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Weather extremes have been responsible for widespread economic damage at global scale in the last decades. Agriculture alone absorbed 26% of the overall impact caused by natural hazards in low- and middle-income countries and even in high-income countries yield losses due to extreme weather are relevant. Vulnerability curves are traditionally used to quickly estimate the damage due to extreme events. This study maps the articles published from January 2000 to May 2022 implementing crop vulnerability curves to weather-related extreme events and climate change. Fifty-two articles have been identified through the use of Scopus, Web of Science, Google Scholar and the references of the selected papers. The selected papers have been analysed to determine for which extreme events vulnerability curves have been proposed, which crops have been studied, which explanatory variables have been used to create the curves, which functions are used to develop vulnerability curves and the number of parameters on which the proposed functions rely. Comparisons among the vulnerability curves for the various extremes are proposed, as well as indications of the main drawback of the developed vulnerability curves. Finally, areas where further research is needed are proposed together with recommendations on which elements should be included in vulnerability curve development. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

The African Sahel countries are inherently fragile, environmentally insecure and economically weak. This paper underscores the compounded impacts brought about by the COVID-19 pandemic on resource supply security and, hence, the long-term development of the region. It outlines the Sahel-specific COVID-19 scenario by firstly highlighting the underlying vulnerabilities and later linking the health sector outcomes to increased political instability and environmental insecurity, particularly the deterioration of food security. In this sense, this paper shows from a region-wide perspective how COVID-19 in the Sahel is associated with enlarged sociopolitical developmental perils. Lower remittance sent by expatriates, violent conflicts, increased cross-border terrorism and migration, discriminant mobility restrictions of people and goods, weak national healthcare infrastructures, bottlenecks in international aid, pressures on the education system and recent climate extremes are some revealing examples of aggravators of the impacts on the supply of vital resources, such as food. This paper also shows the importance of considering the close interlinks between health, food and political stability in the Sahel. There is a paramount need for more comprehensive approaches linking human health to other sectors, and for re-considering local sustainable agriculture. To avoid prolonged or recurrent humanitarian crises, the Sahel countries need to strengthen response capacities through public sector-led responses. Examples of these responses include reinforced national disaster programs for the vulnerable, support to sustainable agriculture and food markets, improved performance and communication of public sector relief, state-based cooperation, building of regional alliances and peacemaking efforts. [ABSTRACT FROM AUTHOR]

Oil refineries are among industrial installations that are vulnerable to climate extreme events, whose frequency and intensity have been increasing over the last decades. Building resilience in resources to withstand climate-related hazards and to recover fast at low human and material cost, for changing climate conditions, is required. In this paper, we present an action research effort for the design of a viable decentralized climate-resilience-providing virtual organization in an oil refinery in Greece using the Viable System Model. The VIPLAN method was employed for the methodological design of a distributed Climate Resilience Providing Organisation for the case of a refinery facility in Greece. The paper presents the process and the results of this effort. [ABSTRACT FROM AUTHOR]

This paper introduces and summarises the main outcomes of the 19th workshop of the International Association for Phytoplankton Taxonomy and Ecology held in Tiszafüred, Hungary, Tisza Balneum Hotel, from 23 to 30 September 2022. The selected theme of the workshop was "Trait-Based Approaches in Micro-Algal Ecology". The discussions presented during the workshop sessions resulted in the 18 articles contained in this Special Issue. There are 6 main thematic aspects developed by the participants: 1. Shape and size: are these traits easy to measure? 2. Spatial scales: when and where to look for microalgae? 3. Climate and extremes of ecological gradients: hot topics of this century. 4. Metaphyton and metaphytic habitats: life beyond plankton and benthos. 5. Microalgae in water management: phycology in practice. 6. Traditional and new methods: perspectives and comments. Trait-based approaches in microalgae ecology, although requiring further investigation and methodological development, represent a valid tool for refining the analysis of environmental variability in aquatic ecosystems. The papers presented in this Special Issue demonstrate that these approaches are extremely useful not only in the study of planktic algae but constitute a thoughtful method for the analysis of benthic and metaphytic microalgae in a wide variety of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau is a driver and amplifier of global climate change. The increased frequency and scale of landslides in this area are one of the manifestations of extreme climate change. Studying the trigger of landslides is of great value to the research, protection, and management of engineering geology and climatic environmental changes. However, to our knowledge, there is no efficient, convenient and intelligent method to recognize the trigger of landslides in the Tibetan Plateau. Therefore, a new high-efficiency and high-precision deep learning algorithm has been proposed in this study to analyze the landslides triggers. Specifically, this paper proposes a novel lightweight neural network landslide classification method (MNTL) based on MobileNet-V2 and transfer learning. Mobilenet-V2 requires few parameters and few floating-point operations per second. Furthermore, it is integrated with transfer learning to improve the representation learning ability of the model. The proposed method was applied to classify landslides induced by rainfall and thawing effect. The method required only 30 samples of each class, and it converged quickly in just 3 min and achieved a 94% forecast accuracy. Compared with six state-of-the-art deep learning classification methods—specifically, VGG-16, VGG-19, ResNet-50, ResNet-101, Inception, and MobileNet-V2, the proposed method exhibited competitive advantages in terms of convergence speed and generalization capability. MNTL can be embedded into the mobile terminal, which is conducive to the rapid application of landslide-related research results at a small cost. More importantly, the work in this paper could serve as a potential basis for advancing research on the correlation between landslide hazards and climate change in southeast Tibet. [ABSTRACT FROM AUTHOR]

The sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events that impact agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered as the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for years 2005–2020. [ABSTRACT FROM AUTHOR]

Quantifying the extent of change in climatic and hydrological variables in the past and the future is essential for climate change-resilient development, especially in the climate change sensitive region of Nepal. This paper analyzed future climatic trends and extremes, historical hydrological extremes and their linkage with historical precipitation extremes, and discussed the impacts of climate change on various sectors in the less-studied Small and Medium-sized Eastern River Basins (SMERB) of Nepal. Applying an ensemble of five Coupled Model Intercomparison Project phase 6 (CMIP6) global climate models (GCMs), we evaluated 10 precipitation and 13 temperature extreme indices using Climpact2 for the historical (1979–2020), near-future (2021–2045), mid-future (2046–2070) and far-future (2071–2100) under two Shared Socioeconomic Pathway (SSP), SSP245 and SSP585. Hydrological extreme indices were assessed using Indicators of Hydrologic Alteration tool. Projected climate revealed significant increase (9–73%) in annual and seasonal rainfall except winter; frequent intense rainfall extremes but prolonged dry spells; significant increasing minimum and maximum temperature trends (0.4–5 °C); rise in extreme heat events; increasing minimum and maximum discharge extreme trends in most hydrological stations; as well as strong association between maximum 1 day precipitation (Rx1day) and 1 day max flow for all stations. Growing dry periods but intense rainfall in few wet days, coupled with warming pattern all-over SMERB with frequent extreme events indicate high risk for future climate-related disasters. The harsher climate will potentially have damaging implications, especially in climate-induced disasters, food security, and water and sanitation infrastructure. [ABSTRACT FROM AUTHOR]

Extreme climate change is an existential threat to humanity and infrastructure development. At the same time, the construction and operation of carbon-intense public–private partnership (PPP) infrastructure such as road transport, water, public houses, energy supply and sanitation unleash most of the greenhouse gas emissions that impacts negatively on the climate. Increasingly, there is a heightened interests in the development and financing of climate-smart PPP solutions to promote resilient and sustainable public infrastructures. Therefore, this article aims at identifying the critical solutions to the provision of climate-smart PPP infrastructure projects together with the driving factors and challenges of its implementation in public facilities. The paper utilized a systematic literature review method where data were sourced from prominent academic databases of Scopus, Web of Science, Google Scholar, and PubMed. The outcomes of the review demonstrate that the adoption of climate finance, renewable energy, and maintaining resilient infrastructures are prominent solutions to attain low-carbon infrastructure development. Key drivers such as the global call to reduce huge emissions from construction projects and transition to sustainable green construction management account for the shift toward climate-smart PPP projects. The barriers identified include poor and unconcerted practice and policy directions to resolve emission problems in the construction industry. The outcomes of this article provide incentives for the development and management of climate-smart public projects. Researchers can harness the results to investigate and develop adaptation and mitigation strategies for low-carbon PPP projects. [ABSTRACT FROM AUTHOR]

Suitable thermal management of photovoltaic (PV) modules can increase their efficiency. Alongside, the extra amount of energy needed for their thermal management should also be minimized to improve the overall efficiency of the PV system. This leads to exploring passive thermal management techniques. Recently, radiative cooling (RC) has been explored widely as a passive thermal management technique for PV systems. This paper explores radiative cooling and heat sink (HS) as passive methods for thermal regulation of the photovoltaic systems to get lower and uniform temperature distribution along the PV module. A comprehensive two-dimensional model of the proposed system is developed and analyzed in commercial COMSOL Multiphysics software. The governing equations are solved numerically using finite element methods, and simulations are carried out. Four different configurations, namely Case-0: photovoltaic-only system, Case-1: photovoltaic + heat sink, Case-2: photovoltaic + radiative cooling, and Case-3: photovoltaic + heat sink + radiative cooling systems, are considered in this analysis. The performance of four cases has been compared regarding PV temperature reduction, power output, and conversion efficiency. The performance analysis is carried out for the climatic conditions of the Atacama Desert. The results indicated that the photovoltaic + heat sink + radiative cooling system, i.e., Case-3, is the most efficient among all cases. The reduction in the maximum PV operating temperature and improvements in the maximum PV power output and minimum PV conversion efficiency of the photovoltaic + heat sink + radiative cooling system compared to that of the photovoltaic system alone are 6.63%, 8.57%, and 11.11%, respectively. The findings of this study can be used to effectively design the cooling system for the thermal management of photovoltaic modules installed in desert locations. [ABSTRACT FROM AUTHOR]

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Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent years have witnessed climate change characterized by increasingly frequent extreme precipitation events, and the assumption of stationarity in traditional frequency analyses is gradually being questioned. In terms of the current research status in China, there is a lack of thorough investigations on the linkage between extreme precipitation and climate change. This paper aims to determine the dominant climate indices as well as the corresponding significant time scales and periods affecting extreme precipitation over China for dynamic assessments of the upcoming rainstorm risk. Correlations between 15 climate indices and precipitation extremes, as well as the correlations among climate indices, are fully explored to identify potential predictors for non-stationary modeling. Then, 21 non-stationary generalized extreme value (GEV) models are constructed, and the optimal covariates as well as their lag times with extreme precipitation at 769 stations are ascertained in a Bayesian framework. Finally, a complete predictive process is developed, and the national rainstorm risk under non-stationary conditions is assessed. The results indicate that precipitation extremes remain stationary only at 74 stations (less than 10%). WPI is dominant in modeling the variability in precipitation extremes for nearly 22% of the total stations, ranking first among all the climate indices. The predominant time scale affecting extreme precipitation at the majority of stations is 3 months. Ignoring the non-stationarity of extreme precipitation inevitably leads to misperceptions of rainstorm risks, and the spatial distribution of the maximum case of the design rainstorms under non-stationary conditions differs remarkably from that under stationary conditions. Our findings have important implications for the in-depth understanding of the real drivers of extreme precipitation non-stationary and enable advanced predictions of rainstorm risks for mitigating subsequent disasters. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) satellite has proven adept at monitoring, characterizing, and predicting hydrological variables. This paper attempted to investigate the applicability of using nonlinear autoregressive with exogenous input (NARX) neural network to forecast GRACE terrestrial water storage anomalies over 11 basins. By using six hydrological indicators as external inputs and forming all possible combinations of these variables, we have found the appropriate external inputs of the optimal NARX model for each basin. In addition, the number of acceptable time delays and the number of hidden neurons are adjusted to train out the optimal NARX model. The results reveal that the NARX models with one time delay perform better than the models with higher than one delay, and the structures of the optimal NARX models vary with basins. The performance of the optimal NARX models of the 11 basins falls into "very good" category whether during training, validating, or testing period. In comparison with GRACE Follow On (GRACE-FO) results, the predictions from the optimal NARX models are satisfactory, with the highest correlation coefficient of 0.97 in the Amazon basin and the lowest correlation coefficient of 0.62 over the Yangtze basin. The results shown here not only bridge the data gap between GRACE and GRACE-FO but also facilitate the applications of using the NARX neural network to predict climate extremes like droughts and flooding. [ABSTRACT FROM AUTHOR]

To ensure the effective development of high-viscosity oil fields in the Arctic region with due regard to their extreme natural and climatic conditions, the Institute of Petroleum Chemistry of the Siberian Branch of the Russian Academy of Sciences (IPC SB RAS) created a number of physicochemical methods and integrated technologies for enhancing oil recovery using "smart" compositions. These compositions are based on surfactants, coordinating solvents, and complex compounds that chemically evolve in situ, thus acquiring colloid-chemical properties optimal for oil displacement. The chemical evolution is induced by thermobaric reservoir conditions and interactions with the reservoir rock and reservoir fluids. This paper reports the data of laboratory research, field testing, and industrial operation of a number of technologies designed to enhance the oil recovery from high-viscosity deposits developed both under natural drive and with thermal steam treatment. These technologies involve the utilization of "smart" acidic and alkaline oil-displacing compositions based on surfactants and buffer systems operable in the Arctic. The paper further describes a laboratory investigation of physicochemical, acid–base, and rheological properties in "surfactant–polybasic acid–carbamide–polyol–aluminum salt–ammonium salt–water" systems. The industrial implementation of the newly-created technologies has good prospects for the development of high-viscosity oil fields in the Arctic region. [ABSTRACT FROM AUTHOR]

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin, East Africa, using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March–May (MAM), June–August (JJA), and October–December (OND)] precipitation climatology is expected over the domain by mid-century (2040–2069). The changes intensify towards the end of the century (2070–2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985–2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events—represented by the width of the right tail distribution of precipitation (99p–90p)—show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region—which is already experiencing conflicts over water and water-related resources. [ABSTRACT FROM AUTHOR]

Due to the climate change–induced extreme rainfall, urban flooding risk is one of the major concerning risks in the near future with accelerating occurrence frequency and intensity. To systematically evaluate the socioeconomic impacts induced by urban flooding, this paper proposed a GIS-based spatial fuzzy comprehensive evaluation (FCE) framework for local government to efficiently take contingency measures especially under urgent rescue conditions. The risk-assessing procedure could be investigated in 4 aspects: 1) application of the hydrodynamic model to simulate the depth and extent of inundation; 2) quantification of the impact of flooding with 6 methodically picked evaluation indexes concerning the transportation attenuation, residential security, and tangible and intangible monetary losses according to depth-damage functions; 3) implementing FCE method: comprehensive evaluation of urban flooding risk with the diverse socioeconomic indexes by fuzzy theory; and 4) presenting the risk maps of single and multiple impact factors intuitively in ArcGIS platform. The detailed case study in SA city validates the effectiveness of the adopted multiple index evaluation framework, which could help detect higher risk areas with low transport efficiency, high economic loss, high social impact, and high intangible damage. The results of single-factor analysis can also provide feasible suggestions for decision-makers and other stakeholders. Theoretically, the proposed method tends to improve the evaluation accuracy as the inundation distribution can be simulated by hydrodynamic model rather than subjective prediction with hazard factors, while the impact quantification with flood-loss models can also directly reflect the vulnerability of involved factors instead of empirical weight analysis of traditional methods. Besides, the results illustrate that the areas with higher risk levels reasonably coincide with severe inundation situations and dense hazard-bearing bodies. This systematic evaluation framework can support applicable references for further extension to other similar cities. [ABSTRACT FROM AUTHOR]

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability. [ABSTRACT FROM AUTHOR]

The costs due to climate change have been increasing day by day. In addition to the risk of losing our planet's natural assets due to the increasing destructiveness of climate-related natural disasters and extreme climate events, we are also faced with grave economic risks. For this reason, researchers have recently focused on environmental issues. Nevertheless, they generally have investigated developed countries and ignored developing and least developed countries such as African countries. The United Nations (2015) report highlights that African countries should be specially investigated. Hence, the paper analyzes whether the ecological footprint in the Economic Community of Central African States (ECCAS) and Economic Community of West African States (ECOWAS) converges for the period from 1961 to 2017. We employ Bahmani-Oskooee et al.'s (2014) panel unit root test with sharp and smooth breaks. The empirical findings demonstrate that the ecological footprint is stationary in ECCAS and ECOWAS. Stated in other words, the ecological footprint in these countries converges. Therefore, policymakers could implement similar policies to reduce the ecological footprint in these countries. This policy framework paves the way for an effective sustainable development. [ABSTRACT FROM AUTHOR]

Current climate projections for Southern Africa indicate an increase in the incidence of extreme weather events in the future. Even though South Africa does not rank among the highest on the world multi-hazard index list, the country is prone to multiple climate-related extreme events which pose substantial human and ecological impacts. Consequently, such climate extremes have serious negative effects on regional water resources, public health, biodiversity, food security, natural systems, and infrastructure. The main aim of this study is to review the literature on climate-change-induced weather events and the implications for urban water resource management in South Africa particularly focusing on QwaQwa. The study reviewed 122 documents which include books, peer-reviewed articles, conference papers, newspaper articles, institutional and government reports, and one news broadcast video. Findings revealed that QwaQwa experiences increasing water challenges as demand for water increases and both quantity and quality decrease to critical levels. This study, therefore, provides preliminary suggestions of strategies to build resilience in this climate change context, such as investment in climate-resilient water infrastructure, effective and transparent management of public resources with accountability, strengthening resilience through addressing poverty and marginalisation, nature-based solutions, and education and awareness. Furthermore, conducting hazard, exposure, and resilience analyses is necessary in order to inform the development of relevant disaster risk reduction strategies. The findings contribute to the literature on climate change impacts on water resource planning in South Africa and similar climate change contexts. The findings could; therefore, be valuable to researchers and applied practitioners such as policymakers, water resource management professionals, and urban planners. [ABSTRACT FROM AUTHOR]

The Lancang–Mekong River Basin (LMRB) is one of the river basins most prone to floods and droughts induced by extreme precipitation (EP). Therefore, it is of great significance to characterize the spatio-temporal changes of EP in the LMRB. Based on the Multi-Source Weighted-Ensemble Precipitation dataset (MSWEP) Version 2.2 (V2.2) dataset from 1979 to 2016, all improved extreme precipitation indices (EPIs) at grid and national levels were computed and their changing trends were investigated by means of the trend-free pre-whitening Mann–Kendall significance test and the Sen's slope; the rationality of the improved EPIs was discussed. The results revealed: (1) The improved EPIs performed better than the original ones in describing the spatio-temporal variations of EP across LMRB; (2) R95p, R99p, SDII-10, SDII-25, SDII-50, Rx1day, Rx3day, and Rx7day showed a significant decreasing trend (p < 0.05) in the northeastern part of the upper, middle, and lower reaches of the Mekong River Basin (MRB), implying a probable reduction in flood risks; (3) Rx15day and Rx30day showed a significant increasing trends (p < 0.05) in the southeast upper MRB and the north middle MRB, indicating a probable increase in flood risks; (4) CDD4 and NDD4 manifested an probably increasing drought risk in the middle and lower reaches of the Lancang River Basin and the lower MRB with a probably reducing drought risk in Lancang River Headwater Region, the middle MRB and the Mekong Delta; (5) the decreasing trend exhibited by most flood-related indices in China, Laos, Thailand, Cambodia, and Vietnam signified a probable reduction in flood risks; (6) the increasing trend of both CDD4 and NDD4 in China and Cambodia indicated a probable increase in drought risks. The outputs of this paper could benefit a better understanding of evolution in daily extreme precipitation, and flood and drought risk in LMRB. [ABSTRACT FROM AUTHOR]

Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia. [ABSTRACT FROM AUTHOR]

Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that, as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme look more and more like one another: they converge towards a typical, i.e. most probable, trajectory called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics to understand the physical mechanisms driving extreme events in climate models. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

This paper presents a synthesis of the main characteristics of precipitation in the State of Rio de Janeiro (Brazil) based on extreme rainfall indicators. Daily precipitation data are derived from 56 rainfall stations during the second half of the twentieth century and the 2000s. Eight indices related to extreme precipitation were analyzed. The Mann–Kendall nonparametric test and the Sen's Curvature were employed to evaluate the significance and magnitude of trends. The primary climatological aspects and identified trends throughout the last decades are discussed, besides the hydrometeorological impacts associated with them. Lower values of annual total precipitation are recorded in northern Rio de Janeiro (around 800 mm) and higher in the southern State (up to 2,200 mm). The Serra do Mar affects the frequency of heavy precipitation, and the areas near the sea and high relief present the highest values of consecutive days with expressive rainfall (more than 150 mm in 5 days). These areas also showed a high concentration of flood and landslides events. Most of Rio de Janeiro exhibits precipitation intensity of about 13 mm/day. The maximum number of consecutive dry days shows a gradient from the coast (about 30 days) to the State's interior (around 50 days). Regarding trends, there is a growth of accumulated extreme precipitation in various stations near the ocean. The extreme rainfall in 24 h displays an increase in most Rio de Janeiro (+ 1 to + 5 mm/decade). The consecutive dry and rainy days present similar signs of decreasing trends, suggesting irregularly distributed precipitation in the State. This study is especially relevant for decision-makers who need detailed information in the short and long term to prevent natural hazards like floods and landslides and the related impacts in the environmental and socioeconomic sectors of the Rio de Janeiro. [ABSTRACT FROM AUTHOR]

This paper presents the results of developing a frost-resistant semi-liquid lubricant for extreme cold climate areas. To this end, the mechanism of chemmotological processes was studied in laboratory installations simulating the operational conditions of engineering equipment. The effect of additive agents on the operational properties of the developed lubricant was investigated in order to optimize its composition. The ability of the developed composition to exhibit its inherent properties was assessed. [ABSTRACT FROM AUTHOR]

Climate change leads to a more frequent occurrence of hot days (HDs) and hot nights (HNs). The consecutive occurrence of HDs and HNs (COHs) is often used as a measure of the persistence of an extremely hot spell. Nonetheless, the combined effect of air temperature and relative humidity on the changing COHs has never been studied. In this paper, we use an ensemble of global climate models and multiple thermal indices to robustly examine the combined effect of air temperature and relative humidity on COHs globally on an hourly basis. Our findings reveal that COHs show an increasing trend in the future and a strong latitudinal gradient increasing from high latitudes to the equator. Compared to COHs based on air temperature, the frequency of COHs based on perceived temperature is amplified by the combined effects of high temperature and humidity for both boreal and austral summers. To investigate the underlying mechanisms, we examine two different diurnal temperature ranges (DTRs), derived from air temperature and perceived temperature, for their corresponding types of COHs. Both DTRs are projected to increase in the future relative to the historical period from 1980 to 2004, but the DTR changes derived from perceived temperature are consistently larger than those derived from air temperature. Due to the nonlinearity in thermal indices, the perceived temperature in HDs and HNs rising faster than air temperature leads to a larger increase in perceived COHs. The COHs are further amplified by the increasing number of HNs and HDs that occur consecutively under wet conditions. [ABSTRACT FROM AUTHOR]

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The relation between the increase in the frequency and the effects of extreme events with climate change has been widely demonstrated and the related consequences are a global concern. In this framework, the strong correlation between significant lightning occurrence and intense precipitation events has been also documented. Consequently, the possibility of having a short-term forecasting tool of the lightning activity may help in identifying and monitoring the evolution of severe weather events on very short time ranges. The present paper proposes an application of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform a nowcasting of Cloud-to-Ground (CG) lightning occurrence over the Italian territory and the surrounding seas during the months of August, September, and October from 2017 to 2019. Results obtained with three different spatial resolutions have been compared, suggesting that, to enhance the skills of the model in identifying the presence or absence of strokes, all the data selected as input should be commonly gridded on the finest available spatial resolution. Moreover, the features' importance analysis performed confirms that meteorological features describing the state of the atmosphere, especially at higher altitudes, have a stronger impact on the final result than topology data, such as Latitude or Digital Elevation Model (DEM). [ABSTRACT FROM AUTHOR]

One of the goals of climate science is to characterize the statistics of extreme and potentially dangerous events in the present and future climate. Extreme events like heat waves, droughts, or floods due to persisting rains are characterized by large anomalies of the time average of an observable over a long time. The framework of Donsker–Varadhan large deviation theory could therefore be useful for their analysis. In this paper we discuss how concepts and numerical algorithms developed in the context of with large deviation theory can be applied to study extreme, rare fluctuations of time averages of surface temperatures at regional scale with comprehensive numerical climate models. When performing this type of analysis, unless a rigorous study of the convergence to the large deviation limit is performed, it can be easy to be misled in thinking to have reached the asymptotic regime. In this paper we provide a systematic protocol to study the convergence of large deviation functions tailored for applications to climate problems. Referring to the existing literature on the subject, we provide explicit formulas to compute large deviation functions directly from time series of a deterministic dynamical system that can be applied to climate records, and we describe how to study the convergence. We show how using a rare event algorithm applied to a numerical model can improve the efficiency of the computation of the large deviation functions. As a case study we consider the time averaged European surface temperature obtained with the numerical climate model Plasim. We show how a precise analysis of the convergence leads to the conclusion that the large deviation limit is nor properly reached for time scales shorter than a few years, and is therefore of no practical interest to study midlatitude heat waves. Finally we show how, even in a case like this, rare event algorithms developed to study large deviation functions can be used to improve the statistics of events on time scales shorter than the one needed to reach the large deviation asymptotic regime. [ABSTRACT FROM AUTHOR]

Ecological engineering is an effective method to control mountain disasters, in which tree roots play an important role in stabilizing the slope and consolidating the soil in face of the landslide. The use of artificial roots to simulate real root is an important means of experimental research. To explore which tree planting patterns could achieve better stability on slopes of 20°, 35°, 50°, and 60° under extreme climatic conditions (rainfall intensity of 118 mm/h), this paper used an artificial root model to construct four types of slopes, i.e., slope with dense trees planted on wide bands, slope with dense trees planted on narrow bands, slope with equally and sparsely planted trees, and barren slope (control group). Sixteen groups of indoor slope rainfall experiments were carried out. To determine the stability effects of different slopes, the failure process and model of slopes during rainfall were analyzed, and the safety factor values Fs were calculated. The following conclusions are drawn: (1) Trees have different effects on the stability of slopes with different slope gradients. Planting trees on 20° and 35° slopes has good reinforcement effect, but planting trees on 50° or 60° slopes will lead to less stability. (2) The optimal planting patterns are different for different slopes. For example, the optimal planting pattern on the 20° and 35° slopes is wide-band dense planting, while it is not suitable to plant tall trees on the 50° and 60° slopes. (3) The change of slope gradient will not affect the failure mode of barren slopes but will change the failure process for vegetated slopes. (4) Wide-band dense planting pattern on all slopes can effectively intercept sediment, inhibit sediment slide, and prevent collapse. All in all, the research results provide a good theoretical guidance and practice to control landslides with ecological engineering. [ABSTRACT FROM AUTHOR]

