Pub Date : 2023-11-09DOI: 10.5194/nhess-23-3407-2023
Franz Livio, Maria Francesca Ferrario, Elisa Martinelli, Sahra Talamo, Silvia Cercatillo, Alessandro Maria Michetti
Abstract. Low-deformation regions are characterized by long earthquake recurrence intervals. Here, it is fundamental to extend back the record of past events as much as possible to properly assess seismic hazards. Evidence from single sites or proxies may be not compelling, whereas we obtain a more substantial picture from the integration of paleo- and archeoseismic evidence at multiple sites, eventually supplemented with historical chronicles. In the city of Como (N Italy), we perform stratigraphic and sedimentological analyses on the sedimentary sequences at Via Manzoni and we document earthquake archeological effects at the Roman baths by means of structure from motion and field surveys. Radiocarbon dating and chronological constraints from the archeological site allow us to bracket the time of occurrence of the deformations to the sixth century CE. We interpret the observed deformations as due to earthquake ground shaking and provide constraints on the lower threshold for the triggering of such evidence. We move toward a regional view to infer possible relevant seismic sources by exploiting a dataset of published paleoseismic evidence in Swiss and N Italy lakes. We perform an inverse grid search to identify the magnitude and location of an earthquake that can explain all the positive and negative evidence consistent with the time interval of the event dated at Como. Our results show that an earthquake (minimum Mw 6.32) with epicenter located at the border between Italy and Switzerland may account for all the observed effects; a similar event in the sixth century CE has not been documented so far by historical sources. Our study calls for the need to refine the characterization of the local seismic hazard, especially considering that this region seems unprepared to face the effects of an earthquake size similar to the one inferred for the sixth-century-CE event.
{"title":"The footprint of a historical paleoearthquake: the sixth-century-CE event in the European western Southern Alps","authors":"Franz Livio, Maria Francesca Ferrario, Elisa Martinelli, Sahra Talamo, Silvia Cercatillo, Alessandro Maria Michetti","doi":"10.5194/nhess-23-3407-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3407-2023","url":null,"abstract":"Abstract. Low-deformation regions are characterized by long earthquake recurrence intervals. Here, it is fundamental to extend back the record of past events as much as possible to properly assess seismic hazards. Evidence from single sites or proxies may be not compelling, whereas we obtain a more substantial picture from the integration of paleo- and archeoseismic evidence at multiple sites, eventually supplemented with historical chronicles. In the city of Como (N Italy), we perform stratigraphic and sedimentological analyses on the sedimentary sequences at Via Manzoni and we document earthquake archeological effects at the Roman baths by means of structure from motion and field surveys. Radiocarbon dating and chronological constraints from the archeological site allow us to bracket the time of occurrence of the deformations to the sixth century CE. We interpret the observed deformations as due to earthquake ground shaking and provide constraints on the lower threshold for the triggering of such evidence. We move toward a regional view to infer possible relevant seismic sources by exploiting a dataset of published paleoseismic evidence in Swiss and N Italy lakes. We perform an inverse grid search to identify the magnitude and location of an earthquake that can explain all the positive and negative evidence consistent with the time interval of the event dated at Como. Our results show that an earthquake (minimum Mw 6.32) with epicenter located at the border between Italy and Switzerland may account for all the observed effects; a similar event in the sixth century CE has not been documented so far by historical sources. Our study calls for the need to refine the characterization of the local seismic hazard, especially considering that this region seems unprepared to face the effects of an earthquake size similar to the one inferred for the sixth-century-CE event.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.5194/nhess-23-3445-2023
Stephanie Mayer, Frank Techel, Jürg Schweizer, Alec van Herwijnen
Abstract. Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.
{"title":"Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations","authors":"Stephanie Mayer, Frank Techel, Jürg Schweizer, Alec van Herwijnen","doi":"10.5194/nhess-23-3445-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3445-2023","url":null,"abstract":"Abstract. Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.5194/nhess-23-3379-2023
Francisco Rodrigues do Amaral, Nicolas Gratiot, Thierry Pellarin, Tran Anh Tu
Abstract. We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on 25 November 2018 when Typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm of rainfall over 24 h which led to flooding and considerable material damage. We propose an in-depth study of TY-induced, compound flood drivers at a short timescale by focusing on the days before and after the event. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and inland effects on the hydrosystem. We found that TY Usagi made landfall without forming a significant storm surge. The extreme rainfall does not translate into immediate river discharge but presents a 16 h time lag between peak precipitation and peak residual discharge. Nevertheless, increased river water levels can be seen at both urban and upstream stations with a similar time lag. At the upstream river station, residual discharge represents 1.5 % of available rainwater, and evidence of upstream widespread flooding was found. At the urban river station, we assess the potential surface runoff during the event to be 8.9 % of the upstream residual discharge. However, a time lag in peak river water level and peak rainfall was found and attributed to the combination of high tide and impervious streets which prevented the evacuation of rainwater and resulted in street flooding of up to 0.8 m. Overall, it was found that despite not having a significant storm surge, the coastal tidal forcing is the predominant compound flood driver even during severe, heavy rainfall with tidal fluctuations in river water level and respective discharge much larger than the residuals.
{"title":"Assessing typhoon-induced compound flood drivers: a case study in Ho Chi Minh City, Vietnam","authors":"Francisco Rodrigues do Amaral, Nicolas Gratiot, Thierry Pellarin, Tran Anh Tu","doi":"10.5194/nhess-23-3379-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3379-2023","url":null,"abstract":"Abstract. We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on 25 November 2018 when Typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm of rainfall over 24 h which led to flooding and considerable material damage. We propose an in-depth study of TY-induced, compound flood drivers at a short timescale by focusing on the days before and after the event. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and inland effects on the hydrosystem. We found that TY Usagi made landfall without forming a significant storm surge. The extreme rainfall does not translate into immediate river discharge but presents a 16 h time lag between peak precipitation and peak residual discharge. Nevertheless, increased river water levels can be seen at both urban and upstream stations with a similar time lag. At the upstream river station, residual discharge represents 1.5 % of available rainwater, and evidence of upstream widespread flooding was found. At the urban river station, we assess the potential surface runoff during the event to be 8.9 % of the upstream residual discharge. However, a time lag in peak river water level and peak rainfall was found and attributed to the combination of high tide and impervious streets which prevented the evacuation of rainwater and resulted in street flooding of up to 0.8 m. Overall, it was found that despite not having a significant storm surge, the coastal tidal forcing is the predominant compound flood driver even during severe, heavy rainfall with tidal fluctuations in river water level and respective discharge much larger than the residuals.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.5194/nhess-23-3337-2023
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, Simon Loew
Abstract. We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time series of rockfall events with volumes of 1 m3 and greater since 2018 and serves as automatic traffic control for an important main road. In the period between January 2018 and October 2022, 6743 events were detected, which is 2 orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow area, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that, due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings.
{"title":"Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)","authors":"Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, Simon Loew","doi":"10.5194/nhess-23-3337-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3337-2023","url":null,"abstract":"Abstract. We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time series of rockfall events with volumes of 1 m3 and greater since 2018 and serves as automatic traffic control for an important main road. In the period between January 2018 and October 2022, 6743 events were detected, which is 2 orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow area, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that, due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"15 51","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.5194/nhess-23-3355-2023
Juliette Godet, Olivier Payrastre, Pierre Javelle, François Bouttier
Abstract. Flash floods have dramatic economic and social consequences, and efficient adaptation policies are required to reduce their impacts, especially in the context of global change. Developing more efficient flash flood forecasting systems can largely contribute to these adaptation requirements. The aim of this study was to assess the ability of a new seamless short-range ensemble quantitative precipitation forecast (QPF) product, called PIAF-EPS (Prévision Immédiate Agrégée Fusionnée ensemble prediction system) and recently developed by Météo-France, to predict flash floods when used as input to an operational hydrological forecasting chain. For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reanalysed, using a hydrological-modelling chain similar to the one implemented in the French Vigicrues Flash operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the forecasts obtained with different deterministic QPFs from which PIAF-EPS is directly derived. The verification method applied in this work uses scores calculated on contingency tables and combines the forecasts issued on each 1 km2 pixel of the territory. This offers a detailed view of the forecast performances, covering the whole river network and including the small ungauged rivers. The results confirm the added value of the ensemble PIAF-EPS approach for flash flood forecasting, in comparison to the different deterministic scenarios considered.
{"title":"Assessing the ability of a new seamless short-range ensemble rainfall product to anticipate flash floods in the French Mediterranean area","authors":"Juliette Godet, Olivier Payrastre, Pierre Javelle, François Bouttier","doi":"10.5194/nhess-23-3355-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3355-2023","url":null,"abstract":"Abstract. Flash floods have dramatic economic and social consequences, and efficient adaptation policies are required to reduce their impacts, especially in the context of global change. Developing more efficient flash flood forecasting systems can largely contribute to these adaptation requirements. The aim of this study was to assess the ability of a new seamless short-range ensemble quantitative precipitation forecast (QPF) product, called PIAF-EPS (Prévision Immédiate Agrégée Fusionnée ensemble prediction system) and recently developed by Météo-France, to predict flash floods when used as input to an operational hydrological forecasting chain. For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reanalysed, using a hydrological-modelling chain similar to the one implemented in the French Vigicrues Flash operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the forecasts obtained with different deterministic QPFs from which PIAF-EPS is directly derived. The verification method applied in this work uses scores calculated on contingency tables and combines the forecasts issued on each 1 km2 pixel of the territory. This offers a detailed view of the forecast performances, covering the whole river network and including the small ungauged rivers. The results confirm the added value of the ensemble PIAF-EPS approach for flash flood forecasting, in comparison to the different deterministic scenarios considered.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"12 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The impact of assimilating GNSS-ZTD (global navigation satellite system–zenith total delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, which was characterized by several moderate to intense precipitation events, especially over northwestern Italy. The WRF (Weather Research and Forecasting) model, version 4.1.3, is used with its 3D-Var data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. The water vapor forecast is verified for the forecast hours of 1–6 h after the last data assimilation time. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The precipitation forecast is verified in the phases of 0–3 and 3–6 h after the last data assimilation time using more than 3000 rain gauges spread over Italy. The application of GNSS-ZTD data assimilation to a case study improved the precipitation forecast by increasing the rainfall maximum and by better focusing the precipitation pattern over northeastern Italy, with the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on rainfall forecast, with an improvement in the performance up to 6 h and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm (3 h)−1).
{"title":"The impact of global navigation satellite system (GNSS) zenith total delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the Weather Research and Forecasting (WRF) model","authors":"Rosa Claudia Torcasio, Alessandra Mascitelli, Eugenio Realini, Stefano Barindelli, Giulio Tagliaferro, Silvia Puca, Stefano Dietrich, Stefano Federico","doi":"10.5194/nhess-23-3319-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3319-2023","url":null,"abstract":"Abstract. The impact of assimilating GNSS-ZTD (global navigation satellite system–zenith total delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, which was characterized by several moderate to intense precipitation events, especially over northwestern Italy. The WRF (Weather Research and Forecasting) model, version 4.1.3, is used with its 3D-Var data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. The water vapor forecast is verified for the forecast hours of 1–6 h after the last data assimilation time. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The precipitation forecast is verified in the phases of 0–3 and 3–6 h after the last data assimilation time using more than 3000 rain gauges spread over Italy. The application of GNSS-ZTD data assimilation to a case study improved the precipitation forecast by increasing the rainfall maximum and by better focusing the precipitation pattern over northeastern Italy, with the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on rainfall forecast, with an improvement in the performance up to 6 h and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm (3 h)−1).","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"61 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-23DOI: 10.5194/nhess-23-3305-2023
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, Wolfgang Wagner
Abstract. In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km2 was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.
摘要2022年8月至9月，巴基斯坦遭受严重洪灾，数百万人受到影响。利用Technische Universität Wien (TU Wien)为哥白尼应急管理服务(CEMS)全球洪水监测(GFM)组件开发的基于sentinel -1的洪水测绘算法，记录了2022年8月10日至9月23日洪水的传播情况。使用CEMS快速制图组件的洪水图对结果进行了评估。总体而言，该算法的性能相当好，临界成功指数高达80%，而检测到的差异主要归因于算法结果与相应参考文献的时差。在6周的时间跨度内，观测到30 492平方公里的面积至少被淹没一次，8月30日发现最大程度。该研究证明了TU Wien洪水映射算法完全自动生成大规模结果的能力，以及如何从这些结果中获得事件的关键数据。
{"title":"Sentinel-1-based analysis of the severe flood over Pakistan 2022","authors":"Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, Wolfgang Wagner","doi":"10.5194/nhess-23-3305-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3305-2023","url":null,"abstract":"Abstract. In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km2 was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.5194/nhess-23-3285-2023
Xabier Blanch, Marta Guinau, Anette Eltner, Antonio Abellan
Abstract. In this publication we address the lack of technical expertise in the geoscience community in the design and construction of photogrammetric systems for monitoring natural hazards at high spatio-temporal resolution. Accordingly, we provide in-depth information on the components, assembly instructions, and programming codes required to build them, making them accessible to researchers from different disciplines who are interested in 3D change detection monitoring. Each system comprises five photographic modules and a wireless transmission system for real-time image transfer. As an alternative to lidar (light detection and ranging), high-end digital cameras offer a simpler and more cost-effective solution for the generation of 3D models, especially in fixed time-lapse monitoring systems. The acquired images, in combination with algorithms that allow the creation of improved 3D models, offer change detection performance comparable to lidar. We showcase the usefulness of our approach by presenting real-world applications in the field of geohazard monitoring. Our findings highlight the potential of our method to detect pre-failure deformation and identify rockfalls with a theoretical change detection threshold of only 3–4 cm, thereby demonstrating the potential to achieve similar accuracies to lidar but at a much lower cost. Furthermore, thanks to the higher data acquisition frequency, the results show how the overlap of events that leads to an erroneous interpretation of the behaviour of the active area is minimized, allowing, for example, more accurate correlations between weather conditions and rockfall activity.
{"title":"Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution","authors":"Xabier Blanch, Marta Guinau, Anette Eltner, Antonio Abellan","doi":"10.5194/nhess-23-3285-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3285-2023","url":null,"abstract":"Abstract. In this publication we address the lack of technical expertise in the geoscience community in the design and construction of photogrammetric systems for monitoring natural hazards at high spatio-temporal resolution. Accordingly, we provide in-depth information on the components, assembly instructions, and programming codes required to build them, making them accessible to researchers from different disciplines who are interested in 3D change detection monitoring. Each system comprises five photographic modules and a wireless transmission system for real-time image transfer. As an alternative to lidar (light detection and ranging), high-end digital cameras offer a simpler and more cost-effective solution for the generation of 3D models, especially in fixed time-lapse monitoring systems. The acquired images, in combination with algorithms that allow the creation of improved 3D models, offer change detection performance comparable to lidar. We showcase the usefulness of our approach by presenting real-world applications in the field of geohazard monitoring. Our findings highlight the potential of our method to detect pre-failure deformation and identify rockfalls with a theoretical change detection threshold of only 3–4 cm, thereby demonstrating the potential to achieve similar accuracies to lidar but at a much lower cost. Furthermore, thanks to the higher data acquisition frequency, the results show how the overlap of events that leads to an erroneous interpretation of the behaviour of the active area is minimized, allowing, for example, more accurate correlations between weather conditions and rockfall activity.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.5194/nhess-23-3261-2023
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, Benjamin B. Mirus
Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.
{"title":"Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA","authors":"Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, Benjamin B. Mirus","doi":"10.5194/nhess-23-3261-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3261-2023","url":null,"abstract":"Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.5194/nhess-23-3247-2023
Jiachang Tu, Jiahong Wen, Liang Emlyn Yang, Andrea Reimuth, Stephen S. Young, Min Zhang, Luyang Wang, Matthias Garschagen
Abstract. This article presents a flood risk assessment for Shanghai, which provides an indication of what buildings (including residential, commercial, office, and industrial) will be exposed to flooding and its damage. Specifically, this assessment provides a risk assessment that buildings may face after construction. To achieve the flood risk assessment on buildings, we developed an integrated flood model and collected data on building shape and number of floors, land use, and construction costs for different building types in Shanghai. The extreme compound flood scenarios (1/200-, 1/500-, 1/1000-, and 1/5000-year floods) and building metadata were aggregated using a risk analysis chain. According to the damage for different flood scenarios, the average annual loss (AAL) can be calculated and is referred to as building flood risk. The AAL of residential, commercial, office, and industrial buildings is USD 12.3, 2.5, 3.7, and 3.4 million, respectively. Among the 15 (non-island) districts in Shanghai, Pudong has the highest AAL. The risk analysis chain developed in this study can be reproduced for other megacities. The results provide a clear picture for future building flood risks which links directly to disaster risk management, which implies the extent of flood risk in building types, sub-districts, and districts related to the Shanghai Master Plan. This assessment takes into consideration future climate change scenarios, information for scenario-based decision making, and a cost–benefit analysis for extreme flood risk management in Shanghai. We also discussed different potential adaptation options for flood risk management.
{"title":"Assessment of building damage and risk under extreme flood scenarios in Shanghai","authors":"Jiachang Tu, Jiahong Wen, Liang Emlyn Yang, Andrea Reimuth, Stephen S. Young, Min Zhang, Luyang Wang, Matthias Garschagen","doi":"10.5194/nhess-23-3247-2023","DOIUrl":"https://doi.org/10.5194/nhess-23-3247-2023","url":null,"abstract":"Abstract. This article presents a flood risk assessment for\u0000Shanghai, which provides an indication of what buildings (including\u0000residential, commercial, office, and industrial) will be exposed to flooding\u0000and its damage. Specifically, this assessment provides a risk assessment\u0000that buildings may face after construction. To achieve the flood risk\u0000assessment on buildings, we developed an integrated flood model and\u0000collected data on building shape and number of floors, land use, and\u0000construction costs for different building types in Shanghai. The extreme\u0000compound flood scenarios (1/200-, 1/500-, 1/1000-, and 1/5000-year floods) and\u0000building metadata were aggregated using a risk analysis chain. According\u0000to the damage for different flood scenarios, the average annual loss (AAL)\u0000can be calculated and is referred to as building flood risk. The AAL of\u0000residential, commercial, office, and industrial buildings is USD 12.3, 2.5,\u00003.7, and 3.4 million, respectively. Among the 15 (non-island) districts\u0000in Shanghai, Pudong has the highest AAL. The risk analysis chain developed\u0000in this study can be reproduced for other megacities. The results provide a\u0000clear picture for future building flood risks which links directly to\u0000disaster risk management, which implies the extent of flood risk in building\u0000types, sub-districts, and districts related to the Shanghai Master Plan.\u0000This assessment takes into consideration future climate change scenarios,\u0000information for scenario-based decision making, and a cost–benefit analysis\u0000for extreme flood risk management in Shanghai. We also discussed different\u0000potential adaptation options for flood risk management.","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：