Controls on the Lateral Flux of Organic Carbon from the Coterminous U.S.

1
Controls on the Lateral Flux of Organic Carbon
from the Coterminous U.S.

• David E. Butman1 Peter Raymond1 Scott Goetz2
• 1 Yale School of Forestry Environmental
  Studies, 205 Prospect Street, New Haven, CT
  06511, United States 2 The Wood Hole Research
  Center, 149 Woods Hole Road, Falmouth, MA 02540
  Corresponding Author David Butman
  (david.butman_at_yale.edu)

4. Results to date

• Introduction
• Although our knowledge base is growing, the
  lateral transport of atmospherically derived
  carbon across the landscape is an area of
  research that lags behind other aspects of global
  carbon cycle science. Smaller in magnitude than
  net primary production, the transport of organic
  and inorganic carbon represents a 2.1Pg C yr-1
  flux into the worlds major rivers of which 1.1Pg
  C yr-1 is exported to the ocean and 1Pg C yr-1 is
  degassed across the rivers and estuarine surface.
  Furthermore, it is a net flux of carbon from the
  terrestrial biosphere.
• Terrestrial organic materials can enter aquatic
  systems as dissolved organic carbon (DOC),
  particulate organic carbon (POC), or dissolved
  inorganic carbon (DIC). Each species results
  from the processes of photosynthesis and
  respiration or in the case of DIC an additional
  portion is a result of the chemical weathering of
  soil carbonate and silicate minerals. Each form
  can significantly influence the aquatic system it
  enters by driving levels of productivity in
  estuaries and coastal margins as well as altering
  CO2 concentrations and the exchange of CO2 with
  the atmosphere. However short term weather
  patterns, land-use change, and abrupt changes in
  climate can have immediate effects on carbon
  transport. Many of these disturbances are
  confounded by positive and negative feedbacks
  from humans influence on the environment as well
  as regional variation in land surface
  characteristics.
• Estimates of total river carbon export are used
  to offset estimates of the magnitude of a
  terrestrial sink for atmospheric carbon. This
  research will improve those estimates and may
  provide insight into how regional variation and
  measurable changes in growing season and
  precipitation are increasing or decreasing any
  long term terrestrial sink.

• 5. Future Direction
• First model the spatial distribution of water
  movement across
• the landscape.
• More spatial diversity may improve
  relationships between
• discharge and DOC.
• Develop spatially explicit discharge maps for
  the US.

• 1,789 USGS gauging stations available for DOC
  with DOC measurements, (Figure 1).
• DOC measurements were made at regular intervals
  since 1960.
• Data is well distributed spatially across the
  U.S.
• Weakness in data coverage exists for Alabama
  and portions of the mid-west and will be
  supplemented by additional sources from the EPA
  data storage and retrieval system, (STORET).

6. Case Study Ohio Basin Discharge is
controlled both by climate and anthropogenic
influence on the land surface. Using the Ohio
river basin as a test case we are able to produce
a spatially explicit map of annual discharge (m)
based on annual precipitation, annual maximum
temperature, impervious cover and canopy
cover. Discharge is strongly related to
precipitation for the year 2004.
Figure 5. Data layers for Ohio River Basin case
study.

• 2. Goal and Hypotheses
• Goal To produce a spatially explicit map of the
  lateral
• Export of DOC from terrestrial landscapes and the
• associated input of DOC to coastal margins,
  highlighting
• the landscape controls on DOC export to rivers.
• Hypothesis
• I. The drivers of DOC export change across
  spatial scales.
• Growing season length, productivity, land-use,
  precipitation, and vegetation type will control
  DOC at small spatial scales - watersheds less
  than 250 km2.
• In-stream processing and land-use will be
  important at large spatial scales.
• Watershed size will dictate the impact climate,
  land use, and storm events can have of DOC export
  
• II. Temporal changes in DOC export will
  correspond to gross changes in Net Ecosystem
  Productivity

• A simple multiple linear regression was able to
  account for 85
• of the variation in discharge and provided the
  following
• equation
•  
• Discharge (m) Impervious0.00493
  Forest0.006055 precip1.012 Tempmax
  0.004339
• Non-linear statistical approaches can improve
  this model

• A Recursive Partitioning and Regression Tree
• (RPart)produces a more robust analysis of the
  controls on DOC
• Regression Tree Model
• DOC (load) f (Precipitation Temperature
  Wetland Urban Forest)
• RPart can explain 70 of the variation in DOC
  load compared
• to the 38 from MLR.
• RPart suggests a positive relationship between
  DOC load and
• temperature at a regional scale.
• Land use and precipitation effect DOC at the
  sub-regional
• scale

3.Datasets River Chemistry and Flow (USGS Water
Data) DOC Flux Ginger Booth, Peter Raymond, and
Neung-Hwan Oh, 2007, LoadRunner. Software and
website, Yale University, New Haven, CT
http//environment.yale.edu/raymond/loadrunner/
Runkel, R.L., Crawford, C.G., and Cohn, T.A.,
2004, Load Estimator (LOADEST) A FORTRAN
Program for estimating Constituent Loads in
streams and Rivers U.S. Geological Survey
Techniques and Methods Book 4, Chapter A5, 69
p. Watershed Delineation / Topography National
Hydrography Dataset Plus NHDPlus (USGS
USEPA) Precipitation and Temperature (PRISM
Group, Oregon State University) Land Use (1992 -
2006) Multi-Resolution Land Characteristics
Consortium (MRLC)

• DOC shows no correlation with precipitation or
  discharge across 162 watersheds along the
  Eastern US, (A).
• DOC load is strongly correlated with
  concentration (B).
• Wetland cover and temperature were the
  strongest and only significant variables after a
  stepwise multiple linear regression, r2.38
  (MLR) (CD)
• Distinct regional separation with latitude
  inherently Introduces bias

Acknowledgements We would like to thank the
members of the Yale Center for Earth Observation
for providing the infrastructure needed for data
storage and analysis. This work has been funded
in part by a NASA Earth Systems Science
Fellowship awarded to David Butman.