Data-Driven Insights into Climate Change and Technological Levels: Data Mining Visualization and TOPSIS Approach

Sufficient samples of extreme precipitation events are needed in order to obtain reliable estimates of the probability of their occurrence. Here, we use a large ensemble simulation with 50 members from the Canadian Earth System Model (CanESM2) under the representative concentration pathway 8.5 (RCP8.5) scenario to give future projection of the intensity and frequency of extreme precipitation events under different warming levels relative to the current climate over China. A bias-correction method based on quantile mapping is first used to remove systematic biases in the ensemble. The return value and return period are obtained by fitting enough annual maximum precipitation samples with the generalized extreme value to represent the intensity and frequency of extreme events, respectively. The results show that the average intensity of extreme precipitation in China will increase by nearly 8% per 1°C of global warming, which closely follows the Clausius–Clapeyron relation. Rarer extreme events will experience greater changes in frequency, especially under higher warming. The nationally averaged extreme precipitation events, presently expected to occur every 50 years (100 years) under the current climate conditions, are expected to occur approximately every 41 years (82 years), 32 years (62 years), 22 years (42 years) and 15 years (29 years) under warming levels of 1.5, 2.0, 3.0 and 4.0°C, respectively. Northwestern China (NW), southwestern China (SW) and the Yangtze River valley (YZ) exhibit the greatest increase in probability ratio (PR) under future climate condition. The risk of extreme precipitation events, currently expected to occur once every 50 years, will be nearly 11 (21) times more likely to occur under a climate warming by 3.0°C (4.0°C). Limiting warming to 1.5°C will help avoid approximately 40%-50%, 70%-80% and over 90% of the increase in the risk of extreme events in almost all subregions if the global mean surface temperature (GMST) continues warming to 2.0°C, 3.0°C and 4.0°C, respectively. Our study provides a useful information for the understanding the impact of climate change on the future risk of extreme events over China.

Risk of extreme events in delta environment: A case study of the Mahanadi delta

<p>For public, scientists and policy-makers, it is important to know to what extent human-induced climate change played (or did not play) a role behind changing risks of extreme weather events. Probabilistic event attribution (PEA) can provide scientific information regarding this association and reveal whether and to what extent external drivers of climate change have influenced the probability of high-impact weather events. To date, most of the PEA-based studies have focused on extreme events of mid-latitudes and predominantly events that have occurred in the developed countries. Developing countries located at the tropical monsoon regions are underrepresented in this field of research, despite that fact that these countries are highly climate vulnerable, often experience extreme weather events that cause severe damages and have the least capacity to adapt. </p><p>Bangladesh, a South Asian country with tropical monsoon climate, is a hotspot of climate change impacts as it is vulnerable to a combination of increasing challenges from record-breaking temperatures, extreme rainfall events, more intense river floods, tropical cyclones, and rising sea levels. The unique geographical location of this country particularly exposes it to high risks of flooding and landslides caused by heavy rainfall events. Observation based studies indicate that the frequency of high-intensity rainfall events may have already increased, with significant repercussions for agriculture, health, ecosystems and economic development.</p><p>Using high resolution regional climate model (RCM) simulations from weather@home, here we quantify the risks of extreme rainfall events in Bangladesh under pre-industrial, present-day and future climate scenarios of the Paris Agreement temperature targets of 1.5°C and 2°C warming. Additionally, we assess the risks under greenhouse gas (GHG)-only climate scenario where anthropogenic aerosols are reduced to pre-industrial levels. In order to test the robustness of the RCM results, available four atmosphere only global circulation model (AGCM) simulations from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project are analysed. This enabled for the first time, a multi-model assessment of the changing risks of extreme rainfall events in Bangladesh considering anthropogenic climate change drivers.</p><p>Findings suggest that both a 1.5°C and 2.0°C warmer world is poised to experience increased seasonal mean and, to a lesser extent, increased extreme rainfall events. The risk of a 1 in 100 year rainfall event under current climate condition has already increased significantly compared with pre-industrial levels. Substantial reduction in the impacts resulting from 1.5°C compared with 2°C warming is reported in this study; however the difference is spatially and temporally variable across Bangladesh. This paper highlights that reduction in the anthropogenic aerosols play an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. The policy-makers therefore need to take stronger climate actions to avoid impacts of 2°C warmer world and consider future changes in the risks of extreme rainfall events in the face of changeable GHG and aerosol impacts.</p>

Introduction: Increasing body of literature suggests frequency, duration, and intensity of extreme events are rising and will increase in response to changing climate. Others have linked such extreme events with adverse health outcomes. Large-scale weather phenomenon (i.e., ENSO) is known to impact US weather patterns, but no studies to date have investigated how ENSO may modulate the associations between extreme events and adverse health outcomes. Methods: We linked hospitalization records for asthma/MI and culture-confirmed campylobacter/salmonella infections with extreme heat and precipitation events in Maryland. Extreme events were identified based on local climatology specific to each county derived from a 30-year baseline. We obtained data on phases of ENSO (El Niño, La Niña, Neutral) from the National Weather Service Climate Prediction Center. A time-stratified case-crossover design was used to examine associations between exposure to extreme events exposure and chronic health outcomes, while infection risk was assessed using multivariate negative binomial regression. We used stratified analysis to investigate how associations varied by ENSO phase. Results: There were 116,470 asthma and 138,343 MI hospitalizations and 4,804 and 9,527 reported infections of campylobacter and salmonella, respectively, during the study periods. Risk of salmonella and campylobacter associated with extreme heat and precipitation events was highest during the La Niña period. For asthma, risk associated with extreme heat and precipitation events was highest during El Niño. For MI, extreme heat related risk was highest during El Niño, while extreme precipitation related risk was highest during La Niña. Conclusion: Our results show extreme heat and precipitation related risk varies considerably during ENSO phases, which has a strong but uneven influence on weather across the globe. Studies investigating the link between climate change and health need to account for this phenomenon.

Type of the article: Research ArticleAbstractThis study examines how small and medium-sized enterprises (SMEs) in Kazakhstan perceive and prioritize global risks within an evolving resource-dependent economy. SMEs play a vital role in Kazakhstan’s economic development but remain highly vulnerable to macroeconomic instability, environmental shocks, and geopolitical uncertainty. The study aims to explore SMEs’ perceptions of global risks, assess how well these perceptions align with the World Economic Forum’s (WEF) global risk rankings, and identify key issues requiring policy attention.The paper employs a structured survey and qualitative risk assessment methodology to analyze data collected from 127 SMEs across all 20 regions of Kazakhstan. The survey, conducted in October 2024, included questions on the perceived likelihood and impact of global risks over a 10-year horizon. Risk categories encompassed economic, environmental, geopolitical, societal, and technological domains. Respondents assessed each risk on a five-point scale for both probability and severity.Findings indicate that inflation and labor shortages are perceived as the most critical risks by Kazakh SMEs, followed by environmental concerns such as extreme weather events and resource depletion. Geopolitical and technological risks were considered important but secondary. A risk matrix was developed to visualize the prioritization of these risks and support policy planning.The results reveal a significant gap between global risk assessments and localized SME perceptions, underscoring the importance of context-specific risk management strategies. Targeted government interventions in workforce development, financial support, and climate resilience are essential to strengthen the adaptive capacity of SMEs facing global challenges.

Are longer and more intense heatwaves more prone to extreme precipitation?

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Radley Horton,1,a Daniel Bader,1,a Yochanan Kushnir,2 Christopher Little,3 Reginald Blake,4 and Cynthia Rosenzweig5 1Columbia University Center for Climate Systems Research, New York, NY. 2Ocean and Climate Physics Department, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY. 3Atmospheric and Environmental Research, Lexington, MA. 4Physics Department, New York City College of Technology, CUNY, Brooklyn, NY. 5Climate Impacts Group, NASA Goddard Institute for Space Studies; Center for Climate Systems Research, Columbia University Earth Institute, New York, NY

Potential increases in the risk of extreme weather events under climate change can have significant socio‐economic impacts at regional levels. These impacts are likely to be particularly high in South Asia where Bangladesh is one of the most vulnerable countries. Regional climate models (RCMs) are valuable tools for studying weather and climate at finer spatial scales than are typically available in global climate models. Quantitative assessment of the likely changes in the risk of extreme events occurring requires very large ensemble simulations due to their rarity. The weather@home setup within theclimateprediction.netdistributed computing project is capable of providing the necessary very large ensembles at regionally higher resolution, but has only been evaluated over the South Asia region for its representation of seasonal climatological and monthly means. Here, we evaluate how realistically the HadAM3P‐HadRM3P model setup of weather@home can reproduce the observed patterns of temperature and rainfall in Bangladesh with focus on the modelled extreme events. Using very large ensembles of regional simulations, we find that there are substantial spatial and temporal variations in rainfall and temperature biases compared with observations. These are highest in the pre‐monsoon, which are largely caused by timing issues in the model. Modelled mean monsoon and post‐monsoon temperatures are in good agreement with observations, whereas there is a dry bias in the modelled mean monsoon rainfall. The rainfall bias varies both spatially and with the data set used for comparison. Despite of these biases, the model‐simulated temperature and rainfall extremes in summer monsoon over Bangladesh are approximately representative of the observed ones. At the wettest parts of northeast Bangladesh, rainfall extremes are underestimated compared to GPCC and APHRODITE but are within the range of CPC observations. Therefore, the weather@home RCM, HadRM3P may provide a sufficiently reliable tool for studying the extreme weather events in Bangladesh.

