74 research outputs found
Digital Elevation Models for topographic characterisation and flood flow modelling along low-gradient, terminal dryland rivers:A comparison of spaceborne datasets for the RĂo Colorado, Bolivia
Get PDFPerspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM
Get PDFOpen-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30 m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in data-sparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a âmust-haveâ for any flood model. A high-accuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation
Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation
Get PDFAt least 10 global digital elevation models (DEMs) at one-arc-second resolution now cover Earth. Comparing derived grids, like slope or curvature, preserves surface spatial relationships, and can be more important than just elevation values. Such comparisons provide more nuanced DEM rankings than just elevation root mean square error (RMSE) for a small number of points. We present three new comparison categories: fraction of unexplained variance (FUV) for grids with continuous floating point values; accuracy metrics for integer code raster classifications; and comparison of stream channel vector networks. We compare six global DEMs that are digital surface models (DSMs), and four edited versions that use machine learning/artificial intelligence techniques to create a bare-earth digital terrain model (DTM) for different elevation ranges: full Earth elevations, under 120 m, under 80 m, and under 10 m. We find edited DTMs improve on elevation values, but because they do not incorporate other metrics in their training they do not improve overall on the source Copernicus DSM. We also rank 17 common geomorphic-derived grids for sensitivity to DEM quality, and document how landscape characteristics, especially slope, affect the results. None of the DEMs perform well in areas with low average slope compared to reference DTMs aggregated from 1 m airborne lidar data. This indicates that accurate work in low-relief areas grappling with global climate change should use airborne lidar or very high resolution image-derived DTMs
DEMIX Method Ranks COPDEM and FABDEM as Top 1'' Global DEMs
Get PDFWe present a practical approach to inter-compare a range of candidate digital
elevation models (DEMs) based on pre-defined criteria and statistically sound
ranking approach. The presented approach integrates the randomized complete
block design (RCBD) into a novel framework which has been named the DEMIX wine
contest. Ranking a collection of wines or a set of DEMs from a given set of
candidates leads to a mathematically similar problem. The method presented
provides a flexible, statistically sound and customizable tool for evaluating
the quality of any raster - in this case a DEM - by means of a ranking
approach, which takes into account a confidence level, and can use both
quantitative and qualitative criteria. The users can design their own criteria
for the quality evaluation in relation to their specific needs. The application
of the wine contest to six 1'' global DEMs, considering a wide set of study
sites, covering different morphological and landcover settings, highlights the
potentialities of the approach. We used a suite of criteria relating to the
differences in the elevation, slope, and roughness distributions compared to
reference DEMs aggregated from 1-5 m lidar-derived DEMs to 1 second DEM.
Results confirmed significant superiority of COPDEM and its derivative FABDEM
as the overall best 1'' global DEMs. They are slightly better than ALOS, and
clearly outperform NASADEM and SRTM, which are in turn much better than ASTER
Increased population exposure to Amphanâscale cyclones under future climates
Get PDFInternational audienceAbstract Southern Asia experiences some of the most damaging climate events in the world, with loss of life from some cyclones in the hundreds of thousands. Despite this, research on climate extremes in the region is substantially lacking compared to other parts of the world. To understand the narrative of how an extreme event in the region may change in the future, we consider Super Cyclone Amphan, which made landfall in May 2020, bringing storm surges of 2â4 m to coastlines of India and Bangladesh. Using the latest CMIP6 climate model projections, coupled with storm surge, hydrological, and socioâeconomic models, we consider how the population exposure to a storm surge of Amphan's scale changes in the future. We vary future sea level rise and population changes consistent with projections out to 2100, but keep other factors constant. Both India and Bangladesh will be negatively impacted, with India showing >200% increased exposure to extreme storm surge flooding (>3 m) under a high emissions scenario and Bangladesh showing an increase in exposure of >80% for lowâlevel flooding (>0.1 m). It is only when we follow a lowâemission scenario, consistent with the 2°C Paris Agreement Goal, that we see no real change in Bangladesh's storm surge exposure, mainly due to the population and climate signals cancelling each other out. For India, even with this lowâemission scenario, increases in flood exposure are still substantial (>50%). While here we attribute only the storm surge flooding component of the event to climate change, we highlight that tropical cyclones are multifaceted, and damages are often an integration of physical and social components. We recommend that future climate risk assessments explicitly account for potential compounding factors
Digital Elevation Models: Terminology and Definitions
Get PDFDigital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earthâs surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background
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