Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

The multifunctionality of urban farming: perceived benefits for neighbourhood improvement

Urban farming – a type of urban agriculture focused on entrepreneurial food production – serves multiple functions in neighbourhoods; yet these are not well delineated. Expectations for urban farming often centre on traditional measures of economic development, potentially overlooking other benefits. Through a qualitative case study conducted in Baltimore, Maryland, we sought to understand community perceptions regarding the ways in which urban farms can benefit cities. Interviews with residents, neighbourhood leaders, and urban farmers in three residential neighbourhoods with urban farms revealed the pathways by which community members view farms as improving neighbourhoods. Benefits stemmed from four primary changes urban farms made to study neighbourhoods: creation of public projects welcoming involvement, physical improvement of degraded space, production of local food, and creation of new businesses. These changes led to multiple perceived benefits including increased social connectedness, a transformed physical landscape, improved neighbourhood reputation, increased access to fresh produce, and educational, youth development, and employment opportunities. Our findings demonstrate the importance of a multifunctional paradigm that accounts for social and educational functions in assessing the value of urban farming and bring empirical evidence to the concept of multifunctional agriculture. Urban farms with strong social aims may appear to contribute little to economic development if measured using traditional indicators of success such as job creation or fiscal impacts, but provide numerous other benefits for community development.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Monitoring Environmental Impact in the Upper Sonoran Lifestyle: A New Tool for Rapid Ecological Assessment

Characterized by expensive housing, high socioeconomic status, and topographic relief, Upper Sonoran Lifestyle communities are found primarily along the Wildland-Urban Interface (WUI) in the Phoenix, Arizona metro area. Communities like these sprawl into the wildlands in the United States Southwest, creating a distinct urban fringe. This article, through locational comparison, introduces and evaluates a new field assessment tool for monitoring anthropogenic impact on soil–vegetation interactions along the well-maintained multi-use recreational trails in Upper Sonoran Lifestyle region. Comparing data from randomly selected transects along other multi-use trails with data from a control site revealed three key indicators of anthropogenic disturbances on soil–vegetation interactions: soil disturbance, vegetation disturbance, and vegetation density. Soil and vegetation disturbance displayed an average distance decay exponent factor of −0.60, while vegetation density displayed a reverse decay average of 0.60. Other important indicators of disturbance included vegetation type, biological soil crusts, and soil bulk density. The predictive ability of this new field tool enhances its applicability, offering a powerful rapid ecological assessment method for monitoring long-term anthropogenic impact in the Upper Sonoran Lifestyle, and other sprawling cities along the WUI.     

可靠性技术的发展与应用

本文介绍了可靠性标准体系与参数体系,可靠性的分析与设计方法,可靠性试验的发展趋势,指出可靠性管理是产品可靠性工作的核心,以及提高电子设备可靠性的发展策略。     

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

Abstract: Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient‐reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight‐digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.     

金竹山土朱煤矿开采地表沉降规律与灰色预测模型研究

针对金竹山矿业公司土朱煤矿煤层赋存条件,依据采动理论的裂缝垂直分带模型,分析地表沉降和塌陷的机理;提出在采煤活动阶段应进行地表实际位移观测,经数据处理后得到地面沉降曲线,以确保地面人类活动的安全;在采煤活动后阶段则实施灰色预测地面沉降,即通过采煤活动阶段的地表实际观测数据为历史原始数据序列,建立灰色Logistic模型;并对采煤活动后阶段的地面沉降进行预测。精度检验表明:灰色Logistic模型预测精度高,利用该模型预测地面沉降可减少地面沉降监测经费和实时提供预警信息,以确保开采区域内人们生命财产安全。     

Best Practices in Environmental Communication: A Case Study of Louisiana's Coastal Crisis

This mixed-method case study of environmental communication best practices in Louisiana, USA, identifies trends and approaches as informed by environmental psychology. The results provide key insights for environmental communicators, especially communicators in coastal regions. In-depth interviews with both environmental psychologists and environmental communicators showed that both groups emphasized knowing the audience, telling local stories, building relationships with target audiences and targeted messaging. Both psychologists and communicators also frequently mentioned general messaging concerns of source credibility, avoiding controversial terms and talking about issues, impacts and solutions to which the target audience can relate. A representative survey revealed that Louisiana residents are most interested in hearing about how environmental issues such as climate change, coastal land loss and flooding are affecting their own communities. This finding supports the idea that environmental communicators could do a better job tapping into strong place attachment and sense of community among coastal residents to promote action.     

Time and cost characterisation of emissions from non-road diesel equipment for infrastructure projects

The construction process contributes to pollutant emissions, particularly through the operation of diesel- and gasoline-powered equipment. In the past decade, a series of investigations were undertaken to quantify these emissions for a variety of non-road construction equipment performing different activities and undergoing different duty cycles, and a model to estimate quantities of six types of pollutant was developed. This paper uses that model to estimate emissions for four street and utility construction projects which no one has done previously. We combined information from company records with standard construction industry manuals to estimate total emissions for the projects and to examine the pollution patterns and magnitudes. The street construction projects all had similar emission profiles with a large peak at the beginning and a steady output of emissions throughout the duration of the project. For example, in two of the projects studied, half of all CO₂ emissions were produced before the projects were 40% completed. Results showed that demolition and earthwork are the activities with the largest contribution. The equipment types with the largest contribution are backhoes, front-end loaders, bulldozers and trenchers. Trenchers, for example, produced 30% of all emissions on the projects on which they were used.