Economic losses from reduced freshwater under future climate scenarios: An example from the Urumqi River,Tianshan Mountains

As important freshwater resources in alpine basins, glaciers and snow cover tend to decline due to climate warming, thus affecting the amount of water available downstream and even regional economic development. However, impact assessments of the economic losses caused by reductions in freshwater supply are quite limited. This study aims to project changes in glacier meltwater and snowmelt of the Urumqi River in the Tianshan Mountains under future climate change scenarios (RCP2.6 (RCP, Representative Concentration Pathway), RCP4.5, and RCP8.5) by applying a hydrological model and estimate the economic losses from future meltwater reduction for industrial, agricultural, service, and domestic water uses combined with the present value method for the 2030s, 2050s, 2070s, and 2090s. The results indicate that total annual glacier meltwater and snowmelt will decrease by 65.6% and 74.5% under the RCP4.5 and RCP8.5 scenarios by the 2090s relative to the baseline period (1980-2010), respectively. Compared to the RCP2.6 scenario, the projected economic loss values of total water use from reduced glacier meltwater and snowmelt under the RCP8.5 scenario will increase by 435.10×10⁶ and 537.20×10⁶ CNY in the 2050s and 2090s, respectively, and the cumulative economic loss value for 2099 is approximately 2124.00×10⁶ CNY. We also find that the industrial and agricultural sectors would likely face the largest and smallest economic losses, respectively. The economic loss value of snowmelt in different sectorial sectors is greater than that of glacier meltwater. These findings highlight the need for climate mitigation actions, industrial transformation, and rational water allocation to be considered in decision-making in the Tianshan Mountains in the future.     

西北干旱区白刺属植物适生环境的模拟分析

白刺属(Nitraria L.)植物是古地中海第三纪孑遗植物,对我国西北干旱、半干旱地区具有极高的生态效益.本研究基于MaxEnt模型和ArcGIS软件及其SDM工具箱预测了过去到未来不同时期下白刺属6个物种在我国的潜在适生区及其迁移路线,以期为该属植物的保护及资源利用方面提供一定的理论依据.结果表明:白刺属植物当代在...     

Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis

Runoff is a major component of the water cycle,and its multi-scale fluctuations are important to water resources management across arid and semi-arid regions.This paper coupled the Distributed Time Variant Gain Model(DTVGM)into the Community Land Model(CLM 3.5),replacing the TOPMODEL-based method to simulate runoff in the arid and semi-arid regions of China.The coupled model was calibrated at five gauging stations for the period 1980–2005 and validated for the period 2006–2010.Then,future runoff(2010–2100)was simulated for different Representative Concentration Pathways(RCP)emission scenarios.After that,the spatial distributions of the future runoff for these scenarios were discussed,and the multi-scale fluctuation characteristics of the future annual runoff for the RCP scenarios were explored using the Ensemble Empirical Mode Decomposition(EEMD)analysis method.Finally,the decadal variabilities of the future annual runoff for the entire study area and the five catchments in it were investigated.The results showed that the future annual runoff had slowly decreasing trends for scenarios RCP 2.6 and RCP 8.5 during the period 2010–2100,whereas it had a non-monotonic trend for the RCP 4.5 scenario,with a slow increase after the 2050 s.Additionally,the future annual runoff clearly varied over a decadal time scale,indicating that it had clear divisions between dry and wet periods.The longest dry period was approximately 15 years(2040–2055)for the RCP 2.6 scenario and 25 years(2045–2070)for the RCP 4.5 scenario.However,the RCP 8.5 scenario was predicted to have a long dry period starting from 2045.Under these scenarios,the water resources situation of the study area will be extremely severe.Therefore,adaptive water management measures addressing climate change should be adopted to proactively confront the risks of water resources.     

