Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data","authors":"Sourav Das, Anuradha Priyadarshana, Stephen Grebby","doi":"10.1007/s00477-024-02713-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02713-3","url":null,"abstract":"<p>Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"2 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother","authors":"Jiuhui Li, Zhengfang Wu, Wenxi Lu, Hongshi He, Yaqian He","doi":"10.1007/s00477-024-02761-9","DOIUrl":"https://doi.org/10.1007/s00477-024-02761-9","url":null,"abstract":"<p>The identification of groundwater contamination sources (IGCSs) is an important requirement for the remediation and treatment of groundwater contamination. The data assimilation methods such as ensemble Kalman filter (EnKF) and ensemble smoother (ES) have been applied to IGCSs in recent years and obtained good identification results. The unscented kalman filter (UKF) is also a data assimilation method with the potential to simultaneously identify hydraulic conductivity and GCSs. However, when UKF is applied to identify hydraulic conductivity and GCSs, it is necessary to use the observed data at different times separately, which increases the complexity of the update process and this may result in low identification accuracy. ES is a variant of EnKF that updates the system parameters with all observed data in all time periods, which makes ES faster and easier to implement than EnKF. Therefore, inspired by the ES, an unscented kalman smoother (UKS) based on UKF was proposed for simultaneously identifying the hydraulic conductivity and GCSs in this study. The UKS can use the data observed in all time periods simultaneously, while it is also simpler to operate and the calculation speed is faster. Present studies have shown that ES can solve IGCS problems. Thus, ES was also applied to identify the hydraulic conductivity and GCSs in this study, and its identification performance was compared with UKS. In contrast to previous applications of ES to IGCSs, both UKS and ES were set up with stop iteration conditions instead of only performing one update process, and thus both methods applied multiple update processes. The results showed that compared with ES, the identification results obtained by UKS were characterized by greater stability, higher accuracy, and the iterative process required less iteration process and computational time.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"9 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic simulation of hydraulic jump in a riverbed in presence and absence of stilling basin","authors":"Farshad Hajizadehmishi, Seyed Mehrab Amiri, Ali Akbar Hekmatzadeh, Parjang Monajemi, Shahin Farahmandpey","doi":"10.1007/s00477-024-02751-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02751-x","url":null,"abstract":"<p>This study examines how the variability of the Manning coefficient (<i>n</i>) affects the position of hydraulic jumps downstream of hydraulic structures. Using a robust finite volume method and random field theory, the study investigates the impact of spatial variations in <i>n</i> on hydraulic jump characteristics. Two scenarios are considered: one with a stilling basin and one without. Both one-dimensional and two-dimensional spatial distributions of <i>n</i> are analyzed. The results show that without a stilling basin, there are significant variations in the location of hydraulic jumps in the riverbed. The uncertainty in the location of the hydraulic jump is much higher than the uncertainty in the values of conjugate depths. Additionally, one-dimensional spatial distribution of <i>n</i> leads to higher standard deviations in the estimated location compared to two-dimensional distribution. In scenarios with a stilling basin, increasing riprap length causes the hydraulic jump to move upstream, while standard deviation remains constant.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"1 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical Analysis of Spatial Distribution of Ambient Air Pollution in Addis Ababa, Ethiopia","authors":"Daniel Mulgeta, Butte Gotu, Shibru Temesgen, Merga Belina, Habte Tadesse Likassa, Dejene Tsegaye","doi":"10.1007/s00477-024-02748-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02748-6","url":null,"abstract":"<p>Ambient air pollution has recently emerged as a major global public health issue, causing a variety of negative health impacts even at the lowest measurable concentrations. This study aims to analyze the spatial distribution of ambient air pollution in Addis Ababa, Ethiopia. The study was based on cross-sectional data collected from 21 selected sites within the period of October 13, 2019 to January 26, 2020, and July 5 to October 29, 2021. The spatial distribution of ambient air pollution was analyzed using spatial autocorrelation (Moran’s I and Geary’s C), and the hotspot areas of ambient air pollution were identified using the Ord and Getis statistics after visualizing via the Moran Scatter Plot. The average concentration of ambient air pollution was modeled against the covariates using a spatial lag model. Moran’s I, and Geary’s C, showed that the spatial distribution of ambient air pollution was globally clustered in the study area. Results revealed that Petros, Tekle Haimanot, and Bob Marley Squares, Legehar, Jamo Mikael, Sholla, Megenagna, African Union traffic signal, Stadium, North and East sampling sites of Akaki Kality's metal welding shade were identified as the hotspot sites of both ambient air pollutants. The results showed that temperature, average wind speed, wind direction, road characteristics, and land use characteristics were statistically significantly associated with the ambient air pollution concentrations. Paying attention to reducing ambient air pollution in pollution hotspot areas is recommended by the government and all concerned bodies.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"33 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data","authors":"Mahalingam Jayaprathiga, A. N. Rohith, Raj Cibin, K. P. Sudheer","doi":"10.1007/s00477-024-02758-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02758-4","url":null,"abstract":"<p>Hydrological models are critical for water resources planning and management. The precision and reliability of the simulations hinge greatly on the accessibility and quality of available input data. Particularly in developing nations, the major challenge in modeling is the scarcity of fine-scale spatiotemporal input data, specifically precipitation. Remotely sensed weather data has been increasingly used in recent years. However, they possess bias compared to ground observations due to the nature of indirect measurement and may affect the simulated water balance. To address these limitations, we explored data assimilation techniques to improve the Global Precipitation Measurement product (IMERG) precipitation using limited ground observations. Multiple assimilation methods are applied by incorporating Linear scaling Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the most effective method identified, is utilized in an eco-hydrological model, and the resulting stream flow simulations are validated against observed flow data. The findings indicate that assimilated precipitation enhances the monthly flow statistics in both CF and PF methods and also in conditional merged precipitation. An ensemble of hydrological simulations, outperformed those based on raw IMERG precipitation. Additionally, the hydrological simulations are compared with observed gauge precipitation data and the widely used Climate Forecast System Reanalysis (CFSR) dataset in data-limited watersheds. The simulations utilizing the assimilated IMERG dataset (NSE = 0.52) are comparable to gauge precipitation-based simulations (NSE = 0.61) and significantly superior to CFSR-based simulations (NSE=-0.2). These results highlight the potential of utilizing assimilated remote sensing data for hydrological modeling in data-limited watersheds, leading to improved simulation accuracy and reliability.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"9 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic flows time series in a flood-prone area: modeling and clustering on extreme values with a spatial constraint","authors":"Maurizio Carpita, Giovanni De Luca, Rodolfo Metulini, Paola Zuccolotto","doi":"10.1007/s00477-024-02735-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02735-x","url":null,"abstract":"<p>Time series of traffic flows, extracted from mobile phone origin–destination data, are employed for monitoring people crowding and mobility in areas subject to flooding risk. By applying a vector autoregressive model with exogenous covariates combined with dynamic harmonic regression to such time series, we detected the presence of many extreme events in the residuals, which exhibit heavy-tailed distribution. For this reason, we propose a time series clustering procedure based on tail dependence which is suitable for data characterized by a spatial dimension, since objects’ geographical proximity is taken into account. The final aim is to obtain clusters of areas characterized by the common tendency to the manifestation of extreme events, that in this case study are represented by extremely high incoming traffic flows. The proposed method is applied to the Mandolossa, a strongly urbanized area located on the western outskirts of Brescia (northern Italy) which is subject to frequent flooding.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"42 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of groundwater pollution sources based on optimal layout of groundwater pollution monitoring network","authors":"Xi Ma, Jiannan Luo, Xueli Li, Zhuo Song","doi":"10.1007/s00477-024-02756-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02756-6","url":null,"abstract":"<p>The accuracy of pollution source identification significantly depends on the amount of effective information derived from monitoring data. Currently, most of the comprehensive studies on groundwater contamination source identification and optimal design of monitoring solutions are based on hypothetical cases, whereas relevant studies on actual cases only consider one characteristic of the pollution source (either locations or fluxes). An optimal monitoring network (OMN)-based pollution source characterisation framework that takes source locations and source fluxes into account is presented to enhance the accuracy of pollution source identification. The genetic algorithm (GA) and particle swarm optimization (PSO) were used to solve the optimization model of pollution source characteristics identification. The framework is applied to a landfill for waste located in Baicheng City, China. The results showed that the identification results based on OMN has a smaller mean relative error and higher accuracy, compared with those based on random monitoring network (RMN). This study shows that OMNs can provide more effective information for pollution source identification and effectively enhance the accuracy of the groundwater sources characteristics identification.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"26 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving future drought predictions – a novel multi-method framework based on mutual information for subset selection and spatial aggregation of global climate models of precipitation","authors":"Muhammad Shakeel, Zulfiqar Ali","doi":"10.1007/s00477-024-02746-8","DOIUrl":"https://doi.org/10.1007/s00477-024-02746-8","url":null,"abstract":"<p>Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"44 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141254449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021","authors":"Jae Yeol Song, Eun-Sung Chung","doi":"10.1007/s00477-024-02742-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02742-y","url":null,"abstract":"<p>Atlantic tropical cyclones often associate with heavy rainfall, which causes inland- and coastal-flooding in the United States, and the storm-induced rainfall is closely related to its storm scale, movement, and location. For a better performance in flood or risk analysis in a region, understanding the characteristics and distribution of tropical storm (TS) induced extreme rainfall is essential. This study proposes dimensionless rainfall-duration curves for designated four-quartile storms that represents the temporal distribution of TS induced extreme rainfall in the Gulf of Mexico from 1979 to 2021. Our study employs spatiotemporal analysis to compute rainfall while TSs are located overseas and inland from satellite based climate forcing data and hurricane track records, annual maximum approach to define TS induced extreme rainfall events, and designated track types to categorize events based on their trajectories. As a result, extreme rainfall relating to TSs in the Gulf of Mexico are found to be considerably higher in inland than overseas. For inland, majority of the TSs was found to be the 1st- and 2nd-quartile storms. However, the 3rd-quartile storms, which case are rare, were found to have the overall largest amount of rainfall per duration compared to the other quartile storms. As for overseas, more than half of the TSs were found to be the 4th-quartile storm while the 2nd-quartile storm has higher overall rainfall per duration. Spatial analysis shows that Texas, Louisiana, Mississippi, Florida, and South Carolina are determined as high-threatened areas by TS induced extreme rainfall.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"35 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monte Carlo simulation of source-specific risks of soil at an abandoned lead-acid battery recycling site","authors":"Andrijana Miletić, Jelena Vesković, Milica Lučić, Antonije Onjia","doi":"10.1007/s00477-024-02747-7","DOIUrl":"https://doi.org/10.1007/s00477-024-02747-7","url":null,"abstract":"<p>Anthropogenic activities predominantly affect environmental Pb pollution, especially during waste lead-acid battery (LAB) recycling operations. In this study, the presence of Pb and nine other potentially toxic elements (PTEs) in the soil at an abandoned LAB recycling site was investigated. The focus was on spatial and vertical distributions and potential health issues related to PTEs. Average concentrations of Cd, As, Hg, Pb, Al, Zn, Cu, and Sb were elevated at all investigated soil depths, whereas the concentrations of Zn, Cu, and Sb were significant only on the soil surface. Positive matrix factorization, correlation and cluster analyses, as well as self-organizing maps, identified four primary pollution sources: recycling activities (Cd, Hg, Pb, and Sb), mixed anthropogenic sources (Zn and Cu), the soil parent material (As, Cr, and Ni), and surface runoff combined with sand application (Al and pH). While the non-carcinogenic risk results revealed a negligible risk for adults, the hazard index (HI) values for children were greater than one in 26% of the samples. For adults and children, the total carcinogenic risk (TCR) values were acceptable for 98% and 94% of the samples, respectively. Geospatial analysis identified the main hotspot in the battery disposal area. Source-specific non-carcinogenic and carcinogenic risks were most influenced by recycling activities. Monte Carlo simulation (MCS) of total HI for children showed that the risk value exceeded the threshold level (HI &gt; 1) at the 10th percentile, whereas the maximum value of total HI for adults was 0.2. Regarding carcinogenic risk, the TCR values at the 95th percentile of all four sources for adults and children were below the limit value (1 × 10⁻⁴), indicating a low probability of cancer development.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"91 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}