Natural Hazards and Earth System Sciences

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy
Nicola Loglisci, Giorgio Boni, Arianna Cauteruccio, Francesco Faccini, Massimo Milelli, Guido Paliaga, and Antonio Parodi
Nat. Hazards Earth Syst. Sci., 24, 2495–2510, https://doi.org/10.5194/nhess-24-2495-2024, 2024
We analyse the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in Genoa. Rainfall observations were made using rain gauge networks based on either official networks or citizen science networks. The merged analysis stresses the spatial variability in the precipitation, which cannot be captured by the current spatial density of authoritative stations. Results show that at minimal distances the variations in cumulated rainfall over a sub-hourly duration are significant.

The Record-Breaking Precipitation Event of December 2022 in Portugal
Tiago M. Ferreira, Ricardo M. Trigo, Tomás H. Gaspar, Joaquim G. Pinto, and Alexandre M. Ramos
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-130,2024
Preprint under review for NHESS (discussion: open, 0 comments)
Here we investigate the synoptic evolution associated with the occurrence of an atmospheric river leading to a 24 h record-breaking extreme precipitation event (120.3 mm) in Lisbon, Portugal, on 13 December 2022. The synoptic background allowed the formation, on 10 December, of an atmospheric river associated with a deep extratropical cyclone and with a high moisture content and an inflow of moisture, due to the warm conveyor belt, throughout its life cycle. The system made landfall on day 12.

Brief Communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-119,2024
Preprint under review for NHESS (discussion: open, 1 comment)
Floods have caused significant damage in the past. To prepare for such events, we rely on historical data, but face issues due to rare rainfall events, lack of data, and climate change. Counterfactuals, or "what if" scenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve future flood preparation.

Revisiting regression methods for estimating long-term trends in sea surface temperature
Ming-Huei Chang, Yen-Chen Huang, Yu-Hsin Cheng, Chuen-Teyr Terng, Jinyi Chen, and Jyh Cherng Jan
Nat. Hazards Earth Syst. Sci., 24, 2481–2494, https://doi.org/10.5194/nhess-24-2481-2024, 2024
Monitoring the long-term trends in sea surface warming is crucial for informed decision-making and adaptation. This study offers a comprehensive examination of prevalent trend extraction methods. We identify the least-squares regression as suitable for general tasks yet highlight the need to address seasonal signal-induced bias, i.e., the phase–distance imbalance. Our developed method, evaluated using simulated and real data, is unbiased and better than the conventional SST anomaly method. 

Optimizing Rainfall-Triggered Landslide Thresholds to Warning Daily Landslide Hazard in Three Gorges Reservoir Area
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-109,2024
Preprint under review for NHESS (discussion: open, 2 comments)
Our research enhances landslide prevention using advanced machine learning to forecast heavy rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.

Estuarine hurricane wind can intensify surge-dominated extreme water level in shallow and converging coastal systems
Mithun Deb, James J. Benedict, Ning Sun, Zhaoqing Yang, Robert D. Hetland, David Judi, and Taiping Wang
Nat. Hazards Earth Syst. Sci., 24, 2461–2479, https://doi.org/10.5194/nhess-24-2461-2024, 2024
We coupled earth system, hydrology, and hydrodynamic models to generate plausible and physically consistent ensembles of hurricane events and their associated water levels from the open coast to tidal rivers of Delaware Bay and River. Our results show that the hurricane landfall locations and the estuarine wind can significantly amplify the extreme surge in a shallow and converging system, especially when the wind direction aligns with the surge propagation direction.

Precipitation extremes in Ukraine from 1979 to 2019: climatology, large-scale flow conditions, and moisture sources
Ellina Agayar, Franziska Aemisegger, Moshe Armon, Alexander Scherrmann, and Heini Wernli
Nat. Hazards Earth Syst. Sci., 24, 2441–2459, https://doi.org/10.5194/nhess-24-2441-2024, 2024
This study presents the results of a climatological investigation of extreme precipitation events (EPEs) in Ukraine for the period 1979–2019. During all seasons EPEs are associated with pronounced upper-level potential vorticity (PV) anomalies. In addition, we find distinct seasonal and regional differences in moisture sources. Several extreme precipitation cases demonstrate the importance of these processes, complemented by a detailed synoptic analysis.

Micro-business participation in collective flood adaptation: lessons from scenario-based analysis in Ho Chi Minh City, Vietnam
Javier Revilla Diez, Roxana Leitold, Van Tran, and Matthias Garschagen
Nat. Hazards Earth Syst. Sci., 24, 2425–2440, https://doi.org/10.5194/nhess-24-2425-2024, 2024
Micro-businesses, often overlooked in adaptation research, show surprising willingness to contribute to collective adaptation despite limited finances and local support. Based on a study in Ho Chi Minh City in Vietnam, approximately 70 % are ready for awareness campaigns, and 39 % would provide financial support if costs were shared. These findings underscore the need for increased involvement of micro-businesses in local adaptation plans to enhance collective adaptive capacity.

Brief communication: Storm Daniel flood impact in Greece in 2023: mapping crop and livestock exposure from synthetic-aperture radar (SAR)
Kang He, Qing Yang, Xinyi Shen, Elias Dimitriou, Angeliki Mentzafou, Christina Papadaki, Maria Stoumboudi, and Emmanouil N. Anagnostou
Nat. Hazards Earth Syst. Sci., 24, 2375–2382, https://doi.org/10.5194/nhess-24-2375-2024, 2024
About 820 km2 of agricultural land was inundated in central Greece due to Storm Daniel. A detailed analysis revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km2 comprised ~ 30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimate that more than 14 000 ornithoids and 21 500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe.

