Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.     

利用空间聚集的贝叶斯网络评估手足口病发病风险

手足口病是一种常见的传染病,以往的研究表明该疾病与气象、环境和社会经济等因素相关联,其影响关系复杂,而疾病本身体现出较强的区域聚集性,采用普通的线性风险建模方法无法捕捉影响因素的复杂性及空间聚集性。因此,本文以山东省为例,在前人研究的基础上,提出了采用贝叶斯网络综合风险建模方法研究手足口病的发病风险与气象、土地利用、社会经济及空气污染等要素间的关系,并通过引入空间扫描统计聚集结果,将空间聚集引入到贝叶斯网络模型加强其空间推理功能,减少模型的偏差,提高评估的精度。结果表明,本文建立的手足口病空间贝叶斯网络风险模型具有较高的估计效果,引入的空间聚集性较好地融入到贝叶斯概率推理模型中,合理建立预测因子同手足口病发病风险之间的关系。通过对建模结果的解译,分析了手足口病的发病风险影响因素,特别是气候、社会经济及空气污染的影响。本文的空间贝叶斯建模方法及研究结果对手足口病暴发的防控预警具有重要的意义。     

地基沉降预测模型的正则化算法

通过分析地基沉降预测模型,指出最小二乘的病态性会导致模型参数求解失败。应用正则化理论,基于矩阵求逆理论,提出了一种沉降预测模型参数的正则化无偏估计算法,说明了新算法的无偏性和方差最小性。在一定条件下,证明了新算法中正则参数的存在性,并给出了正则参数的计算公式。结合文献和工程实例进行的分析表明,新算法降低了矩阵条件数,减轻矩阵病态程度,能有效求得地基沉降预测模型参数。     

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster–Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.     

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space–time heterogeneity of rainfall observations make space–time estimation of precipitation a challenging task. In this paper we propose a Box–Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space–time monthly precipitation in the monsoon periods during 1974–2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space–time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.     

Modeling of environmental influence in structural health assessment for reinforced concrete buildings

One branch of structural health monitoring (SHM) utilizes dynamic response measurements to assess the structural integrity of civil infrastructures. In particular,modal frequency is a widely adopted indicator for structural damage since its square is proportional to structural stiffness. However,it has been demonstrated in various SHM projects that this indicator is substantially affected by fluctuating environmental conditions. In order to provide reliable and consistent information on the health status of the monitored structures,it is necessary to develop a method to filter this interference. This study attempts to model and quantify the environmental influence on the modal frequencies of reinforced concrete buildings. Daily structural response measurements of a twenty-two story reinforced concrete building were collected and analyzed over a one-year period. The Bayesian spectral density approach was utilized to identify the modal frequencies of this building and it was clearly seen that the temperature and humidity fluctuation induced notable variations. A mathematical model was developed to quantify the environmental effects and model complexity was taken into consideration. Based on a Timoshenko beam model,the full model class was constructed and other reduced-order model class candidates were obtained. Then,the Bayesian modal class selection approach was employed to select the one with the most suitable complexity. The proposed model successfully characterizes the environmental influence on the modal frequencies. Furthermore,the estimated uncertainty of the model parameters allows for assessment of the reliability of the prediction. This study not only improves the understanding about the monitored structure,but also establishes a systematic approach for reliable health assessment of reinforced concrete buildings.     

Clarifying the distal to proximal tephrochronology of the Millennium (B–Tm) eruption,Changbaishan Volcano,northeast China

Tephra dispersed during the Millennium eruption (ME), Changbaishan Volcano, NE China provides one of the key stratigraphic links between Asia and Greenland for the synchronization of palaeoenvironmental records. However, controversy surrounds proximal-distal tephra correlations because (a) the proposed proximal correlatives of the distal ME tephra (i.e. B–Tm) lack an unequivocal chronostratigraphic context, and (b) the ME tephra deposits have not been chemically characterized for a full spectrum of element using grain-specific techniques. Here we present grain-specific glass chemistry, including for the first time, single grain trace element data, for a composite proximal sequence and a distal tephra from Lake Kushu, northern Japan (ca. 1100 km away from Changbaishan). We demonstrate a robust proximal-distal correlation and that the Kushu tephra is chemically associated with the ME/B–Tm. We propose that three of the proximal pyroclastic fall units were erupted as part of the ME. The radiocarbon chronology of the Kushu sedimentary record has been utilised to generate a Bayesian age-depth model, providing an age for the Kushu tephra which is consistent with high resolution ages determined for the eruption and therefore supports our geochemical correlation. Two further Bayesian age-depth models were independently constructed each incorporating one of two ice-core derived ages for the B–Tm tephra, providing Bayesian modelled ages of 933–949 and 944–947 cal AD (95.4%) for the Kushu tephra. The high resolution ice-core tephra ages imported into the deposition models help test and ultimately constrain the radiocarbon chronology in this interval of the Lake Kushu sedimentary record. The observed geochemical diversity between proximal and distal ME tephra deposits clearly evidences the interaction of two compositionally distinct magma batches during this caldera forming eruption.     

病态加权总体最小二乘靶向病灶的正则化方法

病态EIV模型的病灶源于设计矩阵的部分数据列之间存在复共线性关系。针对病灶特点制定正则化策略,在克服病态性的同时尽量减小正则化过程所引起的副作用,提出靶向病灶的正则化方法。通过数值试验,与总体最小二乘方法、病态总体正则化方法等进行比较,结果表明靶向病灶的正则化方法最优。     

利用GOCE卫星观测数据反演地球重力场模型

利用GOCE卫星约6个月的重力梯度数据和约1 a的几何轨道数据,联合解算250阶次的地球重力场模型TJGOCE01。GOCE重力梯度数据的低频误差采用ⅡR数字滤波器处理,粗差采用阀值法和移动窗口阀值法组合探测与剔除。直接在梯度仪坐标系中建立GOCE卫星的重力梯度观测方程,采用改进的短弧边值法建立几何轨道观测方程。两类观测值的权根据其先验精度确定,采用Kaula规则约束的正则化方法解算法方程。解算的TJGOCE01模型相对于EIGEN6C2模型在250阶次的大地水准面误差和大地水准面累积误差分别为19.4 mm和177.9 mm。北美地区GPS水准观测数据的检验结果表明,TJGOCE01模型的中误差为0.544 m,略优于欧空局公布的同阶次的第二代时域法和空域法解算的GOCE重力场模型。