A two-stage ensemble Kalman filter for smooth data assimilation

The ensemble Kalman Filter (EnKF) applied to a simple fire propagation model by a nonlinear convection-diffusion-reaction partial differential equation breaks down because the EnKF creates nonphysical ensemble members with large gradients. A modification of the EnKF is proposed by adding a regularization term that penalizes large gradients. The method is implemented by applying the EnKF formulas twice, with the regularization term as another observation. The regularization step is also interpreted as a shrinkage of the prior distribution. Numerical results are given to illustrate success of the new method.     

Designing environmental monitoring networks to measure extremes

This paper discusses challenges arising in the design of networks for monitoring extreme values over the domain of a random environmental space-time field {X _ij } i = 1, . . . , I denoting site and j = 1, . . . denoting time (e.g. hour). The field of extremes for time span r over site domain i = 1, . . . ,I is given by \(\{Y_{i(r+1)}=\max_{j=k}^{k+n-1} X_{ij}\}\) for k = 1 + rn, r = 0, . . . ,. Such networks must not only measure extremes at the monitored sites but also enable their prediction at the non-monitored ones. Designing such a network poses special challenges that do not seem to have been generally recognized. One of these problems is the loss of spatial dependence between site responses in going from the environmental process to the field of extremes it generates. In particular we show empirically that the intersite covariance Cov(Y _i(r+1),Y _i′(r+1)) can generally decline toward zero as r increases, for site pairs i ≠ i′. Thus the measured extreme values may not predict the unmeasured ones very precisely. Consequently high levels of pollution exposure of a sensitive group (e.g. school children) located between monitored sites may be overlooked. This potential deficiency raises concerns about the adequacy of air pollution monitoring networks whose primary role is the detection of noncompliance with air quality standards based on extremes designed to protect human health. The need to monitor for noncompliance and thereby protect human health, points to other issues. How well do networks designed to monitor the field monitor their fields of extremes? What criterion should be used to select prospective monitoring sites when setting up or adding to a network? As the paper demonstrates by assessing an existing network, the answer to the first question is not well, at least in the case considered. To the second, the paper suggests a variety of plausible answers but shows through a simulation study, that they can lead to different optimum designs. The paper offers an approach that circumvents the dilemma posed by the answer to the second question. That approach models the field of extremes (suitably transformed) by a multivariate Gaussian-Inverse Wishart hierarchical Bayesian distribution. The adequacy of this model is empirically assessed in an application by finding the relative coverage frequency of the predictive credibility ellipsoid implied by its posterior distribution. The favorable results obtained suggest this posterior adequately describes that (transformed) field. Hence it can form the basis for designing an appropriate network. Its use is demonstrated by a hypothetical extension of an existing monitoring network. That foundation in turn enables a network to be designed of sufficient density (relative to cost) to serve its regulatory purpose.     

Integrated models to support multiobjective ecological restoration decisions

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state‐and‐transition, species‐distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade‐offs in management outcomes through integrated modeling and structured decision‐support approaches has wide application for situations in which trade‐offs exist between competing conservation objectives.     

西南地区陆地植被生态系统NPP时空演变及驱动力分析

研究西南地区陆地植被生态系统净初级生产力(NPP)的时空演变特征及其驱动力,对区域生态环境保护具有重要的现实意义.利用2000～2021年MODIS NPP、1999～2021年基于站点的气象数据和2000～2020年土地利用类型等数据,结合主成分分析、残差分析、Theil-Sen Median趋势分析和偏相关分析等方法,研究西南地区陆地植被生态系统NPP时空演变及其对驱动力的响应特征.结果表明,时间上,2000～2021年西南地区植被NPP呈波动上升趋势,速率为3.54g·(m²·a)^-1.气候变化和人类活动影响下,农田、草地和森林生态系统NPP均呈上升趋势,但农田生态系统NPP的上升趋势最为显著.空间上,西南地区植被NPP呈上升趋势的面积占比为89.06%,显著上升和极显著上升的区域主要分布在广西南部、四川东部、重庆西部,以及云南和贵州交界处.气候变化和人类活动对西南地区植被生长具有双重影响,气候变化和人类活动影响下农田生态系统NPP呈上升趋势的面积占比均高于草地和森林生态系统.西南地区植被NPP与各气象因子的相关性呈明显地域差异.区域尺...     

气候变化和人类活动对我国典型草原区植被恢复的影响

草原区作为影响我国陆地生态系统碳水循环和生态安全的重点区域,对气候变化和人类活动极为敏感.然而,有关气候变化和人类活动对该区域植被恢复相对贡献的认识尚存分歧.以生态系统净初级生产力(NPP)为评价指标,通过对比MODIS观测的实际NPP和基于Thornthwaite Memorial模型估算的潜在NPP的趋势差异,量化了2000～2020年气候变化和人类活动对我国典型草原区(北方温性草原区和青藏高原高寒草原区)植被恢复的相对作用.结果表明,研究区内93%的草地植被呈恢复趋势,NPP平均增加速率达(以C计)2.12 g·(m²·a)^-1(P<0.01),其中,近一半植被恢复区受气候变化和人为活动共同控制,约36%和10%植被恢复区分别受气候变化和人类活动的独立控制;此外,不同草地类型气候变化主导植被恢复的面积占比差异大,主要表现为高寒草地明显高于温性草地,气候条件越干旱,气候主导面积占比越大.人类活动不是北方温性草原区和青藏高原高寒草原区植被恢复的主要原因,但在气候条件恶化地区,人类活动可降低甚至抵消气候变化对植被的负面影响.未来需加强长...     

