Natural Hazards and Earth System Sciences
ISSN / EISSN : 1684-9981 / 1684-9981
Published by: Copernicus GmbH (10.5194)
Total articles ≅ 3,627
Latest articles in this journal
Natural Hazards and Earth System Sciences, Volume 22, pp 2131-2144; https://doi.org/10.5194/nhess-22-2131-2022
Given trends in more frequent and severe natural disaster events, developing effective risk mitigation strategies is crucial to reduce negative economic impacts, due to the limited budget for rehabilitation. To address this need, this study aims to develop a strategic framework for natural disaster risk mitigation, highlighting two different strategic implementation processes (SIPs). SIP-1 is intended to improve the predictability of natural disaster-triggered financial losses using deep learning. To demonstrate SIP-1, SIP-1 explores deep neural networks (DNNs) that learn storm and flood insurance loss ratios associated with selected major indicators and then develops an optimal DNN model. SIP-2 underlines the risk mitigation strategy at the project level, by adopting a cost–benefit analysis method that quantifies the cost effectiveness of disaster prevention projects. In SIP-2, a case study of disaster risk reservoir projects in South Korea was adopted. The validated result of SIP-1 confirmed that the predictability of the developed DNN is more accurate and reliable than a traditional parametric model, while SIP-2 revealed that maintenance projects are economically more beneficial in the long term as the loss amount becomes smaller after 8 years, coupled with the investment in the projects. The proposed framework is unique as it provides a combinational approach to mitigating economic damages caused by natural disasters at both financial loss and project levels. This study is its first kind and will help practitioners quantify the loss from natural disasters, while allowing them to evaluate the cost effectiveness of risk reduction projects through a holistic approach.
Natural Hazards and Earth System Sciences, Volume 22, pp 2117-2130; https://doi.org/10.5194/nhess-22-2117-2022
A rockfall dataset for Germany is analysed with the objective of identifying the meteorological and hydrological (pre-)conditions that change the probability for such events in central Europe. The factors investigated in the analysis are precipitation amount and intensity, freeze–thaw cycles, and subsurface moisture. As there is no suitable observational dataset for all relevant subsurface moisture types (e.g. water in rock pores and cleft water) available, simulated soil moisture and a proxy for pore water are tested as substitutes. The potential triggering factors were analysed both for the day of the event and for the days leading up to it. A logistic regression model was built, which considers individual potential triggering factors and their interactions. It is found that the most important factor influencing rockfall probability in the research area is the precipitation amount at the day of the event, but the water content of the ground on that day and freeze–thaw cycles in the days prior to the event also influence the hazard probability. Comparing simulated soil moisture and the pore-water proxy as predictors for rockfall reveals that the proxy, calculated as accumulated precipitation minus potential evaporation, performs slightly better in the statistical model. Using the statistical model, the effects of meteorological conditions on rockfall probability in German low mountain ranges can be quantified. The model suggests that precipitation is most efficient when the pore-water content of the ground is high. An increase in daily precipitation from its local 50th percentile to its 90th percentile approximately doubles the probability for a rockfall event under median pore-water conditions. When the pore-water proxy is at its 95th percentile, the same increase in precipitation leads to a 4-fold increase in rockfall probability. The occurrence of a freeze–thaw cycle in the preceding days increases the rockfall hazard by about 50 %. The most critical combination can therefore be expected in winter and at the beginning of spring after a freeze–thaw transition, which is followed by a day with high precipitation amounts and takes place in a region preconditioned by a high level of subsurface moisture.
Natural Hazards and Earth System Sciences, Volume 22, pp 2099-2116; https://doi.org/10.5194/nhess-22-2099-2022
Droughts often have a severe impact on the environment, society, and the economy. The variables and scales that are relevant to understand the impact of drought motivated this study, which compared hazard and propagation characteristics, as well as impacts, of major droughts between 1990 and 2019 in southwestern Germany. We bring together high-resolution datasets of air temperature, precipitation, soil moisture simulations, and streamflow and groundwater level observations, as well as text-based information on drought impacts. Various drought characteristics were derived from the hydrometeorological and drought impact time series and compared across variables and spatial scales. Results revealed different drought types sharing similar hazard and impact characteristics. The most severe drought type identified is an intense multi-seasonal drought type peaking in summer, i.e., the events in 2003, 2015, and 2018. This drought type appeared in all domains of the hydrological cycle and coincided with high air temperatures, causing a high number of and variability in drought impacts. The regional average drought signals of this drought type exhibit typical drought propagation characteristics such as a time lag between meteorological and hydrological drought, whereas propagation characteristics of local drought signals are variable in space. This spatial variability in drought hazard increased when droughts propagated through the hydrological cycle, causing distinct differences among variables, as well as regional average and local drought information. Accordingly, single variable or regional average drought information is not sufficient to fully explain the variety of drought impacts that occurred, supporting the conclusion that in regions as diverse as the case study presented here, large-scale drought monitoring needs to be complemented by local drought information to assess the multifaceted impact of drought.
Natural Hazards and Earth System Sciences, Volume 22, pp 2081-2097; https://doi.org/10.5194/nhess-22-2081-2022
Landslide dams are caused when landslide materials block rivers. After the occurrence of large-scale landslides, it is necessary to conduct a large-scale investigation of barrier lakes and a rapid risk assessment. Remote sensing is an important means to achieve this goal. However, at present, remote sensing is only used for the monitoring and extraction of hydrological parameters, without predicting the potential hazard of the landslide dam. The key parameters of the barrier dam, such as the dam height and the maximum volume, still need to be obtained based on a field investigation, which is time consuming. Our research proposes a procedure that is able to calculate the height of the landslide dam and the maximum volume of the barrier lake using a single remote-sensing image and a pre-landslide DEM. The procedure includes four modules: (a) determining the elevation of the lake level, (b) determining the elevation of the bottom of the dam, (c) calculating the highest height of the dam and (d) predicting the lowest crest height of the dam and the maximum volume. Finally, a sensitivity analysis of the parameters used during the procedure and an analysis of the influence of the image resolution is carried out. This procedure is mainly demonstrated through the Baige landslide dam and the Hongshiyan landslide dam. A single remote-sensing image and a pre-landslide DEM are used to predict the height of each dam and the key parameters of the dam break, which are in good agreement with the measured data. This procedure can effectively support quick decision making regarding hazard mitigation.
Natural Hazards and Earth System Sciences, Volume 22, pp 2057-2079; https://doi.org/10.5194/nhess-22-2057-2022
In slowly deforming intraplate tectonic regions such as the Alps only limited knowledge exists on the occurrence of severe earthquakes, their maximum possible magnitude, and their potential source areas. This is mainly due to long earthquake recurrence rates exceeding the time span of instrumental earthquake records and historical documentation. Lacustrine paleoseismology aims at retrieving long-term continuous records of seismic shaking. A paleoseismic record from a single lake provides information on events for which seismic shaking exceeded the intensity threshold at the lake site. In addition, when positive and negative evidence for seismic shaking from multiple sites can be gathered for a certain time period, minimum magnitudes and source locations can be estimated for paleo-earthquakes by a reverse application of an empirical intensity prediction equation in a geospatial analysis. Here, we present potential magnitudes and source locations of four paleo-earthquakes in the western Austrian Alps based on the integration of available and updated lake paleoseismic data, which comprise multiple mass-transport deposits on reflection seismic profiles and turbidites and soft-sediment deformation structures in sediment cores. The paleoseismic records at Plansee and Achensee covering the last ∼10 kyr were extended towards the age of lake initiation after deglaciation to obtain the longest possible paleoseismic catalogue at each lake site. Our results show that 25 severe earthquakes are recorded in the four lakes Plansee, Piburgersee, Achensee, and potentially Starnbergersee over the last ∼16 kyr, from which four earthquakes are interpreted to have left imprints in two or more lakes. Earthquake recurrence intervals range from ca. 1000 to 2000 years, with a weakly periodic to aperiodic recurrence behavior for the individual records. We interpret that relatively shorter recurrence intervals in the more orogen-internal archives Piburgersee and Achensee are related to enhanced tectonic loading, whereas a longer recurrence rate in the more orogen-external archive Plansee might reflect a decreased stress transfer across the current-day enhanced seismicity zone. Plausible epicenters of paleo-earthquake scenarios coincide with the current enhanced seismicity regions. Prehistoric earthquakes with a minimum moment magnitude (Mw) 5.8–6.1 have occurred around the Inn valley, the Brenner region, and the Fernpass–Loisach region and might have reached up to Mw 6.3 at Achensee. The paleo-earthquake catalogue might hint at a shift in severe earthquake activity near the Inn valley from east to west to east during postglacial times. ShakeMaps highlight that such severe earthquake scenarios do not solely impact the enhanced seismicity region of Tyrol but widely affect adjacent regions like southern Bavaria in Germany.
