基于土壤数据库的动态模型预测未来二氧化碳(CO_2)浓度升高下农田有机碳变化是实施农业固碳的基础,但目前基于不同制图尺度土壤数据库对旱地有机碳模拟结果的影响尚不清晰,一定程度上增加了农业管理措施制定的风险性。基于此,选择江苏北部(简称苏北地区)3.90 * 10~6 hm~2旱地为例,运用生物地球化学过程模型(Denitrification and Decomposition, DNDC)模拟未来CO_2浓度升高下该地区1: 5万、1: 25万、1: 50万、1: 100万、1: 400万、和1: 1 000万制图尺度的土壤有机碳变化。结果表明:20102039年间C02浓度在目前正常增加速率(1.9 ppm a~(-1))的基础上提高0.5倍、1倍和2倍,苏北旱地数据最详细的1: 5万尺度年均固碳速率分别为357 kg hm~(-2)、360 kg km~(-2)和365 kg hm~(-2)。但进一步从其他制图尺度来看,由于使用的土壤数据库不同导致有机碳模拟结果差异很大。以1: 5万尺度年均固碳速率为基准,3种CO_2浓度情景处理下1: 25万 ~ 1: 1 000万尺度的模拟误差分别在0.89% ~ 60.55%、0.81% ~ 60.71%和0.15% ~ 61.02%之间,这说明未来CO_2浓度升高的大背景下我国旱地土壤有机碳模拟中选择适宜的制图尺度非常重要。
英文摘要:
【Objective】 Agro -ecosystem models have been extensively used to predict changes in soil organic carbon (SOC) in farmland in the scenario of elevated CO_2 in future. However, currently most of the studies rely on maps of only one or certain one scale, and little has been done on influence of map scales on prediction of SOC dynamics in the scenario of elevated CO_2. China has a total of 140 million hm2 of farmlands, consisting of 110 M hm~2 of uplands and 30 M hm~2 of paddy fields. As upland soil is enormous in area and high in carbon storage, it plays an important role in sequestrating carbon and mitigating climate change. Owing to the complexity of carbon turnover processes and dynamic response of carbon to environmental conditions, recent years have seen progresses in using process-based models to simulate historic patterns and future trends of SOC variation in agricultural systems. The DeNitrification-DeComposition (DNDC) model based on human activity data, land use, soil parameters, daily temperatures, and precipitation is used to describe biogeochemical processes of C and N recycling in the terrestrial ecosystem. Currently it has been extensively used to explain mechanisms of carbon turnover as affected by the complex interactions among soil management, crops, and climate. 【Method】 Based on the uplands in North Jiangsu, China, the 19802009 meteorological data and 2009 farmland management data of the region, soil databases of six different mapping scales, i.e., 1: 50 000, 1: 250 000,1: 500 000,1:1 000 000, 1:4 000 000, and 1: 10 000 000,and 3 different scenarios set for the period of 2010-2039 with atmospheric CO_2 elevation rate being 1.5, 2.0 and 3.0 times, respectively, the normal rate (1.9 ppm a~(-1)), this study used the DNDC model to predict carbon sequestration rate and potential as affected by C02 elevation rate in the region with the data of the most detailed 1: 50 000 map and quantify the uncertainties of using the soil databases different in mapping scale to simulate SOC dynamics in the upland-crop ecosystem.【Result】 Results show that based on the 1: 50 000 map and in the scenario of the atmospheric CO_2 concentration rising at a rate 1.5, 2.0, and 3.0 times the normal rate, the average annual SOC sequestration rate in the topsoil (0 ~ 50 cm) layer of the upland of North Jiangsu during the period of 20112039 was predicted to be 357, 360, and 365 kg hm~(-2) a~(-1), respectively, and the total SOC sequestration was 42.08、42.38 and 42.93 Tg C, respectively. However, the prediction varied sharply with scale of the map used. When the average annual C sequestration rate predicted based on the 1: 50 000 map was used as baseline,the use of the other maps would generated deviations ranging from 0.89% to 58.09%, 0.81% to 60.13% and 0.88% to 58.92%, in terms of average annual C sequestration rate and from 0.60 to 59.22%, 0.37 to 59.39% and 0.02 to 59.71% in terms of total C sequestration, respectively, in the three scenarios.【Conclusion】 It could be concluded that the effect of scale of the map used on prediction of SOC in the scenarios of elevated CO_2 is significant. In general, heterogeneity of soil properties in a region would often lead to variation of the prediction of SOC, which is mainly attributed to the disappearance of some soil types and spatial distortion when the map of small scales is polygonized. It is, therefore, essential for studies in future to use soil maps of large scales for data in quantifying regional SOC dynamics.