Modelling gross primary production in the Heihe river basin and uncertainty analysis

Abstract

Accurate and timely monitoring of gross primary production (GPP) at regional and global scales is necessary for understanding the terrestrial biosphere carbon balance. In this article, 8-day composite GPP is estimated using the region production efficiency model (REG-PEM) in the Heihe river basin from 2006 to 2008. The result indicates that GPP meets the seasonal cycle well and varies with different land covers. Analysis of uncertainty and sensitivity of the REG-PEM model are implemented by the Monte Carlo method. GPP is simulated with a test data set; the data set includes three groups, and each group has 8000 points. The three groups obey uniform distribution, normal distribution and beta distribution, respectively. Uncertainty is assessed by the quantification of mean values and standard deviation of outputs. The distribution of simulated GPP is slightly different from that of model-calculated GPP over the study sites. Among the input parameters, the land surface water index (LSWI) and air temperature (T) make less of a contribution to model output than photosynthetic active radiation (PAR) and the enhanced vegetation index (EVI). The main sources of model uncertainty are input data uncertainty and uncertainty from the specific model.