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. ARTICLE HISTORY

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.

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.

Hydraulic modelling is now, at increasing rates, used all over the world to provide flood risk maps for spatial planning, flood insurance, etc. This puts heavy pressure on the modellers and analysts to not only produce the maps but also information on the accuracy and uncertainty of these maps. A common means to deliver this is through performance measures or feature statistics. These look at the global agreement between the modelled flood area and the reference flood that is used. Previous studies have shown that the feature agreement statistics do not differ much between models that have been based on digital elevation models (DEMs) of different resolutions, which is somewhat surprising since most researchers agree that high-resolution DEMs are to be preferred over poor resolution DEMs. Hence, the aim of this study was to look into how and under which conditions the different feature agreement statistics differ, in order to see when the full potential of high-resolution DEMs can be utilised. The results show that although poor resolution DEMs might produce high feature agreement scores (around F > 0.80), they may fail to provide good flood extent estimations locally, particularly when the terrain is flat. Therefore, when high-resolution DEMs (1 to 5 m) are used, it is important to carefully calibrate the models by the use of the roughness parameter. Furthermore, to get better estimates on the accuracy of the models, other performance measures such as distance disparities should be considered.

ABS TRACT: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 DSM s/DTM s, an in-house method is being developed to derive them starting from the produced DSM s.

<p>River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood-risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue-green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning.</p>

Abstract. The apparent absoluteness of information presented by crisp-delineated flood boundaries can lead to misconceptions among planners about the inherent uncertainties associated in generated flood maps. Even maps based on hydraulic modelling using the highest-resolution digital elevation models (DEMs), and calibrated with the most optimal Manning's roughness (n) coefficients, are susceptible to errors when compared to actual flood boundaries, specifically in flat areas. Therefore, the inaccuracies in inundation extents, brought about by the characteristics of the slope perpendicular to the flow direction of the river, have to be accounted for. Instead of using the typical Monte Carlo simulation and probabilistic methods for uncertainty quantification, an empiricalbased disparity-distance equation that considers the effects of both the DEM resolution and slope was used to create prediction-uncertainty zones around the resulting inundation extents of a one-dimensional (1-D) hydraulic model. The equation was originally derived for the Eskilstuna River where flood maps, based on DEM data of different resolutions, were evaluated for the slope-disparity relationship. To assess whether the equation is applicable to another river with different characteristics, modelled inundation extents from the Testebo River were utilised and tested with the equation. By using the cross-sectional locations, water surface elevations, and DEM, 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 model inaccuracies caused by the combined effects of slope and DEM resolution, and (2) the DEM-related uncertainties alone do not account for the total inaccuracy of the derived flood map. Decision-makers can apply it to already existing flood maps, thereby recapitulating and re-analysing the inundation boundaries and the areas that are uncertain. Hence, more comprehensive flood information can be provided when determining locations where extra precautions are needed. Yet, when applied, users must also be aware that there are other factors that can influence the extent of the delineated flood boundary.

To inform spatial planning promoting low-carbon travel and well-being, we investigate the potential for experiential diversity by active travel across different residential contexts. We use spatiotemporal tracking and experience data from the Gävle city-region, Sweden, generated by 165 participants over the course of 15 months. Findings reveal a discrepancy between typical travel distances to locations of positive experiences (1.5–5 km) and the distances at which active travel dominates (up to 1.5 km). This discrepancy largely persists across urban, suburban, and peripheral contexts, with urban dwellers travelling further for nature experiences, whereas peripheral dwellers travel further for urbanicity experiences. These results illustrate the importance of spatial scale for promoting diverse positive experiences by active travel, regardless of residential context. Planning strategies include enhancing environmental diversity close to people’s homes and providing infrastructure that promotes switching from motorised to active travel for trips of a few kilometres.