数值模型结构不确定性对模拟结果的影响分析

以内蒙古鄂托克旗为例,基于GMS中的MODFLOW模块构建了地下水流数值模拟模型,分析了模型结构(含水层厚度、参数分区)与模型参数不确定性因素对模拟结果的影响.研究结果表明:含水层不确定情景(含水层下边界概化为隔水底板平均值870 m)与实际情况水头差值绝对值的累计和最大为701 m,对模拟结果起了主控作用;当含水层下边界概化为910,940 m时,累计和分别增加为1 013,1 593 m;与仅考虑单个不确定性因素相比,同时考虑模型参数与含水层不确定情景累计和最大为738 m,同时考虑参数分区与含水层不确定情景累计和最大为791 m.因此,在构建地下水流数值模拟模型时,应优先考虑含水层空间结构概化的合理程度,同时考虑多个不确定性因素对模拟结果的综合影响,使地下水数值模拟模型能更精确地反映真实的地下水流状况.     

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

不确定度评定方法在汽车事故再现中的应用

介绍了不确定度理论及其评定方法，结合车辆碰撞事故的具体特点，给出了基于能量守恒的车速计算表达式．通过选择适当的输入参数作为不确定因子，分别计算各分量等效速度标准不确定度．考虑不确定度累积和传播，得到车速输出结果的合成标准不确定度和展伸不确定度，以此检验事故再现结果的准确程度．通过具体事故案例的分析计算和计算机检验，验证该方法的适用性．     

混合态的不确定关系与压缩效应

在关于混合态的海森堡不确定关系的基础上，研究了纯态和混合态的最小不确定性和压缩效应.虽然最小不确定态必定是纯态，但在某些并非最小不确定态的纯态或混合态中，依然可 以实现力学量不确定度的压缩.还给出了普通统计学的不确定关系，它们不涉及量子相干性却与量子力学的海森堡不确定关系具有相似的数学结构.     

An investigation of uncertainty in field habitat mapping and the implications for detecting land cover change

Land cover data for landscape ecological studies are frequently obtained by field survey. In the United Kingdom, temporally separated field surveys have been used to identify the locations and magnitudes of recent changes in land cover. However, such map data contain errors which may seriously hinder the identification of land cover change and the extent and locations of rare landscape features. This paper investigates the extent of the differences between two sets of maps derived from field surveys within the Northumberland National Park in 1991 and 1992. The method used in each survey was the Phase 1 approach of the Nature Conservancy Council of Great Britain. Differences between maps were greatest for the land cover types with the smallest areas. Overall spatial correspondence between maps was found to be only 44.4%. A maximum of 14.4% of the total area surveyed was found to have undergone genuine land cover change. The remaining discrepancies, equivalent to 41.2% of the total survey area, were attributed primarily to differences of land cover interpretation between surveyors (classification error). Differences in boundary locations (positional error) were also noted, but were found to be a relatively minor source of error. The implications for the detection of land cover change and habitat mapping are discussed.     

Seed production of Chenopodium album in competition with field vegetables

Total weed control within a crop is both difficult and expensive to achieve, so that some weeds will often remain to set seed. The seed production resulting from these weeds will ultimately affect the sustainability of the weed control strategy. If too much is allowed to return each season there could be a gradual, but significant, increase in the potential weed flora over a number of seasons. Field trials were carried out in 2000 and 2001 to quantify the potential magnitude of this weed seed return from Chenopodium album L., grown at two planting densities either in pure stands or in competition with one of two crops (cabbage or onion). Crop and weed weights and weed seed production were notably greater in 2001. Both dry weight and seed production of C. album were suppressed by increasing planting density or by the presence of crop, with cabbage having a more suppressive effect. Despite the plasticity in seed production, a linear relationship was demonstrated between log weed seed production and log weed biomass that was robust over a range of competitive situations with onion and cabbage, at different planting densities and in growing seasons. The study also demonstrated that the relationship could be combined with an existing simple competition model to allow the consequences of incomplete weed control to be assessed in terms of potential weed seed return.     

天然生胶杂质含量测量结果不确定度评定

根据GB/T 8086—2008规定的测定方法,分析了测定过程中的随机效应与系统效应,计算这两种效应对测定过程导致的不确定度和,最终对本方法所产生的不确定度给予综合评定。     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

Movement rules for juvenile steelhead: dynamic linking of movement behaviour to habitat and density

We incorporated explanatory factors including stream habitat type and fish density into individual‐based models with dynamic connections among adjacent habitat units to infer dispersal behaviour of juvenile steelhead Oncorhynchus mykiss in a Great Lakes watershed. We used mark–recapture data and an inverse modelling approach to estimate daily probability of steelhead moving out of a habitat unit, P(move), according to four competing models. The models used included (i) a null model where all fish had equal movement probability; (ii) a habitat‐dependent model where P(move) depended on the habitat type; (iii) a density‐dependent model of P(move); and (iv) a model where P(move) depended on both density and habitat type. The habitat‐dependent model provided the most parsimonious fit to the observed data according to Akaike's information criteria (AICc). In the null model, P(move) averaged 0.70, whereas P(move) averaged 0.75 in pools, 0.68 in riffles and 0.73 in runs in the habitat‐dependent model.