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This article is a response to comments made by researchers from the Universidad de Sevilla regarding a paper on extending climate justice to animals. The authors of the response agree with the commenters that the Iberian Lynx population has increased due to conservation efforts, but they clarify that their paper was not intended as a critique of these measures. The purpose of their paper is to discuss how to help wild animals cope with climate change, using the Iberian Lynx as an illustrative case. They argue that while the population has increased, the species is still at risk of extinction due to climate change. The authors also address the commenters' mention of the lack of originality in their proposed conservation measures, stating that their intention was to examine the moral implications of intervention strategies. Overall, the authors emphasize the importance of climate justice for animals, including the Iberian Lynx. [Extracted from the article]

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research. [ABSTRACT FROM AUTHOR]

Urban flood resilience can critically diminish the negative effects of extreme climatic conditions. In recent decades, green infrastructure has been gaining attention among researchers and authorities in terms of its use in urban contexts to enhance urban resilience. This paper tries to provide knowledge on how urban flood resilience has been recently approached through green infrastructure. To do this, the distribution of the topics of interest, authors, and sources/regions of publication are investigated through a systematic review of recent articles. Additionally, the methodological approaches and green infrastructure typologies are examined. Findings show an agglomeration of publications in developed countries. It was also observed that there is a predominance of quantitative methodological approaches and a low connectivity for some hot topics within this field of research (e.g., biodiversity). The most common green infrastructure typologies used in urban flood resilience research are also discussed. It is noticeable that more than half of the papers used general terms (e.g., urban park/open space) to describe green infrastructure rather than using technical typologies providing more information on water flow management characteristics. The outcomes are discussed to give an overview of the latest hotspots and gaps in this field of research, which gives some future directions/expectations to be followed in forthcoming investigations. [ABSTRACT FROM AUTHOR]

This paper presents a novel theoretical framework for incorporating climate risks and adaptation investments into infrastructure debt pricing. Utilizing the Capital Asset Pricing Model (CAPM), the framework extends the conventional modeling of infrastructure project revenues and costs to include climate risk considerations. It proposes three climate-informed revenue and cost formulations: adjustmentment of mean and standard deviation, incorporation of extreme climate events via Pareto and Poisson distributions, and a climate-informed cost model that includes adaptation investment. The paper demonstrates the application of this model in pricing a loan for a Light Rail Transit project in Costa Rica, introducing the concepts of "flood risk premium" and "adaptation curves". This study not only offers a novel lens through which to view infrastructure investment under climate uncertainty but also sets the stage for transformative policy and practice in financial risk assessment, encouraging a shift towards more sustainable and resilient infrastructure development. [ABSTRACT FROM AUTHOR]

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future. [ABSTRACT FROM AUTHOR]

This paper examines the linear relationship between climate events in the La Plata Basin (LP) from 1980 to 2018 and atmospheric moisture transport from major sources using a Lagrangian approach. The standardized precipitation evapotranspiration index (SPEI-1) was utilized to assess climate events, as monthly water balance variations may be related to changes in atmospheric moisture transport. A total of 49 dry and 46 wet events were identified through sequences of negative and positive SPEI-1 values, respectively. Lagrangian analysis tracked changes in moisture uptake and supply from sources to the LP during these events. Simple linear regression analysis revealed a relationship between moisture transport from the Amazonas (AM), North Atlantic (NA), and Tocantins (TO) basins and the severity and duration of climate events. Increased moisture supply from the São Francisco basin (SF) and Eastern Brazil oceanic (EBO) sources intensified the duration and severity of wet events. Peak wet events were linked to increased moisture supply from the northern South America basins (AM, NA, and TO), while peak droughts were related to decreased moisture uptake from eastern sources (TO, SF, and EBO). Our findings highlight how the water balance in distant regions affects the LP via moisture transport, emphasizing the need for interconnected adaptive strategies. [ABSTRACT FROM AUTHOR]

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse. [ABSTRACT FROM AUTHOR]

Disasters, whether natural or man-made, pose inevitable global challenges. Events such as COVID-19, earthquakes, extreme climatic conditions, and conflicts underscore the urgent demand for effective temporary housing solutions. These temporary housing units (THUs) serve as an aid in assisting displaced people to rebuild their lives as the recovery process unfolds. However, numerous temporary housing units present environmental, economic, and social issues that hinder their sustainability. This paper investigates the underlying causes of these issues, defines the essential requirements that temporary housing units must meet, and proposes an initial design to fulfill these requirements. The methodology comprises three key phases: case study analysis, requirement identification, and the integration of these requirements into the design process. The main findings highlight that the construction of sustainable temporary housing units necessitates a meticulous consideration of various parameters to achieve a balanced equilibrium between economic, social, and environmental impacts. Possible future research directions are emphasized, including the use of digital tools and BIM models to promote the adoption of circular economy practices and the validation of the design solution through value analysis. Possible improvements in the user's well-being are also taken into consideration. [ABSTRACT FROM AUTHOR]

The Canadian Prairies are associated with high natural hydroclimatic variability including the frequent periodic occurrence of droughts and pluvials. These extremes carry various risks including significant damage to the economy, environment and society. The well-documented level of damage necessitates further risk assessment and planned reductions to vulnerability, particularly in light of a warming climate. A logical starting point involves awareness and information about the changing characteristics of such climate extremes. We focus on the compound occurrence of droughts and pluvials as the risks from this type of event are magnified compared to the hydroclimatic extremes in isolation. Compound droughts and pluvials (CDP) are drought and pluvial events that occur in close succession in time or in close proximity in area. Also, research on CDP is limited even for the worldwide literature. Therefore, the purposes of this paper are to synthesize recent literature concerning the risks of CDP, and to provide examples of past occurrences, with a focus on the Canadian Prairies. Since literature from the Prairies is limited, global work is also reviewed. That literature indicates increasing concern and interest in CDP. Relationships between drought and pluvials are also characterized using the SPEI Global Monitor for the Prairies, emphasizing the recent past. Research mostly considers drought and pluvials as separate events in the Prairies, but is integrated here to characterize the relationships of these extremes. The spatiotemporal patterns showed that several of the extreme to record pluvials were found to be closely associated with extreme droughts in the Prairies. The intensities of the extremes and their dry to wet boundaries were described. This is the first research to explore the concept of and to provide examples of CDP for the Prairies and for Canada. Examples of CDP provide insights into the regional hydroclimatic variability. Furthermore, most literature on future projections strongly suggests that this variability is likely to increase, mainly driven by anthropogenic climate change. Therefore, improved methods to characterize and to quantify CDP are required. These findings suggest means of decreasing vulnerability and associated damages. Although the study area is the Canadian Prairies, the work is relevant to other regions that are becoming more vulnerable to increasing risks of and vulnerabilities to such compound extremes. [ABSTRACT FROM AUTHOR]

