The implication of input data aggregation on up-scaling soil organic carbon changes. (October 2017)

• Record Type: Journal Article
• Authors: Grosz, Balázs; Dechow, Rene; Gebbert, Sören; Hoffmann, Holger; Zhao, Gang; Constantin, Julie; Raynal, Helene; Wallach, Daniel; Coucheney, Elsa; Lewan, Elisabet; Eckersten, Henrik; Specka, Xenia; Kersebaum, Kurt-Christian; Nendel, Claas; Kuhnert, Matthias; Yeluripati, Jagadeesh; Haas, Edwin; Teixeira, Edmar; Bindi, Marco; Trombi, Giacomo; Moriondo, Marco; Doro, Luca; Roggero, Pier Paolo; Zhao, Zhigan; Wang, Enli; Tao, Fulu; Rötter, Reimund; Kassie, Belay; Cammarano, Davide; Asseng, Senthold; Weihermüller, Lutz; Siebert, Stefan; Gaiser, Thomas; Ewert, Frank;
• Is Part Of: Environmental modelling & software. Volume 96(2017)
• Page Start: 361
• Page End: 377
• Publication Date: 2017-10
• Subjects: Biogeochemical model -- Data aggregation -- Up-scaling error -- Soil organic carbon; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.046
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. Highlights: Analysis of soil data aggregation on model errors of up-scaled SOC trends. Determination of factors controlling aggregation effects (AE) on modeled SOC trends. Comparison of variability between 7 biogeochemical models and AE. Development of ex ante methods to approximate AE for SOC simulation studies.