Climate Change Scenarios Research Articles

ABSTRACT Rubble-mound breakwaters play a fundamental role in protecting coastal areas against wave action. Understanding the damage evolution and failure of the breakwaters is essential for effective design and adaptation strategies, particularly in light of ongoing climate change scenarios, where extreme events are becoming more frequent and intense. The present study introduces a practical image-based approach for assessing the damage progression of a small-scale model of rubble-mound breakwater in laboratory environment. The physical model, armored with a single-layer of concrete armor units, was subjected to climate-driven extreme storm conditions representative of failure scenarios beyond standard design thresholds. At a number of intervals, an Unmanned Aerial Vehicle (UAV) was operated to capture aerial images of the breakwater. The analysis of these images enabled the assessment of erosion and accretion patterns along the model, the identification of the most vulnerable areas and a detailed understanding of damage evolution and progressive failure mechanisms. The experiment captured the onset and speed of the progressive failure of the single-layer concrete armor units under extreme wave action. The resulting insight into damage evolution may prove highly valuable for the future adaptation strategies for coastal structures originally designed without accounting for the effects of climate change.

Abstract Global lake and reservoir water surfaces were recently estimated to contribute ~10% of global methane (CH 4 ) emissions. The sensitivity of these emissions to climate and environmental change is a growing concern. Here we present data-driven, globally gridded modelling of future open-water CH 4 fluxes under different scenarios. We included multiple potential predictor variables and available peer-reviewed flux data focusing on in situ-verified relationships. The results indicate total lake and reservoir CH 4 emissions increases of 24–91% under the IPCC Shared Socioeconomic Pathway (SSP) climate change scenarios SSP1-2.6 to SSP5-8.5 by 2080–2099. Effects of changed temperature and seasonality dominated these increases. Area and nutrient load changes also contributed substantially to reservoir emissions. Large absolute changes were predicted at all latitudes. The results demonstrate the urgency in minimizing climate change to avoid substantially increased future inland water CH 4 emissions.

Corn (Zea mays L.) yield productivity is driven by a multitude of factors, specifically genetics, environment, and management practices, along with their corresponding interactions. Despite continuous monitoring through proximal or remote sensors and advanced predictive models, understanding these complex interactions remains challenging. While predictive models are improving with regard to accurate predictions, they often fail to explain causal relationships, rendering them less interpretable than desired. Process-based or biophysical models such as the Agricultural Production Systems sIMulator (APSIM) incorporate these causalities, but the multitude of interactions are difficult to tease apart and are largely sensitive to external drivers, which often include stochastic variations. To address this limitation, we developed a novel methodology that reveals these hidden causal structures. We simulated corn production under varied conditions, including different planting dates, nitrogen fertilizer amounts, irrigation rules, soil and environmental conditions, and climate change scenarios. We then used the simulation results to rank features having the largest impact on corn yield through Random Forest modeling. The Random Forest model identified nitrogen uptake and annual transpiration as the most influential variables on corn yield, similar to the existing research. However, this analysis alone provided limited insight into how or why these features ranked highest and how the features interact with each other. Building on these results, we deployed a Causal Bayesian model, using a hybrid approach of score-based (hill climb) and constraint-based (injecting domain knowledge) models. The causal analysis provides a deeper understanding by revealing that genetics, environment, and management factors had causal impacts on nitrogen uptake and annual transpiration, which ultimately affected yield. Our methodology allows researchers and practitioners to unpack the “black box” of crop production systems, enabling more targeted and effective model development and management recommendations for optimizing corn production.

Abstract. River water temperature is a key factor for water quality, aquatic life, and human use. Under climate change, inland water temperatures have increased and are expected to do so further, increasing the pressure on aquatic life and reducing the potential for human use. Here, future river water temperatures are projected for Switzerland based on a multi-fidelity modeling approach. We use 2 different semi-empirical surface water temperature models, 22 coupled and downscaled general circulation to regional climate models, future projections of river discharge from 4 hydrological models, and 3 climate change scenarios (RCP2.6, 4.5, and 8.5). By grouping catchments under representative thermal regimes and by employing hierarchical cluster-based thermal-pattern recognitions, an optimal model and model configuration were selected, thereby improving model performance. Results show that, until the end of the 21st century, average river water temperatures in Switzerland will likely increase by 3.2 ± 0.7 °C (or 0.36 ± 0.1 °C per decade) under RCP8.5, while, under RCP2.6, the temperature increase may remain at 0.9 ± 0.3 °C (0.12 ± 0.1 °C per decade). Under RCP8.5, temperatures of rivers classified as being in the Alpine thermal regime will increase the most, that is, by 3.5 ± 0.5 °C, followed by rivers of the Downstream Lake regime, which will increase by 3.4 ± 0.5 °C. Under RCP2.6, temperatures in the Alpine and Downstream Lake regimes change most, with +1.15 and +0.99 ± 0.5 °C. A general pattern of decreasing river discharge in summer (−10 % to −40 %) and increasing river discharge in winter (+10 % to +30 %), combined with a further increase in average near-surface air temperatures (0.5 °C per decade), bears the potential to result in not only overall warmer rivers but also prolonged periods of extreme summer river water temperatures. This dramatically increases the thermal-stress potential for temperature-sensitive aquatic species such as the brown trout in rivers where such periods occur already but also in rivers where this was previously not a problem. By providing information on future water temperatures, the results of this study can guide the management of climate mitigation efforts.

Climate change and insecticides negatively impact organism development and reproduction. Previous studies on climate change have focused on average temperature while ignoring diurnal temperature fluctuations. Therefore, this study investigates the interaction effects of the nymph experiencing temperature amplitudes (TAs) (+/−0, +/−6, +/−12 °C) at the same average temperature (22 °C) and two insecticides (beta-cypermethrin: negative temperature coefficient NT, imidacloprid: positive temperature coefficient PT) in the adult phenotypes and population parameter of S. avenae. The findings revealed that wide amplitude (+/−12 °C) significantly decreased fecundity, daily nymph reproduction, and the intrinsic rate of increase, while it significantly enhanced early fecundity. Medium amplitude (+/−6 °C) significantly lowered the intrinsic rate of increase. Notably, insecticides mitigated or even reversed the harmful impact of wide amplitude on adults. Under PT treatment, longevity was significantly higher than that under 22 °C. Under NT treatment, survival was noticeably greater than that under 22 °C. The interaction between insecticide and medium amplitude positively influenced adult phenotypes, with both PT and NT treatments resulting in higher survival, longevity, fecundity, and daily nymph production compared to 22 °C. These findings support the theory of climate-induced poison sensitivity and indicate that insecticide temperature coefficient is crucial in assessing insecticide safety risks under climate change scenarios.

There is ample relevant literature on the potential effects of climate change on forest ecosystems. However, the majority of studies have focused on analyzing the effects of increase in temperature or atmospheric CO 2 on specific processes over short periods of time. This may be explained by the difficulty of implementing long-term field experiments to monitor changes that occur very slowly in forest ecosystems. Forest simulation models may contribute to evaluating long-term changes in forest dynamics under different scenarios of climate change. However, as models are continually developed, there is a need to evaluate their biological consistency and realism of their predictions. The gap model ZELIG-CFS was used to simulate the long-term effects of climate change scenarios Representative Concentration Pathways (RCP) 4.5 and 8.5 on the dynamics of seventeen temperate tree species in Nova Scotia, eastern Canada. A dataset of 454 permanent sample plots was assembled, which consisted mostly of mixed stands. The simulation results indicated that the effects of climate change differed among species. Some species, such as balsam fir ( Abies balsamea (L.) Mill.), were negatively affected under RCP 4.5 and 8.5 scenarios by showing a decrease in mean basal area, stand density and diameter at breast height. In contrast, other species, such as trembling aspen ( Populus tremuloides Michx.), increased their abundance. The simulated responses of the species were discussed in the light of their autecology.

ABSTRACT The main goal of this research is to develop a theoretical framework to estimate soil salinity risk in climate and socioeconomic change scenarios using machine learning, namely XGBoost regression (XGR), Decision Trees regression (DTR), AdaBoost (ADB), and CatBoost (CB), along with the Analytic Hierarchy Process (AHP), to model salinity risk in Vietnam’s Red River Delta in multiple climate and development scenarios. Risk was calculated by integrating hazards (the level of salinity), exposure (agricultural land area, aquaculture land area, agricultural production), and vulnerability (poverty rate, population density, number of students per community). The chosen models produced risk maps for 2015, 2020, 2025, and 2035 using input based on several scenarios. The finding indicated that the region at high or very high risk expanded from approximately 10 km2 in 2015 to 18 km2 in 2020 and continued to increase to 25 km2 in 2025. It was then forecast to decline to 14 km2 by 2035. This pattern directly relates to the effects of rising sea level, shifting land use, and a reduction in poverty. Adaptive capacity was strongly related to access to resources and community awareness of the problem. These findings highlight the benefit of integrating scenario-based salinity risk management strategies.

A novel integrated GIS-AI framework for optimal CSP plant site selection: a multi-criteria approach under climate change scenarios in Bushehr, Iran

Abstract Land subsidence driven by groundwater exploitation poses a critical threat under future water stress from population growth, urbanization, and economic expansion. While the relationship between groundwater level (GWL) fluctuations and land subsidence has been studied, limited research has explored their co‐evolution under future development, climate change, and groundwater management aimed at stabilizing or reversing GWL decline. The long‐term effects of large‐scale water transfer projects, such as China's South‐to‐North Water Diversion (SNWD) project, on subsidence remain unclear despite their role in reducing extraction pressures and recovering groundwater level. This study develops a coupled groundwater flow and land subsidence model for a key city in the SNWD's middle route, incorporating water demand projections (from Shared Socioeconomic Pathways, SSPs), climate change scenarios (from CMIP6), and water diversion strategies. Results indicate that future (until 2050) GWL changes and subsidence are primarily driven by water demand (over 50%) and water diversion (up to 45.3%), with climate change having a minor effect (under 18.6%). Over the period extending from 2020 to 2050, subsidence recovery could reach 56.8 mm (averagely 1.9 mm/yr) with reduced demand and increased diversion. However, in a worst‐case scenario characterized by rising demand and absence of diversion optimization, subsidence could worsen by up to 439.9 mm (averagely 14.7 mm/yr). Water diversion could be 15 times more effective in mitigating subsidence under higher water‐stress conditions, while prohibiting deep groundwater extraction for agriculture could lead to a 4.4‐fold improvement in GWL recovery and subsidence mitigation. This study highlights the role of technology, policy, and optimized water diversion in managing GWL and mitigating subsidence under future uncertainties.

Predicting future tropical theileriosis risk in China using tick distribution and climate models.

Co-elevated CO2 concentration and temperature enhance the carbon assimilation and lipid metabolism in a high-oil soybean (Glycine max (L.) Merr.) variety.

Short-term exposure of non-optimal temperature and lung function: The role of relative humidity.

Abstract Understanding how jet streams respond to a warming climate is crucial for anticipating changes in atmospheric circulation and their broader impacts. Previous studies have highlighted the influence of anthropogenic warming on the meridional temperature gradient, which directly affects jet stream dynamics and variability. This study investigates projected trends in upper-level jet stream shear instability under future climate change scenarios using CMIP6 multimodel simulations. Building on previous findings linking anthropogenic warming to strengthened meridional temperature gradients, we analyze annual means of zonal wind speed, vertical wind shear, and stratification profiles from 2015 to 2100 globally. Results show strengthened multimodel annual-mean vertical shear at 250 hPa, particularly in high-emission scenarios, with trends ranging from 0.04 to 0.11 m s−1 (100 hPa)−1 decade−1 depending on the scenario and region, equivalent to a total relative increase of 16%–27% over 86 years. Decreasing trends are observed in the annual-mean Brunt–Väisälä frequency N2 at 250 hPa, with multimodel ensemble mean values across regions ranging from −0.018 to −0.040 × 10−4 s−2 decade−1 for lower and higher emission scenarios, respectively, equating to a total relative decrease of 10%–20%. Similarly, the annual-mean Richardson number Ri shows decreasing trends from −0.014 to −0.050 decade−1 across emission scenarios and regions, which is a total relative decrease of 38%–47%. These findings suggest more favorable conditions for the generation of clear-air turbulence (CAT), posing critical challenges for aviation safety and operations in a warming climate.

Functional bat diversity and the role of the protected areas against climate change projections across Europe.

Abstract The deliberate addition of sulfur dioxide in the stratosphere to form reflective sulfate aerosols, reflect sunlight, and reduce surface temperatures is increasingly being considered as an option for minimizing the impacts of climate change. This strategy would create an unprecedented climate where the relationship between surface temperature and carbon dioxide concentration is decoupled. The implications of stratospheric aerosol intervention (SAI) for global crop protein concentrations have not yet been explored. While elevated CO 2 concentrations are expected to reduce crop protein, higher temperatures may increase crop protein concentrations. Here we report changes of maize, rice, soybean, and wheat protein concentrations under a medium emissions climate change scenario and a SAI scenario to maintain global average temperatures at 1.5 °C above preindustrial levels, as simulated by three global gridded crop models. We show that using SAI to offset surface temperature increases would create decreases in the global protein concentrations of maize and rice, with minimal impact on wheat and soybean. Some already protein-deficient and malnourished nations that rely heavily on these crops to meet protein demands would show large decreases in protein intake under SAI with the current diet pattern, which could exacerbate their nutrient scarcity. The range of results between crop models highlights the need for a more comprehensive analysis using additional crop models, climate models, a broader range of climate intervention scenarios, and advancements in crop models to better represent protein responses to climate changes.

