Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

适应城市消防规划需求的火灾风险评估研究

研究城市火灾风险评估,可以为城市消防规划提供有效参考,对推进城市防灾减灾工程具有重要意义。面向所要研究区域的城市消防规划需求,首先分析区域火灾历史统计数据,提炼区域的火灾特点,从火灾危险性、承灾体易损性、城市抗灾能力3方面分析指标影响因子,建立了城市火灾风险评估指标体系;其次,提出了以建设用地地块为评估单元,基于现场调研和卫星遥感影像、城市用地分类相结合的指标因子快速分析方法,并通过加权平均评估城市火灾风险;最后,通过城市火灾风险评估案例研究,阐释了评估的技术流程和结果形式。研究结果表明:适应城市消防规划需求的火灾风险评估方法可以快速定量评估区域火灾风险的空间分布特征,还能识别出消防安全布局的重点地区,进一步支撑城市消防规划编制。     

2014年我国煤矿事故统计与规律分析

为了更深入地研究我国煤矿事故的一般性规律,基于2014年我国煤矿事故数据,采用数理统计的方法对煤矿百万t死亡率、事故区域、类型、月份、时间段、诱因6个维度进行了研究.结果表明:湖南省、重庆市煤矿事故发生频率较高,贵州省瓦斯事故发生频率较高;瓦斯事故、顶板事故是导致人员伤亡的主要事故类型,瓦斯爆炸是煤矿瓦斯事故的主要类型;8月煤矿事故情况较为严重;10:00-11:59是煤矿事故频发时间;违章作业是导致煤矿事故发生的主要诱因.同时,针对煤矿事故发生规律提出了相应的对策措施.     

基于逻辑树和贝叶斯网络的海洋平台火灾概率分析

火灾概率分析是海洋平台火灾定量风险评估的重要组成部分,考虑到传统事故树和事件树方法存在一定的局限性,提出了基于逻辑树和贝叶斯网络的火灾概率分析模型。首先采用数理统计方法对墨西哥湾地区2 837起火灾事故进行统计分析,依据事故情况构建逻辑树,然后将逻辑树转化为贝叶斯网络,根据历史数据确定贝叶斯网络各节点的先验概率和条件概率。结果表明:海洋平台火灾事故是设备、人因和组织管理多因素耦合作用的结果;基于贝叶斯网络模型得到海洋平台火灾概率约为1.0×10~(-5),为海洋平台火灾定量风险评估提供了基础数据;由贝叶斯网络模型分析得出,人因操作失误与缺乏作业安全分析的后验概率分别达到0.471和0.119,表明人因组织因素对海洋平台火灾事故具有重要影响。     

The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): two novel evaluation methods for developing optimal training programs

Current methodologies in training evaluation studies largely employ a single method entitled random confirmatory trials, prompting several concerns. First, practitioners and researchers often analyze the effectiveness of their entire omnibus training, rather than the individual elements or identifiable components of the training program. This slows the testing of theory and development of optimal training programs. Second, a common training is typically administered to all employees within an organization or workgroup; however, certain factors may cause individualized training to be more effective. Given these concerns, the current paper presents two training evaluation methodologies to overcome these problems: the multiphase optimization strategy and sequential multiple assignment randomized trials. The multiphase optimization strategy is a method to evaluate a standard training, which emphasizes the importance of a multi‐stage training evaluation process to analyze individual training elements. In contrast, sequential multiple assignment randomized trial is used to evaluate an adaptive training that varies over time and/or trainees. These methodologies jointly overcome the problems noted earlier, and they can be integrated to address several of the key challenges facing training researchers and practitioners. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessment of the Extreme Rainfall Event at Nashville,TN and the Surrounding Region on May 1–3, 2010

This paper analyzes the May 1–3, 2010 rainfall event that affected the south‐central United States, including parts of Mississippi, Tennessee, and Kentucky. The storm is evaluated in terms of its synoptic setting, along with the temporal distributions, and spatial patterns of the rainfall. In addition, the recurrence interval of the storm is assessed and the implications for hydrologic structure designs are discussed. The event was associated with an upper‐level trough and stationary frontal boundary to the west of the rainfall region, which remained quasi‐stationary for a period of 48 h. Heavy rainfall was produced by two slow‐moving mesoscale convective complexes, combined with abundant atmospheric moisture. Storm totals exceeding 330 mm occurred within a large elongated area extending from Memphis to Nashville. Isolated rainfall totals over 480 mm were reported in some areas, with NEXRAD weather radar rainfall estimates up to 501 mm. An extreme value analysis was performed for one‐ and two‐day rainfall totals at Nashville and Brownsville, Tennessee, as well as for gridded rainfall estimates for the entire region using the Storm Precipitation Analysis System. Results suggest maximum rainfall totals for some durations during the May 1–3, 2010 event exceeded the 1,000‐year rainfall values from National Oceanic and Atmospheric Administration Atlas 14 for a large portion of the region and reached up to 80% of the probable maximum precipitation values for some area sizes and durations.     

ArcGIS支持下的样本稀疏山区空间插值模拟探讨

样本稀疏地区空间插值法对区域化变量的精准管理具有重要意义。基于ArcGIS 90，在分析土壤属性空间分布特征的基础上，提出并构建了基于不同土壤类型的土壤特性空间预测模拟模型，对比了传统方法与改进方法空间插值精度，实现了数值插值在复杂地理环境区域的应用，得到以下结论：(1)基于经度、纬度、海拔高度及坡度等地理因子的土壤基础环境因子的空间预测模拟模型，突破以往只能描述土壤属性在水平方向变化的局限，较客观、合理地反映土壤属性随地理位置及海拔高度的立体变化特征；(2)基于不同土壤类型回归模型来增加样本点以推断评价指标在无取样地区的分布状况的处理方式具有一定的数学理论支撑，有效降低了插值误差，提高了评价精度，使评价结果更加接近现实。     

IDENTIFICATION OF CHANGES IN STREAMFLOW CHARACTERISTICS1

ABSTRACT: A set of procedures for identifying changes in selected streamflow characteristics at sites having long‐term continuous streamflow records is illustrated by using streamflow data from the Waccamaw River at Freeland, North Carolina for the 55‐year period of 1940–1994. Data were evaluated and compared to streamflow in the adjacent Lumber River Basin to determine if changes in streamflow characteristics in the Waccamaw River were localized and possibly the result of some human activity, or consistent with regional variations. Following 1963, droughts in the Waccamaw Basin seem to have been less severe than in the Lumber Basin, and the annual one‐, seven‐, and 30‐day low flows exhibited a slightly increasing trend in the Waccamaw River. Mean daily flows in the Waccamaw River at the 90 percent exceedance level (low flows) during 1985–194, a relatively dry period, were very nearly equal to flows at the same exceedance level for 1970–1979, which represents the 10‐year period between 1940 and 1994 with the highest flows. Prior to the 1980s, flows per unit drainage area in the Waccamaw Basin were generally less than those in the Lumber Basin, but after 1980, the opposite was true. The ratio of base flow to runoff in the Waccamaw River may have changed relative to that in the Lumber River in the late 1970s. There was greater variability in Waccamaw River streamflow than in Lumber River flow, and flow variability in the Waccamaw River may have increased slightly during 1985–1994.     

COMPARISON OF PARAMETRIC TAIL ESTIMATORS FOR LOW‐FLOW FREQUENCY ANALYSIS1

ABSTRACT: In recent years, several approaches to hydrologic frequency analysis have been proposed that enable one to direct attention to that portion of an overall probability distribution that is of greatest interest. The majority of the studies have focused on the upper tail of a distribution for flood analyses, though the same ideas can be applied to low flows. This paper presents an evaluation of the performances of five different estimation methods that place an emphasis on fitting the lower tail of the lognormal distribution for estimation of the ten‐year low‐flow quantile. The methods compared include distributional truncation, MLE treatment of censored data, partial probability weighted moments, LL‐moments, and expected moments. It is concluded that while there are some differences among the alternative methods in terms of their biases and root mean square errors, no one method consistently performs better than the others, particularly with recognition that the underlying population distribution is unknown. Therefore, it seems perfectly legitimate to make a selection of a method on the basis other criteria, such as ease of use. It is also shown in this paper that the five alternative methods can perform about as well as, if not better than, an estimation strategy involving fitting the complete lognormal distribution using L‐moments.