Search

The quantitative assessment of glacier changes and freshwater availability under future climate change is inevitable for sustainable water resources management and preventing natural disasters. Limiting the uncertainties in glacio-hydrological modeling and exploring spatiotemporal distributions of runoff and its components along the vertical profile are critical for such investigations. The present study quantifies glacio-hydrological changes using the Spatial Processes in HYdrology (SPHY) model forced by CMIP6 climate data (shared socioeconomic pathways: SSP126, SSP245, and SSP585) in the Upper Indus Basin (UIB) over twenty-first century. The model was calibrated based on in situ glacier changes, snow cover changes, and streamflow records to avoid the risk of equifinality. Variations in vertical distribution of runoff components and their impact on glacio-hydrology were investigated using the critical zone approach. The projected remaining glacier area is 55 ± 10%, 32 ± 17%, and 15 ± 5%, and freshwater availability is reduced by − 12 ± 5%, − 30 ± 7%, and − 36 ± 6% in 2100, compared with 2005–2014, under SSP126, SSP245, and SSP585, respectively. The average changes in snowmelt, glacier melt, baseflow, and rain-runoff contributions to total runoff under SSP245 are projected as 25 ± 15%, − 30 ± 11%, − 20 ± 16%, and 242 ± 71%, respectively. The critical zone (3500 – 5500 masl) contributes 63% of total runoff during the reference period, with a significant reduction (51–81%) in the projected period, indicating a diminishing influence of glacier runoff (with 50% reduction) in future hydrology compared with historical period. In turn, low flows (October–March) are projected to increase (9 − 58%), and high flows (April–September) will likely decrease (− 2 to − 13%). Warming temperature was identified as the dominant driver for the glacier area changes (r = − 0.85*) and total runoff (r = − 0.75*) at 0.05 level of significance. Our findings indicate a rainfall-runoff-dominant hydrological regime in future, highlighting critical freshwater availability conditions and associated socioeconomic risks in terms of agricultural applications and natural disasters. We recommend building infrastructure for water storage and conveyance in the Indus basin to prevent the adverse impacts of future climate change. [ABSTRACT FROM AUTHOR]

LULC and climate change are the two most common manifestations of global environmental change. The objective of this study was to quantify past and future LULC and climate change impacts on environmental sustainability in the Vea catchment of Ghana. The past LULC change (1998–2022) was analyzed using ERDAS Imagine 15 upon the application of the Support Vector Machine (SVM) classifier algorithm. The CA-Markov was used to predict land-use changes in 2038 and 2054. Climate change was projected under SSP 4.5 and SSP 8.5 emission scenarios using the R software. The projections from an ensemble mean of SSPs in the near future (2023–2052) and far future (2071–2100) were compared to the reference period (1993–2022). The results show that, between 1998 and 2022, cropland increased from 10.9% to 51.98%, while grassland and forest areas decreased from 54.8–18.14% and 31.7–22.73%, respectively. The results of the predicted LULC change showed an increase in cropland from 181 km2 in 2038 to 183 km2 in 2054 at the expense of grassland and forest, which are expected to decrease from 51–50 km2 and 73–71 km2, respectively. The findings of climate change show the projected increases in average temperature range from 2.10 to 3.5 °C under SSP4.5 and from 2.7 to 4.15 °C under SSP8.5. Under the SSP4.5, annual average precipitation is anticipated to decrease by 12.34–13.1%, while the SSP8.5 projects a decrease of 12.6–13.6%. Results will help in the setting up of long-term environmental management strategies in the catchment as well as the development of appropriate adaptation methods to mitigate the ongoing impacts of LULC and climate change on environmental sustainability. [ABSTRACT FROM AUTHOR]

Soil erosion-induced land degradation is susceptible to climate change, specifically in the sub-tropical third world countries. Simulations of 21st century climate change in India predict notable variation in rainfall that causes soil erosion-induced land degradation. Land degradation susceptibility modelling of the red and lateritic agro-climatic zone of Bengal (Eastern India) has been prepared using random forest (RF), support vector machine (SVM) and extreme gradient boost (XGBoost) algorithms. Assessment of models using validation data in AUC-ROC revealed that XGBoost (0.909 and r = 0.91) is the most optimal followed by SVM (0.881 and r = 0.87) and RF (0.879 and r = 0.85). Furthermore, future land degradation risk dynamics were assessed through Coupled Model Intercomparison Project six (CMIP6) down-scale-based ensembles of nine global climate models (GCMs) on four SSPs scenarios. The combination of deep learning along with climate modelling should be useful to enhance the result more precisely. [ABSTRACT FROM AUTHOR]

Global warming is causing an intensification of extreme climate events with significant changes in frequency, duration, and intensity over many regions. Understanding the current and future influence of this warming in northeastern Brazil (NEB) is important due to the region's greater vulnerability to natural disasters, as historical records show. In this paper, characteristics of climate change projections (precipitation and air temperature) over NEB are analyzed using 15 models of Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios. By using the Taylor diagram, we observed that the HadGEM3-GC31-MM model simulates the seasonal behavior of climate variables more efficiently. Projections for NEB indicate an irreversible increase in average air temperature of at least 1 °C throughout the 21st century, with a reduction of up to 30% in annual rainfall, as present in scenarios of regional rivalry (SSP3-7.0) and high emissions (SSP5-8.5). This means that a higher concentration of greenhouse gases (GHG) will increase air temperature, evaporation, and evapotranspiration, reducing rainfall and increasing drought events. The results obtained in this work are essential for the elaboration of effective strategies for adapting to and mitigating climate change for the NEB. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Whether there is a transition underway, from a warm-dry climate to a warm-wet climate in Northwest China remains a controversial and scientifically significant issue. Will this trend continue in the future? Another hot issue is whether the climate in Northwest China will continue to be warm and humid over the next few decades. In this paper, eight CMIP6 models were employed to investigate temperature and precipitation changes under five principal Shared Socioeconomic Pathway (SSP) scenarios (from 2015 to 2099) to project the future warming and humidification in Northwest China using the SPEI (standardized precipitation evapotranspiration index) method. The results revealed that (1) the simulated temperature and precipitation of eight CMIP6 models were consistent with that of observed data during 1961–2014, which showed an increase of approximately 28.2 mm, while simulated data revealed an increase of approximately 9.4 mm. The annual precipitation gradually decreased from Eastern Inner Mongolia and the Southern Northwest Mongolia region (>700 mm) to the Central Northwest Mongolia region (

This study examines the projected changes in mean and extreme precipitation over the Mediterranean (MED) and Sahara (SAH) regions based on the multi‐model ensemble mean of the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) datasets. The study employs robust statistical analyses to investigate future changes during 2015–2100 relative to a baseline period (1995–2014), under two Shared Socio‐economic Pathways (SSP) scenarios: SSP2‐4.5 and SSP5‐8.5. Selected indices from the Expert Team on Climate Change and Detection Indices are used in this study. They include those that represent maximum daily precipitation (RX1day), simple daily precipitation intensity (SDII), heavy precipitation days (R10mm), consecutive dry days (CDD), and consecutive wet days (CWD). Historical and projected daily precipitation is first bias‐adjusted using a quantile mapping approach before employing them to compute mean and extreme precipitation changes. The results demonstrate that the bias adjustment largely reduces the biases in the modelled mean and extreme precipitation over MED and SAH regions. Projections show a reduction in mean precipitation over most parts of the study region by the end of the 21st century. The areas encompassing Morocco and Algeria, and the Mediterranean area will experience the highest drying. The projected pattern agrees with the "wet gets wetter, dry gets drier" paradigm. The number of consecutive dry days and wet‐day intensity are also projected to increase and decrease, respectively. Under SSP5‐8.5, significant changes and the largest decrease in SDII attributed to global warming are projected in both regions. The reduction in mean precipitation, coupled with an increase in dry days, is likely to exacerbate the region's droughts and aridity situation and worsen the water scarcity status. Although there are uncertainties in the CMIP simulations, the findings support earlier studies based on varying datasets. This increases confidence in the output for decision‐making. [ABSTRACT FROM AUTHOR]

Through the reasonable calculation of water resources, evaluating the irrigation carrying capacity of farmland under the constraints of water resources is crucial for optimizing the spatial distribution of agricultural production and ecology and rationally adjusting the scale of agricultural production. This paper proposes an optimization framework based on Type 2 fuzzy chance-constrained programming (T2FCCP) to solve the problem of regional water resources optimal allocation and evaluation of farmland irrigation carrying capacity under uncertain conditions. To illustrate the applicability of the proposed framework, this paper conducts a case study on Lancang County, Puer City, Yunnan Province. Methods, such as watershed harmony evaluation method, remote sensing data, and shared socioeconomic pathways (SSPs), are applied and integrated into the proposed optimization framework to systematically deal with uncertainties in water resource systems and agricultural systems. The results include the costs and benefits of regional water and soil resources systems, water resources optimal allocation, and crop planting structure results under different SSPs in Lancang County, Puer City. The results also show that the total cost under T2FCCP is about 5% lower than that under fuzzy chance-constrained programming (FCCP) and about 17% lower than that under chance-constrained programming (CCP). By 2025, the water resources carrying capacity of different tributaries in Lancang County, Puer City will increase, and based on the evaluation results of agricultural production irrigation carrying capacity, suggestions are given to ensure agricultural production carrying capacity. [ABSTRACT FROM AUTHOR]

Drought characteristics and propagation of droughts have been extensively studied for future climate scenarios, but studies on drought hazard mapping in response to climate change are very limited. This study investigated the possibility and severity of drought hazard based on the meteorological and hydrological properties of the drought under changing climate change scenarios. Soil and Water Assessment Tool (SWAT) was used for future streamflow generation. Sixth International Coupled Model Intercomparison Project phage 6 (CMIP6) ensemble General Circulation Models (GCMs) were used to obtain information regarding the future precipitation and streamflow. The Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) were constructed under different Socioeconomic Shared Pathways (SSPs) to analyse the Drought Hazard Index (DHI) over the basin. The key findings of the study are: (i) hydrological and meteorological drought properties are influenced by precipitation as well as minimum and maximum temperatures; (ii) SRI showed moderate severity and remained nearly constant in all scenarios, while SPI showed a decrease in severity over the watershed; (iii) the drought severity showed regressive declining conditions and a marked decrease under different climate scenarios especially under SSP585 scenario; and (iv) the drought hazard will be lower for future scenarios compared to reference period. A novel insight to explore the drought hazard under global warming scenario is the major contribution of this study. [ABSTRACT FROM AUTHOR]

Changes in precipitation and temperature, especially in the Himalayan region, will have repercussions for socio-economic conditions in the future. Thus, this study aimed to understand the climatic trend and changes in one of the Himalayan River basins, i.e., Gandaki River Basin (GRB), Nepal. In particular, we analysed the historical (1985–2014) and projected (2015–2100) precipitation and temperature trend and their extremes using observation and 13 bias-corrected Coupled Model Intercomparison Project phase 6 (CMIP6) datasets. Additionally, the relationship between extreme precipitation/temperature indices and ocean-atmospheric circulation patterns were also analysed. The results showed an increasing trend of precipitation amount and temperature at annual and seasonal scales with the highest upward trend for precipitation in monsoon season and temperature in winter season. Among nine precipitation indices analysed, the wet extremes are projected to increase in all Shared Socioeconomic Pathways (SSP) scenarios; with the highest increment of high-intensity related extremes (R10 mm and R20 mm). In contrast, dry spells will decline in the distant-future (2075–2100) as compared to near (2015–2044) and mid-future (2045–2074). Further, increment in temperature trend resulted in a decrease in cold related temperature extremes and an increase in warm related extremes. Furthermore, it was observed that the changes in precipitation and temperature extremes over GRB were influenced by large-scale ocean-atmospheric circulation patterns. The Atlantic Multidecadal Oscillation (AMO), Sea Surface Temperature (SST) and Southern Oscillation Index (SOI) were found to have a major role in driving precipitation extremes while AMO, SST and Pacific Decadal Oscillation (PDO) have strong influence on temperature extremes. The results of this study will be useful for better understanding the implications of historical and future changes in precipitation and temperature and their extremes over the GRB. [ABSTRACT FROM AUTHOR]

It is indisputable that global warming has triggered more frequent extreme weather and in turn led to severe flood disasters. To understand the trend of extreme rainfall under 1.5 °C and 2 °C warming, we investigated the historical variation and future trends in extreme rainfall for the Huai River basin, which has frequently been hit by floods, using recorded meteorological data and a projection of five General Circulation Models in the Coupled Model Intercomparison Project 6. We used the years 1995–2014 as the baseline period to study the temporal and spatial changes in extreme rainfall under 1.5 °C and 2.0 °C warming scenarios. The results indicated that (1) temperatures in the Huai River basin have risen significantly from 1995 to 2014, but there are insignificant variation trends in annual precipitation (AP), intensive precipitation (R95P), maximum daily precipitation (Rx1d) and heavy rain days (Rr50) during the same time span. (2) From 2015 to 2100, both temperature and extreme rainfall indices show increase trends, with a higher rate of increase under a higher emission scenario. (3) Under the warming scenario of 1.5 °C, AP, R95P, Rx1d and Rr50 in the basin will likely increase by 4.6%, 5.7%, 6.2% and 13.4%, respectively, compared with that in the baseline period. Under the warming scenario of 2.0 °C, AP, R95P, Rx1d and Rr50 will probably increase by 7.3%, 7.4%, 10.9% and 19.0%, respectively. (4) Spatially, the changes in extreme rainfall indices under the warming scenarios of 1.5 °C and 2.0 °C generally tend to increase from north to south. Higher intensity extreme rainfall will likely extend to the whole of the Huai River basin. It is therefore essential to study adaptive measures to cope with flooding in the Huai River basin induced by the increase in future rainfall extremes. [ABSTRACT FROM AUTHOR]

Our aim in this research was to detect historical and future water yield coefficient evolution and attribution. Based on the calibrated and validated water yield coefficient model in the Yiluo River Basin, the coefficient for the years 2000–2020 was simulated, along with the future projection for 2030–2050 under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The spatio-temporal evolution of historical and future water yield coefficients was then analyzed. Moreover, the geographical detector model was used to detect the impacts of climate, land use, and terrain factors on the water yield coefficient. The results showed that the water yield coefficient increased by 8.53% from 2000 to 2020, with the coefficient of farmland increasing by 10.47% and that of forestland decreasing by 8.93%. The coefficient was highest under the SSP370 scenario and the lowest under the SSP585 scenario in projections for 2030–2050. Compared to 2000–2020, the coefficients of the two scenarios increased by 12.2% and 2.0%, respectively. Consequently, under the SSP370 and SSP585 scenarios, the coefficient of farmland increased by 13.2% and 2.7%, and that of the forestland decreased by 0.9% and 14.6%, respectively. Driving factors detection indicated that land use types had the strongest explanatory power affecting the water yield coefficient; the explanatory value reached 26.5% in 2000–2020 and will exceed 29.5% in 2030–2050. In addition, the interaction between any two factors was stronger than a single factor. This research provides scientific support for the precise management of watershed and water-land resources.

Methane is a greenhouse gas with a lifespan of about a decade, and its presence in the atmosphere affects the Earth’s temperature and the climate system. Methane is included in short-lived climate forcers (SLCFs) or near-term climate forcers (NTCFs), whose atmospheric composition changes have a near-term effect on climate, predominantly in the first two decades after their emission or formation. In this study, the climate benefits of methane mitigation on global air temperature distribution are examined for the near future (2031–2050). The analysis is based on model simulations conducted by the Earth System Model GFDL-ESM4 for the future scenario SSP3-7.0 with additional air quality mitigation measures either in non-methane NTCFs (aerosol and ozone precursors) or in all NTCFs (including methane). It is shown that additional methane mitigation would potentially further contribute to offsetting the warming from reduced aerosols associated mainly with SO2 reductions that would accompany decarbonization.

