Daiki Ikeshima

Bio: Daiki Ikeshima is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Digital elevation model & Scale (ratio). The author has an hindex of 4, co-authored 9 publications receiving 663 citations.

A high-accuracy map of global terrain elevations

Abstract: 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

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

Abstract: 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.

A Physically Based Empirical Localization Method for Assimilating Synthetic SWOT Observations of a Continental-Scale River: A Case Study in the Congo Basin

Abstract: Water resource management has faced challenges in recent decades due to limited in situ observations and the limitations of hydrodynamic modeling. Data assimilation techniques have been proposed to improve hydrodynamic model outputs of local rivers (river length ≤ 1500 km) using synthetic observations of the future Surface Water and Ocean Topography (SWOT) satellite mission to overcome limited in situ observations and the limitations of hydrodynamic modeling. However, large-scale data assimilation schemes require computationally efficient filtering techniques, such as the Local Ensemble Transformation Kalman Filter (LETKF). Expansion of the assimilation domain to maximize observations is limited by error covariance caused by limited ensemble size in complex river networks, such as the Congo River. Therefore, we tested the LETKF algorithm in a continental-scale river (river length > 1500 km) using a physically based empirical localization method to maximize the observations available while filtering error covariance areas. Physically based empirical local patches were derived separately for each river pixel, considering spatial auto-correlations. An observing system simulation experiment (OSSE) was performed using empirical localization parameters to evaluate the potential of our method for estimating discharge. We found our method could improve discharge estimates considerably without affected from error covariance while fully using the available observations. We compared this experiment using empirical localization parameters with conventional fixed-shape local patches of different sizes. The empirical local patch OSSE showed the lowest normalized root mean square error of discharge for the entire Congo basin. Extending the conventional local patch without considering spatial auto-correlation results in very large errors in LETKF assimilation due to error covariance between small tributaries. The empirical local patch method has the potential to overcome the limitations of conventional local patches for continental-scale rivers using SWOT observations.

High-resolution mapping of global surface water and its long-term changes

Abstract: A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)

Abstract: For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS. For an overview of USGS information products, including maps, imagery, and publications, Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted materials contained within this report. 10. Diagram showing the GMTED2010 layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second SRTM

A high-accuracy map of global terrain elevations

Abstract: 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

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

Abstract: 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.