未来30年川东平行岭谷区县域农田SOC动态模拟

以位于川东平行岭谷的典型县——垫江县为研究对象,探讨在特定气候模式下未来30 a研究区农田土壤有机碳(SOC)及其动态变化,为研究区未来耕地可持续利用与管理提供数据支持和科学依据。利用生物地球化学模型DNDC,选取IPCC AR4报告中的BCCR_BCM 2.0的B1模式,在基于研究区土壤性质和农业管理制度等建立的GIS区域数据库的支持下,模拟研究区2011—2041年SOC动态变化。结果表明:1)DNDC模型能够较好地模拟特定气候条件下SOC及其动态变化,模拟值和观测值的相关系数r为0.981,达到0.01水平下的极显著相关关系;模拟值和观测值的RMSE值为16%,模拟结果较好。2)未来30 a研究区农田0~20 cm土层SOC密度和储量均呈显著增加态势,单位面积碳增量2 637.07~8 091.55 kg(C)·hm~(-2),增幅为10%~34%,新增固碳量2.7×10~5~8.3×10~5 t,年均增速87.9~269.7 kg(C)·hm~(-2)·a~(-1)。3)未来30 a川东平行区县域农田土壤总体呈持续碳增汇状态,研究区固碳、丢碳以及相对平衡三者间的差异将逐渐凸显。     

典型潮土N2O排放的DNDC模型田间验证研究

利用典型潮土N2O排放的田间试验数据对脱氮-分解模型（DNDC）及其参数进行验证。结果表明,DNDC模型能较好地模拟田间实测到的冬小麦、夏玉米季土壤湿度和土壤日平均地表温度的动态变化。小麦和玉米季土壤N2O排放通量与土壤水分（WFPS）呈显著正相关,与土壤温度相关性不大。田间实测到的N2O排放高峰主要受降水和施肥的影响,在N2O排放峰的峰值和出现时间上模拟值与实测值较接近,但准确地捕捉N2O季节性的排放通量仍需对模型进行修正。通过比较施肥、土壤和田间管理等输入参数的改变对DNDC模型进行灵敏性分析,氮肥用量、施肥次数、土壤初始无机氮含量和土壤质地的改变对土壤N2O排放量均很敏感,其中氮肥用量和施肥次数的改变最为敏感。基于当地土壤特性和田间管理的校正,DNDC模型为评价农田生态系统N2O的排放提供了强有力的工具。     

基于贝叶斯综合不确定性估计法的水文模型不确定性研究

在贝叶斯理论框架下利用IBUNE方法探讨了水文模型结构、参数以及输入的不确定性问题。通过在模型中嵌入EMSCEM-UA算法来处理模型平均和参数优化问题,然后利用预报量后验分布参数和综合输入的不确定性进行多模型综合,进而评价水文模拟结果不确定性及其影响特征。以辽宁省铁岭市铁岭水文站为例,研究了水文模型的不确定性。研究表明:水文模型的不确定性可通过贝叶斯综合不确定性估计法进行准确的表征和客观的反映,并且可针对不同情况模拟出合理的概率预报区间。     

耕作方式对华北农田土壤有机碳储量及温室气体排放的影响

耕作方式能够改变土壤有机碳在土层中的分布,进而对土壤有机碳及土壤碳储量产生影响。该研究在模型调整的基础上选取了土壤有机碳(SOC)、土壤碳密度(SCD)、土壤呼吸(SR)以及生物量碳(BC)4个指标对DNDC(denitrification-decomposition)模型在华北麦-玉两熟农田的适用性进行验证,并用该模型模拟当地土壤碳储量(SCS)动态变化以及温室气体排放特征。结果表明,模型模拟值与实测值吻合良好,此模型可以适用于华北麦-玉两熟农田土壤有机碳的模拟研究;2001-2010年SOC和SCS逐年递增;对未来100a模拟发现,前15a旋耕(RT)和翻耕(CT)处理SOC增长迅速,而免耕(NT)SOC的剧烈增长趋势要持续近40a;对比各处理100a碳储量变化可知,前20aCT处理SCS最大,20a后NT处理SCS最大;各处理土壤全球变暖潜势(GWP)大小为CT>RT>NT。通过验证该文证明了DNDC模型可以较好地研究华北麦-玉两熟农田土壤碳循环;长久来看NT有利于农田SCS的积累以及GWP的降低。该研究能够为华北麦-玉两熟农作区固碳减排提供依据。     

稻麦轮作农田氮素循环的DNDC模型分析

基于长江中下游稻麦轮作体系的氮肥施用田间试验,采用Denitrification- Decomposition model (DNDC) 模型研究了气候条件、土壤属性、农业管理等输入因素的不确定性对子粒产量、作物氮吸收、氨挥发、N2O排放等预测结果的影响。结果显示:采用DNDC模型模拟的土壤氨挥发速率和N2O排放通量与田间实测结果较为吻合,氨挥发通量模拟值与实测值相关系数为0.688,N2O排放通量模拟值与实测值相关系数为0.528,均达极显著水平,表明DNDC模型预测农田土壤氮素具有较高可信度。模拟结果显示,气温和氮肥用量是影响作物产量和吸氮量的关键因素;土壤氨挥发主要受氮肥品种影响,并随氮肥用量增加而增加;土壤N2O排放主要受温度、土壤pH值、土壤有机碳含量的影响。为使DNDC能更有效地估算氨挥发和N2O排放,有必要获取更翔实的资料以减少输入数据的不确定性。     

基于MCMC方法的WOFOST模型参数标定与不确定性分析

为探究作物生长模型参数的自动标定技术及其不确定性分析方法,该研究以郑州农业气象试验站为试验点,利用融入了snooker更新(snooker update)的DE-MC(differential evolution Markov chain,差分进化马尔科夫链)方法实现对WOFOST(world food studies)作物生长模型的参数标定和不确定性定量评价。snooker更新增加了DE-MC算法中候选样本的多样性,从而实现利用更少的并行链对多维参数空间进行有效采样,较适合于WOFOST模型参数众多的特性。结果表明:相比于模型默认值,采用MCMC(Markov chain Monte Carlo,马尔科夫链-蒙特卡洛)标定后的参数,叶面积指数(leaf area index,LAI)模拟精度可提高51.40%~53.07%,产量模拟精度提高8.25%~8.88%。标定参数中,SPAN、SLATB070、SLATB040、AMAXTB130和SLATB00的后验分布可近似为高斯分布,其中SPAN的不确定性最低。带入后验参数集合进行模型,LAI在三叶期至返青期之间以及拔节期至抽穗期之间模拟的不确定性较大;产量模拟的不确定性随时间不断增大,至乳熟期前后达到稳定。该方法能够实现对多参数复杂作物生长模型的参数标定和不确定性分析,对作物模型参数估计及提高模拟精度具有重要作用。     

DNDC模型模拟农田温室气体排放研究进展

在简要介绍DNDC模型(脱氮分解模型)及其在中国的应用与改进基础上,综述了中国学者利用该模型模拟与估算农田温室气体排放和减排调控方面的研究进展,提出未来模型在中国的发展应针对中国农业种植体系的特点,增加模型模块,修正模型参数,建立跨尺度农田生态系统综合评估模型,加强大尺度和长时间序列的温室气体排放模拟与预测研究。同时,加强遥感和地理信息系统技术与模型的结合,以提高区域尺度模拟和预测精度,降低模拟结果的不确定性。     

