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摘要:
末次冰期低海平面时期出露的巽他陆架植被变化对全球碳循环影响有着重要影响,但目前植被重建结果仍存在很大争议。利用巽他陆架区域现有末次冰期的碳同位素和植物孢粉记录进行了古植被重建。与全新世相比,末次冰期时热带雨林的分布范围向赤道方向收缩,呈现不均匀的带状分布,而远离赤道的区域草本植物扩张。赤道辐合带的向南移动和热带太平洋的类El Niño状态导致的巽他陆架区域整体降水减少是造成这种现象的重要原因。山地在冰期植被垂直分布结构的演化中起着重要作用。在湿而冷的区域,山地雨林向下扩展;而在干而冷的区域,山地扮演着雨林避难所的角色。巽他陆架古植被重建工作仍面临着隐域性植被、植被指标局限性等难点。
Abstract:Changes in the vegetation of the Sunda shelf exposed during the last ice period at low sea level have important implications for global carbon cycle impacts. However, the results of current vegetation reconstructions are still highly controversial. A paleo-vegetation reconstruction was conducted using the available carbon isotope and pollen records of the last ice age in the Sunda shelf region. Compared to the Holocene, the distribution range of tropical rainforests contracted toward the equator during the last glacial period, showing an uneven zonal distribution pattern, while the herbaceous vegetation expanded in the regions far from the equator. The southward shift of the Intertropical Convergence Zone and the overall decrease of precipitation in the Sunda shelf region due to the El Niño-like state of the tropical Pacific Ocean are important reasons for this phenomenon. Mountains play an essential role in the evolution of the vertical distribution structure of vegetation during the ice age. In the wet and cold regions, montane rainforests expanded downward, while in the dry and cold regions, mountains played the role of rainforest refuges. The reconstruction of ancient vegetation on the Sunda shelf still faces difficulties such as cryptic vegetation and limitations of vegetation indicators, and more work is needed to improve it.
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Key words:
- palaeo-vegetation /
- carbon isotope /
- pollen records /
- Sunda shelf /
- last glacial period
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0. 引言
泥石流作为我国自然灾害重要类型之一,具有暴发突然、波及范围广、破坏性大等特点。泥石流易发性评价也从最早的定性分析发展到定量分析,一直以来是国内外地学专家学者研究的热点[1-2],其评价质量的好坏与评价指标的选取、评价模型的确定有着密不可分的关系[3-8]。地形地貌、地质构造及人类活动等是泥石流易发性的主要影响因素,也成为其评价指标选取的重要参考依据[9-10]。常用的泥石流易发性评价模型有专家系统模型(层次分析、专家打分)、数理统计模型(信息量、证据权和逻辑回归等)和机器学习模型(决策树、随机森林、神经网络和支持向量机等)[11-18],这些模型各有所长,同时也存在一定的缺陷。相对于专家系统模型受人为因素影响明显、机器学习模型存在参数调试较难等问题,信息量模型作为《地质灾害风险调查评价技术要求》(1∶50000)(试行)推荐的方法具有操作简单、应用广泛和客观性好等优势,能够科学的对各指标区间分级[19]。
东川区地质灾害发育,尤以泥石流分布广泛,危害最为严重,素有“世界泥石流天然博物馆”之称,是全国泥石流危害最严重的地区。据不完全统计,解放以来泥石流已造成人员伤亡300余人,直接经济损失近4亿元,并且对生态环境破坏明显,严重制约了东川经济与社会发展。因此,在东川建立科学合理的泥石流易发性评价模型,为泥石流精准防控提供支撑,意义重大[20-21]。
文中在对东川区地质灾害详细调查成果系统梳理基础上,细致分析东川泥石流流域特征、动力学特性与形成机理等,选取坡度、坡向、起伏度、曲率、工程岩组、距水系距离、距断层距离和土地利用类型9个评价指标,基于信息量模型和GIS平台技术,以小流域为单元对东川泥石流进行了易发性评价,以期为东川防灾减灾工作提供参考。
1. 研究区概况及数据来源
1.1 研究区概况
东川区位于云南省昆明市最北端,面积1858.79 km2,属侵蚀剥蚀构造地貌,地势呈东西高中间河谷低、南高北低的特征,最大相对高差达3600 m。区域上属金沙江流域,区域年降水量701~1163 mm,降水量的85%以上集中在5—10月。区内地层岩性以元古代昆阳群碎屑岩分布最广,约占全区面积的43.68%,古生代碎屑岩夹碳酸盐岩约占19.10%,古生代玄武岩、中生代碎屑岩约占32.36%,新生代黏土岩和松散碎石土等约占4.86%。区内地质构造复杂,主构造线为南北向的小江断裂带。复杂的地形地貌、特殊的地质背景及独特的气候条件导致了东川泥石流频发。
通过收集东川区地质灾害详查、隐患排查和风险普查等数据,结合InSAR地质灾害隐患早期识别成果和精细化调查野外验证,系统梳理出研究区典型泥石流144条(图1),以此作为样本数据,开展研究区泥石流易发性定量评价。
1.2 数据来源
DEM数据收集自阿拉斯加卫星设备,制作高程、坡度、坡向、起伏度和曲率5个评价指标;水系数据收集自OSM并与DEM提取水系进行比对制作距水系距离评价指标;地质数据收集自全国地质资料馆东川幅、会理幅、曲靖幅和武定幅20万地质图[22]制作工程岩组分类和距断层距离2个评价指标,土地类型数据收集自欧空局官网制作土地利用类型评级指标(表1)。
表 1. 数据来源及类型Table 1. Data source and types基础数据 评价因子 数据来源及制作 数据格式 DEM 高程 ASF
(阿拉斯加
卫星设备)12.5 m×12.5 m
栅格数据坡度 坡向 起伏度 曲率 水系 距水系距离 DEM提取
Open Street Map矢量数据 地质数据 工程岩组分类 全国地质资料馆 矢量数据 距断层距离 土地类型 土地利用类型 ESA WorldCover 10 m栅格数据 灾害点 泥石流数量 地质灾害详查、排查等 矢量数据 2. 研究方法
2.1 流域单元划分
泥石流易发性常用栅格作为评价单元,虽易于划分和模型计算,但忽略其流域特性,不能有效反映真实的泥石流情况,无法建立合理的评价模型,得出精准的评价结果。文中以收集的12.5 m×12.5 m的DEM栅格数据为基础,利用ArcGIS10.8模型构建器将流域划分过程(填洼→流向→流量→提取河流网络→栅格河网矢量化→盆域分析→栅格转面)模块化,化繁为简,实现自动化生成流域,通过反复调整集水阈值,最终得到最符合实际的流域划分结果。将研究区划分为961个流域,其中平均流域面积为1.94 km2,最大流域面积为10.62 km2,最小流域面积为0.44 km2。
2.2 信息量模型基本原理
信息量模型的理论基础是信息论[23],运用概率论和数理统计的方法以信息熵的概念来分析各种评价指标作用下泥石流易发性的模型。通过各种评价指标与泥石流灾害点空间叠加分析,依托GIS平台计算其单个指标信息量,然后进行多个指标的加权叠加得到综合信息量,从而建立泥石流易发性评价模型,其信息量值越大,表明易发性越高。
I=∑nj=1lnNj/NSj/S (1) 式中:I——各种评价指标加权的总信息量,可作为泥石流 易发性指数;
Nj—单个评价指标特定分级区间内含有泥石流的个数;
N——泥石流总数量;
Sj——单个评价指标特定分级区间内栅格数;
S——总栅格数。
3. 研究区泥石流易发性评价
3.1 评价指标选取原则
泥石流的形成影响因素众多,其演化是一个复杂的多因素耦合作用的地质过程。东川自1961年中科院建立野外观测站至今,对东川泥石流动力地貌过程与区域演化规律、运动学与动力学特征、流体物理力学与流变特性等方面取得了一系列国际先进水平成果。本文在系统分析东川泥石流触发因素基础上,结合大量专家学者泥石流易发性评价研究成果进行了评价指标的优选,选取坡度、坡向、起伏度、曲率、工程岩组、距水系距离、距断层距离和土地利用类型9个评价指标。
3.2 评价流程
