The use of precipitation intensity in estimating gross primary production in four northern grasslands

• Published in: Journal of arid environments Vol. 82; pp. 11 - 18
• Main Authors: Wu, C., Chen, J.M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2012; Elsevier
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; atmospheric precipitation; Biological and medical sciences; Climate change; Climatology. Bioclimatology. Climate change; Earth, ocean, space; ecoregions; ecosystems; Exact sciences and technology; External geophysics; Fundamental and applied biological sciences. Psychology; Grasslands; Gross primary production; Meteorology; model validation; MODIS; photosynthetically active radiation; Precipitation intensity; primary productivity; remote sensing; Synecology; temperature; Terrestrial ecosystems; Gross primary production; Climate change; Grasslands; Precipitation intensity; MODIS; Dynamical climatology; Grassland; Primary productivity; Precipitation; Arid environment
• ISSN: 0140-1963; 1095-922X
• DOI: 10.1016/j.jaridenv.2012.02.014

Remote sensing is a useful tool for the estimation of gross primary production (GPP) in terrestrial ecosystems at regional to global scales. One limitation of remote sensing based GPP models is the inappropriate characterizing of precipitation impacts. In this study, we showed positive relationship between the monthly flux-measured GPP of four grasslands ecosystems and the precipitation intensity, which was calculated from dividing the monthly sums of precipitation by the half-hourly precipitation frequency. Suggested by this finding, two remote sensing based GPP models, i.e. the greenness and radiation model (GR) and the temperature and greenness (TG) model, were selected to test the potential of incorporating this precipitation intensity for the estimation of monthly GPP. A scaled precipitation intensity was proposed by normalizing a multi-year maximum precipitation intensity, considering its dynamical ranges across sites and regions. Results indicated that by adding of this scalar, the revised models can provide better monthly GPP estimates with average 10% improvements in precisions compared to their original outputs. A further analysis showed that such better performances of the revised models can be attributed to the positive relationship between precipitation intensity and the absorbed photosynthetically active radiation (APAR). However, no evident response has been observed on the light use efficiency (LUE), indicating the LUE and precipitation intensity relationship may differ across species and ecoregions. To the best of our knowledge, this is the first report of the potential use of precipitation intensity in the remote sensing based GPP models and it will be useful for the development of future models that can better predict GPP in the context of future precipitation regimes. ► Monthly GPP are independent on precipitation quantity. ► Monthly GPP positively correlates to precipitation intensity. ► Algorithm incorporates precipitation intensity show better GPP estimates. ► No evident impact is observed of precipitation intensity on LUE. ► Precipitation intensity shows positive impacts on APAR.