不同气候情景下中国东北与华北作物产量变化趋势模拟

采用意大利ICTP中心的区域气候模式RegCM3输出的多情景加权平均值简称REA情景和英国Hadley中心的区域气候模式PRECIS输出的A2和B2情景,结合WOFOST作物生长模拟模型,模拟研究了未来40 a(2011—2050年)气候变化对我国华北东北农作物产量的影响。模拟结果表明:从40 a平均结果来看,对于东北玉米区,三种气候情景资料下可能减产区域主要是东北三省的偏西南地区,其中减产高值区主要集中在吉林省的西部地区白城、通榆等地。对于华北冬麦区,REA情景下可能减产区主要是河北省的东部和山东省的东部,其中减产高值区主要集中在京津及其以东地区;A2和B2情景下可能减产区主要是河北省的中部和东部、山东省的东部以及河南省的南部偏南地区,其中减产高值区主要集中在天津及其以东地区和山东省的东部沿海地区。     

未来气候变化对东北地区玉米单产影响的模拟研究

东北地区是我国具有战略意义的商品粮基地,研究气候变化对东北地区玉米产量的影响具有重要意义。采用区域气候模式与作物生长WOFOST模型相结合的方法,模拟了基准气候(BS,1961-1990年)和B2情景下2011-2050年东北地区的玉米单产,初步预测了未来气候情景下玉米单产的变化状况。结果表明:WOFOST作物模型能较好的模拟东北地区的玉米产量,模型检验结果表明,该模型对玉米产量的模拟在70%～80%年份里的模拟精确度在80%以上。模拟结果显示:未来10-20年内,东北地区北部大部地区玉米具有增产潜力,但就北地区整体而言,总体增产幅度较小。2030s和2040s东北地区大部受气候因素影响表现为减产。未来40年,受气候变化影响东北地区玉米单产总体表现为减产,减产幅度为9.5%。由于目前的研究结果存在未考虑农业生产的适应措施等局限性,可能会高估气候变化的负面影响。     

气候变化对干旱缺水区中尺度流域水文过程的影响

基于1997-2003年的气象水文资料,选择了黄土高原泾河流域上游,对半分布式生态水文模型SWIM进行了率定和验证,并以流域历史气象条件为基准,固定其它气候因素不变,针对单一气候因素变化(降水或温度)进行了情景模拟,分析、评价了降水和温度对流域径流量、蒸散量和土壤蓄水量各水文过程的影响。结果表明:流域降水量增加(减少),年径流、蒸散及蒸散分量、土壤蓄水量均增加(减少),且降水转化为各水量平衡分量的比例大小依次为径流量(蓝水)>蒸散量(绿水)>土壤蓄水量;流域温度升高,蒸散及其分量随之增加,但径流和土壤蓄水量则随之相应减少。研究结果对于变化环境下的干旱区流域水资源管理具有一定的意义。     

基于最大熵方法的未来气候变化下狼毒在中国潜在分布的预测

为了解入侵杂草狼毒Stellera chamaejasme在当前(1970—2000年)和未来气候情景下的扩散动态,采用最近邻体距离法选取样本数据,采用相关性分析和主成分分析选择环境变量,应用最大熵方法建立生态位模型用来预测当前(1970—2000年)和未来(2050年和2070年)气候变化下狼毒的潜在分布区,并应用Matlab软件计算气候变化下狼毒地理分布重心、平均海拔和等级分布区范围的动态变化。结果表明,当前(1970—2000年)气候条件下狼毒种群的潜在分布区从西南到东北呈带状分布,适生区等级随纬度的增加逐渐降低;未来气候变化下狼毒的潜在分布区将向西南方向和高海拔地区扩散,平均海拔将增高638 m,高适生区面积增长尤为迅速,2050年的相对增长率达51%;高寒草地将成为未来狼毒扩散的主要区域,因此应进一步加强对该区狼毒的防控。     

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.     

黄土高原半干旱区马铃薯产量对气候变化的响应

为探讨气候变化背景下,黄土高原半干旱区马铃薯生育期气象要素的变化规律及其对马铃薯生长发育的影响,本研究基于黄土高原半干旱区定位观测试验,结合气象要素观测资料,研究了半干旱区马铃薯产量对气候变化的响应。结果表明,1960—2020年61 a间,马铃薯生育期气温和积温显著升高,气候倾向率分别为0.32℃·10a^-1和48.14℃·10a^-1(P<0.01);降水量波动减少,气候倾向率为3.97 mm·10a^-1(P<0.05);日照时数显著减少,气候倾向率为10.92 h·10a^-1(P<0.05)。但2005—2020年16 a间,马铃薯生育期气温显著下降,气候倾向率为-0.54℃·10a^-1(P<0.01),各生育时期气温均呈下降趋势,其中块茎膨大～成熟期气温下降最明显,气候倾向率为-1.18℃·10a^-1,通过0.01水平显著性检验;马铃薯生育期降水量增加显著,气候倾向率为13.89 mm·10a^-1(P&l...     