生物地球化学循环模型DNDC及其应用

生物地球化学模型是模拟研究化学元素动态的新兴领域,可用于陆地生态系统内植物、有机物和无机营养元素动态变化和循环。DNDC模型(DeNitrification-DeComposition Model)是美国新罕布什尔州大学陆地海洋空间研究中心开发研制的,最初是为了模拟农田生态系统固碳、氮流失和水平衡而创建,目前该模型可以模拟草地、湿地、林地等陆地生态系统碳氮动态过程。DNDC模型已经在美洲、欧洲、澳洲以及亚洲的一些地区得到了验证和运用。DNDC模型可用来分析陆地植物生长规律、土壤硝化和反硝化作用、温室气体和痕量气体排放预测研究、不同土壤类型及气候条件对森林生态系统碳氮通量变化的影响以及气候变化对生物地球化学循环的影响预测等。     

根系水质模型中土壤与作物参数优化及其不确定性评价

农业系统模型参数优化存在很高的不确定性,是模型应用研究的重点和难点。该研究利用自动优化程序PEST(parameter estimation software)对根系水质模型(root zone water quality model,RZWQM)中土壤参数(土壤水力学参数和根系生长参数)和作物遗传参数进行了优化,结果表明PEST优化模拟结果明显优于传统试错法的校正结果,且具有较高的参数优化效率。模型参数优化不确定性评价表明校正数据和参数初始值的选择、土壤水力学参数估算方法、不同类型参数间的相互作用以及优化目标方程(误差来源计算)都明显影响模型模拟结果。以上过程中土壤水力学参数优化值差异较小,但其土壤水分特征曲线却明显不同。通过以上评价分析提高了RZWQM相关参数优化结果的可靠性及其模拟功能,降低了模型参数优化的不确定性,为PEST优化其他模型参数提供了重要支持。     

参数优化方法对微生物模型预测土壤有机碳时空演变的影响

准确把握土壤有机碳（SOC）的时空演变规律对于土壤资源的高效持续利用、发挥土壤生态系统服务功能，以及应对气候变化等均具有重要意义。以江苏省南部为研究区，以明确表达微生物分解作用的微生物模型MIMICS为对象，以模型参数敏感性分析为切入点，分析了不同参数优化方法对MIMICS模型预测苏南农田表层（0 ~ 20 cm）SOC时空演变动态的影响。结果表明，批处理和点对点两种参数优化方法下，MIMICS模型均能较好地模拟1980─2015年苏南农田表层SOC密度先增加后减少的总体趋势；采用考虑模型参数空间异质性的点对点参数优化方法时，MIMICS模型预测精度最高，其预测误差（RMSE）较采用默认参数值时分别降低22.2%（2000年独立验证）和14.7%（2015年独立验证），但2015年SOC密度预测精度依然偏低（R2 = 0.13，RMSE = 1.22 kg?m–2）。上述结果表明进一步改进微生物模型的结构、提高模型输入数据的精度及分辨率，将是微生物模型建模区域尺度SOC时空动态所面临的重要挑战。     

Testing the Suitability of the DNDC Model for Simulating Long-Term Soil Organic Carbon Dynamics in Japanese Paddy Soils

The suitability of the DeNitrification-DeComposition (DNDC) model for simulating long-term changes in the content of soil organic carbon (SOC) was validated using 5 sites for long-term experiments related to Japanese paddy soils. Since the model could not simulate crop growth adequately, several crop growth parameters provided by the model as default were changed to adjust crop growth to the observation. Overall, the changes in the content of SOC with time simulated by DNDC using adjusted crop parameters, agreed well with the observation in 9 plots from 5 experimental sites during the 16 to 22-year period of the experiment. The good performance of the decomposition sub-model in the DNDC was verified in the long-term SOC decomposition in paddy soils as well as in upland soils reported in previous studies. However, the simulated SOC did not agree well with the observation in some plots, especially in soils with a very low SOC content, suggesting that care should be exercised when applying the model to soils with a very low SOC content. Moreover, careful tuning of crop growth parameters should be promoted for better simulation, and detailed information about farm management required for input parameters is often difficult to obtain, especially in long-term experiments. In conclusion, the DNDC model is an effective tool for simulating long-term SOC dynamics in paddy soils. The unique kinetic scheme "anaerobic balloon" in the model may play an important role in successful simulation of SOC dynamics in paddy soils that are water-logged during the rice cropping period. This scheme may be helpful for modifying the other turnover models of soil organic matter for use for paddy soils, too.     

