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  • 1.
    Brandt, S. Anders
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Lim, Nancy J.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Visualising DEM-related flood-map uncertainties using a disparity-distance equation algorithm2016In: IAHS-AISH Proceedings and Reports / [ed] A. H. Schumann, G. Blöschl, A. Castellarin, J. Dietrich, S. Grimaldi, U. Haberlandt, A. Montanari, D. Rosbjerg, A. Viglione, and S. Vorogushyn, Göttingen: Copernicus Publications on behalf of International Association of Hydrological Sciences (IAHS) , 2016, Vol. 373, p. 153-159Conference paper (Refereed)
    Abstract [en]

    The apparent absoluteness of information presented by crisp-delineated flood boundaries can lead tomisconceptions among planners about the inherent uncertainties associated in generated flood maps. Even mapsbased on hydraulic modelling using the highest-resolution digital elevation models (DEMs), and calibrated withthe most optimal Manning’s roughness (n) coefficients, are susceptible to errors when compared to actual floodboundaries, specifically in flat areas. Therefore, the inaccuracies in inundation extents, brought about by thecharacteristics of the slope perpendicular to the flow direction of the river, have to be accounted for. Instead ofusing the typical Monte Carlo simulation and probabilistic methods for uncertainty quantification, an empiricalbaseddisparity-distance equation that considers the effects of both the DEM resolution and slope was used tocreate prediction-uncertainty zones around the resulting inundation extents of a one-dimensional (1-D) hydraulicmodel. The equation was originally derived for the Eskilstuna River where flood maps, based on DEM dataof different resolutions, were evaluated for the slope-disparity relationship. To assess whether the equation isapplicable to another river with different characteristics, modelled inundation extents from the Testebo Riverwere utilised and tested with the equation. By using the cross-sectional locations, water surface elevations, andDEM, uncertainty zones around the original inundation boundary line can be produced for different confidences.The results show that (1) the proposed method is useful both for estimating and directly visualising modelinaccuracies caused by the combined effects of slope and DEM resolution, and (2) the DEM-related uncertaintiesalone do not account for the total inaccuracy of the derived flood map. Decision-makers can apply it to alreadyexisting flood maps, thereby recapitulating and re-analysing the inundation boundaries and the areas that areuncertain. Hence, more comprehensive flood information can be provided when determining locations whereextra precautions are needed. Yet, when applied, users must also be aware that there are other factors that caninfluence the extent of the delineated flood boundary.

  • 2.
    Brandt, S. Anders
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Lim, Nancy Joy
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Importance of river bank and floodplain slopes on the accuracy of flood inundation mapping2012In: River Flow 2012: Volume 2 / [ed] Rafael Murillo Muñoz, Leiden, The Netherlands: CRC Press / Balkema (Taylor & Francis) , 2012, p. 1015-1020Conference paper (Refereed)
    Abstract [en]

    Effective flood assessment and management depend on accurate models of flood events, which in turn are strongly affected by the quality of digital elevation models (DEMs). In this study, HEC-RAS was used to route one specificwater discharge through the main channel of the Eskilstuna River, Sweden. DEMs with various resolutions and accuracies were used to model the inundation. The results showed a strong positive relationship between the quality of theDEMand the extent of the inundation. However, evenDEMswith the highest resolution produced inaccuracies. In another case study, the Testebo River, the model settings could be calibrated, thanks to a surveyed old inundation event. However, even with the calibration efforts, the resulting inundation extents showed varying degrees of deviation from the surveyed flood boundaries. Therefore, it becomes clear that not only does the resolution of the DEM impact the quality of the results; also, the floodplain slope perpendicular to the river flow will impact the modelling accuracy. Flatter areas exhibited the greatest predictive uncertainties regardless of the DEM’s resolution. For perfectly flat areas, uncertainty becomes infinite.

  • 3.
    Lim, Nancy J.
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science.
    Brandt, S. Anders
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Assessment of spatial-based decisions and user perspectives in utilisation of flood certainty mapsManuscript (preprint) (Other academic)
  • 4.
    Lim, Nancy Joy
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Modelling, mapping and visualisation of flood inundation uncertainties2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Flood maps showing extents of predicted flooding for a given extreme event have wide usage in all types of spatial planning tasks, as well as serving as information material for the public. However, the production processes that these maps undergo (including the different data, methods, models and decisions from the persons generating them), which include both Geographic Information Systems (GIS) and hydraulic modelling, affect the map’s content, and will be reflected in the final map. A crisp flood boundary, which is a common way of representing the boundary in flood maps, may therefore not be the best representation to be used. They provide a false implication that these maps are correct and that the flood extents are absolute, despite the effects of the entire modelling in the prediction output. Hence, this research attempts to determine how flood prediction outputs can be affected by uncertainties in the modelling process. In addition, it tries to evaluate how users understand, utilise and perceive flood uncertainty information. 

    Three main methods were employed in the entire research: uncertainty modelling and analyses; map and geovisualisation development; and user assessment. The studies in this work showed that flood extents produced were influenced by the Digital Elevation Model (DEM) resolution and the Manning’s  used. This effect was further increased by the topographic characteristic of the floodplain. However, the performance measure used, which quantify how well a model produces result in relation to a reference floor boundary, had also biases in quantifying outputs. Determining the optimal model output, therefore, depended on outcomes of the goodness-of-fit measures used.

     In this research, several ways were suggested on how uncertainties can be visualised based on the data derived from the uncertainty assessment and by characterising the uncertainty information. These can be through: dual-ended maps; flood probability maps; sequential maps either highlighting the degrees of certainty (certainty map) or degrees of uncertainty (uncertainty map) in the data; binary maps; overlain flood boundaries from different calibration results; and performance bars. Different mapping techniques and visual variables were used for their representation. These mapping techniques employed, as well as the design of graphical representation, helped facilitate understanding the information by the users, especially when tested during the evaluations. Note though that there were visualisations, which the user found easier to comprehend depending on the task given. Each of these visualisations had also its advantages and disadvantages in communicating flood uncertainty information, as shown in the assessments conducted. Another important aspect that came out in the study was how the users’ background influence decision-making when using these maps. Users’ willingness to take risks depended not only on the map, but their perceptions on the risk itself. However, overall, users found the uncertainty maps to be useful to be incorporated in planning tasks.

  • 5.
    Lim, Nancy Joy
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Brandt, S. Anders
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance2019In: Geomatics, Natural Hazards and Risk, ISSN 1947-5705, E-ISSN 1947-5713, Vol. 10, no 1, p. 1613-1647Article in journal (Refereed)
    Abstract [en]

    In comprehending flood model results, we performed sensitivity analyses and evaluated how different combinations of digital elevation model (DEM) resolution and Manning’s roughness affect flood maps produced from a 2D hydraulic model. Moreover, we analysed how the estimation of accuracy can further be influenced by the performance measure and the area’s topography. Various combinations of DEM and Manning’s n produced different results, in terms of quantified performance in relation to actual flood extent and the generated flood boundaries. High-resolution DEMs performed better with higher Manning’s n, while lower n values were better for lower resolution DEMs. Furthermore, although lower resolution DEMs (25 and 50 m) received higher quantified performances, there are more discrepancies in the flood maps and water surface elevations (WSE) produced by them. The current statistical estimators of model performance do not necessarily provide an accurate estimate of which combination of DEM resolution and roughness are more suitable for application to modelling. Different statistical estimates have different assumptions, which can affect the model selection. Therefore, a more holistic approach towards model selection should be adopted that gives equal importance to statistical estimators, as well as the quality of flood inundation extents.

  • 6.
    Lim, Nancy Joy
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Brandt, S. Anders
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science. Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Visualisation and evaluation of flood uncertainties based on ensemble modelling2016In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 2, p. 240-262Article in journal (Refereed)
    Abstract [en]

    This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses.

  • 7.
    Lim, Nancy Joy
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Sahlin, Eva
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Quantification, classification and mapping of spatial uncertainties of floods2017Conference paper (Refereed)
    Abstract [en]

    In flood modelling studies, spatial uncertainties may be visualised differently. This will rely on the characteristics of the information produced from the quantification method applied, which may vary depending on the type of model uncertainty taken into account. It is important to be able to characterise and generally classify the different types of spatial uncertainty information in hydraulic model results, because this can help determine how they can be best represented and visualised.

    In this paper, two methods of quantifying uncertainties were employed to derive uncertainty information. The first was ensemble-based modelling, which combined the results of 100 simulations considering the effects of the Digital Elevation Model (DEM) and Manning’s roughness coefficient to the model output. Each result from the individual model run was assessed on how likely it depicted the spatial extent of an observed flood event. Afterwards, the results were weighted and aggregated. In the second method, the most optimal output based on a series of calibrations from one-dimensional flood modelling was used and applied with the empirical disparity-distance equation to account for further errors brought about by the resolution of the underlying DEM and the slope. The equation was implemented with an algorithm that created uncertainty zones based on the 95% prediction confidence. The resulting information from the two quantification methods were then classified, discretised and visualised using different map types, visual variables, and overlay techniques.

    Based on these results, four types of uncertainty information for flood modelling were produced that can be classified according to the characteristics of the data they show: (1) diverging, which is distinguished by two opposing conditions (certain to be dry and flooded) and a middle condition (highly uncertain); (2) sequential, where values range from lowest (uncertain) to highest (certain); (3) multiple calibration results, which show simultaneously the flood extents produced using different parameters for comparative purposes; and, (4) inundation zones which identify areas that are both certain and uncertain to be flooded.

    The results from both diverging and sequential uncertainty information were presented as continuous and discrete data in choropleth and graduated symbol maps. The gradation from uncertain-to-certain conditions was displayed using lightest-to-darkest colour and/or smallest-to-largest point symbols. With certainty/uncertainty zone, the binary statuses were represented in choropleth maps as: (a) blue/red colours; (b) organised/disorganised arrangements; and, (c) fine/coarse grain textures. For multiple calibration results, isopleths maps were used with a combination of at least two visual variables (size, shape, colour) to emphasise the differences in the lines, and facilitate visual comparison of results.

    Furthermore, since giving geographic context to flood uncertainty is an important aspect in the visualisation, three types of overlay were considered: map pairs, sequential and bivariate representations. Sequential representation worked well for all map types. Bivariate maps, on the other hand, were best for uncertainty represented as [one-coloured] symbol, texture, arrangement and linear features, which do not obscure the information behind. The background map had also to be displayed with increased transparency to prevent its dominance over the uncertainty data. Map pairs were the most suitable for choropleth maps using fill colour in order to avoid problems caused by colour blending when two maps are overlain. 

    Classification of the uncertainty information facilitated the choice of data representation. Even when using other quantification methods, hydraulic modellers can adopt the suggested visualisation using similar characteristic data.  This can be an initial step in producing guidelines for flood uncertainty visualisation. Moreover, testing the effectiveness of these visualisations can be the next relevant step to see how the information is communicated, interpreted and used, e.g. in spatial planning, flood risk management and insurance policies.