首先通过GIS平台得到各评价指标的状态分级[24-25],然后对其分级区间进行重分类,与泥石流灾害点图层进行空间叠加分析统计,计算各评价指标图层的信息量值;采用栅格转面-空间连接-面转栅格实现将各评价指标的状态分级信息量值赋值到栅格图层中,运用空间分析工具叠加各评价指标的信息量栅格图层获取总信息量值,并按流域单元划分总信息量栅格图层,以子流域总信息量的平均值作为该子流域的信息量值,并按自然间断法对其进行重分类,实现研究区的泥石流易发性分区。
3.3 评价指标状态分级
因各评价指标的状态分级对信息量模型精度影响较大,科学合理划分其状态分级至关重要。文中在处理各评价指标状态分级时首先采用自然间断法一般将其细分为10~15级,与泥石流灾害点图层进行叠加分析,得出其信息量值,然后优先将信息量值为零的分级与相邻分级合并,接着将信息量值相近的分级与相邻分级合并,最后计算归并后的分级信息值,从而实现最优各评价指标状态分级(图2、表2)。
表 2. 各因素状态信息量表Table 2. Weighted information values of each factor指标因子 分级 泥石流点比例 信息量值 指标因子 分级 泥石流点比例 信息量值 高程/m 660~1500 0.2033 1.238744 曲率 −38~−1 0.2139 −0.131543 1500~2000 0.2598 −0.577567 −1~0 0.3114 0.430833 2000~2500 0.2209 −1.380367 0~2 0.3838 −0.205470 2500~3000 0.1594 −0.831065 >2 0.0909 −1.473365 >3000 0.1566 −1.729497 工程岩组 软岩组 0.0277 1.255712 坡度/(°) 0~10 0.1137 1.315538 较软岩组 0.5647 0.067643 10~20 0.2133 0.545702 较坚硬岩组 0.0693 −0.220774 20~30 0.2821 −0.871237 坚硬岩组 0.3383 −0.330859 30~40 0.2553 −1.206583 距水系距离/m 0 0.0037 1.322303 >40 0.1356 −2.278888 200 0.5022 0.624230 坡向 平坦(−1) 0.0011 0.000000 400 0.2786 −1.899977 北(0~22.5) 0.0638 0.084068 >400 0.2155 −3.435165 北东(22.5~67.5) 0.1268 0.353573 距断层距离/m <1000 0.6126 0.183653 东(67.5~112.5) 0.136 0.392672 1000~2000 0.2277 −0.445741 南东(112.5~157.5) 0.1266 −0.337806 2000~3000 0.0933 0.041246 南(157.5~202.5) 0.1072 −0.791193 >3000 0.0664 −1.158486 南西(202.5~247.5) 0.1119 −0.214621 土地利用类型 林地 0.2839 0.184122 西(247.5~292.5) 0.1379 0.269687 灌木 0.0024 −1.048475 北西(292.5~337.5) 0.1269 −0.266767 草地 0.4877 0.955946 北(337.5~360) 0.0618 −0.394293 耕地 0.1166 −0.437008 起伏度/(°) 0~20 0.2639 1.006637 建筑用地 0.0230 −1.937148 20~40 0.4084 −0.577548 裸地/稀疏植被区 0.0822 −0.994439 40~60 0.2465 −1.960151 开阔水域 0.0042 −0.514259 60~441 0.0812 −1.766161 (1)高程
以研究区12.5 m精度的DEM栅格数据为基础,最高点为拱王山雪岭,海拔4344 m,最低点位于金沙江与小江交汇处,海拔660 m,高差大于3600 m。以660~4344 m为区间,将高程分为5级,见图2(a)。泥石流主要分布在660~3000 m,占泥石流总数量的84.34%,见图3(a)。
(2)坡度
从0°起,以10°为间隔,将坡度分为5级,见图2(b)。泥石流点在坡度各分级均有分布,主要集中在20°~40°,见图3(b)。
(3)坡向
将坡向划为10个分级,见图2(c)。泥石流在南、西南、东南方向比例为34.57%,在北、北西、北东方向为37.93%,可见泥石流在北坡比例大于南坡,见图3(c)。
(4)地形起伏度
地形起伏度可以直观的反映山体的相对高差,是划分地貌类型的一个重要指标,同时还能有效地体现人类活动与地质灾害发育程度的相关性。借助ArcGIS平台提取出研究区地形起伏度在0~441 m,分为0~20 m平坦起伏、20~40 m微起伏、40~60 m小起伏,>60 m较大起伏4个分级,见图2(d)。泥石流在微起伏占比最高,为40.84%,见图3(d)。
(5)曲率
曲率主要是用来反映地形弯曲程度的指标,将曲率划为4个分级,见图2(e)。泥石流主要分布在曲率−1~2,占泥石流总数量的69.52%,见图3(e)。
(6)工程岩组分类
以研究区20万地质图为基础,根据地层岩性的工程地质特性,将研究区工程岩组划分为4大类,见图2(f)。泥石流主要分布在较软岩组内,占泥石流总数量的56.47%,见图3(f)。
(7)距水系距离
研究区水系发育,分布有小江流域和普渡河流域,均属金沙江水系。以研究区内河流为中心作200 m、400 m、>400 m缓冲区分析,见图2(g)。结果表明,在距河流400 m范围内泥石流点分布集中,占泥石流总数量的78.08%,见图3(g)。
(8)距断层距离
研究区位于川滇菱形块体内部小江断裂与普渡河断裂夹持地带,断层发育。以研究区1∶20万构造纲要图为基础,以断层为中心作1 km、2 km、3 km、>3 km缓冲区分析,见图2(h)。分析表明泥石流主要位于断层0~2 km范围内,占比84.03%,见图3(h)。
(9)土地利用类型
土地利用数据采用欧空局(ESA)发布的2020年10 m分辨率的全球土地利用(World Cover)数据。研究区土地利用主要包括林地、灌木、草地、耕地、建筑用地、裸地/稀疏植被区和开阔水域7类,见图2(i)。泥石流主要集中分布在林地、草地和耕地内,占比88.82%,见图3(i)。
3.4 基于GIS的信息量计算和易发性评价
评价指标体系建立完成后,基于GIS平台将各评价指标状态分级(图2)与泥石流灾害点进行空间叠加分析,得出各评价指标状态分级区间内泥石流分布数量(图3),根据信息量模型公式进行各评价指标信息量值计算,然后通过栅格叠加分析计算总信息量值,以自然间断法将其划分为4个等级,得到研究区泥石流易发性分区图,见图4(a)。低易发区[−14.15,−5.60]、中易发区[−5.60,−2.72]、高易发区[−2.72,0.42]及极高易发区[0.42,8.10],占东川区全域栅格比例为18.49%、32.43%、33.30%和15.78%,其灾点占栅格频率比分别为0.038、0.150、0.542、4.843,极高易发区和高易发区内灾点频率比最高,符合客观事实。
3.5 基于小流域的信息量法易发性评价
目前泥石流易发性定量评价多采用直接以栅格单元为评价单元,往往会出现单条泥石流流域内存在不同的易发性分区,忽略了泥石流单元的整体性,与实际的环境不相符合,对指导泥石流的精准防控不利。为真实反映泥石流的环境演变,解决单条泥石流流域内分区差异性,文中将基于栅格单元计算得来的泥石流易发性总信息量值以流域为单元进行划分,然后统计每个子流域范围内信息量值的平均值,作为研究区子流域单元信息量值,通过自然间断法将其划分为4个等级:低易发区[−7.85,−3.97]、中易发区[−3.97,−2.25]、高易发区[−2.25,−0.31]及极高易发区[−0.31,3.39],得到研究区泥石流易发性分区图,见图4(b)。
4. 结果分析与精度评价
4.1 易发性结果分析
研究区泥石流极高易发区和高易发区主要集中在小江流域沿岸的阿旺镇、铜都镇、拖布卡镇一带分布,占全域面积的37.27%,其中著名的蒋家沟、大小白泥沟、大桥河沟以及城市后山4条沟均位于极高易发区,与历史泥石流灾害点吻合度较高。
极高易发区和高易发区内地质环境特征典型,主要表现在以下几点:(1)位于小江活动断裂附近,构造极为发育,地震频发;(2)工程岩组以较软岩组为主,受小江断裂等影响岩石极为破碎,为泥石流发生提供了物质来源;(3)地形陡峻,相对高差大,地貌以侵蚀剥蚀为主;(4)距离水系越近泥石流易发性越高;(5)主要集中在小江河谷一线,人类工程活动强烈,对地形地貌破坏作用明显。
4.2 易发性精度评价
文中采用深度学习二分类中常用评价模ROC型曲线进行检验,构建以易发性分区累计百分比为横轴,泥石流灾点数累计百分比为纵轴,曲线下面积(AUC)表示易发性预测成功率,其值越接近1,代表准确率越高[26-28]。本次采用的信息量模型AUC为0.876,准确度较高(图5)。
5. 结论
(1)构建了云南东川泥石流易发性定量评价指标体系,对评价指标状态分级进行优化处理后发现:泥石流主要分布在高程660~3000 m、坡度20°~40°,曲率−1~2,地形集中在微起伏地形,工程岩组主要以较软岩组为主,距河流400 m内,断层在0~2 km,土地利用类型以林地、草地和耕地为主,而坡向对其影响不大。
(2)东川泥石流极高易发区和高易发区主要集中在小江流域沿岸的阿旺镇、铜都镇、拖布卡镇一带分布,占全域面积的37.27%。
(3)通过信息量模型可以很好建立东川泥石流易发性定量预测模型,ROC曲线检验模型AUC=0.876,准确度较高,建模结果与历史泥石流灾害点吻合度较高,较好地揭示了研究区泥石流易发性特征,为东川防灾减灾工作提供参考。
文中在评价指标选取和状态分级过程中仍存在一些不足之处。评价指标选取和状态分级合并主观性较强,后续可引入类似ROC曲线等验证模型进行定量化评价,提高其合理性和科学性。
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表 1 巽他陆架及周边地区末次冰期碳同位素变化记录
