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Concept of vulnerability (to extreme weather). Socio-economic factors are important drivers of vulnerability. Source: own representation.
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Impacts of extreme weather events are relevant for regional (in the sense of subnational) economies and in particular cities in many aspects. Cities are the cores of economic activity and the amount of people and assets endangered by extreme weather events is large, even under the current climate. A changing climate with changing extreme weather pa...
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Context 1
... and''social vulnerability' or''- inherent vulnerability' when not. In the context of extreme weather events, the likelihood of impacts is also related to in- herent properties of the system, e.g., because of the anthropogenic contribution to climate change. Therefore, it makes sense to use the broader concept of biophysical vulnerability. In Fig. 1, the connection between different terms explaining vulnerability is ...
Context 2
... the following, an attempt is made to combine the regional, temporal and sectoral dimension of losses from extreme weather events, focusing on European conditions. Frei and Kowalewski (2013) have developed a climate change vulnerability index with a regional and sectoral scope. The sensitivity (cf. Fig. 1) for different sectors is measured by the respective water intensity, energy in- tensity, diversity of inputs and dependence on the traffic infra- structure. The regional scale is implemented via regionalized in- put-output tables which will be mentioned again in Section 4. The exposition to climate change is quantified by the regional ...
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... According to the latest United Nations urbanization projections, 55% of the global population resided in urban areas in 2018, displaying a 24% increase from 1950 -furthermore, the rate of urbanization is set to increase in the future, such that 68% of the world's population are expected to be urban by 2050 (United Nations, 2019). The increased agglomeration of people, buildings, and infrastructure suggests a more vulnerable society in climate-induced hydro-hazards (Jahn, 2015). A stressed hydrological cycle due to climate change has increased the frequency of natural hazards, such as floods, in recent years (Ghazali et al., 2018).. Worldwide, the likelihood of flooding is increasing, with most of the damage caused by floods occurring locally. ...
Climate change exacerbates natural hazards, prompting countries to adopt comprehensive adaptation and resilience strategies to mitigate the increasing losses and damages. Resilience is a critical concept that integrates disaster risk management, sustainable development, and climate change adaptation. Despite various community resilience frameworks and flood resilience measurement tools, there is a significant knowledge gap in identifying practical indicators to measure urban flood resilience. This study aims to fill this gap by developing a unified list of indicators to measure urban flood resilience, focusing on Dhaka City, Bangladesh. This research will identify and validate indicators contributing to urban flood resilience through a detailed comparative analysis of different resilience frameworks, expert opinions, focus group discussions, and key informant interviews. Dhaka City, a rapidly growing megacity, is prone to waterlogging and flooding, resulting in significant economic losses, diseases, and stresses. The study’s findings will provide a comprehensive understanding of urban flood resilience in Dhaka City and inform stakeholders and government officials on strategies to enhance resilience and adaptation capacities. The approach will also highlight gaps and issues, enabling policymakers to develop targeted interventions to minimize flood risks and promote sustainable urban development.
... Studies that quantify the hazard-economic impact relationship have considered country-level impacts and hazards, as in the case of Felbermayr and Gröschl [15] and Franzke [25], or impacts of a particular event type in a specific region, as in the case of Zhai and Jiang [26] and Murnane and Elsner (2012), which study hurricane impacts in the United States [27]. Since disaster occurrence and impacts can be highly localized and variable across regions, sectors, and event types [19,23,28], it is important to quantify the hazard-impact relationship at a suitable scale and level of disaggregation. This is particularly true for temperatureinduced disasters, whose economic impacts remain relatively poorly studied. ...
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... Conversely, lower temperatures can result in reduced precipitation. In extreme cases, such as prolonged droughts or floods, fluctuations in temperature and precipitation can have catastrophic consequences, including the failure of crops and damage to property [60,69]. ...
... Sustainable development is particularly vulnerable to disruptions resulting from global climate change, which can lead to the degradation of natural resources crucial for maintaining ecological balance. Such changes can also destabilise economies and cause social issues, such as migration driven by environmental changes [69]. ...
