Norsk Hydro (Germany)

Rivers provide many ecosystem services. However, in densely populated urban areas, they are polluted by multiple sources including combined sewer overflows (CSOs). By studying the behavior of the combined sewer system, whose capacity can be saturated during heavy rainfall episodes, it is possible to anticipate these contamination periods. In addition, by using an inverse method (quantifying causes from effects), it would be possible to determine the quantity of contaminants released by the CSO. In this paper, we present the design of the method on the study site of the Seine River, in Paris. The spatial extension of the combined sewer network is described by a directed graph. Temporal consistency is achieved through event definition. The use of these data for CSO assessment would make it possible to anticipate contamination and guide decisions to contain it.

The Digital Twin platform developed for the “ZwillE” research project in Germany serves different purposes, covering a wide range of urban water management tasks of the wastewater utility of Hanover in Germany. Fundamental concepts, such as model integration issues and different use cases with different levels of complexity are tackled within the project. This contribution focuses on a new approach for the fast estimation of maximum water levels for use in relatively flat urban environments. The approach combines pre-calculated flood maps based on design rainfall for fixed return periods with real-time radar measurements and radar nowcasts, including uncertainty information. An evaluation with 10 extreme precipitation events in Hanover, between 2002 and 2023, was performed, where an observed flooding, and a high number of fire brigade calls were registered throughout the city. Forecast and Maximum forecast precipitation conditions are also included and validated. Results of the new approach for fast flooding estimation are assessed in comparison to fire brigade calls and to a coupled 1D sewer and 2D surface runoff simulation using the model Hystem-Extran/Hystem- Extran2D. The fast estimation shows good results of the maximum water level in comparison to the reference simulation, with absolute differences mostly below 0.1 m or 0.2 m respectively. The approach is suitable for real-time application in the newly developed digital Twin for the city Hanover.

This study investigates the combined effects of salinity and microplastic contamination on the evaporation process and salt crystallization in porous media through complementary column- and pore-scale experiments. Laboratory soil columns were packed with either pure sand or sand mixed with 5% (w/w) poly(vinyl chloride) (PVC) microplastics and subsequently saturated with freshwater or saline (NaCl) solution. Evaporation and crystallization dynamics were monitored by using mass loss measurements, surface optical and thermal imaging, and synchrotron X-ray tomography. Results show that salinity consistently suppressed evaporation by roughly 25–30%, whereas PVC microplastics enhanced it, generating substantial differences in cumulative water loss across treatments. Moreover, thermal imaging revealed distinct surface responses: NaCl samples developed salt crusts that progressively reduced local temperature contrasts and led to more spatially uniform surface conditions, whereas PVC-NaCl samples exhibited lower mean surface temperatures but substantially higher spatial variability, reflected in larger and more persistent temperature anomalies during drying. Pore-scale μCT imaging further confirmed that microplastics altered crystallization patterns by redistributing salt deposition over the upper part of the sand profile and modifying the nucleation behavior. Together, these findings underscore the complex interplay between microplastics and salinity, with implications for soil moisture regulation, surface energy flux, and environmental monitoring strategies.

Soil organic carbon (SOC) plays a central role in regulating soil fertility, water retention, and quantifying risks of soil degradation (Afshar et al., 2025). While climate variability is increasingly recognized as a major pressure on SOC, large-scale assessments of drought impacts on forest soils remain limited. Recent studies emphasize that drought effects on SOC are highly context dependent, shaped by soil carbon status, climate regime, and interacting environmental controls, calling for flexible modeling frameworks that can capture nonlinear responses (Hassani et al., 2024; Shokri et al., 2025).In this study, we analyze SOC change between 2009 and 2018 across European forest soils using generalized additive models (GAMs) applied to harmonized LUCAS topsoil observations. SOC change is modelled as a nonlinear function of initial SOC, drought characteristics derived from the Standardized Precipitation Evapotranspiration Index (SPEI), climate, and soil properties.GAM results show that drought severity exerts a significant, nonlinear impact on SOC change (p < 0.001), strongly modulated by initial SOC and climatic parameters. On average, under severe drought conditions, SOC declines by ~32% relative to mild drought conditions. Overall, the results demonstrate that drought impacts on forest SOC are state-dependent and spatially heterogeneous, governed by the combined influence of drought severity, initial carbon stocks, and regional climate conditions.

Abstract. Urban areas are increasingly experiencing more frequent and intense pluvial flooding due to the combined effects of climate change and rapid urbanization – a trend expected to continue in the coming decades. This highlights the growing need for effective flood forecasting and disaster management systems. While recent advances in GPU computing have made high-resolution hydrodynamic modeling feasible at the urban scale, operational use remains limited, particularly for large domains where single-GPU processing falls short in terms of memory and performance. This study demonstrates the capabilities of the hydrodynamic model RIM2D (Rapid Inundation Model 2D), enhanced with multi-GPU processing, to perform high-resolution pluvial flood simulations across large urban domains such as the whole state of Berlin (891.8 km2) within operationally relevant timeframes. We evaluate RIM2D’s performance across spatial resolutions of 2, 5, and 10 m using GPU configurations ranging from 1 to 8 units. Two flood scenarios are analyzed: the real-world pluvial flood of June 2017 and a standardized 100-year return period (HQ100) event used for official hazard mapping. Results show that RIM2D can deliver detailed flood extents, flow characteristics, and impact estimates for the 48 h 2017 event in 8 min at 10 m resolution, 34 min at 5 m, and approximately 5.5 h at 2 m using 8 A100 GPUs – fast enough to be integrated into real-time early warning systems. Multi-GPU processing proves essential not only for enabling high-resolution simulations (e.g., dx= 2 m or finer), but also for making simulations at resolutions finer than 5 m computationally feasible for flood forecasting and early warning applications. Additionally, we find that beyond 4 GPUs, runtime improvements become marginal for 5 and 10 m resolutions, and similarly, more than 6 GPUs offer limited benefit at dx= 2 m resolution, illustrating the balance between computational nodes of the used GPUs and number of raster cells of the model. Moreover, simulations at a finer dx= 1 m resolution demand more than 8 GPUs to be run. Overall, this work demonstrates that large-scale, high-resolution flood simulations can now be executed rapidly enough to support operational early warning and impact-based forecasting. With models like RIM2D and the continued advancement of GPU hardware, the integration of detailed, real-time flood forecasting into urban flood risk management is both technically feasible and urgently needed.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate extremes like storms, heatwaves, wildfires, droughts and floods significantly threaten society and ecosystems. However, comprehensive data on the socio-economic impacts of climate extremes remains limited. Here we present Wikimpacts 1.0, a global climate impact database built by extracting information from Wikipedia using natural language processing. Our method identifies relevant articles, extracts the information using GPT4o, post-processes the information and consolidates the database. Impact data is stored at the event, national, and sub-national levels, covering 2,928 events from 1034 to 2024, with 20,186 national and 36,394 sub-national entries. The database shows low error scores (range from 0 to 1) for event-level information like timing (0.05), deaths (0.03), and economic damage (0.12), and slightly higher error scores for injuries (0.21), homelessness (0.25), displacement (0.29), and damaged buildings (0.28) compared to manually annotated data from 156 events. Wikimpacts 1.0 provides broader impact coverage on storms than EM-DAT at the sub-national level. In comparing impact values, 38 out of 234 matched events have identical data for deaths, and 7 of 94 for injuries. However, there are notable discrepancies in information on homelessness and damage. Our public database highlights the potential of natural language processing to complement existing impact datasets and to provide robust information on climate impacts.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate extremes like storms, heatwaves, wildfires, droughts and floods significantly threaten society and ecosystems. However, comprehensive data on the socio-economic impacts of climate extremes remains limited. Here we present Wikimpacts 1.0, a global climate impact database built by extracting information from Wikipedia using natural language processing. Our method identifies relevant articles, extracts the information using GPT4o, post-processes the information and consolidates the database. Impact data is stored at the event, national, and sub-national levels, covering 2,928 events from 1034 to 2024, with 20,186 national and 36,394 sub-national entries. The database shows low error scores (range from 0 to 1) for event-level information like timing (0.05), deaths (0.03), and economic damage (0.12), and slightly higher error scores for injuries (0.21), homelessness (0.25), displacement (0.29), and damaged buildings (0.28) compared to manually annotated data from 156 events. Wikimpacts 1.0 provides broader impact coverage on storms than EM-DAT at the sub-national level. In comparing impact values, 38 out of 234 matched events have identical data for deaths, and 7 of 94 for injuries. However, there are notable discrepancies in information on homelessness and damage. Our public database highlights the potential of natural language processing to complement existing impact datasets and to provide robust information on climate impacts.

Embankments such as fluvial dikes or dams may fail due to breaching caused by overtopping or internal erosion. The mechanisms associated with the temporal evolution of embankment breaching are still not fully understood. A deeper knowledge can be attained if novel cost-effective experimental techniques are applied in laboratory studies. Contributing to this general aim, this study presents the application of light detection and ranging (LiDAR) technology in medium-scale experimental models of earth dams and fluvial dikes failures. This technology enables the complete 3D digital reproduction of the embankment structures at various stages of failure using point clouds generated by a sensor with an integrated depth camera operating in the near infrared spectrum and using the time-of-flight (ToF) principle. This study is aimed at discussing the errors associated with the ToF principle, through a benchmark analysis involving structure from motion (SfM), and at discussing its application to real-time laboratory-scale embankment breaching. Emphasis is placed on the challenges associated with mapping the water surface and refractive index correction when the structures are submerged. The applications show the potential of ToF-based real-time monitoring to quantify the volumes of material eroded by hydraulic erosion and mass detachments. Two different laboratory embankment overtopping experiments were conducted to verify the ToF principle. In the first, the flow was stopped at different instants, and the reservoir was drained to allow scanning for 3D digital reconstitution. In the second, a dike breach evolution was continuously scanned at five-point clouds per second to enable near-continuous 3D reconstitutions. This study shows that SfM yields the highest point density per measurement, but that ToF has the same measurement error range. Unlike SfM, ToF enables realistic real-time characterization and quantification of mass detachments, allows for reconstruction of submerged 3D structures, and enables precise identification of the water-free surface position.