气候变化下栎枯萎病菌在中国的潜在适生区预测

为降低未来气候变化下栎树枯萎病暴发的风险,基于现有的栎枯萎病菌Bretziella fagacearum分布数据,筛选影响其分布的关键环境变量,利用优化后的MaxEnt模型预测当前气候条件和未来气候条件（低强迫情景SSP126和高强迫情景SSP585）下其在中国的潜在适生区。结果显示,最暖月最高温度、最干季度平均温度、年降水量、最干月降水量是影响栎枯萎病菌分布的关键环境变量;优化后MaxEnt模型的受试者工作特征曲线下面积值高于0.90,表明该模型预测结果可靠。当前气候条件下栎树枯萎病菌的适生区面积约为1.39×10⁶ km²,占中国陆地总面积的14.48%,高适生区主要分布在湖南省北部、浙江省中南部、湖北省东南部、江西省西部和新疆维吾尔自治区北部。未来气候条件（低强迫情景SSP126和高强迫情景SSP585）下栎树枯萎病菌适生区面积均呈增加趋势,尤其高适生区的气候异常程度最高,造成其面积增加。此外,高适生区的质心均有由湖南省向北迁移的趋势。     

气候变化条件下樱桃绕实蝇在中国的潜在地理分布预测

樱桃绕实蝇Rhagoletis cerasi(L.)被我国列为进境检疫性有害生物,目前在我国尚无分布报道。为明确该虫在我国的适生区及适生程度,利用CLIMEX地点比较模型预测在当前以及未来2030、2080年A1B与A2两种不同排放情景下樱桃绕实蝇在我国的潜在地理分布。结果显示,樱桃绕实蝇在我国的潜在地理分布包括长江以北的大部分地区,随着气候变化,到2030年与2080年,樱桃绕实蝇的低度适生区、中度适生区面积均增加,高度适生区面积减少,其中在2030年A1B排放情景下樱桃绕实蝇的总适生区面积增加了48.4万km~2,在2030年的A2情景下与2080年的A1B与A2情景下总适生区面积分别减少了3.4、5.7、5.7万km~2。因此,为保护我国樱桃种植业的安全生产,应加强对该虫的检疫力度,防止其传入我国。     

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。     

基于物种分布模型对未来气候变化下云南松毛虫在四川省适生区的预测

为明确云南松毛虫Dendrolimus houi于未来气候变化下在四川省的分布情况,运用最大熵（maximum entropy,MaxEnt）模型中的刀切法和Pearson相关系数分析法对未来气候数据（2050年和2070年）、林地因子及人为因子进行权重划分,筛选出影响云南松毛虫潜在分布的重要且相关系数较低的环境因子,并结合openModeller中的人工神经网络（artificial neural network,ANN）模型、生物气候（BioClim）模型、气候空间（climate space,CS）模型、气候信封（envelope score,ES）模型、基于统计概率和规则集的遗传算法（genetic alorithm for rule-set production,GARP）模型、MaxEnt模型和支持向量机（support vector machine,SVM）模型对云南松毛虫未来的适生区进行预测,利用AUC（area under curve）评价模型的精确度。结果发现,最暖季降水量、人类足迹指数、最冷季降水量和海拔对云南松毛虫在未来气候条件下的潜在分布有较强影响,在2050年贡献率分别为27.2%、16.0%、2.0%和4.9%,在2070年贡献率分别为20.6%、16.8%、9.7%和4.9%。对比7种模型的预测结果,发现SVM模型在2050年和2070年对云南松毛虫适生区预测的AUC为0.93,预测精确度最高,具有较高的可信度;该模型预测结果显示,从2050年至2070年云南松毛虫在四川省的总适生面积增加了4 269.8 km²,其中,中、低适生区面积共减少了17 185.8 km²,高适生区面积增加了21 455.6 km²。     

