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Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)

Most estimates of global mean sea-level rise this century fall below 2 m. This quantity is comparable to the positive vertical bias of the principle digital elevation model (DEM) used to assess global and national population exposures to extreme coastal water levels, NASA’s SRTM. CoastalDEM is a new DEM utilizing neural networks to reduce SRTM error. Here we show – employing CoastalDEM—that 190 M people (150–250 M, 90% CI) currently occupy global land below projected high tide lines for 2100 under low carbon emissions, up from 110 M today, for a median increase of 80 M. These figures triple SRTM-based values. Under high emissions, CoastalDEM indicates up to 630 M people live on land below projected annual flood levels for 2100, and up to 340 M for mid-century, versus roughly 250 M at present. We estimate one billion people now occupy land less than 10 m above current high tide lines, including 230 M below 1 m. Accurate estimates of global mean sea-level rise are important. Here the authors employ a new digital elevation model (DEM) utilizing neural networks and show that the new DEM more than triples the NASA SRTM-based estimates of current global population occupying land below projected sea levels in 2100, with more than 200 million people could be affected based on RCP4.5 and 2 degC of warming.

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New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding.

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

High‐resolution raster hydrography maps are a fundamental data source for many geoscience applications. Here we introduce MERIT Hydro, a new global flow direction map at 3‐arc sec resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body data sets (G1WBM, Global Surface Water Occurrence, and OpenStreetMap). We developed a new algorithm to extract river networks near automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand editing, the constructed hydrography map shows good agreement with existing quality‐controlled river network data sets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite‐based global river channel data. Relative error in the drainage area was <0.05 for 90% of Global Runoff Data Center (GRDC) gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography data sets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high‐quality baseline geospatial data sets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales. Plain Language Summary Rivers play important roles in global hydrological and biogeochemical cycles, and many socioeconomic activities also depend on water resources in river basins. Global‐scale frontier studies of river networks and surface waters require that all rivers on the Earth are precisely mapped at high resolution, but until now, no such map has been produced. Here we present “MERIT Hydro,” the first high‐resolution, global map of river networks developed by combining the latest global map of land surface elevation with the latest maps of water bodies that were built using satellites and open databases. Surface flow direction of each 3‐arc sec pixel (~90‐m size at the equator) is mapped across the entire globe except Antarctica, and many supplemental maps (such as flow accumulation area, river width, and a vectorized river network) are generated. MERIT Hydro thus represents a major advance in our ability to represent the global river network and is a data set that is anticipated to enhance a wide range of geoscience applications including flood risk assessment, aquatic carbon emissions, and climate modeling.

MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset

Sandy beaches occupy more than one-third of the global coastline1 and have high socioeconomic value related to recreation, tourism and ecosystem services2. Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones3. However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological4,5, geological6 and anthropogenic factors1,7. A substantial proportion of the world’s sandy coastline is already eroding1,7, a situation that could be exacerbated by climate change8,9. Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world’s sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures.

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Sandy coastlines under threat of erosion

Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors Here we introduce a high-accuracy global DEM at 3″ resolution (~90 m at the equator) by eliminating major error components from existing DEMs We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58% Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill-valley structures, became clearly represented We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (eg, Ganges, Nile, Niger, and Mekong) and swamp forests (eg, Amazon, Congo, and Vasyugan) The newly developed DEM will enhance many geoscience applications which are terrain dependent

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A high-accuracy map of global terrain elevations

Bare-Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models

MERIT DEM: A new high-accuracy global digital elevation model and its merit to global hydrodynamic modeling

Flood disasters have regularly been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and re...

Multi return periods flood hazards and risks assessment in the Congo River Basin

We propose a machine learning‐based approach to estimate the flood defense standard (FDS) for unlabeled sites. We adopted random forest regression (RFR) to characterize the relationship between the declared FDS and 10 explanatory factors contained in publicly available data sets. We compared RFR with multiple linear regression (MLR) and demonstrated the proposed approach in the conterminous United States (CONUS) and England, respectively. The results showed the following: (a) RFR performed better than MLR, with a Nash–Sutcliffe efficiency of 0.85 in the CONUS and 0.76 in England. Unsatisfactory performances of MLR indicated that the relationship between the FDS and explanatory factors did not obey an explicit linear function. (b) RFR revealed river flood factors had higher importance than physical and socio‐economic factors in the FDS estimation. The proposed RFR achieved the highest performance using all factors for prediction and could not provide good predictions (NSE < 0.65) using physical or socio‐economic factors individually. (c) We estimated the FDS for all unlabeled sites in the CONUS and England. Approximately 80% and 29% of sites were identified as high or highest standard (>100‐year return period) in the CONUS and England, respectively. (d) We incorporated the estimated FDS in large‐scale flood modeling and compared the model results with official flood hazard maps in three case studies. We identified obvious overestimations in protected areas when flood defenses were not taken into account; flood defenses were successfully represented using the proposed approach.

J. C. Neal

Papers

A high-accuracy map of global terrain elevations

Bare-Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models

MERIT DEM: A new high-accuracy global digital elevation model and its merit to global hydrodynamic modeling

Multi return periods flood hazards and risks assessment in the Congo River Basin

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling