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.     

Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities

Objective: Road traffic injuries (RTIs) are a major global health issue causing a global burden of mortality and morbidity. Half of all fatalities on the world’s roads are vulnerable road users (VRUs). The targeted intervention strategies based on fatality analysis focusing on VRUs can effectively contribute to reducing RTIs. This study aimed to compare VRUs and motor vehicle occupants (MVOs) in terms of epidemiology and injury profile.

Methods: We utilized a nationwide, prospective database of RTI-related mortality cases for patients who visited 23 emergency departments between January 2011 and December 2015. All fatalities due to RTIs in the prehospital phase or in-hospital were eligible, excluding patients with unknown mode of transport and those admitted to general wards. The primary and secondary outcomes were fracture injuries and visceral injuries diagnosed using the International Classification of Diseases, Tenth Revision (ICD-10). We compared fracture injuries between VRUs and MVOs using Abbreviated Injury Scale (AIS) 2? and 2+ classification.

Results: Among a total 3,694 road traffic fatalities (RTFs), 43.3% were pedestrians, followed by MVOs (27.0%), motorcyclists (18.9), bicyclists (6.6%), and agricultural vehicle users (4.2%). The elderly (>60 years old) accounted for 54.9% of VRU fatalities. RTFs occurred most frequently in the autumn and the VRU group and the MVO group showed significant differences in weekly and diurnal variation in RTFs. The injury severities (AIS 2+) of the head, neck, and thorax were significantly different between the 2 groups (P?Conclusions: Elderly pedestrians should be targeted for decreases in RTFs, and road traffic safety interventions for VRUs should be made based on the analysis of temporal epidemiology and injury profiles of RTFs.     

基于电力历史应急大数据的应急情景规则分析与发现方法研究

为了解决电力突发事件演化过程难于预测,进而导致事件应急缺少针对性,较为被动的问题,提出在现有应急预案指导、事故模拟仿真、事故案例经验总结等方法基础上,引入基于电力历史大数据分析与预测的应急情景规则分析与发现方法,将电力应急突发事件情景构建的结果与相关大数据预测模型（汇总统计、分类与预测）相结合,提出科学、合理且具有良好操作性的情景演化规则的获取方法与技术路线,为进一步提高复杂电力突发事件的应急处置与指挥能力提供技术支撑。     

全国非煤矿山总量情况统计分析及监管建议

为研究全国非煤矿山基本情况并为非煤矿山安全监管部门提供决策支撑,采用统计、对比分析等方法,以各地上报的2017年统计数据及历年安全生产年鉴为依据,对我国“十三五”期间非煤矿山总量、类型、分布以及“头顶库”、高边坡露天矿山等重点监管对象进行分析,并在此基础上提出强化重点对象精准监管、严格安全生产条件、严控建设项目准入、规范尾矿库闭库注销制度4点监管建议。分析汇总了截至2017年底我国非煤矿山总量情况的统计数据,可为非煤矿山行业的深入分析研究提供重要支撑。     

贵州省闪电定位资料中小幅值地闪范围研究

采用2006—2015年贵州省ADTD闪电定位资料,应用Matlab软件中的曲线拟合工具箱对数据进行对数正态分布拟合,利用非参数正态分布检验中的Kolmogorov—Smirnov检验方法(简称K-S检验方法)分析各区间拟合效果,最终确定贵州省闪电定位系统资料的"小幅值地闪"干扰区间为-10~2k A,该结论为贵州省闪电资料修正提供理论依据,在使用ADTD闪电定位资料时可先将此区间误差数据剔除。该研究为利用闪电定位资料确定雷电灾害风险区划提供重要参考。     

基于ANFIS改进的大气腐蚀环境缺失数据填补方法

目的针对大气腐蚀中重要环境数据缺失的复杂问题,提出一种相关因素(Relevance Factors)和自适应神经模糊推理系统(Adaptive Neuro-Fuzzy Inference System)结合的方法(RF-ANFIS)对缺失数据进行填补。方法首先采用相关因素方法计算缺失数据和多项环境因素间的相关程度,筛选出相关系数较大的因子,然后应用ANFIS构建缺失数据与所选环境因子的关系模型。最后以二氧化硫数据为具体对象,采用北京2015年的气象数据对所建立的模型进行检验。结果经过改进的RF-ANFIS模型在最优情况下样本均方误差为0.696,在14个测试样本中有13个相对误差在20%以内,针对有限样本的数据分析中更为适用。结论该方法有效提高了大气腐蚀环境数据缺失的填补精度,对在数据缺失情况下预测大气腐蚀速率具有重要意义。     

水质自动监测站数据质控研究

随着环境监测技术的不断发展,水质自动监测以其高效、精确、连续监测等诸多优点,正逐步成为当今水环境监测的主流.然而中国水质自动监测技术起步较晚,且涉及的领域较多,很多关于水质自动站数据质控的相关标准不能及时出台,而且各个地方的河流分布、水文情况及水质自动站建设情况都不尽相同,这就需要对原有的手工监测标准进行比较和分析,同时结合对水质自动站运维管理的经验,找出适合目前水质自动站运行管理的质控手段.     

基于博弈论的环保数据质量影响因素分析研究

污染源产排污数据是一切环保工作的基础,确保真实可信具有十分重要的意义.为此,分析了影响污染源产排污数据质量的主要因素:受经济利益驱动的自身因素;受政绩利益驱动的管理因素和受地方保护驱动的政府因素等.在新环保法未得到充分贯彻,排污单位、环保主管部门和地方政府三者违法成本均低于守法成本的条件下,以博奕论为分析工具讨论了三种影响因素产生负面作用的必然性,并根据分析结果针对性地提出了提高数据质量的应对策略.     

A Nationwide Analysis of U.S. Army Corps of Engineers Reservoir Performance in Meeting Operational Targets

The United States (U.S.) Army Corps of Engineers operates reservoirs across the U.S. with 89% of reservoirs constructed prior to 1980. Many reservoirs have experienced changes in environmental conditions (e.g., climate and sediment yield) and societal conditions (e.g., water/energy demand and ecological flows) since construction. These changes may challenge the potential for reservoirs to meet their operational targets (OTs) (management goals). Historic daily reservoir data and OTs were collected for 233 reservoirs. Analyses were developed to identify when and where reservoirs may be systematically departing from OTs in terms of the frequency and magnitude of departure. Fifty‐six percent of reservoirs consistently met operating targets, 30% were borderline, and 13% experienced frequent and large magnitude departures. Fifty‐two percent of reservoirs with large departures were due to shortages and were located in the South Pacific and Southwestern divisions. This work provides a framework to identify reservoir performance in relation to management goals, a necessary step for moving toward adaptive management under changing conditions. All individual reservoir analyses are provided via an interactive data visualization tool: https://nicholasinstitute.duke.edu/reservoir-data .     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.