Evaluation of the most appropriate spatial resolution of input data for assessing the impact of climate change on rice productivity in Japan
Evaluation of the most appropriate spatial resolution of input data for assessing the impact of climate change on rice productivity in Japan
复制标题
评估气候变化对日本水稻生产力影响的输入数据的最佳空间分辨率的评估
DOI：
10.2480/agrmet.d-19-00021
复制
发表时间：
2020-04-01
影响因子：
1.3
通讯作者：
Nishimori, Motoki
中科院分区：
文献类型：
--
作者：
Ishigooka, Yasushi;Hasegawa, Toshihiro;Nishimori, Motoki
基于过程的作物生长模型越来越多地被用作在田间、区域和国家尺度上评估气候变化对作物生产力影响的重要工具。模型预测的可靠性在很大程度上取决于作为输入的气象数据的质量。对于大面积的评估，输入数据的空间分辨率会影响计算结果，因为诸如整个网格单元的平均值与网格中作物土地部分之间的海拔差异等因素可能会在输入数据中引入重大的温度偏差。在这项研究中，我们试图确定最合适的空间分辨率，以支持评估气候变化对日本水稻生产力的影响。我们在基线气候条件下（1981年至2000年）使用长谷川-堀江水稻生长模型，然后应用该模型来解释温度升高至比基线高1和3摄氏度。首先，我们使用100米分辨率的输入作为“真实值”计算水稻产量。然后，我们将使用10 km和1 km分辨率输入计算的水稻产量与使用100 m分辨率输入计算的产量进行了比较。我们发现，在地形复杂的地区，10公里分辨率的产量差异大于1公里分辨率的产量差异，但在均匀的平坦地区，差异很小。在地形极其复杂的情况下，与基线气候条件下的产量相比，区域平均产量被低估了11.5%，但在气温升高时，使用10公里分辨率时，区域平均产量被高估了5.4%。这些差异很可能是预测气候变化对区域范围产量影响的不确定性的主要原因。输入数据的空间分辨率，使用10公里的分辨率不影响评估结果时，产量是在全国范围内汇总。
Process-based crop growth models are increasingly utilized as an essential tool for assessing the impact of climate change on crop productivity at field, regional, and national scales. The reliability of model predictions depends strongly on the quality of the meteorological data used as inputs. For evaluations over large areas, the spatial resolution of input data affects the calculation results because factors such as elevation differences between the mean for an entire grid cell and the portion of crop land in the grid can introduce a major temperature bias in the input data. In this study, we attempted to identify the most appropriate spatial resolution to support assessment of the impact of climate change on rice productivity in Japan. We used the Hasegawa-Horie rice growth model under the baseline climate conditions (1981 to 2000) and then applied the model to account for temperature increases to 1 and 3 degrees C higher than the baseline. First, we calculated the rice yield using inputs at 100-m resolution as the "true value". We then compared the rice yield calculated using inputs at 10-km and 1-km resolutions with the yield calculated using inputs at 100-m resolution. We found that the yield differences were larger with 10-km resolution than with 1-km resolution in areas that had complex terrain, but the differences were small in homogeneous flat areas. Where the terrain is extremely complex, regional mean yields were underestimated by 11.5% compared with the yield under baseline climatic conditions but were overestimated by 5.4% at increased temperatures using 10-km resolution. These differences are likely to be a major cause of uncertainty in predicting the impacts of climate change on yield at a regional scale. Spatial resolution of input data, using 10-km resolution did not affect the assessment results when yield is aggregated at a national scale.