基于GWRK的土壤有效磷空间预测及其超标风险评估

以江苏省金坛区土壤有效磷的空间预测为例,构建地理加权回归克里格（GWRK）模型,即采用地理加权回归（GWR）来量化土壤有效磷与主要土壤因子（即：土壤全磷、土壤pH值和土壤有机质）之间的局部空间关系,并结合局部回归残差的插值结果来预测土壤有效磷的空间分布状况.GWR结果显示主要土壤因子对土壤有效磷含量的影响程度随空间位置的变化而变化.同时,采用独立验证样本对比GWRK模型和普通克里格（OK）模型的空间预测精度.结果显示,GWRK预测结果具有更低的平均绝对误差（MAE）、均方根误差（RMSE）和更高的Pearson相关系数（r）,且较OK预测结果的相对提高指数（RI）为19.61%.此外,根据GWRK预测结果,对金坛区土壤有效磷含量的超标风险进行了评估.结果表明土壤有效磷含量超过其环境安全阈值（40mg/kg）的区域集中分布在金坛区北部,其面积为175.58km²,约占金坛区总面积的18%.因此,GWRK模型能有效评估区域土壤元素有效量空间分布状况,且GWR局部空间回归系数能为区域土壤元素有效量的调控提供更精确空间决策支持.     

新疆塔里木河流域洪水发生率非平稳性特征及气候变化影响研究

新疆洪水灾害近年来有加剧趋势,但其特征与机理尚未有深入探讨。论文利用核估计和Bootstrap方法深入分析新疆塔里木河（塔河）流域洪水发生率的非平稳性及不确定性,同时采用广义可加模型（GAMLSS）构建洪水发生频率与协变量（大气环流因子、降水和气温指标等）的关系并定量辨识主要影响因子。研究表明：1）塔河流域洪水在1960年代左右和1990年代左右两个时期高频发生,两个洪水高发期之间洪水发生次数多为2或3次,且与极端降水发生次数较为吻合;2）洪水发生率呈现显著非平稳性,从1990年左右洪水发生次数持续上升,并达到峰值,表明洪水发生频率及强度呈加剧趋势;3）冬季AMO和AO是影响新疆塔河流域洪水发生的重要因子,而冬季NAO和SOI则是影响塔河流域5个州的洪水发生次数最为显著的大气环流指标。论文研究可为新疆塔河流域洪灾预测与预警及流域洪水管理提供关键理论依据。     

具有趋势变异的非一致性东江流域洪水序列频率计算研究

世界众多江河洪水序列形成的环境背景“一致性”已不复存在,传统极值流量分析的“极值理论”需修正。东江流域变化环境后,龙川和河源站年最大日流量序列M-K检验通过0.01 显著水平,呈下降趋势。采用时间变化矩模型对年最大日流量序列作非一致性处理,选择5 种分布线型、8 种趋势模型共40 种模型进行比较。结果表明,龙川站对数正态分布搭配CP趋势（均值和标准差相关且具有抛物线趋势）模型、河源站Gumbel分布搭配CP趋势模型拟合效果最优。水文情势变化后,传统洪水重现期概念应该被修正。基于传统频率分析方法得到的100 a 一遇洪水设计值,均表现出其重现期由水利工程建设前小于100 a 一遇变化到2000 年后的大于400 a 一遇,而非100 a 一遇。若仍采用传统方法计算,两站均会高估设计洪水量级。非一致性背景下,推荐考虑现状时间基点下的洪水设计值。     

A process-convolution approach to modelling temperatures in the North Atlantic Ocean

This paper develops a process-convolution approach for space-time modelling. With this approach, a dependent process is constructed by convolving a simple, perhaps independent, process. Since the convolution kernel may evolve over space and time, this approach lends itself to specifying models with non-stationary dependence structure. The model is motivated by an application from oceanography: estimation of the mean temperature field in the North Atlantic Ocean as a function of spatial location and time. The large amount of this data poses some difficulties; hence computational considerations weigh heavily in some modelling aspects. A Bayesian approach is taken here which relies on Markov chain Monte Carlo for exploring the posterior distribution.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.