Methodology for development of drought Severity-Duration-Frequency (SDF) Curves

Drought monitoring and early warning are essential elements impacting drought sensitive sectors such as primary production, industrial and consumptive water users. A quantitative estimate of the probability of occurrence and the anticipated severity of drought is crucial for the development of mitigating strategies. The overall aim of this study is to develop a methodology to assess drought frequency and severity and to advance the understanding of monitoring and predicting droughts in the future. Seventy (70) meteorological stations across Victoria, Australia were selected for analysis. To achieve the above objective, the analysis was initially carried out to select the most applicable meteorological drought index for Victoria. This is important because to date, no drought indices are applied across Australia by any Commonwealth agency quantifying drought impacts. An evaluation of existing meteorological drought indices namely, the Standardised Precipitation Index (SPI), the Reconnaissance Drought Index (RDI) and Deciles was first conducted to assess their suitability for the determination of drought conditions. The use of the Standardised Precipitation Index (SPI) was shown to be satisfactory for assessing and monitoring meteorological droughts in Australia. When applied to data, SPI was also successful in detecting the onset and the end of historical droughts. Temporal changes in historic rainfall variability and the trend of SPI were investigated using non-parametric trend techniques to detect wet and dry periods across Victoria, Australia. The first part of the analysis was carried out to determine annual rainfall trends using Mann Kendall (MK) and Sen’s slope tests at five selected meteorological stations with long historical records (more than 100 years), as well as a short sub-set period (1949-2011) of the same data set. It was found that different trend results were obtained for the sub-set. For SPI trend analysis, it was observed that, although different results were obtained showing significant trends, SPI gave a trend direction similar to annual precipitation (downward and upward trends). In addition, temporal trends in the rate of occurrence of drought events (i.e. inter-arrival times) were examined. The fact that most of the stations showed negative slopes indicated that the intervals between events were becoming shorter and the frequency of events was temporally increasing. Based on the results obtained from the preliminary analysis, the trend analyses were then carried out for the remaining 65 stations. The main conclusions from these analyses are summarized as follows; 1) the trend analysis was observed to be highly dependent on the start and end dates of analysis. It is recommended that in the selection of time period for the drought, trend analysis should consider the length xvi of available data sets. Longer data series would give more meaningful results, thus improving the understanding of droughts impacted by climate change. 2) From the SPI and inter-arrival drought trends, it was observed that some of the study areas in Victoria will face more frequent dry period leading to increased drought occurrence. Information similar to this would be very important to develop suitable strategies to mitigate the impacts of future droughts. The main objective of this study was the development of a methodology to assess drought risk for each region based on a frequency analysis of the drought severity series using the SPI index calculated over a 12-month duration. A novel concept centric on drought severity-duration-frequency (SDF) curves was successfully derived for all the 70 stations using an innovative threshold approach. The methodology derived using extreme value analysis will assist in the characterization of droughts and provide useful information to policy makers and agencies developing drought response plans. Using regionalisation techniques such as Cluster analysis and modified Andrews curve, the study area was separated into homogenous groups based on rainfall characteristics. In the current Victorian application the study area was separated into six homogeneous clusters with unique signatures. A set of mean SDF curves was developed for each cluster to identify the frequency and severity of the risk of drought events for various return periods in each cluster. The advantage of developing a mean SDF curve (as a signature) for each cluster is that it assists the understanding of drought conditions for an ungauged or unknown station, the characteristics of which fit existing cluster groups. Non-homogeneous Markov Chain modelling was used to estimate the probability of different drought severity classes and drought severity class predictions 1, 2 and 3 months ahead. The non-homogeneous formulation, which considers the seasonality of precipitation, is useful for understanding the evolution of drought events and for short-term planning. Overall, this model predicted drought situations 1 month ahead well. However, predictions 2 and 3 months ahead should be used with caution. Many parts of Australia including Victoria have experienced their worst droughts on record over the last decade. With the threat of climate change potentially further exacerbating droughts in the years ahead, a clear understanding of the impact of droughts is vital. The information on the probability of occurrence and the anticipated severity of drought will be helpful for water resources managers, infrastructure planners and government policy-makers with future infrastructure planning and with the design and building of more resilient communities

Underpass clearance checking in highway widening projects using digital twins

Main road widening can reduce the clearance of the low-level underpass road, restricting the passage of vehicles and leading to collisions with structures. Therefore, checking the clearance of the underpass road effectively should be considered at the design stage. This paper describes a digital twin approach for checking the clearance of underpass roads in highway widening projects using online map data. The underpass road digital twin and BIM model of the newly widened road based on the existing main road digital twin are created to assist the clearance check and redesign. The proposed method presented a cost-effective clearance check for underpass roads in road widening design without field surveys and was successfully implemented in an underpass road in the UK. In future research, more digital twin methods for overpasses, bridges, tunnels, and traffic safety facilities should be employed comprehensively to assist more road widening applications