Table 1. Carbon isotope records in the Sunda shelf and surrounding areas during the last glacial period
站位 经纬度 水深/m 碳同位素值范围/‰ 参考文献 湖泊/海洋记录 BJ8-03-91GGC 2°52′N,118°23′E 2326 –30.8~–32.9 [11] SO189-144KL 1°5′N,98°1′E 481 –28.8~–31.9 [37] 17962 7°11′N,112°5′E 1968 –28.4~–33.9 [38] 18252-3 9°14'N,109°23'E 1273 –27.3~–34.5 [39] MD05-2894 7°2'N,111°33'E 1982 –29.1~–36.6 [40] IDLE-MAT10-2B 2°31′S,121°24′E 137 –32.8~–44.9 [41] GEOB10053-7 8°41′S,112°52′E 1375 –24.2~–31.5 [44] GEOB10069-3 9°36′S,120°55′E 1250 –24.6~–28.9 [11] IDLE-TOW10-9B 2°30′S,121°30′E 154 –25.0~–41.0 [42] SO18515 3°37'S,119°21'E 688 –25.0~–29.6 [16] 陆地记录 Niah洞穴 3°49′N,113°46′E –22.9~–26.2 [9] Gomantong洞穴 5°32'N,118°5'E –26.1~–27.7 [33] Bau Bau洞穴 0°55'S,117°13'E –23.5~–27.8 [33] Batu洞穴 3°13′N,101°42′E –26.2~–22.6 [9] Makangit洞穴 10°28′N,119°27′E –19.5~–30.3 [9] Gangub洞穴 8°31′N,117°33′E –18.0~–26.3 [9] Saleh洞穴 3°1'S,115°59'E –17.2~–27.3 [14] 表 2 巽他陆架及周边地区末次冰期孢粉记录
Table 2. Pollen records in the Sunda shelf and surrounding areas during the last glacial period
站位 经纬度 水深/m 草本植物比例/% 参考文献 湖泊/海洋记录 17962 7°11′N,112°5′E 1968 10~30 [49] 17964 1°5′N,98°1′E 1556 5~20 [50] 18287 5°39′N,110°39′E 598 10~40 [51] 18300 4°21′N,108°39′E 91 5~75 [48] 18302 4°09'N,108°34'E 83 5~40 [48] 18323 2°47'N,107°53'E 92 4~55 [48] CG-2 6°23′N,110°09′E 1239 1~35 [19] NS07-25 6°40′N,113°33′E 2006 0~20 [52] CB-19 7°46′N,114°40′E 1798 0~20 [53] MD06-3075 6°28′N,125°49′E 1878 1~18 [61] G6-4 10°47′S,118°04′E 3510 20~60 [67] SHI-9014 5°46′S,126°58′E 3163 20~55 [68] BRA94-42 6°04'S,102°25'E 2542 2~52 [64] 陆地记录 Niah 3°49′N,113°46′E 0~35 [54] Kelabit 3°34'N,115°33'E 5~40 [55] Sentarum 0°44'N,112°06'E 5~20 [56] NTSH 7°52′N,99°28′E 0~20 [60] Nee Soon 1°24′N,103°48′E 5~30 [59] Sim Sim 2°25′N,98°47′E 0~60 [58] Di-Atas 1°04'S,100°46'E 0 [57] Wanda 2°33'S,121°23'E 5~50 [43] Tondano 1°29'N,124°50'E 0~95 [63] Rawa 6°11′S,105°59′E 0~60 [65] Bandung 7°S,108°E 0~70 [62,66] Misedor 1°N,117°E 0~40 [62] Halmahera 2°N,128°E 0~30 [62] -
[1] Hanebuth T J J, Voris H K, Yokoyama Y, et al. Formation and fate of sedimentary depocentres on Southeast Asia’s Sunda Shelf over the past sea-level cycle and biogeographic implications [J]. Earth-Science Reviews, 2011, 104(1-3): 92-110. doi: 10.1016/j.earscirev.2010.09.006