The modeling of sub-daily extreme rainfall has long constituted a challenge for hydrologists in view of the limited availability of data, both in time and space. In this context, the scale invariance principle, which formally links precipitation intensities across a range of time scales under a rigorous theoretical underpin, comprises a useful and parsimonious tool for deriving probabilistic models for the referred stochastic variables. This paper aims at reviewing the history of application of the scale invariance principle to the marginal distributions of intense precipitation, as well as presenting recent developments in this field. We first address the basic concepts and the mathematical formalism of scale invariant models, discussing distinct scaling regimes across durations and estimation procedures. Next, we present a comprehensive review on at-site and regional stationary scaling models applied in several regions of the world, with focus on their underlying assumptions and main limitations. Finally, we address extensions of the rationale for nonstationary models as a means of accommodating potential effects of climate change in extreme short-duration rainfall. While discussing each of these frameworks, we indicate potential research gaps and modeling developments which could further advance the understanding of scaling behavior of extreme precipitation and improve statistical inference. Hence, this review may constitute a useful guide for practitioners and motivate future research in the modeling of short-duration extreme rainfall. [ABSTRACT FROM AUTHOR]

Cities need to take swift action to deal with the impacts of extreme climate events. The co-production of positive visions offers the potential to not only imagine but also intervene in guiding change toward more desirable urban futures. While participatory visioning continues to be used as a tool for urban planning, there needs to be a way of comparing and evaluating future visions so that they can inform decision-making. Traditional tools for comparison tend to favor quantitative modeling, which is limited in its ability to capture nuances or normative elements of visions. In this paper, we offer a qualitative method to assess the resilience, equity, and sustainability of future urban visions and demonstrate its use by applying it to 11 visions from Phoenix, AZ. The visions were co-produced at two different governance scales: five visions were created at the village (or borough) scale, and six visions were created at the regional (or metropolitan) scale. Our analysis reveals different emphases in the mechanisms present in the visions to advance resilience, sustainability, and equity. In particular, we note that regional future visions align with a green sustainability agenda, whereas village visions focus on social issues and emphasize equity-driven approaches. The visions have implications for future trajectories, and the priorities that manifest at the two scales speak of the political nature of visioning and the need to explore how these processes may interact in complementary, synergistic, or antagonistic ways. [ABSTRACT FROM AUTHOR]

For those involved in planning for regional and local scale changes in future climate, there is a requirement for climate information to be available in a context more usually associated with meteorological timescales. Here we combine a tool used in numerical weather prediction, the 30 weather patterns produced by the Met Office, which are already applied operationally to numerical weather prediction models, to assess changes in the UK Climate Projections (UKCP) Global ensemble. Through assessing projected changes in the frequency of the weather patterns at the end of the 21st Century, we determine that future changes in large-scale circulation tend towards an increase in winter of weather patterns associated with cyclonic and westerly wind conditions at the expense of more anticyclonic, settled/blocked weather patterns. In summer, the results indicate a shift towards an increase in dry settled weather types with a corresponding reduction in the wet and windy weather types. Climatologically this suggests a shift towards warmer, wetter winters and warmer, drier summers; which is consistent with the headline findings from the UK Climate Projections 2018. This paper represents the first evaluation of weather patterns analysis within UKCP Global. It provides a detailed assessment of the changes in these weather patterns through the 21st Century and how uncertainty in emissions, structural and perturbed parameters affects these results. We show that the use of these weather patterns in tandem with the UKCP projections is useful for future work investigating changes in a range of weather-related climate features such as extreme precipitation. [ABSTRACT FROM AUTHOR]

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021-2040 (S) and 2081-2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are a substantial source of precipitation in the eastern U.S. and may be sensitive to regional climatic change. We use a suite of convection-permitting climate simulations to examine possible changes in MCS precipitation. Specifically, annual and regional totals of MCS and non-MCS precipitation generated during a retrospective simulation are compared to end-of-21st-century simulations based on intermediate and extreme climate change scenarios. Both scenarios produce more MCS precipitation and less non-MCS precipitation, thus significantly increasing the proportion of precipitation associated with MCSs across the U.S. [ABSTRACT FROM AUTHOR]

How to effectively monitor the structural condition of slab track and determine its service reliability is a key problem to be solved in the development of high-speed rail (HSR) lines in plateau areas, due to the influence of extreme climate, such as low air pressure, dryness and large temperature difference. To this end, this paper first develops an online displacement monitoring system for double-block slab track. Considering the actual operation condition of the HSR line, this system uses the non-contact vision sensing technique to measure the slab displacement. The slab temperature is also measured synchronously by the system. Then, the relationship between atmospheric temperature and the displacement of the slab track is discussed. The monitoring data are grouped according to three temperature states when being collected: stable temperature, rapid temperature rise, and rapid temperature drop. Finally, the serviceability limit state (SLS) equation of slab track is established and the service reliability of slab track under three temperature conditions is calculated using Monte Carlo method. The results show that track slab deformation is highly sensitive to temperature change and can significantly affect the service reliability of the slab track; compared with the period of "stable temperature", when the temperature rises rapidly, the reliability index of the slab track decreases from 4.48 to 3.92 and the corresponding failure possibility increases around 12 times. This research can provide data support and theoretical basis for intelligent health management of the HSR slab track in the plateau area. [ABSTRACT FROM AUTHOR]

Protecting the ecological security of the Qinghai-Tibet Plateau (QTP) is of great importance for global ecology and climate. Over the past few decades, climate extremes have posed a significant challenge to the ecological environment of the QTP. However, there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP, and few researchers have made quantitative analysis. Hereby, this paper proposed the Ecological Environmental Quality Index (EEQI) for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020, and explored the effects of climate extremes on EEQI based on Geographically and Temporally Weighted Regression (GTWR) model. The results showed that the ecological environment quality in QTP was poor in the west, but good in the east. Between 2000 and 2020, the area of EEQI variation was large (34.61% of the total area), but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level (EEQI change range of 0.05–0.1). The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations, which may be attributed to climate extremes. Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality. Specifically, the effects of daily temperature range (DTR), number of frost days (FD0), maximum 5-day precipitation (RX5day), and moderate precipitation days (R10) on ecological environment quality were positive in most regions. Furthermore, there were significant temporal differences in the effects of consecutive dry days (CDD), consecutive wet days (CWD), R10, and FD0 on ecological environment quality. These differences may be attributed to variances in ecological environment quality, climate extremes, and vegetation types across different regions. In conclusion, the impact of climate extremes on ecological environment quality exhibits complex patterns. These findings will assist managers in identifying changes in the ecological environment quality of the QTP and addressing the effects of climate extremes. [ABSTRACT FROM AUTHOR]