Use of probability distributions by regulatory agencies often focuses on the extreme events and scenarios that correspond to the tail of probability distributions. This paper makes the case that assessment of the tail of the distribution can and often should be performed separately from assessment of the central values. Factors to consider when developing distributions that account for tail behavior include (a) the availability of data, (b) characteristics of the tail of the distribution, and (c) the value of additional information in assessment. The integration of these elements will improve the modeling of extreme events by the tail of distributions, thereby providing policy makers with critical information on the risk of extreme events. Two examples provide insight into the theme of the paper. The first demonstrates the need for a parallel analysis that separates the extreme events from the central values. The second shows a link between the selection of the tail distribution and a decision criterion. In addition, the phenomenon of breaking records in time‐series data gives insight to the information that characterizes extreme values. One methodology for treating risk of extreme events explicitly adopts the conditional expected value as a measure of risk. Theoretical results concerning this measure are given to clarify some of the concepts of the risk of extreme events.

The provenance of siliclastic sediment may be traced using a wide variety of chemical, mineralogical and isotopic proxies. These define three distinct data types: (1) compositional data such as chemical concentrations; (2) point-counting data such as heavy mineral compositions; and (3) distributional data such as zircon U-Pb age spectra. Each of these three data types requires separate statistical treatment. Central to any such treatment is the ability to quantify the ‘dissimilarity’ between two samples. For compositional data, this is best done using a logratio distance. Point-counting data may be compared using the chi-square distance, which deals better with missing components (zero values) than the logratio distance does. Finally, distributional data can be compared using the Kolmogorov–Smirnov and related statistics. For small datasets using a single provenance proxy, data interpretation can sometimes be done by visual inspection of ternary diagrams or age spectra. However, this no longer works for larger and more complex datasets. This paper reviews a number of multivariate ordination techniques to aid the interpretation of such studies. Multidimensional Scaling (MDS) is a generally applicable method that displays the salient dissimilarities and differences between multiple samples as a configuration of points in which similar samples plot close together and dissimilar samples plot far apart. For compositional data, classical MDS analysis of logratio data is shown to be equivalent to Principal Component Analysis (PCA). The resulting MDS configurations can be augmented with compositional information as biplots. For point-counting data, classical MDS analysis of chi-square distances is shown to be equivalent to Correspondence Analysis (CA). This technique also produces biplots. Thus, MDS provides a common platform to visualise and interpret all types of provenance data. Generalising the method to three-way dissimilarity tables provides an opportunity to combine several datasets together and thereby facilitate the interpretation of ‘Big Data’. This paper presents a set of tutorials using the statistical programming language R. It illustrates the theoretical underpinnings of compositional data analysis, PCA, MDS and other concepts using toy examples, before applying these methods to real datasets with the provenance package.

The frequency of extreme rainfall events in Southern Brazil is impacted by El Niño—Southern Oscillation (ENSO) episodes, especially in austral spring. There are two areas in which this impact is more significant: one is on the coast, where extreme events are more frequent during El Niño (EN) and the other one extends inland, where extreme events increase during EN and decrease during La Niña (LN). Atmospheric circulation patterns associated with severe rainfall in those areas are similar (opposite) to anomalous patterns characteristic of EN (LN) episodes, indicating why increase (decrease) of extreme events in EN (LN) episodes is favoured. The most recurrent precipitation patterns during extreme rainfall events in each of these areas are disclosed by Principal Component Analysis (PCA) and evidence the separation between extreme events in these areas: a severe precipitation event generally does not occur simultaneously in the coast and inland, although they may occur inland and in the coastal region in sequence. Although EN predominantly enhances extreme rainfall, there are EN years in which fewer severe events occur than the average of neutral years, and also the enhancement of extreme rainfall is not uniform for different EN episodes, because the interdecadal non‐ENSO variability also modulates significantly the frequency of extreme events in Southern Brazil. The inland region, which is more affected, shows increase (decrease) of extreme rainfall in association with the negative (positive) phase of the Atlantic Multidecadal Variability, with the negative (positive) phase of the Pacific Multidecadal Variability and with the positive (negative) phase of the Pacific Interdecadal Variability. Copyright © 2008 Royal Meteorological Society

The prevention of flood risks and the effective planning and management of water resources require river flows to be continuously measured and analyzed at a number of stations. For a given station, a hydrograph can be obtained as a graphical representation of the temporal variation of flow over a period of time. The information provided by the hydrograph is essential to determine the severity of extreme events and their frequencies. A flood hydrograph is commonly characterized by its peak, volume, and duration. Traditional hydrological frequency analysis (FA) approaches focused separately on each of these features in a univariate context. Recent multivariate approaches considered these features jointly in order to take into account their dependence structure. However, all these approaches are based on the analysis of a number of characteristics and do not make use of the full information content of the hydrograph. The objective of the present work is to propose a new framework for FA using the hydrographs as curves: functional data. In this context, the whole hydrograph is considered as one infinite‐dimensional observation. This context allows us to provide more effective and efficient estimates of the risk associated with extreme events. The proposed approach contributes to addressing the problem of lack of data commonly encountered in hydrology by fully employing all the information contained in the hydrographs. A number of functional data analysis tools are introduced and adapted to flood FA with a focus on exploratory analysis as a first stage toward a complete functional flood FA. These methods, including data visualization, location and scale measures, principal component analysis, and outlier detection, are illustrated in a real‐world flood analysis case study from the province of Quebec, Canada.

