北京市不同土地利用类型的土壤镉含量特征

通过对北京市菜地、稻田、果园、绿化地、麦地以及自然土壤6种土地利用类型共595个土壤样品的调查分析,探讨了不同土地利用方式对土壤镉含量的影响。结果表明,绿化地、麦地以及自然土壤与背景值没有显著差别;菜地、稻田、果园的镉含量显著高于背景值。6种土地利用类型中,菜地土壤的镉含量最高,其次为稻田和果园,绿化地以及麦地土壤镉的含量最低。与土壤基线值相比,果园样品有9.5%的样品超出,绿化地与菜地分别有7.7%和4.6%超出。在全部土壤样品中,共有19个样品超标,超标率为3.2%。石景山、门头沟、丰台、海淀等地区的样品超标较多,应该合理规划这些地区的农业土地利用。     

土壤颜色——一个可靠的气候变化代用指标

将研究剖面自上而下以2cm间隔采取的样品,烘干并研磨至小于45μm,此时,土壤的颜色将不再发生变化。将处理好的样品分别测土壤颜色指标亮度、红度和黄度,将测得的亮度、红度和黄度指标分MIS4、MIS3、MIS2和MIS14个阶段,通过与研究剖面的气候指标磁化率、粒度和CaCO_3在百年尺度、千年尺度上的对比分析,以及在万年尺度上与高纬格陵兰GRIP冰芯记录和反映全球冰量变化的SPECMAP曲线进行对比分析表明：土壤颜色指标作为气候变化的代用指标无论在百年尺度、千年尺度还是在万年尺度上均是可靠的,这种可靠性在末次冰期阶段表现得更为显著,这一结论是否适用于整个第四纪冰期间冰期旋回,尚需更多黄土剖面的研究事实来加以证明。     

Assessment of Terrestrial Ecosystem Sensitivity and Vulnerability in Tibet

The Tibetan Plateau serves an important shelter function for the ecological security of Asia, and especially China. Here, we proposed and improved indicators and methods for assessing the ecological sensitivity and vulnerability of the terrestrial alpine Plateau ecosystems and assessed the freeze-thaw erosion, land desertification, water-caused soil loss, and land salinization sensitivity, together with ecological vulnerability, from the overall ecological sensitivity, ecological pressure, and elasticity aspects in Tibet. The results indicate that the terrestrial ecosystem of Tibet is quite sensitive to freeze-thaw erosion, land desertification and water-caused soil loss. Extremely and highly sensitive regions account for 9.62% and 83.69%, respectively, of the total area of the Tibet Autonomous Region. Extremely and highly vulnerable areas account for 0.09% and 52.61%, respectively, primarily distributed in the Himalayan and Gangdise mountain regions in west Tibet; the Nyainqentanglha, Tanggula, Hoh Xil, and Kunlun mountain regions; and the northwest and northern regions of the Changtang Plateau. The results will aid the development of customized protection schedules according to different ecological issues in each region.     

三峡库区紫色土坡地养分状况及养分流失*

紫色土是三峡库区移民安置区的主要坡地土壤。本研究采用典型区域调查,代表性土壤剖面养分分析,结合实验小区人工降雨的方法,初步研究了发育于侏罗纪紫色砂泥岩母质上紫色土的养分状况、养分流失特点、过程及影响养分流失因素。     

基于机器学习的稀疏样本下的土壤有机质估算方法

采用GRNN（Generalized Regression Neural Network）和RF（Random Forest）2种机器学习方法构建土壤有机质预测模型,以提高稀疏样本情况下的土壤有机质估算精度。依据北京市大兴区农用地2007年的土壤有机质采样数据,按MMSD准则（Minimization of the Mean of the Shortest Distances）抽稀为8种不同采样密度的样本（分别为2703、1352、676、339、169、85、43、22个样本）,分别采用GRNN、RF和Ordinary kriging对各采样密度下的未知采样点进行预测,采用交叉检验的方式验证各采样密度下未知样点的预测精度。随着采样点密度的下降,样点间的空间自相关性逐渐减弱,半变异函数的拟和精度变差,预测点结果误差增大,预测的置信度降低。当抽稀到43个和22个采样点时,样点间的空间自相关性接近歼灭,半变异函数的决定系数较低且残差较大。普通克里格受到采样点数量和采样密度、样点的空间结构的影响比较明显,其预测精度随采样点数量的下降而下降。在85个采样点及以下时,其预测值与观测值之间没有显著的相关性。GRNN和RF的预测精度受采样密度的影响不大,其预测精度在一个较小的范围内波动,其预测值围绕观测值在一定阈值空间内震荡波动,具有较好的相关性,在85个及以下的采样密度时,预测精度相对普通克里格有较大的提升。普通克里格法不适合在稀疏样本条件下空间插值计算,尤其是在空间自相关性比较弱的情况下。机器学习模型能充分学习土壤间环境信息、样点空间邻近效应信息,兼顾属性相似性和空间自相关,具有更好的稳定性和适应性,不容易受到采样点数量、构型和采样密度等因素的影响,即使在采样点空间自相关性很弱的情况下也能做出稳定预测精度。     

Storage and density of soil organic carbon in urban topsoil of hilly cities: A case study of Chongqing Municipality of China

