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This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana's contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana. [ABSTRACT FROM AUTHOR]

Climate change is a major threat to the Middle East and North Africa (MENA) region, which can cause significant harm to its plant and animal species. We predicted the habitat distribution of Cataglyphis nodus (Brullé, 1833) in MENA using MaxEnt models under current and future climate conditions. Our analysis indicates that the cooler regions of the MENA are projected to experience temperature increases of 1–2 °C by 2040 and 2–4 °C by the 2070s. Similarly, the warmer regions may anticipate rises of 0.5–2 °C by 2040 and 2–4 °C by the 2070s. MaxEnt model results for the current climate show good agreement with observations (mean area under the curve value of 0.975 and mean true statistical skill value of 0.8), indicating good potential habitat suitability for C. nodus. Significant factors affecting habitat suitability are elevation, mean monthly precipitation of the coldest quarter, temperature seasonality, and precipitation amount of the driest month. The research predicts that under Shared Socioeconomic Pathway (SSP) 1.2.6, the habitat suitability area may increase by 6% in 2040, while SSP 3.7.0 (0.3%) and SSP 5.8.5 (2.6%) predict a decrease. For 2070, SSP 5.8.5 predicts a 2.2% reduction in habitat suitability, while SSP 1.2.6 (0.4%) and SSP 3.7.0 (1.3%) predict slight increases. The results provide insight into the potential impacts of climate change on the species and regional biodiversity changes associated with the projected species distribution. [ABSTRACT FROM AUTHOR]

Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks, integrate the MCE-CA-Markov and InVEST models to evaluate the spatiotemporal variations of carbon stocks in mid- to high-latitude alpine watersheds in China under historical and future climate scenarios. Additionally, the study employs the Geographic Detector model to explore the driving mechanisms influencing the carbon storage capacity of watershed ecosystems. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Fractional Vegetation Cover (FVC), Digital Elevation Model (DEM), and Mean Annual Temperature (MAT) had the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the watersheds, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the watershed's southwest part. Therefore, given the vulnerability of mid- to high-latitude mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid- to high-latitude watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change. [ABSTRACT FROM AUTHOR]

Abstract This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana’s contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

Abstract The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.

Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions. [ABSTRACT FROM AUTHOR]

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC's) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province's five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. [ABSTRACT FROM AUTHOR]

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on water resources, particularly on runoff. In the present study, impact of climate change on runoff under different shared socio-economic pathways (SSPs) of CMIP6 is analysed over the Parvati catchment of Rajasthan using MIKE11 NAM model. The ensemble of best 3 global climate models (GCM) out of 13 GCMs is taken to generate future climate projections of rainfall, maximum temperature (Tmax), and minimum temperature (Tmin). MIKE11 NAM model is used for runoff simulation. The results showed a good match between observed and model simulated runoff with Nash-Sutcliff efficiency, percent of bias, coefficient of determination, and root mean square error as 0.79, − 2%, 0.79, and 4.95 during model calibration (2005–2013) and 0.67, − 12.4%, 0.68, and 0.83 during model validation (2014–2020). The changes in runoff during three future periods FP1 (2023–2039), FP2 (2046–2064), and FP3 (2081–2099) under three SSPs (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are compared with base period (2005–2020). The projected rainfall, Tmax, and Tmin showed an increasing trend in future. Similarly, future simulated runoff is also found to increase by 2.08–3.78, 3.47–5.15, and 3.13–5.82% during FP1, FP2, and FP3, respectively. The runoff is found to increase during monsoon and pre-monsoon season, whereas decrease in runoff is found during post-monsoon and winter season. These changes in the runoff pattern suggest the need to develop better irrigation management strategies for crop planning and may help planners and policy-makers for effective water resources management. [ABSTRACT FROM AUTHOR]

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation. [ABSTRACT FROM AUTHOR]

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Extreme precipitation events, especially climatic heavy precipitation-induced (CHP-induced) floods, pose substantial negative impacts on a myriad of socio-economic drivers. Incidents related to extreme precipitation events result in revenue loss, thereby negatively affecting national economies, and there is a growing need to advance studies that promote early warning and rapid resilience building across West Africa. Despite the region’s vulnerability to climate change, studies on flood events have barely compared multiple indices in order to provide a true representation of meteorological floods in the region Using multiple precipitation indices (Standardized Rainfall Anomaly, Standardized Precipitation Index and Rainfall Anomaly Index), CHP-induced flood events were assessed both historically and into the far-future. The projections were estimated under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0 and SSP5-8.5) and over three demarcated zones of West Africa (Guinea Coast [GC], Savanna [SAV] and Sahel [SAH]). The findings from the study indicate an expected intensification in future flood potential, particularly in the Savanna and Sahel. Contrastingly, flood potential in the Guinea Coast is projected to remain nearly similar to the historical records. On the impacts of emissions scenarios on projected CHP-induced floods, an increase in the mean flood count and maximum flood could potentially result from rising emission concentrations. The findings of the study are useful for advancing current understanding of CHP-induced floods over West Africa, and planning effective mitigation and adaptation strategies to build resilience in the face of current to future episodes.

