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Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

Extreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter (ξ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events. [ABSTRACT FROM AUTHOR]

Our paper reviews recent progress in the study and understanding of observed trends in extreme rainfall events in the Metropolitan Area of São Paulo (MASP). These are discussed in relation to hydrometeorological hazards that trigger natural disasters, such as flash floods, landslides, and droughts, that affect the population and local economies. A review of the most updated literature on rainfall and extremes in the MASP shows a significant increase in the total volume of rainy-season rainfall during the last seven decades. While there were practically no days with heavy rain (more than 50 mm) in the 1950s, these days have been occurring two to five times a year in the last 10 years. This, together with the inappropriate occupation of risky areas, such as slopes and banks of watercourses, leads to inundation, flooding, and landslides. Changes in extremes can be partly due to natural climate variability but can also be related to global warming and/or urbanization. There is ample evidence of an increasing risk of rainfall-related hazards in theMASP. This is particularly so for landslides in vulnerable areas. Exposure will continue to lead to risk increases. This calls for significant improvement in climate and disaster risk reduction and management efforts in theMASP region. [ABSTRACT FROM AUTHOR]

In this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season. [ABSTRACT FROM AUTHOR]

ABSTRACT Non-stationary modelling of climate extremes has attracted significant attention in recent years. Generalized extreme value ( GEV) distribution is the standard approach for modelling block extremes. The non-stationary form of GEV distribution with location and scale parameters linearly regressing to time has been widely used for single-site time series of climate extremes. In the present paper, such a model is extended to be geoadditive for regional climate extremes with nonparametric spatially varying temporal effects. The model is implemented through the Bayesian hierarchical approach and is applied to the annual minimum surface air temperature in Mainland China as a demonstration. The five bivariate functions representing the regional regression parameters in the model are approximated by low-rank thin plate regression splines. All the necessary software tools for producing a thin plate regression spline basis and Markov Chain Monte Carlo sampling can be seamlessly integrated and called through the R software environment. Results show that the model can capture the spatial inhomogeneity and temporal non-stationarity features of regional climate extremes. Expressions for return levels with different return periods and their trends are also derived accordingly. Trends in different return levels, especially those in the 'extreme state' (e.g., 50-year return level) that cannot be inferred by usual observational trend analysis, can also be derived through samples drawn from posterior predictive distributions. The regional trend in a 50-year return level is quite different to that in a 2-year return level. More attention should be paid to trends in the extreme state of climate under climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent special reports on climate extremes have shown evidences of changes in the patterns of climate extremes at global, regional and local scales. Understanding the characteristics of climate extremes at regional and local levels is critical not only for the development of preparedness and early warning systems, but is also fundamental in the development of any adaptation strategies. There is still very limited knowledge regarding the past, present and future patterns of climate extremes in the Greater Horn of Africa (GHA). This study, which was supported by the World Bank Global Facility for Disaster Reduction and Recovery (WB-GFDRR) and implemented by the World Meteorological Organization, was organized in terms of three workshops with three main objectives; (1) analysis of daily rainfall and temperature extremes for ten countries in the GHA region using observed in situ data running from 1971 to 2006, (2) assessing whether the United Kingdom Met-office and Hadley centre Providing REgional Climates for Impact Studies (UK-PRECIS) modelling system can provide realistic representation of the past and present climate extremes as observed by available in situ data, and (3) studying the future regional climate extremes under different scenarios to further assess the expected changes in climate extremes. This paper, therefore, uses the outputs of these workshops and also includes post-workshop analyses to assess the changes of climate extremes within the GHA. The results showed a significant decrease in total precipitation in wet days greater than 1 mm and increasing warm extremes, particularly at night, while cold extremes are decreasing. Considering a combination of geophysical models and satellite gravimetry observations from the Gravity Recovery and Climate Experiment (GRACE) mission in the frame of GRACE daily Kalman-smoothing models, for the years 2002 to 2010, we explored a decline in total water storage variations over the GHA. [ABSTRACT FROM AUTHOR]