Some empirical evidence on business-IT alignment processes in the public sector: A case study report

An empirical study that explores business-IT alignment processes in a networked organization among the province Overijssel, the municipalities Zwolle and Enschede, the water board district Regge & Dinkel and Royal Grolsch N.V. in The Netherlands, is summarized in this report. The aim of the study was to identify processes that contribute to improve such alignment. This study represents a continuation of previous validation efforts that help us to confirm the business-IT alignment process areas that should ultimately be included in the ICoNOs MM. Evidence was sought for the alignment of business and IT through the use of information systems to support the requirements of the organization in a specific project. The results of this study in the public sector also are relevant to the private sector where (i) business-IT alignment plays an increasingly valuable role, and (ii) the characteristics of collaborative networked organizations are present

Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment

BIM Standards for Roads and Related Transportation Assets

With the industry foundation classes (IFC) building information modeling (BIM) standard (ISO 16739) being adopted by AASHTO as the national standard for modeling bridge and road infrastructure projects, there comes a great opportunity to upgrade the INDOT model development standard of roads and related assets to 2D+3D BIM. This upgrade complies with the national standard and creates a solid foundation for preserving accurate asset information for lifecycle data needs. This study reviewed the current modeling standards for drainage and pavement at different state DOTs and investigated the interoperability between state-of-the-art design modeling software and IFC. It was found that while the latest modeling software is capable of supporting interoperability with IFC, there remain gaps that must be addressed to achieve smooth interoperability for supporting life cycle asset data management. Specifically, the prevalent use of IfcBuildingElementProxy and IfcCourse led to a lack of differentiation in the use of IFC entities for the representations of different components, such as inlets, outfalls, conduits, and different concrete pavement layers. This, in turn, caused challenges in the quality assurance (QA) of IFC models and rendered the conventional model view definition (MVD)-based model checking insufficient. To address these gaps and push forward BIM for infrastructure at INDOT, efforts were made in this project to initially create model development instruction manuals that can serve as the foundation for further development and the eventual establish a consistent and comprehensive IFC-based modeling standards and protocols. In addition, automated object classification leveraging invariant signatures of architecture, engineering, and construction (AEC) objects was investigated. Correspondingly, a QA method and tool was developed to check and identify the different components in an IFC model. The developed tool achieved 91% accuracy on drainage and 100% accuracy in concrete pavement in its tested performance. These solutions aim to support the lifecycle management of INDOT transportation infrastructure projects using BIM and IFC

Review and ranking of crash risk factors related to the road infrastructure

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as “hot topics” of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 ‘Synopses’ (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors)

Building demolition estimation in urban road widening projects using as-is BIM models

Building demolition caused by urban road widening projects can lead to engineering, economic, and environmental issues and should be planned at the design stage. Based on as-is BIM, this paper proposes a method to estimate the building demolition caused by urban road widening using online map data and statistics on government websites. The as-is BIM models of the existing old road and its surrounding buildings are created, and the BIM models of the newly widened road are built based on the as-is BIM models considering road components in accordance with road engineering expressions to assist building demolition estimation using clash detection. This paper presents a cost-effective building demolition estimation in urban road widening projects without field surveys. It was tested on the M4 Motorway project in London. It has been proved to be a very practical approach to facilitate urban road planning and decision making

An Exploration of The Application of Spatial Network Screening Methods On Iowa Rural Road Crashes

Safety on the roadway system is important due to its usage on mobility and accessibility, especially on rural roads in the state of Iowa. Single vehicle run off road crashes have been increasing in the United States and studies and research has increased due to the concern with those. For this effort, a spatial-temporal method of traffic safety network screening is utilized in order to evaluate the concerning type of crashes in particular locations. The study of single vehicle run off road crashes using the proposed method is important since distributions and clusters of crashes along roadways can be observed and further evaluations can be performed

Digital twinning of existing bridges from labelled point clusters

The automation of digital twinning for existing bridges from point clouds has yet been solved. Whilst current methods can automatically detect bridge objects in points clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to detected point clusters remains human dependent to a great extent. 95% of the total manual modelling time is spent on customizing shapes and fitting them to right locations. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are made up of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of existing methods have evaluated the resulting models in terms of spatial accuracy with quantitative measurements. We tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from labelled point clusters. The accuracy of the generated models is gauged using distance-based metrics. Experiments on ten bridge point clouds indicate that the method achieves an average modelling distance smaller than that of the manual one (7.05 cm vs. 7.69 cm) (value included all challenging cases), and an average twinning time of 37.8 seconds. Compared to the laborious manual practice, this is much faster to twin bridge concrete elements