Highlight articles

This page features highlight articles from EGU's open access journals Geoscientific Model Development and Natural Hazards and Earth System Sciences. These articles of particular interest are selected by journal editors.

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All Geoscientific Model Development (GMD) Natural Hazards and Earth System Sciences (NHESS)

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HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution

• Geoscientific Model Development
• DOI 10.5194/gmd-13-3863-2020
• 24 September 2020

Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.

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Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-2365-2020
• 22 September 2020

Forecasting of daily fire weather indices driven by the ECMWF ensemble prediction system is shown to have a good skill up to 10 d ahead in predicting flammable conditions in most regions of the world. The availability of these forecasts through the Copernicus Emergency Management Service can extend early warnings by up to 1–2 weeks, allowing for greater proactive coordination of resource-sharing and mobilization within and across countries.

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Anthropogenic climate change and glacier lake outburst flood risk: local and global drivers and responsibilities for the case of lake Palcacocha, Peru

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-2175-2020
• 27 August 2020

There is increasing interest and need to analyze the contribution of anthropogenic climate change to negative impacts of climate change. We study the case of glacial lake Palcacocha in Peru, which poses a significant flood risk to the city of Huaraz. We found that greenhouse gas emissions; strong urbanization processes without appropriate land use planning; and social, cultural, political, and institutional factors all contribute to the existing flood risk.

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The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500

• Geoscientific Model Development
• DOI 10.5194/gmd-13-3571-2020
• 27 August 2020

This study provides the future greenhouse gas (GHG) concentrations under the new set of so-called SSP scenarios (the successors of the IPCC SRES and previous representative concentration pathway (RCP) scenarios). The projected CO ₂ concentrations range from 350 ppm for low-emission scenarios by 2150 to more than 2000 ppm under the high-emission scenarios. We also provide concentrations, latitudinal gradients, and seasonality for most of the other 42 considered GHGs.

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Invited perspectives: The volcanoes of Naples: how can the highest volcanic risk in the world be effectively mitigated?

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-2037-2020
• 4 August 2020

This paper starts by showing the present low performance of eruption forecasting and then addresses the problem of effectively mitigating the highest volcanic risk in the world, represented by the Naples area (southern Italy). The problem is considered in a highly multidisciplinary way, taking into account the main economic, sociological and urban planning issues. Our study gives precise guidelines to assessing and managing volcanic risk in any densely urbanised area.

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Spatial database and website for reservoir-triggered seismicity in Brazil

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-2001-2020
• 30 July 2020

One of the biggest challenges in studying reservoir-triggered seismicity (RTS) is to identify factors that can trigger seismicity. A spatial database and a web viewer were created, gathering the data pertinent to the RTS study. Results were obtained in processing these data; for example, the occurrence of RTS increases with the height of the dam, the minimum limiting volume value is 1 × 10 ⁻⁴  km ³ for occurrence of RTS, and for geology no correlations were found, among other results.

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Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-1955-2020
• 28 July 2020

Storm Gloria hit the Mediterranean Spanish coastlines between 20 and 23 January 2020, causing severe damages such as flooding of the Ebro River delta. We evaluate its coastal impacts with a numerical simulation of the wind waves and the accumulated ocean water along the coastline (storm surge). The storm surge that reached values up to 1 m was mainly driven by the wind that also generated wind waves up to 8 m in height. We also determine the extent of the Ebro Delta flooded by marine water.

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Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

• Natural Hazards and Earth System Sciences
• DOI 10.5194/nhess-20-1783-2020
• 2 July 2020

To assess snow avalanche mapping with radar satellites in Switzerland, we compare 2 m resolution TerraSAR-X images, 10 m resolution Sentinel-1 images, and optical 1.5 m resolution SPOT-6 images. We found that radar satellites provide a valuable option to map at least larger avalanches, though avalanches are mapped only partially. By combining multiple orbits and polarizations from S1, we achieved mapping results of quality almost comparable to single high-resolution TerraSAR-X images.

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Towards an objective assessment of climate multi-model ensembles – a casestudy: the Senegalo-Mauritanian upwelling region

• Geoscientific Model Development
• DOI 10.5194/gmd-13-2723-2020
• 30 June 2020

The most robust representation of climate is usually obtained by averaging a large number of simulations, thereby cancelling individual model errors. Here, we work towards an objective way of selecting the least biased models over a certain region, based on physical parameters. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the Senegalo-Mauritanian region, but it could potentially be developed for any other region or process.

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RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

• Geoscientific Model Development
• DOI 10.5194/gmd-13-2631-2020
• 23 June 2020

In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.

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