GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR018198

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

Recent floods from intense storms in the southern United States and the unusually active 2017 Atlantic hurricane season have highlighted the need for real-time flood inundation mapping using high-resolution topography. High-resolution topographic data derived from lidar technology reveal unprecedented topographic details and are increasingly available, providing extremely valuable information for improving inundationmapping accuracy. The enrichment of terrain details from these data sets, however, also brings challenges to the application of many classic approaches designed for lower-resolution data. Advanced methods need to be developed to better use lidar-derived terrain data for inundation mapping. We present a new workflow, GeoFlood, for flood inundation mapping using high-resolution terrain inputs that is simple and computationally efficient, thus serving the needs of emergency responders to rapidly identify possibly flooded locations. First, GeoNet, a method for automatic channel network extraction from high-resolution topographic data, is modified to produce a low-density, high-fidelity river network. Then, a Height Above Nearest Drainage (HAND) raster is computed to quantify the elevation difference between each land surface cell and the stream bed cell to which it drains, using the network extracted from high-resolution terrain data. This HAND raster is then used to compute reach-average channel hydraulic parameters and synthetic stage-discharge rating curves. Inundation maps are generated from the HAND raster by obtaining a water depth for a given flood discharge from the synthetic rating curve. We evaluate our approach by applying it in the Onion CreekWatershed in Central Texas, comparing the inundation extent results to Federal Emergency Management Agency 100-yr floodplains obtained with detailed local hydraulic studies. We show that the inundation extent produced by GeoFlood overlaps with 60%~90% of the Federal Emergency Management Agency floodplain coverage demonstrating that it is able to capture the general inundation patterns and shows significant potential for informing real-time flood disaster preparedness and response. Plain Language Summary Simple and computationally efficient flood inundation mapping methods are needed to take advantage of increasingly available high-resolution topography data. In this work, we present a new approach, called GeoFlood, for flood inundation mapping using high-resolution topographic data. This approach combines GeoNet, an advanced method for high-resolution terrain data analysis, and the Height Above Nearest Drainage. GeoFlood can rapidly convert real-time forecasted river flow conditions to corresponding flood maps. A case study in central Texas demonstrated that the flood maps generated with our approach capture the majority of the inundated extent reported by detailed Federal Emergency Management Agency flood studies. Our results show that GeoFlood is a valuable solution for rapid inundation mapping.

/pdf/geoflood-large-scale-flood-inundation-mapping-based-on-high-30o2of9v6b.pdf

GeoFlood: Large-Scale Flood Inundation Mapping Based on High-Resolution Terrain Analysis

https://research-information.bris.ac.uk/files/212291819/1_s2.0_S2589915519300239_main.pdf

A flood inundation forecast of Hurricane Harvey using a continental-scale 2D hydrodynamic model

3-D representations of flood inundation through emerging formats in virtual and augmented realities may be a powerful tool with which to interact with political decision-makers and to engage people with flood hazards. Despite that, environmental scientists are far from fully harnessing these techniques when interacting with non-scientists. Integrations with computer graphic science is considered necessary to such an extent that a distinct and emerging field of research related to the visualization in assisting environmental scientists can be detected in the literature. A key issue could be represented by the implementation of a simple visualization product that can be used by hydraulic engineers as a reasonable balance between the intrinsic informatics complexity of virtual reality and the practical need to represent flooding events in 3-D environments for risk communication purposes. Therefore, this paper aims at defining a suitable path that starts from the river model set-up and, passing through 2-D flood simulations obtained by means of shallow water equations, arrives at 3-D visualizations of the results. In particular, this paper is focused on developing an intentionally simple workflow for the representation of two-dimensional hydraulic simulations within a 3-D virtual reality environment. The level of detail as well as the pro and cons associated with the use of this procedure are discussed and the appropriateness of visualization outputs of the proposed procedure are analysed. These issues are discussed with reference to a case study located in the old town of Cosenza (Calabria, Italy).

Moving to 3-D flood hazard maps for enhancing risk communication

Flood simulation models can provide practitioners of Flood Disaster Management with sophisticated estimates of floods. Despite the advantages that flood simulation modeling may provide, experiences have proven that these models are of limited use. Until now, this problem has mainly been investigated by evaluations of which information is demanded by decision-makers versus what models can actually offer. However, the goal of this study is to investigate how model information is exchanged among participants in flood disaster organizations and how this exchange affects the use of modeling information. Our findings indicate that the extent to which a model is useful not only depends on the type and quality of its output, but also on how fast and flexible a model can be. In addition, methods of model use are required that support a fast exchange of model information between participants in the flood disaster organization. Decisions during flood disasters are little supported by information from flood models.The practical process of decision-making is not well acknowledged in existing evaluations about the limited use of models.Decisions happen in a dynamic network of participants, exchanging information and knowledge.Social Network Analysis provides insight in how model information flows through this network.Delays, caused by technical and organizational limitations, are the main barrier for model use.

/pdf/why-are-decisions-in-flood-disaster-management-so-poorly-191qimcpx7.pdf

W. Schuurmans

Papers

Why are decisions in flood disaster management so poorly supported by information from flood models