Future Changes Research Articles

The Lifetime Health and Economic Burden of Smokeless Tobacco use in Bangladesh, India, and Pakistan: Results From ASTRAMOD.

Under the current policy landscapes, the lifetime health and economic burden of smokeless tobacco (ST) products, consumed by over 297 million ST users in South Asia, is unknown. The aim of this study was to estimate the lifetime health effects and costs attributable to current and future ST use in Bangladesh, India, and Pakistan where the majority of ST users live. We developed a Markov-based state-transition model (ASTRAMOD) to predict the lifetime costs of treatment of four diseases (oral, pharyngeal, esophageal cancers, and stroke) and disability-adjusted life years (DALYs), attributable to the current and future use of ST under existing ST policy scenario. Country-specific Global Adult Tobacco Surveys, life tables, and meta-analyses of South Asian and South East Asian studies were used to populate the model. A probabilistic sensitivity analysis evaluated the uncertainty in model predictions. If there were no change in the current ST policies, the lifetime ST-attributable treatment costs would be over US$19 billion in India, over US$1.5 billion in Bangladesh, and over US$3 billion in Pakistan. For all countries, the attributable costs are higher for younger cohorts with costs declining with increasing age for those over 50. The model predicted that a typical 15-year-old male adoloscent would gain 0.07-0.18 life years, avert 0.07-0.19 DALYs, and generate a cost-savings of US$7-21 on healthcare spending if ST policies were changed to eliminate ST use. Policy interventions aimed at decreasing the uptake of ST and increasing quitting success have the potential to substantially decrease the economic and health burden of ST. This study provides the most comprehensive estimates of the lifetime health and economic burden of ST by 5-year age and sex cohorts. This is also the first study that highlights the scale of health and economic burden of ST in Bangladesh, India, and Pakistan if there were no changes in the current ST policies. Policymakers and practitioners can use the reported data to justify their decisions to improve current ST policies and practices in their country. Researchers can use the ASTRAMOD methodology to estimate the impact of future ST policy changes.

Value change, reprogenetic technologies, and the axiological underpinnings of reproductive choice.

Value change is a phenomenon that is gaining increasing attention in ethical analyses of technologies. However, a comprehensive study of how reprogenetic technologies and values coevolve is lacking. To remedy this gap, in this overview article, I address the relationship between reprogenetics and value change. This contribution thus argues for the importance of investigating the phenomenon of value change in relation to the technological controversies discussed in bioethics. To meet this goal, I begin by clarifying, first, how technologies shape reproductive choice. I then clarify what "values" and "moral values" are, how reprogenetic technologies are value laden, and what values are often discussed in reprogenetics debates. Next, I show five types of value changes that have occurred in advance in reproductive and genetic technologies. Finally, I argue for the bioethical relevance of discussing future techno-value change, pointing out the descriptive and normative challenges of this philosophical endeavor.

A warming-induced glacier reduction causes lower streamflow in the upper Tarim River Basin

Study areaThree headwater basins of the Tarim River Study focusThis study built upon existing knowledge by incorporating threshold melting temperatures into the Spatial Processes in Hydrology (SPHY) model and decomposing the streamflow changes into individual runoff components. Additional glacier retreat rate data were used to calibrate the improved SPHY, which provides better constrained estimates of glacier response to climate change. We employed bias-corrected simulations from Coupled Model Intercomparison Project Phase 6 (CMIP6) models to drive the improved SPHY under four future scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). New hydrological insights for the regionModeling results show accelerated glacier melt in the future, with glacier volume at the end of this century projected to be less than 20% of the current level under SSP5–8.5. Furthermore, future streamflow is expected to decrease, most notably in the Karakash River basin under the SSP1–2.6 scenario. The reduction in streamflow, mainly during the summer months, results from decreased glacier melt runoff, which cannot be offset by increased rainfall runoff. Additionally, the implementation of climate change mitigation measures could delay glacier loss, but the dominant component of runoff would still shift from current glacier melt to future rainfall. Plain language summaryGlobal warming accelerates the hydrological cycle, altering the temporal and spatial patterns of streamflow, especially in cold regions, such as the Tarim River Basin. Understanding future streamflow changes is important for the economic development and ecological protection of this region, which will help to manage the risk of water shortages and to plan engineering works to mitigate water shortage crises. We therefore used state-of-the-art climate simulation to project future streamflow changes over three basins of the Upper Tarim River. We found that under all future scenarios, runoff will decrease in these basins. This decrease is mainly due to glacier retreat and cannot be mitigated by anticipated increases in rainfall in the future.

Impacts of land use and cover changes on ecosystem service values from 1992 to 2052 in Gena District, Southwest Ethiopia

Land use and cover changes alter the functions and structures of ecosystem, resulting in variations in Ecosystem Service Values (ESVs). Thus, we examined the impacts of land use/land cover (LULC) changes on ESVs from 1992 to 2052 using geospatial technologies. The Landsat images were classified using the supervised maximum likelihood classification technique, and future changes in LULC were predicted using the CA-Markov model. Ecosystem Service Values coefficients were adopted from empirical studies and ESVs changes were evaluated based on the benefit transfer method using LULC data for the study periods, with their corresponding modified ESVs coefficients. The results revealed that, the proportions of grassland, forestland and shrubland declined by 58.5 %, 48.15 % and 33.48 %, respectively, from 1992 to 2022. Simultaneously, the highest rate of expansions of waterbodies (34 times), farmland and settlement threefold as well as bareland (60.2 %) from 1992 to 2022 was noticed. As a result, decreasing trends were experienced in the total ESVs of the district from US$33.6 million in 1992 to US$27.79 million in 2022, and are anticipated to further decline to US$25.94 million in 2052. The ESVs of forestland, shrubland and grassland shrank from 53.1 %, 40 % and 2.78 % in 1992 to 33.28 %, 33.16 % and 1.4 % in 2022 these changes are anticipated to continue for the next three decades, except trend for the increase in grassland ecosystem service value. Therefore, the government should redesign effective land management strategies to alleviate the negative consequences of LULC changes, facilitate payment for ecosystem services, and design ecotourism to boost the income of residents for major land use management-based production systems to increase the ESV in the district.

A rapid approach to assessing the vulnerability of Mozambican fisheries’ species to climate change

Mozambique is amongst the most vulnerable of Western Indian Ocean (WIO] countries to the impacts of climate change on its marine fisheries. We used rapid assessment methods to evaluate sensitivity, exposure and vulnerability of fisheries species to climate change, appropriate for data-deficient, developing countries in the region. Species were selected based on their importance in industrial and artisanal fisheries’ landings, further prioritized by local experts. Species’ attributes likely to be sensitive to climate change were identified and scored, utilizing life history or biological characteristics. Sea Surface Temperature (SST] was the most prominent climate exposure factor and for which we could confidently predict likely future change. Most species had low or medium overall sensitivity to climate change, with only eight considered highly sensitive. Climate exposure (SST] scores were high off northern Mozambique, while the central and southern regions were generally of medium exposure. Ten species received a High vulnerability score, 14 were Medium, and 16 had a Low vulnerability score. The highly vulnerable species were all fishes, apart from one crustacean; 4 of the 9 were strongly estuarine-associated; the most vulnerable species was the parrotfish Scarus ghobban. This is the first attempt to use a rapid, semi-quantitative, specialist- and trait-based vulnerability assessment of the anticipated effect of climate change on marine fisheries species in the WIO. Challenges experienced were data paucity, limited resources, the large study area, and the complex oceanography of the region. However, a simple methodology was developed, derived from efforts elsewhere, and which can be used to undertake similar assessments for other WIO countries. Raised awareness of climate change among small-scale fishing communities is a necessity, combined with adaptation by promoting fisheries co-management. Also required is support from government to ensure that people can be flexible to change.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China

Abstract. The subtropical forests of China play a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (e.g. leaf area index (LAI)), CO2 fertilisation, and climate change (CC) on the annual gross primary productivity (GPP) dynamics of different subtropical forest types are essential for mitigating carbon emissions and predicting future climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China's subtropical forests from 2001 to 2018. Simulated GPP showed a significant increasing trend (20.67 gCm-2yr-1, p<0.001) under the interaction effects of FCC, LAI change, rising CO2, and CC. The CO2 fertilisation (6.84 gCm-2yr-1, p<0.001) and LAI change (3.79 gCm-2yr-1, p=0.004) were the two dominant drivers of total subtropical forest GPP increase, followed by the effects of FCC (0.52 gCm-2yr-1, p<0.001) and CC (0.92 gCm-2yr-1, p=0.080). We observed different responses to drivers depending on forest types. The evergreen broad-leaved forests showed the maximum carbon sequestration rate due to the positive effects of all drivers. Both the FCC (0.19 gCm-2yr-1, p<0.05) and CC (1.22 gCm-2yr-1, p<0.05) significantly decreased evergreen needle-leaved forest GPP, while their negative effects were almost offset by the positive impact of LAI changes. Our results indicated that LAI outweighed FCC in promoting GPP, which is an essential driver that needs to be accounted for in studies and ecological and management programmes. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes and provides valuable information for policy makers to better manage subtropical forests to mitigate climate change risks.

Law, Colonial-Capitalist Floods, and the Production of Injustices in Eastern India: Insights for Climate Adaptation

Abstract Floods are not merely ‘natural’ disasters; rather, they emerge as socio-natural phenomena shaped by political, social, and economic processes. Law plays a pivotal role in producing and sustaining these processes and contributes to the creation of unjust environments. Drawing on political ecology and environmental history, this article analyzes the role of law and its interactions with colonialism and capitalism in the Damodar river valley in Eastern India. The Damodar river valley is an intensely engineered and hazardous region, a site of multiple interventions and developmental and ecological experiments for over a century. Colonial and post-colonial legacies have left a lasting imprint on legal, policy, and institutional frameworks, establishing a path-dependent trajectory for addressing future climate change adaptation challenges. While focusing on a specific case study, the article's approach and findings have broader significance, especially in the context of climate adaptation. The central argument underscores the need to understand the political and legal dimensions of flooding, and reinforces the need for a shift beyond incremental adjustments that do not tackle the underlying structures that produce the injustices associated with floods. It highlights the importance of ‘transformative adaptation’ approaches that address the root causes of climate-related disasters, such as restructuring power relations between actors, reconfiguring governance structures, and scrutinizing ideologies that mediate how water is used and distributed.

Model predictive control of blood glucose in critically ill patients using Gaussian processes

Abstract Stress-induced hyperglycemia and high glycemic variability are common in intensive care patients. Several clinical studies show the benefits of tight blood glucose control, including lower mortality. This article presents an algorithm for blood glucose control in the intensive care unit. An Unscented Kalman Filter is developed to estimate the glucose metabolism state and time-varying insulin sensitivity from blood glucose measurements. Gaussian Processes are used to predict future insulin sensitivity changes based on previous measurements. A model predictive controller is designed to estimate optimal insulin infusion based on current state, predicted insulin sensitivity and planned nutrition. The developed control algorithm allows individualized blood glucose control with reduced glycemic variability and reduced risk of hypoglycemia in the intensive care unit.

Vegetation redistribution is predicted to intensify soil organic carbon loss under future climate changes on the Tibetan Plateau

Vegetation redistribution may bring unexpected climate-soil carbon cycling in terrestrial biomes. However, whether and how vegetation redistribution alters the soil carbon pool under climate change is still poorly understood on the Tibetan Plateau. Here, we applied the G-Range model to simulate the cover of herbs, shrubs and trees, net primary productivity (NPP) and soil organic carbon density (SOCD) at the depth of 60 cm on Tibetan Plateau for the individual years 2020 and 2060, using climate projection for Representative Concentration Pathways (RCP) 4.5 and RCP8.5 scenarios with the RegCM4.6 model system. Vegetation redistribution was defined as the transitions in bare ground, herbs, shrubs and trees between 2020 and 2060, with approximately 57.9 % (RCP4.5) and 59 % (RCP8.5) of the area will redistribute vegetation over the whole Tibetan Plateau. The vegetation cover will increase by about 2.4 % (RCP4.5) and 1.9 % (RCP8.5), while the NPP and SOCD will decrease by about −14.3 g C m−2 yr−1 and −907 g C m−2 (RCP4.5), and −1.8 g C m−2 yr−1and −920 g C m−2 (RCP8.5). Shrubs and trees will expand in the east, and herbs will expand in the northwest part of the Plateau. These areas are projected to be hotspots with greater SOCD reduction in response to future climate change, and will include lower net plant carbon input due to the negative NPP. Our study indicates that the SOC pool will become a carbon source under increased air temperature and rainfall on the Tibetan Plateau by 2060, especially for the area with vegetation redistribution. These results revealed the potential risk of vegetation redistribution under climate change in alpine ecosystems, indicating the policymakers need to pay attention on the vegetation redistribution to mitigate the soil carbon emission and achieve the goal of carbon neutrality on the Tibetan Plateau.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Vulnerability of geoheritage sites in South Africa to climate change: Examples from the Eastern Cape Province

Climate change and variability have potential to impact on all types of heritage sites globally, through increasing weathering and erosion of geological and built heritage, changes in biome and ecosystem viability, sea-level rise and coastal flooding, and increased climate hazard events. This study examines the most likely impacts of climate change on different heritage sites in South Africa, with a particular regional focus on sites of potential geoheritage interest in the Eastern Cape Province of the country. The diverse examples described in this study are: (1) Klasies River cave, which is a Middle Stone Age archaeological site; (2) different localities along the coast where faunal footprints are preserved within aeolianite that dates mainly to the last interglacial period; (3) different localities inland where rock art panels are present within caves or rock shelters; and (4) Cape St Francis, which is a coastal foreland where different phases of late Quaternary coastal dunes with archaeological interest are preserved. Results show that many of these very varied sites of geoheritage interest are not protected as National or Provincial Heritage Sites under current legislation, which is weak and inconsistently applied across the country. This means that no specific management plans, under national policy guidelines, are in place for mitigating against the impacts of present and future climate change and variability, either regionally or nationally. These climate changes include: sea-level rise and increased coastal storminess and wave attack; and changes in weathering and erosion regimes as a result of increased temperatures and likely decreased rainfall or higher rainfall variability. A better appreciation of the interconnections that exist between geology and natural and cultural heritage may lead to valuing landscape and site geoheritage more highly at the local, national and international level.

Evaluation and projection of the summer precipitation recycling over the Tibetan plateau based on CMIP6 GCMs

AbstractBased on ERA5 and 23 CMIP6 models under three Shared Socioeconomic Pathways (SSPs), we assess the performance of CMIP6 models in reproducing the summer precipitation, evaporation and precipitation recycling ratio (PRR) over the Tibetan plateau (TP) during 1981–2014. We also project future changes of the summer precipitation, evaporation, PRR and moisture transport over the TP during 2021–2100. CMIP6 models and their ensemble mean to reproduce the temporal and spatial characteristics of precipitation, evaporation, PRR and moisture transport over the TP. And the performance in simulating precipitation is better than that in simulating evaporation and PRR. The precipitation and evaporation under the three SSPs will increase, with the significant upward trends of 0.84%/decade ~2.79%/decade and 0.80%/decade ~2.14%/decade, respectively. However, the PRR will increase slightly (0.13%/decade) and then marginally reduce (−0.22%/decade). In the late 21st century, regional mean summer precipitation, evaporation and PRR over the TP in SSP5‐8.5 will change by about 20.13%, 17.14%, and −0.77%, respectively. From spatial distribution, precipitation will increase in most parts of the TP in the 21st century, especially in the western part of the plateau, reaching more than 50% in of the late 21st century, the area of PRR decreased gradually expanded, with the maximum reduction (15%–20%) of PRR in the western region. It indicates that more precipitation in the western of the plateau depends on the external moisture transport. The PRR in the southern part of the plateau will increase, with a maximum of more than 20%, which indicates the contribution of the external water vapour transport to the southern precipitation will decrease, and that of the internal cycle (local evaporation) to the local precipitation will increase. In conclusion, the increase of the regional mean precipitation over the TP in the near and mid‐21st century is mainly due to the local evaporation, while the increase of the summer precipitation in the late 21st century is due to both the local evaporation and the external water vapour advection, it shows that the precipitation generated by the inner circulation is becoming weaker and more dependent on the external water vapour transport.

The Application of Intelligent Architectural Design in Energy Saving and Sustainable Development

In today's context, energy conservation, emissions reduction, and sustainable development are highly prioritized, and the architectural industry must recognize the value of intelligent buildings to maintain stable growth amidst fierce competition. Intelligent design not only enhances the energy efficiency of buildings, achieving energy savings and emissions reductions, but also significantly improves their safety and convenience, providing a more comfortable living environment for residents. Moreover, it is a key force driving technological innovation in the architecture industry, leading it towards more efficient, environmentally friendly, and sustainable development. In terms of application principles, intelligent architectural design emphasizes coordination, integrity, and flexibility. It requires considering the harmonious unity between architecture and the environment, ecosystems, and human needs during the design process, treating the building as an integrated system, and focusing on the flexibility and adjustability of the design to accommodate potential future changes. In specific applications, intelligent designs for natural lighting, ventilation, and electrical systems are important manifestations of intelligent architectural design. These applications, through intelligent control systems and devices, achieve efficient management and adjustment of the building environment, thereby enhancing the building's energy efficiency and contributing to sustainable development.

Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

ABSTRACT The study examines the complex dynamics of changes in LULC over three decades, focused on the years 1992, 2002, 2012, and 2022. The research highlights the significance of comprehending these alterations within the framework of environmental and socio-economic consequences. The changes in land use and land cover (LULC) have significant and far-reaching effects on ecosystems, biodiversity, and human livelihoods. This study offers useful information for politicians, conservationists, and urban planners by examining historical patterns and forecasting future changes. The study utilized a Multilayer Perceptron Neural Network (MLP-NN), a well-known machine learning technique that excels at collecting intricate patterns. This model’s design had three layers: input, hidden, and output. The model underwent 10,000 iterations during its training process, and a thorough statistical analysis was conducted to assess the impact of each driving component. The MLP-NN model demonstrated impressive performance, with a skill measure of 0.8724 and an accuracy rate of 89.08%. The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence likely was found to be a significant factor that had a substantial impact on the accuracy of the model. The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. Identifying the primary causes of model performance and understanding their implications may help to enhance land management strategies, encourage spatial planning, guide accurate decision-making, and facilitate the development of policies that align with sustainable growth and development.

Feeling climate injustice: Affective climate violence, greenhouse gaslighting and the whiteness of climate anxiety

This paper explores the process of feeling climate injustice. It aims to situate climate distress as an issue of justice, in order to generate more politically accountable and empowering responses. It firstly situates climate anxiety, solastalgia and climate disaster trauma as symptoms of affective climate violence, where harm that could have been prevented was instead consciously and systematically exacerbated by fossil fuelled political regimes. It articulates witnessing as a practice of affective climate justice, an approach that would recognise climate distress as violence, and offer support, apology and redress for this violence, including through seeking to prevent future climate change. However, the second section outlines how, in perverse efforts to maintain fossil fuel interests, climate distress is often further amplified through practices of greenhouse gaslighting – denying, deriding and dismissing people's experiences of harm. Greenhouse gaslighting is outlined as a patriarchal practice of emotional abuse that is enabled by and seeks to perpetuate white-colonial-extractivism. Thirdly, the paper argues that even within progressive circles, current efforts to witness climate distress potentially fail to enact affective climate justice due to discourses that centre whiteness and privilege, rather than recognising and responding to the different and unequal forms of affective climate violence experienced by diverse peoples.

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

AbstractThe present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Combining mesocosms with models reveals effects of global warming and ocean acidification on a temperate marine ecosystem.

Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modeling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming, ocean acidification and fisheries exploitation on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioral responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of fisheries exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fishes. However, under warming and acidification combined, biomass increases at higher trophic levels will be lower or absent, while in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index decline under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multilevel mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.

Future projection of tropical cyclone genesis in the Western North pacific using high-resolution GCMs and genesis potential indices

The study employed high-resolution atmospheric general circulation models (AGCM) to simulate tropical cyclones (TCs) and evaluated two TC genesis potential indices in reflecting projected TC changes in the western North Pacific (WNP) under a warming scenario. Both indices accurately represented the seasonal variation of TC genesis frequency (TCGF) and its spatial distribution in historical simulations and observation data. The widely-used TC genesis potential index (χGPI) projected a significant increase in TCGF in response to a warmer ocean surface. However, this projection conflicted with the significant reduction in the model projection due to the dominant control of SST on the χGPI. Higher SST in remote ocean basins often over dominated the destabilization effect of in-situ warmer SST and caused more stable atmospheric conditions in the WNP, resulting in fewer TC occurrences. By contrast, the revised index (χMqGPI), which considers gross moisture condensation, projected a TCGF decrease that more accurately reflected the decreasing trend of TCGF in the warming simulations by AGCM, although the degree of reduction was smaller than that derived directly from TC detection scheme. The results suggest the plausibility of using χMqGPI, based on the results of multimodel coarse-resolution CMIP6 climate models, to project future changes in TCGF in the WNP.