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

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 ( T NARR ) and downward shortwave radiation ( R NARR ) 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 (GPP VPM ) at seven AmeriFlux crop sites, and investigated the uncertainties in GPP VPM from climate inputs as compared with eddy covariance-based GPP (GPP EC ). Results showed that T NARR agreed well with in-situ measurements; R NARR , however, was positively biased. An empirical linear correction was applied to R NARR , and significantly reduced the relative error of R NARR by ∼25% for crop site-years. Overall, GPP VPM calculated from the in-situ (GPP VPM(EC) ), original (GPP VPM(NARR) ) and adjusted NARR (GPP VPM(adjNARR) ) climate data tracked the seasonality of GPP EC well, albeit with different degrees of biases. GPP VPM(EC) showed a good match with GPP EC 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 GPP VPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean) . GPP VPM(adjNARR) showed a good agreement with GPP VPM(EC) for both crops due to the reduction in the bias of R NARR . The results imply that the bias of R NARR 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. [ABSTRACT FROM AUTHOR]