Evaluation of High-Resolution Simulation of the Urban Heat Island in Vienna, Austria

The recently developed microscale model for urban applications PALM-4U was used to simulate the thermal variability in Vienna on different spatial scales and to evaluate its ability to capture thermal characteristics in real urban environment. The model simulations cover the entire city of Vienna with a spatial resolution of 20 m. The static data related to geographical information and urban infrastructure are based on GIS data provided by the city administration of Vienna, available as spatial multi-purpose maps (Flächen-Mehrzweckkarte - FMZK), street tree cadastre, Digital Elevation Model and Digital Surface Model, which were combined with the national land cover data (Land Information System Austria - LISA) to account for the unresolved vegetation and Open Street Map to include building properties in the surrounding region (Lower Austria) of the model domain. The simulations were performed for a selected clear-sky hot day in August 2022. The results for hourly air temperature were evaluated with conventional weather stations of the national weather service and the city of Vienna and with quality-controlled data from citizen weather stations from the company NETATMO. The results show high intra-urban variability during daytime, but distinct spatial patterns at night with higher air temperatures in urban regions. In addition, spatial patterns of surface temperature were compared to remote sensing data from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and with the modelling results from previous studies, but with coarser grid spacing (e.g. urban climate model MUKLIMO_3 with 100 m spatial resolution). The results indicate that the microscale model PALM-4U shows general agreement with observations and is able to simulate atmospheric processes in urban regions. However, during the night a strong temperature inversion is present in the model, which can be related to the choice of model configuration and requires further investigations. The spatial patterns in urban-rural temperature gradient are similar as found in coarser scale model simulations and remote-sensing data, but show higher variation in surface temperature amplitude

Development Of Distributed Grid-Based Hydrological Model And Floodplain Inundation Management System

A physical based, distributed hydrological model was developed to route overland flows during isolated HISD storms. The model has operated on a grid or cell basis and routed the excess rainfall over the grids, conforming to the DEM-derived drainage paths, to the basin outlet. The rainfall-runoff hydrological modelling was implemented in MATLAB 7.0. The system has integrated GIS, RS, DEM, data management capability and a dynamic basin model within a common Windows environment. The simulation algorithms of the rainfall-runoff model have operated on grid bases compatible with the MATLAB programming language, which has been used to write instructions to many grid-based operations. Due to the MATLAB architecture, the system has been proven successful for large-scale basin modeling, which requires high level resolution, record keeping and technical transfer. The model has estimated the runoff using the Soil Conservation Service-Curve Numbers (SCS-CN), determined by the land use/ land cover and the hydrological soil group found in each grid. The overland flow mechanics were described by the diffusion wave approximation of St Venant equations, which were numerically solved for depth of flow and runoff by the finite volume method (FVM). The grid cell physical properties such as topography, land use, soil, and Manning’s roughness’ coefficient were extracted from published maps for discretized cells of the Klang River basin(KRB) using a GIS. The land use/cover classes were derived from interpreted information of Landsat TM imagery using the combined object-oriented segmentation - fuzzy logic algorithm. The DEM of 90m resolution, used to calculate slopes that generated runoffs, was derived from radar data sets (C-band) of the Shuttle Radar Topography Mission (SRTM) using the interferometric approach. Four criteria were used for the assessment of the model performance - Model bias, Nash– Sutcliffe and model efficiencies for both low and high flows during both calibration and validation periods. The results showed the advantages of integrating RS, DEM and GIS with hydrologic simulation in generating runoff processes in the spatial domain, attaining as well fairly high precision simulation with the general hydrologic trends well captured by the model. This study has also involved the application of flood modeling, which has integrated the results of the grid-based overland flow routing model into MIKE11 onedimensional hydrodynamic model. The discharge hydrographs were extracted from the grid-based overland flow routing model in ASCII format and imported into MIKE11 hydrodynamic modeling system. The MIKE11 model was developed based on surveyed, stream cross-section data to perform hydrodynamic simulation of the flooding process. The MIKE11 modeling was applied to the Klang River system comprising 9 main tributaries. The analysis has considered the river system with and without Stormwater Management and Road Tunnel (SMART) project, which involve structural flood mitigations measures including retention ponds, bypass tunnel and flow diversions, where the river physical condition was modified accordingly. Hourly data for flow were created into compatible MIKE11 time series in a separate file as input to the parameter editors. Initial and boundary conditions were based on the inputs for MIKE11 operational analysis. It has been found that the modeled predictions of depth and discharge matched observed data. A good agreement between the simulated and observed data was achieved for rating curves with RMSE = 0.96, 0.94, 0.95, and 0.97 at respective calibration points. From the results revealed by the MIKE11 modeling simulation, there were evidences that SMART was useful for flood mitigation of Klang River Basin. For instance at Tun Perak Bridge, the normal level for the Klang River was 25m, the alert level was 28m and the danger level was 29.5m. The value from the simulation showed that the maximum water level without SMART was 32m. However this level with SMART was only 27.8m which did not exceed the alert and danger level at Tun Perak Bridge. This area is the most critical part of KL. Once the water level from the Klang River exceeds the flood wall, the whole KL will be badly flooded. Finally, the results of the runoff modeling were integrated in MIKE-GIS model for flood inundation mapping. A digital planimetric view and topographic mapping of the floodplain was developed using the three-dimensional floodplain visualization approach through the integration of a digital terrain model. This model was synthesized from MIKE11 stream cross-sectional coordinate into a digital surface model, generated from aerial stereo pair photos using Ortho Engine PCI image processing software. The resulting formulated surface model provided a good representation of the general landscape and contained additional details within the stream channel. Integration of 3D-GIS and spatial analytical techniques together with hydrologic and hydraulic modeling processes has enhanced the visualization and display techniques for visual presentation and generation of flood inundation maps for early warning and contingency planning

Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data

We have developed a domain agnostic ordinary kriging algorithm accessible via a standards-based service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority.Our sensor data comes from wind in-situ observation stations in an area approximately 200km by 125km. We provide on-demand average wind interpolation maps. These spatial estimates can then be compared with the results of other estimation models in order to identify spurious results that sometimes occur in wind estimation.Our processing is based on ordinary kriging with automated variogram model selection (AVMS). This procedure can smooth time point wind measurements to obtain average wind by using a variogram model that reflects the wind phenomenon characteristics. Kriging is enabled for wind direction estimation by a simple but effective solution to the problem of estimating periodic variables, based on vector rotation and stochastic simulation.In cases where for the region of interest all wind directions span 180 degrees, we rotate them so they lie between 90 and 270 degrees and apply ordinary kriging with AVMS directly to the meteorological angle. Else, we transform the meteorological angle to Cartesian space, apply ordinary kriging with AVMS and use simulation to transform the kriging estimates back to meteorological angle.Tests run on a 50 by 50 grid using standard hardware takes about 5 minutes to execute backward transformation with a sample size of 100,000. This is acceptable for our on-demand processing service requirements

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distributions

A key objective of the smart grid is to improve reliability of utility services to end users. This requires strengthening resilience of distribution networks that lie at the edge of the grid. However, distribution networks are exposed to external disturbances such as hurricanes and snow storms where electricity service to customers is disrupted repeatedly. External disturbances cause large-scale power failures that are neither well-understood, nor formulated rigorously, nor studied systematically. This work studies resilience of power distribution networks to large-scale disturbances in three aspects. First, a non-stationary random process is derived to characterize an entire life cycle of large-scale failure and recovery. Second, resilience is defined based on the non-stationary random process. Close form analytical expressions are derived under specific large-scale failure scenarios. Third, the non-stationary model and the resilience metric are applied to a real life example of large-scale disruptions due to Hurricane Ike. Real data on large-scale failures from an operational network is used to learn time-varying model parameters and resilience metrics.Comment: 11 pages, 8 figures, submitted to IEEE Sig. Pro

Supporting security-oriented, inter-disciplinary research: crossing the social, clinical and geospatial domains

How many people have had a chronic disease for longer than 5-years in Scotland? How has this impacted upon their choices of employment? Are there any geographical clusters in Scotland where a high-incidence of patients with such long-term illness can be found? How does the life expectancy of such individuals compare with the national averages? Such questions are important to understand the health of nations and the best ways in which health care should be delivered and measured for their impact and success. In tackling such research questions, e-Infrastructures need to provide tailored, secure access to an extensible range of distributed resources including primary and secondary e-Health clinical data; social science data, and geospatial data sets amongst numerous others. In this paper we describe the security models underlying these e-Infrastructures and demonstrate their implementation in supporting secure, federated access to a variety of distributed and heterogeneous data sets exploiting the results of a variety of projects at the National e-Science Centre (NeSC) at the University of Glasgow

GMES-service for assessing and monitoring subsidence hazards in coastal lowland areas around Europe. SubCoast D3.5.1

This document is version two of the user requirements for SubCoast work package 3.5, it is SubCoast deliverable 3.5.1. Work package 3.5 aims to provide a European integrated GIS product on subsidence and relative sea level rise. The first step of this process was to contact the European Environment Agency as the main user to discover their user requirements. This document presents these requirments, the outline methodology that will be used to carry out the integration and the datasets that will be used. In outline the main user requirements of the EEA are: 1. Gridded approach using an Inspire compliant grid 2. The grid would hold data on: a. Likely rate of subsidence b. RSLR c. Impact (Vulnerability) d. Certainty (confidence map) e. Contribution of ground motion to RSLR f. A measure of certainty in the data provided g. Metadata 3. Spatial Coverage - Ideally entire coastline of all 37 member states a. Spatial resolution - 1km 4. Provide a measure of the degree of contribution of ground motion to RSLR The European integration will be based around a GIS methodology. Datasets will be integrated and interpreted to provide information on data vlues above. The main value being a likelyhood of Subsidence. This product will initially be developed at it’s lowest level of detail for the London area. BGS have a wealth of data for london this will enable this less detialed product to be validated and also enable the generation of a more detailed product usig the best data availible. One the methodology has been developed it will be pushed out to other areas of the ewuropean coastline. The initial input data that have been reviewed for their suitability for the European integration are listed below. Thesea re the datasets that have European wide availibility, It is expected that more detailed datasets will be used in areas where they are avaiilble. 1. Terrafirma Data 2. One Geology 3. One Geology Europe 4. Population Density (Geoland2) 5. The Urban Atlas (Geoland2) 6. Elevation Data a. SRTM b. GDEM c. GTOPO 30 d. NextMap Europe 7. MyOceans Sea Level Data 8. Storm Surge Locations 9. European Environment Agencya. Elevation breakdown 1km b. Corine Land Cover 2000 (CLC2000) coastline c. Sediment Discharges d. Shoreline e. Maritime Boundaries f. Hydrodynamics and Sea Level Rise g. Geomorphology, Geology, Erosion Trends and Coastal Defence Works h. Corine land cover 1990 i. Five metre elevation contour line 10. FutureCoas

Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved

Spatial Concentration of Opioid Overdose Deaths in Indianapolis: An Application of the Law of Crime Concentration at Place to a Public Health Epidemic

The law of crime concentration at place has become a criminological axiom and the foundation for one of the strongest evidence-based policing strategies to date. Using longitudinal data from three sources, emergency medical service calls, death toxicology reports from the Marion County (Indiana) Coroner’s Office, and police crime data, we provide four unique contributions to this literature. First, this study provides the first spatial concentration estimation of opioid-related deaths. Second, our findings support the spatial concentration of opioid deaths and the feasibility of this approach for public health incidents often outside the purview of traditional policing. Third, we find that opioid overdose death hot spots spatially overlap with areas of concentrated violence. Finally, we apply a recent method, corrected Gini coefficient, to best specify low-N incident concentrations and propose a novel method for improving upon a shortcoming of this approach. Implications for research and interventions are discussed