Nowadays, frequent climate extremes exert a serious impact on agricultural production and social development, which is seldom studied in the previous literature. Production–Living–Ecological (PLE) is a useful analysis framework, and China is a suitable model for such study. This paper takes the Huai River Eco-Economic Belt (HREB), an important agricultural zone in China, to study the relationship among agricultural production (P), society (L), and climate change (E), which is referred to as APLE. This paper constructs a coupled coordination evaluation index system for the APLE system and uses coupling coordination degree models and geographic detector to study the spatial and temporal evolution of the coordinated development of 34 counties (cities) in the HREB from 2009 to 2018. The results show the following: (1) The development of the agricultural subsystem and the social subsystem formed a "scissors difference" from 2009 to 2014, and the three subsystems showed a slight upward trend during 2014–2018. (2) The coupling and coordinated development of the APLE system in the HREB was generally stable, and the coupling coordination degree was improved from low-grade and slightly uncoordinated to barely and primarily coordinated. Furthermore, the spatial differentiation of the coupling coordination degree shows a clear pattern of being high in the southeast and low in the northwest. (3) The main influencing factors are the drought and flood protection rate, the effective irrigation rate, the per capita electricity consumption in agriculture, the number of beds in healthcare facilities per 10,000 people, the per capita disposable income of urban residents, the annual average temperature, and the annual precipitation. (4) The spatial–temporal evolution of the coupling and coordinated development of the APLE system is the result of the comprehensive effect of internal driving forces such as food security, the consumption level of rural residents, and the development level of urbanization construction, and external driving forces such as government public welfare and natural conditions. [ABSTRACT FROM AUTHOR]

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks). [ABSTRACT FROM AUTHOR]

The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery. [ABSTRACT FROM AUTHOR]

For building environments, meteorological factors such as daily mean temperature, extreme temperature and seasonal temperature changes, are essential, as they impact building structures significantly. Due to the importance of detailed and accurate temperature data, and taking Beijing, China, as an example, this paper developed a fast and effective interpolation method to extract hourly meteorological data, based on 30 years' raw meteorological data. With the interpolated data, this paper defined the extreme weather for buildings. Moreover, a temperature model based on probability and statistical analysis was constructed, and the general climate standard for days and extreme climate for typical days with different return periods were obtained. Furthermore, meteorological models for standard annual temperature were also achieved, reflecting the daily variation and annual variation of temperature, and can provide continuous-numerical-simulation parameters for analyzing daily and annual temperature. According to the daily temperature difference obeys the Gumble Distribution, the daily temperature difference in different return periods and extreme climates is obtained by analysis. Therefore, annual temperature ranges of different recurrence intervals and extreme climate are also achieved, and the annual temperature ranges can be used to analyze the effect of different recurrence intervals and extreme weather on building structures. [ABSTRACT FROM AUTHOR]

In the pursuit of more efficient vehicles on the world's roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the 'Octovalve', is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve's performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life. [ABSTRACT FROM AUTHOR]

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the " Hollowing " scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community's microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of "Temperature-Precipitation" combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change. [ABSTRACT FROM AUTHOR]

The processes of overland flow, infiltration through an unsaturated zone (UZ) and groundwater flow discharge to a surface-water network are simulated by a synthetic flow and transport watershed model under a 30-year warming signal. A promising strategy is to use both methods as complementary to each other: machine learning techniques can improve the calibration of numerical models whereas process-based numerical models are suitable to understand the physical system and, on turn, select proper input variables for machine learning models. In general, the study highlights that choosing the modelling approach based on the specific aquifer features is fundamental to obtaining a modelling tool efficient in supporting groundwater management actions aimed at mitigation of the effects of climate change. [Extracted from the article]

Depopulation often leads to the abandonment of agricultural land and the resulting process of afforestation and reforestation. In this paper, we study the land use changes between 2000 and 2018 in two Spanish provinces, Soria and Teruel. The provinces chosen as case studies are of particular interest because they have some of the lowest population densities in Europe and continue to suffer depopulation processes that have been ongoing since the mid-20th century. The reasons for this are not immediately clear given that unlike other European regions with similar population density values, for example in Northern Scandinavia, they are not exposed to extreme climatic conditions, nor are they located in isolated mountain regions. Using the CORINE Land Cover database, we observed that in both provinces, there has been an expansion in shrub and forest land uses and a decline in agriculture due largely to the fact that certain areas have been abandoned by their inhabitants, although in other parts of these provinces, the amount of land used for agriculture has increased. Urban growth over this period has been minimal. In the coming years, it seems likely that the population of these areas will continue to fall, given that the policies carried out over decades to try to revert this trend have not been successful. [ABSTRACT FROM AUTHOR]