Comparing components for seismic risk modelling using data from the 2019 Le Teil (France) earthquake
Konstantinos Trevlopoulos, Pierre Gehl, Caterina Negulescu, Helen Crowley, and Laurentiu Danciu
Nat. Hazards Earth Syst. Sci., 24, 2383–2401, https://doi.org/10.5194/nhess-24-2383-2024, 2024
The models used to estimate the probability of exceeding a level of earthquake damage are essential to the reduction of disasters. These models consist of components that may be tested individually; however testing these types of models as a whole is challenging. Here, we use observations of damage caused by the 2019 Le Teil earthquake and estimations from other models to test components of seismic risk models.

Global application of a regional frequency analysis to extreme sea levels
Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 24, 2403–2423, https://doi.org/10.5194/nhess-24-2403-2024, 2024
Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.

Dynamics and Impacts of Monsoon-Induced Geological Hazards: A 2022 Flood Study along the Swat River in Pakistan
Nazir Ahmed Bazai, Mehtab Alam, Peng Cui, Wang Hao, Adil Poshad Khan, Muhammad Waseem, Yao Shunyu, Muhammad Ramzan, Li Wanhong, and Tashfain Ahmed
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-95,2024
Preprint under review for NHESS (discussion: open, 0 comments)
This study examines the 2022 monsoon in the Swat River basin, Pakistan, where record rainfall exceeded averages by 7–8 %, causing catastrophic debris flows and floods. These events worsened challenges for low-income communities, resulting in extensive damage and financial instability. Field investigations, remote sensing, and simulations identified deforestation and steep topography as key factors. The study advocates for disaster mitigation, reforestation, and better land use planning.

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.

Unravelling the capacity-action gap in flood risk adaptation
Annika Schubert, Anne von Streit, and Matthias Garschagen
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-121,2024
Preprint under review for NHESS (discussion: open, 0 comments)
Households play a crucial role in climate adaptation efforts. Yet, households require capacities to implement measures. We explore which capacities enable German households to adapt to flooding. Our results indicate that flood-related capacities such as risk perception, responsibility appraisal and motivation are pivotal, whereas financial assets are secondary. Enhancing these specific capacities, e.g. through collaborations between households and municipalities, could promote local adaptation.

Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, 2024
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.

Prediction of volume of shallow landslides due to rainfall using data-driven models
Jérémie Tuganishuri, Chan-Young Yune, Manik Das Adhikari, Seung Woo Lee, Gihong Kim, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-90,2024
Preprint under review for NHESS (discussion: open, 0 comments)
To reduce the consequences of landslides due to rainfall, such as of life and economic losses, and disruption of order of our daily living; this study describes the process of building a machine learning model which can help to estimate the volume of landslides material that can occur in a particular region taking into account of antecedent rainfall, soil characteristics, type of vegetation etc. The findings can be useful for land use, infrastructure design and rainfall disaster management.

Flood risk assessment through large-scale modeling under uncertainty
Luciano Pavesi, Elena Volpi, and Aldo Fiori
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-114,2024
Preprint under review for NHESS (discussion: open, 0 comments)
Several sources of uncertainty affect flood risk estimation for reliable assessment for investors, insurance and risk management. Here, we consider the uncertainty of large-scale flood hazard modeling, providing a range of risk values that show significant variability depending on geomorphic factors and land use types. This allows to identify the critical points where single value estimates may underestimate the risk, and the areas of vulnerability to prioritize risk reduction efforts.

Aircraft engine dust ingestion at global airports
Claire L. Ryder, Clément Bézier, Helen F. Dacre, Rory Clarkson, Vassilis Amiridis, Eleni Marinou, Emmanouil Proestakis, Zak Kipling, Angela Benedetti, Mark Parrington, Samuel Rémy, and Mark Vaughan
Nat. Hazards Earth Syst. Sci., 24, 2263–2284, https://doi.org/10.5194/nhess-24-2263-2024, 2024
Desert dust poses a hazard to aircraft via degradation of engine components. This has financial implications for the aviation industry and results in increased fuel burn with climate impacts. Here we quantify dust ingestion by aircraft engines at airports worldwide. We find Dubai and Delhi in summer are among the dustiest airports, where substantial engine degradation would occur after 1000 flights. Dust ingestion can be reduced by changing take-off times and the altitude of holding patterns.

An improved dynamic bidirectional coupled hydrologic–hydrodynamic model for efficient flood inundation prediction
Yanxia Shen, Zhenduo Zhu, Qi Zhou, and Chunbo Jiang
Nat. Hazards Earth Syst. Sci., 24, 2315–2330, https://doi.org/10.5194/nhess-24-2315-2024, 2024
We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify flood-prone areas where high-resolution inundation conditions are needed, and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. The improved model was reliable.

Tsunami hazard assessment in the South China Sea based on geodetic locking of the Manila subduction zone
Guangsheng Zhao and Xiaojing Niu
Nat. Hazards Earth Syst. Sci., 24, 2303–2313, https://doi.org/10.5194/nhess-24-2303-2024, 2024
The purpose of this study is to estimate the spatial distribution of the tsunami hazard in the South China Sea from the Manila subduction zone. The plate motion data are used to invert the degree of locking on the fault plane. The degree of locking is used to estimate the maximum possible magnitude of earthquakes and describe the slip distribution. A spatial distribution map of the 1000-year return period tsunami wave height in the South China Sea was obtained by tsunami hazard assessment.