基于不同空间分辨率土壤数据库的中国江苏省水稻土有机碳的动态模拟

A number of process-based models have been developed for quantifying carbon(C)sequestration in agro-ecosystems.The DeNitrification-DeComposition(DNDC)model was used to simulate and quantify long-term(1980-2008)soil organic carbon(SOC)dynamics in the important rice-producing province,Jiangsu,China.Changes in SOC storages were estimated from two soil databases differing in spatial resolution:a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu.The simulated SOC storage with the coarse resolution county database ranged between 131.0-320.6 Tg C in 1980 and 170.3-305.1 Tg C in 2008,respectively,while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008,respectively.The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results;and when lacking detailed soil datasets,the DNDC model,parameterized with the most sensitive factor(MSF) method to cope with attribute uncertainty,could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.     

A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil

The efficacy of mathematical modeling as a tool for estimating greenhouse gas (GHG) emissions from soil depends on the uncertainty. Systematic evaluation of various sources of uncertainties in GHG emission models is limited. This paper reviews the state-of-the-art knowledge on the parameterization and uncertainty analysis of soil GHG emission models. Major recommendations and conclusions from this work include: (a) uncertainties due to model parameters and structure can be quantified by combining the Bayesian theorem and the Markov Chain Monte Carlo (MCMC) method; (b) uncertainty due to event-based model input may also be assessed by regarding each event as a latent variable; however, the necessity of the simultaneous evaluation of uncertainties from model input, parameters, and structure might be negotiable because strong correlations may exist between input errors and model parameters; (c) uncertainty analysis is essential for a successful model parameterization by reducing both the number of undetermined parameters and the parameter space; and (d) model parameterization (calibration) should be conducted on multiple sites towards multiple objectives. Case studies were presented for comparing the model uncertainties of the denitrification components of four models, DAYCENT, DNDC, ECOSYS, and COMP. The methods discussed in this paper can help to evaluate model uncertainties and performances, and to offer a critical guidance for model selection and parameterization.     

不同气候-土壤-耕作组合条件下水稻土有机碳长期演变模拟的不确定性和敏感性分析

Reporting modeling results with uncertainty information can benefit decision making by decreasing the extent that variability exerts a disproportionate influence on the options selected. For making decisions with more confidence, the uncertainty interval should be as narrow as possible. Here, the soil organic carbon(SOC) dynamics of the major paddy soil subgroup from 4 different paddy field regions of China(located in 4 counties under different climate-soil-management combinations) were modeled using the De NitrificationDe Composition(DNDC) model for the period from 1980 to 2008. Uncertainty intervals associated with the SOC dynamics for these 4 subgroups were estimated by a long-term global sensitivity and uncertainty analysis(i.e., the Sobol′method), and their sensitivities to 7 influential factors were quantified using the total effect sensitivity index. The results, modeled with high confidence, indicated that in the past 29 years, the studied paddy soils in Xinxing, Yixing, and Zhongjiang counties were carbon(C) sinks, while the paddy soil in Helong County was a C source. The 3 C sinks sequestered 12.2(5.4, 19.6), 17.1(8.9, 25.0), and 16.9(-1.2, 33.6) t C ha~(-1)(values in the parentheses are the 5th and 95th percentiles, respectively). Conversely, the C source had a loss of -5.4(-14.2, 0.06) t C ha~(-1) in the past 29 years. The 7 factors, which changed with the climate-soil-management context, exhibited variable influences on modeled SOC. Measures with potential to conserve or sequestrate more C into paddy soils, such as incorporating more crop residues into soils and reducing chemical fertilizer application rates, were recommended for specific soils based on the sensitivity analysis results.     

