Uncertainties in up-scaling N2O flux from field to 1°×1° scale: A case study for Inner Mongolian grasslands in China

Since N₂O emissions cannot be measured easily at large scales, global emission estimates inevitably involve problems with scaling. To date, up-scaling processes depend highly on the models and database. Because of the limitation in resolution of the databases, which provide input parameters to drive the model's regional simulations, the uncertainties generated from the up-scaling processes must be quantified. In this paper, the uncertainties in up-scaling N₂O emissions from the field scale (∼1 km²) to 1°×1° scale (∼10,000 km²) were quantified in a case study from the Xilin River basin of Inner Mongolia, China. A revised process-based DNDC model was applied in the study for quantifying N₂O fluxes with a high-resolution (1 km²) soil database constructed with remote sensing data and GIS technique. The results showed that the uncertainties coming from spatial scaling effect is 63.6%, and from the partitioning of sensitive model parameter (SOC) is 86.4%. We found that inclusion of spatial heterogeneity of soil factors resulted in lower regional N₂O emission estimates. Utilization of the spatial structural information based on soil type was more effective for reducing the spatial scaling effect in comparison with the variability information calculated from Monte Carlo method.     

Using a modified DNDC model to estimate N2O fluxes from semi-arid grassland in China

Field measurement results showed that approximately 40% of the annual N₂O losses occurred during the non-growing season and confirmed the importance of spring and autumn periods for assessment of total N₂O losses from semi-arid temperate grassland in China. In the previous study, we found that the 7.2 version of Denitrification-Decomposition (DNDC) model had significantly lower estimates of N₂O losses in spring and autumn time. In this study, three modifications, which mainly focus on the nitrification sub-model, the impact of soil frost and snow cover on gas production and emission, had been made to the model code. Based on field measurement, we concluded that modified version of DNDC model is more suitable for estimating the magnitude and seasonal trends of N₂O losses from this region on a plot scale. By extrapolating the field data with our modified model, we estimated that the annual N₂O emission rates for natural temperate grasslands of northern China is ca. 0.056T_g N₂O-N y⁻¹, i.e. ca. 40% lower than the estimate based only on field measurements (ca. 0.092T_g N₂O-N y⁻¹).     

太湖地区小麦生育期麦田土壤铵态氮和硝态氮含量的模拟与预测

[目的]以T-FACE(temperature-free air carbon dioxide enrichment)模拟的气候情景下的田间实测数据为参照,对DNDC模型在太湖地区的适用性及拟合效果进行分析,并预测农田土壤中无机氮素的变化趋势。[方法]基于太湖地区稻麦轮作体系的T-FACE田间试验,采用DNDC模型研究了温度升高和CO2含量升高对土壤中硝态氮和铵态氮含量变化的影响。[结果]DNDC模型对试验区耕作层土壤中硝态氮和铵态氮含量的模拟值与田间实测值较为吻合,相关系数分别为0.942 1(P<0.01)和0.763 6(P<0.05),具有较高的可信度。年降水量和氮肥使用量的敏感性指数较大,分别为-2.282、0.692(铵态氮)和-3.417、0.433(硝态氮)。[结论]试验区耕作层土壤内无机氮素含量受气候因子的影响较大,并存在明显的季节性差异。模拟结果在小麦生长季后期与实测值较接近;在生长季前期和施肥后以及CO2含量和温度升高处理后较实测值偏高。降水量和施肥量是影响无机态氮含量的关键因素,年平均温度、p H值也有一定影响。DNDC模型对其他参数的敏感性不高。     

Use of models to evaluate carbon sequestration in agricultural soils

ABSTRACT

The Rothamsted Carbon (RothC) model, which is one of widely used soil carbon (C) models, was validated against long-term experimental datasets in Japan and modified to suit Andosols and paddy soils reflecting unique soil C turnover mechanisms in these soils. Nationwide soil C calculation system was developed by combining these modified models and spatial model input data such as weather, soil type, land use, and agricultural activities. The model was validated in China and Thailand by using long-term field experimental datasets, too. Further studies especially in tropical Asia will be needed. Matching conceptual model C pools with measurable fractions have been big challenges. Using various plant materials, two conceptual pools of plant litter, decomposable plant material (DPM) and resistant plant material (RPM), in the RothC were successfully identified. It was achieved by comparing the default proportions of DPM and RPM pools in the RothC and proportions in plant material fractions determined by two-step acid hydrolysis with H₂SO₄. The trial to match all of five C pools in the model, however, remains unachieved though a study was conducted comparing not only the size of C pools but mean residence time of the pools. A web-based decision support tool called ‘Visualization of CO₂ absorption by soils’ was developed. This allows users to easily calculate changes in soil C, CH₄ and N₂O emissions, and fossil fuel consumption. With this tool, farmers can see how to improve the environmental sustainability of their products and this tool may help spread mitigation options widely. Soil C sequestration can help achieve climate change mitigation and sustainable agricultural production. Importance of long-term field observations should be more highlighted because long-term experiments have supported the development of modeling approaches. I hope models will be more widely used by decision makers. Collaboration between modeling and monitoring studies is important.     