Since the droughts of the 1970s–1980s, populations in the Sahel region have opted for a mass exodus to more humid urban or rural centers. Migrations or exoduses have accelerated in recent decades due to environmental degradation and unfavorable climatic conditions. Insufficient harvests are the main reason for migration for the majority of migrants in the Sahelian areas. Migration is a major adaptation strategy to cope with extreme climatic conditions, thus requiring quantification in the destination area. The aim of this paper is to propose a metapopulation model to approximate reality by identifying the transition from one socioeconomic vulnerability group to another, from a less favorable area to favorable area in terms of natural resources, depending on the strategies, policies, and climate variability. The model was used to analyze the dynamics of socioeconomic vulnerability to study the impact of migration on the dynamics of socioeconomic vulnerability. The developed mathematical model was analyzed. Up to 2050, simulations applied to the Tougou village in northern Burkina Faso show that migration has a positive impact on the socioeconomic vulnerability of the destination area, thereby reducing the vulnerability of the population by 10% when resources are increased by up to 30%. [ABSTRACT FROM AUTHOR]

As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world's water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation. [ABSTRACT FROM AUTHOR]

In recent years, with the superposition of extreme climate, earthquakes, engineering disturbance and other effects, global landslide disasters occur frequently. Due to reservoir landslides being mostly in a multi-field coupling environment, the temperature field will impact the deformation and seepage fields, thereby affecting the stability of the reservoir landslide. The variation in the landslide's surface temperature also directly affects the stress and deformation of deep rock masses. If hidden dangers are not detected in time, and corresponding measures are implemented, it is easy to cause landslide instability. In order to clarify the temperature measurement performance of different optic-electric sensors and the application characteristics of layout techniques, laboratory calibration tests of temperature sensors under different adhesives and attachment materials are carried out in this paper. It was found that the test data of the iron bar had the best effect among the four attachment materials overall. Therefore, the bar with a high-stiffness material should be preferred when selecting a pipe fitting as the fiber Bragg grating (FBG) temperature attachment in the borehole. However, considering the high requirements for the durability of sensors and layout techniques in on-site monitoring, the long-term stability of the adhesives used in actual monitoring needs to be improved. At the same time, it was found that the platinum 100 (PT100) temperature sensor has relatively higher testing accuracy (A: 0.15 + 0.002 × |t|; B: 0.30 + 0.005 × |t|), a larger temperature measurement range (−200~+850 °C) and better temperature measurement stability when compared to conventional sensors. Moreover, its resistance value has a good linear relationship with temperature. Finally, the Xinpu landslide in the Three Gorges Reservoir area was selected as the research object for on-site monitoring. There was a high correlation between the on-site monitoring results with the laboratory calibration test results. Therefore, through the performance test of optic-electric sensors in reservoir landslide temperature fields, more accurate solutions can be provided for selecting sensors and designing layout techniques to monitor the underground temperature field of landslides under different geological conditions. Thereby, grasping the real-time state information of the reservoir landslide temperature field is achieved accurately, providing an important reference for early warning, prediction, prevention and the control of reservoir landslide disasters. [ABSTRACT FROM AUTHOR]

The frequency and severity of extremes, including extreme precipitation events, extreme evapotranspiration and extreme water storage deficit events, are changing. Thus, the necessity for developing a framework that estimates non-stationary conditions is urgent. The aim of this paper is to develop a framework using the geeSEBAL platform, Generalised Extreme Value (GEV) models and spatiotemporal analysis techniques that incorporate the physical system in terms of cause and effect. Firstly, the geeSEBAL platform has enabled the estimation of actual evapotranspiration (ETa) with an unprecedented level of spatial-temporal resolution. Following this, the Non-stationary Extreme Value Analysis (NEVA) approach employs the Bayesian method using a Differential Evolution Markov Chain technique to calculate the frequency and magnitude of extreme values across the parameter space. Station and global climate datasets have been used to analyse the spatial and temporal variation of rainfall, reference evapotranspiration (ETo), ETa and water storage (WS) variables in the Lockyer Valley located in Southeast Queensland (SEQ), Australia. Frequency analysis of rainfall, ETa, and water storage deficit for 14 stations were performed using a GEV distribution under stationary and non-stationary assumptions. Comparing the ETa, ETo and ERA5 rainfall with station data showed reasonable agreement as follows: Pearson correlation of 0.59–0.75 for ETa, RMSE of 45.23–58.56 mm for ETa, Pearson correlation of 0.96–0.97 for ETo, RMSE of 73.13–87.73 mm for ETo and Pearson correlation of 0.87–0.92 for rainfall and RMSE of 37.53–57.10 mm for rainfall. The lower and upper uncertainty bounds between stationary and non-stationary conditions for rainfall station data of Gatton varied from 550.98 mm (stationary) to 624.97 mm (non-stationary), and for ERA5 rainfall datasets, 441.30 mm (stationary) to 450.77 mm (non-stationary). The results demonstrate that global climate datasets underestimate the difference between stationary and non-stationary conditions by 9.47 mm compared to results of 73.99 mm derived from station data. Similarly, the results demonstrate less variation between stationary and non-stationary conditions in water storage, followed by a sharp variation in rainfall and moderate variation in evapotranspiration. The findings of this study indicate that neglecting the non-stationary condition in some hydrometeorological variables can lead to underestimating their amounts. This framework can be applied to any geographical area for estimating extreme conditions, providing valuable insights for infrastructure planning and design, risk assessment and disaster management. [ABSTRACT FROM AUTHOR]

Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia's Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. [ABSTRACT FROM AUTHOR]

With global warming, the frequency, intensity, and period of extreme climates in more areas will probably increase in the twenty first century. However, the impact of climate extremes on forest vulnerability and the mechanisms by which forests adapt to climate extremes are not clear. The eastern Tianshan Mountains, set within the arid and dry region of Central Asia, is very sensitive to climate change. In this paper, the response of Picea schrenkiana and Larix sibirica to climate fluctuations and their stability were analyzed by Pearson's correlation based on the observation of interannual change rates of climate indexes in different periods. Additionally, their ecological adaptability to future climate change was explored by regression analysis of climate factors and a selection of master control factors using the Lasso model. We found that the climate has undergone significant changes, especially the temperature, from 1958 to 2012. Around 1985, various extreme climate indexes had obvious abrupt changes. The research results suggested that: (1) the responses of the two tree species to extreme climate changed significantly after the change in temperature; (2) Schrenk spruce was more sensitive than Siberian larch to extreme climate change; and (3) the resistance of Siberian larch was higher than that of Schrenk spruce when faced with climate disturbance events. These results indicate that extreme climate changes will significantly interfere with the trees radial growth. At the same time, scientific management and maintenance measures are taken for different extreme weather events and different tree species. [ABSTRACT FROM AUTHOR]

