The implication of input data aggregation on up-scaling soil organic carbon changes
The implication of input data aggregation on up-scaling soil organic carbon changes
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DOI：
10.1016/j.envsoft.2017.06.046
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发表时间：
2017-10-01
影响因子：
4.9
通讯作者：
Ewert, Frank
中科院分区：
文献类型：
--
作者：
Grosz, Balazs;Dechow, Rene;Ewert, Frank
在放大研究中，模型输入数据聚合是科普数据可用性不足和限制计算工作量的常用方法。我们分析了模型误差，由于土壤数据汇总建模SOC的趋势。对于西北部德国的一个地区，网格土壤数据的空间分辨率为1公里和100公里之间的多数选择。利用这些数据，用7个生态地球化学模型模拟了30年来土壤有机碳的变化。土壤数据汇总强烈影响建模SOC的趋势。随着模型输出空间分辨率的增加，模拟SOC变化的预测误差减小。输出数据汇总仅略微减少了模型之间的模型输出差异，这表明即使对模型输出的空间分辨率要求很低，模型结构缺陷造成的误差也可能持续存在。（C）2017爱思唯尔有限公司版权所有。
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.