A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27%of the total area of the Tibetan Plateau and is also one of the most important vegetation types.The Dangxiong alpine meadow ecosystem,located in the south-central part of the Tibetan Plateau,is a typical example.To understand the carbon and water fluxes,water use efficiency(WUE),and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area,two parameter estimation methods,the Model-independent Parameter Estimation(PEST)and the Dynamic Dimensions Search(DDS),were used to optimize the Biome-BGC model.Then,the gross primary productivity(GPP)and evapotranspiration(ET)were simulated.The results show that the DDS parameter calibration method has a better performance.The annual GPP and ET show an increasing trend,while the WUE shows a decreasing trend.Meanwhile,ET and GPP reach their peaks in July and August,respectively,and WUE shows a“dual-peak”pattern,reaching peaks in May and November.Furthermore,according to the simulation results for the next nearly 100 years,the ensemble average GPP and ET exhibit a significant increasing trend,and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario.WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario.This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

Projected impacts of climate change on the habitat of Xerophyta species in Africa

Climate change poses a serious long-term threat to biodiversity.To effectively reduce biodiversity loss,conservationists need to have a thorough understanding of the preferred habitats of species and the variables that affect their distribution.Therefore,predicting the impact of climate change on speciesappropriate habitats may help mitigate the potential threats to biodiversity distribution.Xerophyta,a monocotyledonous genus of the family Velloziaceae is native to mainland Africa,Madagascar,and the Arabian Peninsula.The key drivers of Xerophyta habitat distribution and preference are unknown.Using 308 species occurrence data and eight environmental variables,the MaxEnt model was used to determine the potential distribution of six Xerophyta species in Africa under past,current and future climate change scenarios.The results showed that the models had a good predictive ability(Area Under the Curve and True Skill Statistics values for all SDMs were more than 0.902),indicating high accuracy in forecasting the potential geographic distribution of Xerophyta species.The main bioclimatic variables that impacted potential distributions of most Xerophyta species were mean temperature of the driest quarter(Bio9)and precipitation of the warmest quarter(Bio18).According to our models,tropical Africa has zones of moderate and high suitability for Xerophyta taxa,which is consistent with the majority of documented species localities.The habitat suitability of the existing range of the Xerophyta species varied based on the climate scenario,with most species experiencing a range loss greater than the range gain regardless of the climate scenario.The projected spatiotemporal patterns of Xerophyta species help guide recommendations for conservation efforts.

Projected impacts of climate change and anthropogenic effects on habitat distribution of endangered Javan Hawk-Eagle in Indonesia

Changing climate will jeopardize biodiversity,particularly the geographic distribution of endemic species.One such species is the Javan Hawk-Eagle(JHE,Nisaetus bartelsi),a charismatic raptor found only on Java Island,Indonesia.Thus,it is crucial to develop an appropriate conservation strategy to preserve the species.Ecological niche modeling is considered a valuable tool for designing conservation plans for the JHE.We provide an ecological niche modeling approach and transfer its model to future climate scenarios for the JHE.We utilize various machine learning algorithms under sustainability and business-as-usual(BAU)scenarios for 2050.Additionally,we investigate the conservation vulnerability of the JHE,capturing multifaceted pressures on the species from climate dissimilarities and human disturbance variables.Our study reveals that the ensemble model performs exceptionally well,with temperature emerging as the most critical factor affecting the JHE distribution.This finding indicates that climate change will have a significant impact on the JHE species.Our results suggest that the JHE distribution will likely decrease by 28.41%and 40.16%from the current JHE distribution under sustainability and BAU scenarios,respectively.Furthermore,our study reveals high-potential refugia for future JHE,covering 7,596 km^(2)(61%)under the sustainability scenario and only 4,403 km^(2)(35%)under the BAU scenario.Therefore,effective management and planning,including habitat restoration,refugia preservation,habitat connectivity,and local community inclusivity,should be well-managed to achieve JHE conservation targets.

Potential distribution of Haloxylon ammodendron in Central Asia under climate change

Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change.Haloxylon ammodendron shelterbelts are essential for the protection of plant resources and the control of desertification in Central Asia.Thus far,the potential suitable habitats of H.ammodendron in Central Asia are still uncertain in the future under global climate change conditions.This study utilised the maximum entropy(MaxEnt)model to combine the current distribution data of H.ammodendron with its growth-related data to analyze the potential distribution pattern of H.ammodendron across Central Asia.The results show that there are suitable habitats of H.ammodendron in the Aralkum Desert,northern slopes of the Tianshan Mountains,and the upstream of the Tarim River and western edge of the Taklimakan Desert in the Tarim Basin under the current climate conditions.The period from 2021 to 2040 is projected to undergo significant changes in the suitable habitat area of H.ammodendron in Central Asia,with a projected 15.0% decrease in the unsuitable habitat area.Inland areas farther from the ocean,such as the Caspian Sea and Aralkum Desert,will continue to experience a decrease in the suitable habitats of H.ammodendron.Regions exhibiting frequent fluctuations in the habitat suitability levels are primarily found along the axis stretching from Astana to Kazakhskiy Melkosopochnik in Kazakhstan.These regions can transition into suitable habitats under varying climate conditions,requiring the implementation of appropriate human intervention measures to prevent desertification.Future climate conditions are expected to cause an eastward shift in the geometric centre of the potential suitable habitats of H.ammodendron,with the extent of this shift amplifying alongside more greenhouse gas emissions.This study can provide theoretical support for the spatial configuration of H.ammodendron shelterbelts and desertification control in Central Asia,emphasising the importance of proactive measures to adapt to climate change in the future.

A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

Climate warming profoundly affects hydrological changes,agricultural production,and human society.Arid and semi-arid areas of China are currently displaying a marked trend of warming and wetting.The Chinese Tianshan Mountains(CTM)have a high climate sensitivity,rendering the region particularly vulnerable to the effects of climate warming.In this study,we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset(1961-2014)and 24 global climate models(GCMs)of the Coupled Model Intercomparison Project Phase 6(CMIP6)to assess the applicability of the CMIP6 GCMs in the CTM at the regional scale.Based on this,we conducted a systematic review of the interannual trends,dry-wet transitions(based on the standardized precipitation index(SPI)),and spatial distribution patterns of climate change in the CTM during 1961-2014.We further projected future temperature and precipitation changes over three terms(near-term(2021-2040),mid-term(2041-2060),and long-term(2081-2100))relative to the historical period(1961-2014)under four shared socio-economic pathway(SSP)scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).It was found that the CTM had experienced significant warming and wetting from 1961 to 2014,and will also experience warming in the future(2021-2100).Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble(MME)from the CMIP6 GCMs.The MME simulation results indicated an apparent wetting in 2008,which occurred later than the wetting observed from the CN05.1 in 1989.The GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the CTM.Warming and wetting are more rapid in the northern part of the CTM.By the end of the 21st century,all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet transitions.However,the rise in precipitation fails to counterbalance the drought induced by escalating temperature in the future,so the nature of the drought in the CTM will not change at all.Additionally,the projected summer precipitation shows negative correlation with the radiative forcing.This study holds practical implications for the awareness of climate change and subsequent research in the CTM.

Comparative Assessment of Impacts of Future Climate Change on Runoff in Upper Daqinghe Basin,China

Assessing runoff changes is of great importance especially its responses to the projected future climate change on local scale basins because such analyses are generally done on global and regional scales which may lead to generalized conclusions rather than specific ones.Climate change affected the runoff variation in the past in the upper Daqinghe Basin,however,the climate was mainly considered uncertain and still needs further studies,especially its future impacts on runoff for better water resources management and planning.Integrated with a set of climate simulations,a daily conceptual hydrological model(MIKE11-NAM)was applied to assess the impact of climate change on runoff conditions in the Daomaguan,Fuping and Zijingguan basins in the upper Daqinghe Basin.Historical hydrological data(2008–2017)were used to evaluate the applicability of the MIKE11-NAM model.After bias correction,future projected climate change and its impacts on runoff(2025–2054)were analysed and compared to the baseline period(1985–2014)under three shared social economic pathways(SSP1-2.6,SSP2-4.5,and SSP5-8.5)scenarios from Coupled Model Intercomparison Project Phase 6(CMIP6)simulations.The MIKE-11 NAM model was applicable in all three Basins,with both R^(2)and Nash-Sutcliffe Efficiency coefficients greater than 0.6 at daily scale for both calibration(2009–2011)and validation(2012–2017)periods,respectively.Although uncertainties remain,temperature and precipitation are projected to increase compared to the baseline where higher increases in precipitation and temperature are projected to occur under SSP2-4.5 and SSP5-8.5 scenarios,respectively in all the basins.Precipitation changes will range between 12%–19%whereas temperature change will be 2.0℃–2.5℃ under the SSP2-4.5 and SSP5-8.5 scenarios,respectively.In addition,higher warming is projected to occur in colder months than in warmer months.Overall,the runoff of these three basins is projected to respond to projected climate changes differently because runoff is projected to only increase in the Fuping basin under SSP2-4.5 whereas decreases in both Daomaguan and Zijingguan Basins under all scenarios.This study’s findings could be important when setting mitigation strategies for climate change and water resources management.

Potentially Suitable Area and Change Trends of Tulipa iliensis under Climate Change

Tulipa iliensis,as a wild plant resource,possesses high ornamental value and can provide abundant parental materials for tulip breeding.The objective of this research was to forecast the worldwide geographical spread of Tulipa iliensis by considering bioclimatic,soil,and topographic variables,the findings of this research can act as a benchmark for the conservation,management,and utilization of Tulipa iliensis as a wild plant resource.Research results indicate that all 12 models have an area under curve(AUC)of the receiver operating characteristic curve(ROC)values greater than 0.968 for the paleoclimatic,current,and future climate scenarios,this suggests an exceptionally high level of predictive accuracy for the models.The distribution of Tulipa iliensis is influenced by several key factors.These factors include the mean temperature of the driest quarter(Bio9),calcium carbonate content(T_CACO3),slope,precipitation of the driest month(Bio14),Basic saturation(T_BS),and precipitation of the coldest quarter(Bio19).During the three paleoclimate climate scenarios,the appropriate habitats for Tulipa iliensis showed a pattern of expansion-contraction expansion.Furthermore,the total suitable area accounted for 13.38%,12.28%,and 13.28%of the mainland area,respectively.According to the current climate scenario,the High-suitability area covers 61.78472×10^(4)km^(2),which accounts for 6.57%of the total suitable area,The Midsuitability area covers 190.0938×10^(4)km^(2),accounting for 20.2%of the total suitable area,this represents a decrease of 63.53%~67.13%compared to the suitable area of Tulipa iliensis under the paleoclimate scenario.Under the Shared Socioeconomic Pathways(SSP)scenarios,in 2050 and 2090,Tulipa iliensis is projected to experience a decrease in the High,Mid,and Low-suitability areas under the SSP126 climate scenario by 7.10%~12.96%,2.96%~4.27%and 4.80%~7.96%,respectively.According to the SSP245 scenario,the high suitability area experienced a slight expansion of 2.26%in 2050,but a reduction of 6.32%in 2090.In the SSP370 scenario,the High-suitability areas had a larger reduction rate of 11.24%in 2050,while the Mid-suitability and Low-suitability areas had smaller expansion rates of 0.36%and 4.86%,respectively.In 2090,the High-suitability area decreased by 4.84%,while the Mid and Low-suitability areas experienced significant expansions of 15.73%and 45.89%,respectively.According to the SSP585 scenario,in the future,the High,Mid,and Low-suitability areas are projected to increase by 5.09%~7.21%,7.57%~17.66%,and 12.30%~48.98%,respectively.The research offers enhanced theoretical direction for preserving Tulipa iliensis’genetic variety amidst evolving climatic scenarios.

Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling

In recent years,Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas.However,the regions of potential distribution and the main contributing environmental variables for this nematode are unclear.Under the current climate scenario,we predicted the potential geographic distributions of M.enterolobii worldwide and in China using a Maximum Entropy(MaxEnt)model with the occurrence data of this species.Furthermore,the potential distributions of M.enterolobii were projected under three future climate scenarios(BCC-CSM2-MR,CanESM5 and CNRM-CM6-1)for the periods 2050s and 2090s.Changes in the potential distribution were also predicted under different climate conditions.The results showed that highly suitable regions for M.enterolobii were concentrated in Africa,South America,Asia,and North America between latitudes 30°S to 30°N.Bio16(precipitation of the wettest quarter),bio10(mean temperature of the warmest quarter),and bio11(mean temperature of the coldest quarter)were the variables contributing most in predicting potential distributions of M.enterolobii.In addition,the potential suitable areas for M.enterolobii will shift toward higher latitudes under future climate scenarios.This study provides a theoretical basis for controlling and managing this nematode.

Hydrologic Response to Future Climate Change in the Dulong-Irra-waddy River Basin Based on Coupled Model Intercomparison Project 6

Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role as both a valuable hydro-power resource and an essential ecological passageway.However,the water resources and security exhibit a high degree of vulnerabil-ity to climate change impacts.This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin(DIRB)by using a physical-based hydrologic model.We crafted future climate scenarios using the three latest global climate models(GCMs)from Coupled Model Intercomparison Project 6(CMIP6)under two shared socioeconomic pathways(SSP2-4.5 and SSP5-8.5)for the near(2025-2049),mid(2050-2074),and far future(2075-2099).The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow,demonstrating reliable performance in streamflow simulations with a val-idation Nash-Sutcliffe Efficiency(NSE)of 0.72.Results showed that climate change projections showed increases in the annual precip-itation and potential evapotranspiration(PET),with precipitation increasing by 11.3%and 26.1%,and PET increasing by 3.2%and 4.9%,respectively,by the end of the century under SSP2-4.5 and SSP5-8.5.These changes are projected to result in increased annual streamflow at all stations,notably at the basin’s outlet(Pyay station)compared to the baseline period(with an increase of 16.1%and 37.0%at the end of the 21st century under SSP2-4.5 and SSP5-8.5,respectively).Seasonal analysis for Pyay station forecasts an in-crease in dry-season streamflow by 31.3%-48.9%and 22.5%-76.3%under SSP2-4.5 and SSP5-8.5,respectively,and an increase in wet-season streamflow by 5.8%-12.6%and 2.8%-33.3%,respectively.Moreover,the magnitude and frequency of flood events are pre-dicted to escalate,potentially impacting hydropower production and food security significantly.This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers.

The Cloud Model for Climate Change

In 1995, the Intergovernmental Panel on Climate Change (IPCC) released a thermodynamic model based on the Greenhouse Effect, aiming to forecast global temperatures. This study delves into the intricacies of that model. Some interesting observations are revealed. The IPCC model equated average temperatures with average energy fluxes, which can cause significant errors. The model assumed that all energy fluxes remained constant, and the Earth emitted infrared radiation as if it were a blackbody. Neither of those conditions exists. The IPCC’s definition of Climate Change only includes events caused by human actions, excluding most causes. Satellite data aimed at the tops of clouds may have inferred a high Greenhouse Gas absorption flux. The model showed more energy coming from the atmosphere than absorbed from the sun, which may have caused a violation of the First and Second Laws of Thermodynamics. There were unexpectedly large gaps in the satellite data that aligned with various absorption bands of Greenhouse Gases, possibly caused by photon scattering associated with re-emissions. Based on science, we developed a cloud-based climate model that complied with the Radiation Laws and the First and Second Laws of Thermodynamics. The Cloud Model showed that 81.3% of the outgoing reflected and infrared radiation was applicable to the clouds and water vapor. In comparison, the involvement of CO2 was only 0.04%, making it too minuscule to measure reliably.

Rainfall-Runoff Modeling and Hydrological Responses to the Projected Climate Change for Upper Baro Basin, Ethiopia

This paper presents the results of Rainfall-Runoff modeling and simulation of hydrological responses under changing climate using HEC-HMS model. The basin spatial data was processed by HEC-GeoHMS and imported to HEC-HMS. The calibration and validation of the HEC-HMS model was done using the observed hydrometeorological data (1989-2018) and HEC-GeoHMS output data. The goodness-of-fit of the model was measured using three performance indices: Nash and Sutcliffe coefficient (NSE) = 0.8, Coefficient of Determination (R2) = 0.8, and Percent Difference (D) = 0.03, with values showing very good performance of the model. Finally, the optimized HEC-HMS model has been applied to simulate the hydrological responses of Upper Baro Basin to the projected climate change for mid-term (2040s) and long-term (2090s) A1B emission scenarios. The simulation results have shown a mean annual percent decrease of 3.6 and an increase of 8.1 for Baro River flow in the 2040s and 2090s scenarios, respectively, compared to the baseline period (2000s). A pronounced flow variation is rather observed on a seasonal basis, reaching a reduction of 50% in spring and an increase of 50% in autumn for both mid-term and long-term scenarios with respect to the base period. Generally, the rainfall-runoff model is developed to solve, in a complementary way, the two main problems in water resources management: the lack of gauged sites and future hydrological response to climate change data of the basin and the region in general. The study results imply that seasonal and time variation in the hydrologic cycle would most likely cause hydrologic extremes. And hence, the developed model and output data are of paramount importance for adaptive strategies and sustainable water resources development in the basin.

A High-Resolution Modeling Strategy to Assess Impacts of Climate Change for Mesoamerica and the Caribbean

Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.

Estimation of the potential geographical distribution of a new potato pest(Schrankia costaestrigalis) in China under climate change

Global food security is threatened by the impacts of the spread of crop pests and changes in the complex interactions between crops and pests under climate change.Schrankia costaestrigalis is a newly-reported potato pest in southern China.Early-warning monitoring of this insect pest could protect domestic agriculture as it has already caused regional yield reduction and/or quality decline in potato production.Our research aimed to confirm the potential geographical distributions(PGDs)of S.costaestrigalis in China under different climate scenarios using an optimal MaxEnt model,and to provide baseline data for preventing agricultural damage by S.costaestrigalis.Our findings indicated that the accuracy of the optimal MaxEnt model was better than the default-setting model,and the minimum temperature of the coldest month,precipitation of the driest month,precipitation of the coldest quarter,and the human influence index were the variables significantly affecting the PGDs of S.costaestrigalis.The highly-and moderately-suitable habitats of S.costaestrigalis were mainly located in eastern and southern China.The PGDs of S.costaestrigalis in China will decrease under climate change.The conversion of the highly-to moderately-suitable habitat will also be significant under climate change.The centroid of the suitable habitat area of S.costaestrigalis under the current climate showed a general tendency to move northeast and to the middle-high latitudes in the 2030s.The agricultural practice of plastic film mulching in potato fields will provide a favorable microclimate for S.costaestrigalis in the suitable areas.More attention should be paid to the early warning and monitoring of S.costaestrigalis in order to prevent its further spread in the main areas in China’s winter potato planting regions.

Monitoring the little fire ant,Wasmannia auropunctata(Roger 1863),in the early stage of its invasion in China:Predicting its geographical distribution pattern under climate change

Invasive alien ants(IAAs)are among the most aggressive,competitive,and widespread invasive alien species(IAS)worldwide.Wasmannia auropunctata,the greatest IAAs threat in the Pacific region and listed in“100 of the world’s worst IAS”,has established itself in many countries and on islands worldwide.Wild populations of W.auropunctata were recently reported in southeastern China,representing a tremendous potential threat to China’s agricultural,economic,environmental,public health,and social well-being.Estimating the potential geographical distribution(PGD)of W.auropunctata in China can illustrate areas that may potentially face invasion risk.Therefore,based on the global distribution records of W.auropunctata and bioclimatic variables,we predicted the geographical distribution pattern of W.auropunctata in China under the effects of climate change using an ensemble model(EM).Our findings showed that artificial neural network(ANN),flexible discriminant analysis(FDA),gradient boosting model(GBM),Random Forest(RF)were more accurate than categorical regression tree analysis(CTA),generalized linear model(GLM),maximum entropy model(MaxEnt)and surface distance envelope(SRE).The mean TSS values of ANN,FDA,GBM,and RF were 0.820,0.810,0.843,and 0.857,respectively,and the mean AUC values were 0.946,0.954,0.968,and 0.979,respectively.The mean TSS and AUC values of EM were 0.882 and 0.972,respectively,indicating that the prediction results with EM were more reliable than those with the single model.The PGD of W.auropunctata in China is mainly located in southern China under current and future climate change.Under climate change,the PGD of W.auropunctata in China will expand to higher-latitude areas.The annual temperature range(bio7)and mean temperature of the warmest quarter(bio10)were the most significant variables affecting the PGD of W.auropunctata in China.The PGD of W.auropunctata in China was mainly attributed to temperature variables,such as the annual temperature range(bio7)and the mean temperature of the warmest quarter(bio10).The populations of W.auropunctata in southern China have broad potential invasion areas.Developing strategies for the early warning,monitoring,prevention,and control of W.auropunctata in southern China requires more attention.

Modeling Climate Change Impacts on the US Agricultural Exports

Climate change is expected to have substantial effects on agricultural productivity worldwide. However, these impacts will differ across commodities, locations and time periods. As a result, landowners will see changes in relative returns that are likely to induce modifications in production practices and land allocation. In addition, regional variations in impacts can alter relative competitiveness across countries and lead to adjustments in international trade patterns. Thus in climate change impact studies it is likely useful to account for worldwide productivity effects. In this study, we investigate the implications of considering rest of world climate impacts on projections of the US agricultural exports. We chose to focus on the US because it is one of the largest agricultural exporters. To conduct our analyses, we consider four alternative climate scenarios, both with and without rest of world climate change impacts. Our results show that considering/ignoring rest of world climate impacts causes significant changes in the US production and exports projections. Thus we feel climate change impact studies should account not only for climate impacts in the country of focus but also on productivity in the rest of the world in order to capture effects on commodity markets and trade potential.

Modeling the impacts of climate change on China's agriculture

The impacts of climate change on China's agriculture are measured based on Ricardian model. By using county-level cross-sectional data on agricultural net revenue, climate, and other economic and geographical data for 1275 agriculture-dominated counties in the period of 1985-1991, we find that both higher temperature and more precipitation will have overall positive impact on China's agriculture. However, the impacts vary seasonally and regionally. Higher temperature in all seasons except spring increases agricultural net revenue while more precipitation is beneficial in winter but is harmful in summer. Applying the model to five climate scenarios in the 2020s and 2050s shows that the North, the Northeast, the Northwest, and the Qinghai-Tibet Plateau would always benefit from climate change while the South and the Southwest may be negatively affected. For the East and the Central China, most scenarios show that they may benefit from climate change. In conclusion, climate change would be beneficial to the whole China.

Assessment of Climate Change Impact on Water Requirement and Rice Productivity

Assessing the impact of climate change(CC)on agricultural production systems is mainly done using crop models associated with climate model outputs.This review is one of the few,with the main objective of providing a recent compendium of CC impact studies on irrigation needs and rice yields for a better understanding and use of climate and crop models.We discuss the strengths and weaknesses of climate impact studies on agricultural production systems,with a particular focus on uncertainty and sensitivity analyses of crop models.Although the new generation global climate models(GCMs)are more robust than previous ones,there is still a need to consider the effect of climate uncertainty on estimates when using them.Current GCMs cannot directly simulate the agro-climatic variables of interest for future irrigation assessment,hence the use of intelligent climate tools.Therefore,sensitivity and uncertainty analyses must be applied to crop models,especially for their calibration under different conditions.The impacts of CC on irrigation needs and rice yields vary across regions,seasons,varieties and crop models.Finally,integrated assessments,the use of remote sensing data,climate smart tools,CO_(2)enrichment experiments,consideration of changing crop management practices and multi-scale crop modeling,seem to be the approaches to be pursued for future climate impact assessments for agricultural systems。

Prediction of forest fire occurrence in China under climate change scenarios

Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).

Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments

Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)and future climate change scenarios(different Representative Concentration Pathways(RCPs)in different future time periods)are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield.This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments(Shiraz,Hamedan,Sanandaj,Kermanshah and Khorramabad)in eastern and southern Iran.These five representative locations can be categorized into three climate classes:arid cold(Shiraz),semi-arid cold(Hamedan and Sanandaj)and semi-arid cool(Kermanshah and Khorramabad).Accordingly,the downscaled daily outputs of 29 GCMs under two RCPs(RCP4.5 and RCP8.5)in the near future(2030s),middle future(2050s)and far future(2080s)were used as inputs for the Agricultural Production Systems sIMulator(APSIM)-wheat model.Analysis of variance(ANOVA)was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield.Years from 1980 to 2009 were regarded as the baseline period.The projection results indicated that wheat grain yield was expected to increase by 12.30%,17.10%,and 17.70%in the near future(2030s),middle future(2050s)and far future(2080s),respectively.The increases differed under different RCPs in different future time periods,ranging from 11.70%(under RCP4.5 in the 2030s)to 20.20%(under RCP8.5 in the 2080s)by averaging all GCMs and locations,implying that future wheat grain yield depended largely upon the rising CO2 concentrations.ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations,followed by scenarios,GCMs,and their interactions.Specifically,at the semi-arid climate locations(Hamedan,Sanandaj,Kermanshah and Khorramabad),most of the variations arose from the scenarios(77.25%),while at the arid climate location(Shiraz),GCMs(54.00%)accounted for the greatest variation.Overall,the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions,particularly in dryland environments characterized by large fluctuations in rainfall and temperature.Moreover,the current research suggested some GCMs(e.g.,the IPSL-CM5B-LR,CCSM4,and BNU-ESM)that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.

Are Polyploid Species Less Vulnerable to Climate Change? A Simulation Study in North American Crataegus

Understanding the mechanisms underlying plant responses to climate change is an important step toward developing effective mitigation strategies. Polyploidy is an important evolutionary trait that can influence the capacity of plants to adapt to climate change. The environmental flexibility of polyploids suggests their resiliency to climate change, however, such hypotheses have not yet received empirical evidence. To understand how ploidy level may influence response to climate change, we modeled the current and future distribution of 54 Crataegus species under moderate to severe environments and compared the range change between diploids and polyploids. The majority of studied species are predicted to experience considerable range expansion. We found a negative interaction between ploidy and ecoregions in determining the response to climate change. In extreme environments, polyploids are projected to experience a higher range expansion than diploids with climate change, while the opposite is true for moderate environments. The range expansion of Crataegus species can be attributed to their tolerance for a wide range of environmental conditions. Despite the higher tolerance of polyploids to extreme environments, they do not necessarily outperform diploids in moderate environments, which can be attributed to the varying nature of species interactions along a stress gradient.