SHAW模型模拟积雪覆盖下土壤热过程的不确定性分析

为探究SHAW(Simultaneous heat and water)模型中输入参数不确定性在模拟积雪覆盖条件下土壤热过程中对输出结果造成的影响以及关键影响因素，以松嫩平原黑土区东北农业大学试验场为研究区域，运用SHAW模型模拟积雪覆盖条件下6个不同深度土层热过程动态变化情况，并结合拉丁超立方取样(Latinhypercubesampling,LHS)方法，采用标准秩逐步回归探究参数不确定性对土壤冻结深度和温度输出不确定性的影响。结果表明：SHAW模型能够反映土壤冻融规律，6个深度土层温度的模拟值与实测值平均绝对误差小于2℃，选取的参数对土壤温度的输出敏感性较弱，而初始积雪厚度对土壤冻结深度的输出起主导作用。总体而言，SHAW模型基于LHS抽样和标准秩逐步回归方法可用于模拟积雪覆盖条件下土壤热过程模拟研究。     

海涂围垦区土壤盐分空间变异模拟的比较研究

以苏北海涂围垦区为例,利用人工神经网络(ANN)、普通克里格(OK)插值和序贯高斯模拟(SGS)对典型地块土壤盐分空间分布进行了模拟、插值与预测,获取了各方法的优化结构与参数,并就各方法对土壤盐分分布特征与空间结构的预测能力进行了比较分析。结果表明:ANN、OK和SGS法均较好地模拟和预测了土壤盐分的空间分布,达到了较高模拟、插值与预测精度;ANN获得的土壤盐分空间分布最为连续,SGS法整体分布相对离散;ANN能较好地预测盐分较低的样点,但ANN对高盐分样点的预测结果不如SGS和OK;SGS预测结果最符合实测值的波动特点,ANN预测结果波动范围最窄,SGS较ANN和OK更能反应数据随机变量的结构性和波动性,在整体上要优于ANN和OK法。该结果为滨海地区盐渍土壤的精准评估与高效改良提供了参考依据。     

基于最小数据集的塔里木河上游绿洲土壤质量评价

以塔里木河上游绿洲阿拉尔垦区为靶区,综合考虑表征土壤物理、化学和生物学性质的14个土壤指标,利用数理统计方法确定最小数据集并评价土壤质量,同时与不含土壤酶指标的土壤质量评价结果进行对比分析。结果表明:(1)研究区土壤质量评价适用的最小数据集(MDS)包括:土壤水分、全盐、全氮和过氧化氢酶活性;(2)研究区棉田、新开垦农田、果园和林地等大部分耕地土壤质量属于中等及以上水平(SQI≧0.5的占78.12%),大部分分布于河岸及绿洲区域;(3)不包含土壤酶活性指标的土壤质量评价使土壤全盐含量的权重降低,且导致土壤质量评价结果偏低。     

中国亚热带土壤可蚀性K值预测的不确定性研究

土壤可蚀性K值是土壤侵蚀模型(如USLE和RUSLE)的必要参数,直接套用经验模型估算土壤可蚀性K值会给土壤侵蚀预报带来不可估计的误差。本文以我国亚热带7种典型土壤可蚀性K值的观测值为依据,选用平均绝对误差(MAE)、平均相对误差(MRE)、均方根误差(RMSE)和精度因子(Af)四种数学统计项为指标,评价了诺谟图模型、修正诺谟图模型、EPIC模型、几何平均粒径模型和Torri模型等5种土壤可蚀性K值预测模型的不确定性。结果表明,5种模型的不确定性从小到大的顺序为:Torri模型<修正诺谟图模型和诺谟图模型相似文献   

基于集对分析与三角模糊数耦合的土壤重金属污染评价模型

土壤重金属污染程度综合评价问题是一个前沿的、复杂的不确定性问题。本文尝试利用集对分析方法描述土壤重金属污染评价过程中的确定性和不确定性特征,并应用角模糊数刻画集对差异度系数以改进集对分析方法,进而提出了基于集对分析与三角模糊数耦合的土壤重金属污染综合评价新模型。实例应用结果及与其他方法对比分析表明:该模型评价过程直观、计算简便、结果客观合理,在土壤重金属污染评价中具有参考价值。     

基于高密度电阻率成像法的陇中半干旱区土壤含水量监测研究

土壤含水量是影响半干旱区农作物生长的重要因素。为了准确测定土壤含水量的变化动态以指导农业高效用水,近年来,利用地球物理测量方法研究高分辨率的水流入渗,已经越来越受到欢迎和重视。本文以陇中半干旱区玉米田为例,通过在土壤表面布置电极,利用高密度电阻率成像法(ERT)对降雨前后土壤二维剖面进行电阻率数据测量,实现对土壤二维剖面电阻率值和含水量监测,解释不同条件下土壤含水量变化的原因,建立陇中半干旱区农田土壤电阻率和含水量之间的相关关系。结果表明:降水入渗使得二维剖面土壤电阻率整体呈明显降低趋势,反演得到的电阻率图像局部电阻值"高-低-高"的变化过程,与一次降水过程前后"干-湿-干"的循环过程一致。土壤含水量实测值与估计值之间有较为显著的线性关系(R2=0.651 8,n=96)。在0~2.0 m深度范围内,总体估计偏差较小,为0.74%;土壤含水率的估计精度较高,为2.64%。0~0.5 m土层(H1)含水量监测探头分布密集,数据采集较为准确,故H1层估计精度略高于0.5~2.0 m层(H2)。相比之前利用实测工具进行野外测量,ERT测量方法精度较高。本文提供了一个高分辨率的土壤结构二维分布与水分运移过程的图像,同时为实现精确和高效的农业用水管理提供一种新途径。     

基于常规Olsen法的全自动化学分析仪(SmartChem 200)测定土壤有效磷的研究

土壤有效磷含量在评价土壤肥力、指导作物磷肥施用等方面有重要的意义。常规测定方法(碳酸氢钠浸提-人工钼锑抗比色法)步骤复杂、费时费力,提高常规方法测试效率和准确性是测土配方施肥研究中亟待解决的技术问题。选取4组不同有效磷含量的土壤为样品,探究基于碳酸氢钠浸提-全自动化学分析仪测定土壤有效磷含量的可行性。结果表明:此方法有效磷含量在0.00~2.00 mg/L范围内线性良好,相关系数r为1;用于测定土壤样品中有效磷含量,测定结果与常规方法接近,相对误差在0.00%~2.07%之间,有较高的相关性;用于测定土壤标准物质(NSA-2)中有效磷,测定结果的相对标准偏差(RSD,n=6)为0.37%,加标回收率为99.00%~101.00%。采用全自动化学分析仪测定土壤有效磷准确度高、精密度好,测定结果达到土壤有效磷的检测要求;同时该仪器操作简单、测试效率高,可用于大批量土壤有效磷含量的分析测试。     

探针有限特性对热脉冲技术测定土壤热特性的影响

在利用热脉冲方法测定热特性时,通常对探针形状做理想化处理,即假设探针为线性热源,热导率无限大而热容量为零。在实际应用中,探针本身的有限特性(有限半径以及有限热容量)会导致热特性测定误差。为了研究探针有限特性对热脉冲技术测定土壤热特性的影响,该研究采用改进的热脉冲探针(直径2 mm、长度40 mm、间距8 mm)测定土壤热特性,并分别使用PILS(pulsed infinite line source,无限长线性脉冲热源)和ICPC(identical cylindrical perfect conductors,近似圆柱形完美导体)2种理论估计土壤热特性,比较分析了探针有限特性对热脉冲技术测定热特性结果的影响。结果表明:1)与PILS理论相比,利用ICPC理论拟合得到的温度升高曲线,可以有效减少探针有限半径和热容量对土壤热特性测定结果的影响。与ICPC理论相比,在0.03~0.25 m3/m3的含水率范围内,用PILS理论得到的砂土热扩散率和热导率分别偏低11.8%和5.2%;与模拟热容量相比,PILS和ICPC理论分别将热容量高估16.1%和7.9%;2)探针有限特性对土壤热特性的影响与含水率有关:在干土上最大;随着土壤含水率的增加,其影响逐渐降低。该研究对提高热脉冲技术测定土壤热特性的准确性具有指导意义。     

