GeoHazards最新文献

{"title":"ENSO Impacts on Jamaican Rainfall Patterns: Insights from CHIRPS High-Resolution Data for Disaster Risk Management","authors":"Cheila Avalon-Cullen, Rafea Al Suhili, Nathaniel K. Newlands, Christy Caudill, Harvey Hill, Jaqueline Spence-Hemmings, Markus Enenkel","doi":"10.3390/geohazards5010005","DOIUrl":"https://doi.org/10.3390/geohazards5010005","url":null,"abstract":"This study examines the influence of the El Niño Southern Oscillation (ENSO) on Jamaica’s rainfall patterns, leveraging CHIRPS data from 1981 to 2021 in 370 locations. Our analysis reveals a distinct ENSO imprint on rainfall, with La Niña phases showing a consistently higher probability of exceeding various rainfall thresholds compared to El Niño. Notably, La Niña increases the likelihood of heavier rainfall, particularly in the wet seasons, with probabilities of exceeding 200 mm reaching up to 50% during wet season II. Spatially, the probability of total monthly rainfall (TMR) during La Niña is elevated in the northeastern regions, suggesting regional vulnerability to excess rainfall. Additionally, during El Niño, the correlation between TMR and the maximum air temperature (Tmax) is significantly stronger, indicating a positive and more pronounced relationship between higher temperatures and rainfall, with correlation coefficients ranging from 0.39 to 0.80. Wind speed and evapotranspiration show a negligible influence on TMR during both ENSO phases, maintaining stable correlation patterns with only slight variations. The results of this study underscore the necessity for differentiated regional strategies in water resource management and disaster preparedness, tailored to the unique climatic characteristics imposed by ENSO variability. These insights contribute to a refined understanding of climate impacts, essential for enhancing resilience and adaptive capacity in Jamaica and other small island developing states.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"236 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139877312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee","authors":"George K. Darkwah, A. Kalyanapu, Collins Owusu","doi":"10.3390/geohazards5010004","DOIUrl":"https://doi.org/10.3390/geohazards5010004","url":null,"abstract":"The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models. Even though forecast stations are expanding across the United States, the coverage is usually limited to major rivers and urban areas. Most rural and sub-urban areas, including recreational areas such as the Window Cliffs State Natural Area, do not have such forecast stations and as such, are prone to the dire effects of unforeseen flooding. In this study, four machine learning model architectures were developed based on the long short-term memory, random forest, and support vector regression techniques to forecast water depths at the Window Cliffs State Natural Area, located within the Cane Creek watershed in Putnam County, Tennessee. Historic upstream and downstream water levels and absolute pressure were used to forecast the future water levels downstream of the Cane Creek watershed. The models were tested with lead times of 3, 4, 5, and 6 h, revealing that the model performances reduced with an increase in lead time. Even though the models yielded low errors of 0.063–0.368 ft MAE, there was an apparent delay in predicting the peak water depths. However, including rainfall data in the forecast showed a promising improvement in the models’ performance. Tests conducted on the Cumberland River in Tennessee showed a promising improvement in model performance when trained with larger data.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"24 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139597918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines","authors":"K. Yuen, Adam D. Switzer, Paul P. S. Teng, Janice S. H. Lee","doi":"10.3390/geohazards5010002","DOIUrl":"https://doi.org/10.3390/geohazards5010002","url":null,"abstract":"Typhoons are destructive multi-hazard events. To assess the relationship between typhoon intensity and agricultural loss, there is a need for accurate and standardized information on loss and damage, which is currently lacking. To address this, a database for Vietnam and the Philippines was created to provide aggregated information on the magnitude of rice damage and to highlight the rice-growing areas which were prone to being damaged by typhoons. Our study period was from 1970 to 2018, and we focused on Vietnam and the Philippines as these two countries experience frequent and intense typhoons. As different crops respond differently to wind and rain, we limit our research to a single crop. In this study, we focused on rice as it is a major staple food in Southeast Asia, and rice fields were often damaged by typhoons in the two countries. Of the 829 typhoon events recorded, only 15% of the events resulted in rice damage. The average area of rice damaged per typhoon event ranged from 42,407 ha in Vietnam to 83,571 ha in the Philippines. Meanwhile, the average production loss per typhoon event ranged from 190,227 metric tonnes in the Philippines to 539,150 metric tonnes in Vietnam. The monetary value of rice crops lost was only reported in the Philippines, and this amounted to an average of US$ 42 million per typhoon event. There was a weak relationship between landfall wind speed and the three indicators of rice damage, which suggests that rice damage was not primarily due to strong winds. Our results showed that the rice fields in the coastal provinces of Vietnam and the northern parts of the Philippines were more vulnerable to being damaged by typhoons.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Lac Fallère Area as an Example of the Interplay between Deep-Seated Gravitational Slope Deformation and Glacial Shaping (Aosta Valley, NW Italy)","authors":"Stefano Dolce, M. G. Forno, M. Gattiglio, Franco Gianotti","doi":"10.3390/geohazards5010003","DOIUrl":"https://doi.org/10.3390/geohazards5010003","url":null,"abstract":"The Lac Fallère area in the upper Clusellaz Valley (tributary of the middle Aosta Valley) is shaped in micaschist and gneiss (Mont Fort Unit, Middle Penninic) and in calcschist and marble (Aouilletta Unit, Combin Zone). Lac Fallère exhibits an elongated shape and is hosted in a WSW–ENE-trending depression, according to the slope direction. This lake also shows a semi-submerged WSW–ENE rocky ridge that longitudinally divides the lake. This evidence, in addition to the extremely fractured rocks, indicates a wide, deep-seated gravitational slope deformation (DSGSD), even if this area is not yet included within the regional landslide inventory of the Aosta Valley Region. The Lac Fallère area also shows reliefs involved in glacial erosion (roches moutonnée), an extensive cover of subglacial sediments, and many moraines essentially referred to as Lateglacial. The DSGSD evolution in a glacial environment produced, as observed in other areas, effects on the facies of Quaternary sediments and the formation of a lot of wide moraines. Glacial slope sectors and lateral moraines displaced by minor scarps and counterscarps, and glaciers using trenches forming several arched moraines, suggest an interplay between glacial and gravitational processes, which share part of their evolution history.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139626870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Soil Loss from Land Cover Changes in the Nan River Basin, Thailand","authors":"K. Pakoksung","doi":"10.3390/geohazards5010001","DOIUrl":"https://doi.org/10.3390/geohazards5010001","url":null,"abstract":"This study investigates soil loss erosion dynamics in the Nan River Basin, Thailand, focusing on the impact of land cover changes. Utilizing the Universal Soil Loss Equation (USLE) model, key factors, including rainfall erosivity, soil erodibility, topography, and land cover, are analyzed for the years 2001 to 2019. The findings reveal a substantial increase in human-induced soil erosion, emphasizing the pressing need for effective mitigation measures. Severity classification demonstrates shifting patterns, prompting targeted conservation strategies. The examination of land cover changes indicates significant alterations in the satellite image (MODIS), particularly an increase in Deciduous forest (~13.21%), Agriculture (~0.18%), and Paddy (~0.43%), and decrease in Evergreen Forest (~13.73%) and Water (~0.12%) cover types. Deciduous forest and Agriculture, associated with the highest soil loss rates, underscore the environmental consequences of specific land use practices. Notably, the increase in Deciduous forest and Agriculture significantly contributes to changes in soil loss rates, revealing the interconnectedness of land cover changes and soil erosion in ~18.05% and ~8.67%, respectively. This study contributes valuable insights for informed land management decisions and lays a foundation for future research in soil erosion dynamics. Additionally, the percentage increase in Agriculture corresponds to a notable rise in soil loss rates, underscoring the urgency for sustainable land use practices.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"33 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network","authors":"B. Skahill, C. H. Smith, Brook T. Russell","doi":"10.3390/geohazards4040030","DOIUrl":"https://doi.org/10.3390/geohazards4040030","url":null,"abstract":"This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"968 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139177024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near Real-Time Detection and Moment Tensor Inversion of the 11 May 2022, Dharchula Earthquake","authors":"Pankaj Kumar, V. Silwal, Rinku Mahanta, V. Maurya, Kamal, Mukat Lal Sharma, Ambikapathy Ammani","doi":"10.3390/geohazards4040029","DOIUrl":"https://doi.org/10.3390/geohazards4040029","url":null,"abstract":"On 11 May 2022, an earthquake of Mw 5.2 occurred in the Dharchula region of Uttarakhand Himalayas, India. The Uttarakhand State Earthquake Early Warning System (UEEWS) detected and transmitted the warning within 11.61 s from the origin time, taking only 4.26 s for processing, location, and magnitude estimation and warning dissemination. The complete analysis was performed using three seconds of waveforms. Using the initial earthquake parameters provided by the UEEWS, moment tensor inversion was performed using the broadband seismometers network installed in northern India. The moment tensor (MT) inversion was performed for the event using both the body waves and the surface waves. The first motion polarity was used along with waveform data to enhance the solution’s stability. This paper discusses the importance of real-time event detection and efforts towards real-time MT source inversion of earthquakes in the Uttarakhand Himalayas. Relocation of two past earthquakes near Dharchula is also undertaken in this study. The outcome of this study provides insights into mitigating seismic hazards, understanding earthquake source mechanisms, and enhancing knowledge of local fault structures.","PeriodicalId":502457,"journal":{"name":"GeoHazards","volume":"49 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}