Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data

Published Date

15 June 2016, Vol.223:116–131, doi:10.1016/j.agrformet.2016.04.003

Title 

Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data

• Author 

• Yulong Zhang ^a,b,,
• Conghe Song ^a,c,,
• Ge Sun ^d
• Lawrence E. Band ^a,b
• Steven McNulty^d
• Asko Noormets ^d
• Quanfa Zhang ^e
• Zhiqiang Zhang ^f

• aDepartment of Geography, University of North Carolina at Chapel Hill, Chapel Hill 27599, NC, USA
• bInstitute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill 27599, NC, USA
• cSchool of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
• dEastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, Raleigh 27606, NC, USA
• eKey Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
• fSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China

Received 8 July 2015. Revised 17 February 2016. Accepted 5 April 2016. Available online 16 April 2016.

Highlights

• •
  Developed a coupled monthly carbon and water (CCW) model to estimate global GPP and ET.
• •
  Modeled GPP based on LUE and UWUE was used to estimate ET.
• •
  CCW explained over 65% of variations of tower-derived GPP and ET.
• •
  CCW improved global GPP and ET estimates.

Abstract

Terrestrial gross primary productivity (GPP) and evapotranspiration (ET) are two key ecosystem fluxes in the global carbon and water cycles. As carbon and water fluxes are inherently linked, knowing one provides information for the other. However, tightly coupled and easy to use ecosystem models are rare and there are still large uncertainties in global carbon and water flux estimates. In this study, we developed a new monthly coupled carbon and water (CCW) model. GPP was estimated based on the light-use efficiency (LUE) theory that considered the effect of diffuse radiation, while ET was modeled based on GPP and water-use efficiency (WUE). We evaluated the non-linear effect of single (GPP_OR) or combined (GPP_AND) limitations of temperature and vapor pressure deficit on GPP. We further compared the effects of three types of WUE (i.e., WUE, inherent WUE, and underlying WUE) on ET (i.e., ET_WUE, ET_IWUE and ET_UWUE). CCW was calibrated and validated using global eddy covariance measurement from FLUXNET and remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2007. Modeled GPP_ANDand GPP_OR explained 67.3% and 66.8% of variations of tower-derived GPP, respectively, while ET_UWUE, ET_IWUE and ET_WUE explained 65.7%, 59.9% and 58.1% of tower-measured ET, respectively. Consequently, we chose GPP_AND and ET_UWUE as the best modeling framework for CCW, and estimated global GPP as 134.2 Pg C yr⁻¹ and ET as 57.0 × 10³ km³ for vegetated areas in 2001. Global ET estimated by CCW compared favorably with MODIS ET (60.5 × 10³ km³) and ET derived from global precipitation (56.5 × 10³ km³). However, global GPP estimated by CCW was about 19% higher than MODIS GPP (109.0 Pg C yr⁻¹). The mean global WUE value estimated by CCW (2.35 g C kg⁻¹ H₂O) was close to the mean tower-based WUE (2.60 g C kg⁻¹ H₂O), but was much higher than the WUE derived from MODIS products (1.80 g C kg⁻¹ H₂O). We concluded that the new simple CCW model provided improved estimates of GPP and ET. The biome-specific parameters derived in this study allow CCW to be further linked with land use change models to project human impacts on terrestrial ecosystem functions.

Keywords

• Gross primary productivity
• Evapotranspiration
• Light-use efficiency
• Water-use efficiency
• FLUXNET
• MODIS
• CCW

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  Corresponding authors at: Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill 27599, NC, USA.

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