Presenter/Author Information

Matthias Kuhnert, Institute of Biological and Environmental Sciences, University of AberdeenFollow
Jagadeesh Yeluripati, Institute of Biological and Environmental Sciences, University of Aberdeen, The James Hutton Institute
Pete Smith, Institute of Biological and Environmental Sciences, University of Aberdeen
Holger Hoffmann, Crop Science Group, INRES, University of Bonn
Marcel van Oijen, Centre for Ecology and Hydrology, CEH-Edinburgh
Julie Constantin, INRA
Elsa Coucheney, Swedish University of Agricultural Sciences
Rene Dechow, Thünen-Institute of Climate-Smart-Agriculture
Henrik Eckersten, Swedish University of Agricultural Sciences
Thomas Gaiser, Centre for Ecology and Hydrology, CEH-Edinburgh
Balász Grosz, Thünen-Institute of Climate-Smart-Agriculture
Edwin Haas, Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology
Kurt-Christian Kersebaum, Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research
Ralf Kiese, Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology
Steffen Klatt, Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology
Elisabet Lewan, Swedish University of Agricultural Sciences
Claas Nendel, Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research
Helene Raynal, INRA
Carmen Sosa, Swedish University of Agricultural Sciences
Xenia Specka, Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research
Edmar Teixeira, Systems Modelling Team (Sustainable Production Group), The New Zealand Institute for Plant and Food Research Limited, Canterbury Agriculture & Science Centre
Enli Wang, CSIRO Land and Water
Lutz Weihermüller, Institute of Bio- & Geosciences Agrosphere (IBG-3)
Gang Zhao, Crop Science Group, INRES, University of Bonn
Zhigan Zhao, CSIRO Land and Water
Stephen Ogle, Natural Resource Ecology Laboratory, Colorado State University
Frank Ewert, Crop Science Group, INRES, University of Bonn

Keywords

net primary production, NPP, scaling, extreme events, crop modelling, climate, data aggregation

Location

Session B5: Managing Uncertainty

Start Date

11-7-2016 5:30 PM

End Date

11-7-2016 5:50 PM

Description

In ecosystem modelling studies, data are often collected at a small scale but are used in models to predict ecosystem responses e.g. net primary production (NPP) at coarser scale, using data aggregation. The data aggregation causes errors that are difficult to predict, because of model complexity and our limited knowledge of the spatial heterogeneity underlying our aggregated input values. Input aggregation thus adds to the uncertainty associated with model predictions. One way of reducing uncertainty related to input aggregation error is to evaluate models at different resolutions for areas where high-resolution input data are available. The objective of this study was the quantification of the aggregation error on modelled NPP introduced by climate data aggregation. Therefore, climate data at 1 km x 1 km resolution (>30 000 grid cells) were used as baseline to simulate NPP with 11 different crop and biogeochemical models for the federal state North Rhine-Westphalia (Germany). These results, for 29 year monocultures of wheat and maize cropping systems, respectively, were compared with simulation results using aggregated climate data for four resolutions (10, 25, 50, 100 km grid cell side length).The aggregation effect is represented by the maximum differences between the NPP, averaged for wheat and maize cropping systems over the growing season, simulated for the five different resolutions. Input data aggregation had little impact on NPP of 29 year averages (0.5 – 7.8 % for wheat and 0.3 – 10 % for maize), while the climate data aggregation effect was higher for single years; up to 9 % and 13 % for wheat and maize, respectively, gradually decreasing to low effects for averages over 10 year periods or longer. The scale effect differed among models and shows only a minor impact (2%) for an ensemble run.

 
Jul 11th, 5:30 PM Jul 11th, 5:50 PM

Effects of climate data aggregation on regional net primary production (NPP) modelling

Session B5: Managing Uncertainty

In ecosystem modelling studies, data are often collected at a small scale but are used in models to predict ecosystem responses e.g. net primary production (NPP) at coarser scale, using data aggregation. The data aggregation causes errors that are difficult to predict, because of model complexity and our limited knowledge of the spatial heterogeneity underlying our aggregated input values. Input aggregation thus adds to the uncertainty associated with model predictions. One way of reducing uncertainty related to input aggregation error is to evaluate models at different resolutions for areas where high-resolution input data are available. The objective of this study was the quantification of the aggregation error on modelled NPP introduced by climate data aggregation. Therefore, climate data at 1 km x 1 km resolution (>30 000 grid cells) were used as baseline to simulate NPP with 11 different crop and biogeochemical models for the federal state North Rhine-Westphalia (Germany). These results, for 29 year monocultures of wheat and maize cropping systems, respectively, were compared with simulation results using aggregated climate data for four resolutions (10, 25, 50, 100 km grid cell side length).The aggregation effect is represented by the maximum differences between the NPP, averaged for wheat and maize cropping systems over the growing season, simulated for the five different resolutions. Input data aggregation had little impact on NPP of 29 year averages (0.5 – 7.8 % for wheat and 0.3 – 10 % for maize), while the climate data aggregation effect was higher for single years; up to 9 % and 13 % for wheat and maize, respectively, gradually decreasing to low effects for averages over 10 year periods or longer. The scale effect differed among models and shows only a minor impact (2%) for an ensemble run.