According to the latest projections of the Intergovernmental Panel on Climate Change, at the end of the century, coastal zones and low-lying ecosystems will be increasingly threatened by rising global mean sea levels. In order to support integrated coastal zone management and advance the basic "source-pathway-receptor-consequence" approach focused on traditional receptors (e.g., population, infrastructure, and economy), a novel risk framework is proposed able to evaluate potential risks of loss or degradation of ecosystem services (ESs) due to projected extreme sea level scenarios in the Italian coast. Three risk scenarios for the reference period (1969-2010) and future time frame up to 2050 under RCP4.5 and RCP8.5 are developed by integrating extreme water-level projections related to changing climate conditions, with vulnerability information about the topography, distance from coastlines, and presence of artificial protections. A risk assessment is then performed considering the potential effects of the spatial-temporal variability of inundations and land use on the supply level and spatial distribution of ESs. The results of the analysis are summarized into a spatially explicit risk index, useful to rank coastal areas more prone to ESs losses or degradation due to coastal inundation at the national scale. Overall, the Northern Adriatic coast is scored at high risk of ESs loss or degradation in the future scenario. Other small coastal strips with medium risk scores are the Eastern Puglia coast, Western Sardinia, and Tuscany's coast. The ESs Coastal Risk Index provides an easy-to-understand screening assessment that could support the prioritization of areas for coastal adaptation at the national scale. Moreover, this index allows the direct evaluation of the public value of ecosystems and supports more effective territorial planning and environmental management decisions. In particular, it could support the mainstreaming of ecosystem-based approaches (e.g., ecological engineering and green infrastructures) to mitigate the risks of climate change and extreme events while protecting ecosystems and biodiversity. Integr Environ Assess Manag 2022;18:1564-1577. © 2021 SETAC.

1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.1.1 Measures of location. 3.1.2 Measures of variability. 3.1.3 Measures of heterogeneity. 3.1.4 Measures of concentration. 3.1.5 Measures of asymmetry. 3.1.6 Measures of kurtosis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.4.1 Independence and association. 3.4.2 Distance measures. 3.4.3 Dependency measures. 3.4.4 Model-based measures. 3.5 Reduction of dimensionality. 3.5.1 Interpretation of the principal components. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.1.1 Euclidean distance. 4.1.2 Similarity measures. 4.1.3 Multidimensional scaling. 4.2 Cluster analysis. 4.2.1 Hierarchical methods. 4.2.2 Evaluation of hierarchical methods. 4.2.3 Non-hierarchical methods. 4.3 Linear regression. 4.3.1 Bivariate linear regression. 4.3.2 Properties of the residuals. 4.3.3 Goodness of fit. 4.3.4 Multiple linear regression. 4.4 Logistic regression. 4.4.1 Interpretation of logistic regression. 4.4.2 Discriminant analysis. 4.5 Tree models. 4.5.1 Division criteria. 4.5.2 Pruning. 4.6 Neural networks. 4.6.1 Architecture of a neural network. 4.6.2 The multilayer perceptron. 4.6.3 Kohonen networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.8.1 Association rules. 4.8.2 Retrieval by content. 4.9 Uncertainty measures and inference. 4.9.1 Probability. 4.9.2 Statistical models. 4.9.3 Statistical inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.11.1 Main inferential results. 4.12 Generalised linear models. 4.12.1 The exponential family. 4.12.2 Definition of generalised linear models. 4.12.3 The logistic regression model. 4.13 Log-linear models. 4.13.1 Construction of a log-linear model. 4.13.2 Interpretation of a log-linear model. 4.13.3 Graphical log-linear models. 4.13.4 Log-linear model comparison. 4.14 Graphical models. 4.14.1 Symmetric graphical models. 4.14.2 Recursive graphical models. 4.14.3 Graphical models and neural networks. 4.15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.1.1 Distance between statistical models. 5.1.2 Discrepancy of a statistical model. 5.1.3 Kullback-Leibler discrepancy. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business case studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.4.1 Cluster analysis. 6.4.2 Kohonen networks. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.4.1 Log-linear models. 7.4.2 Association rules. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.