The implication of input data aggregation on up-scaling soil organic carbon changes

Balazs Grosz*, Rene Dechow, Soeren Gebbert, Holger Hoffmann, Gang Zhao, Julie Constantin, Helene Raynal, Daniel Wallach, Elsa Coucheney, Elisabet Lewan, Henrik Eckersten, Xenia Specka, Kurt-Christian Kersebaum, Claas Nendel, Matthias Kuhnert, Jagadeesh Yeluripati, Edwin Haas, Edmar Teixeira, Marco Bindi, Giacomo Trombi & 14 others Marco Moriondo, Luca Doro, Pier Paolo Roggero, Zhigan Zhao, Enli Wang, Fulu Tao, Reimund Roetter, Belay Kassie, Davide Cammarano, Senthold Asseng, Lutz Weihermueller, Stefan Siebert, Thomas Gaiser, Frank Ewert

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Citations (Scopus)

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. (C)2017 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)361-377
Number of pages17
JournalEnvironmental Modelling and Software
Volume96
Early online date1 Aug 2017
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Biogeochemical model
  • Data aggregation
  • Up-scaling error
  • Soil organic carbon
  • DIFFERENT SPATIAL SCALES
  • NITROUS-OXIDE EMISSIONS
  • MODELING SYSTEM
  • DATA RESOLUTION
  • CROP MODELS
  • CLIMATE
  • LONG
  • PRODUCTIVITY
  • CROPLANDS
  • DAYCENT

Cite this

Grosz, B., Dechow, R., Gebbert, S., Hoffmann, H., Zhao, G., Constantin, J., ... Ewert, F. (2017). The implication of input data aggregation on up-scaling soil organic carbon changes. Environmental Modelling and Software, 96, 361-377. https://doi.org/10.1016/j.envsoft.2017.06.046

The implication of input data aggregation on up-scaling soil organic carbon changes. / Grosz, Balazs; Dechow, Rene; Gebbert, Soeren; 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; Roetter, Reimund; Kassie, Belay; Cammarano, Davide; Asseng, Senthold; Weihermueller, Lutz; Siebert, Stefan; Gaiser, Thomas; Ewert, Frank.

In: Environmental Modelling and Software, Vol. 96, 10.2017, p. 361-377.

Research output: Contribution to journalArticle

Grosz, B, Dechow, R, Gebbert, S, Hoffmann, H, Zhao, G, Constantin, J, Raynal, H, Wallach, D, Coucheney, E, Lewan, E, Eckersten, H, Specka, X, Kersebaum, K-C, Nendel, C, Kuhnert, M, Yeluripati, J, Haas, E, Teixeira, E, Bindi, M, Trombi, G, Moriondo, M, Doro, L, Roggero, PP, Zhao, Z, Wang, E, Tao, F, Roetter, R, Kassie, B, Cammarano, D, Asseng, S, Weihermueller, L, Siebert, S, Gaiser, T & Ewert, F 2017, 'The implication of input data aggregation on up-scaling soil organic carbon changes', Environmental Modelling and Software, vol. 96, pp. 361-377. https://doi.org/10.1016/j.envsoft.2017.06.046
Grosz, Balazs ; Dechow, Rene ; Gebbert, Soeren ; 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 ; Roetter, Reimund ; Kassie, Belay ; Cammarano, Davide ; Asseng, Senthold ; Weihermueller, Lutz ; Siebert, Stefan ; Gaiser, Thomas ; Ewert, Frank. / The implication of input data aggregation on up-scaling soil organic carbon changes. In: Environmental Modelling and Software. 2017 ; Vol. 96. pp. 361-377.
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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. (C)2017 Elsevier Ltd. All rights reserved.",
keywords = "Biogeochemical model, Data aggregation, Up-scaling error, Soil organic carbon, DIFFERENT SPATIAL SCALES, NITROUS-OXIDE EMISSIONS, MODELING SYSTEM, DATA RESOLUTION, CROP MODELS, CLIMATE, LONG, PRODUCTIVITY, CROPLANDS, DAYCENT",
author = "Balazs Grosz and Rene Dechow and Soeren Gebbert and Holger Hoffmann and Gang Zhao and Julie Constantin and Helene Raynal and Daniel Wallach and Elsa Coucheney and Elisabet Lewan and Henrik Eckersten and Xenia Specka and Kurt-Christian Kersebaum and Claas Nendel and Matthias Kuhnert and Jagadeesh Yeluripati and Edwin Haas and Edmar Teixeira and Marco Bindi and Giacomo Trombi and Marco Moriondo and Luca Doro and Roggero, {Pier Paolo} and Zhigan Zhao and Enli Wang and Fulu Tao and Reimund Roetter and Belay Kassie and Davide Cammarano and Senthold Asseng and Lutz Weihermueller and Stefan Siebert and Thomas Gaiser and Frank Ewert",
note = "Acknowledgments This work was supported by the FACCE MACSUR knowledge hub (http://macsur.eu) and founded by the German Federal Ministry of Food and Agriculture (BMEL), (031A103A) and the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning(220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRAACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. ET was funded by the Royal Society of New Zealand and the Climate Change Impacts and Implications for New Zealand project (CCII) financed by the Ministry of Business, Innovation and Employment (MBIE). FE and SS acknowledge support by the German Science Foundation (project EW119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. JY and MK thank Scottish Government for providing travel grant for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",
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T1 - The implication of input data aggregation on up-scaling soil organic carbon changes

AU - Grosz, Balazs

AU - Dechow, Rene

AU - Gebbert, Soeren

AU - Hoffmann, Holger

AU - Zhao, Gang

AU - Constantin, Julie

AU - Raynal, Helene

AU - Wallach, Daniel

AU - Coucheney, Elsa

AU - Lewan, Elisabet

AU - Eckersten, Henrik

AU - Specka, Xenia

AU - Kersebaum, Kurt-Christian

AU - Nendel, Claas

AU - Kuhnert, Matthias

AU - Yeluripati, Jagadeesh

AU - Haas, Edwin

AU - Teixeira, Edmar

AU - Bindi, Marco

AU - Trombi, Giacomo

AU - Moriondo, Marco

AU - Doro, Luca

AU - Roggero, Pier Paolo

AU - Zhao, Zhigan

AU - Wang, Enli

AU - Tao, Fulu

AU - Roetter, Reimund

AU - Kassie, Belay

AU - Cammarano, Davide

AU - Asseng, Senthold

AU - Weihermueller, Lutz

AU - Siebert, Stefan

AU - Gaiser, Thomas

AU - Ewert, Frank

N1 - Acknowledgments This work was supported by the FACCE MACSUR knowledge hub (http://macsur.eu) and founded by the German Federal Ministry of Food and Agriculture (BMEL), (031A103A) and the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning(220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRAACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. ET was funded by the Royal Society of New Zealand and the Climate Change Impacts and Implications for New Zealand project (CCII) financed by the Ministry of Business, Innovation and Employment (MBIE). FE and SS acknowledge support by the German Science Foundation (project EW119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. JY and MK thank Scottish Government for providing travel grant for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2017/10

Y1 - 2017/10

N2 - 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. (C)2017 Elsevier Ltd. All rights reserved.

AB - 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. (C)2017 Elsevier Ltd. All rights reserved.

KW - Biogeochemical model

KW - Data aggregation

KW - Up-scaling error

KW - Soil organic carbon

KW - DIFFERENT SPATIAL SCALES

KW - NITROUS-OXIDE EMISSIONS

KW - MODELING SYSTEM

KW - DATA RESOLUTION

KW - CROP MODELS

KW - CLIMATE

KW - LONG

KW - PRODUCTIVITY

KW - CROPLANDS

KW - DAYCENT

U2 - 10.1016/j.envsoft.2017.06.046

DO - 10.1016/j.envsoft.2017.06.046

M3 - Article

VL - 96

SP - 361

EP - 377

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -