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 language | English |
|---|---|
| Pages (from-to) | 361-377 |
| Number of pages | 17 |
| Journal | Environmental Modelling and Software |
| Volume | 96 |
| Early online date | 1 Aug 2017 |
| DOIs | |
| Publication status | Published - Oct 2017 |
Bibliographical note
AcknowledgmentsThis 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.
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
