Bayesian analyses of multiple random change points in survival models with applications to clinical trials.

Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 - α)% highest posterior density (HPD) intervals calculated from Chen-Shao's algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.

Climate risk analysis of low-altitude tea gardens in central Taiwan using a Bayesian network.

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

Future climate-driven drought events across Lake Urmia, Iran.

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016-2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985-2015) and future (2016-2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045-2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

Unevenly distributed CO2 and its impacts on terrestrial carbon uptake under the changing land uses.

Changes in land-use structure and pattern can affect both atmospheric CO2 concentrations and the terrestrial carbon budget. To explore the effects of non-uniformly distributed CO2 concentration on terrestrial carbon uptake under land-use changes, this study integrated global CO2 concentrations, Net Primary Productivity (NPP), and land-use data under historical period and SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios from 1850 to 2100. Land-use intensity (LUI) and the CO2 correlation to NPP were calculated using partial correlation analysis by controlling LUI. The results showed that NPP growth over the forest was the highest among the land-use types, reaching 0.54 g C·m2, 2.06 g C·m2 and 4.64 g C·m2, respectively, under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Among all the scenarios, the average correlation levels of atmospheric CO2 and NPP considering the LUI effect and controlling LUI ranged respectively from 0.34 to 0.68 and from 0.32 to 0.61 at a 5 % level of significance. It suggested that sensible land use planning might enhance the CO2 fertilization effect and that rises in CO2 concentrations could stimulate terrestrial carbon absorption. The findings add to the body of knowledge about the effects of atmospheric CO2 on terrestrial carbon uptake and serve as a scientific guide for protecting terrestrial carbon stocks and managing land use.

Future climate projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan.

This study investigates the projections of precipitation and temperature at the local scale in the Upper Indus Basin (UIB) in Pakistan using six Regional Climate Models (RCMs) from CORDEX under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). For twenty-four stations spread across the study area, the Long Ashton Research Station Weather Generator, version six (LARS-WG6), was used to downscale the daily data from the six different RCMs for maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (pr) at a spatial resolution of 0.44°. Investigations were made to predict changes in mean annual values of Tmax, Tmin, and precipitation during two future periods, i.e., the mid-century (2041-2070) and end-century (2071-2100). The model results from statistical and graphical comparison validated that the LARS-WG6 can simulate the temperature and the precipitation in the UIB. Each of the six RCMs and their ensemble revealed a continuously increased temperature projection in the basin; nevertheless, there is variation in projected magnitude across RCMs and between RCPs. The rise in average Tmax and Tmin was more significant under RCP 8.5 than RCP 4.5, possibly due to unmitigated greenhouse gas emissions (GHGs). The precipitation projections follow the non-uniform trend, i.e., not all RCMs agree on whether the precipitation will increase or decrease in the basin, and no orderly variations were detected during any future periods under any RCP. However, an overall increase in precipitation is projected by the ensemble of RCMs.

Uncertainties in future monsoon flow predictions in the context of projected climate change: A study of the Chaliyar River Basin.

A major part of the annual rainfall in most parts of India is received during the monsoon. The Chaliyar River Basin in the state of Kerala is no exception with more than 85% of the annual rainfall occurring during the monsoon season. Evidences pointing towards the influence of anthropogenic activities on climate change have been reported from all over the world in recent years. One of the major problems encountered in the projection of future climate is the accumulation of uncertainties arising from different sources. This, in turn, would result in uncertainties in the predicted future streamflows. In this work, uncertainties in the monsoon flow predictions for a future period (2070-2099), stemming from the use of different climate models, hydrological models, and representative concentration pathways are analyzed. Uncertainty due to each of these sources and their interactions are partitioned by performing three-way analysis of variance. Results of the study indicate that the major source of uncertainty in the monsoon flow predictions is uncertainty from the climate models, which is about 83.73% of the total uncertainty in future monsoon flow predictions. Hydrological models account for about 5.38% and RCPs account for about 4.3% of the total uncertainty. About 6.57% is attributed to interactions between these three factors. Evaluation of the uncertainties in future monsoon flow predictions would facilitate informed decision making while formulating strategies for water management in the future.

Sources and uncertainties of future global drought risk with ISIMIP2b climate scenarios and socioeconomic indicators.

