Climate Change Models Research Articles

Assessing rainfall-runoff models for climate change: simple and differential split-sample tests for conceptual and artificial intelligence models

ABSTRACT In this study, the performance of two popular conceptual models, a classic artificial intelligence (AI) model, and a deep learning model is compared using simple and differential split-sample tests (SSST and DSST). Finally, the two conceptual models and the classic AI model are combined employing a model ensemble technique. The SSST and DSST schemes confirmed the ability of both types of models (conceptual and AI) to capture the hydrological response to rainfall events, although the conceptual models showed a better performance in the DSST scheme. The ensemble technique enhanced the modelling performance up to 22% and 35% in the calibration and verification steps. This study highlights the importance of applying DSST using existing data to build further confidence in model projections, and suggests that the use of model ensemble techniques combining conceptual and AI models can enhance the performance of rainfall-runoff modelling in the context of climate change.

Modeling Climate Change Scenarios for Spring Barley in Southeast of Almaty in Kazakhstan Using the LINTUL Approach

Barley stands as a cornerstone in agricultural landscape of Kazakhstan, weaving through diverse climate zones, and annually gracing over 1.5 million hectares. The intricate interplay between climate and food systems necessitates thorough analysis and strategic measures to food safety and nutritional security, as the evolving climate significantly influences both the quantity and quality of our food resources. This study aims to employ the LINTUL-MULTICROP Model to assess how spring barley adapts to both today’s climatic conditions and potential climate change scenarios to elevated levels of carbon dioxide and temperature under the specific conditions of southeast of Almaty. Three different global climate change models were studied (GCMs); i) GFDL-ESM2M, ii) HadGEM2-AO, and iii) MPI-ESM-MR for historical period (1986-2005) under RCP 4.5 and RCP 8.5 during the periods of i) 2040-2059 years scenarios, ii) 2060-2079 years scenarios, and iii) 2080-2099 years scenarios. Overall, the HADGEMAO and MPIESMMR models exhibited promising results in simulating yield, projecting an increase in spring barley yield for both RCP4.5 and RCP8.5 scenarios in GFDL-ESM2M model case also demonstrated stable increase in rainfed conditions. In conclusion, it should be noted that in the conditions of Kazakhstan, the cultivation of spring barley tends to change to growth in the southeast of Almaty.

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

Evolving Narratives in Tourism and Climate Change Research: Trends, Gaps, and Future Directions

This study presents a comprehensive overview of the evolving landscape of tourism and climate change research over the past decade by leveraging bibliometric analysis and a dataset sourced from the Scopus Database. The study scrutinised over 3400 English language articles. The analysis reveals a remarkable surge in publications, signifying the growing recognition of climate change’s multifaceted impact on tourism. However, a noteworthy geographical disparity emerges, with many regions remaining underrepresented in the literature, particularly in Africa and the Middle East. This oversight is concerning, given the vulnerability of these regions to climate change and their burgeoning tourism industries. The study also highlights the pivotal role of influential scholars, funding organisations, and publication outlets in shaping the research landscape. The European Commission and the National Natural Science Foundation of China are major funders. At the same time, journals like Sustainability and the Journal of Sustainable Tourism serve as prominent platforms for disseminating research findings. The analysis uncovers thematic trends, including the growing focus on climate change modelling and its implications for destination planning. However, research gaps persist, notably in sports tourism and climate resilience within the tourism sector. In conclusion, this study offers valuable insights into the current state of tourism and climate change research, pinpointing areas that demand increased attention and inclusivity. It is a valuable resource for scholars, policymakers, and stakeholders working towards a sustainable and resilient future for the global tourism industry in the face of climate change.

Risk of climate change to sweet potato cultivation in the American continent

ABSTRACT Sweet potato, Ipomoea potatoes L., is an essential crop for food security in various countries. However, the effects of climate change could reduce or increase the yield and profitability of this crop, especially for small farmers. Thus, we developed species distribution models for the current and six climate change scenarios based on environmental variables, species occurrence, and global climate change model. Also, we identified the most relevant environmental variables for cultivation. Future scenarios showed gradual shifts in suitable areas for cultivation. Losses in suitable areas were more relevant than gains at the low concentration of greenhouse gas emissions (RCP 2.6). This trend is maintained in the worst scenario of a high concentration of greenhouse gas emissions (RCP 8.5) with more significant losses of suitable areas. The temperature and precipitation of warmest and wettest quarters, temperature seasonality, and isothermality are the most critical variables for sweet potato cultivation. The cultivation is expected to expand the suitable areas in some regions and retract in others due to climate change. Knowledge about climate change effects in cultivated areas is essential for developing management plans that could mitigate the adverse effects of climate change.

Building energy savings by green roofs and cool roofs in current and future climates

The global energy demand has greatly impacted greenhouse gas emissions and climate change. Since buildings are responsible for a large portion of global energy consumption, this study investigates the energy-saving potential of green roofs and cool roofs in reducing building energy consumption. Using an integrated approach that combines climate change modeling and building energy simulation, the study evaluates these strategies in six global cities (Cairo, Hong Kong, Seoul, London, Los Angeles, and Sao Paulo) under current and future climate change scenarios. The results show that in future climates, the implementation of green and cool roofs at the city level can lead to substantial annual energy reductions, with up to 65.51% and 71.72% reduction in HVAC consumption, respectively, by 2100. These findings can guide the implementation of these strategies in different climatic zones worldwide, informing the selection and design of suitable roof mitigation strategies for specific urban contexts.

Distribution models in invasive plants with climatic niche expansion: a case study of Ulex europaeus L. in Colombian Andes

Invasive plants have a wide distribution worldwide. Their ability to adapt and colonize different climates is attributed to the phenotypic plasticity and the expansion of the climatic niche of the species. Simultaneously, their distribution patterns will be affected by climate change, creating or reducing the ideal areas for their establishment. We evaluated the current and potential distribution of Ulex europaeus L. in Colombia, under the global circulation models ACCESS-ESM1-5 and MIROC6 in two scenarios, for the years 2041–2060, taking into account its climate niche expansion. An ecological niche model was built for this purpose using Maxent algorithm of the KUENM, the records of the species in native and invasive condition from the GBIF portal, and records obtained through field trips and bioclimatic variables from Worldclim2 were used. Changes in potential distribution were analyzed with the Biomod2. A total of 1072 occurrence records were found for U. europaeus and 109 records were found in Colombia in the Cordillera Central and Oriental, where an expansion of the altitudinal range was evidenced from 2000 to 4052 m above sea level. Climate change models for Colombia showed that the creation of new areas for potential establishment is less than 1% and there is a reduction in potential distribution of 12 and 31% due to unsuitable climatic conditions that would affect their germination capacity. However, given its potential for adaptation, the invasions will persist in the new conditions and will continue their colonization process at higher altitudes where they find optimal temperatures for their germination and establishment.

Effect of rainfall on White Rhino calf survival depends on home range choice of the mother

Abstract Within African savannas, seasonal rainfall influences the survival of mammalian grazers by determining the availability and quality of food. The strength of these effects may, however, vary depending on the availability of reserve and buffer resources within the home range of an individual. From 1999 to 2019, 24% of the white rhino (Ceratotherium simum simum) calves born in Ithala Game Reserve died without a known cause. To explore this, we investigated the impacts of seasonal rainfall on calf survival, and whether these relationships were modified by the availability of woodlands (i.e., reserve resources) and bunch grasslands (i.e., buffer resources) within the home ranges established by the mothers. We found that nearly all of the deceased calves died during their first dry season after weaning had commenced. The likelihood of a calf surviving this period was positively influenced by rainfall during the dry season and negatively influenced by its duration. However, these effects were more pronounced when the availability of woodlands within the home range of the mother was high. Ultimately, calf deaths were caused by a combination of low dry season rainfall, long dry seasons, and the selection of home ranges with insufficient bunch grasslands by some mothers. With climate change models predicting increased dry season durations and a reduction in dry season rainfall, our results highlight future challenges for the conservation of white rhinos and other large herbivores.

A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops

Changes in the frequency of extreme weather events related to climate change potentially pose significant challenges to UK agricultural production. There is a need for improved climate change risk assessments to support adaptation strategies and to ensure security of food production in future.We describe an innovative and practical framework for spatially explicit modelling of climate change impacts on crop yields, based on the UKCP18 climate projections. Our approach allows the integration of relatively simple crop growth models with high spatial and temporal resolution Earth Observation datasets, describing changes in crop growth parameters within year and over the longer term. We focus on modelling winter wheat, a commercially important crop. We evaluate the results of the model against precision yield data collected from 719 fields. We show that the assimilation of leaf area index data from Sentinel-2 satellite observations improves the agreement of the modelled yields with those observed. Our national-scale results indicate that wheat production initially becomes more favourable under climate change across much of the UK with the projected increase in temperature. From 2050 onwards, yields increase northwards, whilst they decline in South East England as the decrease in precipitation offsets the benefits of rising temperature.Our framework can readily accommodate growth models for other crops and LAI retrievals from other satellite sensors. The ability to explore impacts of crop yields at fine spatial resolutions is an important part of assessing the potential risks of climate change to UK agriculture and of designing more climate resilient agricultural systems.

Can we project well-being? Towards integral well-being projections in climate models and beyond

Scientists have proposed many “Beyond-GDP” indicators to replace the Gross Domestic Product (GDP) in order to quantify genuine societal development. While GDP is regularly projected, research on future trajectories of Beyond-GDP indicators is lacking, failing to meet policymakers’ needs. Focusing on the Human Development Index (HDI), this paper attempts to calculate one of the first global well-being projections for 161 countries by 2100 using the shared socioeconomic pathways (SSPs), the socioeconomic inputs underlying global climate change scenarios. The results indicate a potential global well-being improvement from medium to very high level, depending on the SSPs, with most countries reaching high human development under SSPs 1&5. While serving as an initial step in well-being projection, the results highlight a crucial gap in existing climate change models which are used by the IPCC—they inadequately account for the feedback effects of climate change on well-being. This oversight results in counterintuitive or potentially misleading well-being projections. Therefore, we propose steps to improve this situation. By synthesizing climate change feedback effects on HDI determinants, this assessment delves into their implications for well-being and further underscores the necessity for interdisciplinary collaborations among well-being researchers, climate scientists and policy modelers to achieve sound integral well-being projections.

Deterioration of steel structures due to corrosion considering the global effects of climate change

The randomness of atmospheric conditions is among the key contributing factors that affect the ability to accurately predict the corrosion growth in steel structures. Climate change has the potential to alter the long-term characteristics of these factors over the lifespan of steel structures, both those already existing and those newly built. The impact of climate variability on the stochastic nature of atmospheric variables, which greatly influence corrosion conditions, can add complexity to corrosion predictions in these structures. This paper introduces an integrated framework to quantify the impact of climate change on corrosion rates of steel structures worldwide. It considers the changes in environmental conditions, specifically temperature, relative humidity and wind speed and their effects on atmospheric corrosion. Global Climate Models are employed to evaluate the long-term impacts of climate change on these environmental conditions. An analytical model for predicting corrosion rate is integrated with climate change models to predict alterations in the corrosion rates of steel components relative to historical conditions. This paper also discusses the impact of climate change on the variations of these climatic parameters and offers a comparison between historical data and projected conditions across the globe. The results indicate that the effect of climate change on the corrosion rate depends on the considered region. While regions such as Australia, North America and Europe see an increase, others such as Asia and Africa observe a decline. Notably, all oceans, especially the Antarctic and Arctic, experience a significant increase in corrosion by the end of the century.

Predicting future climate change impacts on the potential distribution of the black howler monkey (Alouatta pigra): an endangered arboreal primate.

Climate change is one of the main factors affecting biodiversity worldwide at an alarming rate. In addition to increases in global extreme weather events, melting of polar ice caps, and subsequent sea level rise, climate change might shift the geographic distribution of species. In recent years, interest in understanding the effects of climate change on species distribution has increased, including species which depend greatly on forest cover for survival, such as strictly arboreal primates. Here, we generate a series of species distribution models (SDMs) to evaluate future projections under different climate change scenarios on the distribution of the black howler monkey (Alouatta pigra), an endemic endangered primate species. Using SDMs, we assessed current and future projections of their potential distribution for three Social Economic Paths (SSPs) for the years 2030, 2050, 2070, and 2090. Specifically, we found that precipitation seasonality (BIO15, 30.8%), isothermality (BIO3, 25.4%), and mean diurnal range (BIO2, 19.7.%) are the main factors affecting A. pigra distribution. The future climate change models suggested a decrease in the potential distribution of A. pigra by projected scenarios (from - 1.23 to - 12.66%). The highly suitable area was the most affected above all in the more pessimist scenario most likely related to habitat fragmentation. Our study provides new insights into the potential future distribution and suitable habitats of Alouatta pigra. Such information could be used by local communities, governments, and non-governmental organizations for conservation planning of this primate species.

Climate change and economic prosperity: Evidence from a flexible damage function

The damage function used to assess the economic impact of secular changes in temperature is one of the most speculative components of integrated assessment models of climate change. Existing work informing this debate is based on pooled empirical models incorporating limited non-linearity and giving little regard to dynamics. We use aggregate and agricultural data for 151 countries over the past six decades to estimate dynamic heterogeneous models which (a) allow the weather-output nexus to differ freely across countries, (b) help distinguish short-run from long-run effects, and (c) account for unobserved time-varying heterogeneity. Overall, we find that, in low-income or high-temperature countries, a permanent 1 °C rise in temperature is associated with a fall in income per capita of about 1.3% in the short-run and 8.5% in the long run. These long-run effects are substantially larger than those commonly suggested in the literature.

Synergising Simulated Annealing and Generative Adversarial Network for Enhanced Wind Data Imputation in Climate Change Modelling

Climate models help us simulate and predict how the Earth’s climate is going to change in the future. Wind speed data is critical for developing and validating such models. However, in the real world, often owing to many factors such as station maintenance and sensor failures, a considerable amount of wind data goes missing. The Generative Adversarial Network (GAN) has been used to impute missing wind data, but the handling of unrealistic GAN output has remained largely unstudied. In this paper, we propose a novel hybrid approach that combines both the GAN and dual annealing algorithms to not only impute missing wind speed data but also counter unrealistic GAN outcomes. The hourly mean wind data has been collected from the National Centers for Environmental Information for four Indian stations, viz. Ahmedabad, Indore, Mangaluru and Mumbai. We compared the performance of the proposed approach with those of k-nn, soft imputation, and plain GAN-based approaches on mean, variance, standard deviation, kurtosis, skewness, and R-square. We found that our approach ranks number one based on the R-square value for all the considered stations. Our model consistently produces realistic results, unlike plain GAN. We observed that Mumbai has the lowest percentage of missing data (13.14%) and the highest R-square value (0.9999186451). However, Indore has the highest percentage of missing data (46.6463%) and the lowest R-square value (0.9046885604).

Biodiversity mitigates drought effects in the decomposer system across biomes

Multiple facets of global change affect the earth system interactively, with complex consequences for ecosystem functioning and stability. Simultaneous climate and biodiversity change are of particular concern, because biodiversity may contribute to ecosystem resistance and resilience and may mitigate climate change impacts. Yet, the extent and generality of how climate and biodiversity change interact remain insufficiently understood, especially for the decomposition of organic matter, a major determinant of the biosphere-atmosphere carbon feedbacks. With an inter-biome field experiment using large rainfall exclusion facilities, we tested how drought, a common prediction of climate change models for many parts of the world, and biodiversity in the decomposer system drive decomposition in forest ecosystems interactively. Decomposing leaf litter lost less carbon (C) and especially nitrogen (N) in five different forest biomes following partial rainfall exclusion compared to conditions without rainfall exclusion. An increasing complexity of the decomposer community alleviated drought effects, with full compensation when large-bodied invertebrates were present. Leaf litter mixing increased diversity effects, with increasing litter species richness, which contributed to counteracting drought effects on C and N loss, although to a much smaller degree than decomposer community complexity. Our results show at a relevant spatial scale covering distinct climate zones that both, the diversity of decomposer communities and plant litter in forest floors have a strong potential to mitigate drought effects on C and N dynamics during decomposition. Preserving biodiversity at multiple trophic levels contributes to ecosystem resistance and appears critical to maintain ecosystem processes under ongoing climate change.

Biomineralisation of corals inhabiting extreme and marginal environments

ABSTRACT Climate change is the primary threat to coral reefs, causing catastrophic coral mortality on a global scale. Stressors such as ocean acidification limit calcium carbonate availability needed to build skeletons. A wealth of research has shown that climate stressors impair the ability of coral to produce and maintain their skeletons, thereby disrupting functions critical to maintaining coral health as well as threatening the ecologically significant framework for reef building. There are natural marginal and extreme environments however, where coral reefs thrive despite unfavourable conditions. These habitats provide a natural long-term setting to examine the impact of multiple interacting climate change factors on corals and understand the adaptations required by corals to inhabit these conditions. We review studies of coral skeletogenesis in extreme and marginal environments, with a particular focus on mangrove habitats due to their natural fluctuations in temperature, pH and oxygen, analogous to those projected under climate change models. The changes in coral skeletal morphology under stress are described as are the techniques used to visualise these changes. In marginal and extreme environments, corals experience lower calcification rates and produce more porous, less robust skeletons whilst maintaining normal rates of linear extension, suggesting the integrity of coral skeletons are likely to be threatened under future ocean conditions. The biomineralisation processes of corals inhabiting extreme and marginal environments remains an underexplored area of research and has the potential to yield valuable insights into how corals might adapt to climate change and the mechanisms that underlie their resilience to global environmental change.

Dynamics of changes in the temperature coefficient of electrical conductivity of distilled water in conductometric cells during heating and cooling

The problem of taking into account the influence of air on the properties of distilled water is considered, namely the lack of a single generally accepted method for calculating such an influence. The sensitivity of the structure of water to the influence of external factors is described and the possibility of recording and studying such factors by changes in the temperature coefficient of electrical conductivity of water is shown. The dependences of the temperature coefficient of electrical conductivity of distilled water on the rate of change in water temperature, the degree of filling of conductometric cells, as well as on the intensity of the exchange of carbon dioxide between water and air across their interface have been studied. It is noted that these metabolic processes are currently insufficiently studied. A hardware-software measuring complex has been developed and manufactured to study the temperature coefficient of electrical conductivity of water when its temperature changes within the range of 20–55 °C. The temperature coefficient of electrical conductivity of water in sealed conductometric cells was measured at different degrees of filling the cells with distilled water and the rate of heating and cooling of water. The degree of filling of the cells varied within the range of 10–100 %, the rate of change in water temperature varied within the range of 0.04–2.00 °C/min. With a constant heating and cooling time of 15 minutes in all experiments, the change in speed was achieved by changing the temperature of the heating element. The integral temperature coefficient of electrical conductivity is calculated based on the initial and final values of electrical conductivity and water temperature in each measurement cycle. The dependences of the temperature coefficient of electrical conductivity on the rate of change in water temperature at several constant degrees of cell filling were obtained. It has been shown that with a constant ratio of the volumes of water and air in the cell and an increase in the rate of heating of water, the temperature coefficient of electrical conductivity of water decreases by 19–22 %. It has been established that at a constant rate of water heating, with a decrease in the volume of water in the cell, the temperature coefficient of electrical conductivity of water decreases by 40–42 %. The results obtained can be used to quantify the dissociation coefficient of carbonic acid, the mobility of hydrogen ions, as well as the intensity of the gas exchange process under various external influences on water. Refinement of data on the electrical conductive properties of water and processes at the water/air interface is necessary for the development of models of atmospheric phenomena and climate change, as well as for the creation of sensors for weak changes in environmental parameters for the purposes of both environmental monitoring and medical diagnostics.

Monitoring the Expansion of Unplanned Urbanization and its Impact on Climate Change based on Google Earth Engine Service, a Case Study of Baghdad / Iraq

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C.

At the Crossroads of Physics and Climate: A Comprehensive Review of Climate Change in Afghanistan

Climate change in Afghanistan is important because it has severe impacts on various aspects of life, particularly the agricultural sector, which is the main source of income for the country's economy. More than 80% of Afghanistan's population is involved in farming, herding, or both, despite only 12% of the country's total land area being arable. Climate change is causing successive droughts, health problems, and damage to the environment, animals, and agriculture products in the country. The country has been experiencing an increased frequency of drought since the early 1960s, and it is anticipated that drought could become the norm by 2030. Furthermore, higher temperatures are expected to exacerbate flooding events due to more rapid snow melt in the spring. Climate change also has adverse effects on human health, water resources, and environmental diversity. Therefore, understanding the impact of climate change in Afghanistan is crucial for developing strategies to mitigate its effects and preserve the country's production capacity.This research delves into the mathematical and physical modeling of climate change in Afghanistan.

Construction of wetlands in La Piedad Lagoon: a strategy to mitigate climate change in Mexico

ABSTRACT The freshwater systems located within the Metropolitan Area of Mexico City (Mexico City and the State of Mexico) are depleted. La Piedad Lagoon is recognized as a critical water resource as it serves as a run-off reservoir during the rainy season. However, the Lagoon is highly contaminated by untreated wastewater discharge due to disorderly and unplanned urban development. Inadequate sanitation has caused significant biodiversity loss and negatively impacted the population's health. Climate change models estimate that surface water availability could be reduced by over 30% in the short term, increasing the risk of a regional water crisis. This complex situation is urgent and demands the ecological restoration of La Piedad Lagoon as it provides an alternative source of water for Mexico City. Here, the intervention and efforts currently performed to rescue La Piedad Lagoon are described, involving its transformation and analysis of the environmental conditions of the area, land uses and ownership, as well as available infrastructure. Finally, it examines key environmental parameters for the construction of wetlands in the Lagoon. The removal contamination capacities of Eichhornia crassipes and Lemna minor, two aquatic plants in the area, were assessed. The lessons learned from this intervention can provide valuable lessons.