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Title: Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model

Abstract

Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature (TNARR) and downward shortwave radiation (RNARR) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPPVPM) at seven AmeriFlux crop sites, and investigated the uncertainties in GPPVPM from climate inputs as compared with eddy covariance-based GPP (GPPEC). Results showed that TNARR agreed well with in-situ measurements; RNARR, however, was positively biased. An empirical linear correction was applied to RNARR, and significantly reduced the relative error of RNARR by ~25% for crop site-years. Overall, GPPVPM calculated from the in-situ (GPPVPM(EC)), original (GPPVPM(NARR)) and adjusted NARR (GPPVPM(adjNARR)) climate data tracked the seasonality of GPPEC well, albeit with different degrees of biases. GPPVPM(EC) showed a good match with GPPEC for maize (Zea mays L.), but was slightly underestimated for soybean (Glycine max L.). Replacing the in-situ climate data with the NARR resultedmore » in a significant overestimation of GPPVPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean). GPPVPM(adjNARR) showed a good agreement with GPPVPM(EC) for both crops due to the reduction in the bias of RNARR. The results imply that the bias of RNARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF); U.S. Department of Agriculture - National Institute of Food and Agriculture; USDOE Office of Science - Office of Biological and Environmental Research
OSTI Identifier:
1237681
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Agricultural and Forest Meteorology (Print)
Additional Journal Information:
Journal Volume: 213; Journal ID: ISSN 0168-1923
Country of Publication:
United States
Language:
English
Subject:
AmeriFlux; Downward shortwave radiation; MODIS; North American Regional Reanalysis (NARR); Vegetation Photosynthesis Model (VPM); Vegetation indices

Citation Formats

Jin, Cui, Xiao, Xiangming, Wagle, Pradeep, Griffis, Timothy, Dong, Jinwei, Wu, Chaoyang, Qin, Yuanwei, and Cook, David R. Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model. United States: N. p., 2015. Web. doi:10.1016/j.agrformet.2015.07.003.
Jin, Cui, Xiao, Xiangming, Wagle, Pradeep, Griffis, Timothy, Dong, Jinwei, Wu, Chaoyang, Qin, Yuanwei, & Cook, David R. Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model. United States. https://doi.org/10.1016/j.agrformet.2015.07.003
Jin, Cui, Xiao, Xiangming, Wagle, Pradeep, Griffis, Timothy, Dong, Jinwei, Wu, Chaoyang, Qin, Yuanwei, and Cook, David R. 2015. "Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model". United States. https://doi.org/10.1016/j.agrformet.2015.07.003.
@article{osti_1237681,
title = {Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model},
author = {Jin, Cui and Xiao, Xiangming and Wagle, Pradeep and Griffis, Timothy and Dong, Jinwei and Wu, Chaoyang and Qin, Yuanwei and Cook, David R.},
abstractNote = {Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature (TNARR) and downward shortwave radiation (RNARR) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPPVPM) at seven AmeriFlux crop sites, and investigated the uncertainties in GPPVPM from climate inputs as compared with eddy covariance-based GPP (GPPEC). Results showed that TNARR agreed well with in-situ measurements; RNARR, however, was positively biased. An empirical linear correction was applied to RNARR, and significantly reduced the relative error of RNARR by ~25% for crop site-years. Overall, GPPVPM calculated from the in-situ (GPPVPM(EC)), original (GPPVPM(NARR)) and adjusted NARR (GPPVPM(adjNARR)) climate data tracked the seasonality of GPPEC well, albeit with different degrees of biases. GPPVPM(EC) showed a good match with GPPEC for maize (Zea mays L.), but was slightly underestimated for soybean (Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPPVPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean). GPPVPM(adjNARR) showed a good agreement with GPPVPM(EC) for both crops due to the reduction in the bias of RNARR. The results imply that the bias of RNARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales.},
doi = {10.1016/j.agrformet.2015.07.003},
url = {https://www.osti.gov/biblio/1237681}, journal = {Agricultural and Forest Meteorology (Print)},
issn = {0168-1923},
number = ,
volume = 213,
place = {United States},
year = {Sun Nov 01 00:00:00 EDT 2015},
month = {Sun Nov 01 00:00:00 EDT 2015}
}