Quantification of Blue Carbon Burial in Seagrass Ecosystems and the Impact of Projected Climate Change

Grants

Abstract

  • Background: Seagrasses represent some of the most productive and valuable ecosystems on earth. In addition to being important primary producers, seagrasses store large quantities of organic C in vegetation stocks and sediments, and generate alkalinity that can counter the effects of ocean acidification on coastal ecosystems. However, they are vulnerable to local anthropogenic disturbance and climate warming, particularly in areas with high water column turbidity and warming sea temperatures. Our ability to quantify the contribution to global carbon burial from these ecosystems suffers from large uncertainties in standing inventories of seagrass abundance. Advances in shallow water optics, submerged vegetation canopies and seagrass physiology, combined with the shallow depth distributions of seagrasses and improved spatial resolution of radiometrically calibrated remote sensing platforms, now enable seagrass ecosystems to be monitored on a global scale from orbiting platforms such as LandSat-8, WV-2,-3. Objectives: We will quantify abundances and map distributions of seagrasses across two regions of the coastal United States threatened by changes in water quality, climate warming and sea level rise. We will exploit archived remote sensing imagery available from the LandSat-8 (USGS) and the WV-2,3 satellites (DigitalGlobe), and determine temporal changes in these resources as permitted from the archive data. Remotely sensed changes will be linked to a predictive bio-optical model that can account for the differential effects of water quality, ambient temperature and CO2 availability on seagrass abundance and distribution which has direct impacts on Blue Carbon.Methods: Our study will focus on Chesapeake Bay and the Gulf of Mexico coastline from Mobile Bay to Tampa Bay. They are routinely imaged by existing remote sensing assets, are covered by extensive networks of water quality monitoring stations, and represent areas where we have significant prior working knowledge regarding ecosystem composition & function and water column optical properties. We will employ our remote sensing algorithms, combined with 30-m digital elevation models of submarine bathymetry to retrieve seagrass abundance and water quality parameters across the image domains. We will use existing relationships between seagrass density, above and below ground biomass to quantify carbon burial. A seagrass growth model which includes water column bio-optical conditions, ambient temperature and CO2 availability will be used to explore the impacts of predicted climate change on carbon burial. Scenarios will be simulated that include improvements or worsening of water column clarity, sea level rise, rise in ambient temperature, and increasing CO2 availability. These scenarios will be used to determine the overall impact of these changes on Blue Carbon Burial, and can be used to identify areas within our study region that can be expected to increase or decrease seagrass mediated burial.Significance: Coupling our ability to retrieve absolute estimates of biomass, areal distribution and carbon stocks by fusing remote sensing imagery with digital elevation models of nearshore bathymetry and a predictive bio-optical model of seagrass response to climate warming and ocean acidification will offer important tools for assessing Blue Carbon burial within seagrass and responses to future changes in water quality and climate. Further, it will provide analytical and predictive tools that can be applied to other locations, enabling global estimates of seagrass abundance and carbon burial to be derived from existing image archives, and transitioned to new remote sensing assets (e.g. PACE, HyspIRI) as they become available.
  • Date/time Interval

  • 2017 - 2019
  • Total Award Amount

  • $553,876.00
  • Contributor

  • Blake Schaeffer   Investigator  
  • Jiang Li   Investigator  
  • Richard Zimmerman   Investigator  
  • Victoria Hill   Investigator