Meteorological drought features in northern and northwestern parts of Mexico under different climate change scenarios

Meteorological drought has been an inevitable natural disaster throughout Mexican history and the northern and northwestern parts of Mexico(i.e., the studied area), where the mean annual precipitation(MAP) is less than 500 mm, have suffered even more from droughts in the past. The aim of this study was to conduct a meteorological drought analysis of the available MAP data(1950–2013) from 649 meteorological stations selected from the studied area and to predict the drought features under the different IPCC-prescribed climate change scenarios. To determine the long-term drought features, we collected 1×10~4 synthetic samples using the periodic autoregressive moving average(PARMA) model for each rainfall series. The simulations first consider the present prevailing precipitation conditions(i.e., the average from 1950 to 2013) and then the precipitation anomalies under IPCC-prescribed RCP 4.5 scenario and RCP 8.5 scenario. The results indicated that the climate changes under the prescribed scenarios would significantly increase the duration and intensity of droughts. The most severe impacts may occur in the central plateau and in the Baja California Peninsula. Thus, it will be necessary to establish adequate protective measures for the sustainable management of water resources in these regions.     

气候变化下基于GIS的农田恶性杂草旱雀麦在中国的分布与发展趋势

为确定旱雀麦在我国的空间分布及其对气候变化的响应,以期进一步开展生态防控,本研究利用旱雀麦在中国的地理分布数据,结合当前气候数据和未来气候变化情景（RCP8.5情景下2050s,2070s）,建立最大熵模型（MaxEnt模型）,确定影响旱雀麦分布的主导环境因子。应用地理信息系统（GIS）对中国地区旱雀麦的适生区进行划分,以ROC曲线作为模拟的准确性评价指标。结果表明,MaxEnt模型模拟效果极好（AUC=0.965）;当前气候条件下,旱雀麦适生面积为2.5534×10⁶ km²,主要集中分布于青海省东北部、甘肃省与青海省接壤的地区、四川省的西北部,以及新疆的西北部;其中影响旱雀麦分布的主要环境因子为海拔、bio12（年降水量）、bio9（最干季度平均温度）和bio15（降水量季变异系数）,其贡献率分别为45.0%、17.5%、9.7%、9.7%,累计贡献率达81.9%;在RCP8.5情景下,未来2个时期,旱雀麦潜在高适生区分布面积与当前相比增加了12.2%~23.3%,但RCP8.5情景下2070s较RCP8.5情景下2050s旱雀麦的潜在高度适生区分布面积减少了8.9%。综上所述,气候变化情景下旱雀麦的潜在分布面积呈现出扩大趋势,且RCP8.5情景下2070s较RCP8.5情景下2050s的适生区分布面积有缩减趋势。     

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin,Xinjiang, China

Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020-2029 and 2040-2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990-1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.     

21世纪开都-孔雀河流域未来气候变化情景预估

利用Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections (DCHP)提供的31个 CMIP5降尺度数据和CRU逐月气温、降水格点数据集,通过评估PLS(偏最小二乘回归)、RR（岭回归）和EE（等权平均）3种多模式集合平均预估模型对历史气候变化的模拟能力,确定最优集合方法,进而预估开都-孔雀河流域21世纪气候变化情景。结果表明：① 所建立的PLS模型对流域的气温和降水具有较好的模拟能力,尤其对气温的模拟, r值均达到了0.64以上,明显优于降水（0.19~0.36）,但存在空间异质性;② 21世纪开都-孔雀河流域各子区气温呈显著增加趋势,且RCP8.5情景下的增温速率〔0.58~0.67 ℃·（10a）-1〕是RCP4.5情景下〔0.25~0.31 ℃·（10a）-1〕的2倍以上,21世纪中叶是2种情景产生明显差异的开始。整个流域增温速率由西北山区向东南荒漠区逐渐增大;③ 未来降水在不同排放情景下变化速率的分布状况略有不同,但均呈显著增加趋势,且RCP8.5情景下的增加速率〔1.22%~1.54%·（10a）-1〕总体上高于RCP4.5〔0.80%~1.32%·（10a）-1〕。     

1961-2013年新疆雪雨比变化

基于中国国家气象信息中心发布的1961-2013年全国0.5°×0.5°逐日降水量和逐日平均气温数据集以及气象站点日降水量和气温实测资料,评估了该套格点降水资料在新疆地区的可信度,并利用格点降水资料研究了新疆地区降雪量、降雨量以及雪雨比的时空变化和突变信息.结果表明:内插到气象站点的格点降水数据和气象站点实测降水数据之间的偏差普遍较小,偏差在0.2 ～1.0 mm的站点个数占总站点数的72.55％,在绝大多数区域二者之间的相关系数均在0.80以上;1961-2013年,新疆降雪量呈微弱的增加趋势,增加幅度为0.11 mm·a-1,但没有通过0.05的显著性水平检验.降雨量以0.63 mm·a-1的速率呈明显的增加趋势(P ＜0.000 1),突变出现在1992年左右.雪雨比呈下降趋势,幅度为0.01 a-1 (P ＜0.05),突变出现在1991年左右;降雪量、降雨量及雪雨比表现出一定的空间分布差异,降雪量和降雨量在全区普遍呈增加趋势,而雪雨比在天山山地以北呈增加趋势,在天山山地以及天山山地以南主要呈减少趋势.在新疆冰川分布区域,降雪量倾向率在新疆北部呈增加趋势,而在新疆东南部呈减少趋势;降雨量倾向率呈增加趋势,且新疆东南部降雨量倾向率大于北部;雪雨比倾向率呈现出与降雪量倾向率相似的变化趋势.     

Impact of climate change on the streamflow in the glacierized Chu River Basin,Central Asia

Catchments dominated by meltwater runoff are sensitive to climate change as changes in precipitation and temperature inevitably affect the characteristics of glaciermelt/snowmelt, hydrologic circle and water resources. This study simulated the impact of climate change on the runoff generation and streamflow of Chu River Basin(CRB), a glacierized basin in Central Asia using the enhanced Soil and Water Assessment Tool(SWAT). The model was calibrated and validated using the measured monthly streamflow data from three discharge gauge stations in CRB for the period 1961–1985 and was subsequently driven by downscaled future climate projections of five Global Circulation Models(GCMs) in Coupled Model Inter-comparison Project Phase 5(CMIP5) under three radiative forcing scenarios(RCP2.6, RCP4.5 and RCP8.5). In this study, the period 1966–1995 was used as the baseline period, while 2016–2045 and 2066–2095 as the near-future and far-future period, respectively. As projected, the climate would become warmer and drier under all scenarios in the future, and the future climate would be characterized by larger seasonal and annual variations under higher RCP. A general decreasing trend was identified in the average annual runoff in glacier(–26.6% to –1.0%), snow(–21.4% to +1.1%) and streamflow(–27.7% to –6.6%) for most of the future scenario periods. The projected maximum streamflow in each of the two future scenarios occurred one month earlier than that in the baseline period because of the reduced streamflow in summer months. Results of this study are expected to arouse the serious concern about water resource availability in the headwater region of CRB under the continuously warming climate. Changes in simulated hydrologic outputs underscored the significance of lowering the uncertainties in temperature and precipitation projection.     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Predicting the potential distribution of Pseudomonas syringae pv. actinidiae in China using ensemble models

Pseudomonas syringae pv. actinidiae (Psa) is a causal agent of kiwifruit bacterial canker worldwide, which has affected kiwifruit vines in China since 1996 and has subsequently spread to the main cultivation areas. Based on occurrence of Psa and pseudo-absences randomly generated in China, the consensus-based modelling technique was used to estimate the spatial spread of Psa epidemics within China. Environmental variables that related to Psa development were identified, and their contributions to Psa development were evaluated. Three modelling algorithms, namely generalized boosting models (GBM), random forests (RF) and classification tree analysis (CTA) within the BIOMOD2 framework, were employed to construct the model. The ensemble models weighted by the true skill statistic (TSS) value were used to predict the current habitat suitability of Psa, and were projected using the four general circulation models (GCMs) to assess range shifts under two types of representative concentration pathways (RCP 4.5 and RCP 8.5) by 2050. The results indicated that precipitation in March and mean temperature of warmest quarter were the most important limiting factors for distribution of Psa. The predictive accuracy of the ensemble model showed acceptable predictive powers (TSS = 0.852). Under future climate conditions, substantial net loss of suitability for Psa was estimated to be 3.03–12.5% under RCP 4.5 (except one GCM), and 2.46–9.89% under RCP 8.5. Shrinkage of suitable habitats was detected mainly in the areas currently infected by Psa. Special attention should be given to recent infectious regions in south and southwest China, considering the locally expanding kiwifruit commercial plantations.     

气候变化对新疆意大利蝗潜在分布的影响

意大利蝗Calliptamus italicus(L.)是新疆草原主要优势蝗虫之一,每年给新疆畜牧业经济带来严重损失,气候变化对其潜在分布影响的预测对其科学防治有重要意义。本研究采用意大利蝗的分布数据和生物气候数据,结合MaxEnt模型和ArcGIS软件,预测了BCC_CSM1.1气候模式下政府间气候变化专门委员会第五次工作报告(IPCC AR5)采用的RCP2.6、RCP4.5和RCP8.5三种气候情景在2021-2040年(2030s)、2041-2060年(2050s)和2061-2080年(2070s)的意大利蝗新疆潜在适生区分布范围。结果表明:在BCC_CSM1.1的各情景下,意大利蝗适生区在北疆及天山一带分布格局基本保持不变,但高度适生区面积都有所增加,其中在天山和阿尔泰山地区,意大利蝗中、高度适生区范围将向更高海拔区域蔓延,在北疆阿勒泰地区高度适生区明显增加。极端水分条件和水热条件对意大利蝗在新疆潜在分布发挥主要作用,其中4月、10月、3月和11月降水量对意大利蝗在新疆潜在分布影响最大,因其直接影响土壤相对含水量和土壤温度,从而决定意大利蝗卵的存活量。     

Climate change impacts on the streamflow of Zarrineh River,Iran

Zarrineh River is located in the northwest of Iran, providing more than 40% of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth. Lake Urmia is a highly endangered ecosystem on the brink of desiccation. This paper studied the impacts of climate change on the streamflow of Zarrineh River. The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5 (coupled model intercomparison project phase 5) data series (namely, BCC-CSM1-1, BNU-ESM, CSIRO-Mk3-6-0, GFDL-ESM2G, IPSL-CM5A-LR, MIROC-ESM and MIROC-ESM-CHEM) under RCP2.6 (RCP, representative concentration pathways) and RCP8.5. The model data series were statistically downscaled and bias corrected using an artificial neural network (ANN) technique and a Gamma based quantile mapping bias correction method. The best model (CSIRO-Mk3-6-0) was chosen by the TOPSIS (technique for order of preference by similarity to ideal solution) method from seven CMIP5 models based on statistical indices. For simulation of streamflow, a rainfall-runoff model, the hydrologiska byrans vattenavdelning (HBV-Light) model, was utilized. Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5, respectively. In the case of temperature, the numbers change from 12.33°C and 12.37°C in 2015 to 14.28°C and 14.32°C in 2050. Corresponding to these climate scenarios, this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m³/s in 2015 to 22.61 and 23.19 m³/s in 2050. The finding is of important meaning for water resources planning purposes, management programs and strategies of the Lake's endangered ecosystem.     

Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran

Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(ROC),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range(Bio2)and annual precipitation(Bio12)explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P.juliflora.At all thresholds,the ensemble model showed a significant difference in comparison with single model.However,Max Ent and RF outperformed the others models.Under climate change scenarios,it is predicted that suitable areas for this invasive species will increase in Khuzestan Province,and increasing climatically suitable areas for the species in future will facilitate its future distribution.These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.     

Sensitivity of runoff to climatic variability in the northern and southern slopes of the Middle Tianshan Mountains,China

Temperature and precipitation play an important role in the distribution of intra-annual runoff by influencing the timing and contribution of different water sources.In the northern and southern slopes of the Middle Tianshan Mountains in China,the water sources of rivers are similar;however,the proportion and dominance of water sources contributing to runoff are different.Using the Manas River watershed in the northern slope and the Kaidu River watershed in the southern slope of the Middle Tianshan Mountains as case studies,we investigated the changes in annual runoff under climate change.A modified hydrological model was used to simulate runoff in the Kaidu River and Manas River watersheds.The results indicated that runoff was sensitive to precipitation variation in the southern slope and to temperature variation in the northern slope of the Middle Tianshan Mountains.Variations in temperature and precipitation substantially influence annual and seasonal runoff.An increase in temperature did not influence the volume of spring runoff;but it resulted in earlier spring peaks with higher levels of peak flow.Damages caused by spring peak flow from both slopes of the Middle Tianshan Mountains should be given more attention in future studies.     

Impact of rainfed and irrigated agriculture systems on soil carbon stock under different climate scenarios in the semi-arid region of Brazil

Understanding the dynamics of soil organic carbon (SOC) is of fundamental importance in land use and management, whether in the current researches or in future scenarios of agriculture systems considering climate change. In order to evaluate SOC stock of the three districts (Delmiro Gouveia, Pariconha, and Inhapi districts) in the semi-arid region of Brazil in rainfed and irrigated agriculture systems under different climate scenarios using the Century model, we obtained RCP4.5 and RCP8.5 climate scenarios derived from the Eta Regional Climate Model (Eta-HadGEM2-ES and Eta-MIROC5) from the National Institute for Space Research, and then input the data of bulk density, pH, soil texture, maximum temperature, minimum temperature, and rainfall into the soil and climate files of the Century model. The results of this study showed that the Eta-HadGEM2-ES model was effective in estimating air temperature in the future period. In rainfed agriculture system, SOC stock under the baseline scenario was lower than that under RCP4.5 and RCP8.5 climate scenarios, while in irrigated agriculture system, SOC stock in the almost all climate scenarios (RCP4.5 and RCP8.5) and models (Eta-HadGEM2-ES and Eta-MIROC5) will increase by 2100. The results of this study will help producers in the semi-arid region of Brazil adopt specific agriculture systems aimed at mitigating greenhouse gas emissions.     

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network(GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data(including climate and topography data), and the performance of each network model was assessed using the mean estimation error(MEE), model efficiency factor(MEF), and correlation coefficient(r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future(in 2030 and 2080) under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm~2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production(0–100 kg/hm~2) will increase while the areas with moderate, moderately high and high levels of forage production(≥100 kg/hm~2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.     

A look into the past,present and future potential distributions of Talinopsis frutescens,a North American endemic lineage closely related to Cactaceae

Talinopsis frutescens(Anacampserotaceae,a family that is close related to Cactaceae)is a succulent species endemic to North America.The aim of this study was to explore,using Ecological Niche Modeling(ENM),changes in potential distribution ranges considering different climate scenarios:past conditions during the Last Inter Glacial(LIG)and the Last Glacial Maximum(LGM),the present and projections for 2070(RCP 2.6 to 8.5).A pattern of contraction is observed during the LIG,which agrees with other studies focused in species from arid environments.This pattern was followed by a migration towards the south during the LGM and a possible recent expansion to the north as is observed in the present scenario.All future projections show the same contraction and fragmentation patterns,resulting in three discontinuous areas:the northern part of the Chihuahuan Desert,the southern-central part of the Mexican Plateau,and the smallest one in the Tehuacán-Cuicatlán Valley.Our projections for future scenarios agree with other studies and support that global climate change tends to alter the current distribution of arid environment species.     

Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios

Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015-2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046-2075, but degenerates during 2015-2044 and 2076-2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.     

气候变化对台兰河年平均径流量的影响

以台兰河为研究对象,选用台兰水文站1957-2011年平均流量序列,采用SPSS统计软件对其55 a序列进行趋势分析。结果显示：台兰河径流与降水量的年内分配极不均匀,径流主要集中在夏秋两季,降水量集中在春夏两季,径流与降水量集中期主要分布在7月。识别和提取台兰河年平均流量时间序列的趋势函数QS(t),获得了相应的趋势回归模型;台兰河年平均流量序列存在1957-1981年、1981-1999年、1999-2011年3个时段呈递减→递增→递减变化态势;降水、气温变化是影响台兰河不同时段年平均流量变化的主要因素,年平均径流量变化趋势与气温、降水变化趋势一致。