植被复垦对露天煤矿排土场土壤化学及生物学特性的影响

为探讨农牧交错带煤矿排土场不同植被复垦模式对土壤肥力的影响,以黑岱沟露天煤矿排土场不同植被类型(山杏+苜蓿(A1)、杨树+苜蓿(A2)、杨树+沙棘(A3)、杨树+披肩草+苜蓿(A4)、未复垦(CK))土壤为研究对象,通过简单相关分析和通径分析研究浅层(0—20cm)土壤化学性质和酶活性与土壤微生物量间的相关关系,揭示不同植被复垦下土壤微生物量差异的驱动因子,并采用土壤恢复指数(RI)评价不同植被复垦对露天煤矿排土场土壤质量的影响。结果表明:(1)与未复垦(CK)相比,不同植被复垦样地土壤有机质含量、碱解氮含量、土壤酶活性和微生物量均显著增加(p<0.05),但pH无明显变化规律且差异不显著(p>0.05),并且0—10cm明显高于10—20cm土层。(2)土壤微生物量碳、氮与土壤酶活性和有机质含量呈显著或极显著正相关关系,与pH和有效磷含量则均未表现出相关性。由于土壤微生物量碳和氮对有机质含量和蔗糖酶活性变化比较敏感,因此对土壤性质变化具有指示作用。(3)不同植被复垦土壤恢复指数在0—10,10—20cm土层分别表现为A3>A2>A4>A1和A3>A2>A1>A4。因此,杨树+沙棘(A3)和杨树+苜蓿(A2)复垦方式对矿区排土场土壤质量的改善较好。     

干旱区土壤剖面无机碳分布及其与盐碱性的关系

土壤无机碳作为干旱区土壤碳库的主要存在形式,其数量分布影响着区域内土壤剖面碳聚积和存储的格局。以往由于缺乏深层土壤剖面数据,无法准确量化土壤剖面碳分布与碳存储特征,使得土壤无机碳的数量存在很大的不确定性。以三工河流域典型的农田和相邻荒地土壤作为研究对象,共6个剖面190个采样点,挖掘取样深度至潜层地下水位,分析了剖面土壤无机碳(SIC)和可溶性盐离子的分布特征,并且通过冗余分析探究无机碳与土壤盐碱性之间的相互关系。结果表明:(1)农田的SIC含量显著大于荒地的SIC含量(p0.05),相比于荒地,农田的SIC含量增加了27.9%,变化范围增大了3.66倍;荒地和农田的SIC含量在剖面上分别表现为"S"形和"M"形分布。(2)在整个剖面上,同层次的农田土壤中的可溶性离子含量显著小于荒地中的含量(p0.05),并且在剖面上分布荒地表现为增加—减少趋势,而农田为逐渐减小趋势,表明农业活动显著改变了可溶性离子的数量和分布特征。(3)所有剖面土壤无机碳储量为0—100cm土层100—300cm土层300cm以下土层(p0.05),虽然层次间其数值差异较大,但在相同层次,农田和荒地的土壤无机碳储量所占比例却基本相同,为10%,35%,55%(p0.05)。(4)通过冗余分析得到土壤盐碱性因素对SIC的贡献作用排序,正相关性,pHESPSARCO_3~(2-)HCO_3~-;负相关性,K~+Ca~(2+)Mg~(2+)Cl~-盐分SO_4~(2-)Cl~-Na~+。     

华北平原典型区土壤肥力低下区识别及限制因子分析

采用序贯高斯模拟方法对山东禹城市土壤质量指数的空间表征进行了评价,并深入分析了土壤肥力低下区的范围及其主要限制因子。结果表明,禹城市西南和中部土壤肥力质量指数较高,肥力质量较低的区域主要分布在该市北部、西北和东南三个区域。利用土壤肥力质量与产量的关系,确定禹城市土壤肥力低下的判定阈值为0.55,该市大部分区域土壤质量指数处于该阈值以上,仅在北部、西北和东南三个相对独立的区域共有2 494 hm2的耕地土壤肥力低下的风险较高。该市北部肥力低下区的主要限制因子是土壤质地和全磷含量,西北部主要受土壤盐化限制,而东南部的土壤肥力低下区则受土壤速效养分低、土壤盐化、质地较差以及土壤全氮含量不足等多个限制因子影响。     