气候变化情景下基于MaxEnt的麦瘟病在全球及中国的适生性分析

麦瘟病是小麦生产上的一种毁灭性真菌病害, 过去仅在南美洲流行?2016年该病害在亚洲的孟加拉国首次出现, 给世界尤其我国小麦生产带来重大潜在威胁?本研究基于麦瘟病当前在全球(包括孟加拉国)的分布数据和生物气候数据, 采用MaxEnt生态位模型和ArcGIS软件, 预测了当前气候条件下麦瘟病在全球特别是在我国的潜在适生区, 以及RCP2.6?RCP4.5?RCP6.0和RCP8.5 4种气候变化情景下2030s?2050s和2070s麦瘟病在中国潜在适生区分布范围, 进而以刀切法分析了影响其分布的主要气候因子, 并采用ROC对预测结果进行检验?结果表明, 在当前和未来不同气候情景下, 麦瘟病在中国的中风险和高风险区面积将呈增加趋势, 未来情景下与当前气候情景下相比, 中风险和高风险区的面积比例增加范围为0.08%~1.63%, 其中温度和湿度条件对麦瘟病的潜在分布区影响最大?此研究结果对于我国麦瘟病的检疫和防控提供了技术支撑?     

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.     

基于MaxEnt模型预测气候变化下玉米根萤叶甲在中国的潜在地理分布

为评估不同气候条件下玉米根萤叶甲Diabrotica virgifera virgifera在我国的潜在地理分布情况及适生区的空间格局变化趋势,通过筛选影响该虫分布的关键环境变量并基于其在全球的分布数据,运用MaxEnt模型和ArcGIS软件预测其在历史和未来气候情景下的潜在地理分布范围和适生区空间格局变化。结果表明,所构建MaxEnt模型的受试者工作特征 （receiver operating characteristic, ROC）曲线下面积（area under curve, AUC）平均为0.960,说明模型预测结果为优秀,具有较高的可信度。关键气候变量中最冷月最低温对玉米根萤叶甲的潜在地理分布具有十分重要的影响,累积贡献率为44.5%。历史气候条件下,玉米根萤叶甲的总适生区面积占我国陆地总面积的23.78%,高适生区主要分布于我国河南、湖北、陕西、甘肃、重庆、四川和云南等省市。未来气候情景下,玉米根萤叶甲在我国的总适生区面积略有减少,整体上呈现出南部收缩、北部扩张的趋势,原中南部的中、高适生区逐步转变为低适生区或非适生区。玉米根萤叶甲在我国的适生区较为广泛,适生范围涵盖多个重要玉米产区,对玉米安全生产威胁较大,应给予足够的重视,严防该虫传入我国。     

Effects of climate change and land use/cover change on the volume of the Qinghai Lake in China

Qinghai Lake is the largest saline lake in China.The change in the lake volume is an indicator of the variation in water resources and their response to climate change on the Qinghai-Tibetan Plateau(QTP)in China.The present study quantitatively evaluated the effects of climate change and land use/cover change(LUCC)on the lake volume of the Qinghai Lake in China from 1958 to 2018,which is crucial for water resources management in the Qinghai Lake Basin.To explore the effects of climate change and LUCC on the Qinghai Lake volume,we analyzed the lake level observation data and multi-period land use/land cover(LULC)data by using an improved lake volume estimation method and Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.Our results showed that the lake level decreased at the rate of 0.08 m/a from 1958 to 2004 and increased at the rate of 0.16 m/a from 2004 to 2018.The lake volume decreased by 105.40×10⁸ m³ from 1958 to 2004,with the rate of 2.24×10⁸ m³/a,whereas it increased by 74.02×10⁸ m³ from 2004 to 2018,with the rate of 4.66×10⁸ m³/a.Further,the climate of the Qinghai Lake Basin changed from warm-dry to warm-humid.From 1958 to 2018,the increase in precipitation and the decrease in evaporation controlled the change of the lake volume,which were the main climatic factors affecting the lake volume change.From 1977 to 2018,the measured water yield showed anincrease-decrease-increasefluctuation in the Qinghai Lake Basin.The effects of climate change and LUCC on the measured water yield were obviously different.From 1977 to 2018,the contribution rate of LUCC was -0.76% and that of climate change was 100.76%;the corresponding rates were 8.57% and 91.43% from 1977 to 2004,respectively,and -4.25% and 104.25% from 2004 to 2018,respectively.Quantitative analysis of the effects and contribution rates of climate change and LUCC on the Qinghai Lake volume revealed the scientific significance of climate change and LUCC,as well as their individual and combined effects in the Qinghai Lake Basin and on the QTP.This study can contribute to the water resources management and regional sustainable development of the Qinghai Lake Basin.