2015-2060中国农田土壤碳汇空间数据集

本数据是利用四种粮食作物（水稻、小麦、玉米和大豆）产量数据校正和验证Agro-C模型，验证后的Agro-C模型用管理情景驱动，模拟四种粮食作物产量变化和粮食作物生产过程中土壤密度的空间分布，现状模拟2015/2020中国农田土壤碳密度的空间分布，得到当前农田管理情景下2015年、2020年中国农田土壤碳密度空间分布图。在未来气候变化条件下，利用气候模式输出的不同气候变化情景气象数据驱动模型，利用Agro-C模型和基线/优化两种农田管理情景，选择我国碳达峰和碳中和目标年2030/2060年对我国粮食作物生产的农田土壤碳汇潜力进行评估，分别得到在基准农田管理和优化农田管理情景下，四种气候变化情景下2030/2060年中国农田土壤碳密度空间分布图。

This dataset is used to calibrate and validate the Agro-C model with yield data of four grain crops: rice, wheat, maize, and soybean. The validated Agro-C model is driven by management scenarios to simulate the yield changes of the four grain crops and the spatial distribution of soil carbon density during crop production. Under the current farm management scenario, we simulate the spatial distribution of soil carbon density in China's croplands for the years 2015 and 2020, and obtain the corresponding spatial distribution maps of cropland soil carbon density in China for these two years.

Under future climate change conditions, we drive the Agro-C model using meteorological data from different climate change scenarios output by climate models, combined with two farm management scenarios: baseline and optimized. We assess the soil carbon sequestration potential of China's croplands for grain crop production in the target years of China's carbon peak and carbon neutrality, namely 2030 and 2060. Finally, we respectively generate the spatial distribution maps of cropland soil carbon density in China for 2030 and 2060 under four climate change scenarios for both the baseline and optimized farm management scenarios.

搜集汇总

背景与挑战

背景概述

该数据集提供了2015-2060年中国农田土壤碳密度的空间分布数据，通过Agro-C模型模拟了四种粮食作物在不同管理情景和气候变化条件下的土壤碳汇潜力。数据时间分辨率为年，空间分辨率为10km，适用于中国农田土壤碳汇研究。