Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions

With the extensive use of 2D flood models, the resolution and quality of Digital Elevation Models (DEMs) have come under greater focus especially in urban hydrology. One of the major research areas, in this regard, is the effect of DEM resolution on flood modelling. This study first investigates the root causes of the impact of DEM resolution on urban fluvial flood modelling outputs using DEMs with grid resolutions ranging from 1 m to 50 m. The study then investigates how DEM resolution affects the definition and characterisation of the river channel and the consequences of this for the modelled results. For this purpose, a separate set of merged DEMs was generated where the river channel as defined by the 1 m resolution DEM is merged with coarser resolution DEMs. Data obtained during the flood event caused by Storm Desmond (2015) in Cockermouth (Cumbria, UK) was used for this study. The HEC-RAS 2D model was used for all of the simulations. The benchmark model obtained with the 1 m resolution DEM was calibrated using measured water levels at two locations within the rivers. Results show that there is a 30% increase in flood extent from 58.9 ha to 79.0 ha and a 150% increase in mean flood depth from 1.74 m to 4.30 m when the resolution reduces from a 1 m grid to a 50 m grid. The main reason for this is the increasing lack of definition of the river channel with an associated reduction in the estimated depth of the river resulting in reduced river channel conveyance. This then leads to an increase in the flood extent and depth especially in the immediate vicinity of the river. This effect is amplified when the DEM grid size is greater than the river width. When the 1 m resolution DEM for the river channel is used in conjunction with coarser resolution DEMs for the surrounding areas (merged DEMs), there is a significant improvement in the agreement between the modelled and the reference case (obtained from the benchmark model) flood extents and depths. The use of merged DEMs reduces the error in mean flood depth from 90% to 4% and reduces the overall RMSE in flood depths from 2.6 m to 0.9 m at 30 m resolution. The use of merged DEMs, where a higher resolution DEM is used to characterise the river channel in conjunction with, for example, a 30 m resolution DEM (e.g., the freely available NASA Shuttle Radar Topography Mission DEMs) for the wider area could be a cost-effective solution for locations where higher resolution DEMs may not be available.

Slope is an important topographical parameter in hydrological models. Dramatic changes in slope are produced due to micro variations of topography in flow diversion terrace (FDT) systems. Assessing the effectiveness of FDT on soil conservation yields different results using different slope calculation methods with different resolution and accuracy digital elevation models (DEMs). In this study, the ArcGIS built-in average-neighborhood-slope (ANS) method and a downhill-slope (DHS) method were used to calculate slopes for a small agricultural watershed in New Brunswick, Canada. Six DEMs were used, i.e., 1, 5 and 10 m resolution DEMs generated with conventional photogrammetric techniques, and 1, 5, and 10 m resolution DEMs derived from light detection and ranging (LiDAR) data. Calculated slopes were summarized for the whole watershed, a patch of FDT systems, and grassed flow channels in front of embankments in the patch. Results indicated that the DHS method produced smaller slopes than the ANS method with LiDAR-1m DEM along grasses flow channels. Furthermore, the mean L-factor derived from the DHS method was smaller than that derived from the ANS in terraces and grassed flow channels for the LiDAR-1m DEM. Finally, soil loss estimated from the DHS method using RUSLE was more consistent with measurement for the watershed than that from the ANS method when the LiDAR-1m DEM was used. The DHS method can provide accurate slopes along grassed flow channels and can be used to assess FDT systems using USLE-based models with high resolution and accuracy DEMs.

To understand how the produced flood boundaries from hydraulic modeling can be affected by the resolution of the Digital Elevation Model (DEM) and the roughness values used, and how different performance measures can affect the choice of most optimal models, different simulations were conducted using varying pairs of DEM and Manning’s n. Each result was then compared to the reference flood data and analyzed using different performance measures that account for the sizes of inundation (F-statistics), and the distance between the modeled and reference flood (disparity measures). The results showed that the different pairs of DEM resolution and Manning’s n produced different performance scores and flood extents. High-resolution DEMs performed generally better with higher resolution DEMs, while lower resolution DEMs received better performance when paired with lower Manning’s n. The different model performances also produced different combinations having the most optimal performances depending on the assumptions they applied in the computations. Additionally, the results showed that models from coarser resolution DEMs can produce higher maximum scores than higher resolution DEMs. However, when the maps, cross-sectional profiles, and the water surface elevations from the coarser resolution DEMs were compared with the higher resolution DEMs, they are more inaccurate in representing the terrain, flood boundaries, and the water depths. Thus, interpreting model results only based on performance measure, particularly for lower resolution DEMs, may give incorrect conclusion on the optimality of the result. It is important that the flood extent maps, cross-sectional profiles, and the derived water depths are analyzed together with the performance scores to gain better understanding of the reliability of flood model results.

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

The topography of many floodplains in the developed world has now been surveyed with high resolution sensors such as airborne LiDAR (Light Detection and Ranging), giving accurate Digital Elevation Models (DEMs) that facilitate accurate flood inundation modelling. This is not always the case for remote rivers in developing countries. However, the accuracy of DEMs produced for modelling studies on such rivers should be enhanced in the near future by the high resolution TanDEM-X WorldDEM. In a parallel development, increasing use is now being made of flood extents derived from high resolution Synthetic Aperture Radar (SAR) images for calibrating, validating and assimilating observations into flood inundation models in order to improve these. This paper discusses an additional use of SAR flood extents, namely to improve the accuracy of the TanDEM-X DEM in the floodplain covered by the flood extents, thereby permanently improving this DEM for future flood modelling and other studies. The method is based on the fact that for larger rivers the water elevation generally changes only slowly along a reach, so that the boundary of the flood extent (the waterline) can be regarded locally as a quasi-contour. As a result, heights of adjacent pixels along a small section of waterline can be regarded as samples with a common population mean. The height of the central pixel in the section can be replaced with the average of these heights, leading to a more accurate estimate. While this will result in a reduction in the height errors along a waterline, the waterline is a linear feature in a two-dimensional space. However, improvements to the DEM heights between adjacent pairs of waterlines can also be made, because DEM heights enclosed by the higher waterline of a pair must be at least no higher than the corrected heights along the higher waterline, whereas DEM heights not enclosed by the lower waterline must in general be no lower than the corrected heights along the lower waterline. In addition, DEM heights between the higher and lower waterlines can also be assigned smaller errors because of the reduced errors on the corrected waterline heights. The method was tested on a section of the TanDEM-X Intermediate DEM (IDEM) covering an 11 km reach of the Warwickshire Avon, England. Flood extents from four COSMO-SKyMed images were available at various stages of a flood in November 2012, and a LiDAR DEM was available for validation. In the area covered by the flood extents, the original IDEM heights had a mean difference from the corresponding LiDAR heights of 0.5 m with a standard deviation of 2.0 m, while the corrected heights had a mean difference of 0.3 m with standard deviation 1.2 m. These figures show that significant reductions in IDEM height bias and error can be made using the method, with the corrected error being only 60% of the original. Even if only a single SAR image obtained near the peak of the flood was used, the corrected error was only 66% of the original. The method should also be capable of improving the final TanDEM-X DEM and other DEMs, and may also be of use with data from the SWOT (Surface Water and Ocean Topography) satellite.

The objective of this paper is to study the impact of the mesh size of the digital elevation model (DEM) on terrain attributes within an Annualized AGricultural NonPoint Source pollution (AnnAGNPS) Model simulation at watershed scale and provide a correction of slope gradient for low resolution DEMs. The effect of different grid sizes of DEMs on terrain attributes was examined by comparing eight DEMs (30, 40, 50, 60, 70, 80, 90, and 100 m). The accuracy of the AnnAGNPS stimulation on runoff, sediments, and nutrient loads is evaluated. The results are as follows: (1) Rnoff does not vary much with decrease of DEM resolution whereas soil erosion and total nitrogen (TN) load change prominently. There is little effect on runoff simulation of AnnAGNPS modeling by the amended slope using an adjusted 50 m DEM. (2) A decrease of sediment yield and TN load is observed with an increase of DEM mesh size from 30 to 60 m; a slight decrease of sediment and TN load with the DEM mesh size bigger than 60 m. There is similar trend for total phosphorus (TP) variation, but with less range of variation, the simulation of sediment, TN, and TP increase, in which sediment increase up to 1.75 times compared to the model using unadjusted 50 m DEM. In all, the amended simulation still has a large difference relative to the results using 30 m DEM. AnnAGNPS is less reliable for sediment loading prediction in a small hilly watershed. (3) Resolution of DEM has significant impact on slope gradient. The average, minimum, maximum of slope from the various DEMs reduced obviously with the decrease of DEM precision. For the grade of 0∼15°, the slopes at lower resolution DEM are generally bigger than those at higher resolution DEM. But for the grade bigger than 15°, the slopes at lower resolution DEM are generally smaller than those at higher resolution DEM. So it is necessary to adjust the slope with a fitting equation. A cubic model is used for correction of slope gradient from lower resolution to that from higher resolution. Results for Dage watershed showed that fine meshes are desired to avoid large underestimates of sediment and total nitrogen loads and moderate underestimates of total phosphorus loads even with the slopes for the 50 m DEM adjusted to be more similar to the slopes from the 30 m DEM. Decreasing the mesh size beyond this threshold does not substantially affect the computed runoff flux but generated prediction errors for nitrogen and sediment yields. So the appropriate DEM will control error and make simulation at acceptable level.

Digital elevation models (DEM) or digital terrain elevation models (DTED) are widely used in radio propagation simulation and prediction. The resolution of DEM's is getting higher and higher with the latest resolution of centimeters generated by LIDAR (light detection and ranging). These high resolution DEM's provide more realistic representation of the environments and will enhance the accuracy of propagation modeling results. But they also have some drawbacks. First, the storage of high resolution DEM's will cost large computer disk space and slowdown the I/O process. Second, not all propagation scenarios need high resolution DEM's depending on the frequency and range. Third, the DEM's do not explicitly have the three dimensional (3D) information about topographic structures, such as ridges, which is physically important in diffracted field calculations. For example, 3D ridges are key contributors of diffracted field in certain mountainous regions. But many widely used and DEM-based methods (e.g., knife-edge models) ignore the 3D features. Our previous work has shown that the orientation and interior angle of a ridge can cause 3dB difference in path loss prediction for a single ridge. For two ridges, the difference is almost doubled.

Machine Learning (ML) is extensively used in diverse topic domains, including geographical information. Despite its limitations, using Digital Elevation Models (DEMs) is gradually being considered in various operational applications. This study explores the application of machine learning algorithms, Shuttle Radar Topography Mission (SRTM) data with different resolutions, and data collected by Unmanned Aerial Vehicle (UAV) technology to produce high-resolution DEMs. The proposed construction approach is based on eight algorithms Linear Regression, Decision Tree, Random Forest, Ridge, Lasso, SVR, K-Neighbors Regressor, and XGB Regressor to deal with the complex features of urban topography to reconstruct high-resolution urban DEMs. The proposed algorithms were applied to two different study areas in Egypt. The results were contrasted with a reference DEM obtained from the ground measurement data (UAV). The numerical accuracy and terrain feature preserving effects of the Linear Regression algorithm in the first study area can generate reconstructed DEMs that better match the Reference DEMs, show lower mean absolute error (MAE), mean square error (MSE), and improve the accuracy of the terrain parameters by the overall fitness R2 of .971. The results showed that the Linear Regression algorithm is the most accurate with an R2 of .985. Compared to other commonly used methods, the current proposed approach offers a cost-effective and innovative method for acquiring high-resolution DEMs in other data-scarce regions, resulting in superior results. Our research is a comprehensive examination of geographical artificial intelligence (Geo AI), which is a term that refers to the processing of geographic information.

Topography plays an important role in the accuracy of flood inundation maps. Most current flood inundation maps are created using topographic information derived from Light Detection and Ranging (LiDAR) data. Although LiDAR data are very accurate, handling of very high resolution Digital Elevation Models (DEMs) from LiDAR can be computationally challenging and expensive in long river reaches. As a result, the DEMs from LiDAR are sometimes resampled to coarser resolutions to expedite the creation and processing of flood inundation maps. The first objective of this study is to quantify the errors arising from DEM properties such as resolution and accuracy on flood inundation maps using the principle of resampling and error analysis. The results show that water surface elevations and flood inundation area have a linear relationship with the DEM resolution and accuracy. The second objective of this study is to use the linear relationship between the flood inundation extent and the DEM resolution or accuracy to create an approach for developing more accurate flood inundation maps by modeling the spatial distribution of errors using less accurate and coarser resolution DEMs. The results show that the hydraulic modeling outputs obtained from coarser resolution DEM using this approach are comparable to the results obtained using high resolution accurate DEMs. The accuracy of flood inundation maps can be improved significantly by modeling the spatial distribution of DEM errors. Therefore, by using the approach developed in this study, it is possible to obtain more accurate flood maps from less accurate topographic data in areas where LiDAR or any other form of accurate topography information is not available.

UAV-based topographic change detection is widely used in geoscience communities. The change detection involves comparison of two digital elevation models (DEMs) produced by UAV surveys, which are affected by the DEM resolution. Coarse resolution DEMs introduce errors in change detection, but the DEM resolution effect remains poorly understood. Moreover, effective strategies for mitigating the resolution effect have yet to be investigated. This study generated UAV-based DEMs at resolutions ranging from 0.1 m to 10 m with various resampling methods. The impact of DEM resolution on topographic change detection was then evaluated by analyzing the difference of DEM (DoD) and volume budget errors with indices such as mean error (ME), standard deviation (STD), and Moran’s I. The results from two human-altered landscapes showed that the random errors of DoD increase rapidly with the DEM resolution coarsening, while DoD systematic errors (spatial distribution of errors) become stable after 4 m resolution. The volume budget errors also increase with DEM coarsening. Coarser resolution DEMs tend to underestimate the volume budget (gross erosion, gross deposition, and net changes). Moreover, selecting an appropriate method for generating DEM is beneficial in decreasing the errors caused by the resolution effect. Among the seven methods (MAX, MIN, MEAN, BIL, NEAR, NEB, and TIN), the BIL method is optimum for mitigating both DoD and volume errors. The NEAR, NEB, and TIN methods are equivalent, and they are superior to the aggregation methods (MAX, MIN, MEAN). The slope of DoD (SDoD) should be considered when selecting a resolution for change detection. Large errors tend to appear in areas with large SDoD and vice versa. Coarse resolution DEMs are tolerable in areas with low SDoD, while high resolution DEMs are necessary in areas with large SDoD.

At the end of the nineties the emergence of high resolution (1 m) digital elevation models (DEMs) settled the context of high precision geomorphological analysis. These new elevation models permitted to reveal structures that remained heretofore undetectable. Earth scientists henceforth benefit from a field of analysis with a textural richness that was never attained before. However, the complexity and the volume of the data reveal a series of questions and problems. The storage size has increased, the computational processes have become heavier, and the visual or digital interpretation has become more complex. Moreover, these new models make it possible to characterize and analyse much smaller phenomena than previously. Traditional DEMs with resolutions ranging from 10 m to 90 m can be used to analyse a valley or a hillside. Transposed to a cartographic scale, this corresponds at best to a 1 : 25 000 ratio. As for high resolution DEMs, they show much more detailed structural levels and can be used to analyse geomorphological features of 2 – 3 meters, this corresponding to scales ranging from 1 : 10 000 to 1 : 1 000. Yet, in the abundance offered by this growing resolution, large geomorphological structures are still present, including the finer structures. They are even the actuators of processes relevant to larger cartographic scales. Consequently, high resolution DEMs contain a multitude of structures, which exist throughout their interactions with other structures at other scales. This is the context of the present study. Geomorphometry – the quantitative counterpart of exploratory geomorphology – permits to explore and quantify a wide range of shapes and terrain indicators. At higher resolution however, the methods of this discipline can hardly be used. Geomorphometrical methods are based on a geometric model (a quadratic surface) and few of these methods can be applied it in a multiscale context. Furthermore inappropriate techniques are frequently used, hence the idea to move to a multiscale approach called the wavelet transform. The latter had previously been explored by few researchers within the geomorphometry community, but never thoroughly to micro- and to mesoscales. Due to the non-stationarity of DEMs, the wavelet transform was preferred to the Fourier transform in order to decompose DEMs into multiscale spaces. This facilitates a coherent navigation from scale to scale, but also makes new scale specific phenomena emerge for different frequencies. The wavelet transform is a technique widely used in image analysis. It allows decomposing a signal according to its frequency components, but also according to the position of the frequencies in the signal. Its multi-scale capacity is an effective analytical tool in multiple domains. More particularly in geomorphology, structural components – specific to a specific phenomenon – are well determined in these sub-spaces specific to the scale continuum. Finally an in-depth analysis of the phenomena enabled us to understand processes and their and their phenomenological inter-dependencies. In order to understand the effects and outcomes of the approach we developed an artificial landslide. We then computed some profiles and analysed the autocorrelation, slope attenuation and local fractal indicator. The resulting high-pass information of the wavelet transform has also been analysed and filtered using several types of filters. In a case study we used a real-world landslide to validate the transform and to understand its impact on geological structures. Within this case-study we conducted a web-based survey that allowed the participants to analyse the landslide using wavelet results and to make comments on the potential of the wavelet transform in the field of geomorphometry. Moreover, important contributions of this thesis are new algorithms that allow the illustration of the structural coherence in relation to each subspace. These are based on the theory of vision of Marr and on structure tensors. The results of our studies show a high consistency. The wavelet transform thereby extends the range of tools in geomorphometry. The different structural scale levels show that such these methods are needed to better understand the phenomenology of geomorphological processes.

A 10-metre meshed high resolution Digital Elevation Model (DEM) has been integrated with Geographic Information Systems (GIS). High resolution DEM plays a crucial role in future GIS applications such as constructing 3D systems in Japan, but it is not sufficient for precise requirements from various applications. Also, a one-metre high resolution pansharpen image from the IKONOS satellite has been integrated with a digital map on the GIS system at the same time. IKONOS images cover 3-dimensional surfaces constructed by 10 m DEM. A precise GIS map of Kitakyushu City in Japan has been used as a digital map, which is quite accurate.

Digital Elevation Model (DEM) is an important data used in varies geographical researches but it is difficult and costly to obtain a high resolution DEM. This paper evaluates the accuracy of interpolated high resolution DEM established from a low resolution DEM data obtained from an online resource which is freely accessible to researchers and academician. The low resolution DEM available online was obtained from the ASTER Global Digital Elevation Model (ASTER GDEM) project by Japan Space Systems (JSS). Six types of interpolation techniques, namely High Power Inverse Distance Weighted (IDW), Low Power IDW, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging are applied to the low resolution ASTER GDEM to produce the high resolution interpolated DEM. The accuracy of the interpolated DEM is performed by comparing the interpolated DEM with the reference DEM data obtained from the Malaysian Centre for Geospatial Data Infrastructure (MACGDI). Based on the experimental results, the ordinary kriging and universal kriging have shown a good result of 13.99m and 13.95m of average absolute difference respectively. The study suggests that the interpolated DEM generated from ASTER GDEM by JSS can be applied to conduct future studies if high resolution DEM is required.

Cantel Medical Corp, USA

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments