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 (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.
Evidence suggests that several elements (i.e., subsystems) of the Earth’s climate system could tip into a qualitatively different state due to on-going and future anthropogenically induced climate change. Risks associated with tipping could form a component of critical climate risks, and their consideration should be indispensable in decision-making. However, there is lack of scientific knowledge about the risks associated with tipping elements, inhibiting their incorporation into comprehensive risk assessments of climate change (i.e., assessments of impact, adaptation, and mitigation with uncertainty). Using two major tipping elements (Arctic summer sea-ice loss and Greenland ice-sheet melting) as examples, this study attempted to address this lack of knowledge by conducting several calculations under various policy choices based on target temperature, including (i) the probability of passing a threshold temperature in this century and (ii) the potential impact of passing a threshold temperature on a millennial timescale beyond this century. The first theme of this study [Item (i) above] suggested that probability of exceeding the threshold within this century is 24.8% for the Greenland ice sheet and 2.7% for Arctic summer sea ice under a 1.5 °C temperature goal. However, it should be noted that the estimated probabilities of exceeding the threshold are largely dependent on the specification of the probability density function and key assumptions. With regard to the second theme of this study [Item (ii) above], estimation of the potential global coastal exposure using the estimated sea level exhibited a significant gap between scenarios not exceeding the threshold (1.5 °C target) and those exceeding the threshold.
Abstract. Studies have indicated that submarine landslides played an important role in the 2018 Sulawesi tsunami event, damaging the coast of Palu Bay in addition to the earthquake source. Most of these studies relied on visible landslides to reproduce tsunamis but could still not fully explain the observational data. Recently, several numerical models included hypothesized submarine landslides that were taken into account to obtain a better explanation of the event. In this study, for the first time, submarine landslides were simulated by applying a numerical model based on Hovland’s 3D slope stability analysis for cohesion-frictional soils. To specify landslide volume and location, the model assumed an elliptical slip surface on a vertical slope of 27 m of mesh-divided terrain and evaluated the minimum safety factor in each mesh area based on the surveyed soil property data extracted from the literature. The landslide output was then substituted into a two-layer numerical model based on a shallow-water equation to simulate tsunami propagation. The results were combined with the other tsunami sources, i.e., earthquakes and observed coastal collapses, and validated with various postevent field observational data, including tsunami runup heights and flow depths around the bay, the inundation area around Palu city, waveforms recorded by the Pantoloan tide gauge, and video-inferred waveforms. The model generated several submarine landslides, with lengths of 0.2–2.0 km throughout Palu Bay. The results confirmed the existence of submarine landslide sources in the southern part of the bay and showed agreement with the observed tsunami data, including runups and flow depths. Furthermore, the simulated landslides also reproduced the video-inferred waveforms in 3 out of 6 locations. Although these calculated submarine landslides still cannot fully explain some of the observed tsunami data, they emphasize the possible submarine landslide locations in southern Palu Bay that should be studied and surveyed in the future.
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