RESPONSE OF GREEN‐AMPT MEIN‐LARSEN SIMULATED RUNOFF VOLUMES TO TEMPORALLY AGGREGATED PRECIPITATION1

ABSTRACT: Data collection frequency in automated systems is user determined and can range from seconds to hours or days. Currently, there is no standard or recommended frequency interval for collecting precipitation data from automated systems for input to event‐based models such as Green‐Ampt Mein‐Larsen (GAML). Data from 47 storm events at seven locations were used to simulate the response of GAML excess rainfall to temporally aggregated precipitation data. No difference in model efficiency was recognized when comparing one‐minute interval data (R²= 1.00) to five‐minute data (R²= 1.00). Very little model efficiency was lost at a 10‐minute (R²= 0.96) interval. After 10‐minutes, decline in efficiency became more rapid with R²= 0.16 at one hour. The combined effect of time interval with respect to drainage area, hydraulic conductivity, maximum 30‐minute intensity, and total precipitation also revealed similar results.     

A data-oriented strategy to support water resource managers and researchers

Given the wide diversity of data services provided to national water management agencies, the Group on Earth Observations (GEO) in collaboration with the Committee on Earth Observation Satellites (CEOS) developed the approach described in the report, Implementing the GEOSS Water Strategy—From Observations to Decisions to develop more coherent and equitable data services for water management through the use of Earth observations. Among other water resource issues, it recognized the need to enhance data-enriched water management services to support decision making related to drought monitoring, flood warning, tracking and improving sustainable development and monitoring and ameliorating the impacts of climate change. Needs associated with the Strategy's four themes: improved data acquisition for essential water variables, research and product development, interoperability and coordination, and capacity development and decision support, are reviewed. Responses to the recommendations have been undertaken by GEO, led by its Global Water Sustainability (GEOGloWS) initiative which includes NASA contributions, CEOS, and the Global Terrestrial Network for Hydrology (GTN-H). Progress on the themes is reviewed and benefits of these developments for international and US water management are identified. The commentary concludes with a summary of what has been achieved, what remains to be done, and the priority focus areas for implementation in the final year of the Strategy.     

Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer

A benefit function transfer obtains estimates of willingness-to-pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the benefit transfer model. A more expensive alternative to estimate WTP is to analyze only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian model averaging (BMA) techniques can be used to optimally combine information from all models.The Bayesian algorithm searches for the set of sites that can form the basis for estimating a benefit function and reveals whether such information can be transferred to new sites for which only a small data set is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is ‘poolable’.     

环境监测数据管理系统的开发

在论述开发环境监测数据管理系统重要性的基础上,根据环境监测站的主要工作职能,确定了环境监测数据管理系统的功能,进行了系统总体设计和程序设计,开发出了操作方便、灵活实用的环境监测数据管理系统。     

飞机铝合金结构腐蚀损伤预测方法对比性研究

依据飞机LD2结构材料的加速腐蚀试验数据,提出了数据拟合、神经网络、时间序列3种腐蚀损伤预测方法,并对3种预测方法的基本原理、预测精度及预测外延性进行了对比分析。研究发现神经网络和时间序列方法预测精度较高;时间序列方法的预测外延性较好,能够以较高精度预测未来一段时期腐蚀损伤的发展趋势及损伤程度。因此,应根据研究需要选用合适的方法进行腐蚀损伤预测。     

Emergency evacuation model assuming leakage of toxic substances in a chemical plant

Preparedness of emergency evacuation for the leakage of toxic substances in chemical plants is very important in order to reduce damage. In order to implement an emergency evacuation properly, it is necessary to comprehensively and concretely determine the conditions of the leakage and atmospheric conditions and predict the consequences of the dispersed gases. Repeated training for emergencies is also essential. In order to realize effective evacuation, a prediction model of the evacuation area that anyone can use to obtain the same results both accurately and promptly is developed in the present study. The prediction model is designed such that the wind speed and atmospheric conditions are automatically set, and the leakage rate is the only input parameter, so that anyone can use the model easily. In addition, the model can also predict the atmospheric parameters for up to 3 h and can calculate the evacuation distance so that smooth evacuation can be achieved for changing atmospheric conditions. Finally, the evacuation area is defined by statistically analysed wind fluctuations, and a series of emergency evacuation measures is implemented.     

Performance of the National Water Model in Iowa Using Independent Observations

This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (r_s), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km²) basins (r_s = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km²) basins (r_s = 0.6, E = ?0.25, KGE = ?0.2). Analysis with independent IFCBS observations was used to report performance at large basins (r_s = 0.6, KGE = 0.1) and small basins (r_s = 0.2, KGE = ?0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak.     

基于专利数据分析的污染地下水修复技术发展现状及趋势分析

为明确地下水污染修复技术在世界范围的研究布局和发展态势,本研究基于智慧芽全球专利数据库对1989-2018年地下水污染修复技术专利进行计量学统计,并探讨该技术的研究布局、研究重点及授权趋势。结果显示,从申请量来看,世界范围内地下水污染修复技术涉及的申请量逐渐增长,但绝对申请量和专利授权比相对较低,地下水污染修复技术研发的重视程度依然不够,有待进一步加强;中国是该技术领域主要专利申请国,也是最大的技术来源国;科研单位在该领域的研发实力显著高于企业机构。基于以上研究,认为今后应加强研发,并且创造高效经济的地下水污染修复技术,注重科研机构与企业单位合作及已有专利成果的转化,促进专利的市场应用,使其发挥更大的社会和经济效益。     

三维变分在PM2.5重污染数值模拟中的应用研究

为了探讨三维变分法(3DVAR)对成渝城市群冬季PM2.5重污染模拟的改善效果,采用3DVAR对成渝城市群2017年12月至2018年1月的空气质量数值模拟结果进行资料同化,对比评估嵌套网格空气质量预报模式(NAQPMS)原始数据与同化再分析数据的准确率,并分析成渝重污染特征。研究结果显示,3DVAR在PM2.5、PM10和NO2的同化实验中均取得较好的改善效果,成渝地区检验站点各污染物相关系数(r)的平均提升比例依次为44%、90%和332%,r改善的站点占检验站点总数的比例分别为98%、100%和82%;检验站点均方根误差(RMSE)的平均下降比例分别为15%、37%和31%,RMSE改善的站点占检验站点总数的比例为65%、98%和84%。与原始模拟结果相比,同化结果能够更准确地反映成渝地区冬季重污染期间的PM2.5和PM10空间分布特征。     

Development of injury risk models to guide CT evaluation in the emergency department after motor vehicle collisions

Abstract

Objective: The clinical evaluation of motor vehicle collision (MVC) victims is challenging and commonly relies on computed tomography (CT) to detect internal injuries. CT scans are financially expensive and each scan exposes the patient to additional ionizing radiation with an associated, albeit low, risk of cancer. Injury risk prediction based on regression modeling has been to be shown to be successful in estimating Injury Severity Scores (ISSs). The objective of this study was to (1) create risk models for internal injuries of occupants involved in MVCs based on CT body regions (head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine, and lumbar spine) and (2) evaluate the performance of these risk prediction models to predict internal injury.

Methods: All Abbreviated Injury Scale (AIS) 2008 injury codes were classified based on which CT body region would be necessary to scan in order to make the diagnosis. Cases were identified from the NASS-CDS. The NASS-CDS data set was queried for cases of adult occupants who sought medical care and for which key crash characteristics were all present. Forward stepwise logistic regression was performed on data from 2010–2014 to create models predicting risk of internal injury for each CT body region. Injury risk for each region was grouped into 5 levels: very low (<2%), low (2–5%), medium (5–10%), high (10–20%), and very high (20%). The models were then tested using weighted data from 2015 in order to determine whether injury rates fell within the predicted risk level.

Results: The inclusion and exclusion criteria identified 5,477 cases in the NASS-CDS database. Cases from 2010–2014 were used for risk modeling (n?=?4,826). Seven internal injury risk models were created based on the CT body regions using data from 2010–2014. These models were tested against data from 2015 (n?=?651). In all CT body regions, the majority of occupants fell in the very low or low predicted injury rate groups, except for the head. On average, 57% of patients were classified as very low risk and 15% as low risk for each body region. In most cases the actual rate of injury was within the predicted injury risk range. The 95% confidence interval overlapped with predicting injury risk range in all cases.

Conclusion: This study successfully demonstrated the ability for internal injury risk models to accurately identify occupants at low risk for internal injury in individual body regions. This represents a step towards incorporating telemetry data into a clinical tool to guide physicians in the use of CT for the evaluation of MVC victims.