This paper used original survey data in Shandong province of China to depict local farmers' perceptions of combined climatic and market risks, and their adaptive behaviors. Two Logit models were developed to capture the empirical relationship between farmers' risk perception of the combined risks and adaptive behaviors. Results show that farmer's risk perception of climate change and market significantly affected their adoption behavior of adaptive measures, including perception of rainfall decrease, gale decrease, drought increase, and price fall of agricultural products. Moreover farmers' adaptive behaviors are more sensitive to their perceptions of extreme climatic events. Addition of demographic factors can improve the explanatory capacity of the Logit model. As illustrated by the models, male and better educated farmers have greater willingness to take actions for averting risks, while household heads with bigger farm are more reluctant. A majority of respondents chose to construct farm structure for irrigation and water conservation to avert climatic risks. Insurance, which is regarded as an effective tool to help farmers mitigate risks and avoid losses, has not been sufficiently accepted by farmers in rural areas in Shandong province. This underscores the need of effective communication of knowledge to enhance farmers' coping capacity and to encourage their active response to risk. [ABSTRACT FROM AUTHOR]

Based on bias-corrected future projections from the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model under both RCP4.5 and 8.5 scenarios over China, extreme climatic events at the middle and end of the twenty-first century are investigated in this paper. The model performance of PRECIS is validated using comparisons with observations and HadGEM2-ES projections, and the bias correction adds fidelity to the projections of basic climate variables and extreme climate events. In the future, our single-realisation estimates show that the number of frost days is projected to decrease and days with tropical nights are projected to increase. Including northeast China, Sichuan Basin, middle and lower reaches of Yangtze River and south China, the number of consecutive dry days will increase from our single-realisation estimates, which will exhibit a spatial distribution almost similar to that of consecutive wet days in the coming decades. Although precipitation indices associated with duration will increase, a simple precipitation index depicting the intensity of extreme events will decrease over east and south China in the future from our single-realisation projections. Daily rainfall above 50 mm, which is usually regarded as a rainstorm event, is predicted to increase under the RCP4.5 scenario over most of China from our single-realisation estimates; while the same change pattern will occur in southernmost China, similarly spatially distributed for the RCP8.5 scenario, the number of rainstorm will first increase in the middle of the twenty-first century and then slow at the end of the twenty-first century in the Yangtze River region. In conclusion, our single-realisation estimates indicate that the persistence of extreme precipitation will increase with time, but the change of extreme precipitation intensity is not significant in the future. The comparison of climatic extreme events under the RCP4.5 scenario with those under the RCP8.5 scenario shows that extreme climate events will be enhanced under the higher emissions scenario; hence, reductions in greenhouse gas emissions will help alleviate climate change effects in the future. [ABSTRACT FROM AUTHOR]

Quantitative and qualitative knowledge about the potential impacts of climate change on extreme hydrological events is crucial for water resource management and extreme risk management under climate change. This has theoretical and realistic implications to study and couple the climate system with hydrologic processes, to understand the system and solve related problems in water resources and extreme hydrology, such as decision making, plan management, environmental protection, and ecological balance. This paper reviews recent studies investigating climate change impact on hydrological extremes using a perspective of the integrated modeling framework comprising climate change scenarios, climate models, bias correction methods, hydrological modeling (model structure and parameterization), and reducible uncertainty arising from these sources characterized by a paucity of knowledge. The available research outcomes show the extreme high flows are likely to increase under climate change in the most parts of Europe, Asia, and the USA, but greatly vary and decrease in Africa and Latin America, which is highly variable and uncertain in space and time. Each component in integrated modeling has an important role in shifting and producing uncertainty in projected extreme flow. Among them, the climate model's discrepancy and hydrological models' structure are the more dominant source of uncertainty in projection of extreme high and low (or mean) flow in most of the regions, respectively. However, the quality of input data and hydrological model structures are the most dominant source of uncertainty in Africa, Latin America, and some parts of Asia. This indicated that these regions have strong hydrological cycle and higher physiographic heterogeneity. We believe that our existing knowledge and skills need to be improved and transformed into an accurate mathematical and physical representation, to minimize the uncertainty due to the effect of choices in the methodology chain. So, disentangling the aggregated uncertainty in the cascade modeling chain can be done by using variances, which can help understand the interaction effect and identify their contribution to the projected extreme flow. This comprehensive review can help modelers to identify and reduce uncertainty in projecting hydrological extremes and policy makers for full awareness of the various uncertainties to make a robust decision for water resources management under climate change. Article Highlights: This study provides a comprehensive review on reducing uncertainty in projecting hydrological extremes from the perspective of an integrated modeling framework Existing knowledge and skills need to be improved and transformed into an accurate mathematical representation The impact of climate change on hydrological extremes is highly variable and uncertain in space and time Uncertainties of the integrated modeling framework can be reduced by decomposing the aggregated uncertainty using variance analysis and/or the modeling chain [ABSTRACT FROM AUTHOR]