Spatio‐temporal change of soil organic matter content of Jiangsu Province,China, based on digital soil maps

Estimation of spatio‐temporal change of soil is needed for various purposes. Commonly used methods for the estimation have some shortcomings. To estimate spatio‐temporal change of soil organic matter (SOM) in Jiangsu province, China, this study explored benefits of digital soil maps (DSM) by handling mapping uncertainty using stochastic simulation. First, SOM maps on different dates, the 1980s and 2006–2007, were constructed using robust geostatistical methods. Then, sequential Gaussian simulation (SGS) was used to generate 500 realizations of SOM in the area for the two dates. Finally, E‐type (i.e. conditional mean) temporal change of SOM and its associated uncertainty, probability and confidence interval were computed. Results showed that SOM increased in 70% of Jiangsu province and decreased in the remaining 30% during the past decades. As a whole, SOM increased by 0.22% on average. Spatial variance of SOM diminished, but the major spatial pattern was retained. The maps of probability and confidence intervals for SOM change gave more detailed information and credibility about this change. Comparatively, variance of spatio‐temporal change of SOM derived using SGS was much smaller than sum of separate kriging variances for the two dates, because of lower mapping variances derived using SGS. This suggests an advantage of the method based on digital soil maps with uncertainty dealt with using SGS for deriving spatio‐temporal change in soil.     

Estimating the Contribution of Sampling,Sample Pretreatment,and Analysis in the Total Uncertainty Budget of Agricultural Soil pH and Organic Carbon Monitoring

Monitoring of soil organic carbon (SOC) and pH is needed to manage soil protection and tackle possible degradation in support of, i.e, the upcoming European Soil Framework Directive. Harmonized monitoring procedures and protocols produced under the auspices of the International Organization for Standardization (ISO) and the European Committee for Standardization (CEN) will be recommended. The uncertainty contributions of sampling, sample pretreatment, and analysis in the monitoring of soil pH and organic carbon in agricultural parcels using these harmonized monitoring procedures have been studied.

A within-laboratory comparison between the different analytical methods and sample pretreatments was made on 451 soil samples for SOC and 150 samples for soil acidity. Thereafter, a field study was performed to evaluate the contribution of the sampling method. Finally, an interlaboratory trial (including sampling) was organized to assess the overall monitoring uncertainty.

The results indicate that the influence of different sample pretreatments (e.g., milling) in combination with different analytical methods (elemental combustion versus chemical oxidation) are the main contributions to the observed uncertainty in the monitoring of SOC. For the monitoring of soil acidity, a similar observation was made, showing that differences in the practical implementation of the analytical method (e.g., mechanical shaking) are the main contributions to the monitoring uncertainty. The monitoring uncertainties derived from an interlaboratory trial (including sampling) amounted to ±20% (95% confidence interval, CI) for SOC and ±0.3 pH units (95% CI) for soil acidity on an agricultural parcel.     

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。     

Turfgrass Nutrition and Irrigation Water Quality

Turfgrass sites are increasingly irrigated with low-quality water sources, which may complicate nutritional programs by excessive addition of nutrients or problem ions by causing imbalances. Irrigation sources of most concern are nutrient-rich reclaimed water (i.e., reuse water) and those containing high concentrations of soluble salts. Factors contributing to difficulties in fertility programming are (a) increased temporal and geospatial (by soil depth and across the landscape) variability in soil nutrient/ion status; (b) addition of high levels of chemical constituents to the soil–plant system via the irrigation water, irrigation water treatments (i.e., acidification), and soil amendments such as gypsum; (c) salinity leaching programs that also leach soil nutrients; (d) changes in irrigation lake water quality, such as seasonal fluctuations due to rainfall dilution (i.e., dry and rainy seasons), intake locations across the lake surface, or lake depth; (e) attention to environmental and sustainability issues; and (f) on saline sites, achieving fertilization goals are more complex, requiring attention to maintaining root viability, maximizing grass salinity tolerance, and addressing unique nutritional requirements of new halophytic grasses. Addressing these issues requires proactive and frequent soil, water, and tissue testing; appropriate soil tests; and improved means to quantify spatial soil nutrient and salinity status via spatial mapping.     