This article presents an analysis of monthly precipitation totals based on data from the Global Precipitation Climatology Centre and monthly mean temperatures from the National Oceanic and Atmospheric Administration for 377 catchments located worldwide. The data sequences, spanning 110 years from 1901 to 2010, are analysed. These long-term precipitation and temperature sequences are used to assess the variability in climate characteristics, referred to here as polarisation. This article discusses the measures of polarisation used in the natural sciences. This study adopts two measures to evaluate the phenomenon of polarisation. The first measure is defined based on a stationary time series, calculated as the ratio of the amplitude of values to the standard deviation. The second measure is proposed as the difference in trends of these values. Based on the analysis of monthly precipitation data in the studied catchments, polarisation components are confirmed in 25% of the cases, while in the remaining 75%, they are not. For temperature data, polarisation is confirmed in 12.2% of the cases and not in the remaining 88.8%. The trend analysis employs Mann–Kendall tests at a 5% significance level. The Pettitt test is used to determine the point of trend change for precipitation and temperature data. This article underscores the complex relationship between climate polarisation and sustainable development, reaffirming that sustainable development cannot be pursued in isolation from the challenges posed by climate change. It emphasises the importance of integrating environmental, social, and economic strategies to adapt to extreme climatic events and mitigate their effects. This research is supported by detailed graphical analyses, with the results presented in tabular form.
... Global climate change, especially extreme weather and climate disasters worldwide, has caused significant casualties and damages since the 21st century (Intergovernmental Panel on Climate Change 2023). Due to the extreme events, cities, as the main areas of human settlements, have been impacted on a drastic scale and various dimensions, such as disruptions in transportation, damages to infrastructure, and reduced economic activities (Jahn 2015, Markolf et al. 2019, Haghshenas et al. 2022, Montoya-Rincon et al. 2023. Among these most affected aspects, human mobility is a crucial one. ...
Understanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatiotemporal characteristics, causing the uncertainty problem for identifying meaningful travel behaviors. Moreover, there is a lack of robust methodological approaches to extracting stable and genuine travel patterns under normal or disruptive situations. To address these issues, this study proposes a data-driven approach to spatiotemporal flow decomposition based on non-negative matrix factorization. With sparseness factored in the decomposition, stable disaggregate travel patterns can be extracted from origin-destination mobility flows. By combining temporal, spatial, and urban functional perspectives, heterogeneous travel behaviors can be analyzed and inferred. With a case study of the Zhengzhou ‘7.20’ heavy rainfall in 2021, the most extreme rainfall ever recorded in China, this study validated the effectiveness of the proposed approach and managed to identify representative and interesting travel patterns and behaviors, facilitating a better understanding of human travel behaviors under external impacts. In practice, this study can provide valuable insights for coping strategies in the face of increasingly frequent disruptive events.
... FuXi-ENS demonstrates remarkable performance, outperforming the ECMWF ensemble across multiple metrics, including the accuracy of the ensemble mean forecast, ensemble verification metrics, and the prediction of extreme weather events. Extreme weather events have significantly impacted, and will continue to impact society and the economy [40][41][42]. Recent decades have witnessed a notable increase in the frequency of such events globally, including heat wave and extreme rainfall, due to climate change [43][44][45]. ...
Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble 1 arXiv:2405.05925v3 [cs.LG] 9 Aug 2024 2 FuXi-ENS prediction system (EPS) based on conventional numerical weather prediction (NWP) models is highly computationally expensive. Machine learning (ML) models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 Ensemble of Data Assimilations (EDA) or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25°, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the continuous ranked probability score (CRPS) and the Kullback-Leibler (KL) divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Evaluation results demonstrate that FuXi-ENS outperforms ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.
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The cost effectiveness of medical treatments is not precisely known due to the compounding effect of multiple determining factors. Ambulance response time (RT) to emergency calls is exploited to learn more about the effect of the timing of treatment on health outcomes. This causal relation is identified by exploiting rainfall at the time of the ambulance run as a shock to RT. The analysis focuses on patients who have undergone a cardiac event and shows that a one‐minute increase in average RT leads to 105 more deaths each year in one Italian region. Finally, the economic value of the lives that would be saved by reducing RT is quantified to facilitate policymaking.
... As is well known, before designing an effective flood risk analysis strategy, the likelihood, timing, and magnitude of flooding must be quantified, especially for extreme flood events that could exceed flood control planning. However, regions with frequent extreme weather restrict regional economic development (Jahn 2015). Due to the slow pace of economic growth, precipitation data series are short and only cover a small area. ...
