Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany

In Germany, a county-resolution data set that consists of 35 land-use and animal-stock categories has been used extensively to assess the impact of agriculture on the environment. However, because such environmental effects as emission or nutrient surplus depend on the location, even a county resolution might produce misleading results. The aim of this article is to propose a Bayesian approach which combines two sorts of information, with one being treated as defining the prior and the other the data to form a posterior, used to estimate a data set at a municipality resolution. We define the joint prior density function based on (i) remote sensing data, thus accounting for differences in county data and missing data at the municipality level, and (ii) the results of a cluster analysis that was previously applied to the micro-census, whereas the data are defined by official statistics at the county level. This approach results in a fairly accurate data set at the municipality level. The results, using the proposed method, are validated by the national research data centre by comparing the estimates to actual observations. The test statistics presented here demonstrate that the proposed approach adequately estimates the production activities.     

Evaluation of oceanic transport parameters using transient tracers from observations and model output

A method is presented to find the age distribution of ocean waters, the transit-time distribution (TTD), by combining an eddying global ocean model’s estimate of the TTD with hydrographic observations of CFC-11, temperature, and salinity. The method uses a mixture model of an assumed form of the TTD, an inverse Gaussian (IG), and an established Bayesian statistical method. All known significant sources of uncertainty are propagated to arrive at estimates of two oceanic transport parameters associated with the IG TTD, the mean age (Γ) and either the half-variance (${\mathrm{\Delta }}^2$) or the Peclet number ($\mathit{Pe}={\mathrm{\Gamma }}^2/{\mathrm{\Delta }}^2$). It is found that the uncertainties on Γ do not overlap zero in most locations using only CFC-11, temperature, and salinity. However, the uncertainty on the other IG parameter does not overlap zero in only a few locations. With the inclusion of another transient tracer (³He/³H), the uncertainty on this other IG parameter does not overlap zero in just a few additional locations in the deep North Atlantic Ocean. Neither a single- nor mixture-IG representation is adequate for representing the full TTD in the ocean, particularly in the Southern Ocean.Differences between the IG parameters estimated using the model’s tracers as data (BayesPOP) and those estimated using tracer observations as data (BayesObs) provide information about the sources of model biases, and give a more nuanced picture than can be found by comparing the simulated CFCs with observed CFCs. Using the differences between each of the oceanic transport parameters from BayesObs and those from BayesPOP with and without a constant Pe assumption along each of the hydrographic cross-sections considered here, it is found that the model’s eddy mixing biases often lead to larger model errors than the model’s mean advection time biases. It is also found that mean advection time biases in the model can be statistically significant at the 95% level where mode water is found in the Southern Ocean.     

湘江竹埠港段河床沉积物元素地球化学特征

湘江是我国重金属污染最严重的河流之一。本次工作利用X射线荧光(XRF)和电感耦合等离子质谱(ICP-MS)分析技术,对湘江竹埠港沉积物进行主量和微量元素分析。结果表明沉积物主量元素含量变化相对稳定,富集Mn O、P2O5、Fe2O3、Al2O3,亏损Ca O、Na2O。微量元素含量变化较大,亏损Ba、Sc、Mn、Sr而富集其他微量元素。对主量元素和稀土元素进行了分析,表明沉积物物源主要是由上游花岗岩风化所提供。对沉积物进行因子分析和相关性分析,表明沉积物源主要以陆源碎屑为主,并有磷灰石和软锰矿等自生副矿物,Ni、Pb、Cu可能来自人为污染源。地累积指数表明Ba为清洁无污染,重金属V、Cr、Co、Ni、Th、U为轻度污染,重金属Cu、Pb、Zn为偏中度污染,Mn为中度污染,Sc为偏重度污染。其中重金属Cr、Ni、Cu、Pb、Zn、Mn、Sc污染应引起关注。     

基于贝叶斯层次时空模型的甘肃省土地利用程度演变分析

以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。     

基于贝叶斯网络的生态系统服务空间格局优化——以泾河流域为例

城市高速发展引起了区域土地利用格局的改变,不仅影响生态环境质量,而且还对生态系统服务的空间格局产生影响,在此基础上优化生态系统服务显得至关重要。在泾河流域2000—2020年净初级生产力（Net primary productivity, NPP）、农业生产力、土壤保持和产水服务空间评估的基础上,将贝叶斯网络和生态系统服务相结合,在关键变量子集和可视化的最优状态子集的基础上,评估了4种生态系统服务需要优化的区域,为区域经济和生态和谐发展提供参考。结果表明：（1） 水文评价模型（Soil and water assessment tool, SWAT）模型能较准确地模拟区域的径流量。通过模拟值和观测值对比分析,该模型具有较高的决定性系数（R²>0.6）和纳什效率系数（NSE>0.5）,可为进一步评估产水服务提供保障。（2） 2000—2020年泾河流域4种生态系统服务的时空差异性较为显著。在时间尺度上,4种生态系统服务均呈现波动中上升的趋势,在空间尺度上呈现较为稳定的变化趋势。（3） 通过对4种生态系统服务优化区域进行叠加分析,发现综合优化区域集中在彭阳县的中部和西南部以及环县的零星区域。研究结果对指导优化区域进行生态系统的可持续管理以及改善生态系统的退化状况具有重要意义。     

Bayesian area-to-point kriging using expert knowledge as informative priors

Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.     

Bayesian modelling the retreat of the Irish Sea Ice Stream

We present an 8000‐year history spanning 650 km of ice margin retreat for the largest marine‐terminating ice stream draining the former British–Irish Ice Sheet. Bayesian modelling of the geochronological data shows the ISIS expanded 34.0–25.3 ka, accelerating into the Celtic Sea to reach maximum limits 25.3–24.5 ka before a collapse with rapid marginal retreat to the northern Irish Sea Basin (ISB). This retreat was rapid and driven by climatic warming, sea‐level rise, mega‐tidal amplitudes and reactivation of meridional circulation in the North Atlantic. The retreat, though rapid, is uneven, with the stepped retreat pattern possibly a function of the passage of the ice stream between normal and adverse ice bed gradients and changing ice stream geometry. Initially, wide calving margins and adverse slopes encouraged rapid retreat (～550 m a^?1) that slowed (～100 m a^?1) at the topographic constriction and bathymetric high between southern Ireland and Wales before rates increased (～200 m a^?1) across adverse bed slopes and wider and deeper basin configuration in the northern ISB. These data point to the importance of the ice bed slope and lateral extent in predicting the longer‐term (>1000 a) patterns and rates of ice‐marginal retreat during phases of rapid collapse, which has implications for the modelling of projected rapid retreat of present‐day ice streams. Copyright © 2013 John Wiley & Sons, Ltd.     

基于贝叶斯网络的Web访问模式挖掘模型系统

根据用户的访问记录,以及每次检索输入的关键词、用户的动作来动态更新用户的兴趣点.提出了一个能较好吻合这一思想的数据挖掘模型,以用户的停留时间、访问频率和用户输入的检索关键词为数据对象,进行用户兴趣分析,动态跟踪用户需求意向,简化检索过程,提高WEB资源的使用效率.