuring chlorophyll, a common indicator of algal biomass, with Landsat imagery.  Satellite data storage and cloud calculation techniques from Google Earth Engine were used to automatically and rapidly produce long-term whole-lake characterizations of algal biomass using Landsat satellite imagery and our chlorophyll measurement algorithm. Application of this tool improves the accuracy of lake assessment and produces results that can be used for national scale assessments, evaluation of historic conditions in lakes to relate algae to climate and land use change, and forecast vulnerability of lakes to climate change.

Progress Summary:

Our hydrologic and nutrient loading model for the Lower Peninsula of Michigan has been undergoing extensive validation, scenario analysis, and testing in three of its subregions (Grand Traverse Bay, Muskegon River, and St Joseph/Maumee Rivers). These efforts, along with extensive refinements to irrigation routines developed for the Northern High Plains, have greatly reduced model bias at all scales. For example, in the Muskegon River watershed, bias in annual simulated streamflows at larger scales (>1,000 km2) averages approximately 1-2%, down from 5-6% in previous simulations.

The landscape hydrology modeling platform that we are applying for climate downscaling in this project requires spatially and temporally continuous weather data at hourly frequency, which presents challenges when running large regions for long spans of time. We developed and refined a method to create continuous climate simulations for the regional hydrologic models over arbitrarily long time spans. This method is an adaptation of the Binary Coupled Statistical Downscaling (BCSD), in which historical observations of key variables (such as temperature, precipitation, and solar radiation) are perturbed by either individual or ensemble climate model forecasts. We applied this method for simulations within the Lower Peninsula of Michigan. We plan to apply it next to the Northern High Plains and Missouri regional models.

Twenty-eight years of lake assessments using Landsat imagery show long-term changes in algal biomass are positively related to increases in lake surface temperatures, even in lakes with high summer temperatures. Annual average algal biomass, the intensity of storm events and water temperature increased over this 28-year period in four Missouri reservoirs, whereas average summer algal biomass did not. Annual average chlorophyll increased by 0.4 µg/L (σ = 0.2) when lake surface temperature increased by 1 °C, but changed little with storm events. These results show that lake algal biomass will increase with climate change, even though summer temperatures exceed optimal conditions for algal growth.

Remotely sensed whole-lake chlorophyll in 585 lakes in continental United States showed that hydraulic conditions such as basin slope, soil, and land use/cover were more important than lake surface temperature and precipitation in explaining the spatial variation of lake chlorophyll during May-August. That implied algal biomass in lakes were more controlled by internal nutrients that were related to long-term (years) precipitation, basin hydrogeomorphology, and land use, rather than short-term (months) variation in temperature or precipitation. With climate change, lakes will be at a higher risk of algal blooms mainly due to short-term effects of increasing temperature and long-term effect of increasing intensity of precipitation events. Our studies reveal temperature and precipitation affected algal biomass on different time scales, providing evidence and tools for the assessment of climate change impacts on water quality.  


Journal Articles on this Report : 2 Displayed | Download in RIS Format

Other project views: All 6 publications 6 publications in selected types All 6 journal articles
Type Citation Project Document Sources
Journal Article Martin SL, Hayes DB, Kendall AD, Hyndman DW. The land-use legacy effect: towards a mechanistic understanding of time-lagged water quality responses to land use/cover. Science of the Total Environment 2017;579:1794-1803. R835203 (2015)
R835203 (2016)
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  • Journal Article Tang T, Stevenson RJ, Infante DM. Accounting for regional variation in both natural environment and human disturbance to improve performance of multimetric indices of lotic benthic diatoms. Science of the Total Environment 2016;568:1124-1134. R835203 (2016)
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  • Progress and Final Reports:

    Original Abstract
    2012 Progress Report
    2013 Progress Report
    2014 Progress Report
    2015 Progress Report