Soil–climate contribution to DNDC model uncertainty in simulating biomass accumulation under urban vegetable production on a Petroplinthic Cambisol in Tamale,Ghana

Crop yield simulation using the Denitrification–Decomposition (DNDC) model can help to understand key bottlenecks for improved nitrogen (N) use efficiency and estimate greenhouse gas (GHG) emissions in West African urban vegetable production. The DNDC model was successfully calibrated using high‐resolution weather records, information on management practices and soils, and measured biomass accumulation and N uptake by amaranth (Amaranthus L.), jute mallow (Corchorus olitorius L.), lettuce (Lactuca sativa L.), and roselle (Hibiscus sabdariffa L.) for different input intensities (May 2014–November 2015) in urban vegetable production of Tamale (N‐Ghana, West Africa). The root mean square error (RMSE) and relative error (E) values fell within the confidence interval (α 5%) of the measurements, and there was a high correlation (0.91 to 0.98) between measurements and predictions. However, the analysis of uncertainty and factor importance indicated that soil properties (pH, SOC, and clay content) and weather (precipitation) variability contributed highly to yield uncertainty of vegetable biomass.     

Modeling a long-term effect of rice straw incorporation on SOC content and grain yield in rice field

ABSTRACT

The long-term effects of rice straw incorporation on soil organic carbon (SOC) content and rice yield were evaluated from rice cultivation with different treatments: no rice straw (control), rice straw (RS), and rice straw compost (RSC) as a main-plots; five levels of nitrogen (N) fertilizer application: 0, 100, 150, 200, and 250 of N (kg ha^?1) as a sub-plots. The denitrification and decomposition (DNDC) model was employed to simulate changes in SOC content and rice grain yield over 35 years. Additionally, scenario analysis on continuous or discontinuous RS incorporation in rice fields was conducted using the DNDC model. The long-term results indicated that RS and RSC treatments played a crucial role in increasing grain yields by 9% and 11% due to the increased SOC contents compared to control treatment. The validated DNDC model on SOC contents and yields showed a good agreement between the observed and simulated values based on the normalized root mean square errors. The model predicted a rapid decline of SOC contents without RS incorporation. Results suggested that incorporating rice straw or amending manure to paddy soils is a preferred practice for maintaining SOC contents.     

基于Logistic回归和MCMC方法评价地震滑坡敏感性

Logistic回归模型(Logistic Regression,LR)在滑坡敏感性评价上应用广泛,但目前对于模型参数不确定性的研究较为缺乏。马尔可夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)方法能够结合参数的先验信息得到其后验分布,从而对估计参数的不确定性进行分析。为探索MCMC方法在Logistic滑坡敏感性模型构建中的有效性; 量化模型参数估计值的不确定性,以西南地区2013年4·20芦山地震,2017年8·8九寨沟地震和2014年8·3鲁甸地震为例,基于MCMC方法对Logistic回归模型的回归系数进行估计。构建了区域的地震滑坡敏感性模型,对模型参数的估计值进行了不确定性分析,并绘制了区域的滑坡敏感性图。结果表明:在芦山地震案例中,模型参数估计值的不确定性都比较低; 在九寨沟案例中,岩性因子的参数估计值不确定性较高; 在鲁甸地震中,岩性、剖面曲率和平面曲率的参数不确定性较高。总的来说,模型中的大多数参数估计值不确定性都较低。所构建的Logistic回归模型在三次地震滑坡事件中的预测精度都较高,AUC(Area Under ROC Curve)值均在0.9以上,这证明了MCMC方法对Logistic模型参数估计的准确性。在三次地震滑坡事件中,因子相对重要性最大的为高程,其次为距离断层的距离以及修正麦卡利烈度。研究为利用LR模型进行滑坡敏感性评价提供了一种新的思路和方法。     

