Climate Variability Change Research Articles

Dampak Perubahan Iklim terhadap Keanekaragaman Hayati: Literature Review

Climate change is one of the biggest challenges faced by ecosystems and biodiversity worldwide. This research examines climate change's impact on biodiversity through a comprehensive literature review. By collecting and analyzing various previous studies, this research identifies patterns of change in flora and fauna species in response to changing climate variables, such as temperature, rainfall, and the frequency of extreme weather events. Literature results show that many species experience changes in distribution, behavior and reproductive timing, which have the potential to disrupt ecosystem interactions and damage ecological balance. This research also discusses the impact of climate change on natural habitats and the conservation efforts needed to protect biodiversity. These findings highlight the urgency of taking effective mitigation and adaptation measures to maintain biodiversity amidst the increasing challenges of climate change.

How does a change in climate variability impact the Greenland ice sheet surface mass balance?

Abstract. Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data.

Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in Southern China

Abstract Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001-2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale Water Supply Stress Index (WaSSI) ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr-1, p<0.05) and gross primary productivity (GPP) (−10.4 gC m-2 yr-1, p<0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr-1, p<0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 23.2% increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m-2 to 998 gC m-2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modelling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g., urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q. Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modelling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water.

Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Forecasting changes in the earth’s climate system by instrumental measurements and paleodata in the phase-time region, consistent with changes in the barycentric motions of the sun. Part 1

The relevance of the research is due to the need to establish the root cause of climate change on Earth and predict changes in heliocosmic, climatic variables, natural disasters by intramental measurements and paleodata, as time series, for long intervals of the time horizon in the phase domain, taking into account their cyclicity and interdependence of changes. Purpose of research: application of the time series analysis method of the time series to establish the strength of the influence of the barycentric movements of the Sun on the variability of heliocosmic, climatic variables, natural disasters and fires, high-precision prediction of variables in the phase-time domain for long horizons of hundreds and thousands of years by instrumental measurements and paleodata. Objects of research: time series of changes in heliocosmic and climatic variables, natural disasters and fires, curves of changes in climatic changes obtained by analysis of ice cores in Antarctica and bottom sediments in Antarctica. Methods of research: wavelet phase-frequency and phase-time analysis of images of heliocosmic and climatic variables, natural disasters and fires; calculation of the consistency of changes in selected groups on a set of phase-time characteristics of variables in a sliding mode. Main results of research: In the images of wavelet phase-time functions of many variables constructed from observations in 1600–2010, Jose periods of ~178 years were found, which are contained in changes in the characteristics of barycentric movements of the Sun, solar activity, solar constant, CO2,N2OEl Nino, solar wind, the level of the Caspian Sea, temperature in Greenland, the speed of rotation of the Earth, snow accumulation rates in the Indian Ocean sector of Antarctica, global temperature; cyclicity of changes in heliocosmic and climatic variables are consistent with changes in Baricentre; there is a significant difference in the consistency of changes in the phase-time characteristics of variables obtained at the southern hot and northern cold latitudes of the planet by about 2.1 times; in the northern part of the planet, changes in variables are more chaotic, due to the different influence of changes in the Baricentre variable, the magnetic fields of the Sun and the Earth on the variability of variables at different latitudes of the planet; significant variability and resonances of the phase characteristics of heliocosmic and climatic variables are observed by changes in Baricentre on the graphs of coordinated changes in groups of variables in the observed and predicted time intervals. The studies reveal multiple oscillatory responses of the Earth’s climate system to the impact of the Baricentre variable due to the heterogeneity of its components and the Earth’s magnetosphere in space. Changes in the set of phase-time characteristics of variables on the same graph in the observed and predicted time intervals in the interval ± π are displayed as changes in autowaves characteristic of self-organizing systems, characterizing climatic changes in environments in combination with the influence of the variable Baricentre.

