The Eurasian epicontinental sea was an important carbon sink during the Palaeocene-Eocene thermal maximum

The Palaeocene-Eocene Thermal Maximum (ca. 56 million years ago) offers a primary analogue for future global warming and carbon cycle recovery. Yet, where and how massive carbon emissions were mitigated during this climate warming event remains largely unknown. Here we show that organic carbon burial in the vast epicontinental seaways that extended over Eurasia provided a major carbon sink during the Palaeocene-Eocene Thermal Maximum. We coupled new and existing stratigraphic analyses to a detailed paleogeographic framework and using spatiotemporal interpolation calculated ca. 720–1300 Gt organic carbon excess burial, focused in the eastern parts of the Eurasian epicontinental seaways. A much larger amount (2160–3900 Gt C, and when accounting for the increase in inundated shelf area 7400–10300 Gt C) could have been sequestered in similar environments globally. With the disappearance of most epicontinental seas since the Oligocene-Miocene, an effective negative carbon cycle feedback also disappeared making the modern carbon cycle critically dependent on the slower silicate weathering feedback.</p

Variation in landslide-affected area under the control of ground motion and topography

Earthquake-Induced Landslide (EQIL) inventories are the key to improve our understanding of the relationship between landslides and their causes, including environmental settings and ground shaking parameters. However, creating a high-quality inventory can take years. As a result, reliable information on landslide-affected areas typically remains unknown until a complete inventory is compiled. In this paper, we analyze 20 digital EQIL inventories of varying quality and completeness that represent a range of geologic and climatic settings around the globe. We examine the landslide-affected area with respect to Peak Ground Acceleration (PGA) and topography, and develop a statistical model to estimate the landslide distribution without prior knowledge of the actual landslide triggering locations. For each EQIL inventory, we initially calculated the PGA contours where 90% of the total landslide population fell into. Subsequently, we define landslide susceptible areas as those pixels with slope > 5° and local relief>100 m. The latter is used to normalize the total landslide-affected area and to compute correlations with local PGA values. We find that the landslide-affected area may be predicted from PGA values only, with the mean error ranging from −20.0% to +7.1%, with respect to total landslide population. This relationship can be used immediately following a disaster to identify areas of greatest landslide impact and to prioritize emergency response actions, even without a landslide inventory

Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides

The coupled effect of earthquakes and rainfall is rarely investigated in landslide susceptibility assessments although it could be crucial to predict landslide occurrences. This is even more critical in the context of early warning systems and especially in cases of extreme precipitation regimes in post-seismic conditions, where the rock masses are already damaged due to the ground shaking. Here, we investigate this concept by accounting for the legacy of seismic ground shaking in rainfall-induced landslide (RFIL) scenarios. We do this to identify whether ground shaking plays a role in the susceptibility to post-seismic rainfall-induced landslides and to identify whether this legacy effect persists through time. With this motivation, we use binary logistic regression and examine time series of landslides associated with four earthquakes occurred in Indonesia: 2012 Sulawesi (Mw = 6.3), 2016 Reuleut (Mw = 6.5), 2017 Kasiguncu (Mw = 6.6) and 2018 Palu (Mw = 7.5) earthquakes. The dataset includes one co-seismic and three post-seismic landslide inventories for each earthquake. We use the peak ground acceleration map of the last strongest earthquake in each case as a predisposing factor of landslides representing the effect of ground shaking. We observe that, at least for the study areas under consideration and in a probabilistic context, the earthquake legacy contributes to increase the post-seismic RFIL susceptibility. This positive contribution decays through time. Specifically, we observe that ground motion is a significant predisposing factor controlling the spatial distribution of RFIL in the post-seismic period 110 days after an earthquake. We also show that this effect dissipates within 3 years at most

The potential application of statistical post processing techniques on landslide early warning system

With the increase of frequency and intensity of heavy precipitation in the future, rainfall triggered landslides (RTL) can be one of the major threat to human life and property security. Early warning systems of natural hazards are one of the most effective measure for reducing disaster losses and risks. However, the forecast of RTL in near-real-time (NRT) is extremely difficult since the quality of NRT precipitation data is relatively poor. Quantile regression forest (QRF), a state-of-the-art statistical postprocessing method, has been proved to reduce the difference existing between NRT satellite precipitation estimates and ground-based rainfall data. When predicted rainfall maps are put side by side with raw NRT satellite product, the pattern of the first matches much more closely the locations where landslide events have been mapped in a test site in North-Eastern Turkey. This leave an optimistic perspective on the application of statistical postprocessing techniques in the field of weather science and in general for natural hazard assessment. Ideally, by correcting the continuous information in space and time provided by satellite rainfall estimates, one could create a new operational tool for landslide early warning system, not bound to the financial and deployment requirement typical of rain gauge and terrestrial radar stations