Weather generators can serve as an important tool for estimating the probability distributions of hydrometeorological variables to determine extreme rainfall quantiles. It is really of great significance for flood disaster management in data-deficient areas. The annual maximum daily rainfall (MDR) dataset was expanded in this study using a multi-site weather generator model coupled with maximum entropy resampling (MWG-MER), resulting in a sizable resampled dataset of extreme variables. The MDR records from 14 weather stations in the Jing River Basin were chosen to confirm the MWG-MER model's applicability on the Chinese Loess Plateau. There, the best-fitting probability distributions were discovered using the L-moments approach, and the resampled data was utilized to determine the spatial pattern of the probability distribution of MDR. The findings demonstrate that the MWG-MER model implemented a limited extrapolation and offered a larger resampling dataset that only used the first principal component to preserve the primary statistical properties of the observed MDR data. The resampling data had good fitting performance compared to the measured data at all sites and revealed different best-fit probability distributions from the measured data at some sites, implying an improvement in the MWG-MER model's distribution fitting accuracy. With the aid of the resampling data, the spatial pattern of the MDR distribution in the Jing River Basin was discovered, and more logical partitions were provided. This information is crucial for scientifically determining the MDR probability distribution in data-deficient areas and for directing the management of flood disasters in the Loess Plateau and other similar regions.
... Several studies have revealed the drawback of heatwaves worldwide, particularly in Asia [9], in the Middle East [10], in America [11,12], in Europe [13][14][15] in France [16,17], in Russia [13,18], in Africa [19,20], and in West Africa [21][22][23]. These investigations have shed light on important damage, losses of life, and downturns in economies, as highlighted by [24]. ...
... In fact, CORDEX simulations were mainly used in many studies conducted over West Africa for a validation of the mean climate state, its variability, and extreme events [35][36][37][38][39][40], as well as to better understand relevant regional/local climate phenomena and their variability and changes through downscaling [41]. This study aims to provide information about the future climate changes under different Representative Concentration Pathways (RCPs) and global warming levels [23,[42][43][44][45][46][47]. The RCPs are a set of scenarios containing emission, concentration, and land use trajectories based on radiative forcing in w/m 2 [48,49]. ...
This study analyzes the potential response of the seasonal cycle of heatwaves (HWDI) and dry (CDD) and wet (CWD) spell indices over West Africa for the near- (2031–2060) and the far-future periods (2071–2100) under RCP4.5 and RCP8.5 scenarios using Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations. Despite the fact that some relative biases (an underestimation of 30% for CDD, an overestimation of about 60% for CWD, and an overestimation of about 50% for HWDI) exist, during the historical period (1976–2005) in general, the CORDEX simulations and their ensemble mean outperform the seasonal variability in the above-mentioned indices over three defined subregions of West Africa (i.e., the Gulf of Guinea and Western and Eastern Sahel). They show high correlation coefficients (0.9 in general) and less RMSE. They project an increase (about 10 and 20 days) in heatwave days for both the near- and far-future periods over the whole West African region under both RCP scenarios. In addition, projections indicate that the Sahel regions will experience a decrease (about 5 days) in wet spell days from March to November, while in the Gulf of Guinea, a decrease (about 3 days) is projected throughout the year, except in the CCCLM simulation, which indicates an increase (about 5 days) during the retreat phase of the monsoon (October to December). Our results also highlight an increase (about 80%) in dry spells over the Sahel regions that are more pronounced during the March–November period, while over the Gulf of Guinea, an increase (about 40%) is projected over the entire year. Moreover, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be simultaneously exposed to dry seasons associated with a high risk of drought and heatwaves under future climate conditions.
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Understanding the dynamics of temperature
trends is vital for assessing the impacts of climate
change on a regional scale. In this context, the present
study focuses on Madhya Pradesh state in Central
Indian region to explore the spatial-temporal distribution
patterns of temperature changes from 1951
to 2021. Gridded temperature data obtained from the
Indian Meteorological Department (IMD) in 1° × 1°
across the state are utilised to analyse long-term trends
and variations in temperature. The Mann-Kendall
(MK) test and Sen’s slope (SS) estimator were used
to detect the trends, and Pettitt’s test was utilised for
change point detection. The analysis reveals significant
warming trends in Madhya Pradesh during the
study period during specific time frames. The temperature
variables, such as the annual mean temperature
(Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the
most pronounced warming observed during winter.
The trend analysis reveals that the rate of warming
has increased in the past few years, particularly since
the 1990s. However, Pettitt’s test points out significant
changes in the temperature, with Tmean rising from
25.46 °C in 1951–2004 to 25.78 °C in 2005–2021
(+0.33 °C), Tmax shifting from 45.77 °C in 1951–2010
to 46.24 °C in 2011–2021 (+0.47°C), and Tmin increasing
from 2.65 °C in 1951–1999 to 3.19 °C in 2000–
2021 (+0.46 °C). These results, along with spatialtemporal
distribution maps, shed important light on the
alterations and variations in monthly Tmean, Tmax, and
Tmin across the area, underlining the dynamic character
of climate change and highlighting the demand for
methods for adaptation and mitigation.
