The implication of input data aggregation on up-scaling soil organic carbon changes
Balázs Grosz
(1)
,
Rene Dechow
(1)
,
Sören Gebbert
(1)
,
Holger Hoffmann
(2)
,
Gang Zhao
(2)
,
Julie Constantin
(3)
,
Helene Raynal
(4, 3)
,
Daniel Wallach
(3)
,
Elsa Coucheney
(5)
,
Elisabet Lewan
(5)
,
Henrik Eckersten
(6)
,
Xenia Specka
(7)
,
Kurt-Christian Kersebaum
(7)
,
Claas Nendel
(7)
,
Matthias Kuhnert
(8)
,
Jagadeesh Yeluripati
(9)
,
Edwin Haas
(10)
,
Edmar Teixeira
(11)
,
Marco Bindi
(12)
,
Giacomo Trombi
(12)
,
Marco Moriondo
(13)
,
Luca Doro
(14)
,
Pier Paolo Roggero
(15)
,
Zhigan Zhao
(16)
,
Enli Wang
(16)
,
Fulu Tao
(17)
,
Reimund P. Rötter
(17, 18)
,
Belay Kassie
(19)
,
Davide Cammarano
(19)
,
Senthold Asseng
(19)
,
Lutz Weihermüller
(20)
,
Stefan Siebert
(2)
,
Thomas Gaiser
(2)
,
Frank Ewert
(2)
1
Institute of Climate-Smart Agriculture
2 Crop Science Group
3 AGIR - AGroécologie, Innovations, teRritoires
4 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
5 Departement of Soil and Environment
6 Department of Crop Production Ecology
7 Institute of Landscape Systems Analysis
8 Biological and Environmental Sciences, School of Biological Sciences
9 The James Hutton Institute
10 IMK-IFU - Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung
11 Systems Modeling Team (Sustainable Production Group)
12 Department of Agri-food Production and Environmental Sciences
13 IBIMET - Istituto di Biometeorologia [Firenze]
14 Desertification Research Centre
15 Desertification Research Centre - Department of Agricultural Sciences
16 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
17 Climate Impacts Group
18 Department of Crop Sciences
19 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
20 Institute of Bio- and Geosciences Agrosphere (IBG-3)
2 Crop Science Group
3 AGIR - AGroécologie, Innovations, teRritoires
4 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
5 Departement of Soil and Environment
6 Department of Crop Production Ecology
7 Institute of Landscape Systems Analysis
8 Biological and Environmental Sciences, School of Biological Sciences
9 The James Hutton Institute
10 IMK-IFU - Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung
11 Systems Modeling Team (Sustainable Production Group)
12 Department of Agri-food Production and Environmental Sciences
13 IBIMET - Istituto di Biometeorologia [Firenze]
14 Desertification Research Centre
15 Desertification Research Centre - Department of Agricultural Sciences
16 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
17 Climate Impacts Group
18 Department of Crop Sciences
19 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
20 Institute of Bio- and Geosciences Agrosphere (IBG-3)
Julie Constantin
- Fonction : Auteur
- PersonId : 736250
- IdHAL : julie-constantin
- ORCID : 0000-0001-9647-5374
Helene Raynal
- Fonction : Auteur
- PersonId : 745427
- IdHAL : helene-raynal
- ORCID : 0000-0002-3492-0564
Claas Nendel
- Fonction : Auteur
- PersonId : 772081
- ORCID : 0000-0001-7608-9097
Luca Doro
- Fonction : Auteur
- PersonId : 773546
- ORCID : 0000-0003-1404-2255
Davide Cammarano
- Fonction : Auteur
- PersonId : 968228
Résumé
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.