Basin ecohydrological processes are essential for informing policymaking and social development in response to growing environmental problems. In this paper, we review watershed ecohydrology, focusing on the interaction between watershed ecological and hydrological processes. Climate change and human activities are the most important factors influencing water quantity and quality, and there is a need to integrate watershed socioeconomic activities into the paradigm of watershed ecohydrological process studies. Then, we propose a new framework for integrated watershed management. It includes (1) data collection: building an integrated observation network; (2) theoretical basis: attribution analysis; (3) integrated modeling: medium- and long-term prediction of ecohydrological processes by human–nature interactions; and (4) policy orientation. The paper was a potential solution to overcome challenges in the context of frequent climate extremes and rapid land-use change. [ABSTRACT FROM AUTHOR]

The catastrophic floods that hit Pakistan in summer 2022 represent the latest example of climate change-induced extreme events occurring in South Asia. In addition to the dramatic impact on population and infrastructures, this event threatened UNESCO World Heritage Sites (WHS) and properties of national interest. However, while a wealth of national and international mapping initiatives were conducted based on satellite imagery to assess damage to people, buildings and economic goods, the impact caused to archaeological sites and monuments has not been fully unveiled yet. To bridge this gap and provide an integrated approach that can be used by local end-users to assess damage and, in turn, collect evidence to inform and improve risk management plans, the present paper integrates Sentinel-1 and 2 imagery and mapping products derived from them (e.g., Copernicus Emergency Management Service Global Flood Mapping) with ground-truthing and geospatial datasets. Through a multidisciplinary collaboration between geologists, archaeologists, remote sensing and satellite image analysts, the integration methodology was tested on UNESCO and other heritage sites of national relevance located in the two mostly affected regions of Khyber Pakhtunkhwa and Sindh that were intentionally selected to represent different typologies of cultural heritage and governance in Pakistan. Finally, the information extracted from the present analysis was assessed in relation to the current national and international legislations, the official state of conservation reports and the activities conducted at each site to protect them against flood events. Given the accessibility to ready-to-use Copernicus products and that the present analysis can be replicated over time and other sites, the proposed methodology provides a feasible means to exploit satellite data in post-disaster mapping situations and contribute to the decision-making process for risk management. [ABSTRACT FROM AUTHOR]

Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid spatiotemporal model which offers comprehensive data preprocessing techniques, focusing on data decomposition, feature extraction and dimensionality upgrading. This model provides a feasible solution to the puzzling problem of long-term climate prediction. Firstly, we put forward a Period Division Region Segmentation Property Extraction (PD-RS-PE) approach, which divides the data into a stationary series (SS) for an Extreme Learning Machine (ELM) prediction and an oscillatory series (OS) for a Long Short-term Memory (LSTM) prediction to accommodate the changing trend of data sequences. Secondly, a new type of input-output mapping mode in a three-dimensional matrix was constructed to enhance the robustness of the prediction. Thirdly, we implemented a multi-layer technique to extract features of high-speed input data based on a Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) for parameter searching of a neural network, thereby enhancing the overall system's learning ability. Consequently, by integrating all the above innovative technologies, a novel hybrid SS-OS-PSO-DBN-ELM-LSTME (SOPDEL) model with comprehensive data preprocessing was established to improve the quality of long-time-series forecasting. Five models featuring partial enhancements are discussed in this paper and three state-of-the-art classical models were utilized for comparative experiments. The results demonstrated that the majority of evaluation indices exhibit a significant optimization in the proposed model. Additionally, a relevant evaluation system showed that the quality of "Excellent Prediction" and "Good Prediction" exceeds 90%, and no data with "Bad Prediction" appear, so the accuracy of the prediction process is obviously insured. [ABSTRACT FROM AUTHOR]

Simple Summary: Anthropogenic climate change is one of the greatest threats to biodiversity. Extreme temperature events associated with longer-term climate change are increasing in frequency, duration and intensity. The effects of climatic extremes on ectotherms, such as insects, have been well-studied in recent years. However, the effects of extreme temperatures on other arthropod groups, such as spiders, has received much less attention. Spiders are important organisms as predators in natural and agricultural ecosystems. In this paper, we describe spider responses to extreme temperatures and highlight the most important knowledge gaps that urgently need to be filled to better understand how vulnerable spiders are to climate change and climatic extremes. Unlike insects, traits such as body size and niche breadth may differ markedly in male and female spiders. Therefore, we argue that research needs to address the effects of heat exposure on the physiology, behavior and ecology of male and female spiders across multiple taxa. Observed declines in some terrestrial insects have been widely reported in recent years, with climate change, along with other anthropogenic threats, being implicated. Longer-term data on trends in spider abundance, where available, may also shed possible light on the role of climate change. Climatic extremes, such as heat waves, are increasing in frequency, intensity and duration under anthropogenic climate change. These extreme events pose a great threat to many organisms, and especially ectotherms, which are susceptible to high temperatures. In nature, many ectotherms, such as insects, may seek cooler microclimates and 'ride out´ extreme temperatures, especially when these are transient and unpredictable. However, some ectotherms, such as web-building spiders, may be more prone to heat-related mortality than more motile organisms. Adult females in many spider families are sedentary and build webs in micro-habitats where they spend their entire lives. Under extreme heat, they may be limited in their ability to move vertically or horizontally to find cooler microhabitats. Males, on the other hand, are often nomadic, have broader spatial distributions, and thus might be better able to escape exposure to heat. However, life-history traits in spiders such as the relative body size of males and females and spatial ecology also vary across different taxonomic groups based on their phylogeny. This may make different species or families more or less susceptible to heat waves and exposure to very high temperatures. Selection to extreme temperatures may drive adaptive responses in female physiology, morphology or web site selection in species that build small or exposed webs. Male spiders may be better able to avoid heat-related stress than females by seeking refuge under objects such as bark or rocks with cooler microclimates. Here, we discuss these aspects in detail and propose research focusing on male and female spider behavior and reproduction across different taxa exposed to temperature extremes. [ABSTRACT FROM AUTHOR]

Weather and climate extremes such as heat waves, droughts and floods are projected to become more frequent and intense in several regions. There is compelling evidence indicating that changes in climate and its extremes over time influence the living conditions of society and the surrounding environment across the globe. This study applies max-stable models to capture the spatio–temporal extremes with dependence. The objective was to analyse the risk of drought caused by extremely high temperatures and deficient rainfall. Hopkin's statistic was used to assess the clustering tendency before using the agglomerative method of hierarchical clustering to cluster the study area into n = 3 temperature clusters and n = 3 precipitation clusters. For the precipitation and temperature data, the values of Hopkin's statistic were 0.7317 and 0.8446, respectively, which shows that both are significantly clusterable. Various max-stable process models were then fitted to each cluster of each variable, and the Schlather model with several covariance functions was found to be a good fit on both datasets compared to the Smith model with the Gaussian covariance function. The modelling approach presented in this paper could be useful to hydrologists, meteorologists and climatologists, including decision-makers in the agricultural sector, in enhancing their understanding of the behaviour of drought caused by extremely high temperatures and low rainfall. The modelling of these compound extremes could also assist in assessing the impact of climate change. It can be seen from this study that the size, including the topography of the location (cluster/region), provides important information about the strength of the extremal dependence. [ABSTRACT FROM AUTHOR]

Since land use/cover change profoundly impacts climate change and global warming has become an irreversible trend in the Anthropocene, there have been numerous global studies on the impact of climate change on vegetation growth (VG). However, the effects of climate extremes on the growth and direction of various vegetation types need to be better investigated, especially in the climate transition zones. In this paper, we examined the effect of diurnal warming on the growth of various types of vegetation in China's north–south transition zone. Based on the daily observation data of 92 meteorological stations in the Qinling-Daba (Qinba) mountainous area from 1982 to 2015, coupled with the Normalized Difference Vegetation Index (NDVI) and data on the type of vegetation. This research examined the temporal changes in the highest and lowest temperatures during the last 33 years using trend analysis. Second-order correlation analysis was used to investigate vegetation NDVI response characteristics to diurnal warming and to examine the effect of diurnal warming on the growth of different vegetation types. Our results showed that maximum temperature (Tmax) and minimum temperature (Tmin) showed an obvious upward trend, with the daytime temperature increase rate 1.2 times that at night, but failing the t-test. In addition, diurnal warming promoted vegetation growth, with NDVI associated positively correlated with Tmax at approximately 91.2% of the sites and 3492 rasters and with Tmin at roughly 53.25% of the sites and 2864 rasters. Spatial significance analysis showed an apparent difference, but few areas passed the t-test. Furthermore, daytime warming enhanced the growth of grasses, shrubs, deciduous broad-leaved forests, crops, and conifers, while the effect of nighttime warming on VG had a positive effect only on the growth of evergreen broad-leaved forest vegetation. These findings reveal the mechanisms of the impact of climate extremes on VG under global change, particularly the extent to which different vegetation types in climatic transitional zones respond to climate extremes. [ABSTRACT FROM AUTHOR]

The kingdom of Saudi Arabia (SA) ranks fifth in Asia in terms of area. It features broad biodiversity, including interesting flora, and was the historical origin of Islam. It is endowed with a large variety of plants, including many herbs, shrubs, and trees. Many of these plants have a long history of use in traditional medicine. The aim of this review is to evaluate the present knowledge on the plants growing in SA regarding their pharmacological and biological activities and the identification of their bioactive compounds to determine which plants could be of interest for further studies. A systematic summary of the plants' history, distribution, various pharmacological activities, bioactive compounds, and clinical trials are presented in this paper to facilitate future exploration of their therapeutic potential. The literature was obtained from several scientific search engines, including Sci-Finder, PubMed, Web of Science, Google Scholar, Scopus, MDPI, Wiley publications, and Springer Link. Plant names and their synonyms were validated by 'The Plant List' on 1 October 2021. SA is home to approximately 2247 plant species, including native and introduced plants that belong to 142 families and 837 genera. It shares the flora of three continents, with many unique features due to its extreme climate and geographical and geological conditions. As plants remain the leading supplier of new therapeutic agents to treat various ailments, Saudi Arabian plants may play a significant role in the fight against cancer, inflammation, and antibiotic-resistant bacteria. To date, 102 active compounds have been identified in plants from different sites in SA. Plants from the western and southwestern regions have been evaluated for various biological activities, including antioxidant, anti-cancer, antimicrobial, antimalarial, anti-inflammatory, anti-glycation, and cytotoxic activities. The aerial parts of the plants, especially the leaves, have yielded most of the bioactive compounds. Most bioactivity tests involve in vitro assessments for the inhibition of the growth of tumour cell lines, and several compounds with in vitro antitumour activity have been reported. More in-depth studies to evaluate the mode of action of the compounds are necessary to pave the way for clinical trials. Ecological and taxonomical studies are needed to evaluate the flora of SA, and a plan for the conservation of wild plants should be implemented, including the management of the protection of endemic plants. [ABSTRACT FROM AUTHOR]