Natural Hazards and Earth System Sciences, Volume 22, pp 2001-2029; https://doi.org/10.5194/nhess-22-2001-2022
Population information is a fundamental issue for effective disaster risk reduction. As demonstrated by numerous past and present crises, implementing an effective communication strategy is, however, not a trivial matter. This paper draws lessons from the seismo-volcanic “crisis” that began in the French overseas department of Mayotte in May 2018 and is still ongoing today. Mayotte's case study is interesting for several reasons: (i) although the seismo-volcanic phenomenon itself is associated with moderate impacts, it triggered a social crisis that risk managers themselves qualified as “a communication crisis”, (ii) risks are perceived mostly indirectly by the population, which poses specific challenges, in particular to scientists who are placed at the heart of the risk communication process, and (iii) no emergency planning or monitoring had ever been done in the department of Mayotte with respect to volcanic issues before May 2018, which means that the framing of monitoring and risk management, as well as the strategies adopted to share information with the public, has evolved significantly over time. Our first contribution here is to document the gradual organization of the official response. Our second contribution is an attempt to understand what may have led to the reported “communication crisis”. To that end, we collect and analyze the written information delivered by the main actors of monitoring and risk management to the public over the last 3 years. Finally, we compare its volume, timing, and content with what is known of at-risk populations' information needs. Our results outline the importance of ensuring that communication is not overly technical, that it aims to inform rather than reassure, that it focuses on risk and not only on hazard, and that it provides clues to possible risk scenarios. We issue recommendations for improvement of public information about risks, in the future, in Mayotte but also elsewhere in contexts where comparable geo-crises may happen.
Natural Hazards and Earth System Sciences, Volume 22, pp 1973-2000; https://doi.org/10.5194/nhess-22-1973-2022
Recreationists are responsible for developing their own risk management plans for travelling in avalanche terrain. To help recreationists mitigate their exposure to avalanche hazard, many avalanche warning services include explicit travel and terrain advice (TTA) statements in their daily avalanche bulletins where forecasters offer guidance about what specific terrain to avoid and what to favour under the existing conditions. However, the use and effectiveness of this advice has never been tested to ensure it meets the needs of recreationists developing their risk management approach for backcountry winter travel. We conducted an online survey in Canada and the United States to determine which user groups are paying attention to the TTA in avalanche bulletins, what makes these statements useful, and if modifications to the phrasing of the statements would improve their usefulness for users. Our analysis reveals that the core audience of the TTA is users with introductory-level avalanche awareness training who integrate slope-scale terrain considerations into their avalanche safety decisions. Using a series of proportional-odds ordinal mixed-effect models, we show that reducing the jargon used in the advice helped users with no or only introductory-level avalanche awareness training understand the advice significantly better and adding an additional explanation made the advice more useful for them. These results provide avalanche warning services with critical perspectives and recommendations for improving their TTA so that they can better support recreationists who are at earlier stages of developing their avalanche risk management approach and therefore need the support the most.
Natural Hazards and Earth System Sciences, Volume 22, pp 2031-2056; https://doi.org/10.5194/nhess-22-2031-2022
Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to assess the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF 1) was trained relying on the forecast danger levels published in the official Swiss avalanche bulletin. To reduce the uncertainty resulting from using the forecast danger level as target variable, we trained a second classifier (RF 2) that relies on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the models, i.e. the percentage of correct danger level predictions, ranged between 74 % and 76 % for RF 1 and between 72 % and 78 % for RF 2. We assessed the accuracy of forecasts with nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the agreement rate between forecast and nowcast assessments of the current experience-based Swiss avalanche forecasts (which is estimated to be 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. Our results suggest that the models may well have potential to become a valuable supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.
Natural Hazards and Earth System Sciences, Volume 22, pp 1969-1972; https://doi.org/10.5194/nhess-22-1969-2022
Natural Hazards and Earth System Sciences, Volume 22, pp 1955-1968; https://doi.org/10.5194/nhess-22-1955-2022
The estimation of debris flow velocity and volume is a fundamental task for the development of early warning systems and the design of control structures and of other mitigation measures. Debris flow velocity can be calculated using seismic data recorded at two monitoring stations located along the channel, and previous analysis of the seismic energy produced by debris flows showed that the peak discharge of each surge can be estimated based on the maximum amplitude of the seismic signal. This work provides a first approach for estimating the total volume of debris flows from the integrated seismic energy detected with simple, low-cost geophones installed along a debris flow channel. The developed methods were applied to seismic data collected from 2014 to 2018 in three different test sites in the European Alps: Gadria and Cancia (Italy) and Lattenbach (Austria). An adaptable cross-correlation time window was used to calculate the velocity of the different surges, which can offer a better estimation of the velocity compared to a constant window length. The analyses of the seismic data of 14 debris flows show the strong control of the sampling rate and of the inter-station distance on velocity estimation. A linear relationship between the squares of seismic amplitudes – a proxy for seismic energy – and independent measurements of the debris flow volume is proposed for a first-order estimation of the latter. Uncertainties in the volume estimations are controlled by flow properties – such as liquid or viscous surges generating low-amplitude signals and thus underestimating the calculated volume – but in most cases (9 out of 11 events of the test dataset of the Illgraben basin, CH) the order of magnitude of the debris flow volume is correctly predicted.