The severity of potential drought impacts is influenced not only by physical characteristics, such as precipitation, soil moisture, and temperature but also by local socioeconomic conditions that influence a region's exposure and vulnerability. This study aims to demonstrate projected future global drought risk, which is quantified based on indicators representing three risk components, namely, hazard, exposure, and vulnerability. Drought hazard is evaluated using the standardized precipitation-evapotranspiration index. Drought exposure considers population and agricultural land use, and drought vulnerability accounts for gross domestic product, total water storage, and water consumption. This global-scale study was conducted for the historical and future periods of 1975-2005 and 2070-2099, respectively, and employed three combined scenarios consisting of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) with datasets from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). To evaluate the proposed approach, the results obtained for the historical period were compared with drought records. The projections suggest that in addition to increasing drought hazards caused by climate change, populous regions, or areas heavily dependent on agriculture are at a higher risk than other regions because of high water consumption levels. The contributions analysis indicates that agricultural land use is the largest contributor to drought risk, except for Africa, where the population makes the largest contribution. Model uncertainty of the General Circulation Models (GCMs) and Hydrological Models (HMs) is dominant compared to the RCP and SSP scenarios, with uncertainty from the GCMs the most dominant. This study provides possible depictions and their uncertainties of future drought risks and can assist decision-makers in developing better adaptation and mitigation strategies for climatic, environmental, and socioeconomic changes.

Envisaging the actual evapotranspiration and elucidating its effects under climate change scenarios on agrarian lands of bilate river basin in Ethiopia.

The earth's natural water and energy systems rely on actual evapotranspiration (AET). Climate change plays a crucial role in affecting the hydrologic processes of Abayya-Chamo lake basin in Ethiopia's Rift Valley, resulting into a distributed actual evapotranspiration (DAET) system. Various studies have already been undertaken on the effects of climate change (CC) on AET but forecasted precipitation and temperature to determine space-time distribution of AET across the basin have not been studied yet. Estimates for precipitation and temperature were acquired from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa platform, using RCP4.5 and RCP8.5 scenarios, during 1986-2015, 2041-2070, and 2071-2100 periods. WetSpass-M model was employed to investigate seasonal and annual DAET under varied climate amplitude and distribution. For the baseline period (1986-2015), the maximum annual AET was predicted to be 2815.8 mm/yr. For 2041-2070, and 2071-2100 periods, the estimated maximum annual AET for RCP4.5 scenarios was 3019.2 and 3212.1 mm/yr, respectively, while for RCP8.5 scenarios, it was 3116 and 3352.2 mm/yr, respectively. The baseline annual AET was 516.6 mm/yr, while the mid-term (RCP4.5 and RCP8.5) and long-term (RCP4.5 and RCP8.5) models predicted mean annual AETs of 423.8 and 432 mm/yr and 429.6, and 438.5 mm/yr, respectively. Between 2041 and 2070, the RCP4.5 and RCP8.5 scenarios predicted a 92.8 and 84.6 mm/yr decrease in mean annual AET, respectively. The model predicted a decline in mean annual AET of 87 and 78.2 mm/yr for both scenarios in 2071 and 2100, respectively. With the exception of the basin's maximum AET, the mean annual AET for both RCP4.5 and RCP8.5 emission scenarios may decline during 2041-2070 and 2071-2100. As rainfall declines and temperature rises and the projected AET in the basin gets disrupted in the future decades. This research may add information to the water management and utilization, and a better knowledge of how climate change directly affects AET systems.

Effects and mechanism of natural phenolic acids/fatty acids on copigmentation of purple sweet potato anthocyanins.

Anthocyanins are attractive alternatives to colorants; however, their low color stability hinders practical application. Copigmentation can enhance both the color intensity and color stability of complexes. Herein, we report an investigation of copigmentation reactions between purple sweet potato anthocyanins (PSA1) and phenolic acids (tannic, ferulic, and caffeic acids) or fatty acids (tartaric and malic acids) at pH 3.5. The effects of the mole ratios of the copigment and the reaction temperature were examined. In addition, quantum mechanical computations were performed to investigate molecular interactions. The optimum PSA:copigment molar ratio was found to be 1:100. The strongest bathochromic and hyperchromic effects were observed for copigmentation with tannic acid (Tan), which might be attributable to the fact that its HOMO-LUMO energy gap was the smallest among the investigated copigments, and because it has a greater number of phenolic aromatic and groups to form more van der Waals and hydrogen bond interactions. However, the formation of the PSA-caffeic acid (Caf) complex was accompanied by the greatest drop in enthalpy (-33.18 kJ/mol) and entropy (-74.55 kJ/mol), and this was the most stable complex at 90 °C. Quantum mechanical calculations indicated that hydrogen bonds and van der Waals force interactions contributed to the color intensification effect of copigmentation. These findings represent an advancement in our understanding of the properties of PSA, expanding the application scope of this natural product.

Combined impacts of future climate-driven vegetation changes and socioeconomic pressures on protected areas in Africa.

Africa's protected areas (PAs) are the last stronghold of the continent's unique biodiversity, but they appear increasingly threatened by climate change, substantial human population growth, and land-use change. Conservation planning is challenged by uncertainty about how strongly and where these drivers will interact over the next few decades. We investigated the combined future impacts of climate-driven vegetation changes inside African PAs and human population densities and land use in their surroundings for 2 scenarios until the end of the 21st century. We used the following 2 combinations of the shared socioeconomic pathways (SSPs) and representative greenhouse gas concentration pathways (RCPs): the "middle-of-the-road" scenario SSP2-RCP4.5 and the resource-intensive "fossil-fueled development" scenario SSP5-RCP8.5. Climate change impacts on tree cover and biome type (i.e., desert, grassland, savanna, and forest) were simulated with the adaptive dynamic global vegetation model (aDGVM). Under both scenarios, most PAs were adversely affected by at least 1 of the drivers, but the co-occurrence of drivers was largely region and scenario specific. The aDGVM projections suggest considerable climate-driven tree cover increases in PAs in today's grasslands and savannas. For PAs in West Africa, the analyses revealed climate-driven vegetation changes combined with hotspots of high future population and land-use pressure. Except for many PAs in North Africa, future decreases in population and land-use pressures were rare. At the continental scale, SSP5-RCP8.5 led to higher climate-driven changes in tree cover and higher land-use pressure, whereas SSP2-RCP4.5 was characterized by higher future population pressure. Both SSP-RCP scenarios implied increasing challenges for conserving Africa's biodiversity in PAs. Our findings underline the importance of developing and implementing region-specific conservation responses. Strong mitigation of future climate change and equitable development scenarios would reduce ecosystem impacts and sustain the effectiveness of conservation in Africa.

Las áreas protegidas (AP) de África son el último bastión de la biodiversidad distintiva del continente, pero cada vez están más amenazadas por el cambio climático, crecimiento sustancial de la población humana y cambio de uso de suelo. La planificación de la conservación enfrenta el reto de la incertidumbre de cuan fuerte y donde interactuarán estos factores a lo largo de las siguientes décadas. Investigamos los impactos futuros combinados de los cambios en la vegetación impulsados por el clima dentro de AP africanas y las densidades de población humana y el uso de suelo en sus alrededores en 2 escenarios hasta el final del siglo 21. Utilizamos las siguientes 2 combinaciones de las trayectorias socioeconómicas compartidas (SSP) y las trayectorias representativas de concentración de gases de invernadero (RCP): el escenario de "mitad del camino" SSP2-RCP4.5 y el escenario recurso intensivo "desarrollo impulsado por combustibles fósiles" SSP5-RCP8.5. Los impactos del cambio climático sobre la cobertura de árboles y el tipo de bioma (i. e., desierto, pastizal, sabana y bosque) fueron simulados con el modelo vegetación global dinámica adaptativo (aDGVM). En ambos escenarios, la mayoría de las AP fueron afectadas adversamente por lo menos por 1 de los factores, pero la coocurrencia de los factores fue mayoritariamente específica por región y escenario. Las proyecciones de MVGDa sugieren incrementos considerables en la cobertura de árboles impulsados por el clima en las AP en pastizales y sabanas actuales. Para AP en África Occidental, los análisis revelaron cambios en la vegetación impulsados por el clima combinados con sitios clave con numerosa población y gran presión de uso de suelo en el futuro. Excepto en muchos PA de África del Norte, los decrementos en la población y presiones de uso de suelo en el futuro fueron raros. A escala continental, SSP5-RCP8.5 condujo a mayores cambios impulsados por el clima en la cobertura arbórea y en la presión de cambio de uso de suelo, mientras que SSP5-RCP8.5 se caracterizó por una mayor presión demográfica en el futuro. Ambos escenarios SSP-RCP implicaron mayores retos para la conservación de la biodiversidad en AP africanas. Nuestros hallazgos subrayan la importancia de desarrollar e implementar respuestas de conservación específicas para cada región. Medidas sólidas para la mitigación del cambio climático así como escenarios de desarrollo equitativo podrían reducir los impactos en el ecosistema y sustentar la efectividad de la conservación en África.

Climate change influences on the geographic distributional potential of the spotted fever vectors Amblyomma maculatum and Dermacentor andersoni.

Amblyomma maculatum (Gulf Coast tick), and Dermacentor andersoni (Rocky Mountain wood tick) are two North American ticks that transmit spotted fevers associated Rickettsia. Amblyomma maculatum transmits Rickettsia parkeri and Francisella tularensis, while D. andersoni transmits R. rickettsii, Anaplasma marginale, Coltivirus (Colorado tick fever virus), and F. tularensis. Increases in temperature causes mild winters and more extreme dry periods during summers, which will affect tick populations in unknown ways. Here, we used ecological niche modeling (ENM) to assess the potential geographic distributions of these two medically important vector species in North America under current condition and then transfer those models to the future under different future climate scenarios with special interest in highlighting new potential expansion areas. Current model predictions for A. maculatum showed suitable areas across the southern and Midwest United States, and east coast, western and southern Mexico. For D. andersoni, our models showed broad suitable areas across northwestern United States. New potential for range expansions was anticipated for both tick species northward in response to climate change, extending across the Midwest and New England for A. maculatum, and still farther north into Canada for D. andersoni.

Hourly future climate scenario datasets for impact assessment of climate change considering simultaneous interactions among multiple meteorological factors.

Assessing the impacts of climate change in multiple fields, such as energy, land and water resources, and human health and welfare is important to find effective strategies to adapt to a changing climate and to reduce greenhouse gases. Many phenomena influenced by climate change have diurnal fluctuations and are affected by simultaneous interactions among multiple meteorological factors. However, climate scenarios with detailed (at least hourly) resolutions are usually not available. To assess the impact of climate change on such phenomena while considering simultaneous interactions (e.g., synergies), climate scenarios with hourly fluctuations are indispensable. However, because meteorological indicators are not independent, the value of one indicator varies as a function of other indicators. Therefore, it is almost impossible to determine the functions that show all relationships among meteorological elements considering the geographical and temporal (both seasonal and time of a day) characteristics. Therefore, generating hourly scenarios that include possible combinations of meteorological indicators for each hourly observation unit is a challenging problem. In this study, we provide secondary future climate scenario datasets that have hourly fluctuations with reasonable combinations of meteorological indicator values that are likely to occur simultaneously, without losing the long-term climate change trend in the existing daily climate scenarios based on global climate models. Historical hourly weather datasets observed from 2017 to 2019 (the reference years) are used to retrieve short-term fluctuations. Bias-corrected daily future climate scenario datasets generated using four global climate models (GFDL CM3, HadGEM2-ES, MIROC5, and MRI-CGCM3) and two Representative Concentration Pathways (RCP8.5 and 2.6) are used to model long-term climate change. A total of 48 different types of hourly future scenario datasets for five meteorological indicators (temperature, solar radiation, humidity, rainfall, and wind speed) were acquired, targeting a projection period from 2020 to 2080, for 10 weather stations in Japan. The generated hourly climate scenario datasets can be used to project the quantitative impacts of climate change on targeted phenomena considering simultaneous interactions among multiple meteorological factors.

Twenty-first century hydrologic and climatic changes over the scarcely gauged Jhelum river basin of Himalayan region using SDSM and RCPs.

Climatic and hydrological changes of the scarcely gauged mountainous basins remain a challenge to study due to unavailability of observed data. The recent study aims to assess these changes using spatial decision tool statistical downscaling method (SDSM) and snowmelt runoff model (SRM) for the twenty-first century under representative concentration pathways (RCPs). SDSM considered absolute partial correlation coefficient (abs. Pr.) to evaluate efficiency predictors or the predictands of the Jhelum river basin. The performance evaluation of SDSM assessed using coefficient of determination (R2) values for RCP 4.5 and RCP 8.5 under CMIP5 (CCSM4). The biases of the daily time series downscaled data removed by using mean-based biased correction method (MB-BC). Stream projection carried out using SRM by incorporating MODIS snow product. Statistical parameters R2 and volume difference (Dv %) calculated for accuracy assessment of SRM for the simulated and observed discharge (2001-2018). Streamflow projections for the twenty-first century carried out by SRM using de-biased downscaled data. The R2 indicator of SDSM ranged between 78-81% for temperature and 82-86% for precipitation under RCP 4.5 and RCP 8.5, respectively. The temperature results indicated an increasing trend of 1.5oC and 3.8oC for the twenty-first century under RCP 4.5 and RCP 8.5, respectively. The mean annual precipitation showed a rise of 2-7% while surface runoff projected a rising trend of 3.3-7.4% for RCP-4.5 and RCP-8.5 respectively till the end of the twenty-first century. The study results revealed that Jhelum basin will be wetter and warmer for the twenty-first century as compare to the baseline period. The hydrographs of the river predicted the occurrence of more extreme events in the region for the twenty-first century. These hydrographs may help for better water conservation and management strategies in the Jhelum basin for the twenty-first century.

Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6.

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020-2039 (near future), 2040-2069 (mid-century), and 2080-2099 (end-of-the-century), relative to the baseline period (1995-2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region's climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models' outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

Doubling of the population exposed to drought over South Asia: CMIP6 multi-model-based analysis.

Drought has a substantial socioeconomic impact under the changing climate. The estimation of population exposure to drought could be the pivotal signal to predict future water scarcity in the climate hotspot of South Asia. This study examines the changing population exposure to drought across South Asia using 20 climate model ensembles from the latest CMIP6 and demographic data under shared socioeconomic pathways (SSPs). Underpinning the latest version of the IPCC 6th Assessment Report (AR6), this paper focuses on the 2021-2040 (near-term), 2041-2060 (mid-term), and 2081-2100 (long-term) periods to project population exposure changes relative to the reference period (1995-2014) under four SSP-RCP scenarios. Drought events are detected by adopting the standardized precipitation evapotranspiration index (SPEI) and run theory method. Model validation suggests that CMIP6-GCM performs well in projecting climate variables and capturing drought events. The results show that the projected increases in frequent drought events and affected areal coverage are stronger during the early part of the century and weaker at the end under all scenario combinations. In relative terms, the projected increase in the number of people exposed to drought is dominant (>1.5-fold) in the near-term and mid-term periods but decreases in the long-term period. Compared to the reference period, the leading increase in population exposure (2.3-fold) is projected under the newly designed gap scenario (SSP3-7.0) in the mid-term period. A surprising decline in the number of exposed populations was estimated to be 18.8% under SSP5-8.5 by the end of the century. The mitigating effect of the predicted heavy precipitation will decrease droughts in the late future. Spatially, increasing exposure will become more pronounced across India and Afghanistan. Furthermore, the population change effect is mainly responsible for the exposure changes in South Asia. However, this study strongly recommends future 'plausible world' regional rivalry pathways (SSP3) scenario-combinations into consideration for policymaking in regard to water management as well as migration planning over South Asia.

Future air pollution related health burdens associated with RCP emission changes in the UK.

Three Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) are used to simulate future ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5) in the United Kingdom (UK) for the 2050s relative to the 2000s with an air quality model (AQUM) at a 12 km horizontal resolution. The present-day and future attributable fractions (AF) of mortality associated with long-term exposure to annual mean O3, NO2 and PM2.5 have accordingly been estimated for the first time for regions across England, Scotland and Wales. Across the three RCPs (RCP2.6, RCP6.0 and RCP8.5), simulated annual mean of the daily maximum 8-h mean (MDA8) O3 concentrations increase compared to present-day, likely due to decreases in NOx (nitrogen oxides) emissions, leading to less titration of O3 by NO. Annual mean NO2 and PM2.5 concentrations decrease under all RCPs for the 2050s, mostly driven by decreases in NOx and sulphur dioxide (SO2) emissions, respectively. The AF of mortality associated with long-term exposure to annual mean MDA8 O3 is estimated to increase in the future across all the regions and for all RCPs. Reductions in NO2 and PM2.5 concentrations lead to reductions in the AF estimated for future periods under all RCPs, for both pollutants. Total mortality burdens are also highly sensitive to future population projections. Accounting for population projections exacerbates differences in total UK-wide MDA8 O3-health burdens between present-day and future by up to a factor of ~3 but diminishes differences in NO2-health burdens. For PM2.5, accounting for future population projections results in additional UK-wide deaths brought forward compared to present-day under RCP2.6 and RCP6.0, even though the simulated PM2.5 concentrations for the 2050s are estimated to decrease. Thus, these results highlight the sensitivity of future health burdens in the UK to future trends in atmospheric emissions over the UK as well as future population projections.

Snow cover loss compounding the future economic vulnerability of western China.

Decline in snow mass threatens the regional economy that critically depends on meltwater. However, the economic scale of snow mass loss is hardly understood, and its role in the vulnerability of future economic development is unclear. We investigate the current reserves of snow cover and the value of its loss. The result showed that the total annual snow mass in western China declines at a rate of 3.3 × 109 Pg per decade (p < 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 × 1011 Pg). Snow mass loss over the past 40 years in western China turns into an average loss value of CN¥0.1 billion (in the present value) every year ($1 = CN¥7). If the trend continues at the current rate, the accumulated loss value would rise to CN¥63 billion by 2040. Furthermore, subject to the combinations of RCPs and SSPs scenario, the future economic value of snow mass loss in western China appears to accelerate driven by both declining snowmelt resources and socioeconomic development demand. RCP26-SSP1 is the pathway among all to have the least economic cost in replacing the snowmelt loss, and the cost would be quadrupled in RCP80-SSP3 scenario by 2100. At a basin scale, the declining snow mass would turn the regional economy to be more vulnerable except Junggar and Ili endorheic basin. The Ertis river and Qaidam endorheic basins display to be most vulnerable. It highlights that the snow value can be economically important in the regions of west China and should be considered more properly in water resources management.

Rice drought risk assessment under climate change: Based on physical vulnerability a quantitative assessment method.

Drought is the most serious natural disaster causing severe damage to agriculture. Drought impacts on rice (Oryza sativa) production present a major threat to future global food security. In this paper, the Environmental Policy Integrated Climate (EPIC) model was used to simulate the growth of rice, in different periods (short-term (2019-2039), medium-term (2040-2069), long-term (2070-2099)), based on multiple Representative Concentration Pathways (RCP) scenarios. Drought intensity and rice physical vulnerability curves were assessed, based on the output parameters of EPIC, to evaluate global rice yield risk, due to drought. The results show that the average expected loss rate of global rice yield may reach 13.1% (±0.4%) in the future. The high-risk area of rice drought is mainly located in the north of 30°N. The fluctuation of rice drought risk and the proportion of increased risk areas will increase significantly. About 77.6% of the changes in rice drought risk are explained by variations in shortwave radiation (r = 0.88). Projections show that the average value of daily shortwave radiation increases by 1 W/m2 during the rice growth period, accompanied by an expected rice yield loss rate of about 12.7%. The rice drought risk methods presented in this paper provide plausible estimates of forecasting future drought risk under climate change, and address challenges of sparse data; we believe these methods can be applied to decisions for reducing drought-related crop losses and ensuring global food security.

Estimating the distribution and productivity characters of Larix kaempferi in response to climate change.

Understanding the distribution, net primary productivity (NPP) and environmental constraints of Larix kaempferi is crucial to predict how global climate change will affect its growth and future dynamics. We simulated future changes in the globally suitable distribution patterns and the NPP dynamics under different representative concentration pathways (RCPs) using MaxEnt and Physiological Principles in Predicting Growth (3-PG) models. The results showed that suitable distribution areas for Larix kaempferi were concentrated in Europe and Asia, followed by North America, under current climate conditions. Globally, about 33.75% of the suitable area was in China. Suitable areas decreased and shifted northward in Asia, Europe and China in the RCP scenarios. Larix kaempferi could adapt or move to higher latitudes/altitudes to mitigate the negative impacts of climate change. The NPP of Larix kaempferi in China was 241.85-863.57 g m-2 a-1 simulated by the 3-PG model after local parameterization, which was consistent with the measured NPP. Changes in NPP were predicted in future climates. When the correlations between climate factors and NPP were examined, under the more optimistic scenarios, NPP would increase significantly. The key parameters of the 3-PG model were the optimal temperature for growth, forest age, and the number of days of lost productivity in each frost period. Therefore, climate change has a quantitative and significant impact on the distribution and productivity of L. kaempferi, which was estimated successfully with the two modeling approaches. Our results will contribute to the improved cultivation, environment and management of L. kaempferi and potentially of other deciduous gymnosperms.

Climate change impact assessment on Northeast China's grain production.

The international community is paying more attention to climate change because a consensus has been reached that climate change has an adverse effect not only on the environment but also on agriculture. Therefore, in this study, present and future climate datasets (obtained from general circulation models) including atmospheric carbon concentration were used to assess the impact of climate change on grain production for an important base of China (Northeast). An empirical model has been developed using climate and other additional variables (effective irrigation area, fertilizer, and labor force) to assess the effect of climate change on grain production. The results revealed that maximum temperature is a key climate determinant in grain production of the study area. Atmospheric carbon concentration showed a significant impact on grain outputs in most of the cases. During the analysis, it was observed that precipitation displayed a declining trend while an effective irrigation area showed positive non-significant contribution to grain production. Analysis based on different representative concentration pathways exhibited that maximum temperature may contribute negatively to grain production in the future. Overall, the analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.