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

Tum Markus, Strauss Franziska, Mccallum Ian, Günther Kurt, Schmid, Schmid Erwin

Description

Process 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. Results For 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. Discussion This 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.

Sujets

• Primary production
• EPIC
• Land cover
• Weather

Informations

• Publié par: biomed
• Publié le: 01 janvier 2012
• Nombre de lectures: 6
• Langue: English

Extrait

Tum et al . Carbon Balance and Management 2012, 7 :3 http://www.cbmjournal.com/content/7/1/3

R E S E A R C H Open Access How sensitive are estimates of carbon fixation in agricultural models to input data? Markus Tum 1,3* , Franziska Strauss 2 , Ian McCallum 3 , Kurt Günther 1 and Erwin Schmid 2

Abstract Background: Process 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. Results: For 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. Discussion: This 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. Keywords: agricultural models, net primary productivity, EPIC, BETHY/DLR, land cover, weather

Background interaction of plants with the atmosphere can be applied Modelling the net carbon uptake by vegetation (Net Pri-  to simulate the rate of carbon dioxide uptake of the mary Productivity, NPP) and estimating the yields of plant through photosynthesis (called Gross Primary Pro- agricultural plants have become important tools to ductivity, GPP). These models follow the concept of [1] study the mechanisms of carbon exchange between the and [2] to simulate the process of photosynthesis. More- atmosphere and vegetation, as well as issues of food over, carbon uptake of well-watered and fertilized security. Different approaches are currently tracked annual plants is linearly related to the amount of which can be grouped to their approaches how photo-  absorbed Photosynthetically Active Radiation (PAR), synthesis is modelled. which can be derived from satellite data (i.e. the fraction Models describing the chemical, physical and plant of PAR which is absorbed by the canopy; cp. [3] or cal- physiological processes of plant development and the culated by the accumulation of dry matter. NPP is defined as the difference between GPP and * Correspondence: [e-mail] autotrophic respiration. Therefore, it is important to 1 DeutschesZentrumfürLuft-undR , au O m b f e a r h p r f t af ( f D en LR h ) o , fe Deutsches estimate the autotrophic respiration of plants following F G e e r r n m e a rk n undungsdatenzentrum(DFD)n,D-82234Wessling, the determination of GPP. Autotrophic respiration is Fulllisto y fauthorinformationisavailableattheendofthearticle defined as the oxidation of organic compounds found in © 2012 Tum et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.