The projected climate change substantially impacts agricultural productivity and global food security. The cropping system models (CSM) can help estimate the effects of the changing climate on current and future crop production. The current study evaluated the impact of a projected climate change under shared socioeconomic pathways (SSPs) scenarios (SSP2-4.5 and SSP5-8.5) on the grain yield of winter wheat in the North China Plain by adopting the CSM-DSSAT CERES-Wheat model. The model was calibrated and evaluated using observed data of winter wheat experiments from 2015 to 2017 in which nitrogen fertigation was applied to various growth stages of winter wheat. Under the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) SSP2-4.5 and SSP5-8.5 scenarios, the future climate projections were based on five global climate models (GCMs) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The GCMs projected an increase in grain yield with increasing temperature and precipitation in the near-term, mid-term, and long-term projections. In the mid-term, 13% more winter wheat grain yield is predicted under 1.3 °C, and a 33 mm increase in temperature and precipitation, respectively, compared with the baseline period (1995–2014). The increasing CO2 concentration trends projected an increase in average grain yield from 4 to 6%, 4 to 14%, and 2 to 34% in the near-term, mid-term, and long-term projections, respectively, compared to the baseline. The adaptive strategies were also analyzed, including three irrigation levels (200, 260, and 320 mm), three nitrogen fertilizer rates (275, 330, and 385 kg ha−1), and four sowing times (September 13, September 23, October 3, and October 13). An adaptive strategy experiments indicated that sowing winter wheat on October 3 (traditional planting time) and applying 275 kg ha−1 nitrogen fertilizer and 260 mm irrigation water could positively affect the grain yield in the North China Plain. These findings are beneficial in decision making to adopt and implement the best management practices to mitigate future climate change impacts on wheat grain yields. [ABSTRACT FROM AUTHOR]

Droughts significantly affect socioeconomic and the environment primarily by decreasing the water availability of a region. This study assessed the changes in drought characteristics in Central Asia's transboundary Amu river basin for four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The precipitation, maximum, and minimum temperature (Pr, Tmx, and Tmn) simulations of 19 global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) were used to select the best models to prepare the multimodel ensemble (MME) mean. The standard precipitation evapotranspiration index (SPEI) was used to estimate droughts for multiple timescales from Pr and potential evapotranspiration (PET) derived from Tmx and Tmn. The changes in the frequency and spatial distribution of droughts for different severities and timescales were evaluated for the two future periods, 2020–2059 and 2060–2099, compared to the base period of 1975–2014. The study revealed four GCMs, AWI-CM-1–1-MR, CMCC-ESM2, INM-CM4-8, and MPI-ESM1-2-LR, as the most suitable for projections of droughts in the study area. Results revealed a decrease in Pr by 3 to 12% in the near future and by 3 to 9% in the far future in most parts of the basin for different SSPs. However, there is almost no change in PET in the near future while increasing by 10 to 70% in the far future. The changes in Pr and PET would cause a noticeably decrease in drought occurrence in the near future, particularly for moderate droughts by − 50% for SSP5-8.5 and an increase in the far future up to 30% for SSP3-7.0. The increase in all severities of droughts was projected mostly in the center and northwest of the basin. Overall, the results showed a drought shift from the east to the northwest of the basin in the future. [ABSTRACT FROM AUTHOR]

Tamarix chinensis (Tamaricaceae), a halophytic plant, is native to China. Tamarix chinensis has shown significant advantages in improving soil desertification and enhancing soil quality in coastal saline lands and can be used for ornamental and medicinal purposes with high scientific, ecological and economic values. The scarcity of information about geographic distribution under global climate change makes it difficult to promote better cultivation of this shrub. For this study, we modeled the current and future suitable growth areas in 2050 and 2070 for T. chinensis by the Maxent model using the latest Coupled Model Comparison Program 6 (CMIP6) data set. The results revealed that annual mean temperature, precipitation of wettest quarter, annual mean UV-B and elevation were identified as the most important factors affecting T. chinensis distribution. The total suitable potential distribution areas for T. chinensis encompassed ca. 191.38×104 km², in which the highly suitable areas were mainly distributed in the middle and lower reaches of the Yellow River and the eastern coastal areas in China. Under the Shared Socioeconomic Pathway (SSP) 126 scenario, we predicted an expansion of the suitable habitat range in 2050 followed by a contraction in 2070; however, under the SSP585 scenario, the suitable habitat range of T. chinensis would decrease in 2050 and 2070. Overall, T. chinensis showed a shift trend in distribution to higher latitudes and elevations with global warming. This study could provide a theoretical guidance for formulating management plans for T. chinensis and saline soil rehabilitation in the future. [ABSTRACT FROM AUTHOR]

Studies on multivariate drought assessment and the climate change impact on a river basin scale are limited in India. Drought monitoring is a challenging subject due to its dependence on different climatic variables. To overcome this, a copula‐based probabilistic multivariate drought index (MDI) has been developed which simultaneously represents the meteorological, hydrological and agricultural drought phenomenon. The four variate Archimedean copula is used in this study to integrate the precipitation, evapotranspiration, soil moisture and streamflow. Hydrologic variables like evapotranspiration, soil moisture and streamflow were simulated using the Soil and Water Assessment Tool model. The future MDI is also analysed to identify the impact of climate change on drought phenomenon using Coupled Model Intercomparison Project‐6 Global Climate Models under four Socio‐Economic Shared Pathways. Drought characteristics like severity and duration were evaluated to identify the present and future drought events. The precipitation and minimum and maximum temperatures were identified to have increasing tendencies in the future scenarios. Most of the future scenarios showed lower drought duration and severity when compared to the reference period. The drought duration and severity are likely to decrease in the future time scales especially under the high emission scenarios. The present study used a novel approach to examine the drought from various perspectives and the study will be useful for drought mitigation and adaptation strategies over the basin. [ABSTRACT FROM AUTHOR]

Starch and proteins are main storage product to determine the appearance, cooking, texture, and nutritional quality of rice (Oryza sativa L.). OsNAC20 and OsNAC26, as pivotal transcription factors, redundantly regulate the expression of genes responsible for starch and protein synthesis in the rice endosperm. Any knockout of OsNAC20 or OsNAC26 did not result in visible endosperm defects. In this study, we had isolated and characterized a mutant named as floury endosperm25 (flo25). The caryopsis of the flo25 mutant exhibits a floury endosperm, accompanied by reductions in both the 1000-grain weight and grain length, as well as diminished levels of total starch and protein. Through map-based cloning, it was determined that FLO25 encodes a NAM, ATAF, and CUC (NAC) transcription factors, namely OsNAC26, with a lysine to asparagine substitution at position 98 in the flo25 mutant. Remarkably, lysine 98 is conserved across plants species, and this mutation does not alter the subcellular localization of OsNAC26 but significantly attenuates its transcriptional activity and its ability to activate downstream target genes. Furthermore, the mutant protein encoded by OsNAC26 -flo25 could interact with OsNAC20, disrupting the native interaction between OsNAC20 proteins. Additionally, when lysine 98 is substituted with asparagine in OsNAC20, the resulting mutant protein, OsNAC20(K98N), similarly disrupts the interaction between OsNAC26 proteins. Collectively, these findings underscore the pivotal role of Lysine 98 (K) in modulating the transcriptional activity of NAC20/NAC26 within the rice endosperm. • The flo25 mutant showed floury endosperm along with reduced protein and starch content. • The cytosine-to-adenine at position 294 in OsNAC26 caused floury endosperm phenotype in flo25. • Lysine at position 98 is crucial for the transcriptional activation of OsNAC26 and OsNAC20. [ABSTRACT FROM AUTHOR]

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management.

• Over 97%/89% of grids under SSP5-8.5/ SSP1-2.6 scenario will face higher risk. • The rainstorm risk in the southern and southeast coasts of Asia will be the highest. • Except Europe, risk changes in other continents are mainly due to exposure changes. • Annual rainstorm days have the largest uncertainty among three rainstorm indicators. The extreme weather caused by global warming is increasing day by day. It is of great significance to reasonably assess the risk from extreme precipitation in the world to reduce the losses caused by disasters. Based on the daily precipitation simulation data of 22 global climate models (GCMs) under three scenarios, shared socioeconomic pathways (SSPs) 1–2.6, 2–4.5, and 5–8.5 in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study calculated three rainstorm indicators (annual rainstorm days, ARDs; annual rainstorm intensity, ARI; and annual rainstorm maximum, ARM), combined them into an index to indicate rainstorm hazard, calculated the socioeconomic exposure index using the projected future population and GDP data of SSP1, SSP2, and SSP3, and finally combined the rainstorm hazard index and socioeconomic exposure index to calculate the socioeconomic risk index of rainstorms. The socioeconomic risks from rainstorms in different periods (1995–2014, 2041–2060, and 2081–2100) were compared at global and continental scales. The main factors that cause the differences in the socioeconomic risks from rainstorms on different continents were explored. The uncertainties of the three rainstorm indicators calculated from the 22 GCMs were analyzed. The main conclusions are as follows: (1) The socioeconomic risk from rainstorms in most regions of the world will increase in the future, with the largest increase in the SSP5-8.5 scenario and the smallest increase in the SSP1-2.6 scenario. Compared with the historical period, the number of grids with increased risk in the mid-term (2041–2060) and long-term (2081–2100) is 97.58% and 97.98% under the SSP5-8.5 scenario, respectively. Under the SSP1-2.6 scenario, the number of grids with increased risk in the mid-term (2041–2060) and long-term (2081–2100) is 89.20% and 86.57%, respectively. (2) Overall, in the two future periods, the least socioeconomic risk from rainstorms is in Europe, and the largest socioeconomic risk from rainstorms is in Asia, especially the southern and southeastern parts of Asia, which have both the highest mean socioeconomic risk and the largest growth of socioeconomic risk. (3) The socioeconomic risk changes of rainstorms in North and South America, Africa, Asia, and Oceania are more closely related to socioeconomic exposure changes, while the socioeconomic risk changes of rainstorms in Europe are more closely related to the three rainstorm indicators. (4) Among the three scenarios, the SSP5-8.5 scenario has the largest uncertainty, and among the three rainstorm indicators, ARDs have the largest uncertainty. The regions with the greatest uncertainty are concentrated in central North America, Central America, central and southern Africa, central South America, eastern Asia, and southern Oceania. [ABSTRACT FROM AUTHOR]

Climate change is becoming a leading issue in the 21st century due to its devastating environmental and socioeconomic impacts. It is believed to have significant impacts on the water resources and groundwater in the Middle East region, especially in Iraq. In this study, three different global climate models, under the 6th IPCC report, were downscaled under two SSPs scenarios in the middle and west of Iraq. The downscaled models were fixed for bias using CMhyd (climate model data for hydrologic modeling) over the study area. This was achieved using data from four locally chosen weather stations from 1983 to 2022. Three CMIP6 global models were considered and compared. These were the Australian Community Climate and Earth-System Simulator version 1 (ACCESS-CM2), the Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR), and the sixth version of the Model for Interdisciplinary Research on Climate (MIROC6).The findings of this study show that the temperature and precipitation changes in the three models under two scenarios in the future period (2023–2050) show an increasing trend compared to the observation period (1983–2014). The results of the generated predictions show that there will be an increase in annual maximum temperatures by this century's end, by 1.26–2.08 °C under scenario SSP2–4.5 and 1.57–2.34 °C under scenario SSP5–8.5 across all study stations. The study found that the annual increase in precipitation over the observed period varies between scenarios (SSP2–4.5 and SSP5–8.5), ranging from +3.56 to +3.87% for SSP2–4.5 and + 7.22% to +12.61% for SSP5–8.5. The results should help improve our understanding of the effects of climate change on the study region and encourage planners and stakeholders to identify optimal strategies for mitigating them. • CMIP6 datasets were used to project future climate change for the first time in the study area. • CMhyd is an effective method for bias correction of precipitation and temperature data. • The projected increase in annual temperatures by this century's end is 1.57–2.34 °C. • Precipitation is projected to increase from 3.56 to 12.61% for the years 2023–2050. • The ACCESS-CM2 projected a higher increase in precipitation and temperature compared with the other GCMs. [ABSTRACT FROM AUTHOR]

• The economy-energy-carbon (EEC) system is discussed from the city level. • Consider the diversity of the carbon system and the energy system. • Combined system dynamics with scenario analysis to predict the state of EEC. • The optimal solution for different subsystems and overall systems is different. • Positive population growth has a positive impact on all systems. Climate change profoundly impacts the transformation of social and economic systems. This study utilized ridge regression, the TOPSIS method, and a coupling coordination model to analyze the coupling coordination of the Economy-Energy-Carbon (EEC) system in the Chengdu-Chongqing Economic Circle from 2005 to 2021. Additionally, this study establishes a system dynamics model that integrates the sharing economy scenario to predict the coupling coordination status of the EEC system in typical cities from 2022 to 2030. The study findings reveal that (1) Enhancing the economic subsystem has a major positive impact on coupling coordination, followed closely by the carbon subsystem. (2) The overall coupling coordination level in the Chengdu-Chongqing economic circle has experienced a slight average annual decrease of 0.75 % from 2005 to 2021. (3) Chengdu and Chongqing demonstrate significantly higher coupling coordination levels, surpassing 0.65 annually. (4) Only four cities (Chongqing, Chengdu, Suining, and Yaan) are gradually improving dynamic coupling coordination. (5) The most effective development scenarios for different subsystems differ from those for coupling coordination. The research methods proposed for the EEC system have the potential to achieve regional sustainable development goals and apply to other developing urban clusters. [ABSTRACT FROM AUTHOR]

[Display omitted] • EPG-SSPS had the lowest molecular weight and the most hydroxyl groups. • EPG-SSPS increased sulfhydryl content and decreased carbonyl content in surimi. • EPG-SSPS maintained stability of surimi protein secondary and tertiary structure. • EPG-SSPS imparted superior cryoprotective effect on the myofibrillar protei. Cryoprotective effect and potential mechanism of soluble soybean polysaccharides (SSPS) and enzymatic hydrolysates on surimi was investigated. After hydrolysis, the molecular weight of SSPS significantly decreased, and the hydrolysates prepared by endo -polygalacturonase (EPG-SSPS) was the lowest (154 kDa). Infrared spectrum analysis revealed that enzymatic hydrolysis didn't alter the functional groups of SSPS, but it did augment the exposure to hydroxyl groups. Surimi containing 5 % EPG-SSPS had the lowest freezable water after 20 days of frozen storage. Furthermore, the 5 % EPG-SSPS group manifested the highest metrics in total sulfhydryl (8.0 × 10−5 mol/g), active sulfhydryl content (6.7 × 10−5 mol/g), Ca2+-ATPase activity, and exhibited the lowest level in carbonyl content, surface hydrophobicity (153 μg). Notably, the 5 % EPG-SSPS maintained the stability of protein structure. Conclusively, SSPS enzymatic hydrolysate using endo -polygalacturonase imparted superior cryoprotective effect on the myofibrillar protein of surimi, and the mechanism might be a decrease in molecular weight and exposure of hydroxyl groups. [ABSTRACT FROM AUTHOR]

China has responded to the sustainability challenges via a range of policies with evolving objectives in the Yangtze River Economic Belt (YEB) since the 1990s. However, the multi-scale analysis of YEB's comprehensive sustainability and the human-environment nexus amid policy impact remains unexplored. A comprehensive review and forecast of historical and future sustainability pathways under policy impacts are imperative. Here, we systematically examined sustainability under ten National Policies (NPs) and Regional Development Policies (RDPs), forecasting Shared Socioeconomic Pathways (SSPs) in the 21st Century using the entropy weight method and machine learning models. We found that RDPs have contributed to 1.6 times increase in human development from 2000 to 2018 across dimensions of demography, economy, and society in YEB. However, it also revealed that the west-east disparity has widened from 0.14 to 0.19 during this period. Conservation efforts and national initiatives for ecological civilization have led to environmental improvement in the YEB, especially after the RDP3 in 2014, when the eastern provinces, like Shanghai, have already surpassed the peak inverted U-shape curve of the environmental-human nexus. Among the SSPs, SSP1 shows the most sustainable scenario with the lowest ecological footprint at 6.2 hm2 per capita and the highest value of regional sustainability and environmental subsystem. To achieve SSP1, an iterative, inclusive, and context-specific Science-Policy-Practice dialogue is essential. This enables feedback loops and collaboration among policymakers, scientists, and practitioners to foster regional sustainability by studying the implications of historical and future pathways. [Display omitted] • YEB's development transitioned from economic to high-quality. • Regional Policies' effect: Wellbeing surges, overshooting ecological footprints. • The systematic east-west sustainability gradient widened in the past 2 decades. • Eastern provinces of YEB experienced early sustainability turning points. • Optimal SSP1: Lower ecological footprint & climate mitigation, higher development. [ABSTRACT FROM AUTHOR]

Globally, non-cereal crops such as vegetable oils and their associated products will surpass cereals in demand by 2050, according to the World Bank (WB). Despite being considered an energy-efficient food crop, the production and supply capability of rapeseed is mostly influenced by climate conditions. Aiming in this context, the study explored how temperature and rainfall patterns influence rapeseed production, as well as how rapeseed prices in major trading countries may influence production and consumption patterns in developing countries. To do this, a supply and demand model approach has been employed for major exporting (Canada) and importing countries, i.e., China, the United States (U.S.) along with Bangladesh, a developing nation. The baseline study period was considered from 1991 to 2018, and simulations were performed up to 2040. The findings revealed that the most important effect on rapeseed yield is directly related to changes in temperature, which are positively related to the growing season but negatively related to the maturity stages of rapeseed in all studied countries. Rapeseed exports from Canada are expected to rise by 2040, while imports from China and the U.S. will rise simultaneously. In Canada, the per capita consumption of rapeseed oil is expected to increase from 16 to 24 kg per year between 2019 and 2040. Over the projection period, oil per capita consumption has steadily increased in China, the U.S., and Bangladesh. The relative demand for rapeseed oil is projected to increase by 2060, according to representative concentration pathways (RCPs). Therefore, it is necessary to determine market prices considering the probable climatic effect and increasing market demand for rapeseed to sustain the international market access of trading nations. [ABSTRACT FROM AUTHOR]

Objective(s): Bioactive compounds like curcumin can be incorporated into food packaging formulation either to enhance physico-mechanical properties or to improve the biological activity of the packaging systems. Furthermore, it enables the packaging to monitor the changes in food quality. Materials and Methods: In the present study, the effect of curcumin concentration (0.2, 0.4 and 0.6%) on physico-mechanical and biological activity of soluble soy bean polysaccharide (SSPS)/TiO2 nanoparticles nanocomposites were investigated. Additionally, the release behavior of this bioactive compound from the developed film was tested. Finally, the color changing of SSPS/TiO2 nanoparticles/curcumin nanocomposites in contact with different mediums were examined. Results: When the curcumin concentration increased up to a certain point (0.4 %), the physical and mechanical properties of the film improved, but beyond this point, an opposite effect was observed. SSPS/TiO2 nanocomposite showed strong antibacterial activity against both gram positive and negative bacteria. Small amount of curcumin released in ethanol as a food simulant. Conclusion: The films incorporated by curcumin can be used as promising packaging systems for non-destructively detecting quality and freshness of foods.

Heat waves are continuous high temperature processes, which are defined as the daily maximum temperature exceeding the absolute-relative combined high temperature thresholds for more than three consecutive days in this study. Because of its unprecedented casualties, devastating compound disasters and irreversible deterioration trends, heat waves have attracted worldwide concern, while its global changes and socioeconomic impacts still need further study. Using three historical reanalysis data and multi scenario CMIP6 modeled data, Global Heat Wave Toolbox (GHWT) was developed to generate heat wave matrix from 1971 to 2100. The long-term changing characteristics of global heat waves were also analyzed. Next, population and GDP projections are employed to estimate future socioeconomic exposure and risks. The results show that except for high latitudes (latitude greater than 60°), high elevations and some coastal areas, heat waves have visited about 45% (±3%) of the global land area (excluding Antarctica). North Africa, North Australia, South Asia, and the Arabian Peninsula are detected as high heat wave area, where experience more than three heat waves with total duration over 15 days annually. Additionally, the average growth rate of global heat waves under SSP2 4.5 and SSP5 8.5 may be 2–3 times and 3–5 times of that under SSP1 2.6, respectively. Under SSP2 4.5, there may be about 7.32 billion people exposed to heat waves in 2100, accounting for 82.65% of the global population. Economic exposure may reach $433.37 trillion in 2100, accounting for 82.23% of the total global economy in that year. India could have the highest population and economic exposure, follow by populous countries, such as Pakistan and Bangladesh, and major economies, such as China and the United States. Under SSP1 2.6, the increase in heat waves may slow down significantly. Moreover, North Africa, Australia, and Brazil are identified as rapid heat wave growth area. Our study highlights the heat wave of future growth and its socioeconomic impacts, as well as the necessity for climate mitigation and adaptation measures.

Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

Understanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only the Gini coefficient (a summary statistic of the distribution) or utilize the Gini along with the assumption of a lognormal distribution. We test the lognormal assumption and find that it typically underestimates income in the highest deciles and over-estimates it in others. We find that a new model based on two principal components of national time series data for income distribution provides a better fit to the data for all deciles, especially for the highest and lowest. We also construct a projection model in which the first principal component is driven by the Gini coefficient and the second captures deviations from this relationship. We use the model to project income distribution by decile for all countries for the five shared socioeconomic pathways. We find that inequality is consistently higher than projections based on the Gini and the lognormal functional form, with some countries reaching ratios of the highest to lowest income deciles that are almost three times their value using the lognormal assumption.

Climate change is a critical issue in the 21st century. Assessment of the impacts of climate change is beneficial for assisting advanced recommendations for adaptations. Climate change impact assessments require high quality local-scale climate scenarios. The future climate projections from Global Climate Models (GCMs) are problematic to use at local scale due to their coarse spatial and temporal resolution, and existing biases. It is important to have climate change scenarios based on the GCMs ensemble downscaled to local scale to account for inherent uncertainty in climate projections, and to have a sufficient large number of years to account for inter-annual climate variability and low frequency, but high impact, extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across Great Britain based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator, LARS-WG 8.0. The data set consists of climate scenarios of daily weather of 1,000 realizations of typical years for a baseline, and very near (2030) and near-future (2050) climates, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz . SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 8.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz .; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of daily weather for the baseline, as well as for very near and near-future climate change scenarios, are essential for estimating inter-annual variation, and for detecting low frequency, but high impact, extreme climatic events, such as heat waves, floods and droughts. The present dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessments of climate change and decision support for mitigation and adaptation to climate change., (© 2024 The Authors.)

Global warming is causing an intensification of extreme climate events with significant changes in frequency, duration, and intensity over many regions. Understanding the current and future influence of this warming in northeastern Brazil (NEB) is important due to the region’s greater vulnerability to natural disasters, as historical records show. In this paper, characteristics of climate change projections (precipitation and air temperature) over NEB are analyzed using 15 models of Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios. By using the Taylor diagram, we observed that the HadGEM3-GC31-MM model simulates the seasonal behavior of climate variables more efficiently. Projections for NEB indicate an irreversible increase in average air temperature of at least 1 °C throughout the 21st century, with a reduction of up to 30% in annual rainfall, as present in scenarios of regional rivalry (SSP3-7.0) and high emissions (SSP5-8.5). This means that a higher concentration of greenhouse gases (GHG) will increase air temperature, evaporation, and evapotranspiration, reducing rainfall and increasing drought events. The results obtained in this work are essential for the elaboration of effective strategies for adapting to and mitigating climate change for the NEB.

The projected global temperature increase in the 21st century is expected to have consequences on energy consumption due to increase (decrease) in energy demand to cool (heat) the built environments. Such increase (decrease) also depends on the number of end users for such energy, thus it is crucial to include population into the analyses. This study presents population‐weighted (w) cooling (CDD), heating (HDD), and energy (EDD) degree‐day projections at global, regional, and local scales for the 21st century. We used a large ensemble of high‐resolution (0.44°) climate simulations from the COordinated Regional‐climate Downscaling EXperiment (CORDEX) to compute degree‐days for baseline (1981–2010) and global warming levels (GWLs from 1.5°C to 4°C), based on two representative concentration pathways. We used population projections from the NASA‐SEDAC datasets, driven by five socio‐economic scenarios (SSPs). The progressive increase in CDD outbalances the decrease in HDD in Central and South America, Africa, and Oceania and the opposite situation is likely to occur in North America, Europe, and Asia; at global scale, they are balanced. However, if results are weighted according to population, the increase in wCDD outbalances the decrease in wHDD almost everywhere for most GWLs and SSPs. Few regions show a decreasing tendency in wEDD at high GWLs for all SSPs: central Europe, northwestern, northeastern, and eastern Asia. Globally, wEDD are likely to double at 2°C compared to 1981–2010 independently of the SSP. Under the worst‐case scenario (SSP3), at 4°C wCDD are approximately 380% higher and wHDD approximately 30% lower than in the recent past, leading to an increase in wEDD close to 300%. [ABSTRACT FROM AUTHOR]

Through the reasonable calculation of water resources, evaluating the irrigation carrying capacity of farmland under the constraints of water resources is crucial for optimizing the spatial distribution of agricultural production and ecology and rationally adjusting the scale of agricultural production. This paper proposes an optimization framework based on Type 2 fuzzy chance-constrained programming (T2FCCP) to solve the problem of regional water resources optimal allocation and evaluation of farmland irrigation carrying capacity under uncertain conditions. To illustrate the applicability of the proposed framework, this paper conducts a case study on Lancang County, Puer City, Yunnan Province. Methods, such as watershed harmony evaluation method, remote sensing data, and shared socioeconomic pathways (SSPs), are applied and integrated into the proposed optimization framework to systematically deal with uncertainties in water resource systems and agricultural systems. The results include the costs and benefits of regional water and soil resources systems, water resources optimal allocation, and crop planting structure results under different SSPs in Lancang County, Puer City. The results also show that the total cost under T2FCCP is about 5% lower than that under fuzzy chance-constrained programming (FCCP) and about 17% lower than that under chance-constrained programming (CCP). By 2025, the water resources carrying capacity of different tributaries in Lancang County, Puer City will increase, and based on the evaluation results of agricultural production irrigation carrying capacity, suggestions are given to ensure agricultural production carrying capacity.

Changes in precipitation and temperature, especially in the Himalayan region, will have repercussions for socio-economic conditions in the future. Thus, this study aimed to understand the climatic trend and changes in one of the Himalayan River basins, i.e., Gandaki River Basin (GRB), Nepal. In particular, we analysed the historical (1985–2014) and projected (2015–2100) precipitation and temperature trend and their extremes using observation and 13 bias-corrected Coupled Model Intercomparison Project phase 6 (CMIP6) datasets. Additionally, the relationship between extreme precipitation/temperature indices and ocean-atmospheric circulation patterns were also analysed. The results showed an increasing trend of precipitation amount and temperature at annual and seasonal scales with the highest upward trend for precipitation in monsoon season and temperature in winter season. Among nine precipitation indices analysed, the wet extremes are projected to increase in all Shared Socioeconomic Pathways (SSP) scenarios; with the highest increment of high-intensity related extremes (R10 mm and R20 mm). In contrast, dry spells will decline in the distant-future (2075–2100) as compared to near (2015–2044) and mid-future (2045–2074). Further, increment in temperature trend resulted in a decrease in cold related temperature extremes and an increase in warm related extremes. Furthermore, it was observed that the changes in precipitation and temperature extremes over GRB were influenced by large-scale ocean-atmospheric circulation patterns. The Atlantic Multidecadal Oscillation (AMO), Sea Surface Temperature (SST) and Southern Oscillation Index (SOI) were found to have a major role in driving precipitation extremes while AMO, SST and Pacific Decadal Oscillation (PDO) have strong influence on temperature extremes. The results of this study will be useful for better understanding the implications of historical and future changes in precipitation and temperature and their extremes over the GRB.

It is indisputable that global warming has triggered more frequent extreme weather and in turn led to severe flood disasters. To understand the trend of extreme rainfall under 1.5 °C and 2 °C warming, we investigated the historical variation and future trends in extreme rainfall for the Huai River basin, which has frequently been hit by floods, using recorded meteorological data and a projection of five General Circulation Models in the Coupled Model Intercomparison Project 6. We used the years 1995–2014 as the baseline period to study the temporal and spatial changes in extreme rainfall under 1.5 °C and 2.0 °C warming scenarios. The results indicated that (1) temperatures in the Huai River basin have risen significantly from 1995 to 2014, but there are insignificant variation trends in annual precipitation (AP), intensive precipitation (R95P), maximum daily precipitation (Rx1d) and heavy rain days (Rr50) during the same time span. (2) From 2015 to 2100, both temperature and extreme rainfall indices show increase trends, with a higher rate of increase under a higher emission scenario. (3) Under the warming scenario of 1.5 °C, AP, R95P, Rx1d and Rr50 in the basin will likely increase by 4.6%, 5.7%, 6.2% and 13.4%, respectively, compared with that in the baseline period. Under the warming scenario of 2.0 °C, AP, R95P, Rx1d and Rr50 will probably increase by 7.3%, 7.4%, 10.9% and 19.0%, respectively. (4) Spatially, the changes in extreme rainfall indices under the warming scenarios of 1.5 °C and 2.0 °C generally tend to increase from north to south. Higher intensity extreme rainfall will likely extend to the whole of the Huai River basin. It is therefore essential to study adaptive measures to cope with flooding in the Huai River basin induced by the increase in future rainfall extremes.

Abstract It is challenging to estimate how the regional climate will be shifted under future global warming. To reduce the potential risk of regional climate shift under future climates, examining the change in climate features over Asia is important, as approximately 60% of the world's population resides there. In this study, climate shifts are assessed over the Asian monsoon region under global mean temperature warming targets from 1.5°C to 5.0°C above preindustrial (PI) levels based on different shared socioeconomic pathway (SSP) scenarios. Global warming impacts the individual climate variables, and consequently, it impacts the regional climate features across the Asian region. Temperature change patterns are more dominant contributors to the spatial extent and magnitude of climate shifts than precipitation change patterns. Changes in regional climates show different behaviors according to the degree of global warming rather than the type of SSP scenario. Climate shifts are intensified under a higher level of global warming that is above the PI levels. The largest climate shifts in this region are shown under global warming of 5.0°C based on the SSP5‐8.5 scenario, especially in current polar climate zones. Future change patterns in individual climate zone can differ. Regions with tropical climates and arid climates are likely to be expanded, whereas some regions with warm temperate climates, cold climates, and polar climates are likely to shrink under global warming conditions. Therefore, this study supports the necessity of mitigating greenhouse gas (GHG) emissions and establishing an adaptation plan for future global warming conditions.

Thailand’s cement industry shows a significant share of energy consumption and GHG emissions in industries. Therefore, the industry will need to curb its emissions to meet the first national determined contribution (NDC). This study attempts to clarify two research questions: firstly, what is the cement demand under several scenarios of economic and demographic development pathways in Thailand’s cement industry? Secondly, how Thailand’s cement industry achieves the 1.5°. The scopes of this study focus on the per capita cement consumption in 2050, the energy consumption pathways, technology mix, and GHG emissions and other air pollutants. The scenarios include the cement industry target proposed in Thailand’s first NDC and the perspective in 2050. The socioeconomic indicators follow the SSPs narrative. The AIM/Enduse model used in this study is based on linear optimized bottom-up approach. Results show that the energy efficiency improvement and the increase of alternative energy in cement industry will reduce GHG emissions in 2050. To achieve the 1.5-degree pathway, the CCS technology will be deployed after 2022 with a carbon tax rate of US$ 500/t CO2. The use of biomass, municipal solid waste, and sewage sludge will offer a variety of environmental, social, and economic advantages. However, other air pollutants will be increased. Therefore, the policy makers should provide alternative fuel use limitations. Additionally, the CO2 transport and storage need to be assessed in the long-term climate policies.

It is challenging to estimate how the regional climate will be shifted under future global warming. To reduce the potential risk of regional climate shift under future climates, examining the change in climate features over Asia is important, as approximately 60% of the world's population resides there. In this study, climate shifts are assessed over the Asian monsoon region under global mean temperature warming targets from 1.5°C to 5.0°C above preindustrial (PI) levels based on different shared socioeconomic pathway (SSP) scenarios. Global warming impacts the individual climate variables, and consequently, it impacts the regional climate features across the Asian region. Temperature change patterns are more dominant contributors to the spatial extent and magnitude of climate shifts than precipitation change patterns. Changes in regional climates show different behaviors according to the degree of global warming rather than the type of SSP scenario. Climate shifts are intensified under a higher level of global warming that is above the PI levels. The largest climate shifts in this region are shown under global warming of 5.0°C based on the SSP5‐8.5 scenario, especially in current polar climate zones. Future change patterns in individual climate zone can differ. Regions with tropical climates and arid climates are likely to be expanded, whereas some regions with warm temperate climates, cold climates, and polar climates are likely to shrink under global warming conditions. Therefore, this study supports the necessity of mitigating greenhouse gas (GHG) emissions and establishing an adaptation plan for future global warming conditions. Key Points: Potential global warming based on CMIP6 causes the significant changes in regional climates and consequently climate shifts over AsiaPace of climate zone shifts will become faster as the global warms, but the differences exist among the regional climate featuresCharacteristics of climate shifts are more sensitive to the degree of global mean temperature increase than the type of SSP scenarios [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally. • PET estimates an increase of 0.68 mm/annum across Africa from 1979 to 2014. • PET is projected to increase under all scenarios, shows stronger in high forcing scenario. • Solar radiation contributed to PET in the past andmaximum temperatures in future but varies across periods and regions. • The study underscores the implications of PET estimations for drought evaluation. [ABSTRACT FROM AUTHOR]

Stainless steel pickling sludge (SSPS) is a noxious solid waste containing heavy metals (Fe, Cr, Mn, and Ni). Herein, an ultrafast ultrasonic-assisted leaching (UAL) extraction method for heavy metals from SSPS is proposed. The effects of key leaching factors, including acid lixiviant, acid dosage, leaching time, liquid-to-solid ratio, and leaching temperature, on the leaching efficiency of Fe, Cr, Ni and Mn in SSPS were systematically studied, followed by the theoretical optimization by response surface methodology. The leaching efficiencies of Fe, Mn, Ni, and Cr were significantly enhanced to 98.6%, 90.74%, 90.83%, and 92.25% within 15 min, which were nearly nine times higher than the acid leaching process. The ultrasonic burst broke up the SSPS and increased the contact area (mass transfer effect) of the heavy metal leaching reaction. In addition, kinetic and thermodynamic studies shown that sonochemical action could significantly promote the initial leaching efficiency of heavy metals. This work provided a super-fast method to recovery of heavy metal from SSPS, which shows great potential for the actual application. [Display omitted] • The efficient removal of heavy metals in ultra-short time was demonstrated. • The optimal process conditions for leaching heavy metals are provided. • The use of ultrasound accelerated the disintegration of SSPS. • The leaching rate achieved is higher compared to conventional methods. [ABSTRACT FROM AUTHOR]

Increased urban land use efficiency (ULUE) is a pivotal symbol of urbanization's evolution from rapid expansion to a focus on high-quality development. However, the extent to which the spatial-temporal trends in China's future urban sprawl efficiency could hinder the attainment of Sustainable Development Goals (SDGs) 11: "Building Sustainable Cities and Communities" remains uncertain. This study endeavors to shed light on this issue, offering insights that could facilitate the dynamic adjustment of land resource use efficiency assessment strategies to align with the SDG11 urbanization process. We leverage socio-economic shared pathways (SSPs) thematic data through the application of the slack-variable-based data envelopment analysis (SBM-DEA) methodology to project ULUE for China's 368 prefectural-level administrative divisions from 2020 to 2050. In addition, we utilize the coupled coordination degree (CCD) model to anticipate the future harmonization trend of the ULUE with the UN's endorsed SDG indicator, SDG11.3.1: "Land Consumption Rate to Population Growth Rate (LCRPGR)". We harness tools such as Kernel Density Analysis, Moran's Index, and Multi-scale Geographically Weighted Regression (MGWR) to unveil the principal factors influencing the CCD changes and spatial clustering trends. We find that: the higher ULUE areas in China gradually shift from coastal cities and provincial capitals to the western regions, and the value of China's LCRPGR gradually decreases. By 2050, China's ULUE and LCRPGR will reach a high degree of coupling under the SSP2, SSP4, and SSP5 routes. The spatial heterogeneity of the CCD is increasing, with urban land areas having the most substantial adverse effect on CCD, and Gross Domestic Product (GDP), population, and metropolitan green space areas having sequentially decreasing positive effects on CCD. This study assesses the future SDG 11.3.1 process, which helps to adjust the assessment strategy of SDG11 in due time and promote sustainable urban development. [Display omitted] • To predict the ULUE and LCRPGR of prefecture-level divisions in China 2020–2050. • Revealing the coupled coordination between ULUE and LCRPGR in China 2020–2050. • ULUE and LCRPGR reach excellent coordination under the SSP2, SSP4, SSP5 in 2050. • CCD has the strongest negative and positive correlation with land area and GDP. • SDG11.3.1 needs to include economic development and ecological service indicators. [ABSTRACT FROM AUTHOR]

The projected climate change substantially impacts agricultural productivity and global food security. The cropping system models (CSM) can help estimate the effects of the changing climate on current and future crop production. The current study evaluated the impact of a projected climate change under shared socioeconomic pathways (SSPs) scenarios (SSP2-4.5 and SSP5-8.5) on the grain yield of winter wheat in the North China Plain by adopting the CSM-DSSAT CERES-Wheat model. The model was calibrated and evaluated using observed data of winter wheat experiments from 2015 to 2017 in which nitrogen fertigation was applied to various growth stages of winter wheat. Under the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) SSP2-4.5 and SSP5-8.5 scenarios, the future climate projections were based on five global climate models (GCMs) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The GCMs projected an increase in grain yield with increasing temperature and precipitation in the near-term, mid-term, and long-term projections. In the mid-term, 13% more winter wheat grain yield is predicted under 1.3 °C, and a 33 mm increase in temperature and precipitation, respectively, compared with the baseline period (1995–2014). The increasing CO2 concentration trends projected an increase in average grain yield from 4 to 6%, 4 to 14%, and 2 to 34% in the near-term, mid-term, and long-term projections, respectively, compared to the baseline. The adaptive strategies were also analyzed, including three irrigation levels (200, 260, and 320 mm), three nitrogen fertilizer rates (275, 330, and 385 kg ha−1), and four sowing times (September 13, September 23, October 3, and October 13). An adaptive strategy experiments indicated that sowing winter wheat on October 3 (traditional planting time) and applying 275 kg ha−1 nitrogen fertilizer and 260 mm irrigation water could positively affect the grain yield in the North China Plain. These findings are beneficial in decision making to adopt and implement the best management practices to mitigate future climate change impacts on wheat grain yields.

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is an economically important pseudocereal crop, which can adapt well to extreme environments, including low nitrogen (LN) stress. However, little is known regarding the associated molecular mechanisms. In this study, the molecular mechanism of Tartary buckwheat roots in response to different doses of nitrate was investigated by combining physiological changes with transcriptional regulatory network. LN improved elongation and branching of lateral roots, indicating that the plasticity of lateral roots drives the adaption of Tartary buckwheat under LN condition. The roots of the seedlings that were cultivated under four N conditions were selected for RNA-Seq analysis. In total 1686 nitrate dose-responsive genes were identified. Of these genes, 16 genes encoding N transporters showed response to N availability, and they may play important roles in N transport and root system architecture in Tartary buckwheat roots. 108 transcription factors (TFs) showed dose-response to N availability, and they may regulate N response and root growth under varied N conditions by modulating the expression of N transporters. A NIN-like protein, FtNLP7, was identified and it may contribute to the transcriptional regulation of N transporters. Furthermore, 81 N-responsive genes were identified as the small secreted peptides (SSPs). 48 N-responsive SSPs were annotated as hypothetical proteins and they may be the species-specific proteins of Tartary buckwheat. This paper provides useful information for further investigation of the mechanisms underlying the adaptation of Tartary buckwheat under N-deficient condition. • Low nitrogen improved elongation and branching of lateral roots of Tartary buckwheat. • 108 TFs may involve in N transport and root system architecture of Tartary buckwheat by modulating N transporter genes. • 81 SSPs involved in N response were identified in Tartary buckwheat. • 48 N-responsive SSPs were the specific proteins of Tartary buckwheat. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations are continuously threatening the environment and may cause huge economic losses. In present study, the precipitation over China has been evaluated under five principal shared socioeconomic pathways (SSPs) scenarios during 2015–2099 based on eight CMIP6 models bias-corrected by the method of Equidistant Cumulative Distribution Functions. The results showed that (1) the simulated precipitation in China was in good agreement with observed precipitation for the eight CMIP6 models during 1961–2014, especially for the UKESM1-0-LL and MIROC6. However, the simulated annual mean precipitation has been significantly overvalued in the Southwest River basin (> 50%), while it was undervalued in the higher elevations of the Northwest River basin (< − 60%); (2) the annual mean precipitation will show a fluctuating upward trend during 2015–2099 over China under all the SSPs scenarios for the eight CMIP6 models. The rate of precipitation increase over north China will be higher than that in south China, especially in the Northwest River basin (reach 57.44% in the 2090s under the SSP5-8.5 for the ensemble mean). This increase of the precipitation in north China might alleviate the shortage of water there, but will not change the pattern of more rain in the south and less in the north; (3) in the southeastern basins, the precipitation of the multi-model ensemble (MME) and MIROC6 during 2011–2020 will be lower than that of 1961–2010 (− 6.53 to − 0.06%) under all SSPs scenarios. While the precipitation will increase obviously under all the SSPs scenarios, especially for the SSP5-8.5 scenario after the year of 2060; (4) the bias of the MME was much lower than that of individual CMIP6 models, and the bias of lower SSPs scenarios will be relatively lower. Generally, uncertainty ranges of precipitation fluctuations in north China (15.31–79.26%) will be higher than those in south China (16.06–7.55%). These findings revealed the projections and uncertainties of CMIP6 precipitation over China, which will be helpful for a better understanding of the future evolution of precipitation in China at large scale and in other regions of the world. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scenarios are used in climate change research to explore potential impacts, assess vulnerability, and identify adaptation options. In developing scenarios, however, there is a challenge in moving between global, national, and local scales in a way that connects complex adaptive systems in meaningful ways for stakeholders. Some emulate the global parallel scenario framework of Representative Concentration Pathways, Shared Socio-economic Pathways, and Shared Policy Assumptions, collecting and refining expert data and projections into relatively complex scenarios for specific regions. However, such approaches can be expensive, time-consuming, and privilege expert biophysical knowledge. Others use participatory approaches, working with local people to co-create scenarios based on experiential knowledge, risk perception, and community aspirations. While useful, these highly localized scenarios are often unable to account for linkages and feedbacks between national and international processes. Here we seek to overcome some of these challenges through a combination of elements from the global scenario architecture, and locally specific data that bridge a range of social issues, including political debates, land use, and socio-economic inequalities. We illustrate the approach through a case study of the West Coast, New Zealand, which shows that meaningful climate change scenarios that are credible, legitimate, and relevant can be used to open up material discussions. Our methodology provides a robust process that connects international best practice to local contexts and communicates climate change scenarios through accessible textual and visual boundary objects. The results provide a basis for further process refinement and application elsewhere, highlighting key methodological challenges and opportunities.

The food and land use sector is a major contributor to India’s total greenhouse gas (GHG) emissions. On one hand, India is committed to sustainability targets in the Agriculture, Forestry and Other Land Use (AFOLU) sectors, on the other, there is little clarity whether these objectives can align with national developmental priorities of food security and environmental protection. This study fills the gap by reviewing multiple corridors to sustain the AFOLU systems through an integrated assessment framework using partial equilibrium modeling. We create three pathways that combine the shared socio-economic pathways with alternative assumptions on diets and mitigation strategies. We analyze our results of the pathways on key indicators of land-use change, GHG emissions, food security, water withdrawals in agriculture, agricultural trade and production diversity. Our findings indicate that dietary shift, improved efficiency in livestock production systems, lower fertilizer use, and higher yield through sustainable intensification can reduce GHG emissions from the AFOLU sectors up to 80% by 2050. Dietary shifts could help meet EAT-Lancet recommended minimum calorie requirements alongside meeting mitigation ambitions. Further, water withdrawals in agriculture would reduce by half by 2050 in the presence of environmental flow protection and mitigation strategies. We conclude by pointing towards specific strategic policy design changes that would be essential to embark on such a sustainable pathway.

Understanding population exposure to precipitation-related extreme events is important for effective climate change adaptation and mitigation measures. We analyze extreme precipitation using indices (EPIs), including consecutive dry days (CDD), annual total precipitation, simple daily intensity, and the number of extremely wet days, under the past and future climatic conditions over East Africa. The exposure of the East African population to these extreme events at 1.5 °C and 2.0 °C global warming levels (GWLs) is analyzed based on Climate Model Intercomparison Project phase 6 models. Exposure is computed from extremely wet and dry days (R95p and CDD, respectively). Under both GWLs, EPIs (except CDD) averaged over East Africa are projected to increase under the Shared Socio-economic Pathways (SSP)2-4.5 and SSP5-8.5 scenarios. The largest increase in wet events will likely occur in eastern and northern Kenya. The results also reveal an intensification of precipitation extremes over Burundi, Rwanda, and some parts of Uganda. However, small changes are expected over most parts of Kenya and Tanzania. Examination of population exposure to EPIs shows that the most prominent and net intense occurrence is over Burundi, Rwanda, and some parts of Uganda. In contrast, less change is noted to occur over vast parts of Kenya and Tanzania. Meanwhile, limiting the warming target to less than 1.5 °C but not more than 2.0 °C has 37% (44.2%) and 92% (4%) less impact on the occurrence of EPIs for R95p (CDD) under SSP2-4.5 (SSP5-8.5) scenarios, respectively. The study establishes that future exposure is predominantly driven by changes in population compared to other factors such as climate or concurrent changes in climate and population (the nonlinear interaction effect). For instance, climate effects are anticipated to contribute ∼10.6% (12.6%) of the total change in population exposure under 1.5 °C (2.0 °C) warming levels, while population and interaction effects are expected to contribute ∼77.4% (71.9%) and 12% (15.5%), respectively, under 1.5 °C (2.0 °C) scenarios. Interestingly, the projected changes in regional exposure due to the interaction effects under SSP2-4.5 are greater than the climate effect, while the reverse pattern is observed under SSP5-8.5. For example, under SSP5-8.5, climate effects for 1.5 °C and 2.0 °C are larger (after population effect) with ∼3.8 × 10 ^5 (15.7%) and ∼6.1 × 10 ^5 (17.5%) billion person-mm, respectively. The high exposure noted over East Africa calls for a shift in policies to instate suitable adaptation measures to cushion the already vulnerable population.