Rapid urbanization results in the conversion of natural soil to urban soil,and consequently,the storage and density of the soil carbon pools change.Taking Chongqing Municipality of China as a study case,this investigation attempts to better understand soil carbon pools in hilly cities.First,the vegetated areas in the study area were derived from QuickBird images.Then,topsoil data from 220 soil samples(0-20 cm) in the vegetated areas were collected and their soil organic carbon(SOC) densities were analyzed.Using the Kriging interpolation method,the spatial pattern of SOC was estimated.The results show that the SOC density exhibited high spatial variability in the urban topsoil of Chongqing.First,the SOC density in topsoil decreased according to slope in the order 2°-6° < 25°-90° < 0°-2° < 6°-15° < 15°-25°.Second,the newly developed areas during 2001-2010 had a lower SOC density than the areas built before 1988.Third,urban parks and gardens had a higher SOC density in topsoil,residential green land followed,and scattered street green land ranked last.For hilly cities,the variability of terrain affects the distribution of SOC.The Kriging results indicate that Kriging method combining slope with SOC density produced a high level of accuracy.The Kriging results show that the SOC density to the north of the Jialing River was higher than the south.The vegetated areas were estimated to amount to 73.5 km2 across the study area with an SOC storage of 0.192 Tg and an average density of 2.61 kg/m2.     

Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau

Soil erosion is a major threat to our terrestrial ecosystems and an important global environmental problem. The Loess Plateau in China is one of the regions that suffered more severe soil erosion and undergoing climate warming and drying in the past decades. The vegetation restoration named Grain-to-Green Program has now been operating for more than 10 years. It is necessary to assess the variation of soil erosion and the response of precipita- tion and vegetation restoration to soil erosion on the Loess Plateau. In the study, the Revised Universal Soil Loss Equation （RUSLE） was applied to evaluate annual soil loss caused by water erosion. The results showed as follows. The soil erosion on the Loess Plateau between 2000 and 2010 averaged for 15.2 t hm-2 a 1 and was characterized as light for the value less than 25 t hm-2 a-1. The severe soil erosion higher than 25 t hm-2 a-~ was mainly distributed in the gully and hilly regions in the central, southwestern, and some scattered areas of earth-rocky mountainous areas on the Loess Plateau. The soil erosion on the Loess Plateau showed a deceasing trend in recent decade and reduced more at rates more than 1 t hm 2 a 1 in the areas suffering severe soil loss. Benefited from the improved vegetation cover and ecological construction, the soil erosion on the Loess Plateau was significantly declined, es- pecially in the east of Yulin, most parts of Yah＇an prefectures in Shaanxi Province, and the west of Luliang and Linfen prefectures in Shanxi Province in the hilly and gully regions. The variation of vegetation cover responding to soil erosion in these areas showed the relatively higher contribution than the precipitation. However, most areas in Qingyang and Dingxi pre- fectures in Gansu Province and Guyuan in Ningxia Hui Autonomous Region were predomi- nantly related to precipitation.     

土壤粒径空间预测方法研究综述

土壤粒径（砂粒、粉粒和黏粒）是各种陆表过程和生态系统服务评估等模型的关键参数。作为一种土壤成分数据,土壤粒径的空间预测方法有和为1（或100%）等特殊要求,其空间分布精度受预测方法影响较大。本文针对土壤粒径相较于其他土壤属性的特殊性,提出了土壤粒径空间预测方法框架,综述了土壤粒径数据变换、空间插值和精度验证等系列方法,总结了提升土壤粒径空间预测精度的各种途径,包括通过有效的数据变换改善数据分布、结合数据分布特点选择合适的预测方法、结合辅助变量提升制图精度和分布合理性、使用混合模型提升插值精度、使用多成分联合模拟模型提升预测的系统性等。最后,提出了今后土壤粒径空间预测方法研究的未来方向,包括从考虑数据变换原理和机制角度改善数据分布、发展多成分联合模拟模型和高精度曲面建模方法,以及引入土壤粒径函数曲线并与随机模拟结合等。     

分布式侵蚀预报模型中网格面积的选定——以黄土高原丘陵沟壑区为例

在分布式土壤侵蚀过程模型中 ,承载数据以及进行运算的最小单元 ,即基本地块的选取是非常关键的 ,它直接关系到模型的模拟精度和运算数据量。目前大多数分布式模型都采用平均布设矩形网格的方法 ,这种方法在基本网格的大小选取上存在着盲目性和不统一性。本文以黄土高原丘陵沟壑区为例 ,利用GIS和SPSS分析了黄土高原丘陵沟壑区属性均一的基本地块在面积上的统计规律 ,给出了基本地块选取的合理依据 ,以促进建立更好的分布式模型     

科尔沁沙质草甸土壤微生物数量的垂直分布及季节动态

通过对中国农牧交错带科尔沁沙质草甸土壤微生物数量的垂直分布及其季节动态的研究分析表明:(1)微生物总数、细菌和放线菌数量均表现出与降雨量同步的季节动态,即6月份较5月份有所减少,7月份增至最多,7月份以后微生物数量逐渐下降,真菌则表现出从5月份到8月份一直增加,9月份开始回落;(2)土壤微生物具有明显的垂直分布差异.细菌和放线菌的垂直分布表现出随土壤深度增加逐渐减少的趋势,真菌数量表层最高,20 cm以下变化不够规律;(3)土壤微生物的层化比率均大于2(5月份放线菌除外);(4)不同土壤生态因子对微生物的影响不同,相同因子对不同微生物类群的影响也不相同.细菌受水分影响较大,真菌与地温的变化趋势相近,放线菌与水热条件的共同作用有关.土壤养分(有机碳和全氮)与微生物数量呈显著的正相关.