Stochastic Environmental Research and Risk Assessment最新文献

Fluvial floods are a severe hazard resulting from the interplay of climatic and anthropogenic factors. The most critical anthropogenic factor is urbanization, which increases land imperviousness. This study uses the paired catchment approach to investigate the effect of urbanization vs. climate drivers on river floods in Poland. Long-term daily river flow data until 2020 was used for four selected urban catchments and their non-urban counterparts, along with extreme precipitation, soil moisture excess, and snowmelt data generated from the process-based Soil & Water Assessment Tool (SWAT) model. Changes in impervious areas were assessed using two state-of-the-art Copernicus products, revealing a consistent upward trend in imperviousness across all selected urban catchments. A range of statistical methods were employed to assess changes in the magnitude and frequency of floods and flood drivers, including the Pettitt test, the Mann Kendall (MK) multitemporal test, the Poisson regression test, multi-temporal correlation analysis and multiple linear regression. The MK test results showed a contrasting behaviour between urban (increases) and non-urban (no change) catchments for three of the four analysed catchment pairs. Flood frequency increased significantly in only one urban catchment. Multiple regression analysis revealed that non-urban catchments consistently exhibited stronger relationships between floods and climate drivers than the urban ones, although the results of residual analysis were not statistically significant. In summary, the evidence for the impact of urbanization on floods was found to be moderate. The study highlights the significance of evaluating both climatic and anthropogenic factors when analysing river flood dynamics in Poland.

Regional debris flow susceptibility assessment is an effective method to prevent debris flow hazards, and deep learning is emerging as a novel approach in this discipline with the development of computers. However, when debris flow samples are insufficient, there will be problems like overfitting or misclassification. To overcome these problems, this paper proposes a semi-supervised deep neural network model (LPA-DNN) combined with label propagation algorithm (LPA), which utilizes high confidence unlabeled samples as pseudo-samples reasonably in few-label scenarios. Xinzhou, Shanxi Province, was selected as the study area, and a dataset containing 292 debris flow samples and 10 types of impact factors was compiled based on watershed units. Using the dataset and pseudo-samples, the LPA-DNN model was built to get debris flow susceptibility map. Meanwhile, DNN and SVM were set up for comparison to demonstrate that the proposed LPA-DNN model has excellent performance and higher accuracy. LPA-DNN alleviates the problem of low accuracy that caused by samples lacking in deep learning to a certain extent, and obtains great classification results, which proves that it is quite potential in regional debris flow susceptibility assessment.

The present study focuses on characterizing the time–frequency aspects of seasonal temperatures in India by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm with the Hilbert–Huang transform (HHT) decomposition method. The investigation also explores the connections between maximum temperature (T_max) and minimum temperature (T_min) with global climate oscillations, such as the El Nino Southern Oscillation (ENSO), Sunspot Number (SN), and Pacific Decadal Oscillations (PDO). The findings indicate that intra and inter-decadal modes play a pivotal role in influencing temperature series across various seasons, with notable changes observed in the amplitudes of inter-decadal modes for seasonal T_min and T_max. The analysis of intrinsic mode functions (IMFs) reveals that IMF2 closely align to ENSO with a periodicity of 5–7 years, IMF3 to the sunspot cycle with a frequency of approximately 11 years, and IMF5 to PDO with a long periodicity exceeding 60 years. The association between the IMF components of T_min and T_max temperature series and the three climate indices is most evident for low-frequency modes, demonstrating a consistent evolution of trend components.

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems.

As a leading economic center in China and an international metropolis, Shenzhen has great significance in promoting sustainable urban development. To predict its amount of domestic waste clearance, a new multivariable grey prediction model with combinatorial optimization of parameters is established in this paper. Firstly, the new model expands the value range of the order r of a grey accumulation generation operator from positive real numbers (R +) to all real numbers (R), which enlarges the optimization space of parameter and has positive significance for improving model performance. Secondly, the dynamic background-value coefficient λ is introduced into the new model to improve the smoothing effect of the nearest neighbor generated sequences. Thirdly, with the objective function of minimizing the mean absolute percentage error (MAPE), the particle swarm optimization (PSO) is employed to optimize parameters r and λ to improve the overall performance of the new model. The new model is used to simulate and predict the amount of domestic waste clearance in Shenzhen, and the MAPE of the new model is only 0.27%, which is far superior to several other similar models. Lastly, the new model is applied to predict the amount of domestic waste clearance in Shenzhen. The results indicate the amount of domestic waste clearance in 2028 could be 9.96 million tons, an increase of 20.58% compared to 2021.This highlights the significant challenge that Shenzhen faces in terms of urban domestic waste treatment. Therefore, some targeted countermeasures and suggestions have been proposed to ensure the sustainable development of Shenzhen's economy and society.

In probabilistic coastal hazard assessments based on the Joint Probability Method, historical storm data is used to build distribution models of tropical cyclone atmospheric parameters (i.e., central pressure deficit, forward velocity, radius of maximum wind, and heading direction). Recent models have used a range of assumptions regarding the dependence structure between these random variables. This research investigates the performance of a series of joint distribution models based on assumptions of parameter independence, partial-dependence (i.e., dependence between only central pressure deficit and radius of maximum wind), and full dependence (i.e., dependence between each pair of tropical cyclone atmospheric parameters). Full dependence models consider a range of copula models, such as the Gaussian copula and vine copulas that combine linear-circular copulas with Gaussian or Frank copulas. The consideration of linear-circular copulas allows for the characterization of correlation between linear variables (e.g., central pressure deficit) and circular variables (e.g., heading direction). The sensitivity of the results to different joint distribution models is assessed by comparing hazard curves at representative locations in New Orleans, LA (USA). The stability of hazard curves generated using a Gaussian copula considering variation in the selection of the zero-degree convention is also assessed. The tail dependence between large central pressure deficit and large radius of maximum wind associated with various copula models are also compared using estimated conditional probability. It is found that the linear-circular Frank vine copula model improve the stability of hazard curves and maximize tail dependence between large central pressure deficit and large radius of maximum wind. However, the meta-Gaussian copula model exhibits performance within this study region that was generally consistent with the tested vine copulas and have the advantage of being easier to implement.

Amid economic policy uncertainty, recognizing green bonds as stabilizing instruments underscores the imperative to address climate change. Existing research assesses the asymmetric effect of economic policy uncertainty on green bonds in the top 10 green bond-issuing countries (China, USA, Spain, France, Japan, Canada, Germany, the Netherlands, the UK, and Sweden). While past investigations have predominantly used panel data methodologies to probe the correlation between economic policy uncertainty and green bonds, it often overlooked the unique disparities among various economies. Contrarily, our approach utilizes the ‘Quantile-on-Quantile’ methodology, which offers a comprehensive global yet country-specific viewpoint for each economy. The study reveals a significant reduction in green bond prices associated with economic policy uncertainty across various quantile levels in most selected economies. Furthermore, our findings underscore the discrepancies in the connections among our variables across different countries. These discoveries stress that policymakers must manage thorough assessments and execute efficient tactics to manage fluctuations in economic policy uncertainty and green bonds at various levels.

Abstract

Synergizing air pollution control and carbon emission reduction has been widely proposed and highlighted. Evaluating the synergy of air pollution and carbon emissions has been the primary concern and essential support for synergistic control. Current research and works have attempted to assess synergy from multiple perspectives, but the informativeness and comprehensiveness of the synergy of air pollution and carbon emissions have been limited. This study develops a framework evaluating the synergy of PM_2.5, ozone, and CO₂ emission from the correlation and aggregate perspectives based on the large-scale and deep exploitation of the correlation and additivity of the data samples. A case study on the monthly synergy of air pollution and CO₂ emission has been performed in major Chinese urban agglomerations at the city level. The results informatively present the seasonal and city-level characteristics and heterogeneity of synergy for PM_2.5-ozone-CO₂ while providing partitioned and classified recommendations for synergistic control. A comprehensive synergy typology of synergy, bare, aggregate, and correlation for air pollution and CO₂ emission provides a reference for planning short-period synergistic control strategies.

In this article, we take a look at an Ordinary Differential Equation model that describes the bacteria’s role in anaerobic biodegradation dynamics of domestic garbage in a landfill. A nonlinear Ordinary Differential Equation system is used to describe biological activities. In the current study, the Levenberg–Marquardt Backpropagation Neural Network is used to locate alternate solutions for the model. The Runge–Kutta order four (RK-4) method is employed to produce reference solutions. Different scenarios were looked at to analyse our surrogate solution models. The reliability to verify the equilibrium of the mathematical model, physical quantities such as the half-saturation constant ((K_S)), the maximum growth rate ((mu _m)), and the inhibition constant ((K_I)), can be modified. We categorise our potential solutions into training, validation and testing groups in order to assess how well our machine learning strategy works. The advantages of the Levenberg-Marquardt Backpropagation Neural Network scheme have been shown by studies that compare statistical data based on Mean Square Error Function, efficacy, regression plots, and error histograms. From the whole process we conclude that Levenberg–Marquardt Backpropagation Neural Network is accurate and authentic.