Mineralisation and humification of plant matter in soil samples as a tool in the testing of soil quality

Since life is inevitably dependent on the assimilation and dissimilation of carbon, and since most of the organic carbon is bound in soil humic matter, the mineralisation and humification of plant carbon in soil should be monitored, so as to evaluate soil quality and avoid ecological risks. For this reason we suggested an incubation test of thirty days in duration with soil samples amended or not with lucerne meal as a source of plant carbon. During the incubation period, CO₂ release is to be measured repeatedly, and thereafter contents of humic acids (HA) and fulvic acids (FA) should be estimated and compared with those determined in the original soil samples. Our results obtained with samples from non-fertilized or long-term (?>?40 years) fertilized plots of two field trials indicate that fertilization by NPK?+ farm yard manure resulted in (i) a slight enhancement of C mineralisation, i.e., CO₂ release from soil, and (ii) a significant increases of HA and FA contents. Soil samples from a reclaimed mine spoil, and either fertilised or not with sewage sludge did not show distinct differences in the same parameters. The practicability of the incubation test as a tool in the monitoring of soil quality should be further proved using samples from differently affected soils.     

A Review of Probabilistic Risk Assessment of Contaminated Land (12 pp)

Background, Aims and Scope   The management and decisions concerning restoration of contaminated land often require in-depth risk analyses. An environmental risk assessment is generally described as proceeding in four separate steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. The risk assessment should acknowledge and quantify the uncertainty in risk predictions. This can be achieved by applying probabilistic methods which, although they have been available for many years, are still not generally used. Risk assessment of contaminated land is an area where probabilistic methods have proved particularly useful. Many reports have appeared in the literature, mostly by North American researchers. The aim of this review is to summarize the experience gained so far, provide a number of useful examples, and suggest what may be done to promote probabilistic methods in Europe and the rest of the world. Methods   The available literature has been explored through searches in the major scientific and technical databases, WWW resources, textbooks and direct contacts with active researchers. A calculation example was created using standard simulation software. Results and Discussion   Uncertainty and variability are part of every risk assessment. Much work on risks from contaminated soil has focussed on exposure, and choice and structure of the exposure model is then a basic uncertainty factor. Other factors, e.g. parameter uncertainty, are easier to characterize. Variability can be separated into inter-individual, spatial and temporal components. Both uncertainty and variability in the exposure variables can be investigated using Monte Carlo simulation methods. These simulations enable not only the estimation of the probability for a given risk or exposure, but also add information on the sensitivity of the various input variables. This will assist the assessor in further refining the risk analysis. The large number of applications published encompasses soil contamination by lead, arsenic, chromium, uranium, polychlorinated biphenyls (PCB), polycyclic aromatic hydrocarbons (PAH), hexachlorobenzene, pentachlorophenol and chlorinated solvents. Probabilistic risk assessments have been used in widely different settings, such as the metallurgical industry (mining and smelting operations), manufacturing, gas plants, wood impregnation, infrastructure, and waste landfills. Site-specific remediation goals can be specified using probabilistic methods, and a guideline document has been issued within the US Superfund programme. The usability of probabilistic risk assessment is illustrated by a calculation example. The current Swedish generic guideline value for benzo[a]pyrene in contaminated soil, with ingestion of vegetables as the major route of exposure, is compared with a probabilistic estimate. The toxicological reference value corresponds well with the upper 95th percentile of the estimated variability in intake, but does not account for uncertainty in the partition coefficients. Conclusions and Outlook   The probabilistic approach to risk assessment has proved its value in characterizing variability and uncertainty, and thereby contributing to a more informed and transparent decision-making process. The management of contaminated land is a major environmental application for probabilistic risk assessments. A substantial number of studies have been published and the method is now well established in the scientific community. This development has progressed further in the United States than elsewhere, but similar applications are now being reported from Europe and Asia. Probabilistic risk assessment is used to derive soil guideline values in the United Kingdom, and other countries may be anticipated to follow. However, efficient use of probabilistic methods for risk assessment of contaminated land requires certain components. There is a requirement for quality assurance and transparency that can be met by guidelines specifying data requirements and which items to report on. Both federal and state governments in the United States have issued such guidelines, and we see a similar need from a European perspective. A second component, necessary for a successful implementation of probabilistic methods, is education. We have ourselves developed undergraduate curricula, but we also see a need for continuous education of risk assessors and decision makers. The third component required is case studies, showing how probabilistic risk assessment can be implemented successfully in the cleanup of contaminated land. Most published studies originate from the United States, so here too there is a need for the rest of the world to catch up. In addition to the three components mentioned, there is an obvious need to develop and improve methods and practice of risk communication.     