The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

Events characterized by widespread landslides provide rare but valuable opportunities to investigate the spatial and size distributions of landslides in relation to seismic, climatic, geological and morphological factors. This study presents a unique event inventory for the co-seismic landslides induced by the February 25, 2018 Mw 7.5 Papua New Guinea earthquake. The mainshock rupture was dominated by reverse fault motion, and this was also the case for the aftershocks. The latter also triggered widespread landslides in combination with rainfall during the period between February 26 and March 19. We mapped approximately 11,600 landslides of which, more than 10,000 were triggered by the mainshock, with a total failed planimetric area of about 145 km². Such a large area makes this inventory the world's second-largest recorded landslide event after the 2008 Mw 7.9 Wenchuan earthquake, where the motion changed from predominantly thrust to strike-slip. Large landslides are abundant throughout the study area located within the remote Papua New Guinea Highlands. Specifically, more than half of the landslide population is larger than 50,000 m² and overall, post-seismic landslides are even larger than their co-seismic counterparts. To understand the factors controlling the distribution of landslides' occurrence and size, we combine descriptive statistics as well as more rigorous bivariate and multivariate analyses. We statistically show that the 15-day antecedent precipitation plays a role in explaining the spatial distribution of co-seismic landslides. Also, we examine four strong aftershocks (Mw ≥ 6.0) within 9 days after the mainshock and statistically demonstrate that the cumulative effect of aftershocks is the main factor disturbing steep hillslopes and causing the initiation of very large landslides, up to ~5 km². Overall, the dataset and the findings presented in this paper represent a step towards a holistic understanding of the seismic landslide hazard assessment of the entire Papua New Guinea mainland

The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

This dataset was provided as the supplementary material of the paper (Tanyas and others, 2021) presenting a unique event inventory for the co-seismic landslides induced by the February 25, 2018 Mw 7.5 Papua New Guinea earthquake. The authors mapped approximately 11,600 landslides of which, more than 10,000 were triggered by the mainshock, with a total failed planimetric area of about 145 km2. Such a large area makes this inventory the world's second-largest recorded landslide event after the 2008 Mw 7.9 Wenchuan earthquake, where the motion changed from predominantly thrust to strike-slip. Large landslides are abundant throughout the study area located within the remote Papua New Guinea Highlands. Specifically, more than half of the landslide population is larger than 50,000 m2 and overall, post-seismic landslides are even larger than their co-seismic counterparts. To understand the factors controlling the distribution of landslides' occurrence and size, the authors combine descriptive statistics as well as more rigorous bivariate and multivariate analyses. They statistically show that the 15-day antecedent precipitation plays a role in explaining the spatial distribution of co-seismic landslides. Also, they examine four strong aftershocks (Mw ≥ 6.0) within 9 days after the mainshock and statistically demonstrate that the cumulative effect of aftershocks is the main factor disturbing steep hillslopes and causing the initiation of very large landslides, up to ~5 km2. Overall, the dataset and the findings presented in the paper represent a step towards a holistic understanding of the seismic landslide hazard assessment of the entire Papua New Guinea mainland

An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake, New Zealand

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha)

Identification of possible source areas of stone raw materials combining remote sensing and petrography

Understanding the location and distribution of raw materials used in the production of prehistoric artefacts is a significant part of archaeological research that aims to understand the interregional interaction patterns in the past. The aim of this study is to explore the regional locations of the source rock utilized in the production of stone bowls, which were unearthed at the Neolithic (approximately 6500–5500 BC) site of Domuztepe (Kahramanmaraş-Turkey), via a combination of remote-sensing methods, petrographic and chemical analyses. To accomplish this task, the stone bowls were identified mineralogically, geochemically and spectrally, and then mapped with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors. According to the defined mineralogical composition, which is iron-rich chlorite, the target areas were selected among geologically potential areas that would bear similar source rocks in near vicinity and the target spectral signature was searched within these target areas. In order to overcome the problem of spectral similarity of chlorite group to some other minerals such as carbonate or epidote group minerals, band ratioing (BR) and feature-oriented principal component analysis (FOPCA) were used with an integrated approach and then their results were filtered according to the outcomes of the relative absorption band-depth (RBD) images. The areas with highest potentials were vectorized and then field checked. Mineralogical investigations on the collected field samples reveal that there is a mineralogical match between the source and target material. One group of stone bowls samples have similar geochemical signatures as the field samples having ultramafic origins. However, there is another group of stone bowls samples which are geochemically dissimilar to the first group of field and bowls samples. The data regarding the geochemical signatures of these two groups indicate a genetic relation between the sample sets. Therefore, it is concluded that the source rock of a major portion of the stone bowls unearthed at Domuztepe most probably originated from the near vicinity of the site

Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories