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Climate change caused by global warming involves crucial plant growth factors such as atmospheric CO2 concentration, ambient temperature or water availability. These stressors usually co-occur, causing intricate alterations in plant physiology and development. This work focuses on how elevated atmospheric CO2 levels, together with the concomitant high temperature, would affect the physiology of a relevant crop, such as broccoli. Particular attention has been paid to those defence mechanisms that contribute to plant fitness under abiotic stress. Results show that both photosynthesis and leaf transpiration were reduced in plants grown under climate change environments compared to those grown under current climate conditions. Furthermore, an induction of carbohydrate catabolism pointed to a redistribution from primary to secondary metabolism. This result could be related to a reinforcement of cell walls, as well as to an increase in the pool of antioxidants in the leaves. Broccoli plants, a C3 crop, grown under an intermediate condition showed activation of those adaptive mechanisms, which would contribute to coping with abiotic stress, as confirmed by reduced levels of lipid peroxidation relative to current climate conditions. On the contrary, the most severe climate change scenario exceeded the adaptive capacity of broccoli plants, as shown by the inhibition of growth and reduced vigour of plants. In conclusion, only a moderate increase in atmospheric CO2 concentration and temperature would not have a negative impact on broccoli crop yields.

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Projections of coral reefs under climate change have important policy implications, but most analyses have focused on the intensification of climate-related physical stress rather than explicitly modelling how coral populations respond to stressors. Here, we analyse the future of the Great Barrier Reef (GBR) under multiple, spatially realistic drivers which allows less impacted sites to facilitate recovery. Under a Representative Concentration Pathway (RCP) 2.6 CMIP5 climate ensemble, where warming is capped at ~2°C, GBR mean coral cover declined mid-century but approached present-day levels towards 2100. This is considerably more optimistic than most analyses. However, under RCP4.5, mean coral cover declined by >80% by late-century, and reached near zero under RCP ≥6.0. While these models do not allow for adaptation, they significantly extend past studies by revealing demographic resilience of coral populations to low levels of additional warming, though more pessimistic outcomes might be expected under CMIP6. Substantive coral populations under RCP2.6 would facilitate long-term genetic adaptation, adding value to ambitious greenhouse emissions mitigation.

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Meteorological conditions have important impacts on surface ozone (O3) formation. To evaluate the influence of future climate change on O3 concentrations in different regions of China, this study employed the climate data from the community earth system model provided by the CMIP5 under the RCP4.5, RCP6.0, and RCP8.5 scenarios to generate the initial and boundary conditions for the WRF model. Then, the dynamic downscaling WRF results were fed into a CMAQ model as meteorological fields with fixed emission data. Two 10-year periods (2006-2015 and 2046-2055) were selected in this study to discuss the impacts of climate change on O3. The results showed that climate change increased boundary layer height, mean temperature, and heatwave days in China during summer. Relative humidity decreased and wind speed near the surface showed no obvious change in the future. O3 concentration showed an increasing trend in Beijing-Tianjin-Hebei, Sichuan Basin, and South China. The extreme value of O3 maximum daily 8-hour moving average (MDA8) showed an increasing trend, following the order of RCP8.5 (0.7 µg·m-3)>RCP6.0 (0.3 µg·m-3)>RCP4.5 (0.2 µg·m-3). The number of days exceeding the standard for summer O3 had a similar spatial distribution with the heatwave days in China. The increase in heatwave days led to the increase in O3 extreme pollution events, and the possibility of a long-lasting O3 pollution event will increase in China in the future.

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Recently, the International Panel for Climate Change released the 6th Coupled Model Intercomparison Project (CMIP6) climate change scenarios with shared socioeconomic pathways (SSPs). The SSP scenarios result in significant changes to climate variables in climate projections compared to their predecessor, the representative concentration pathways from the CMIP5. Therefore, it is necessary to examine whether the CMIP6 scenarios differentially impact plant-disease ecosystems compared to the CMIP5 scenarios. In this study, we used the EPIRICE-LB model to simulate and compare projected rice blast disease epidemics in the Korean Peninsula using five selected family global climate models (GCMs) of the CMIP5 and CMIP6 for two forcing scenarios. We found a similar decrease in rice blast epidemics in both CMIP scenarios; however, this decrease was greater in the CMIP6 scenarios. In addition, distinctive epidemic trends were found in North Korea, where the rice blast epidemics increase until the mid-2040s but decrease thereafter until 2100, with different spatial patterns of varying magnitudes. Controlling devastating rice blast diseases will remain important during the next decades in North Korea, where appropriate chemical controls are unavailable due to chronic economic and political issues. Overall, our analyses using the new CMIP6 scenarios reemphasized the importance of developing effective control measures against rice blast for specific high-risk areas and the need for a universal impact and vulnerability assessment platform for plant-disease ecosystems that can be used with new climate change scenarios in the future. Supplementary information: The online version contains supplementary material available at 10.1007/s10584-022-03410-2.

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The impact of different climate scenarios, drought, and water level management on the outflow water quality of peatlands has been investigated. A mesocosm experiment has been conducted within climate control chambers to simulate current (2016-2019 real-time) and future representative concentration pathway (RCP) climate scenarios (RCP 2.6, 4.5 and 8.5). To assess the efficiency of a management strategy for improving peatland water quality, water level adjustment was applied to half of the system at the same time for each climate scenario. Furthermore, the mesocosm experienced the 2018 European drought during the simulation years, and the corresponding impact was analyzed. The results of this study revealed a substantial and favorable impact of water level management on water quality of peatlands under different climate scenarios. The effect of water level management was the largest for ammonium (NH4-N) and 5-day biochemical oxygen demand (BOD5), and the smallest for total phosphorus (TP). Drought had a strong impact on chemical variables, increasing their concentration and deteriorating the water quality of peatland outflow. However, water level management can stabilize the nutrient levels in peatland outflows, particularly during drought and under warmer climate scenarios, thus mitigating the adverse effects of climate change.

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Climate change (CC) is a global threat to the agricultural system. Changing climatic conditions are causing variations in temperature range, rainfall timing, humidity percentage, soil structure, and composition of gases in environment. All these factors have a great influence on the phenological events in plants' life cycle. Alternation in phenological events, especially in crops, leads to either lower yield or crop failure. In light of respective statement, the present study is designed to evaluate the climatic impacts on two heat-resistant wheat varieties (Sialkot-2008 and Punjab-2018). During the study, impacts of CC on wheat phenology and annual yield were predicted considering six climatic factors: maximum temp, minimum temperature, precipitation, humidity, soil moisture content, and solar radiation using two quantitative approaches. First, a two-year field experimental plot was set up at five different sites of study-each plot a bisect of two sites. Phenological changes of both varieties were monitored with respect to climatic factors and changes were recorded in a scientific manner. Secondly, experimental results were compared with Global climate models (GMC) models with a baseline range of the past 40 years (1970-2010) and future fifty years (2019-2068) under Representative Concentration Pathway (RCP) 8.5 model analysis. Field experiment showed a (0.02) difference in maximum temperature, (0.04) in minimum temperature, (0.17) in humidity, and about (0.03) significant difference in soil moisture content during 2019-2021. Under these changing climatic parameters, a 0.21% difference was accounted in annual yield. Furthermore, the results were supported by GMC model analysis, which was analyzed by Decision Support System for Agrotechnology Transfer (DSSAT) model. Results depicted that non-heat-resistant wheat varieties could cause up to a 6~13% reduction in yield during future 50 years (2019-2068)) compared with the last 40 years (1970-2010). A larger decline in wheat grain number relative to grain weight is a key reducer of wheat yield, under future climate change circumstances. Using heat-tolerant wheat varieties will not only assist to overcome this plethora but also provide a potential increase of up to 7% to 10% in indigenous environment. On the other hand, it was concluded that cultivating these heat-resistant varieties that are also ripening late culminates into enhanced thermal time chucks during the grain-filling period; hence, wheat yield will increase by 8% to 12%. In changing climatic conditions and varieties, 'Punjab-2018' will be the better choice for peasants and farm-land owners to obtain a better yield of wheat to cope with the necessities of food on the domestic and national level.

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The present study assesses the sensitivity of dairy animals to thermal stress, and projects the economic losses due to heat stress in the Trans and Upper Gangetic plains region of India with Representative Concentration Pathway (RCP) 4.5 climate scenario for the time slice 2010-2039 and two subperiods, 2020-2029 and 2030-2039. The projections were carried out for two different scenarios of population and productivity growth of dairy animals, Business-as-Usual (BAU) and Alternate, whereby land, feed and fodder constraints were applied. The potential annual loss in milk production due to heat stress in the region was estimated to be around 361 and 377 thousand tons for the time slice 2010-2039 under BAU and Alternate scenario, respectively. In economic terms these losses, at current prices, would be equivalent to INR 11.93 billion and INR 12.44 billion, respectively. This gives an indication of the level of financial investment that can be made in adaptation measures to arrest the loss due to climate change.

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Litterfall production is a major process within forest ecosystems that plays a crucial role in the global carbon cycle. Accordingly, studies have explored the abiotic and biotic features that influence litterfall production. In addition to traditional statistical models, the rapid development of nonparametric and nonlinear machine learning models, such as random forest (RF), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), have provided new methods of predicting the production of forest litterfall. Here, we evaluated the ability of the abovementioned models and mixed effect random forest (MERF) models to predict total annual litterfall production-based on several abiotic and biotic features-using 968 records from 314 forest sites covering the full geographical range of Chinese forests. In general, machine learning models were found to outperform linear mixed models. In particular, the MERF models ranked the highest in terms of performance (R2 = 0.7), which may be attributed to their ability to characterize nonlinear relationships between features and litterfall production. The key drivers were climate-related features and forest age, with the mean annual temperature and age positively correlated with litterfall production. Furthermore, the correlation between forest type and litterfall production was more significant for needleleaf forests than for other forest types. For needleleaf and broadleaf forests in several regions in China, the future litterfall production was predicted to be the highest under IPCC representative concentration pathway (RCP) 8.5, followed by RCP 4.5, RCP 2.6, and the original scenarios (sample data). Improved models to better understand and estimate litterfall production in forests at present and in the future are required for forest management planning to minimize the negative impacts of climate change on forest ecosystems.

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This study projected the future temperature change for Egypt during the late of this century (2071-2100) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5), by correcting regional climate model (RCM) simulations of average, maximum, and minimum daily temperature with reference to observed data of 26 stations. Four commonly used methods of bias correction have been applied and evaluated: linear scaling, variance scaling, and theoretical and empirical quantile mapping. The compromise programing results of the applied evaluation criteria show that the best method is the variance scaling, and thus it was applied to transfer the correction factor to the projections. All temperature indices are expected to increase significantly under all scenarios and reach the highest record by the end of the century, i.e., the expected increase in average, maximum, and minimum temperature ranges between 4.08-7.41 °C, 4.55-7.89 °C, and 3.88-7.23 °C, respectively. The largest temperature rise will occur in the summer, with the highest increase in the maximum (minimum) temperature of 10.9 °C (10 °C) in July and August under RCP8.5. The maximum (minimum) winter temperature, on the other hand, will drop by a maximum of 2 °C (1.35 °C) under RCP2.6. The Western Desert and Upper Egypt are the regions most affected by climate change, while the northern region of Egypt is the least affected. These findings would help in impact assessment and adaptation strategies and encourage further investigation to evaluate various climate models in order to obtain a comprehensive assessment of the climate change impacts on different hydrometeorological processes in Egypt.

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Stress factors such as climate change and drought may switch the role of temperate peatlands from carbon dioxide (CO2 ) sinks to sources, leading to positive feedback to global climate change. Water level management has been regarded as an important climate change mitigation strategy as it can sustain the natural net CO2 sink function of a peatland. Little is known about how resilient peatlands are in the face of future climate change scenarios, as well as how effectively water level management can sustain the CO2 sink function to mitigate global warming. The authors assess the effect of climate change on CO2 exchange of south Swedish temperate peatlands, which were either unmanaged or subject to water level regulation. Climate chamber simulations were conducted using experimental peatland mesocosms exposed to current and future representative concentration pathway (RCP) climate scenarios (RCP 2.6, 4.5 and 8.5). The results showed that all managed and unmanaged systems under future climate scenarios could serve as CO2 sinks throughout the experimental period. However, the 2018 extreme drought caused the unmanaged mesocosms under the RCP 4.5 and RCP 8.5 switch from a net CO2 sink to a source during summer. Surprisingly, the unmanaged mesocosms under RCP 2.6 benefited from the warmer climate, and served as the best sink among the other unmanaged systems. Water level management had the greatest effect on the CO2 sink function under RCP 8.5 and RCP 4.5, which improved their CO2 sink capability up to six and two times, respectively. Under the current climate scenario, water level management had a negative effect on the CO2 sink function, and it had almost no effect under RCP 2.6. Therefore, the researchers conclude that water level management is necessary for RCP 8.5, beneficial for RCP 4.5 and unimportant for RCP 2.6 and the current climate.

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Investments in forestry are long-term and thus subject to numerous sources of risk. In addition to the volatility from markets, forestry investments are directly exposed to future impacts from climate change. We examined how diversification of forest management regimes can mitigate the expected risks associated with forestry activities in New Zealand based on an application of Modern Portfolio Theory. Uncertainties in the responses of Pinus radiata (D. Don) productivity to climate change, from 2050 to 2090, were simulated with 3-PG, a process-based forest growth model, based on future climate scenarios and Representative Concentration Pathways (RCPs). Future timber market scenarios were based on RCP-specific projections from the Global Timber Model and historical log grade prices. Outputs from 3-PG and the market scenarios were combined to compute annualized forestry returns for four P. radiata regimes for 2050-2090. This information was then used to construct optimal forestry portfolios that minimize investment risk for a given target return under different RCPs, forest productivity and market scenarios. While current P. radiata regimes in New Zealand are largely homogenous, our results suggest that regime diversification can mitigate future risks imposed by climate change and market uncertainty. Nevertheless, optimal portfolio compositions varied substantially across our range of scenarios and portfolio objectives. The application of this framework can help forest managers to better account for future risks in their management decisions.

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Water purification is one of the most essential services provided by wetlands. A lot of concerns regarding wetlands subjected to climate change relate to their susceptibility to hydrological change and the increase in temperature as a result of global warming. A warmer condition may accelerate the rate of decomposition and release of nutrients, which can be exported downstream and cause serious ecological challenges; e.g., eutrophication and acidification. The aim of this study is to investigate the effect of climate change on water quality in peatland and constructed wetland ecosystems subject to water level management. For this purpose, the authors simulated the current climate scenario base on the database from Malmö station (Scania, Sweden) for 2016 and 2017 as well as the future climate scenarios for the last 30 years of the century based on the Representative Concentration Pathway (RCP) and different regional climate models (RCM) for a region wider than Scania County. For future climate change, the authors simulated low (RCP 2.6), moderate (RCP 4.5) and extreme (RCP 8.5) climate scenarios. All simulations were conducted within climate chambers for experimental peatland and constructed wetland mesocosms. Our results demonstrate that the effect of climate scenario is significantly different for peatlands and constructed wetlands (interactive effect) for the combined chemical variables. The warmest climate scenario RCP 8.5 is linked to a higher water purification function for constructed wetlands, but to a lower water purification function and a subsequent deterioration of peatland water qualities, even if subjected to water level management. The explanation for the different response of constructed wetlands and peatlands to climate change could be due to the fact that the substrate in the constructed wetland mesocosms and peatlands was different in terms of the organic matter quality and quantity. The utilization of nutrients by the plants and microbial community readily exceed the mineralization under a limited nutrient content (as we had in constructed wetland) when the temperature rises. However, concerning the extreme scenario RCP 8.5, the peatlands have shown a tendency to have reverse processes.

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BACKGROUND: Extreme temperature events have been observed to appear more frequently and with greater intensity in Taiwan in recent decades due to climate change, following the global trend. Projections of temperature extremes across different climate zones and their impacts on related mortality and adaptation have not been well studied. METHODS: We projected site-specific future temperature extremes by statistical downscaling of 8 global climate models followed by Bayesian model averaging from 2021 to 2060 across Taiwan under the representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5. We then calculated the attributable mortality (AM) in 6 municipalities and in the eastern area by multiplying the city/county- and degree-specific relative risk of mortality according to the future population projections. We estimated the degree of adaptation to heat by slope reduction of the projected AM to be comparable with that in 2018. RESULTS: The annual number of hot days with mean temperatures over 30 °C was predicted to have a substantial 2- to 5-fold increase throughout the residential areas of Taiwan by the end of 2060 under RCP8.5, whereas the decrease in cold days was less substantial. The decrease in cold-related mortality below 15 °C was projected to outweigh heat-related mortality for the next two decades, and then heat-related mortality was predicted to drastically increase and cross over cold-related mortality, surpassing it from 2045 to 2055. Adjusting for future population size, the percentage increase in heat-related deaths per 100,000 people could increase by more than 10-fold under the worst scenario (RCP8.5), especially for those over 65 years old. The heat-related impacts will be most severe in southern Taiwan, which has a tropical climate. There is a very high demand for heat-adaptation prior to 2050 under all RCP scenarios. CONCLUSIONS: Spatiotemporal variations in AM in cities in different climate zones are projected in Taiwan and are expected to have a net negative effect in the near future before shifting to a net positive effect from 2045 to 2055. However, there is an overall positive and increasing trend of net effect for elderly individuals under all the emission scenarios. Active adaptation plans need to be well developed to face future challenges due to climate change, especially for the elderly population in central and southern Taiwan.

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It is important to understand how species distributions will shift under climate change. While much focus has been on species tracking temperature changes in the northern hemisphere, changing precipitation patterns in tropical regions have received less attention. The aim of the study was to estimate the current distribution of wet and dry miombo woodlands of sub-Saharan Africa and to predict their distributions under different climate change scenarios. A maximum entropy method (Maxent) was used to estimate the distributions and for projections. Occurrence records of dominant tree species in each woodland were used for modeling, together with altitude, soil characteristics, and climate variables as the environmental variables. Modeling was done under all four representative concentration pathways (RCPs) and three general circulation models. Three dominant tree species were used in models of dry miombo while seven were used for wet miombo. Models estimated dry miombo to cover almost the entire known distribution of miombo woodlands while wet miombo were estimated to predominate in parts of Angola, southern Democratic Republic of Congo, Malawi, Tanzania, Zambia, and Zimbabwe. Future climate scenarios predict a drier climate in sub-Saharan Africa, and as a result, the range of dry miombo will expand. Dry miombo were predicted to expand by up to 17.3% in 2050 and 22.7% in 2070. In contrast, wet miombo were predicted to contract by up to - 28.6% in 2050 and - 41.6% in 2070. A warming climate is conducive for the proliferation of dry miombo tree species but unfavorable for wet miombo tree species.

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This study assesses the climate boundary shifts from the historical time to near/mid future by using a slightly modified Köppen-Geiger (KG) classification scheme and presents comprehensive pictures of historical (1960-1990) and projected near/mid future (1950s: 2040-2060/1970s: 2060-2080) climate classes across Nepal. Ensembles of three selected general circulation models (GCMs) under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were used for projected future analysis. During the 1950s, annual average temperature is expected to increase by 2.5 °C under RCP 8.5. Similarly, during the 1970s, it is even anticipated to rise by 3.6 °C under RCP 8.5. The rate of temperature rise is higher in the non-monsoon period than in monsoon period. During the 1970s, annual precipitation is projected to increase by 8.1% under RCP 8.5. Even though the precipitation is anticipated to increase in the future in annual scale, winter seasons are estimated to be drier by more than 15%. This study shows significant increments of tropical (Am and Aw) and arid (BSk) climate types and reductions of temperate (Cwa and Cwb) and polar (ET and EF). Noticeably, the reduction of the areal coverage of polar frost (EF) is considerably high. In general, about 50% of the country's area is covered by the temperate climate (Cwa and Cwb) in baseline scenario and it is expected to reduce to 45% under RCP 4.5 and 42.5% under RCP 8.5 during the 1950s, and 42% under RCP 4.5 and 39% under RCP 8.5 during the 1970s. Importantly, the degree of climate boundary shifts is quite higher under RCP 8.5 than RCP 4.5, and likewise, the degree is higher during the 1970s than the 1950s. We believe this study to facilitate the identification of regions in which impacts of climate change are notable for crop production, soil management, and disaster risk reduction, requiring a more detailed assessment of adaptation measures. The assessment of climate boundary shifting can serve as valuable information for stakeholders of many disciplines like water, climate, transport, energy, environment, disaster, development, agriculture, and tourism.

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The joint and relative effects of future land-use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land-use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041-2070) and late (2071-2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071-2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best-case land-use scenario ("Sustainability") alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse-case land-use scenario ("Fragmentation") causes a 73.2% increase. The optimistic climate-land-use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071-2100 compared to the baseline period. In contrast, the most pessimistic climate-land-use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land-use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land-use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

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Limited information exists on potential impacts of climate change on nitrous oxide (N2O) emissions by including N2-fixing legumes in crop rotations from rain-fed cropping systems. Data from two 3-yr crop rotations in northern NSW, Australia, viz. chickpea-wheat-barley (CpWB) and canola-wheat-barley (CaWB), were used to gain an insight on the role of legumes in mitigation of N2O emissions. High-frequency N2O fluxes measured with an automated system of static chambers were utilized to test the applicability of Denitrification and Decomposition model. The DNDC model was run using the on-site observed weather, soil and farming management conditions as well as the representative concentration pathways adopted by the Intergovernmental Panel on Climate Change in its Fifth Assessment Report. The DNDC model captured the cumulative N2O emissions with variations falling within the deviation ranges of observations (0.88±0.31kgNha-1rotation-1 for CpWB, 1.44±0.02kgNha-1rotation-1 for CaWB). The DNDC model can be used to predict between modeled and measured N2O flux values for CpWB (n=390, RSR=0.45) and CaWB (n=390, RSR=0.51). Long-term (80-yr) simulations were conducted with RCP 4.5 representing a global greenhouse gas stabilization scenario, as well RCP 8.5 representing a very high greenhouse gas emission scenario based on RCP scenarios. Compared with the baseline scenarios for CpWB and CaWB, the long-term simulation results under RCP scenarios showed that, (1) N2O emissions would increase by 35-44% for CpWB and 72-76% for CaWB under two climate scenarios; (2) grain yields would increase by 9% and 18% under RCP 4.5, and 2% and 14% under RCP 8.5 for CpWB and CaWB, respectively; and (3) yield-scaled N2O-N emission would increase by 24-42% for CpWB and 46-54% for CaWB under climate scenarios, respectively. Our results suggest that 25% of the yield-scaled N2O-N emission would be saved by switching to a legume rotation under climate change conditions.

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Climate projections from 20 downscaled global climate models (GCMs) were used with the 3-PG model to predict the future productivity and water use of planted loblolly pine (Pinus taeda) growing across the southeastern United States. Predictions were made using Representative Concentration Pathways (RCP) 4.5 and 8.5. These represent scenarios in which total radiative forcing stabilizes before 2100 (RCP 4.5) or continues increasing throughout the century (RCP 8.5). Thirty-six sites evenly distributed across the native range of the species were used in the analysis. These sites represent a range in current mean annual temperature (14.9-21.6°C) and precipitation (1,120-1,680 mm/year). The site index of each site, which is a measure of growth potential, was varied to represent different levels of management. The 3-PG model predicted that aboveground biomass growth and net primary productivity will increase by 10%-40% in many parts of the region in the future. At cooler sites, the relative growth increase was greater than at warmer sites. By running the model with the baseline [CO2 ] or the anticipated elevated [CO2 ], the effect of CO2 on growth was separated from that of other climate factors. The growth increase at warmer sites was due almost entirely to elevated [CO2 ]. The growth increase at cooler sites was due to a combination of elevated [CO2 ] and increased air temperature. Low site index stands had a greater relative increase in growth under the climate change scenarios than those with a high site index. Water use increased in proportion to increases in leaf area and productivity but precipitation was still adequate, based on the downscaled GCM climate projections. We conclude that an increase in productivity can be expected for a large majority of the planted loblolly pine stands in the southeastern United States during this century.

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In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: (1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems, and (2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT, to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, whereas changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer, drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big sagebrush regeneration and survival will be negatively impacted, potentially resulting in shifts in the relative abundance of big sagebrush plant functional groups. Our results also highlight the importance of assessing multiple GCMs to understand the range of climate change outcomes on ecohydrology, which was contingent on the GCM chosen.

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This study proposed a robust prioritization framework to identify the priorities of treated wastewater (TWW) use locations with consideration of various uncertainties inherent in the climate change scenarios and the decision-making process. First, a fuzzy concept was applied because future forecast precipitation and their hydrological impact analysis results displayed significant variances when considering various climate change scenarios and long periods (e.g., 2010-2099). Second, various multi-criteria decision making (MCDM) techniques including weighted sum method (WSM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy TOPSIS were introduced to robust prioritization because different MCDM methods use different decision philosophies. Third, decision making method under complete uncertainty (DMCU) including maximin, maximax, minimax regret, Hurwicz, and equal likelihood were used to find robust final rankings. This framework is then applied to a Korean urban watershed. As a result, different rankings were obviously appeared between fuzzy TOPSIS and non-fuzzy MCDMs (e.g., WSM and TOPSIS) because the inter-annual variability in effectiveness was considered only with fuzzy TOPSIS. Then, robust prioritizations were derived based on 18 rankings from nine decadal periods of RCP4.5 and RCP8.5. For more robust rankings, five DMCU approaches using the rankings from fuzzy TOPSIS were derived. This framework combining fuzzy TOPSIS with DMCU approaches can be rendered less controversial among stakeholders under complete uncertainty of changing environments.

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