The urban population of the world houses almost 13% in low-elevation coastal zones (LECZ). Major cities with a population of more than five million, which account for almost two-thirds of the world's large cities, reside in low-elevation coastal zones. These areas house some of the most important socioeconomic activities, and they are also environmentally delicate. According to the IPCC 2007 report, there will be an 8–88 cm rise in sea level between 2000 and 2100 AD, which will have an unpredictable and massive impact on land up to 9 m elevation above MSL. This study discusses the rise in sea level, climate vulnerability, and future problems using elevation-based GIS analysis, accomplishing socioeconomic vulnerability and exposure. The statistics suggest that more than 26% of the population lies within a 9 m contour interval, whereas a 20 m contour accommodated 81% of the total population. In figures, it is estimated that over 1,632,200 people by 2011, which is expected to rise to 4,048,894 people by 2050, are exposed to 100-year flood events. Thus, the study clinched that a significant number of people in Surat confront flooding due to the extreme rise in water level during this period. HIGHLIGHTS Approximately 81.10% district population lived within 20 m of MSL in 2011.; The major population is accumulated within 9–20 m range approximately 54.41%.; By 2080, in worst-case SSPs585 (80 cm rise), approximately 9,536,712 individuals with US $3,004.35 billion value of assets will be subjected to 1 in 100-YRP.; For SSPs245, 3.617 m SLR would put around 4,168,260 individuals and assets worth over US$710.73 billion at risk of flooding by 2050.;

Study region: Mokgam River watershed, South Korea Study focus: In this study, the uncertainty contribution of three sources and their interaction effects on future climate and runoff projections were quantified. General circulation models (GCMs), shared socioeconomic pathways (SSPs), and bias correction (BC) methods were considered as the three sources. 20 GCMs under four SSPs (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) were used to project the future climate of the study area. Seven BC methods were used to adjust the GCMs’ daily climate data. The storm water management model (SWMM) was used as a hydrological model to simulate runoff, incorporating both natural and conduit flows according to GCMs’ climate projection. The normalized Nash-Sutcliffe efficiency (NNSE), normalized root mean square error (NRMSE), Kling-Gupta efficiency (KGE), and modified index of agreement (MD) were used to evaluate the performance of the GCMs’ climate simulations and the SWMM runoff simulations, which were based on the GCMs’ climate data. The analysis of variance (ANOVA) method was used to quantify the uncertainty. New hydrological insights for the study region: The results showed that the assumptions of the BC method had a significant impact on the variation in climate and runoff projections. In the uncertainty of future climate and runoff projection results, BC methods exhibited the predominant contribution, while SSPs showed the least contribution. However, the uncertainty contribution from SSPs and GCMs was predominant in temperature projections, and these results could vary depending on the assumptions and the number of BC methods used. Overall, this study emphasizes not only the influence of GCMs but also the impact of BC methods on future climate and runoff projections.

Drought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security. Plain Language Summary: Estimating drought‐induced cropland exposure is the pivotal aspect of agricultural risk assessment for drought impacts. The purposes of this study are to explore (a) how much of the cropland area would be exposed to drought under SSP scenarios; and (b) the degree to which climate change and cropland change effects contribute to changes in exposure. In this regard, we conducted a multi‐model and multi‐scenario‐based analysis to reveal variations in the cropland area exposed to drought in South Asia. Among the seven scenario combinations, the highest percentage of cropland exposed to drought was found at 49.2% (SSP3‐7.0) in the mid‐term epoch (2041–2060). The climate change effect seems to be the key contributor in the near‐term to mid‐term period, whereas it is the cropland change effect long‐term. To reduce drought risk, we recommend focusing on strong climate change mitigation policy development in the near‐ and mid‐term periods at a global scale and strict land‐use management policy interventions in the long‐term period at the regional level. Key Points: Larger cropland area will experience drought in the 2040–2060 period, while it will decline by the end of 21st centuryThe exposed cropland will increase by 49.2% under SSP3‐7.0 than that of historical timeThe climate change effect is the pivotal contributor in exposure changes over South Asia [ABSTRACT FROM AUTHOR]

Climate extremes pose significant risks to human health, agriculture, and water resources. These extremes are defined as long-term, unusual events that fall into the 10th or 90th percentile of a probability density function derived from observations at a certain location (e.g., drought and wildfire). The quantification of future climate risks is based on climate model predictions. Here, we present a review of literature focusing on extreme climate projections in the latest generation of climate models, namely, Coupled Model Intercomparison Project Phase 6 (CMIP6) from 2020 to the present. We highlight the extreme events that could cause potential societal risks, including precipitation (the 90th percentile of the cumulative frequency distribution of daily precipitation), temperature (the 10th or 90th percentile of daily temperature within a reference period), droughts (meteorological, hydrological, and agricultural), floods, heat waves, and compound/concurrent extremes. Regionally, the precipitation extremes are projected to increase in North Africa (Ethiopia, Uganda, and Kenya), followed by drying in South Africa. Heatwaves will increase in a warming scenario (SSP3-7.0) in Asia (Indo-Gangetic Plain) and Afghanistan. The rise in heat stress intensity in Asia will augment the climate risks to agriculture under the SSP2-4.5 and SSP5-8.5 scenarios. On a global scale, land areas are projected to face severe drought specifically in higher biomass regions, under the SSP5-8.5 scenario. Future droughts bring hazards to Europe and the Amazon River basin with severe aridification over Australia, the Middle East, South and North Africa, and Central Asia. The CMIP6 model projections on a regional and global scale over the US Southwest predict intense drought and hot dry summers. The study supplements the discussion section by providing insights on sources of uncertainty in extreme event projections, the role of emergent constraints in uncertainty reduction, and the impact of extremes on water resources, agriculture, and human health. [ABSTRACT FROM AUTHOR]

Droughts have a severe impact on the environment and social economy, and predicting their future changes is challenging due to significant uncertainties in climate change and human activities. Many countries have pledged to achieve carbon neutrality to limit global warming; however, few studies have focused on drought changes during the carbon-neutral period. Here, we analyzed the variations in drought characteristics across the Yellow River Basin (YRB) during the carbon-neutral period under two low-emission scenarios from 7 CMIP6 model outputs. The results show that the temperature and precipitation will increase significantly during the 2015–2100 period under both SSP1-1.9 and SSP1-2.6 scenarios. Compared to the historical period (1979–2014), the hydrological drought frequency is projected to decrease by 15.5% (13.0–18.1%), while drought severity is expected to increase by 14.4% (13.2–15.7%) during the carbon-neutral period. Meteorological droughts exhibit a similar changing trend, although the results vary between different regions. In general, more severe hydrological droughts may occur in the southern YRB in the carbon-neutral period under low-emission scenarios. This study has implications for future drought mitigation within the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Climate change is a critical issue in the 21st century. Assessment of the impacts of climate change is beneficial for assisting advanced recommendations for adaptations. Climate change impact assessments require high quality local-scale climate scenarios. The future climate projections from Global Climate Models (GCMs) are problematic to use at local scale due to their coarse spatial and temporal resolution, and existing biases. It is important to have climate change scenarios based on the GCMs ensemble downscaled to local scale to account for inherent uncertainty in climate projections, and to have a sufficient large number of years to account for inter-annual climate variability and low frequency, but high impact, extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across Great Britain based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator, LARS-WG 8.0. The data set consists of climate scenarios of daily weather of 1,000 realizations of typical years for a baseline, and very near (2030) and near-future (2050) climates, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 8.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of daily weather for the baseline, as well as for very near and near-future climate change scenarios, are essential for estimating inter-annual variation, and for detecting low frequency, but high impact, extreme climatic events, such as heat waves, floods and droughts. The present dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessments of climate change and decision support for mitigation and adaptation to climate change.

In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario. HIGHLIGHTS The output of five Global Climate Models (GCMs) was used to extract climate variables for SSP2-4.5 and SSP5-8.5 emission scenarios.; Temperature and precipitation systematic errors were corrected using the distribution mapping bias correction approach.; We integrated the bias-corrected climate variables with a calibrated SWAT model to evaluate streamflow response due to the impact of climate change.;

Climate change is a major threat to the Middle East and North Africa (MENA) region, which can cause significant harm to its plant and animal species. We predicted the habitat distribution of Cataglyphis nodus (Brullé, 1833) in MENA using MaxEnt models under current and future climate conditions. Our analysis indicates that the cooler regions of the MENA are projected to experience temperature increases of 1–2 °C by 2040 and 2–4 °C by the 2070s. Similarly, the warmer regions may anticipate rises of 0.5–2 °C by 2040 and 2–4 °C by the 2070s. MaxEnt model results for the current climate show good agreement with observations (mean area under the curve value of 0.975 and mean true statistical skill value of 0.8), indicating good potential habitat suitability for C. nodus. Significant factors affecting habitat suitability are elevation, mean monthly precipitation of the coldest quarter, temperature seasonality, and precipitation amount of the driest month. The research predicts that under Shared Socioeconomic Pathway (SSP) 1.2.6, the habitat suitability area may increase by 6% in 2040, while SSP 3.7.0 (0.3%) and SSP 5.8.5 (2.6%) predict a decrease. For 2070, SSP 5.8.5 predicts a 2.2% reduction in habitat suitability, while SSP 1.2.6 (0.4%) and SSP 3.7.0 (1.3%) predict slight increases. The results provide insight into the potential impacts of climate change on the species and regional biodiversity changes associated with the projected species distribution.

Fitting probability distribution functions to observed data is the standard way to compute future design floods, but may not accurately reflect the projected future pattern of extreme events related to climate change. In applying the latest coupled model intercomparison project (CMIP5 and CMIP6), this research investigates how likely it is that precipitation changes in CMIP5 and CMIP6 will affect both the magnitude and frequency of flood analysis. GCM output from four modelling institutes in CMIP5, with representative pathway concentration (RCP8.5) and the corresponding CMIP6 shared socioeconomic pathways (SSP585), were selected for historical and future periods, before the project precipitation was statistically downscaled for selected cities by using delta, quantile mapping (QM), and empirical quantile mapping (EQM). On the basis of performance evaluation, a rainfall-runoff hydrological model was developed by using the stormwater management model (SWMM) for CMIPs (CMIP5 and CMIP6) in historical and future horizons. The results reveal an unprecedented increase in extreme events, for both CMIP5 (historical) and CMIP6 (future) projections. The years 2070–2080 were identified by both CMIP5 and CMIP6 as experiencing the most severe flooding. [ABSTRACT FROM AUTHOR]

From the middle of the 20th century, the majority of the world's highest quality wine-producing regions experienced an increase in temperatures during the growing season. Even if, through technological processes, this issue has been seen as an improvement, recent changes of climatic factors (mainly temperature, sunshine duration and rainfall) show a worrying trend. This study reveals the impacts of climate change in one of the oldest and most important vineyards in Eastern Europe (Odobești vineyard in Romania) on the quality of grapes. The varieties taken into study are ‘Șarbă, Băbească gri’ and ‘Fetească regală’, the first two being considered new created cultivars, while the latter is found on the largest areas of viticultural use. Grapevine phenology and composition were analysed according to literature and standard OIV regulations. Climatic data was gathered, throughout 50 years (1971-2021), by using a weather station of the Odobești Viticulture and Wine-making Research Development Station. A climatic assessment using Shared Socioeconomic Pathways (SSPs) was achieved, focusing on two SSPs (SSP1-1.9 and SSP5-8.5). The climatic predictions show that for the analysed region, phenophases will change due to a temperature increase of over 1.5 oC, leading to an acceleration of 15 (SSP1-1.9) or 24 days (SSP5-8.5) in the case of grape maturation. The hastening of the yearly vine life cycle is correlated to a wide range of undesirable effects, among which an unequilibrated physicalchemical composition of fruits for wine production is considered a sore point in wine sector economy. [ABSTRACT FROM AUTHOR]

Abstract Drought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security.

From the middle of the 20th century, the majority of the world's highest quality wine-producing regions experienced an increase in temperatures during the growing season. Even if, through technological processes, this issue has been seen as an improvement, recent changes of climatic factors (mainly temperature, sunshine duration and rainfall) show a worrying trend. This study reveals the impacts of climate change in one of the oldest and most important vineyards in Eastern Europe (Odobești vineyard in Romania) on the quality of grapes. The varieties taken into study are ‘Șarbă, Băbească gri’ and ‘Fetească regală’, the first two being considered new created cultivars, while the latter is found on the largest areas of viticultural use. Grapevine phenology and composition were analysed according to literature and standard OIV regulations. Climatic data was gathered, throughout 50 years (1971-2021), by using a weather station of the Odobești Viticulture and Wine-making Research Development Station. A climatic assessment using Shared Socioeconomic Pathways (SSPs) was achieved, focusing on two SSPs (SSP1-1.9 and SSP5-8.5). The climatic predictions show that for the analysed region, phenophases will change due to a temperature increase of over 1.5 oC, leading to an acceleration of 15 (SSP1-1.9) or 24 days (SSP5-8.5) in the case of grape maturation. The hastening of the yearly vine life cycle is correlated to a wide range of undesirable effects, among which an unequilibrated physical-chemical composition of fruits for wine production is considered a sore point in wine sector economy.

Understanding the potential impact of anthropogenic climate change on the hydrological regime of the Koshi River Basin (KRB) is very important for sustainable water resources and ecosystem management. The hydrological studies are mainly focused on the annual, seasonal, and peak flows of the hydrological regime; however, the ecologically relevant flows of the hydrological regime are less explored. In this study, we analyzed the different flow characteristics based on the magnitude, intensity, and duration using the Indicator of Hydrologic Alterations (IHA) under the influence of shared socio-economic pathway (SSP) scenarios. We found that the KRB will experience a shift in hydro-climatic events, an increase in rise and fall rates of flow, increase in monthly low flows of the hydrological regime, eventually affecting the livelihoods and ecosystem of the basin. This study highlights the importance of environmental flow components (EFCs) in a hydrological regime to better understand the flow characteristics during the future hydro-climatic variability. HIGHLIGHTS IHA and EFCs were grouped into five based on magnitude, intensity, and duration.; Increased uncertainty has increased the chances of untimely extreme events in the KRB.; Future post-monsoon and winter streamflow increases, sustaining the water table and aquatic life.;

Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions.

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC’s) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province’s five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings.

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation.

Introduction: Heatwaves (HWs) are the serious natural disaster that exert great impacts on human health and social economy. Projecting future changes in HWs is crucial for the development of effective adaptation strategies.Method: This study investigates the variations of HWs in Xinjiang for three time periods (near-term, mid-term, and long-term) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using multi-model ensemble (MME). To enhance confidence in HWs projection, we evaluate the performance of 17 climate models in simulating HWs in terms of interannual variability and spatial patterns using interannual variability skill score (IVS) and distance between indices of simulation and observation (DISO), respectively, and generate MME using the overall performance.Results: Compared to the observed data, the overall performance of MME outperforms most of individual models, although many models effectively capture the characteristics of HWs. Projections indicate that HWs in Xinjiang will become more longer lasting and severe. Specifically, heatwave frequency (HWF) and heatwave duration (HWD) are projected to reach 7.5 times and 61 days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of HWs exhibits significant heterogeneity, with high value regions primarily distributed in eastern Xinjiang and the eastern part of southern Xinjiang. Although the HWs area is projected to expand under all scenarios, the spatial pattern is anticipated to remain largely unchanged.Conclusion: These findings provide a comprehensive assessment of future variations in HWs, which are necessary for improving regional adaptive capacity to extreme heat risk.

Climate change has emerged as a global challenge with varied impacts across regions. The Vea catchment, for instance, is identified as particularly susceptible to climate change due to its location and ecological fragility. This study projects climate change's impact on the area's temperature, precipitation, and stream flow. Data on temperature and precipitation obtained from the Ghana Meteorological Agency were augmented by incorporating precipitation data from twelve satellite sources using CHIRPS data with a 0.05′ (5km) resolution to complement the existing in-situ datasets. The temperature data for the satellite station was sourced from the NASA power project. Modelled climate data from six GCMs, considering SSP 4.5 and SSP 8.5 emission scenarios, were retrieved from the CMIP6 data portal. R software facilitated the extraction of NetCDF-format GCM data to Excel format, and CMhyd was applied for downscaling GCM data. Temperature and precipitation bias correction utilized the variance and local intensity scaling methods, respectively. The statistical significance of changes and trends in temperature, precipitation and stream flow was determined using the Mann-Kendall (MK) method. The results indicate that temperature is expected to increase on average between 2.10 and 3.5°C under SSP4.5 and between 2.7 and 4.15°C under SSP8.5. For SSP4.5, the anticipated decrease in annual average precipitation ranges between 12.34 and 13.1%, while SSP8.5 projects a decrease of 12.6 to 13.6%. Under SSP4.5 and SSP8.5, the projected annual average stream flow will decrease by 28 to 37% and 36 to 42%, respectively. These findings suggest the need for the formulation of long-term water resource management strategies, and existing strategies should be strengthened to address the impact of climate change on the catchment.

AbstractLULC and climate change are the two most common manifestations of global environmental change. The objective of this study was to quantify past and future LULC and climate change impacts on environmental sustainability in the Vea catchment of Ghana. The past LULC change (1998–2022) was analyzed using ERDAS Imagine 15 upon the application of the Support Vector Machine (SVM) classifier algorithm. The CA-Markov was used to predict land-use changes in 2038 and 2054. Climate change was projected under SSP 4.5 and SSP 8.5 emission scenarios using the R software. The projections from an ensemble mean of SSPs in the near future (2023–2052) and far future (2071–2100) were compared to the reference period (1993–2022). The results show that, between 1998 and 2022, cropland increased from 10.9% to 51.98%, while grassland and forest areas decreased from 54.8–18.14% and 31.7–22.73%, respectively. The results of the predicted LULC change showed an increase in cropland from 181 km2 in 2038 to 183 km2 in 2054 at the expense of grassland and forest, which are expected to decrease from 51–50 km2 and 73–71 km2, respectively. The findings of climate change show the projected increases in average temperature range from 2.10 to 3.5 °C under SSP4.5 and from 2.7 to 4.15 °C under SSP8.5. Under the SSP4.5, annual average precipitation is anticipated to decrease by 12.34–13.1%, while the SSP8.5 projects a decrease of 12.6–13.6%. Results will help in the setting up of long-term environmental management strategies in the catchment as well as the development of appropriate adaptation methods to mitigate the ongoing impacts of LULC and climate change on environmental sustainability.

Study region: Haihe River Basin (HRB) in North China Plain. Study focus: Changes in streamflow due to climate change and human activities are highly uncertain. An improved Soil and Water Assessment Tool (SWAT) model equipped with a dynamic CO2 input method was used to quantify the environmental change impacts on streamflow in the HRB. Streamflow changes were analyzed based on 22 bias-corrected CMIP6 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 during two 30-year periods of the middle (2041–2070) and end (2071–2100) of the 21st century relative to the historical period (1971–2000). New hydrological insights for the region: Long-term simulations of annual streamflow in the HRB demonstrated substantial discrepancies between the dynamic and constant CO2 input methods under the SSP1-2.6 and SSP5-8.5 scenarios, which highlighted the importance of using the dynamic CO2 input method for streamflow simulations. Spatio-temporal analysis using the improved SWAT model revealed that streamflow generally increased under four emission scenarios compared to the historical period. In the HRB, annual streamflow in the downstream plains was higher than in the upstream mountains, and this was more evident under the highest emission scenario of SSP5-8.5. Monthly streamflow showed considerable intra-annual variability that was generally greater from July to November. The strongest increment in streamflow was projected in the SSP3-7.0 and SSP5-8.5 scenarios, indicating that flood risk would possibly increase in the late 21st century.

The world is currently in the midst of an energy transition, in which renewable and low-carbon energy is replacing the use of fossil fuels. Along the way, however, planning for and adapting to impacts of climate change is urgently needed, as these are projected to intensify in the future, despite ambitious mitigation efforts. Since the low-carbon energy transition is likely to involve many international interdependencies and connections between countries and regions, assessments of cross-border impacts of climate change, i.e., consequences of climate change that occur remotely from the location of their initial impact, are of utmost importance to ensure the decarbonisation of society is safe and sustainable. This paper utilises expert interviews and a general morphological analysis with the shared socioeconomic pathways to situate national decarbonisation efforts within a global context and identify cross-border impacts of climate change that may affect the energy transition, using the Finnish energy sector as a case study. Interestingly, many of the global development trends that were found to have a boosting effect on the Finnish energy transition, also increased the risk from cross-border climate change impacts, stressing the importance of rigorous adaptation planning. The findings affirm the need for studying national energy transitions from a global perspective and highlight the tendency of climate change impacts to be transmitted across borders via complex pathways. The study offers valuable insights into the importance of cross-border impacts for adaptation planning pertinent to any country or region currently engaged, or planning to engage, in the global low-carbon transition. [ABSTRACT FROM AUTHOR]

Climate change is the most important problem of the earth in the current century. In this study, the effects of climate change on precipitation, temperature, wind speed, relative humidity and surface runoff in Saghez watershed in Iran investigated. The main methods were using the Coupled Model Intercomparison Project phase 6 (CMIP6), the Soil and Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN) model under the Shared Socio-economic Pathway scenarios (SSPs) using the Linear Scaling Bias Correction (LSBC) for the future period (2021–2050) compared to the base period (1985–2014). Additionally, MAE, MSE, RMSE and R2 indices used for model calibration and validation. The average projected precipitation was forecasted to decrease by 6.1%. In terms of the temperature, 1.4 Cº, and 1.6 Cº increases were predicted for minimum and maximum temperatures, respectively. Prediction of surface runoff using the SWAT model also illustrated that based on SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios, runoff will decrease in the future period, which based on three mentioned scenarios is equals to 17.5%, 23.7% and 26.3% decrease, respectively. Furthermore, the assessment using the artificial neural network (ANN) also showed that the parameters of precipitation in the previous two days, wind speed and maximum relative humidity have the greatest effect on the watershed runoff. These findings may be helpful to reduce the impacts of climate change, and make the suitable long-term plans for management of the watersheds and water resources in the region. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities. [ABSTRACT FROM AUTHOR]

Droughts have a severe impact on the environment and social economy, and predicting their future changes is challenging due to significant uncertainties in climate change and human activities. Many countries have pledged to achieve carbon neutrality to limit global warming; however, few studies have focused on drought changes during the carbon-neutral period. Here, we analyzed the variations in drought characteristics across the Yellow River Basin (YRB) during the carbon-neutral period under two low-emission scenarios from 7 CMIP6 model outputs. The results show that the temperature and precipitation will increase significantly during the 2015–2100 period under both SSP1-1.9 and SSP1-2.6 scenarios. Compared to the historical period (1979–2014), the hydrological drought frequency is projected to decrease by 15.5% (13.0–18.1%), while drought severity is expected to increase by 14.4% (13.2–15.7%) during the carbon-neutral period. Meteorological droughts exhibit a similar changing trend, although the results vary between different regions. In general, more severe hydrological droughts may occur in the southern YRB in the carbon-neutral period under low-emission scenarios. This study has implications for future drought mitigation within the Yellow River Basin.

Our study was designed to spatially assess the regulation potential of watershed water-land resources, and determine its regulation objectives for subsequent optimal allocation. We centred around the Yiluo River Basin, being a soil conservation demonstration plot in the middle course of the Yellow River Basin. In accordance with the criteria of the green development, the high efficiency and the safety, the evaluation system of water-land resources regulation potential was established. Then the fuzzy comprehensive evaluation method was applied to assess the watershed water-land resources during 2001–2020 and 2030–2050, and the evaluation results were represented by the eigenvalue T. The research findings pointed out that the assessment grade of watershed water-land resources was mainly moderate and better state, and presented significant spatial agglomeration features in the historical and forecast years. In the context of dual influences of climatic variation and water-land resources exploitation, the assessment eigenvalue T of watershed water-land resources increased by 6% in the historical periods, and decreased by 4.57 ∼ 7.74% in the future periods. Based on the assessment results, it was recommended to restrict the water-land resources exploitation in the upstream, control the soil erosion in the midstream, and coordinate the relationship between the exploitation and protection of water-land resources in the lower reaches. Our study offers scientific evidence for the elaborate regulation of watershed water-land resources.

Crop productivity is a highly vulnerable criterion to drought onset and offset. Therefore, it is necessary to assess the crop-drought relationship from the perspective of climate change for sustainable development in agricultural practices. Aurangabad and Pune districts in the state of Maharashtra are considered as the study area. The temporal evolution of the Standardised Yield Residual Series (SYRS) is investigated, and then the impact of the detrended Standard Precipitation Evapotranspiration Index (SPEI) on different crops was studied. The future crop yields for three crops maize, cotton, and wheat, are projected based on the AquaCrop model by considering the bias-corrected ensemble Coupled Model Intercomparison Model Project Phage 6-Global Climate Models (CMIP6-GCMs) under four different Shared Socioeconomic Scenarios (SSPs). The outcomes from the simulation indicated a high increase in crop yield, especially in the high-emission scenario (SSP585). The increase in crop productivity could be attributed to the favourable thermal range, enhanced CO2 concentration, and increase in water productivity of crops. SPEI has a moderate association with the SYRS at various crop productivity phases. The greatest yield-drought relationship existed for wheat and the least for maize during the study period for the Aurangabad region for the Reference Period (RP). Cotton is expected to be more sensitive to drought onset in the future for the Pune region. This approach can assist stakeholders to understand better the impact of drought on the agricultural sectors, which is critical for reducing adverse situations related to drought. [ABSTRACT FROM AUTHOR]

Drought projection is critical for water resource planning and management, as well as disaster prevention and mitigation. As a strategic national water source for China, the Yangtze River Basin (YRB) plays a vital role in the connectivity of rivers and economic development, flowing through 11 provincial administrative regions and is injected into the East China Sea, with a total length of 6,397 km. The watershed covers an area of 1.8 million square kilometers, accounting for about 1/5 of China's total land area. However, frequent droughts have caused water shortages in the YRB in recent years. Based on observed meteorological and hydrological data, the CMIP6 model and SPEI (standardized precipitation evapotranspiration index) drought models were used to elucidate the risk of future simultaneous droughts in the upper and mid-lower reaches of the YRB from 2015 to 2100. SRI has been used based on SWAT model to study the transfer process of meteorological drought to hydrological drought. The results indicated that, (1) The average of 10 CMIP6 models showed a good verification of historical precipitation and temperature for drought predictions. The MMK and Sen's slope demonstrated consistency for historical and future droughts in the YRB. From a historical perspective (1961–2019), the middle reaches of the YRB experienced intensifying drought frequency with the highest total drought (Moderate and above drought events) frequency (> 17%); (2) In the future (2020–2100), the higher emission signifies higher moderate and total drought frequency, intensity, and scope of the YRB in FF, lower in NF. The ratio of autumn severe and extreme droughts would increase in mid-twenty-first century; (3) Severe drought risk encounters were projected in the upper and meanwhile in the middle-lower reaches in YRB, especially in the 2030–2040 period. Under all three scenarios, severe droughts occurred more frequently with SPEI close to − 2. The middle-lower reaches of the YRB are forecast to witness the largest scope and highest intensity of drought under the SSP1-2.6 scenario.; (4) The future runoff in the YRB during the dry period varied less, but in May and June during the main flood season the runoff under SSP1-2.6 would be the largest. Maximum decrease in runoff in the mid-lower reaches under the SSP2-4.5 scenario would be 2045, reaching 13.9%. Extreme flooding events and extreme meteorological droughts would happen accompanying with hydrological droughts would occur more frequently and severely under different scenarios. More attention and improved strategies should be brought to bear to address future simultaneous droughts in the upper and mid-lower YRB. [ABSTRACT FROM AUTHOR]

Based on historical land use for eight periods from 1980 to 2020 and the projected land use under seven Shared Socioeconomic Pathways (SSPs: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5) from 2021 to 2100, we conducted a study on past and future land use changes in the Qinghai-Tibet Plateau (QTP). This work aims to reveal the land use changes during the carbon peak (2021–2040) and carbon neutrality (2051–2070) periods and at the end of the 21st century (2081–2100). The results show that: (1) in the historical period (1980–2020), the land use types in the QTP were grassland (1475×103 km2, 58.2%), barren land (685×103 km2, 27.0%), forest land (243×103 km2, 9.6%), water (114×103 km2, 4.5%), cropland (18.6×103 km2, 0.7%) and urban land (0.3×103 km2, 0.01%). (2) Relative to the baseline period (1995–2014), the area of grassland is projected to decrease by 0.7% (SSP4-6.0)–5.4% (SSP2-4.5) (0.5–3.9% of the total area of the QTP), 2.8% (SSP4-6.0)–12.5% (SSP3-7.0) (2.1–9.4% of the total area of the QTP) and 6.1% (SSP4-6.0)–21.7% (SSP4-3.4) (4.6–16.4% of the total area of the QTP) in the future three periods. In contrast, the forest land area is projected to increase, by approximately 2.5% (SSP4-6.0) to 30.1% (SSP3-7.0) (0.3–4.3% of the total area of the QTP), 9.2% (SSP4-6.0) to 56.5% (SSP2-4.5) (1.3–8.0% of the total area of the QTP), and 21.2% (SSP4-6.0) to 72.8% (SSP2-4.5) (3.0–10.2% of the total area of the QTP) in the future three periods, respectively. (3) Approximately 0.4 (SSP4-6.0) to 6.9% (SSP5-8.5), 0.9 (SSP4-6.0) to 2.7% (SSP4-3.4), and 0.04 (SSP5-8.5) to 3.5% (SSP1-1.9) of land is expected to convert from grassland to forest land in the future three periods, respectively. The shift from grassland to forest land area is likely to enhance the carbon sink potential of the QTP in the future period. [ABSTRACT FROM AUTHOR]

Precipitation is the main component of the hydrological cycle. It has a significant effect on the ecosystem especially, irrigation and drainage system design and management, crop production, and flood and drought management. In this study, 11 global circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) were investigated under three shared socioeconomic pathway (SSP) scenarios for precipitation simulation and projection in the future period (2021–2050). Then, the results were compared with the base period (1985–2014). The research was conducted in the MENAP region. The evaluation of GCMs’ performances by the Taylor diagram, R2, MSE, MAE, and RMSE indices showed that among the 11 models, the MPI-ESM1-2-HR model with an average R2 and RMSE of 0.6 and 18.9, respectively, was more accurate than other models in precipitation simulation in the entire region. The projection of precipitation indicated that the precipitation will mainly decrease except in areas such as the Black Sea, Mediterranean, and Red Sea coastal areas as well as mountainous and higher altitude regions in the eastern part of the study area. In addition, highest decrease rates will happen in the Middle East countries, Afghanistan, Morocco, Algeria, and Sudan. Based on different scenarios in the MENAP region, precipitation will vary between −77.3 and + 51.1 mm compared to the base period. Moreover, the lowest and highest precipitation changes were estimated based on SSP1-2.6 and SSP5-8.5 scenarios. According to the various scenarios, the amount of precipitation in the future period will decrease compared to the historical period in most parts of the areas under study. HIGHLIGHTS Analysis of the 11 global circulation models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6).; Using the three shared socioeconomic pathway (SSP) scenarios.; Study of 220 meteorological stations in the MENAP (Middle East, North Africa, Afghanistan, and Pakistan) region.; Assessment of CMIP6 model's performance by the Taylor diagram, R2, MSE, RMSE, and MAE.; Using the grid data of ERA5 and GCMs.;

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management. [ABSTRACT FROM AUTHOR]

India has set ambitious solar energy targets to meet its climate commitments. However, climate change, already evident in the country, poses significant challenges to solar power generation. Therefore, assessing the impact of climate change on future photovoltaic potential in India is essential. This study evaluates the mid-century (2041–2050) solar photovoltaic potential across Indian power grids using CMIP6 models under two scenarios: SSP2-4.5 (moderate climate action with intermediate air pollution) and SSP5-8.5 (weak climate action with strong air pollution control). The results indicate that the nationally averaged photovoltaic potential is projected to decrease by −2.3 ± 0.6% (SSP5-8.5) to −3.3 ± 0.9% (SSP2-4.5) compared to the 1985–2014 baseline, primarily due to reduced radiation and increased temperatures. Additionally, cell temperatures are expected to rise by 1.5 ± 0.13 °C (SSP2-4.5) to 2 ± 0.11 °C (SSP5-8.5), leading to efficiency losses and additional 18 ± 5 d under SSP2-4.5 (26 ± 3 d under SSP5-8.5) of efficiency de-rating, particularly in solar-rich regions. This translates to a loss of 600 ± 160 GWh under SSP2-4.5 and 840 ± 100 GWh under SSP5-8.5, based on the solar generation status of 2023–24. Overall, SSP5-8.5 projects a smaller reduction in photovoltaic potential, it also predicts greater temperature-induced efficiency losses compared to SSP2-4.5, due to aerosol direct effect and weak climate action. Finally, this assessment highlights the need for combined climate and pollution mitigation efforts to boost India’s photovoltaic potential and secure a sustainable, resilient energy future.

Fitting probability distribution functions to observed data is the standard way to compute future design floods, but may not accurately reflect the projected future pattern of extreme events related to climate change. In applying the latest coupled model intercomparison project (CMIP5 and CMIP6), this research investigates how likely it is that precipitation changes in CMIP5 and CMIP6 will affect both the magnitude and frequency of flood analysis. GCM output from four modelling institutes in CMIP5, with representative pathway concentration (RCP8.5) and the corresponding CMIP6 shared socioeconomic pathways (SSP585), were selected for historical and future periods, before the project precipitation was statistically downscaled for selected cities by using delta, quantile mapping (QM), and empirical quantile mapping (EQM). On the basis of performance evaluation, a rainfall-runoff hydrological model was developed by using the stormwater management model (SWMM) for CMIPs (CMIP5 and CMIP6) in historical and future horizons. The results reveal an unprecedented increase in extreme events, for both CMIP5 (historical) and CMIP6 (future) projections. The years 2070–2080 were identified by both CMIP5 and CMIP6 as experiencing the most severe flooding.

Climate extremes have more far-reaching and devastating effects than the mean climate shift, particularly on the most vulnerable societies. Ethiopia, with its low economic adaptive capacity, has been experiencing recurrent climate extremes for an extended period, leading to devastating impacts and acute food shortages affecting millions of people. In face of ongoing climate change, the frequency and intensity of climate extreme events are expected to increase further in the foreseeable future. This study provides an overview of projected changes in climate extremes indices based on downscaled high-resolution (i.e., 10 × 10 km 2) daily climate data derived from global climate models (GCMs). The magnitude and spatial patterns of trends in the projected climate extreme indices were explored under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The performance of the GCMs to reproduce the observed climate extreme trends in the base period (1983–2012) was evaluated, the changes in the climate projections (2020–2100) were assessed and the associated uncertainties were quantified. Overall, results show largely significant and spatially consistent trends in the projected temperature-derived extreme indices with acceptable model performance in the base period. The projected changes are dominated by the uncertainties in the GCMs at the beginning of the projection period while by the end of the century proportional uncertainties arise both from the GCMs and SSPs. The results for precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Our work provides a comprehensive insight into the projected changes in climate extremes at relatively high spatial resolution and the related sources of projection uncertainties.

Introduction: An assessment of the potential impacts of future urban expansion on multiple ecosystem services (ESs) represents an essential contribution to the sustainable development of the transnational area of Changbai Mountain (TACM). However, the potential ES losses attributed to future urban expansion have been assessed in few studies.Methods: In this study, we evaluated the potential ES losses attributable to urban expansion in the TACM over the period from 2015 to 2050. To achieve this objective, we initially quantified multiple ESs (namely, food production (FP), carbon storage (CS), water retention (WR), and air purification (AP)) based on 1992 data, and subsequently simulated urban expansion from 2015 to 2050 by coupling Shared Socioeconomic Pathways scenarios (SSPs) with the zoned Land Use Scenario Dynamics-urban model. Finally, the potential impacts of future urban expansion on ESs were evaluated on multiple scales.Results: The results indicated that during the period from 2015 to 2050, the extent of urban land will grow by between 157.59 and 517.77 km2, resulting in FP, CS, WR, and AP losses of 193.76–684.39 thousand tons, 1.47–5.24 million tons, 8.74–30.00 million tons, and 0.34–1.18 thousand tons, respectively.Discussion: The subregion falling within the Democratic People’s Republic of Korea (DPRK) will experience the most severe future losses of ESs, with losses of FP, CS, WR, and AP being 1.06–3.77-fold, 1.15–3.46-fold, 1.10–3.54-fold, and 1.08–3.21-fold higher, respectively than those that occurred in this subregion the period of 1992–2015. Specifically, the encroachment of new urban land onto cropland and woodland will be the main contributing factor in these future declines in ESs. Furthermore, the reductions in ESs attributable to urban expansion in this subregion will have serious effects on human wellbeing. By 2050, the loss of FP will affect approximately 9.58%–30.00% of the population. Moreover, among the residents of this subregion, the loss of AP will contribute to the need for additional expenditures of 6.63–19.81 million euros on air purification measures. On the basis of these findings, we suggest that effective policies and regulations should be implemented to promote the sustainable development of the TACM.