Extreme climatic events may be translated into a higher frequency of both dry and wet years. Frequent droughts pose a challenge to water supply in terms of both quantity and quality. In order to cope with this, there has to be a documentation on the concentration of chemicals in water during such events. The southeast region of Brazil experienced a major drought event in 2014 leading to number of social, economic, and environmental impacts. The objective of the present paper is to understand the effect of an extreme drought on dissolved organic and inorganic carbon concentrations of the Piracicabal river, which is under high human pressure. Concentrations of both dissolved organic (DOC) and inorganic carbon (DIC) were monitored daily, for 30 days, during a dry month in 2014. DOC values found here were at least twice those observed in the river in non-extreme conditions. Rain events within this drought led to significant differences in discharge, DOC, and DIC. We built a dilution-concentration model which described an exponential relationship between discharge and both carbon forms. This indicated that drastic changes in concentrations of DOC and DIC are expected during such events. Our results may also apply to a number of regions in the world especially those of the developing countries where rivers are highly subjected to sewage impact. [ABSTRACT FROM AUTHOR]

Mercury concentrations in Lake Champlain fish increased (2011–2017) for the first time in more than two decades. The increase, however, was not consistent among species or throughout the lake. Mercury concentrations in smallmouth bass and yellow perch from the three Main Lake segments increased significantly while concentrations in the eastern portions of the lake (Northeast Arm and Malletts Bay) remained unchanged or decreased; mercury concentrations in white perch remained unchanged. Factors examined to explain the increase included: atmospheric deposition, lake temperature, chlorophyll-a, fishery dynamics, lake flooding and loading of total suspended solids (TSS). This paper examines how each factor has changed between study periods and the spatial variability associated with the change. We hypothesize fishery dynamics, flooding and TSS loading may be partially responsible for the increase in fish mercury. Both growth efficiency and biomass of fish suggest mercury concentrations would increase in the Main Lake segments and decrease in the eastern portion of the lake. Additionally, two extreme climate events in 2011 resulted in extensive flooding and a four-fold increase in annual TSS loading, both potentially increasing biotic mercury with the impact varying spatially throughout the lake. Changes to the fishery and disturbance caused by extreme climatic events have increased biotic mercury and the processes responsible need further study to identify possible future scenarios in order to better protect human and wildlife health. [ABSTRACT FROM AUTHOR]

Finance ministries are increasingly involved in UN climate finance negotiations, yet this development received very limited attention in the literature on climate finance or climate negotiations. It is not obvious from the literature on bureaucratic politics how these ministries will position themselves on climate finance: they may frame climate finance as expenditure to be limited or as an instrument for correcting the market failure of climate change. This paper investigates which frames have characterised the positions of finance ministries on key issues in the climate finance negotiations, and whether the use of a given frame corresponds to particular factors. Case studies of Denmark, India, Indonesia and the USA based on official documents and interviews show that the position of each finance ministry is generally consistent with one particular frame. The Indonesian and Danish finance ministries predominantly framed climate finance as a way of correcting a market failure. The Indian Ministry of Finance emphasised Common but Differentiated Responsibilities, which fits with the budget frame. The US Treasury's position similarly fits with the budget frame while sharing elements of the market failure frame. Finance ministries that had the lead on climate finance were more likely follow the budget frame. The use of both frames cuts across the divide between industrialised and emerging economies. With the exception of the USA, left- and right-wing governments were equally likely to adopt either frame. These findings indicate that strengthening finance ministry forums built around the market failure frame can be a way of reducing norm fragmentation. [ABSTRACT FROM AUTHOR]

In general, it is known that extreme climatic conditions such as El Niño and positive Indian Ocean Dipole (IOD+) cause prolonged drought in Indonesia's tropical peatlands so that groundwater levels (GWL) drop and peat is prone to fire. However, 27 years of GWL measurements in Central Kalimantan peat forests show the opposite condition, where the lowest GWL occurs several weeks before El Niño and after IOD+ reaches its peaks. We show that the dropped sea surface temperature anomaly induced by anomalously easterly winds along the southern Java-Sumatra occurs several weeks before the GWL drop to the lowest value. Local rainfall decreased, and GWL dropped sharply by 1.0 to 1.5 m, during the super El Niño events in 1997/98 and 2015, as well as remarkable events of IOD+ in 2019. It is suggested that the tropical peatland ecohydrological system (represented by the GWL), El Niño Southern Oscillation (ENSO), and IOD+ are teleconnected. Hence, monitoring GWL variability of peatland over the IMC is a possibility an alert for extreme climate events associated with El Niño and/or moderate IOD+. [ABSTRACT FROM AUTHOR]

Based on the government's historical records and personal documents of the pre-modern Chosŏn Dynasty, this paper examines the socio-economic impacts in Korea in response to climatic variability from 1809 until 1819 that may have been influenced to some degree by the eruption of the "unknown volcano" (1809) and the Tambora eruption (1815). In the early 1800s, when volcanic eruptions occurred successively, the Korean Peninsula experienced a temporal precipitation variation—drought, abundant rainfall, and normalcy—twice. The precipitation variation in this period had a heavy impact on the yields of rice, major crop on the peninsula. In the phase of drought in 1809 and extreme climatic anomalies in 1814, the country suffered record poor harvests, and in the abundant rainfall phase in 1810 and 1816–1817, it had bumper crops. For this reason, 1816–1817 were the halcyon years for Korea, unlike the case of Europe and the northeastern USA which suffered from extreme climatic anomalies in those years. This case of the Korean Peninsula indicates that the climate change and natural disasters of the 1810s were influenced by not only of the single event of the Tambora eruption but of the successive eruptions of volcanoes in the 7 years from 1809 to 1815, which also affected other areas on the globe for 11 years (1809–1819). [ABSTRACT FROM AUTHOR]