A Forensic Investigation of Climate Model Biases in Teleconnections: The Case of the Relationship Between ENSO and the Northern Stratospheric Polar Vortex

AbstractTeleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively.

Advances in understanding physical and biological controls on eggs and larval distribution in Pacific fisheries: A review

The early stages of fish, comprising eggs and larvae, are exceptionally fragile and sensitive to environmental dynamics and climate change. Pacific Ocean (PO) currents play an important role in shaping the distribution of marine organisms, influencing global climate patterns, heat distribution, coastal temperatures, and nutrient redistribution. These currents reveal significant changes within the climate system. Their variability across different timescales can be attributed to the complex interplay of physical forces. These currents are subjected to diverse anthropogenic factors, exerting detrimental effects on the dispersal of fish larvae. Furthermore, climate change variables, including alterations in tropical PO temperature associated with the ENSO cycle, Atlantic Nino modes influencing equatorial Atlantic temperature, changes in ocean salinity, polar ice cap melting, increasing greenhouse gases, marine heatwaves, and fluctuations in subsurface flows, directly impact the distribution, abundance, and species composition of early life stages. Major Pacific fisheries, such as those targeting Pacific sardines, saury, and anchovies, undergo population booms and declines due to significant alterations in the current dynamics of currents and fronts within the PO. The anticipated intensification of the ENSO cycle, characterized by more frequent and severe El Niño (warm) and La Niña (cold) events as a result of climate change, is expected to significantly impact the early developmental stages of important commercial fish stocks regularly. This review synthesizes the current understanding of the physical and biological parameters driving changes in ocean currents and their implications for early fish dispersion, emphasizing the critical need for research in this area to inform global conservation efforts, fisheries management, and food security.

Quantifying Climate Change Variability for the Better Management of Water Resources: The Case of Kobo Valley, Danakil Basin, Ethiopia

Alterations in the hydrological cycle due to climate change are one of the key threats to the future accessibility of natural resources. This study used 12 GCM climate models from CMIP6 to evaluate future climate change scenarios by applying model performance measures and trend analysis in Kobo Valley, Ethiopia. The models were ranked based on their ability to analyze the historical datasets. The result of this study showed that the outputs of the FIO-ESM-2-0 CIMP6 model had a good overall ranking for both precipitation and temperature. After bias correction of the model-based projections with the observed data, the average annual precipitation in the average scenario (SSP2-4.5) decreased by 4.4% and 13% in 2054 and 2084, respectively. Similarly, in the worst-case scenario (SSP5-8.5), by the end of 2054 and 2084, decreases of 4% and 12.8%, respectively, were predicted. The average annual maximum temperature under the SSP2-4.5 scenario increased by 1.5 °C in 2054 and by 2.1 °C in 2084. The average annual maximum temperature under the worst-case (SSP5-8.5) scenario increased by 1.7 °C in 2054 and by 3.2 °C in 2084. In the middle scenario (SSP4.5), the average annual minimum temperature increased by 2.2 °C in 2054 and by 3 °C in 2084. The average annual minimum temperature under the worst-case (SSP5-8.5) scenario increased by 2.6 °C in 2054 and by 4.3 °C in 2084. The seasonal variability in precipitation in the studied valley will decrease in the winter and increase in the summer. A decrease in precipitation combined with an increase in temperature will strengthen the risk of drought events in the future.

Vertical Distribution of Rocky Intertidal Organisms Shifts With Sea-Level Variability on the Northeast Pacific Coast.

Disentangling the effects of cyclical variability in environmental forcing and long-term climate change on natural communities is a major challenge for ecologists, managers, and policy makers across ecosystems. Here we examined whether the vertical distribution of rocky intertidal taxa has shifted with sea-level variability occurring at multiple temporal scales and/or long-term anthropogenic sea-level rise (SLR). Because of the distinct zonation characteristic of intertidal communities, any shift in tidal dynamics or average sea level is expected to have large impacts on community structure and function. We found that across the Northeast Pacific Coast (NPC), sea level exhibits cyclical seasonal variability, tidal amplitude exhibits ecologically significant variability coherent with the 18.6-year periodicity of lunar declination, and long-term sea-level rise is occurring. Intertidal taxa largely do not exhibit significant vertical distribution shifts coherent with short-term (monthly to annual) sea-level variability but do exhibit taxa-specific vertical distribution shifts coherent with cyclical changes in lunar declination and long-term SLR at decadal timescales. Finally, our results show that responses to cyclical celestial mechanics and SLR vary among taxa, primarily according to their vertical distribution. Long-term SLR is occurring on ecologically relevant scales, but the confounding effects of cyclical celestial mechanics make interpreting shifts in zonation or community structure challenging. Such cyclical dynamics alternatingly amplify and dampen long-term SLR impacts and may modify the impacts of other global change related stressors, such as extreme heat waves and swell events, on intertidal organisms living at the edge of their physiological tolerances. As a result, intertidal communities will likely experience cyclical periods of environmental stress and concomitant nonlinear shifts in structure and function as long-term climate change continues. Our results demonstrate that consistent, large-scale monitoring of marine ecosystems is critical for understanding natural variability in communities and documenting long-term change.

Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information

Given the link between climatic factors on one hand, such as climate change and low frequency climate oscillation indices, and the occurrence and magnitude of heat waves on the other hand, and given the impact of heat waves on mortality, these climatic factors could provide some predictive skill for mortality. We propose a new model, the Mortality-Duration-Frequency (MDF) relationship, to relate the intensity of an extreme summer mortality event to its duration and frequency. The MDF model takes into account the non-stationarities observed in the mortality data through covariates by integrating information concerning climate change through the time trend and climate variability through climate oscillation indices. The proposed approach was applied to all-cause mortality data from 1983 to 2018 in the metropolitan regions of Quebec and Montreal in eastern Canada. In all cases, models introducing covariates lead to a substantial improvement in the goodness-of-fit in comparison to stationary models without covariates. Climate change signal is more important than climate variability signal in explaining maximum summer mortality. However, climate indices successfully explain a part of the interannual variability in the maximum summer mortality. Overall, the best models are obtained with the time trend and the North Atlantic Oscillation (NAO) used as covariates. No country has yet integrated teleconnection information in their heat-health watch and warning systems or adaptation plans. MDF modeling has the potential to be useful to public health managers for the planning and management of health services. It allows predicting future MDF curves for adaptive management using the values of the covariates.

A Fuzzy Logic Framework for Modeling Climate Change Impacts on Ecosystems

Climate change poses significant challenges to ecosystems, necessitating robust models to predict and manage its impacts. This paper presents a novel fuzzy logic framework designed to model the complex and uncertain interactions between climate change variables and ecosystem responses. The proposed framework leverages fuzzy logic's ability to handle imprecise and ambiguous data, providing a more nuanced understanding of how temperature fluctuations, precipitation changes, and extreme weather events affect biodiversity, species distribution, and ecosystem services. By integrating ecological knowledge with fuzzy inference systems, the model offers a flexible tool for simulating various climate scenarios and their potential effects on ecosystems. Case studies demonstrate the framework's applicability across different ecosystems, highlighting its potential to inform conservation strategies and policy- making. This work contributes to the growing body of research on climate change modeling, offering a powerful approach to anticipating and mitigating the adverse effects of environmental changes on natural habitats.

Quantifying Sahel Runoff Sensitivity to Climate Variability, Soil Moisture and Vegetation Changes Using Analytical Methods

AbstractWhilst substantial efforts have been deployed to understand the “Sahel hydrological paradox”, most of the studies focused on small experimental watersheds around the central and western Sahel. To our knowledge, there is no study on this issue covering all the watersheds located within the Sahelian belt. The absence of relevant studies may be attributed to a sparsity of in situ data leading to a dearth of knowledge on the Sahel hydrology. To fill this knowledge gap, the present study leverages analytical methods and freely available geospatial datasets to understand the effects of climatic factors, soil moisture and vegetation cover changes on surface runoff in 45 watersheds located within the Sahelian belt over two decades (2000–2021). Analyses show increasing trends in annual precipitation and potential evapotranspiration (PET) in more than 80% of the watersheds. Surface runoff, soil moisture (SM), and vegetation cover measured using the normalised difference vegetation index (NDVI) also show increasing trends in all the watersheds. Multivariable linear regression (MLR) analyses reveal that precipitation, PET, SM, and NDVI contribute about 62% of surface runoff variance. Further analyses using MLR, and the partial least squares regression (PLSR) show that precipitation and NDVI are the main factors influencing surface runoff in the Sahel. Elasticity coefficients reveal that a 10% increase in precipitation, SM and NDVI may lead to about 22%, 26% and 45% increase in surface runoff respectively. In contrast, a 10% increase in PET may lead to a 61% decline in surface runoff in the Sahel. This is the first hydrological study covering all the watersheds located within the Sahelian belt with results showing that surface runoff is influenced by climate, SM and NDVI to varying degrees. Given the unique hydrological characteristics of the Sahel, a better understanding of the different factors influencing surface runoff may be crucial for enhancing climate adaptation and ecological restoration efforts in the region such as the Great Green Wall Initiative.

Analysis of climate change and climate variability impacts on coastal storms induced by extratropical cyclones: a case study of the August 2015 storm in central Chile

ABSTRACT The projected increase in coastal risk requires a reevaluation of coastal risk reduction strategies. A multi-model approach is proposed to examine the variability of coastal storms influenced by climate change and El Niño Southern Oscillation (ENSO). To this end, the historic coastal storm of August 8 2015, resulting from a local extratropical cyclone (ETC) off the central Chilean coast, was analyzed through the coupling of the WRF atmospheric model, Delft3D FM (D-FLOW and D-WAVE modules), and EOT20 astronomical tide model. The results show that the characteristics of local ETCs are susceptible to regional temperature gradients associated with climate change and ENSO. The coastal storm of August 8 2015, presented a decrease in wave height and counterclockwise rotation of wave direction along the Chilean coast under the climate change scenario. Meanwhile, the ENSO scenarios under cold conditions generated a ETC track’s displacement toward the north, causing both an increase in wave height along the coast of the Antofagasta and Atacama regions and a decrease in wave height in the Valparaíso, O’Higgins, and Maule regions. Findings from this study emphasize the importance of considering dynamic design for coastal structures rather than traditional methods to adapt to changing storm patterns.

Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

Understanding the drivers of the catchment hydrological cycle of the Jonkershoek Valley catchment, South Africa

Understanding trends in hydro-climate variables and the impacts of land use on the streamflow is crucial for the development of appropriate catchment water management strategies. Jonkershoek (146 km2) is an important headwater catchment in the Table Mountain Group (TMG) geological region, contributing flow to the Cape Winelands District Municipality in the Western Cape. This study analyses hydro-climate variables at annual, monthly, and seasonal scales using an integrated approach composed of statistical homogeneity testing for abrupt changes, Mann-Kendall tests for trend analysis, and the Indicators of Hydrological Alteration (IHA) tool for streamflow alterations. Analyses were conducted for three headwater sub-catchments within the Jonkershoek value, each with different land use history.Homogeneity test of rainfall and streamflow data spanning from 1946 to 2019 identified gradual downwards change points for annual rainfall and streamflow across the sub-catchments. Moreover, the change points for streamflow were inconsistent with those of rainfall. The identified change points in streamflow were consistent with the timing of afforestation activities in Tierkloof, whereas in Bosboukloof and Langrivier they could be attributed to earlier climatic variability. Furthermore, Mann-Kendall test detected significant (p < 0.05) decreasing trends for both annual and seasonal rainfall which coincided with most of the streamflow trends. Trends were strongest for the winter season suggesting a possible shift in climate patterns which influence winter rainfall. Winter streamflows declined by 15.5%–39.5%. Analysis of hydrological flow indices indicated significant decrease in 1-,7- and 30-day annual maximum extremes during afforestation which were attributed to high evapotranspiration rates of pines. The opposite was observed during clearfelling period in Bosboukloof. The median monthly flow also showed a decrease for winter months. This shows that climate variability and land-use change by afforestation have major impacts on streamflow. The findings of this study are important to inform policymakers on the impacts of climate change and land use, allowing pro-active mitigation and adaptation.

Comparing household heads’ perception of climate change variability with meteorological trends and understanding mitigation measures to combat the adverse effects in coastal areas of Bangladesh

Comparing household heads’ perception of climate change variability with meteorological trends and understanding mitigation measures to combat the adverse effects in coastal areas of Bangladesh

Investigation of the time-dependent bearing capacities of concrete structures in different environments considering climate change

The escalating trend of emissions of greenhouse gases, including CO2, prompts significant modifications in climate variables, such as temperature and relative humidity (RH). For concrete civil infrastructures, the carbonation and chlorination corrosion processes are sensitive to changes in climate variables. The focus of this study is to evaluate the time-sensitive deterioration of concrete components in different corrosion environments with consideration of large uncertainties of climate change. Future projections for atmospheric CO2, temperature, and RH until the end of the twenty first century are examined. By implementing an aging corrosion model that incorporates climate change, the study observes that under the SSP5-8.5 emission scenario, the average carbonation depth for the RC structures in the general atmospheric environment increases by 58.7%. Furthermore, the chlorination corrosion rate in marine environments nearly doubles that under aging conditions alone. By 2100, corrosion-affected RC beams in the SSP5-8.5 scenario will lose 9.5 and 29.2% of their flexural capacity, respectively. Moreover, under carbonation and chlorination environments, the beams will face an additional loss of 20.7 and 27.6% in shear capacity, respectively. In performing the above calculations, we have considered the uncertainty prediction of climate change and the uncertainty analysis of the influence of model parameters and material variability.

Evaluation of the impact of hydrological changes on reservoir water management: A comparative analysis the CanESM5 model and the optimized SWAT-SVR-LSTM

This research examines the impacts of climate change and socio-economic variables on the hydrological cycle, reservoir water management, and hydropower capacity at the Gezhouba Dam. The Gezhouba Dam serves as a crucial hydroelectric power station and dam, playing a vital role in regulating river flow and generating electricity. In this study, an innovative method is employed, combining the Soil and Water Assessment Tool (SWAT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) models. This model is optimized using the Developed Thermal Exchange Optimizer. This optimized combined model significantly enhances the reliability and precision of the forecasting inflow and reservoir levels. By utilizing the Canadian Earth System Model version 5 (CanESM5), we examine climate variables across various scenarios of Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP). Under the SSP5-RCP8.5 scenario, the most aggressive in terms of emissions, we project a temperature rise of 2.6 % and a precipitation decrease of 2.7 %. This scenario leads to the most substantial changes in the hydrological cycle and altered river flow patterns. The results show a direct correlation between precipitation and inflow (0.952) and a strong inverse correlation between temperature and inflow (0.893). The study predicts significant decreases in all flow metrics, with mean high flow (Q5) periods affecting hydropower generation, especially under the SSP5-RCP8.5 scenario. Additionally, the filling frequency rate (FFR) and mean filling level (MFL) are projected to decrease, with a more pronounced decline in the far future, indicating a potential compromise of the reservoir's water storage and power generation capabilities, especially under the SSP5-RCP8.5 scenario.

The impact of climate change variability on horticultural productivity: A review

The impact of climate change variability on horticultural productivity: A review