黑土有机碳变化的DNDC模拟预测

为探讨黑土有机碳的长期变化规律及DNDC模型在土壤有机碳预测方面的适用性,本文利用吉林省公主岭地区黑土不同施肥措施下的长期定位试验数据,选取不施肥(CK)、单施化肥(NPK)、配施有机肥(NPKM)和增施有机肥(M2+NPK)4个处理进行土壤有机碳分析,并将数据用作DNDC模型验证。验证结果表明:各处理DNDC验证中RMSE值均小于10%(分别为5.09%、6.11%、9.38%、8.36%),说明模拟值与观测值一致性良好,模型可用于该地区土壤有机碳模拟。选取了化肥施用、有机肥施用、秸秆还田比率、温度及降水5个因子进行模型的敏感性分析,结果表明:有机肥的施用对土壤有机碳含量的影响最显著,且这种影响具有持久性。最后模拟了4种施肥情境下未来(至2100年)的土壤有机碳变化情况。结果表明:对照不施肥处理(CK)土壤有机碳含量略有下降,至2100年土壤有机碳含量为11.55 g·kg-1,较试验前土壤初始有机碳(13.2 g·kg-1)下降约12.5%。单施化肥处理(NPK)土壤有机碳含量较为稳定,并未出现土壤有机碳含量下降。配施有机肥(NPKM)和增施有机肥(M2+NPK)处理土壤有机碳含量增加明显,至2100年土壤有机碳含量为24.4 g·kg-1和27.6 g·kg-1,分别较初始有机碳含量上升84.8%和109.1%。     

县域尺度红壤丘陵区水稻土有机碳模拟

区域尺度土壤有机碳储量的时空变化及其管理是全球气候变化和农业可持续发展研究的重要内容。本文以中亚热带红壤丘陵区的江西省余江县为例,基于12a的长期试验和1998年、2001年的野外定位采样对比研究,利用反硝化分解模型?DNDC(Denitrification-Decomposition)在田块和县域尺度研究了县域尺度表层(0~20 cm)水稻土有机碳储量的时空变化规律。结果表明,以长期试验数据验证,DNDC模型可以较好地模拟水稻土表层有机碳的长期动态变化。2001年农田水稻土(面积为3.6×108m2)表层(0~20 cm)有机碳总储量为2.9×109kg,平均土壤有机碳密度为6.0 kg m-2。1998年至2001年余江县水稻土表层土壤有机碳库逐年增加,年际平均变化量为3.0×107kg。通过对余江县水稻田模拟不同碳投入的情景,分析预测1998年至2017年土壤有机碳储量,种植绿肥提高秸秆还田比率同时减少化肥的投入,可有效地增加红壤区域有机碳蓄积。     

中国农田表土有机碳含量变化特征——基于国家耕地土壤监测数据

通过对299个国家级耕地土壤监测点20余年数据的统计分析,评价了我国农田表土有机碳含量变化情况和固碳潜力。结果表明,全国约80％试验点有机碳年平均相对增长率（Average relative annual increment,ARAI）在-1．5％～7．5％。中国农田表土有机碳含量整体呈上升趋势。东北、华北等6个地理区域分析得出,华北、华东、西南农田表土有机碳含量显著增加;华东地区有机碳增加的农田面积占全国农田比例最大,东北最小。旱地和水田有机碳含量增加显著;水田有机碳增加的试验点所占比例大于旱地;对ARAI与初始有机碳含量进行相关分析得出,我国旱地和水田有机碳潜在储存能力估计值分别为17．2和27．7g·kg^-1。农田土壤类型中水稻土和褐土有机碳含量增加显著;黑土有机碳含量下降样本所占比例最高。对我国各典型种植制度分析得出,双季稻、麦-稻、麦-玉、单季小麦种植制度下农田有机碳有了显著增加;麦玉轮作较其他种植制度的农田有机碳年平均相对增长率高。