Abstract Understanding the relative magnitude of uncertainties in total water level estimation is critical for prioritizing research efforts and developing more accurate models and projections of future scenarios. This paper examines the uncertainty factors affecting total water levels at beaches around New Zealand, characterized by different processes and conditions. Using a variance‐based global sensitivity analysis, we assess the relative contributions of sea level rise (SLR), astronomical tide, storm surge and runup dynamics, including their associated models and scenarios, to total water levels and their uncertainties, both historically and for different projections up to 2099. Our analysis highlights that waves and tides predominantly influence uncertainty in the early 21st century. As the century progresses, the impact of SLR and climate change scenarios increases. However, the temporal evolution of the uncertainty sources exhibits site‐specific characteristics, with beaches dominated by waves or tides showing a less important role for climate effects also in the long‐term future.

ABSTRACTThe Bengal tiger (Panthera tigris tigris), a flagship and umbrella species of the South Asian forest ecosystem, has declined dramatically in population and geographic distribution due to human‐caused habitat fragmentation and poaching over the past century. Global tiger populations may persist in the next century only if the size and quality of the current habitat remain unchanged. Our first‐of‐its‐kind study in Nepal assesses whether these habitat requirements are in place through an analysis of habitat suitability to predict the future habitat of tigers in varying climatic scenarios across the country. We collected tiger‐presence location (GPS points) from tiger surveys conducted by the Department of National Parks and Wildlife Conservation, Nepal, in 2018 and 2022 across the country. We used MaxEnt software in varying Shared Socio‐economic Pathways (SSP 245 and 585) employing eight bioclimatic and two topographic variables to predict the future habitats of the tiger in 2050, 2070, and 2090. In the SSP 245 scenario, tiger habitat could increase for all three time periods, but in the SSP 585 scenario, the habitat will increase only in 2050. Interestingly, in both scenarios, tiger habitat will increase by more than 80% in 2050. The expanded habitat in all scenarios is outside of protected areas and northeast of the current habitat. This indicates that extreme climate change scenarios with more industrialization, urbanization, and land use change have a greater impact on tiger habitat. Furthermore, tiger habitat qualitatively shifts from protected areas to outside protected areas in the human‐dominated landscape. This creates more challenges for conservationists and managers as human‐tiger interaction may surge. Proactive management solutions to protect Nepal's tigers for the next century could include expanding or establishing new protected areas, establishing connectivity and corridors between the tiger habitats, in addition to anticipatory efforts to address human‐wildlife conflicts that will emerge in this changing landscape.

Understanding soil health and aggregation potential in dryland soils is vital for tackling global food security and environmental degradation. However, empirical evidence of such changes is limited in semiarid dryland ecosystems, highlighting the need for further research to examine the impacts of land-use changes on soil health and soil aggregation in these regions. This study explored the effects of five land use practices (forestland, agro-horticulture land, wheat cropland, grassland, and barren land) and three soil depths (0-15, 15-30, and 30-45cm) on soil health and soil aggregation in dry semiarid regions of Pakistan. The experiment was conducted via a randomized complete block factorial design with three replications. The data were analyzed statistically via two-way analysis of variance (ANOVA), and correlation coefficients were calculated. The results indicate a significant increase in most soil health and soil aggregation indicators under forestland and grassland compared with those under the other land uses. Under forestland and grassland, there were significant changes in the soil organic carbon, boron, sodium, calcium, magnesium, total nitrogen, available phosphorus, and exchangeable potassium contents compared with those in barren land at depths of 0-15cm. Macroaggregates (97.2%) were greater in grasslands than in barren lands, and microaggregates (26.4%) were greater under barren land at depths of 0-15cm. The soil fertility parameters were significantly positively correlated with each other. This study concluded that land use practices have a profound effect on soil fertility and quality traits in semiarid regions. Therefore, forest and grassland should increase in dryland semiarid regions to cope with future climate change scenarios.

Impact of tropical cyclones on the suspended sediment transport in the Bohai Sea since the 21st century.

Climate change-induced instability in water resource utilization affects industries across the board and can cause indirect economic shocks during droughts. This study evaluates the indirect economic losses in the industrial sector - caused by insufficient water availability during the 2015 drought - using the Water-constrained Input-Output Linear Programming (WIOLP) model. The analysis was based on the 2015 input-output table and water use data for the manufacturing, construction, and agriculture sectors. It quantified the indirect economic losses industry-wise under water-use constraint scenarios. The analysis revealed that indirect economic losses in the industrial sector expand as water usage decreases, and when the total water supply decreases by more than 60%, economic activity across the entire industrial sector effectively comes to a halt. Additionally, a structural characteristic was identified in which losses between industries were interconnected and propagated owing to water usage constraints. This highlights the necessity of an integrated analysis that reflects interindustry linkages rather than a fragmented approach focused on a single industry. This study demonstrates that the WIOLP model can quantitatively evaluate indirect economic losses across industries under water-use constraints. The findings can serve as a foundation for developing drought response policies through regional-level analyses and integration with climate change scenarios.