气候变化背景下人类活动对承德接坝区植被净初级生产力的影响

承德接坝区位于农牧过渡区,对气候变化和人类活动极为敏感.以植被净初级生产力(NPP)作为评价指标,基于Thornthwaite Memorial模型计算潜在NPP和MODIS NPP遥感影像获取实际NPP数据,利用潜在NPP与实际NPP间的差值衡量人类活动作用下NPP的大小,运用Slope趋势和变异系数法分析实际NPP、潜在NPP和人类活动作用下NPP的变化趋势及稳定性分布,并采用相关系数法分析实际NPP与年降水量和年均气温间的相关性,最终量化气候变化和人类活动对该区域植被变化的影响.结果表明,潜在NPP自西北向东南递增,其变化趋势和稳定性均为自西向东递增.实际NPP与年降水量和年均气温呈正相关区域面积占比分别是99.87%和91.66%.该区域99.85%的植被得到改善且变化稳定,主要是由气候因素和人类活动共同主导(99.71%),而植被退化完全是由人为因素所导致(0.15%).     

基于多源数据的PM2.5浓度时空分布预测与制图

随着我国经济、工业化、城市化进程迅速发展,PM_(2.5)污染在中国已经成为一个极端的环境和社会问题,并引起广泛关注.采用新技术估算的地表PM_(2.5)质量浓度,收集并处理了遥感反演的气溶胶光学厚度(AOD),气象数据,其他地理数据和污染物排放数据,采用贝叶斯最大熵(BME)结合地理加权回归(GWR)来分析2015年冬季的PM_(2.5)暴露在我国东部大范围区域的时空变异特征.结果表明,BME模型的十折交叉验证结果的决定系数R~2为0.92,均方根误差(RMSE)为8.32μg·m~(-3),平均拟合误差(MPE)为-0.042μg·m~(-3),平均绝对拟合误差(MAE)为4.60μg·m~(-3),与地理加权回归模型的结果相比(R~2=0.71,RMSE=15.68μg·m~(-3),MPE=-0.095μg·m~(-3),MAE=11.14μg·m~(-3)),BME的预测结果有极大的提高.空间上,PM_(2.5)高浓度地区主要集中在华北、长江三角洲、四川盆地,低浓度地区主要集中在中国的最南部如珠江三角洲和云南的西南部;时间上,不同月份的研究区域PM_(2.5)空间分布所有差别,2015年的12月、2016年1月PM_(2.5)污染最为严重,2015年的11月,2016年的2月污染相对较低.     

国际湿地保护政策及形式的演变研究

湿地丧失与退化已被公认为全球性的生态环境和社会问题。本文在综述相关文献的基础上,将国际湿地政策的演变划分为三个阶段：20世纪60年代以前的湿地开发期,20世纪60年代至90年代间的政策转型期以及20世纪90年代至今的湿地全面保护期。湿地被围垦、转换为农地是湿地开发期最主要的特质,是导致湿地大量丧失与退化的主要肇因;《湿地公约》的签署标志着国际湿地政策进入转型期阶段,倡导湿地保护与合理利用;而美国政府于上个世纪90年代初期提出的湿地“零净损失”政策目标,成为继《湿地公约》签署后推动全球湿地政策发生深刻改变的最主要驱动力之一,标志着国际湿地政策进入全面保护期阶段。     

无定河流域地表水硝酸盐浓度的时空分布特征及来源解析

无定河流域作为黄河的一级支流,其水生态环境质量深刻影响着黄河流域生态保护与高质量发展.为识别无定河流域硝酸盐污染来源,对2019～2021年期间无定河的地表水样品进行了采集,探究了流域地表水体硝酸盐浓度的时空分布特征及影响因素,借助水化学方法、氮氧同位素示踪技术以及MixSIAR模型定性和定量地确定了地表水硝酸盐各来源及其贡献率.结果表明,无定河流域硝酸盐浓度存在显著时空差异.时间上,丰水期地表水NO^-₃-N浓度均值高于平水期;空间上,下游地表水NO^-₃-N浓度均值高于上游.地表水硝酸盐浓度的时空差异主要受降雨径流、土壤类型以及土地利用类型的影响.无定河流域地表水丰水期硝酸盐的主要来源是生活污水及粪肥、化学肥料和土壤有机氮,其贡献率分别为43.3%、 27.6%和22.1%,降水的贡献率仅占7.0%.不同河段地表水硝酸盐污染源贡献率存在差异,上游土壤氮贡献率明显高于下游,为26.5%;而下游生活污水及粪肥的贡献率明显高于上游,为48.9%.可为无定河乃至干旱及半干旱地区的河流硝酸盐来源解析和污染治理...     

Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential, which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.