How sensitive are estimates of carbon fixation in agricultural models to input data?

Abstract

BackgroundProcess based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.ResultsFor our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.DiscussionThis study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.

Availability assessment of bioenergy and power plant location optimization: A case study for Pakistan

“…It can be used to track the transformation of atmospheric carbon dioxide into energy storing sugars, a process known as photosynthesis. BETHY/DLR has recently been used to assess Net Primary Productivity (NPP) for parts of Europe and Asia [9,37], and has been validated and cross-compared with other process based models for agriculture [38] and eddy covariance data [40]. The scheme of modeling photosynthesis with models like BETHY/DLR is widely accepted and serves as an input to global dynamic vegetation models, such as the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH) by Knorr and Heimann [21], and the Lund-Potsdam-Jena (LPJ) by Prentice et al [31] and Bondeau et al [5].…”

Can Carbon in Bioenergy Crops Mitigate Global Climate Change?

“…The ability of some perennial species to fix N [11,309,310] and recycle mineral nutrients on an annual basis by programmed senescence and mobilization of mineral nutrients from vegetative tissues to rhizomes for subsequent reuse is a promising strategy for minimizing external fertilizer inputs [290]. However, N demand of a high-yielding bioenergy crop may not be fully met without such inputs or else soil nutrient reserves will be seriously depleted [294].…”