Exploring the sensitivity of coastal inundation modelling to DEM vertical error

© 2018 Informa UK Limited, trading as Taylor & Francis Group. As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover

Evaluación de modelos de elevación digital de acceso libre para estimar áreas de inundación por ascenso de nivel del mar en Cartagena de Indias

El actual aumento en el nivel medio del mar coloca en riesgo a más de 600 millones de personas en el planeta. Una herramienta fundamental en el análisis de la vulnerabilidad frente a este factor es la información espacial en forma de modelos digitales de elevación, que funcionan como la base en la representación de escenarios de inundación que ponen en riesgo a las comunidades costeras. Debido a esto, la calidad de los resultados al analizar los efectos del aumento en el nivel del mar dependerá en gran medida de la calidad de la información espacial utilizada. Actualmente, existen diversas fuentes de información espacial que generan productos para este fin a nivel global; cada uno con características diferentes. En este trabajo se comparan las características de los modelos digitales de elevación ALOS WORLD 3D, SRTM DEM V3 y ASTER GDEM V3, a fin de establecer si estos modelos permiten generar zonas inundables derivadas de escenarios futuros de aumento en el nivel del mar en la ciudad de Cartagena de Indias. Los resultados indican que el modelo ALOS WORLD 3D presenta el menor error vertical en el área de estudio. A pesar de lo anterior, la magnitud en el error vertical de los 3 modelos utilizados supera la recomendada para la generación de zonas inundables en un futuro cercano (2100), por lo que se recomienda el uso de esta información en la delimitación general de zonas vulnerables solamente cuando no existan fuentes de mayor resolución espacial disponibles.PregradoGeólog

Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure

Elevation data are critical for assessments of sea-level rise (SLR) and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of SLR and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a SLR or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (<1 m) of SLR or with shorter planning horizons (<100 years) and thus they should not be used for such mapping, but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative SLR scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative SLR and coastal inundation assessments at high confidence levels

Sampling‐based methods for uncertainty propagation in flood modeling under multiple uncertain inputs: finding out the most efficient choice

In probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field-scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar-wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online

Assessment of inundation risk from sea level rise and critical area for barrier construction: a GIS-based framework and application on the eastern coastal areas of Qatar

Climate has changed throughout geological history as part of the natural process, which consequently altered the extent and the level of seas. However, the rate of these changes has accelerated from the second half of the last century. There is much scientific evidence that climate change has and will continue to accelerate the rate of sea level rise in the 21st century. This creates a significant risk for many countries in terms of flooding, coastal erosion and wetland inundation, which in turn will impact human communities (socially and economically) and ecosystems. It is therefore vital to have reliable strategies for modelling and reducing the impact of climate change. This study provided a methodology based on geospatial technology to provide stakeholders with a decision-making tool for better understanding of uncertainties in climate change study and future flood defence planning. This study integrated the uncertainties from both DEM and RCPs to provide a better projection of flooding that results from sea level rise. The study also looked at the errors and the spatial autocorrelation aspect and provides evidence that the independency of the error did not improve the outcomes significantly. Identifying the critical area in studying sea level rise inundation is crucially important for the decision makers to plan to and prevent future flooding by building barriers. This study developed a method to include the factors affecting the site selection by integrating the multi-criterial evaluation with GIS tool for site selection. In Qatar and many other countries, industrial activities, especially from the oil and gas industry, are concentrated in the coastal areas. The economic benefit of protecting the coastal areas from flooding is important for the wider economy of the country. Therefore, prioritising the areas based on the risk of flooding and identification of the critical areas to build barriers will help in making decisions on future investments by governments and companies operating in those areas

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD