Uncertainty Analysis of a Coupled Hydrological-plant Growth Model for Grassland under Elevated CO2

摘要: The continuous increase in atmospheric carbon dioxide (CO2) contributes to changes plant evapotranspiration and terrestrial water budgets two ways. Firstly, elevated CO2 can result a saving effect, since increasing reduces stomatal opening therefore decreases transpiration. Secondly, fertilization increases biomass accumulation leaf area at canopy level, likely transpiration1. Vegetation hydrological models be used investigate the response bidirectional effects outlined above, including their relative contribution cycle. However, intrinsic plant-soil interaction uncertainty related model parameterization have rarely been considered. Hence, we coupled detailed growth soil by using generic frameworks Plant Modelling Framework (PMF)2 Catchment (CMF)3. Up date mechanisms implemented PMF simulate various ways of how physiology is influenced CO2. Both interact Python computer language. Applying PMF-CMF number physiological environmental variables such as biomass, index moisture field data long-term Free Air Carbon Enrichment (FACE) experiment Giessen, Germany. In this experiment, grassland varieties (herbs, legumes, grass) grow under (+20%) ambient 1997. A Monte Carlo based analysis (GLUE) conducted parameter space. focus will on identification parameters for soil, which are drivers balance. We present first results simulation transpiration concentrations.