CERES-Wheat模型在我国小麦区的应用效果及误差来源

气候模型与作物模型耦合是评价未来气候变化对作物生产影响的常用方法之一, 但当两者结合时, 存在着空间和时间尺度差异问题, 将作物模型升尺度到区域是解决该差异的一种方法。将CERES-Wheat模型升尺度进行区域模拟, 利用区域校准后的CERES-Wheat模型, 模拟了1981—2000年全国各网格小麦产量, 与同期农调队调查产量相比较, 以探讨CERES-Wheat模型在我国小麦区的模拟效果及误差来源。结果表明:全国小麦产量的区域模拟值与农调队调查产量的相对均方根误差为27.9%, 符合度为0.75, 全国59.2%的模拟网格相对均方根误差在30%以内, 其中相对均方根误差小于15%的占26.3%;各区的效果不同, 种植面积最大的小麦种植生态2区, 模拟效果最好。总体来说, CERES-Wheat的区域模拟, 可以反映产量变化规律, 能为宏观决策提供相应信息, 尤其是在主产区; 但区域模拟中还存在一系列误差, 今后还需进一步研究。

The Performance of CERES-Wheat Model in Wheat Planting Areas and Its Uncertainties

Crop models, coupling with climate data from climate models(GCMs, RCMs), are often employed to assess the impacts of climate change on crop production. However, there is a systematic mismatch of resolutions between climate models and crop models. Scaling up the crop model to regional scale is an appropriate method to resolve this problem. CERES Wheat crop model is used to simulate the wheat yields of 1981—2000 at 50 km×50 km grid scale. Performances of this simulation in wheat planting areas are evaluated based on the comparison of simulated yields to census values. The relative root mean square error(RMSE)between simulated and census yields for whole China is 27.9%, and the agreement index is 0.75. Of 2206 simulation units(50 km×50 km grid), 59.2% show relative RMSE less than 30%, in which 26.3% less than 15%. The performances differ among regions. Smallest bias occurs in agro-ecological zone 2(the largest wheat planting areas accounting for 39.9% of China's wheat planting area), with relative RMSE of 16.6% and D=0.68. To sum up, CERES Wheat crop model is able to produce reasonable results temporally and spatially. It can provide simulation information for policy making at macro scale despite existing uncertainties. The uncertainties of this regional simulation are ascribed to simplification and limitations of crop models, the aggregated inputs in wheat planting area, and errors in dataset etc, which need to be addressed in future.