Evaluation of ASTER GDEM and SRTM and their suitability in hydraulic modelling of a glacial lake outburst flood in southeast Tibet

The resolution and accuracy of digital elevation models (DEMs) can affect the hydraulic simulation results for predicting the effects of glacial lake outburst floods (GLOFs). However, for the Tibetan Plateau, high‐quality DEM data are often not available, leaving researchers with near‐global, freely available DEMs, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and the Shuttle Radar Topography Mission data (SRTM) for hydraulic modelling. This study explores the suitability of these two freely available DEMs for hydraulic modelling of GLOFs. Our study focused on the flood plain of a potentially dangerous glacial lake in southeastern Tibet, to evaluate the elevation accuracy of ASTER GDEM and SRTM, and their suitability for hydraulic modelling of GLOFs. The elevation accuracies of ASTER GDEM and SRTM were first validated against field global position system (GPS) survey points, and then evaluated with reference to the relatively high precision of 1:50 000 scale DEM (DEM5) constructed from aerial photography. Moreover, the DEM5, ASTER GDEM and SRTM were used as basic topographic data to simulate peak discharge propagation, as well as flood inundation extent and depth in the Hydrologic Engineering Center's River Analysis System one‐dimensional hydraulic model. Results of the three DEM predictions were compared to evaluate the suitability of ASTER GDEM and SRTM for GLOF hydraulic modelling. Comparisons of ASTER GDEM and SRTM each with DEM5 in the flood plain area show root‐mean‐square errors between the former two as ± 15·4 m and between the latter two as ± 13·5 m. Although SRTM overestimates and ASTER GDEM underestimates valley floor elevations, both DEMs can be used to extract the elevations of required geometric data, i.e. stream centre lines, bank lines and cross sections, for flood modelling. However, small errors still exist in the cross sections that may influence the propagation of peak discharge. The flood inundation extent and mean water depths derived from ASTER GDEM predictions are only 2·2% larger and 2·3‐m deeper than that of the DEM5 predictions, whereas the SRTM yields a flood zone extent 6·8% larger than the DEM5 prediction and a mean water depth 2·4‐m shallower than the DEM5 prediction. The modelling shows that, in the absence of high‐precision DEM data, ASTER GDEM or SRTM DEM can be relied on for simulating extreme GLOFs in southeast Tibet. Copyright © 2011 John Wiley & Sons, Ltd.     

An assessment of large-scale flood modelling based on LiDAR data

Large-scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high-resolution (1 m) LiDAR DEMs are employed to present a novel large-scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large-scale 1D/2D flood model LISFLOOD-FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS-automated procedure allows to obtain the average width required to run LISFLOOD-FP. The GIS-automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10-m resampled LiDAR DEM. Results show significant improvements to large-scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.     

Evaluation of remotely‐sensed DEMs and modification based on plausibility rules and initial sediment budgets of an artificially‐created catchment

To quantify landscape change resulting from processes of erosion and deposition and to establish spatially distributed sediment budgets, ‘models of change’ can be established from a time series of digital elevation models (DEMs). However, resolution effects and measurement errors in DEMs may propagate to these models. This study aimed to evaluate and to modify remotely‐sensed DEMs for an improved quantification of initial sediment mass changes in an artificially‐created catchment. DEMs were constructed from photogrammetry‐based, airborne (ALS) and ground‐based laser scanning (TLS) data. Regions of differing morphological characteristics and vegetation cover were delineated. Three‐dimensional (3D) models of volume change were established and mass change was derived from these models. DEMs were modified region‐by‐region for rill, interrill and alluvial areas, based on logical and hydro‐geomorphological principles. Additional DEMs were constructed by combining multi‐source, modified data. Models were evaluated by comparison with d‐GPS reference data and by considering sediment budget plausibility. Comprehensive evaluation showed that DEM usability depends on a relation between the technique used to obtain elevation data, surface morphology and vegetation cover characteristics. Photogrammetry‐based DEMs were suited to quantification of change in interrill areas but strongly underestimated surface lowering in erosion rills. TLS DEMs were best suited to rill areas, while ALS DEMs performed best in vegetation‐covered alluvial areas. Agreement with reference data and budget plausibility were improved by modifications to photogrammetry‐ and TLS‐based DEMs. Results suggest that artefacts in DEMs can be reduced and hydro‐geomorphic surface structures can be better represented by applying region‐specific modifications. Photogrammetry‐based DEMs can be improved by combining higher and lower resolution data in defined structural units and applying modifications based on principles given by characteristic hydro‐geomorphic evolution. Results of the critical comparative evaluation of remotely‐sensed elevation data can help to better interpret DEM‐based quantifications of earth‐surface processes. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluation of DEM generation based on Interferometric SAR using TanDEM-X data in Tokyo

This study is focused on the evaluation of a Digital Elevation Model (DEM) for Tokyo, Japan from data collected by the recently launched TerraSAR add-on for Digital Elevation Measurements (TanDEM-X), satellite of the German Aerospace Center (DLR). The aim of the TanDEM-X mission is to use Interferometric SAR techniques to generate a consistent high resolution global DEM dataset. In order to generate an accurate global DEM using TanDEM-X data, it is important to evaluate the accuracy at different sites around the world. Here, we report our efforts to generate a high-resolution DEM of the Tokyo metropolitan region using TanDEM-X data. We also compare the TanDEM-X DEM with other existing DEMs for the Tokyo region. Statistical techniques were used to calculate the elevation differences between the TanDEM-X DEM and the reference data. Two high-resolution LiDAR DEMs are used as independent reference data. The vertical accuracy of the TanDEM-X DEM evaluated using the Root Mean Square Error (RMSE) is considerably higher than the existing global digital elevation models. However, the local area DEM generated by Geospatial Information Authority of Japan (GSI DEM) showed the highest accuracy among all non-LiDAR DEM’s. The vertical accuracy in terms of RMSE estimated using the 2 m LiDAR as reference is 3.20 m for TanDEM-X, 2.44 m for the GSI, 7.00 m for SRTM DEM and 10.24 m for ASTER-GDEM. We also compared the accuracy of TanDEM-X with the other DEMs for different types of land cover classes. The results show that the absolute elevation error of TanDEM-X is higher for urban and vegetated areas, likewise to those observed for other global DEM’s. This is probably because the radar signals used by TanDEM-X tend to measure the first reflective surface that is encountered, which is often the top of the buildings or canopy. Hence, the TanDEM-X based DEM is more akin to a Digital Surface Model (DSM).     

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, digital surface models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub‐metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. This paper describes the development of a LiDAR post‐processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post‐processing produces a digital terrain model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR‐derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ～1 m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a two‐dimensional finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features (such as buildings and roads) and taller vegetation features (such as trees and hedges). This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features. Copyright © 2007 John Wiley & Sons, Ltd.     

Application of satellite products and hydrological modelling for flood early warning

Floods have caused devastating impacts to the environment and society in Awash River Basin, Ethiopia. Since flooding events are frequent, this marks the need to develop tools for flood early warning. In this study, we propose a satellite based flood index to identify the runoff source areas that largely contribute to extreme runoff production and floods in the basin. Satellite based products used for development of the flood index are CMORPH (Climate Prediction Center MORPHing technique: 0.25^° by 0.25^°, daily) product for calculation of the Standard Precipitation Index (SPI) and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for calculation of the Topographic Wetness Index (TWI). Other satellite products used in this study are for rainfall-runoff modelling to represent rainfall, potential evapotranspiration, vegetation cover and topography. Results of the study show that assessment of spatial and temporal rainfall variability by satellite products may well serve in flood early warning. Preliminary findings on effectiveness of the flood index developed in this study indicate that the index is well suited for flood early warning. The index combines SPI and TWI, and preliminary results illustrate the spatial distribution of likely runoff source areas that cause floods in flood prone areas.     

Evaluating DEM source and resolution uncertainties in the Soil and Water Assessment Tool

DEMs as important input parameters of environmental risk assessment models are notable sources of uncertainties. To illustrate the effect of DEM grid size and source on model outputs, a widely used watershed management model, the Soil and Water Assessment Tool (SWAT), was applied with two newly available DEMs as inputs (i.e. ASTER GDEM Version 1, and SRTM Version 4.1). A DEM derived from 1:10,000 high resolution digital line graph (DLG) was used as a baseline for comparisons. Eleven resample resolutions, from 5 to 140?m, were considered to evaluate the impact of DEM resolution on SWAT outputs. Results from a case study in South-eastern China indicate that the SWAT predictions of total phosphorus and total nitrogen decreased substantially with coarser resample resolution. A slightly decreasing trend was found in the SWAT predicted sediment when DEMs were resampled to coarser resolutions. The SWAT predicted runoff was not sensitive to resample resolution. For different data sources, ASTER GDEM did not perform better than SRTM in SWAT simulations even it was provided with a smaller grid size and higher vertical accuracy. The predicted outputs based on ASTER GDEM and SRTM were similar, and much lower than the ones based on DLG. This study presents potential uncertainties introduced by DEM resolutions and data sources, and recommends strategies choosing DEMs based on research objects and maximum acceptable errors.     

Assessment of the Accuracy of SRTM C- and X-Band High Mountain Elevation Data: a Case Study of the Polish Tatra Mountains

Global digital elevation models (DEMs) are an invaluable source of information in large area studies. Of particular interest are shuttle radar topography mission (SRTM) data that are freely available for the scientific community worldwide. Prior to any application, global datasets should be evaluated using reference data of higher accuracy. Therefore, the main objective of this study was to assess the accuracy of the SRTM C-band (version 4) DEM and SRTM X-band DEM of mountainous areas located in Poland and to examine the quality of data in relation to topographic parameters, radar beam geometry, initial voids in data and the presence of forest cover. A DEM from the Central National Geodetic and Cartographic Inventory, Poland, served as a reference. The study consisted of three steps: (i) the computation of vertical errors of the SRTM C- and X-band DEMs, (ii) the examination of any systematic bias in the data, and (iii) the analysis of the relationships between the elevation errors and terrain slope, aspect, local incidence angle, occurrence of voids and land cover. We found that the SRTM C- and X-band DEMs have mean errors equal to 4.31 ± 14.09 and 9.03 ± 37.40 m and root mean square errors equal to 14.74 and 38.47 m, respectively. Only 82 % of the C-band DEM and 74 % of the X-band DEM vertical errors had absolute values below 16 m. We found that the most important factors determining the occurrence of high errors were the distribution of initial voids and high slope angles for the C-band DEM, and local incidence angle, slope, aspect and radar beam geometry for the X-band DEM. In both cases, the presence of forest cover increased the mean error by approximately 10 m.     

Two‐dimensional hydraulic flood modelling using a finite‐element mesh decomposed according to vegetation and topographic features derived from airborne scanning laser altimetry

Airborne scanning laser altimetry (LiDAR) is an important new data source that can provide two‐dimensional river flood models with spatially distributed floodplain topography for model bathymetry, together with vegetation heights for parameterization of model friction. Methods are described for improving such models by decomposing the model's finite‐element mesh to reflect floodplain vegetation features such as hedges and trees having different frictional properties to their surroundings, and significant floodplain topographic features having high height curvatures. The decomposition is achieved using an image segmentation system that converts the LiDAR height image into separate images of surface topography and vegetation height at each point. The vegetation height map is used to estimate a friction factor at each mesh node. The spatially distributed friction model has the advantage that it is physically based, and removes the need for a model calibration exercise in which free parameters specifying friction in the channel and floodplain are adjusted to achieve best fit between modelled and observed flood extents. The scheme was tested in a modelling study of a flood that occurred on the River Severn, UK, in 1998. A satellite synthetic aperture radar image of flood extent was used to validate the model predictions. The simulated hydraulics using the decomposed mesh gave a better representation of the observed flood extent than the more simplistic but computationally efficient approach of sampling topography and vegetation friction factors on to larger floodplain elements in an undecomposed mesh, as well as the traditional approach using no LiDAR‐derived data but simply using a constant floodplain friction factor. Use of the decomposed mesh also allowed velocity variations to be predicted in the neighbourhood of vegetation features such as hedges. These variations could be of use in predicting localized erosion and deposition patterns that might result in the event of a flood. Copyright © 2003 John Wiley & Sons, Ltd.     

Unstructured mesh generation and landcover-based resistance for hydrodynamic modeling of urban flooding

Urban flood inundation modeling with a hydrodynamic flow solver is addressed in this paper, focusing on strategies to effectively integrate geospatial data for unstructured mesh generation, building representation and flow resistance parameterization. Data considered include Light Detection and Ranging (LiDAR) terrain height surveys, aerial imagery and vector datasets such as building footprint polygons. First, a unstructured mesh-generation technique we term the building-hole method (BH) is developed whereby building footprint data define interior domain boundaries or mesh holes. A wall boundary condition depicts the impact of buildings on flood hydrodynamics. BH provides an alternative to the more commonly used method of raising terrain heights where buildings coincide with the mesh. We term this the building-block method (BB). Application of BH and BB to a flooding site in Glasgow, Scotland identifies a number of tradeoffs to consider at resolutions ranging from 1 to 5 m. At fine resolution, BH is shown to be similarly accurate but execute faster than BB. And at coarse resolution, BH is shown to preserve the geometry of buildings and maintain better accuracy than BB, but requires a longer run time. Meshes that ignore buildings completely (no-building method or NB) also support surprisingly good flood inundation predictions at coarse resolution compared to BH and BB. NB also supports faster execution times than BH at coarse resolution because the latter uses localized refinements that mandate a greater number of computational cells. However, with mesh refinement, NB converges to a different (and presumably less-accurate) solution compared to BH and BB. Using the same test conditions, Hunter et al. [Hunter NM, Bates PD, Neelz S, Pender G, Villanueva I, Wright NG, Liang D, et al. Benchmarking 2D hydraulic models for urban flood simulations. ICE J Water Manage 2008;161(1):13–30] compared the performance of dynamic-wave and diffusive-wave models and reported that diffusive-wave models under-predicted the longitudinal penetration of the flood zone due to important inertial effects. Here, we find that a relatively coarse-mesh implementation of a dynamic-wave model suffers from the same drawback because of numerical diffusion. This shows that whether diffusion is achieved through the mathematics or numerics, the effect on flood extent is similar. Finally, several methods of distributing resistance parameters (e.g., Manning n) across the Glasgow site were evaluated including methods that utilize aerial imagery-based landcover classification data, MasterMap^® landcover classification data and LiDAR-based feature height data (e.g., height of shrubs or hedges). Results show that landcover data is more important than feature height data in this urban site, that shadows in aerial imagery can cause errors in landcover classification which degrade flood predictions, and that aerial imagery offers a more detailed mapping of trees and bushes than MasterMap^® which can locally impact depth predictions but has little impact on flood extent.     

Evaluating the performance of topobathymetric LiDAR to support multi-dimensional flow modelling in a gravel-bed mountain stream

Stream biophysical processes are commonly studied using multi-dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high-resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground-based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR-derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR-derived topobathymetry in support of multi-dimensional flow modelling and ecohydraulics calculations in two gravel-bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km-long stream segment) along a 15 m-wide river in central Idaho, USA. We compared metre and sub-metre-resolution DEMs generated from RTK-GPS ground and Experimental Advanced Airborne Research LiDAR-B (EAARL-B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two-dimensional (2D) hydraulic models supported by LiDAR and ground-surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground-survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s⁻¹ (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m⁻²) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s⁻¹ and SD = 0.22 m s⁻¹ for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.     

Bank erosion in agricultural drainage networks: new challenges from structure‐from‐motion photogrammetry for post‐event analysis

Drainage channels are an integral part of agricultural landscapes, and their impact on catchment hydrology is strongly recognized. In cultivated and urbanized floodplains, channels have always played a key role in flood protection, land reclamation, and irrigation. Bank erosion is a critical issue in channels. Neglecting this process, especially during flood events, can result in underestimation of the risk in flood‐prone areas. The main aim of this work is to consider a low‐cost methodology for the analysis of bank erosion in agricultural drainage networks, and in particular for the estimation of the volumes of eroded and deposited material. A case study located in the Veneto floodplain was selected. The research is based on high‐resolution topographic data obtained by an emerging low‐cost photogrammetric method (structure‐from‐motion or SfM), and results are compared to terrestrial laser scanning (TLS) data. For the SfM analysis, extensive photosets were obtained using two standalone reflex digital cameras and an iPhone5® built‐in camera. Three digital elevation models (DEMs) were extracted at the resolution of 0.1 m using SfM and were compared with the ones derived by TLS. Using the different DEMs, the eroded areas were then identified using a feature extraction technique based on the topographic parameter Roughness Index (RI). DEMs derived from SfM were effective for both detecting erosion areas and estimating quantitatively the deposition and erosion volumes. Our results underlined how smartphones with high‐resolution built‐in cameras can be competitive instruments for obtaining suitable data for topography analysis and Earth surface monitoring. This methodology could be potentially very useful for farmers and/or technicians for post‐event field surveys to support flood risk management. Copyright © 2015 John Wiley & Sons, Ltd.     

Topographic structure from motion: a new development in photogrammetric measurement

The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image‐based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three‐dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image‐based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high‐resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three‐dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand‐held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low‐altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.     

Extracting drainage networks and their connectivity using LiDAR data

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north‐eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.     

Improvement of streams hydro‐geomorphological assessment using LiDAR DEMs

Hydro‐geomorphological assessments are an essential component for riverine management plans. They usually require costly and time‐consuming field surveys to characterize the spatial variability of key variables such as flow depth, width, discharge, water surface slope, grain size and unit stream power throughout the river corridor. The objective of this research is to develop automated tools for hydro‐geomorphological assessments using high‐resolution LiDAR digital elevation models (DEMs). More specifically, this paper aims at developing geographic information system (GIS) tools to extract channel slope, width and discharge from 1 m‐resolution LiDAR DEMs to estimate the spatial distribution of unit stream power in two contrasted watersheds in Quebec: a small agricultural stream (Des Fèves River) and a large gravel‐bed river (Matane River). For slope, the centreline extracted from the raw LiDAR DEM was resampled at a coarser resolution using the minimum elevation value. The channel width extraction algorithm progressively increased the centerline from the raw DEM until thresholds of elevation differences and slopes were reached. Based on the comparison with over 4000 differential global positioning system (GPS) measurements of the water surface collected in a 50 km reach of the Matane River, the longitudinal profile and slope estimates extracted from the raw and resampled LiDAR DEMs were in very good agreement with the field measurements (correlation coefficients ranging from 0 · 83 to 0 · 87) and can thus be used to compute stream power. The extracted width also corresponded very well to the channel as seen from ortho‐photos, although the presence of bars in the Matane River increased the level of error in width estimates. The estimated maximum unit stream power spatial patterns corresponded well with field evidence of bank erosion, indicating that LiDAR DEMs can be used with confidence for initial hydro‐geomorphological assessments. Copyright © 2013 John Wiley & Sons, Ltd.     

Mapping detention basins and deriving their spatial attributes from airborne LiDAR data for hydrological applications

This paper presents a new technique for mapping detention basins and measuring their spatial attributes using high‐resolution airborne LiDAR (Light Detection and Ranging) data. An efficient least‐cost search algorithm is employed to identify surface depressions from a bare‐earth LiDAR digital elevation model (DEM). Surface depressions are automatically delineated into hydrological objects using the connected component identification and indexing algorithm. Various spatial attributes are derived for these hydrologic objects, including location, perimeter, surface area, depth, storage volume and shape properties. Based on spatial attributes, a rule‐based classifier is established to separate detention basins from other types of surface depressions. We have successfully applied our technique to an urban watershed in the Houston Metropolitan area, Texas. Detention basins at regional and residential subdivision levels are mapped out for the watershed, and measurements on the spatial attributes are derived for each detention basin. The quantitative information derived from LiDAR data provides a scientific basis for formulating an appropriate management plan for detention basins and for assessing their effects on flood control and storm water quality treatment. Copyright © 2007 John Wiley & Sons, Ltd.     

The potential of digital filtering of generic topographic data for geomorphological research

High resolution terrain models generated from widely available Interferometric Synthetic Aperture Radar (IfSAR) and digital photogrammetry are an exciting resource for geomorphological research. However, these data contain error, necessitating pre‐processing to improve their quality. We evaluate the ability of digital filters to improve topographic representation, using: (1) a Gaussian noise removal filter; (2) the proprietary filters commonly applied to these datasets; and (3) a terrain sensitive filter, similar to those applied to laser altimetry data. Topographic representation is assessed in terms of both absolute accuracy measured with reference to independent check data and derived geomorphological variables (slope, upslope contributing area, topographic index and landslide failure probability) from a steepland catchment in northern England. Results suggest that proprietary filters often degrade or fail to improve precision. A combination of terrain sensitive and Gaussian filters performs best for both IfSAR and digital photogrammetry datasets, improving the precision of photogrammetry digital elevation models (DEMs) by more than 50 per cent relative to the unfiltered data. High‐frequency noise and high‐magnitude gross errors corrupt geomorphological variables derived from unfiltered photogrammetry DEMs. However, a terrain sensitive filter effectively removes gross errors and noise is minimized using a Gaussian filter. These improvements propagate through derived variables in a landslide prediction model, to reduce the area of predicted instability by up to 29 per cent of the study area. Interferometric Synthetic Aperture Radar is susceptible to removal of topographic detail by oversmoothing and its errors are less sensitive to filtering (maximum improvement in precision of 5 per cent relative to the raw data). Copyright © 2008 John Wiley and Sons, Ltd.     

Testing new sources of topographic data for flood propagation modelling under structural,parameter and observation uncertainty

ABSTRACT

This study assessed the utility of EUDEM, a recently released digital elevation model, to support flood inundation modelling. To this end, a comparison with other topographic data sources was performed (i.e. LIDAR, light detection and ranging; SRTM, Shuttle Radar Topographic Mission) on a 98-km reach of the River Po, between Cremona and Borgoforte (Italy). This comparison was implemented using different model structures while explicitly accounting for uncertainty in model parameters and upstream boundary conditions. This approach facilitated a comprehensive assessment of the uncertainty associated with hydraulic modelling of floods. For this test site, our results showed that the flood inundation models built on coarse resolutions data (EUDEM and SRTM) and simple one-dimensional model structure performed well during model evaluation.

Editor Z.W. Kundzewicz; Associate editor S. Weijs