Stochastic Environmental Research and Risk Assessment最新文献

Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990-2015) and Chattogram (1999-2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions.

Urbanization affects atmospheric boundary layer dynamics by altering cloud formation and precipitation patterns through the urban heat island (UHI) effect, perturbed wind flows, and urban aerosols, that overall contribute to the urban rainfall effect (URE). This study analyzes an ensemble of numerical simulations with the Weather Research and Forecasting (WRF) model and its version with coupled chemistry and aerosols (WRF-Chem) through a Functional ANalysis Of VAriance (FANOVA) approach to isolate the urban signature from the regional climatology and to investigate the relative contributions of various mechanisms and drivers to the URE. Different metropolitan areas across the United States are analyzed and their urban land cover and anthropogenic emissions are replaced with dominant land-use categories such as grasslands or croplands and biogenic only emissions, as in neighboring regions. Our findings indicate a significant role of the urban land cover in impacting surface temperature and turbulent kinetic energy over the city, and precipitation patterns, both within and downwind of the urban environment. Moreover, simulations of a deep convection event suggest that the aerosols impact dominates the sign and spatial extent of the changes in the simulated precipitation compared to the UHI effect, leading to a significant precipitation enhancement within the urban borders and suppression in downwind regions.

Landslide susceptibility maps can provide important information for managing regional landslide risks. Traditionally, data-driven and physically-based models are widely used for rainfall-induced landslide susceptibility mapping, but each method has limitations. In this study, a hybrid method that integrates a data-driven model and a physically-based model is proposed for rainfall-induced landslide susceptibility mapping, where the uncertainty in the soil properties can be explicitly considered. The proposed method is illustrated with landslide susceptibility mapping in Shengzhou County, Zhejiang Province, China. Logistic regression is used as the data-driven model, and the regional assessment of rainfall-induced landslides model (RARIL) is used as the physically-based model. Three hybrid models are developed. Hybrid model I, which considers soil parameters uncertainty, is compared with hybrid models II and III, which do not consider it. Results indicate that all the three hybrid models outperform the conventional logistic regression and RARIL models. Notably, hybrid model I, which considers the soil parameters uncertainty, outperforms hybrid models II and III, which do not consider it.

Urban flood risk management has been a hot issue worldwide due to the increased frequency and severity of floods occurring in cities. In this paper, an innovative modelling approach based on the Bayesian convolutional neural network (BCNN) was proposed to simulate the urban flood inundation, and to provide a reliable prediction of specific water depth. To develop the model, a series of historical rainfall data during the last 20 years were collected in Rushan China and the responding flood events were reproduced using physically based hydraulic model. The flood condition factors used in modeling include spacial factors and precipitation factors. The results showed that the BCNN model not only inherits the powerful ability of aggregating spacial information from CNNs to perform high level of accuracy and computational efficiency in predicting 2D urban flood inundation maps, but also offers a measure of uncertainty in the form of predictive variance, providing insights into the confidence and reliability of its predictions. The proposed BCNN method offered a new perspective for the analysis of surrogate model regarding real-time forecasting of flood inundation.

Green innovation is essential in achieving sustainable development goals of enhancing resource efficiency, reducing environmental impact, and promoting renewable resource consumption. Identifying the factors that promote green innovation is necessary to capitalise on the benefits of green innovation. The literature has overlooked the economic and geopolitical factors influencing sustainable technological development. Limited studies have analysed the multifaced drivers of green innovations. Therefore, this study fills the gap by exploring the impact of geopolitical risk, economic complexity, and R&D expenditures on green innovation in China from 1995 to 2022. The study employed the “bootstrapping autoregressive distributed lag” (BARDL) method and examined the long-term cointegration. Diagnostic tests confirm that the data series are normally distributed, and unit root tests establish an integration order of I(1). Outcomes of the BARDL approach indicate that increases in economic complexity, R&D expenditures and economic growth significantly enhance the green innovation initiatives. Conversely, rising geopolitical risk deters steady investment in green innovation in the short and long run. The results highlight that while economic complexity and R&D expenditures have greater capabilities and resources to support innovative activities, geopolitical risk acts as a mitigator and diverts focus and resources away from long-term environmental sustainability projects; therefore, effective policy measures focusing on these variables can increase investments in green innovations.

The Bagmati River Basin is experiencing significant water stress due to a reduction of surface and groundwater resources, especially during the dry season. The basin’s heavy reliance on monsoon-dominated precipitation, without the buffer of snow or glacier melt, exacerbates these issues. Dam construction is seen as a viable solution for maintaining river flow and regulating river ecosystems. Thus, this study leveraged multi-criteria decision-making tools, particularly the analytical hierarchy process (AHP) and fuzzy AHP (FAHP) in conjunction with the Geographic Information System(GIS), to identify suitable dam construction sites in the Bagmati River Basin. Through an extensive literature review, nine criteria were identified: stream density, rainfall, slope, land use, elevation, soil type, distance from faults, distance from settlements, and distance from roads. Pairwise comparison matrices, based on expert surveys, were used to assign weights to each criterion, with validation against existing and proposed dams. Results show that approximately 31% of the basin area is suitable for dam construction, with about 4.45% area being highly suitable. FAHP only slightly outperforms AHP in assessing existing dam locations, demonstrating the robustness of both methodologies. For the validation of suitability analysis, location of existing dams are compared. While Nepal is not generally water-stressed, inter-seasonal water availability is high. Dam construction for multiple uses is nascent in Nepal, and location analysis studies are rare. The methodology used here can be replicated in other regions, offering valuable insights for decision-makers.

Continuous hydrological simulation is a powerful approach for generating long-term series of river discharges used for hydrological analyses. This approach requires as inputs precipitation time series generated by a stochastic weather generator (WGEN) to simulate discharge time series. For small catchments where a lumped hydrological model is suitable, the weather generator needs to generate time series of mean areal precipitation (MAP). Here we assess the ability of an at-site hybrid WGEN to generate time series of MAP for a set of test areas ranging from 9 to 1,089 km(^2). The generator is composed of a model based on a Markov chain model used to generate time series of daily MAP, and a multiplicative random cascade used to disaggregate them to an hourly resolution. The work is carried out at several test locations in Switzerland with different precipitation regimes. The parameters of the model are estimated on the observed MAP time series extracted from CombiPrecip, a 1 km(^2) resolution radar-gauge product of precipitation assimilating rain gauges and radar data. For each test location and each test area, 100-year time series are generated and compared with the observed MAP time series. Whatever the location and spatial scale considered, the performance of the WGEN is satisfactory. The model reproduces the observed standard statistics and extreme precipitation of observed MAP very well. At an hourly resolution, better results are obtained at larger spatial scales, while no difference is noticed at a daily resolution. The study shows that using this hybrid WGEN is possible to model and generate MAP for areas ranging from 9 to 1,089 km(^2). Moreover, this particular WGEN is easy to implement for end-user applications. The modelling approach is even more promising as high-resolution gridded precipitation data are expected to become increasingly available worldwide, offering a source of data to calibrate the hybrid model.

Monetary policy uncertainty casts long shadows, shaping the future of financial greenscapes by influencing investment decisions and sustainability initiatives, ultimately determining the pace of our transition to a greener, renewable energy-driven economy. This research analyses the asymmetric impact of monetary policy uncertainty on green finance in the top ten advocates of green funding (USA, China, Germany, UK, France, Sweden, Japan, the Netherlands, Canada, and Spain). Moving beyond traditional panel data methods that ignore country-specific nuances, we adopt the Quantile-on-Quantile approach for a more nuanced understanding. This approach enhances accuracy by offering a global overview and detailed insights for each country individually. The findings reveal that monetary policy uncertainty curtails green finance in most of the selected economies across various quantiles. Our estimation underscores the imperative for policymakers to conduct thorough analyses and develop strategies to address the changes in monetary policy uncertainty and green finance at various levels.

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies.

Achieving the coordination and symbiosis of cold chain logistics and green finance is notably critical for promoting regional green and sustainable development. However, The existing research on the coupling coordination relationship between cold chain logistics and green finance, as well as its driving factors, remains limited and lacks in-depth analysis. This study portrays the coupling coordination degree (CCD) from the perspectives of measurement, spatial patterns, and driving factors in China with multi-source data and the optimal parameters-based geographical detector. Results show that the CCD in China demonstrates an overall increasing trend of fluctuations, along with obvious regional differences. The spatial distribution of the CCD demonstrates a positive correlation, characterized by H-H and L-L clustering. The spatial pattern of the CCD is high in the eastern, southern regions and low in the western, northern regions, this gap is gradually narrowing between the east and west, south and north gap is widening. This spatial pattern is marked by infrastructure, economic factors, human capital, energy intensity, technological factors, and natural factors. Notably, the interactive impact among human capital, financial markets, and digital intelligence technology contributes to further integration, with the impact of individual factors ranging from 7.11 to 632.79%. It offers valuable implications for policymakers and logistics companies for sustainable development, and contributes empirical insights to emerging countries.