Global warming is causing an intensification of extreme climate events with significant changes in frequency, duration, and intensity over many regions. Understanding the current and future influence of this warming in northeastern Brazil (NEB) is important due to the region's greater vulnerability to natural disasters, as historical records show. In this paper, characteristics of climate change projections (precipitation and air temperature) over NEB are analyzed using 15 models of Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios. By using the Taylor diagram, we observed that the HadGEM3-GC31-MM model simulates the seasonal behavior of climate variables more efficiently. Projections for NEB indicate an irreversible increase in average air temperature of at least 1 °C throughout the 21st century, with a reduction of up to 30% in annual rainfall, as present in scenarios of regional rivalry (SSP3-7.0) and high emissions (SSP5-8.5). This means that a higher concentration of greenhouse gases (GHG) will increase air temperature, evaporation, and evapotranspiration, reducing rainfall and increasing drought events. The results obtained in this work are essential for the elaboration of effective strategies for adapting to and mitigating climate change for the NEB. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy source generation is becoming more and more common with a higher penetration level in the smart grid because of PV energy's falling production costs. PV energy is intermittent and uncertain due to its dependence on irradiance. To overcome these drawbacks, and to guarantee better smart grid energy management, we need to deal with PV power prediction. The work presented in this paper concerns the study of the performance of the fuzzy MPPT approach to extract a maximum of power from solar panels, associated with PV power estimation based on short time scale irradiance forecasting. It is particularly applied to a case study of a tropical insular region, considering extreme climatic variability. To validate our study with real solar data, measured and predicted irradiance profiles are used to feed the PV system, based on solar forecasting in a tropical insular context. For that, a spatio-temporal autoregressive model (STVAR) is applied. The measurements are collected at three sites located on Guadeloupe island. The high variability of the tropical irradiance profile allows us to test the robustness and stability of the used MPPT algorithms. Solar forecasting associated with the fuzzy MPPT technique allows us to estimate in advance the produced PV power, which is essential for optimal energy management in the case of smart energy production systems. Simulation of the proposed solution is validated under Matlab/Simulink software. The results clearly demonstrate that the proposed solution provides good PV power prediction and better optimization performance: a fast, dynamic response and stable static power output, even when irradiation is rapidly changing. [ABSTRACT FROM AUTHOR]

While timber harvesting has plateaued, repeat-logging and conversion into plantations (especially oil palm) are still active in the tropics. The associated hydrological impacts especially pertaining to enhanced runoff, flood, and erosion have been well-studied, but little attention has been given to water resource availability in the humid tropics. In the light of the increasing climate extremes, this paper compared baseflow values and baseflow recession constants (K) between headwater catchments of five differing land-uses in Sabah, Malaysian Borneo, namely primary forest (PF), old growth/virgin jungle reserve (VJR), twice-logged forest with 22 years regeneration (LF2), multiple-logged forest with 8 years regeneration (LF3), and oil palm plantation (OP). Hydrological and meteorological sensors and dataloggers were established in each catchment. Daily discharge was used for computing K via four estimation methods. Catchment ranks in terms of decreasing K were VJR (0.97841), LF3 (0.96692), LF2 (0.90347), PF (0.83886), and OP (0.86756). Catchment ranks in terms of decreasing annual baseflow were PF (1877 mm), LF3 (1265 mm), LF2 (812 mm), VJR (753 mm), and OP (367 mm), corresponding to 68%, 55%, 51%, 42%, and 38% of annual streamflow, respectively. Despite the low K, PF had the highest baseflow magnitude. OP had the fastest baseflow recession and lowest baseflow magnitude. Baseflow persistence decreased with increasing degree of disturbance. K showed strong association to catchment stem density instead of basal area. For dynamic catchments in this study, the Kb3 estimator is recommended based on its lowest combination of coefficient of variation (CoV) and root mean squared error (RMSE) of prediction. For wetter catchments with even shorter recession events, the Kb4 estimator may be considered. Regarding climate change, logging and oil palm agriculture should only be conducted after considering water resource availability. Forests (even degraded ones) should be conserved as much as possible in the headwaters for sustainable water resource. [ABSTRACT FROM AUTHOR]

Simple Summary: Vector-borne diseases (VBDs) are a major threat to human health. Climate change has a significant impact on VBDs. To clarify the complex effects of climate change on VBDs, we concluded the effects of climate on the transmission and spread of VBDs from an ecological perspective and summarized VBD changes in response to climate change, specifically including: the nonlinear effects of local climate (temperature, precipitation and wind) on VBD transmission, especially temperature showing n-shape effects; regional climate (the El Niño–Southern Oscillation and North Atlantic Oscillation) has time-lag effects on VBD transmission through indirect impact on local climate; and the u-shaped effect of extreme climates can lead to the geographical spread of VBDs. In terms of non-climatic factors, land use and human mobility through the interactions with climatic factors, will affect transmission and spread of VBD. We further explored the uncertainty of the impact of climate change on VBDs under the COVID-19 pandemic. A systematic understanding of the impact of climate change on the transmission and spread of VBD can provide insights and suggestions for future research on VBD prevention and control. Climate change affects ecosystems and human health in multiple dimensions. With the acceleration of climate change, climate-sensitive vector-borne diseases (VBDs) pose an increasing threat to public health. This paper summaries 10 publications on the impacts of climate change on ecosystems and human health; then it synthesizes the other existing literature to more broadly explain how climate change drives the transmission and spread of VBDs through an ecological perspective. We highlight the multi-dimensional nature of climate change, its interaction with other factors, and the impact of the COVID-19 pandemic on transmission and spread of VBDs, specifically including: (1) the generally nonlinear relationship of local climate (temperature, precipitation and wind) and VBD transmission, with temperature especially exhibiting an n-shape relation; (2) the time-lagged effect of regional climate phenomena (the El Niño–Southern Oscillation and North Atlantic Oscillation) on VBD transmission; (3) the u-shaped effect of extreme climate (heat waves, cold waves, floods, and droughts) on VBD spread; (4) how interactions between non-climatic (land use and human mobility) and climatic factors increase VBD transmission and spread; and (5) that the impact of the COVID-19 pandemic on climate change is debatable, and its impact on VBDs remains uncertain. By exploring the influence of climate change and non-climatic factors on VBD transmission and spread, this paper provides scientific understanding and guidance for their effective prevention and control. [ABSTRACT FROM AUTHOR]

Rice is a major staple food grain for more than half of the world's population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production. [ABSTRACT FROM AUTHOR]

Due to climate change, hydroclimatic extremes are becoming more frequent and intense and their characterization and analysis is essential for climate modelling. One of the regions that will be most affected by these extremes is the Mediterranean coast of Spain. Therefore, this paper analyses the significant wave height (Hs), peak wave period (Tp) and sea level (SL) extremes and their correlation along the Spanish Mediterranean coast. After conducting this analysis, it is finally concluded that and adjustment of the extreme long-term distribution of Tp is urgently needed to create accurate models and projections, which must be considered in combination with the intense extremes happening in the Levantine basin when modelling this area and designing new projects. [ABSTRACT FROM AUTHOR]

Agricultural practices of smallholder farming systems of Latin America can play an important role in reducing their exposure to the risks associated with climate extremes. To date, however, there is no systematic analysis of scientific evidence for the extent to which these practices can provide the multiple benefits needed for smallholders to adapt to climate extremes. In this paper, we searched scientific databases to review scientific evidence of the benefit provided by twenty-six practices in crops commonly farmed by smallholders in the region and highly relevant for their food and nutrition security; namely, coffee, maize and beans. We reviewed scientific documents (n = 304) published in the period 1953–2021 to register evidence of the practices' effects on fifty-five benefits. Our analysis of these documents found measurement records (n = 924) largely based on field experiments (85%). Our results show strong evidence of the multiple benefits that some ecosystem-based practices (e.g., tree-based practices for coffee and no tillage for maize) can provide to support the adaptation to climate extremes of smallholder farming systems and enhance a farm's natural assets (e.g., biodiversity, water, soil). We also found that the majority of research on practices in the region focused more on the socioeconomic dimension (54%) rather than on the capacity of practices to improve the natural assets of a smallholder farmers or reduce the impact of climate extremes. Given these knowledge gaps, we discuss the importance of a renovated investment in research to address existing knowledge gaps. Our concluding suggestions for future research include the need for systematizing existing knowledge from different sources (e.g., peer-reviewed, gray literature, farmers, extension agencies, etc.), and to assess the extent to which these practices can provide multiple benefits for smallholder farming systems by improving their wellbeing, reducing their vulnerability to different hydroclimatic extremes while also contributing to ecosystem services provision at the landscape level. [ABSTRACT FROM AUTHOR]

The impact of climate change on the Niger Delta is severe, as extreme weather events have inflicted various degrees of stress on critical oil/gas infrastructure. Typically, assets managers and government agencies lack a clear framework for evaluating the vulnerability of these systems. This paper presents a participatory framework for the vulnerability assessment of critical oil/gas infrastructure to climate change impacts in the Niger Delta context. Through a critical review of relevant literature and triangulating observational and exploratory data from the field, this paper has developed a conceptual framework with three elements: (1) a preliminary scoping activity; (2) the vulnerability assessment; and (3) mainstreaming the results into institutional asset management codes. Scoping involves the definition of research aims and objectives, review of prevailing climate burdens and impacts, exploratory investigation, screening for new (planned) assets and selection of relevant infrastructure. The emphasis on screening for planned infrastructure is to facilitate the incorporation of sustainable adaptive capacities into the original design of identified systems. A conceptual framework for vulnerability assessment is presented as a robust systematic iterative model for the evaluation of selected assets using an appropriate methodology. In this study, analytic hierarchy process (AHP) is applied while mainstreaming as part of the research framework is emphasised to aid commercial implementation from an expert-based perspective. The study recommends the use of other suitable methodologies and systematic approaches to test the flexibility of the framework. [ABSTRACT FROM AUTHOR]

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent. [ABSTRACT FROM AUTHOR]

The concept of low impact development (LID) plays a crucial role in rainstorm management and non-point source pollution prevention and control. Sorting and summarizing related research through the knowledge map can objectively present the disciplinary structure, research focus, and research hotspots of the LID research. Based on 2103 LID pieces of literature in Chinese and English included in the web of science (WOS) database and China's integrated knowledge resources system (CNKI) database from 2004 to 2021, this paper aims to perform statistical analysis from three aspects: bibliometrics, keyword hotspot co-occurrence and clustering, and literature co-citation clustering. The obtained results reveal that research on LID-based issues maintains a high degree of enthusiasm in China and abroad, but their corresponding focuses are dissimilar. Foreign research essentially focuses on the environmental field with frequent interdisciplinary phenomena, combining the triple goals of water quality improvement, runoff reduction, and multi-functional expansion, and is committed to solving the impact of uncertain factors on urban stormwater management in extreme climates. Chinese research is mostly aimed at unlocking practical engineering problems, which also leads to the majority of research works in the field of building science and engineering. This is mainly due to a series of water-related problems caused by the change in land use types in China. The researchers have determined the type, quantity, location, and combination of the optimal LID measures by establishing appropriate models, using optimization algorithms, and developing multi-level analysis methods. Although the multi-dimensional results of LID in recent years have greatly expanded the framework paradigm, most of the conducted research works are still biased towards the micro-scale. The present hotspot research considers how to make a macroscopic overall layout and efficiently cooperate with the pipelines network, rivers, and lakes systems to unlock the problems pertinent to urban rainwater and non-point source pollution. [ABSTRACT FROM AUTHOR]

Morphodynamic changes in the riverbed may be accelerated by the climate change-induced effects, mostly through the increase of the frequency of extreme climatic events such as floods. This can lead to scouring of the riverbed around the bridge substructure and consequently reduces its overall stability. In order to better understand hydromorphological processes at the local scale, the influence of floods on bridge scour requires a detailed analysis of several interacting flood hydrograph characteristics. This paper presents a multivariate analysis of the annual maximum (AM) flood discharge data at four gauging stations on the Drava and Sava Rivers in Croatia (Europe). As part of the hydrograph analysis, multiple baseflow separation methods were tested. Flood volumes and durations were derived after extracting the baseflow from measured discharge data. Suitable marginal distribution functions were fitted to the peak discharge (Q), flood volume (V) and duration (D) data. Bivariate copula analyses were conducted for the next pairs: peak discharge and volume (Q–V), hydrograph volume and duration (V–D) and peak discharge and hydrograph duration (Q–D). The results of the bivariate copula analyses were used to derive joint return periods for different flood variable combinations, which may serve as a preliminary analysis for the pilot bridges of the R3PEAT project where the aim is to investigate the influences on the riverbed erosion around bridges with installed scour countermeasures. Hence, a design hydrograph was derived that could be used as input data in the hydraulic model for the investigation of the bridge scour dynamics within the project and a preliminary methodology is proposed to be applied. The results indicate that bivariate frequency analysis can be very sensitive to the selected baseflow separation methodology. Therefore, future studies should test multiple baseflow separation methods and visually inspect the performance. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Increased insulation reduces the energy needed during operations, but this may be less than the energy required for the extra insulation material. If so, there must be an optimal insulation thickness. This paper describes the development of a tool to determine the optimal insulation thickness, including what parameters are decisive, and presents some results along with a discussion of the success criteria and limitations. To make these considerations manageable for regular practitioners, only the transmission heat loss through walls is calculated. Although the tool is universal, Greenland is used as an example, because of its extreme climatic conditions. The tool includes climate change, 10 locations and 8 insulation materials. It focuses on greenhouse gas emissions, considers oil and district heating as heating sources, and evaluates four different climate change scenarios expressed in terms of heating degree days. The system is sensitive to insulation materials with high CO2 emissions and heating sources with high emission factors. This is also the case where climate change has the highest impact on the insulation thickness. Using the basic criterion, emitting a minimum of CO2-eq, the Insulation Thickness Optimizer (ITO), generally identifies higher insulation thicknesses as optimal than are currently seen in practice and in most building regulations. [ABSTRACT FROM AUTHOR]

Ecological restoration and climate change in the Loess Plateau region have become research hotspots. Climate change and anthropogenic activities have led to spatial–temporal pattern variations in vegetation and extreme climatic indices and meteorological factors. Therefore, obtaining a better understanding is necessary of the internal relations between vegetation and meteorological factors. In this paper, the interplay between vegetation index and various factors, including extreme climatic indices and meteorological factors, during a long-term time series is investigated using Mann–Kendall trend analysis, and Pearson correlation coefficient analysis. The mechanisms of interaction between vegetation growth and various factors in the Loess Plateau are then analyzed. Results reveal that (i) the rapid growth of vegetation during 2000–2015 has made a major contribution to the growth trend of the Loess Plateau in the past 33 years (1982–2015). During 2000–2015, the increase of vegetation may inhibit the increase of extreme warm index and the decrease of extreme cold index; (ii) a warm and dry climate developed with decreasing relative humidity and increasing temperature; (iii) the normalized vegetation index (NDVI) is strongly correlated with extreme climatic indices and meteorological factors, especially precipitable water vapor (PWV), with a correlation coefficient of 0.94; and (iv) the daily temperature range, diurnal temperature range and sunshine duration (SSD) exerted different time-delay effects on vegetation growth in the Loess Plateau. The above findings provide an essential theoretical basis for ecological policy formulation in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, Δ H , and the corresponding values of the water storage, δ , computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes. [ABSTRACT FROM AUTHOR]

Recent climate changes have prompted changes in the hydrological cycle at a global scale, creating instability when predicting future climate conditions and related changes. Perturbations in global climate models have created the need to concentrate consequent changes in hydro climatic factors to comprehend the regional and territorial impacts of climate and environmental changes. Syria, as a Middle East country, is exposed to extreme climate events such as drought and flood. The aim of this study is to analyze rainfall trends in Syria in response to the likely climate change. The analysis was conducted for rainfall data collected from 71 stations distributed all over the country for the period (1991–2009). The trend analysis was performed in monthly and seasonal scales using Mann–Kendall non-parametric statistical tests. The results attained from Mann–Kendall trend analysis revealed decreasing trends at most of the stations. Additionally, rainfall analysis was conducted for the stations with significant trends for wet and dry periods, which also revealed decreasing trends at almost all the stations. From the analysis of the results, it is obvious that slight increasing trends in rainfall in Syria occurred in the fall period. However, in the winter and spring periods, significant decreasing trends have been observed at almost all the stations. This reveals that the country will suffer from shortage of water, because most rainfall occurs in the winter and spring, infrequently in fall and rarely in summer. The results are consistent with the IPCC's fifth report that predicted a decrease in rainfall in the Mediterranean and southern Asia. The results of this paper could help the management of water resources in Syria considering future climate changes. [ABSTRACT FROM AUTHOR]

In recent years, the number of geological disasters in Sichuan Province has significantly increased due to the influence of earthquakes and extreme climate, as well as the disturbance to the geological environment by human activities. Thus, geological disaster monitoring is particularly important, which can provide some scientific basis for disaster prevention and reduction. In this paper, the interferometric synthetic aperture radar (InSAR) technology was introduced to monitor potential geological hazards, taking parts of Dujiangyan City, Wenchuan County, and Mao County in Sichuan Province, China as examples. Firstly, the data such as Sentinel-1A Terrain Observation with Progressive Scans (TOPS) Synthetic Aperture Radar (SAR) images and Precision Orbit Determination (POD) precise orbit ephemerides from 2018 to 2020, high-resolution optical satellite images and Digital Elevation Model (DEM) were collected. Secondly, the Differential InSAR (D-InSAR), Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), Offset-Tracking, and Distributed Scatterer InSAR (DS-InSAR) algorithms were used to invert the surface deformation of the study area. Thirdly, according to the deformation results obtained by experiments, we used GF-1 and GF-2 optical images as a reference and combine the experimental results of InSAR algorithms to delineate the areas affected by geological disasters. A total of 49 geological disaster areas were obtained, mainly including landslides, collapses, and debris flow. Through field verification, the overall accuracy rate of InSAR deformation monitoring achieved 69.23%, and the accuracy rate of new potential hazards monitoring reached 63.64%. Among all InSAR methods, the DS-InSAR method outperformed and monitored the geological disaster areas well. Finally, the study area was divided into three elevation intervals and the applicability of different InSAR algorithms in different elevation intervals was discussed. [ABSTRACT FROM AUTHOR]