Application of the DNDC model to predict N2O emissions from sandy arable soils with differing fertilization in a long‐term experiment

Modeling crop growth and soil N dynamics is difficult due to the complex nature of soil–plant systems. In several studies, the DNDC model has been claimed to be well‐suited for this purpose whereas in other studies applications of the model were less successful. Objectives of this study were to test a calibration and validation scheme for DNDC‐model applications to describe a field experiment with spring wheat on a sandy soil near Darmstadt (SW Germany) using different fertilizer types (either application of mineral fertilizer and straw—MSI; or application of farmyard manure—FYM) and rates (low—MSI_L, FYM_L; and medium—MSI_M, FYM_M). The model test is based on a model parameterization to best describe the case MSI_L and applies this parameterization for a retrospective simulation of the other cases (MSI_M, FYM_L, FYM_M) including crop growth and N₂O emissions. Soil water contents were not accurately simulated using either the DNDC default values for a loamy sand or for the next finer texture class or using results from the pedotransfer function provided by ROSETTA. After successful calibration of the soil water flow model using a soil texture class that led to the best fit of the measured water content data, grain yield of spring wheat and cumulative N₂O emission were slightly underestimated by DNDC and were between 91% and 86% of the measured data. A subsequent calibration of the yields and cumulative N₂O emissions from soils of the MSI_L treatment gave a good prediction of crop growth and N₂O emissions in the MSI_M treatment, but a marked underestimation of yields of the FYM treatments. Cumulative N₂O emissions were predicted well for all MSI and FYM treatments, but seasonal dynamics were not. Overall, our results indicated that for the sandy soil in Germany, site‐specific calibration was essentially required for the soil hydrology and that a calibration was useful for a subsequent prediction where greater amounts of the same fertilizer were used, but not useful for a prediction with a different fertilizer type.     

草地退化过程中典型草原氮储量的变化——以内蒙古锡林浩特市典型草原为例

以内蒙古自治区锡林浩特市为研究区,探讨了典型草原不同退化程度草地的植被-土壤系统氮储量的变化及差异,运用DNDC(Denitrification-Decomposition)模型对植被-土壤系统氮储量模拟并对结果进行了验证。结果表明,随着草地退化程度的加剧,土壤氮储量呈显著下降趋势(P0.05),中度退化(MD)和重度退化(HD)样地较轻度退化(LD)样地土壤氮储量分别减少了7.74%和44.40%。不同退化程度样地的地上植物氮储量总体上呈现HDMDLD,与土壤氮储量的变化趋势相反;在生长季中,植物根系氮储量是逐渐增加的,且在生长季末表现为HDMDLD。植物-土壤系统中,土壤氮储量占总氮储量的比例高于根系和地上植物,占系统氮储量的95.05%~97.62%。DNDC模型在草原点位模拟土壤氮储量的效果比较好。     

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。     

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。     

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。     

DNDC模型评估苜蓿绿肥对水稻产量和温室气体排放的影响

DNDC(denitrifiction-decompostion)模型是以生物地球化学进程为基础模拟碳氮循环的模型,被广泛用来预测稻田温室气体的排放,但利用DNDC模型研究苜蓿绿肥对稻田生态系统的相关研究尚未见报道。因此,本研究结合两种绿肥在上海地区的使用,模拟了4个不同处理:对照(未施氮肥和绿肥)、氮肥(200 kg/hm²)、紫花苜蓿绿肥(3000 kg DM/hm²)+氮肥和蚕豆绿肥(3000 kg DM/hm²)+氮肥,研究苜蓿绿肥对水稻产量和稻田温室气体排放的影响,同时,对DNDC模型进行本地化修正,建立适宜我国长江中下游地区绿肥-水稻轮作生态系统的DNDC模型,结果表明,与对照相比,苜蓿、蚕豆和氮肥处理下的水稻产量分别提高了41.85%,29.81%和25.36%;蚕豆绿肥处理下的CH₄排放量高于苜蓿绿肥处理,温室气体的排放强度在苜蓿绿肥处理下未显著提高。通过对DNDC模型多个参数的调整和模拟,DNDC模型对水稻产量和CH₄排放的模拟值与实测值十分接近,其中,水稻产量实测值和模拟值的决定系数R²为0.89,相对平均误差RMD为-0.8%。大气温度、大气CO₂浓度、土壤有机碳和土壤粘粒对稻田CH₄和N₂O排放十分敏感,其中,大气温度、CO₂浓度和土壤有机碳与CH₄和N₂O的排放强度呈显著的正相关关系,而土壤粘粒与CH₄排放呈显著的负相关关系,本研究结果说明本地化改进的DNDC模型能够准确模拟紫花苜蓿绿肥对水稻产量和稻田温室气体排放的作用效果。