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ABSTRACT The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region. [ABSTRACT FROM AUTHOR]

Forest restoration is a powerful tool that combats forest loss and mitigates climate change. Our review asks how climate change considerations are integrated into forest restoration. We asked: (1) How many articles about forest restoration practices discuss climate change; (2) has this changed over time; and (3) when climate change is mentioned, how is it considered? We used the framework created by Simonson et al. in 2021, which describes seven unique areas where practitioners can consider climate change impacts in their projects. These areas include adjusting species and location for projected changes to fitness, mitigating risks, and aligning practice with policy. We reviewed the literature through Web of Science and evaluated publications for inclusion of these considerations. We found that the number of publications about forest restoration projects has increased significantly since the early 2000s, exceeding 200 in 2021. The proportion of these including the terms "climate change" or "global warming," has also increased (35% in 2021). Climate change concepts were only considered in what we defined as an "in‐depth" context in 83 of the 186 publications we reviewed (45%). However, the majority of publications (115 of 186, 61%) did incorporate at least one of the seven considerations, even if they did not satisfy our definition for in‐depth climate change consideration. Several articles discussed other considerations not described by Simonson et al. (2021), leading us to recommend adding an eighth criterion to their framework: consider place‐based communities and Indigenous Knowledge Holders' responses to climate change in forest restoration. [ABSTRACT FROM AUTHOR]

In high-altitude regions, such as the Peruvian Andes, understanding the transformation of precipitation types under climate change is critical to the sustainability of water resources and the survival of glaciers. In this study, we investigate the distribution and types of precipitation on a tropical glacier in the Peruvian Central Andes. We utilized data from an optical-laser disdrometer and compact weather station installed at 4709 m ASL, combined with future climate scenarios from the CMIP6 project, to model potential future changes in precipitation types. Our findings highlight that increasing temperatures could lead to significant reductions in solid-phase precipitation, including snow, graupel and hail, with implications for the mass balance of Andean glaciers. For instance, a 2 °C rise might result in less than 10% of precipitation as solid, in regard to the present day, transforming the hydrological processes of the region. The two future climate scenarios from the CMIP6 project, SSP2-4.5 and SSP5-8.5, offer a broad perspective on potential climate outcomes that could impact precipitation patterns in the Andes. Our study underscores the need to revisit and expand our understanding of high-altitude precipitation in the face of climate change, paving the way for improved water resource management strategies and sustainable glacier preservation efforts in these fragile ecosystems. [ABSTRACT FROM AUTHOR]

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information. [ABSTRACT FROM AUTHOR]

The long-term evolution of wind resources under climate change is investigated and discussed in the two largest markets of the Western world: Europe and North America. Wind speed projections from the Coupled Model Intercomparison Project 6 are employed considering the Shared Socioeconomic Pathways, the most up-to-date and elaborate climate scenarios proposed by the Intergovernmental Panel on Climate Change - specifically, a middle-of-the-road scenario and an augmented emissions scenario. In Europe, projections indicate a widespread, substantial decline in wind resources (~15% in wind power density). Whereas an overall decrease in wind energy is also anticipated in North America, this appears to be highly concentrated in different foci. Additionally, the evolution of wind resources in this continent is projected to be notably season-dependent, with strong increases of over 60% and decreases of up to 50%. Conversely, the evolution in Europe shows changes of lesser magnitude but more constant throughout the year. [ABSTRACT FROM AUTHOR]

Abstract Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Abstract In high-altitude regions, such as the Peruvian Andes, understanding the transformation of precipitation types under climate change is critical to the sustainability of water resources and the survival of glaciers. In this study, we investigate the distribution and types of precipitation on a tropical glacier in the Peruvian Central Andes. We utilized data from an optical-laser disdrometer and compact weather station installed at 4709 m ASL, combined with future climate scenarios from the CMIP6 project, to model potential future changes in precipitation types. Our findings highlight that increasing temperatures could lead to significant reductions in solid-phase precipitation, including snow, graupel and hail, with implications for the mass balance of Andean glaciers. For instance, a 2 °C rise might result in less than 10% of precipitation as solid, in regard to the present day, transforming the hydrological processes of the region. The two future climate scenarios from the CMIP6 project, SSP2-4.5 and SSP5-8.5, offer a broad perspective on potential climate outcomes that could impact precipitation patterns in the Andes. Our study underscores the need to revisit and expand our understanding of high-altitude precipitation in the face of climate change, paving the way for improved water resource management strategies and sustainable glacier preservation efforts in these fragile ecosystems.

Water conservation is an essential parameter for maintaining the ecological balance. The Three-River Source Region (TRSR) cannot be an exception, since it is one of the most influential water conservation reserves in the Qinghai–Tibet Plateau in China. Therefore, the realization of its scientific significance can determine its future regional sustainable development and the optimal allocation of water resources. The study of the past is critical to predict the future temporal and spatial changes in the water conservation of the TRSR. The first task of this study was to obtain the optimal runoff simulations in the TRSR from 1981 to 2014 by calibrating the adjustable the parameters of the Soil and Water Assessment Tool (SWAT) model. Then, the water conservation of the TRSR from 1981 to 2014 was quantified. Finally, the future water conservation of the TRSR was also predicted using the optimal SWAT model. The predication took into consideration the three terms including the near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2099) in three different climate scenarios of SSP1-1.9 (SSP119), SSP2-4.5 (SSP245), and SSP5-8.5 (SSP585). The main findings are as follows: (1) both the coefficient of determination (R2) and Nash–Sutcliffe efficiency coefficient (NSE) for runoff simulation on the three sub-basins reached above 0.78 during the calibration and validation periods, which indicates the reasonableness of the SWAT model in the TRSR. (2) From 1981 to 2014, the water conservation capacity of the TRSR showed an increasing trend (0.5135 mm/year), and its changes had significant positive correlations with precipitation and temperature. The Yellow River Source (YR) and the Yangtze River Source (YZ) had the strongest and weakest water conservation capacities, respectively. (3) From 2015 to 2099, the water conservation in the TRSR in the SSP119, SSP245, and SSP585 scenarios decreased first and then increased, increased first and then decreased, and increased steadily, respectively. [ABSTRACT FROM AUTHOR]

This study proposes a novel fusion framework for flood forecasting based on machine-learning, statistical, and geostatistical models for daily multiple-step-ahead and near-future under climate change scenarios. An efficient machine-learning model with three remote-sensing precipitation products, including ERA5, CHIRPS, and PERSIANN-CDR, was applied to gap-fill data. Four individual machine-learning models, including Random Forest, Multiple-Layer Perceptron, Support Vector Machine, and Extreme Learning Machine, were developed twelve days ahead of streamflow modeling. Then, three fusion models, including Random Forest, Bayesian Model Averaging, and Bayesian Maximum Entropy, were applied to combine the outputs of individual machine-learning models. The proposed framework was also implemented to downscale the precipitation variables of three general climate models (GCMs) under SSP5-8.5 and SSP1-2.6 scenarios. The application of this approach is investigated on the Kan River, Iran. The results indicated that individual models illustrated weak performance, especially in far-step-ahead flood forecasting, so it is necessary to utilize a fusion technique to improve the results. The RF model indicated high efficiency in the fusion step compared to other fusion-based models. This technique also demonstrated effective proficiency in downscaling daily precipitation data of GCMs. Finally, the flood forecasting model was developed based on the fusion framework in the near future (2020–2040) by using precipitation data from two scenarios. We conclude that flood events based on SSP5-8.5 and SSP1-2.6 will increase in the future in our case study. Also, the frequency evaluation shows that floods under SSP1-2.6 will occur about 10% more than SSP5-8.5 in the Kan River basin from 2020 to 2040. [ABSTRACT FROM AUTHOR]

This paper's objective and novelty is to understand the impact of different natural ventilation strategies as well as the effect of a proposed smart controlled ventilation strategy on cooling energy saving and thermal comfort in a residential building. Afterwards, an investigation of the Climatic Potential of Natural Ventilation (CPNV) in hot and dry climates, and particularly the potential of the night ventilation strategy under present and future climates, was made. Two prediction climate change scenarios, RCPs 2.6 and 8.5, were considered for two periods. The results, obtained by a validated simulation, have shown the beneficial effect of night ventilation, since it could reduce cooling load by 9% and would keep the operative temperature below 30°C. Nevertheless, the potential of this natural ventilation strategy would be reduced in the future due to climate change. Indoor overheating has been projected to increase drastically. The summer thermal behaviour will be greatly aggravated in the future. Indeed, the CPNV in the summer nights will be decreased by 25%. However, the climate change will affect the winter natural ventilation potential in the future. The results also highlighted that the annual Total Climatic Potential of Natural Ventilation could be increased by 1% for RCP 2.6 and decreased by 4.9% for RCP 8.5. [ABSTRACT FROM AUTHOR]

This research focuses on assessing the environmental impact of heating and cooling systems in public housing projects, built between 2000 and 2020, in Tucuman, Argentina. It considers current and projected climate change scenarios. The study compares existing conditions with improvements suggested by IRAM 11603 for a thermo-energetic transition. Anticipating future energy consumption changes is vital for proposing sustainable retrofitting options to enhance affordability and energy efficiency, while ensuring occupants' thermal comfort. A public housing prototype in Tucumán serves as the case study. The methodology combines energy simulation and Life Cycle Assessment (LCA) to analyze current and future energy demands. The results show climate change's potential impact on housing thermal behavior and the necessity for improvements. In the base case, cooling demand exceeds 11 kWh/m2·year, while heating demand decreases by approximately 4 kWh/m2·year. Rehabilitation could reduce cooling demand by 57% and heating demand by 32.5%, considering future climate scenarios. Active architectural strategies are proposed for enhancing thermal performance and reducing energy consumption and carbon dioxide emissions. This study underscores the importance of analyzing future scenarios and implementing strategies for the thermo-energetic transition of existing social housing. [ABSTRACT FROM AUTHOR]

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of −9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (−0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows. [ABSTRACT FROM AUTHOR]

Strong night frosts in spring have the potential to cause severe damage to fruit trees, causing up to €3.3 billion of damage in Europe in 2017. If temperatures drop below − 2.2 °C after the growing season starts, this phenomenon is referred to as a false spring. For the Netherlands, the risk of false spring damage increased during the past 70 years, due to the decreasing time between the last strong night frost and the flower budburst. This raises the question of whether this trend will continue under future influences of climate change. To analyse the development of false spring damage risks, we developed the False Spring Damage Indicator (FSDI) model. Applying this model to the Dutch KNMI’14 climate change scenarios for 2050 and 2085 showed a decrease in false spring damage risk. The historical risk for 60% bud damage was reduced to around 40% for the moderate scenario in 2050 and to less than 15% for the severe scenario in 2050 and all scenarios in 2080. False spring damage varied spatially throughout the Netherlands. Currently, more severe damage can be expected in the east, while coastal regions in the north and west are less prone to false spring damage. In the future, this is expected to become more uniform over the Netherlands, as the risk in the east is predicted to decrease strongly, becoming similar to the low damage risk in the coastal regions. [ABSTRACT FROM AUTHOR]

Climate change has an immense impact on the environment, ecology, agriculture, and economy. As the most influential climate prediction platform in the world, the Intergovernmental Panel on Climate Change provides important data on predicted future climate change trends. Based on 10 models under the RCP8.5 and RCP4.5 emission scenarios, in this study, we obtained 0.5 × 0.5° data through down-scaling data processing and predicted future rainfall and temperature changes in the Beijing–Tianjin–Hebei region of China. Combined with historical observed data of 198 meteorological stations in the region, the relationships between the predicted values of the model and the measured values were analyzed using the Taylor diagram method. The results show that (1) the future precipitation capacity assessed by the global climate system is generally higher than observed data; (2) for assessment of future temperatures, RCP8.5 shows a larger increase than RCP4.5; (3) for the Beijing–Tianjin–Hebei region, our results show that the Atmospheric Chemistry Coupled Version of Model for Interdisciplinary Research on Climate-Earth System (MIROC-ESM-CHEM) model is more consistent with the evaluation of future precipitation capacity, the Hadley Centre Global Environment Model, version 2-Earth System(HadGEM2-ES) model is more consistent with the future RCP4.5 scenario temperature evaluation, and the MIROC-ESM-CHEM model is more consistent with the future RCP8.5 scenario temperature evaluation. HIGHLIGHTS The future changes and related uncertainties of rainfall and temperature in the Beijing–Tianjin–Hebei region on an annual average scale was analyzed.; Combined with observed historical data the relationships between the model's predicted values and the measured values were analyzed.; A more suitable climate model in Beijing-Tianjin-Hebei region was selected after comparison.;

Cotton cultivation in India is of vital importance to the national economy. However, the susceptibility of cotton crops to climate change poses significant challenges. Recognizing the spatial heterogeneity of climate change’s influence, comprehending its potential repercussions on cotton productivity is crucial. This research examines the effects of climate change on forthcoming cotton yields by utilizing the AquaCrop crop simulation model alongside eight climate models. A sensitivity analysis using the EFAST method is conducted to identify the key parameters influencing cotton yield, revealing eight influential parameters (Kcb, CGC, CCx, CDC, CCmin, wp, HIo, and Zmax) among the 30 parameters considered. These influential parameters are further employed to calibrate the model, automating, and optimizing the calibration process using a genetic algorithm to reduce the root mean square error (RMSE) between observed and simulated values. The AquaCrop model simulations demonstrate favourable agreement with observed data, exhibiting an RMSE of 0.15 t/ha during the calibration period (2001-2010) and 0.24 t/ha during the validation period (2011-2018). Utilizing the well-calibrated model, future projections are generated under different climate scenarios (SSP126 and SSP245) until the end of the century. The results indicate median yield increases of 4.4% and 1.9% in the SSP126 and SSP245 scenarios, respectively. Conversely, under the SSP370 and SSP585 scenarios, median yields are projected to decline by approximately 1.2% and 2% by the year 2100. It is an innovative study that combines AquaCrop, sensitivity analysis, and optimization to assess climate change impact on Indian cotton yields.

Study Region: Egypt is a country located in northeastern Africa. Study Focus: The research evaluated the random forest (RF) and extreme gradient boosting (XGB) as single models and the models' hybrid to predict the ETo for the baseline and future (2015–2099) period from Shared Socioeconomic Pathways (SSP1–26, SSP2–45 and SSP5–85) based on 18 GCMs models. New Hydrological Insights for the Region: The hybrid model has performed better than single models; compared RF and XGB to RF-XGB, the RMSE values were decreased in all zones esepically in zone 3 by 16.2 %, these results indicate that the highest performances of all models are observed in the middle and south Egypt, which exhibit the strongest correlation between temperature and ETo. For the SSP5–8.5 scenario, the ETo increased over the years for all zones; the ETo will increase by 4.38 %,3.71 %, 4.27 %, 2.16 %, 3.26 %, 1.35 %, 5.22 % at the year 2099 compared to the year 2015 for zone 1, 2, 3, 4, 5, 6 and 7 respectively. The Tmin and Tmax are the most critical factors that affect the ETo in all zones in the baseline and future scenarios. This study provides important insights into applying machine learning models to estimate ETo and its implications for future water management strategies. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.

The order Chiroptera includes 55 species in Nepal, which have been recorded within a wide range of elevation between 64 and 4154 m above sea level. Assessing the potential distribution ranges in present climatic scenarios and forecasting changes in distribution ranges in future climate have been adopted to predict likely adverse impacts on biodiversity and natural ecosystems. However, very few studies have assessed the impact of climate change on bats globally and locally. We assessed the current potential distribution ranges of six representative species of bats occurring in Nepal using species distribution models (SDMs) and predicted their responses in future climatic scenarios. The occurrences of the representative species of bats were projected with six to eight environmental variables under the different climatic scenarios; present, and socioeconomic pathways 4.5 and 8.5 for the years of 2050 and 2070 deploying maximum entropy modeling. We used Arc GIS 10.7.1 to calculate the distribution range, area, and elevation for the bat species. Among 18 uncorrelated bioclimatic variables eight variables significantly contributed to the SDMs. Among the six species, two showed a wider range of current distribution. Under the future climatic scenarios, distribution ranges and latitudinal and elevation shifting were found to be species specific. Future distribution ranges for two species are predicted to be constricted, but no significant changes are predicted for the others. Major parts of the current and future distribution ranges of the bat species lie outside the current protected areas of the country. Landscape level and species‐specific conservation policies are necessary for bat conservation, and future surveys of bats should be targeted in western Nepal. [ABSTRACT FROM AUTHOR]

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions. [ABSTRACT FROM AUTHOR]

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy's future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the main components of the hydrological cycle and plays a crucial role for water resources. It is sensitive to climate change, and therefore, estimating ET under changing climatic conditions is essential in comprehending hydrological processes, particularly in agricultural water management. In this study, the impact of climate change on ET in the Central Anatolia region of Türkiye was assessed. For this purpose, RCP4.5 and RCP8.5 climate change scenarios based on two Earth System Models, HadGEM2-ES and MPI-ESM-MR, were employed for three future time periods: 2025-2049, 2050-2074, and 2075-2098. As a baseline period for comparison, the time interval spanning 1980-2000 was considered. ET values were computed by using the Penman-Monteith equation, recommended by the Food and Agriculture Organization, along with five widely utilized methods. The study revealed a consistent increase in ET depending on the employed methods for the future period in response to climate change. The average of the ET amounts for the region was determined as 1089 mm for the reference period 1980-2000. As the average of the six methods utilized, amounts estimated by HadGEM RCP4.5, HadGEM RCP8.5, MPI RCP4.5, and MPI RCP8.5 models for the future period were obtained as 1 199, 1 285, 1 166, and 1 248 mm, respectively. Considering the results, it is found that the ET amount in the Central Anatolia region of Türkiye will increase by up to 11% by the end of this century under the optimistic RCP 4.5 scenario and by up to 19% under the extreme RCP 8.5 scenario. These findings regarding increased evapotranspiration play a significant role in water resource management and agricultural production planning in the region, holding crucial implications for sustainable agriculture.

The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a novel physiography-based method that uses soil texture-dependent potential infiltration coefficients and monthly rainfall data. The developed transient flow model was then applied to compute the temporal variation in the groundwater level in 34 observation wells from 1973 to 2020, taking into account the time series of spatially variable groundwater recharge, artificial groundwater recharge from 5 surface infiltration basins, pumping rates on 740 wells, and internal prescribed head cells to mimic water exchange between the wadis and aquifer. The quantified deviations in the computed hydraulic heads from measured water levels are acceptable because the database used to construct a scientifically sound and reliable groundwater model was limited. Further work is required to collect field data to quantitatively assess the local inflow and outflow rates between surface water and groundwater. The simulation of 12 climate scenarios highlighted a bi-structured north—south behaviour in the hydraulic heads: an increase in the north and a depletion in the south. A further increase in the pumping rate would, thus, be severe for the southern part of the Wadi El Bey aquifer. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the main components of the hydrological cycle and plays a crucial role for water resources. It is sensitive to climate change, and therefore, estimating ET under changing climatic conditions is essential in comprehending hydrological processes, particularly in agricultural water management. In this study, the impact of climate change on ET in the Central Anatolia region of Türkiye was assessed. For this purpose, RCP4.5 and RCP8.5 climate change scenarios based on two Earth System Models, HadGEM2-ES and MPI-ESM-MR, were employed for three future time periods: 2025-2049, 2050-2074, and 2075-2098. As a baseline period for comparison, the time interval spanning 1980-2000 was considered. ET values were computed by using the Penman-Monteith equation, recommended by the Food and Agriculture Organization, along with five widely utilized methods. The study revealed a consistent increase in ET depending on the employed methods for the future period in response to climate change. The average of the ET amounts for the region was determined as 1089 mm for the reference period 1980-2000. As the average of the six methods utilized, amounts estimated by HadGEM RCP4.5, HadGEM RCP8.5, MPI RCP4.5, and MPI RCP8.5 models for the future period were obtained as 1 199, 1 285, 1 166, and 1 248 mm, respectively. Considering the results, it is found that the ET amount in the Central Anatolia region of Türkiye will increase by up to 11% by the end of this century under the optimistic RCP 4.5 scenario and by up to 19% under the extreme RCP 8.5 scenario. These findings regarding increased evapotranspiration play a significant role in water resource management and agricultural production planning in the region, holding crucial implications for sustainable agriculture. [ABSTRACT FROM AUTHOR]

In the face of uncertain future climate change, city policy decision-makers must choose strategies that promote climate-resilient development. Nature-based solutions (NbSs) are considered important for solving urban problems, such as carbon neutrality, improving human well-being, and enhancing biodiversity. This study aimed to examine the possibility of using ecosystem services in decision-making for the future scenarios of the city, considering land use change and climate change in combination. Representative ecosystem services of eco-friendly urban paradigms were selected, and ecosystem services and synergies were analyzed when (1) only land use change was considered, and (2) climate change (RCP 8.5) and land use change were both considered. The selected ecosystem services were analyzed using statistical and model-based methods (InVest, MaxEnt) applied to each characteristic and collectible data. The results revealed that food (rice and fishery production) was affected by land use change, recreation and leisure was affected by external activities and climate change. Carbon uptake was affected by both climate change and land use change. Regarding synergy, the amount of carbon uptake decreased by − 1.60% in BAU (S1) and increased by 24.39% in Smart Shrinkage City (S3); however, regarding recreation, the differences depended on the scenarios, such as an increase in S1 by 5.26% and an increase in S3 by 0.99%. We expect that land use change and ecosystem services can provide policymakers with quantitative information, and they can discuss priorities and alternatives. [ABSTRACT FROM AUTHOR]

Many studies predict shifts in species distributions and community size composition in response to climate change, yet few have demonstrated how these changes will be distributed across marine food webs. We use Bayesian Additive Regression Trees to model how climate change will affect the habitat suitability of marine fish species across a range of body sizes and belonging to different feeding guilds, each with different habitat and feeding requirements in the northeast Atlantic shelf seas. Contrasting effects of climate change are predicted for feeding guilds, with spatially extensive decreases in the species richness of consumers lower in the food web (planktivores) but increases for those higher up (piscivores). Changing spatial patterns in predator–prey mass ratios and fish species size composition are also predicted for feeding guilds and across the fish assemblage. In combination, these changes could influence nutrient uptake and transformation, transfer efficiency and food web stability, and thus profoundly alter ecosystem structure and functioning. [ABSTRACT FROM AUTHOR]

Water conservation is an essential parameter for maintaining the ecological balance. The Three-River Source Region (TRSR) cannot be an exception, since it is one of the most influential water conservation reserves in the Qinghai–Tibet Plateau in China. Therefore, the realization of its scientific significance can determine its future regional sustainable development and the optimal allocation of water resources. The study of the past is critical to predict the future temporal and spatial changes in the water conservation of the TRSR. The first task of this study was to obtain the optimal runoff simulations in the TRSR from 1981 to 2014 by calibrating the adjustable the parameters of the Soil and Water Assessment Tool (SWAT) model. Then, the water conservation of the TRSR from 1981 to 2014 was quantified. Finally, the future water conservation of the TRSR was also predicted using the optimal SWAT model. The predication took into consideration the three terms including the near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2099) in three different climate scenarios of SSP1-1.9 (SSP119), SSP2-4.5 (SSP245), and SSP5-8.5 (SSP585). The main findings are as follows: (1) both the coefficient of determination (R2) and Nash–Sutcliffe efficiency coefficient (NSE) for runoff simulation on the three sub-basins reached above 0.78 during the calibration and validation periods, which indicates the reasonableness of the SWAT model in the TRSR. (2) From 1981 to 2014, the water conservation capacity of the TRSR showed an increasing trend (0.5135 mm/year), and its changes had significant positive correlations with precipitation and temperature. The Yellow River Source (YR) and the Yangtze River Source (YZ) had the strongest and weakest water conservation capacities, respectively. (3) From 2015 to 2099, the water conservation in the TRSR in the SSP119, SSP245, and SSP585 scenarios decreased first and then increased, increased first and then decreased, and increased steadily, respectively.

This study investigates the potential relevance, usefulness, and usability of climate change projections for Swedish agricultural planning and management. Although research indicates the importance of specific users acting as knowledge brokers for climate information, there are knowledge gaps concerning agricultural extension officers’ use of climate information. Through a survey and stakeholder workshops, perspectives of Swedish agricultural extension officers on climate change projections were collected. The results provide insights into “what” information in climate change projections that is relevant and “how” climate information may be presented and used. Based on the analysis of the workshop dialogues, four themes outlining the “what” and “how” were identified: (i) a need for additional climate indicators for Swedish agriculture, (ii) the criticalness of temporal precision, (iii) trade-offs between providing precision and an overview, and (iv) a relevance – usability contradiction. These results inform the basis for ongoing research and practical applications focused on agriculturally tailored climate information, as well as the broader development of climate service methodology. The study reveals a latent demand for climate change projections among respondents, indicating a perceived relevance of information on future climates, but limited current use and usability among agricultural extension officers. The requisite for tailored climate indicators is clear – in this case, for Swedish agricultural planning and management – but critical usability challenges need to be addressed to move from providing relevant information to achieving actual usage that can enhance the climate resilience in Swedish agriculture.

Climate change presents a significant challenge to lagoon ecosystems, which are highly valued coastal environments known for their provision of unique ecosystem services. As important as fragile, lagoons are vulnerable to both natural processes and anthropogenic activities, and this vulnerability is exacerbated by the impacts of climate change, which are likely to result in severe ecological consequences. The complexity of water quality (WQ) processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on lagoon environments. In this setting, a hybrid framework is introduced where Machine Learning (ML) and biogeochemical (BGC) models are integrated in a sequential modelling approach. This integration exploits the unique strengths offered by both models. The ML model allows capturing and learning linear and nonlinear correlations from historical data; the BGC interprets and simulates complex environmental systems subject to compounded pressures, building on identified causal relationships. Multi-Layer Perceptron (MLP) and Random Forest (RF) ML algorithms are trained, validated and tested within the Venice lagoon case study to assimilate historical WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the BGC model SHYFEM-BFM for 2019, 2050, and 2100 timeframes under RCP 8.5 are integrated into the ML model (composing the hybrid ML-BGC model) to evaluate Chl-a variations under future biogeochemical conditions forced by climate change projections. Moreover, the SHYFEM-BFM standalone Chl-a projections are also used to compare the hybrid and the BGC scenarios. Annual and seasonal Chl-a predictions are developed by classes based on two classification modes (median and quartiles) established on the descriptive statistics computed on historical data. Results from the case study showed as the RF successfully classifies Chl-a with an overall model accuracy of about 80% for the median and 61% for the quartiles modes. Concerning future climate change scenarios, results revealed a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) moving to the far future (2100), with an opposite rising trend for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2100 a strong increase in higher Chl-a values is also expected during the springtime one. The proposed hybrid framework represents a valuable approach to strengthen both multivariate Chl-a modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models.

In recent years, lifestyle changes and urbanization of societies, as well as macro-environmental changes, i.e. climate changes (CCs), have caused changes in the land spatial structure and the transfer of resources between different economic sectors of the land. The development of long-term spatial development plans (SDPs) needs to be compatible with CCs, especially in hyperarid areas with low supplies and high demands. In this research, machine learning methods; including Cellular Automata (CA), Random Forest (RF) and regression models through PLUS model were used to simulate the amount of supplies and demands based on land cover (LC) maps during the years 2000, 2010 and 2020 in the hyperarid areas of Kerman, Iran. Then, the best predicted model (Kappa = 0.94, overall accuracy = 0.98) was used to simulate changes in LC classes under climate change scenarios (CCSs) for 2050. The results showed the efficiency of machine learning in simulating land cover changes (LCCs) under CCSs. Findings revealed that SDPs of these areas are not compatible under any possible consideration of CCSs. The modeling results showed that spatial development plans under CCSs is not environmentally efficient and there is no compatibility between supplies, based on agricultural lands, and demands, based on increased population, by 2050. Overall, under the scenario of RCP 8.5, man-made, agriculture and natural LC classes with 106.9, 2.9, and 18.6% changes, respectively, showed the greatest changes compared to 2020. Population control, adjustment of infrastructures, and changes in LC plans can reduce socio-economical and socio-environmental problems in the future of hyperarid areas to some extent.

The urban lagoons receive strong anthropic pressures and the tensions often coexist between the "urban" and the "natural," and this consequently generates pollution and risks to the environment and human health. Our main objective was to study the water quality and to assess the bacteriological and eutrophication risks in the temperate shallow urban lagoon of the Parque Unzué (Gualeguaychú, Argentina), and to predict these risks in climate change scenarios considering the temperature and the rains as indicators. This urban shallow lagoon is in a recreative multiuse park (Gualeguaychú city), in the floodplain of the Gualeguaychú river in the Center-East of Argentina (Neotropical region). Twenty-seven sampling in 3 sampling points (n = 81) were carried out during 2015–2019, and physicochemical and bacteriological parameters were measured. Phosphorus, organic matter, chlorophyll-a (Chl-a), and total coliforms (TC) frequently had a moderate and very high contamination factor (CF), and the pollution load index (PLI) indicated contamination with a frequency of 74.1 %. Moreover, the index (WQI) indicated poor (66.7 %) and good (33.3 %) water quality. Bacteriological and eutrophication predictive risk models showed an increase of the TC and the Chl-a concentration generating a current and future high risk of contamination of the lagoon under climate change scenarios that could generate ecosystemic function losses in the short-term. [ABSTRACT FROM AUTHOR]

The growing sensitivity towards environmental sustainability, particularly in the light of climate change, requires a reflection on the role that historical buildings can play in heritage conservation. This research proposed an interdisciplinary approach combining climate and biological expertise to evaluate the biodeterioration risk associated with different IPCC outdoor climate scenarios. Conduction heat transfer functions and dose–response functions were used to model the indoor climate of a historical building and the related climate-induced risk of mould and pest proliferation. The approach was applied to a case study in the Mediterranean area, i.e., the State Archives of Palermo (Italy) housed in a 15th-century convent. In 2018, a survey conducted by ICPAL-MiC experts warned about past infestations and risks deriving from climate conditions. An environmental monitoring campaign conducted in 2021 allowed for the characterisation of the buffering effect in a historical building in response to the outdoor climate and the simulation of future indoor climate. Since indoor temperature and mixing ratio are expected to raise in future scenarios, it was found that there is an increased risk of insects' proliferation, combined with a decreased risk of spore germination and mould growth. Such evidence-based evaluation allows for the design of tailored preventive conservation measures to enhance the durability of both the archival collections and the building. [ABSTRACT FROM AUTHOR]

The potential impacts of climate change on the distribution of tuna in Pacific Island countries' exclusive economic zones have yet to be investigated rigorously and so their persistence and abundance in these areas remain uncertain. Here, we estimate optimal fisheries areas for four tuna species: albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares). We consider different climate change scenarios, RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, within a set of tuna catch records in the exclusive economic zone of Tonga. Using environmental and CPUE datasets, species distribution modelling estimated and predicted these fisheries areas in the current and future climatic scenarios. Our projections indicate an expansion in area and a shift of productive areas to the southern part of this exclusive economic zone of Tonga. This is an indication that future climatic scenarios might be suitable for the species under study; however, changes in trophic layers, ocean currents, and ocean chemistry might alter this finding. The information provided here will be relevant in planning future national actions towards the proper management of these species. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of climate change on saffron land rents in Khorasan Razavi province. To achieve this goal, relevant data and statistics were gathered from the National Meteorological Organization and the Agricultural Jihad Organization of Khorasan Razavi province between 2011-2020. In doing so, panel data of the Ricardian technique was employed. The net present value of land rent was subsequently calculated. Considering three climate change scenarios, A1, B1, and AB (pessimistic, optimistic, and intermediate) till 2100 of IPCC, the ultimate effect of climate change on saffron land rents in Khorasan Razavi province was predicted. The results indicated that all of the cities in Khorasan Razavi province had positive saffron land rents throughout the study period. Torbat-e-Jam had the highest value of saffron land rent with 535,079,456 Tomans, while Kashmar and Roshtkhar had the lowest value with 160,079,456 Tomans. Moreover, the trend of changes in land rents is positive in some cities and negative in others. Bajestan, Torbat-e- Jam, Jooybar, and Khoshab have an increasing trend in the value of saffron land rent. In addition, the trend of land rent in Bakharz, Taybad, Bardaskan, Torbat Heydariyeh, Joghatai, Chenaran, Khalilabad, and Khaf is decreasing. Based on a fixed effects model, the average annual temperature, cumulative precipitation, and maximum average temperature have a positive and significant effect. In contrast, the interactive effect of temperature, precipitation and minimum average temperature negatively and significantly affect saffron land rents. Considering climate scenarios until 2025, land rent will decrease by 11.0% per hectare. Moreover, changes in land rent caused by temperature and precipitation climate scenarios until 2100 are estimated to be 326.0%. Considering the average land rent and the total saffron cultivation area in Khorasan Razavi province, we will have a decrease of 7.93 billion Tomans in land rent. Given the inevitability of climate change, evaluating its effects can be effective in managing this phenomenon. [ABSTRACT FROM AUTHOR]

Net primary production is a sensitive index to changes in climatic factors and human activities. The present study was conducted with the aim of quantitatively evaluating the relative role of climate change and human activities on the development of desertification in Torbat Heydarieh and Bojnoord based on the spatio-temporal variations of net primary production. First, primary net production was estimated by ground measurement. Subsequently, it was simulated using the CASA model in statistical period between 1986 and 2017. Six scenarios were designed to determine the relative role of climate change and human activities in the expansion or inversion of desertification. The results of the research showed that net primary production has declined over the 31-year period and that the trend in its changes is negative. An examination of different desertification scenarios showed that the city of Bojnord is under the scenario of expansion of desertification due to climate change, and Torbet Heydarieh city is under the scenario of desertification expansion due to the interaction between climate change and human activities. Based on this, the trend of changes in actual and potential primary net production in Bojnord and Torbat Heydarieh cities was negative and its intensity was classified in the low to medium decline category. Also, the slope trend of changes in primary net production caused by human activities in Torbat Heydarieh city was positive and in the low to medium increase class, and in Bojnord city, it was negative and in the low decrease class.The study of the development or return of desertification scenarios showed that 61.23% of the total area of the study area was affected by the expansion and development of desertification due to the impacts of climatic factors, and 38.77% of those affected by the interaction of climatic factors and human activities in expansion of desertification.

This study aims to outline governance actions to mitigate impacts of climate change scenarios (drought) in Lake Trasimeno. A water balance model was integrated with a decision support system to evaluate water levels and water management scenarios. Water quality indices, based on Sentinel-2 images, allow the monitoring of parameters such as chlorophyll-a and turbidity. The possibility of diverting water volume from Montedoglio or Casanova reservoirs into Trasimeno Lake, according to the hypothesis of different "intervention thresholds levels" and volumes immitted, is investigated. Simulation results show that hydrometric zero represents the optimal threshold, maximizing the volume supplied and mitigating a water level decrease. An inverse correlation was observed between the lake water levels, turbidity, and chlorophyll-a concentration indices. Even if further studies to test the effects of mixing water with different physical and chemical characteristics are necessary, the proposed mitigation strategy could avoid the rapid growth of the above-mentioned quality parameters. [ABSTRACT FROM AUTHOR]

Regarding the predisposition of individuals to change when confronted with future climate scenarios, it is necessary to understand the perception of the groups under analysis and how to engage with society to collectively act toward climate change mitigation. A question remains to be answered: how to ensure that people engage, participate, and gain awareness of the undergoing problem that urban climate change poses for the future of cities' management? This research intends to contribute to solving this question by raising another question: "How willing are residents of the Alvalade neighborhood (Lisbon) to change their behavior and way of life when faced with climate change scenarios?" We present a methodology using microclimatic modeling with ENVI-met and questionnaires for the resident community. The present and future local climate scenarios (representing a present hot day versus the extreme IPCC RCP 8.5 scenario) were presented to a set of residents. These figures show the projected UTCI (Universal Thermal Climate Index) in a dramatic but accurate aggravated way. The inquiries allowed us to conclude that people are more aware of local climate change and health risks from extremely hot summer weather, but they continue to resist adopting behavior change for adaptation, although most of them declared this could be important to tackle climate change. The second conclusion is that people think that local authorities should be more active than themselves and will have greater responsibility for climate change adaptation and interventions. In times of climate crisis, the participation of the resident community can be an important help in decision making and finding measures to mitigate adverse climate effects in urban environments. [ABSTRACT FROM AUTHOR]

Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015–2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Since most of the countries in Africa rely on Agriculture supported by rainfall, they are especially vulnerable to dangers brought on by climate change, such as floods and droughts. Droughts in the Horn of Africa are immediately in danger of starvation, particularly in Ethiopia. This study uses ensemble GCM data at the Coupled Model Intercomparison Project 5 (CMIP5) to examine the future metrological and hydrological drafts of the chosen gauged watershed, specifically Gilgel Abay, Gumara, Megech, and Ribb. For the analysis, the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) were used, along with data from the RCP4.5 and RCP8.5 scenarios. The SPI and RDI were used to analyze metrological drought, and the SDI was used to identify hydrological drought in the mid (2040–2079) to long-term (2050–2079) period. Overall meteorological drought results show that annual time scale drought occurrence is higher than biannual time scale in both indices. Under the biannual time scale (SPI6) and annual time scale (RDI12) for RCP8.5 scenarios of all time horizons, 20.69% of the maximum drought record was observed. While the (SPI12) indices of RCP4.5 and RCP8.5 for all time domains (mid and far) display a drought frequency of 17.24%. And according to the RCP8.5 scenario, RDI6 also predicts that droughts would occur 18.10% more frequently in the far future. Additionally, the RCP8.5 scenario has the greatest frequency record for hydrological droughts across all time horizons at 18.10%. Gumara and Ribb gauged watersheds are typically the most susceptible to drought in the mid periods. The study revealed that drought analysis is important to take planning activities for those who are working as researchers, water resource managers and other professions in the basin. As a result, the study will assist the government, policymakers, and decision-makers in putting drought adoptions and early warning actions in place.

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a novel physiography-based method that uses soil texture-dependent potential infiltration coefficients and monthly rainfall data. The developed transient flow model was then applied to compute the temporal variation in the groundwater level in 34 observation wells from 1973 to 2020, taking into account the time series of spatially variable groundwater recharge, artificial groundwater recharge from 5 surface infiltration basins, pumping rates on 740 wells, and internal prescribed head cells to mimic water exchange between the wadis and aquifer. The quantified deviations in the computed hydraulic heads from measured water levels are acceptable because the database used to construct a scientifically sound and reliable groundwater model was limited. Further work is required to collect field data to quantitatively assess the local inflow and outflow rates between surface water and groundwater. The simulation of 12 climate scenarios highlighted a bi-structured north—south behaviour in the hydraulic heads: an increase in the north and a depletion in the south. A further increase in the pumping rate would, thus, be severe for the southern part of the Wadi El Bey aquifer.

Chelmos-Vouraikos National Park is a floristic diversity and endemism hotspot in Greece and one of the main areas where Greek endemic taxa, preliminary assessed as critically endangered and threatened under the IUCN Criteria A and B, are mainly concentrated. The climate and land-cover change impacts on rare and endemic species distributions is more prominent in regional biodiversity hotspots. The main aims of the current study were: (a) to investigate how climate and land-cover change may alter the distribution of four single mountain endemics and three very rare Peloponnesian endemic taxa of the National Park via a species distribution modelling approach, and (b) to estimate the current and future extinction risk of the aforementioned taxa based on the IUCN Criteria A and B, in order to investigate the need for designing an effective plant micro-reserve network and to support decision making on spatial planning efforts and conservation research for a sustainable, integrated management. Most of the taxa analyzed are expected to continue to be considered as critically endangered based on both Criteria A and B under all land-cover/land-use scenarios, GCM/RCP and time-period combinations, while two, namely Alchemilla aroanica and Silene conglomeratica, are projected to become extinct in most future climate change scenarios. When land-cover/land-use data were included in the analyses, these negative effects were less pronounced. However, Silene conglomeratica, the rarest mountain endemic found in the study area, is still expected to face substantial range decline. Our results highlight the urgent need for the establishment of micro-reserves for these taxa. [ABSTRACT FROM AUTHOR]

Introduction: Long and short term weather forecasting have been as two of the most important challenges to water and climate researchers. In order to overcome this challenge, several tools, including atmospheric-ocean general circulation model forecasting scenarios, and downscaling models have been developed and used. These tools downscale forecasting scenarios by creating relationship between parameters of synoptic stations and Large-scale data of general circulation models. Material and methods: In this study large-scale predictor parameters from 1961 to 2003 from the database of National Centers for Environmental Prediction (NCEP), large-scale data for the A1B and A2 forecast scenarios of the HadCM3 model from 2001 to 2100 from the Canadian Centre for Climate Modelling and Analysis (CCCMA), and the meteorological synoptic data of Ardabil stations from the Meteorological Organization were gathered. To this end, three downscaling models such as Statistical Downscaling Model (SDSM), least squares support vector machine (LS-SVM) and multi-layer perceptron (MLP) were determined for downscaling; and correlation coefficient (CC), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Normalized mean square error (NMSE), Nash-Sutcliffe, Mean Absolute Error (MAE), and Taylor diagram were used to evaluate the efficiency of the models. Results and discussion: The results showed that the MLP obtained the best results based on the average of the stations with the values of (CC=0.85), (NMSE=0.63), (NSH=0.73) and (MAE=0.52), and LS-SVM and SDSM are ranked second and third, respectively. Taylor's diagram also identified the SDSM as the weakest and the LS-SVM and MLP as superior models with a slight difference. Based on downscaling results of all forecasting scenarios, an increase in average daily temperature is also predicted by 2100 in all studied stations. Conclusion: Based on the results of the study, all forecasting scenarios and all methods of downscaling show increasing the daily average temperature by 2100. Hence, it is necessary to be taken this issue account for making environmental and developing policies in this area. [ABSTRACT FROM AUTHOR]

Climate is one of the most important elements affecting the distribution of species, and it is expected that the distribution of species will be widely influenced by climate change. In plants, edaphic factors also play a special role along with climate in determining the distribution range. The current study aimed to predict the future distribution of the Persian manna (Astragalus adscendens), an endemic perennial shrub in Zagros Mountains of Western Iran. For this purpose, two sets of static (i.e. edaphic and physiographic) and dynamic (i.e. climatic) data and an ensemble approach were used to develop two edaphic-physiographic and climatic models. Current and future suitability maps are representative of the climatic and the edaphic-physiographic niches of A. Adscendens that were obtained based on climatic suitable areas filtered by edaphic-physiographic model. The filtered map has less suitable habitats compared to the climatic model. Three dynamic variables (mean temperature of wettest quarter, temperature seasonality, temperature annual range) and two static variables (altitude and volumetric fraction of coarse fragments) were identified as the most important factors in determining the habitat of A. Adscendens. The importance of altitude was greater than latitude in maintaining or losing suitable habitats under different climate change scenarios, suggesting that the species will not have range expansion or northward shift due to no significant shift in latitude and longitude. Results revealed a sharp decline in the suitable habitats in such that 67% and 91% of the current habitat may be lost by the year 2050 and 2070, respectively. Area reduction was more extreme in future scenarios with the higher level of CO2 emission. Range contraction of A. Adscendens will increases the risk of extinction. This study provides insights into the response of mountain plants, especially range restricted species, to climate change, revealing major dimensions of plant niche. Therefore, developing habitat management and conservation plans to preserve the predicted habitats of such species are required to preserve the predicted sustainable habitats. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei (BTH) is one of the key areas with PM 2.5 air pollution in China. Driven by the PM 2.5 target accessibility of the Interim Target-1 (IT-1) by World Health Organization (WHO) and China's carbon neutrality, this study explored and quantified the contribution of climate change and anthropogenic emission to future PM 2.5 in the region. The experiments considered future climate change scenarios RCP8.5, RCP4.5, and RCP2.6 with the baseline (Base) and reduced emission (EIT1) inventories in 2030, and RCP4.5 climate scenario with 3 emission inventories in 2050, the additional strong control emission scenario called Best-Health-Effect (BHE). Under various climate scenarios, the future air quality research modelling system projected annual PM 2.5 concentrations nearing 35 μg/m3 in 2030. However, considering only the effect of emission reduction, the annual PM 2.5 concentrations under EIT1 emission scenario is about 35% less than under Base scenario in different key years. The future PM 2.5 concentrations are highly related to anthropogenic emission from human activities, while climate change by 2030 or 2050 has little impact on future air quality over the BTH region. The BHE emission reduction is significantly required for China to meet the new PM 2.5 guideline value of WHO in the future. [Display omitted] • More air pollution will result from low radiative forcing climate scenario. • Significantly less than 1% of heavy air pollution will happen under reduced emission. • Under BHE and RCP4.5 scenarios, 78% of PM 2.5 can reach daily guideline by WHO. [ABSTRACT FROM AUTHOR]