Influence of estimation procedure on soil organic carbon stock assessment in Flanders, Belgium

Abstract. The purpose of the study was to determine the soil organic carbon (SOC) stock for Flanders, Belgium and to evaluate various methods for assessing SOC stock. The assessment methods first determined the SOC density (C mass per unit area) for pedons in a database of soil properties, and then spatially distributed the SOC density to soil and soil/land use categories on a map. The results showed that the pedon SOC density is influenced by drainage class, texture and land use/land cover. The SOC density estimation method significantly influences results and leads to differences of up to 6% in total estimated SOC stock for Flanders. Use of various spatial distributing methods creates differences of up to 2% in total estimated SOC stock. The largest difference in SOC stock estimate between any combination of assessment methods was 7% (125.6 Tg vs 134.9 Tg). These findings emphasize the importance of complete spatial soil databases of high quality that reduce uncertainty of estimates for use in research examining the role of soils in the C cycle. The results indicate that the need for these databases is greater than the need to standardize methods to determine the spatial distribution of SOC. A map of the distribution of SOC density shows that in Flanders a large proportion of SOC is stored in sandy soils in the north of the territory.     

Accounting for variability within map units when linking a pesticide fate model to soil survey

The aim of this study was to estimate the probability of exceedance (POE) of the USEPA health advisory level for particular pesticides in groundwater beneath citrus groves in southwestern Florida. The approach included bootstrapping to assess the uncertainty of the model output due to the variability of soil input data; a weather generator to provide daily rainfall amounts; daily evapotranspiration by the Blaney-Criddle (FAO) method; and a program written to compute POE values. Bootstrapping enabled us to assess the uncertainty of soils inputs by generating pseudo-profiles of soils from pedon characterization data. These pseudo-profiles were used in Monte Carlo simulations that captured the variance of selected soil parameters within soil taxonomic units. Single-name map units (i.e., consociations) were represented by no fewer than three actual pedon characterization data sets for the named soil and/or closely similar soil(s). In the case of a multiple-name map unit (i.e., soil association or soil complex), no fewer than three actual pedons of named and/or similar soils were used to generate pseudo-profiles for each of the named soils in the map unit. Inputs were linked to the pesticide fate model Chemical Movement in Layered Soil, CMLS (Nofziger and Hornsby) to produce cumulative probability curves showing the fraction of applied pesticide leaching below the 1-m depth. This curve was then used to determine the POE for various soil delineations in the groves within the watershed. Multiple POEs were generated for multiple-named map units; the rule was to select the higher(est) POE value as an estimator of environmental risk for each such map unit. Thematic maps are then created using soil and land use coverages and POE's for various pesticides evaluated.

Our approach suggests that the use of cumulative probability functions and probabilities of exceedance of health standards (USEPA HAL) provides needed integration of the uncertainties associated with input parameters, the significance of which can be easily grasped by decision officials. The use of environmental fate models to predict pesticide behavior in soils throughout a landscape has brought about intense scrutiny of soil attribute data and map unit delineations far beyond that envisioned for the present progressive soil survey. The authors also recognize that uncertainties in model formulation, pesticide fate parameters and toxicity further complicate assessments of this nature.