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Qualitative risk assessment of sensitive infrastructures at the local level: flooding and heavy rainfall

Natural Hazards and Earth System Sciences
DOI 10.5194/nhess-25-2097-2025
4 July 2025

In post-disaster reconstruction, emphasis should be placed on critical and sensitive infrastructures. In Germany, as in other countries, sensitive infrastructures have not yet been focused on; therefore, we developed a method for determining the risk that sensitive infrastructures are facing in the context of riverine and pluvial flooding. The easy-to-use assessment framework can be applied to various sensitive infrastructures, e.g., to qualify and accelerate decisions in the reconstruction process.

Using 3D observations with high spatio-temporal resolution to calibrate and evaluate a process-focused cellular automaton model of soil erosion by water

SOIL
DOI 10.5194/soil-11-413-2025
16 June 2025

This study develops a new method to improve the calibration and evaluation of models that predict soil erosion by water. By using advanced imaging techniques, we can capture detailed changes in the soil surface over time. This helps improve models that forecast erosion, especially as climate change creates new and unpredictable conditions. Our findings highlight the need for more precise tools to better model erosion of our land and environment in the future.

Calcium is associated with specific soil organic carbon decomposition products

SOIL
DOI 10.5194/soil-11-381-2025
20 May 2025

This study shows that calcium (Ca) preserves soil organic carbon (SOC) in acidic soils, challenging beliefs that their interactions were limited to near-neutral or alkaline soils. Using spectromicroscopy, we found that Ca was co-located with a specific fraction of carbon, rich in aromatic and phenolic groups. This association was disrupted when Ca was removed but was reformed during decomposition with added Ca. Overall, this suggests that Ca amendments could enhance SOC stability.

Social sensing a volcanic eruption: application to Kīlauea, 2018

Natural Hazards and Earth System Sciences
DOI 10.5194/nhess-25-1681-2025
13 May 2025

Protecting lives and livelihoods during volcanic eruptions is the key challenge in volcanology. Analysing social media usage during volcanic crises can help us better understand the impacts of volcanic eruptions and how warning messages are received and actioned, to eventually better protect those people and their livelihoods. Our work shows how social media data could be used in real time during a volcanic crisis to learn more about volcanic eruptions.

Missing the input: the underrepresentation of plant physiology in global soil carbon research

Plant physiology has been addressed by less than 10 % of peer-reviewed soil organic carbon research published in the last century. Thus, our understanding of soil carbon dynamics is overwhelmingly built on research that neglects the fundamental processes underlying organic carbon inputs. Active engagement of plant scientists in soil carbon research is imperative for shedding light on this blind spot and developing holistic policies that support soil carbon sequestration.

Graphical representation of global water models

Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.

Meteorological ingredients of heavy precipitation and subsequent lake-filling episodes in the northwestern Sahara

The Sahara was wetter in the past and may become wetter in the future. Lake remnants are evidence of the desert’s wetter past. If the Sahara gets wetter in the future, these lakes may serve as a water resource. However, it is unclear how these lakes get filled and how moisture is carried into the desert and converted into rain in the first place. Therefore, we examine processes currently leading to the filling of a dry lake in the Sahara, which can help assess future water availability.

CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland

This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.

RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland">Read more

Creating a national urban flood dataset for China from news texts (2000–2022) at the county level

We create China’s first open county-level urban flood dataset (2000–2022) using news media data with the help of deep learning. The dataset reflects both natural and societal influences and includes 7595 urban flood events across 2051 counties, covering 46 % of China’s land area. It reveals the predominance of summer floods, an upward trend since 2000, and a decline from southeast to northwest. Notably, some highly developed regions show a decrease, likely due to improved flood management.

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.

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