  • 8.
    Lim, Nancy Joy
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Sahlin, Eva A. U.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Brandt, S. Anders
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    A cartographic framework for visualising flood uncertainties2018In: Article in journal (Other academic)
  • 9.
    Lim, Nancy Joy
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Åhlén, Julia
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Seipel, Stefan
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science.
    Geovisualisation of uncertainty in simulated flood maps2014In: Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2014, Game and Entertainment Technologies 2014 and Computer Graphics, Visualization, Computer Vision and Image Processing 2014 - Part of the Multi Conference on Computer Science and Information Systems, MCCSIS 2014 / [ed] Katherine Blashki and Yingcai Xiao, IADIS Press , 2014, p. 206-214Conference paper (Refereed)
    Abstract [en]

    The paper presents a three-dimensional (3D) geovisualisation model of uncertainties in simulated flood maps that can help communicate uncertain information in the data being used. An entropy-based measure was employed for uncertainty quantification. In developing the model, Visualisation Toolkit (VTK) was utilised. Different data derived from earlier simulation study and other maps were represented in the model. Cartographic principles were considered in the map design. A Graphical User Interface (GUI), which was developed in Tkinter, was also created to further support exploratory data analysis. The resulting model allowed visual identification of uncertain areas, as well as displaying spatial relationship between the entropy and the slope values. This geovisualisation has still to be tested to assess its effectiveness as a communication tool. However, this type of uncertainty visualisation in flood mapping is an initial step that can lead to its adoption in decision-making when presented comprehensively to its users. Thus, further improvement and development is still suggested for this kind of information presentation.

  • 10.
    Moreira, J. M. M.
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management. University of Extremadura, Spain .
    Nex, F.
    3D Optical Metrology Unit, Bruno Kessler Foundation (FBK), Trento, Italy .
    Agugiaro, G.
    3D Optical Metrology Unit, Bruno Kessler Foundation (FBK), Trento, Italy .
    Remondino, F.
    3D Optical Metrology Unit, Bruno Kessler Foundation (FBK), Trento, Italy .
    Lim, Nancy Joy
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    From DSM to 3D building models: A quantitative evaluation2013In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives / [ed] Sorgel U., Heipke C., Jacobsen K., Rottensteiner F., Gottingen: Copernicus gesellschaft MBH , 2013, Vol. 40, no 1W1, p. 213-219Conference paper (Refereed)
    Abstract [en]

    The paper reviews the state-of-the-art in 3D city models and building block generation, with a description of the most common solutions and approaches. Then the digital reconstruction and comparison of LoD1 and LoD2 building models obtained with commercial packages and using different input data are presented. As input data, a DSM at 1m resolution derived from a GeoEye-1 stereo-pair, a DSM from an aerial block at 50 cm GSD and a LiDAR-based DSM at 1m resolution are used. The geometric buildings produced with each dataset are evaluated with respect to some ground-truth measurements but also compared between them. Problems such as the quality of the input DSM, the accuracy of the necessary vector datasets containing the building footprints, the flexibility of the approaches and the potentialities of each dataset will be discussed. As reconstruction of the building models is largely influenced by the quality of the building footprints, which may be out-of-date or slightly shifted with respect to the employed DSMs/DTMs, an in-house method is being developed to derive them starting from the produced DSMs.

  • 11.
    Seipel, Stefan
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science.
    Lim, Nancy J.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Color map design for visualization in flood risk assessment2017In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 31, no 11, p. 2286-2309Article in journal (Refereed)
    Abstract [en]

    Visualizations of flood maps from simulation models are widely used for assessing the likelihood of flood hazards in spatial planning. The choice of a suitable type of visualization as well as efficient color maps is critical to avoid errors or bias when interpreting the data. Based on a review of previous flood uncertainty visualization techniques, this paper identifies areas of improvements and suggests criteria for the design of a task-specific color scale in flood map visualization. We contribute a novel color map design for visualizing probabilities and uncertainties from flood simulation ensembles. A user study encompassing 83 participants was carried out to evaluate the effects of this new color map on user’s decisions in a spatial planning task. We found that the type of visualization makes a difference when it comes to identification of non-hazardous sites in the flood risk map and when accepting risks in more uncertain areas. In comparison with two other existing visualization techniques, we observed that the new design was superior both in terms of task compliance and efficiency. In regions with uncertain flood statuses, users were biased toward accepting less risky locations with our new color map design.

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