[2] Hanebuth T J J, Stattegger K, Bojanowski A. Termination of the Last Glacial Maximum sea-level lowstand: The Sunda-Shelf data revisited [J]. Global and Planetary Change, 2009, 66(1-2): 76-84. doi: 10.1016/j.gloplacha.2008.03.011
[3] 贾国东. 冰期出露的巽他陆架?: 重要的陆地碳储库?[J]. 地球科学进展, 2017, 32(11):1157-1162 doi: 10.11867/j.issn.1001-8166.2017.11.1157
JIA Guodong. Exposed Sunda Shelf during the glacial times: An important component of the terrestrial carbon reservoir? [J]. Advances in Earth Science, 2017, 32(11): 1157-1162. doi: 10.11867/j.issn.1001-8166.2017.11.1157
[4] Bird M I, Taylor D, Hunt C. Palaeoenvironments of insular Southeast Asia during the Last Glacial Period: A savanna corridor in Sundaland? [J]. Quaternary Science Reviews, 2005, 24(20-21): 2228-2242. doi: 10.1016/j.quascirev.2005.04.004
[5] Montenegro A, Eby M, Kaplan J O, et al. Carbon storage on exposed continental shelves during the glacial-interglacial transition [J]. Geophysical Research Letters, 2006, 33(8): L08703.
[6] Anhuf D, Ledru M P, Behling H, et al. Paleo-environmental change in Amazonian and African rainforest during the LGM [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2006, 239(3-4): 510-527. doi: 10.1016/j.palaeo.2006.01.017
[7] Prentice I C, Harrison S P, Bartlein P J. Global vegetation and terrestrial carbon cycle changes after the last ice age [J]. New Phytologist, 2011, 189(4): 988-998. doi: 10.1111/j.1469-8137.2010.03620.x
[8] Heaney L R. A synopsis of climatic and vegetational change in Southeast Asia[C]//Tropical Forests and Climate. Dordrecht: Springer Netherlands, 1991: 53-61.
[9] Wurster C M, Bird M I, Bull I D, et al. Forest contraction in north equatorial Southeast Asia during the Last Glacial Period [J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(35): 15508-15511. doi: 10.1073/pnas.1005507107
[10] O’Leary M H. Carbon isotope fractionation in plants [J]. Phytochemistry, 1981, 20(4): 553-567. doi: 10.1016/0031-9422(81)85134-5
[11] Dubois N, Oppo D W, Galy V V, et al. Indonesian vegetation response to changes in rainfall seasonality over the past 25, 000 years [J]. Nature Geoscience, 2014, 7(7): 513-517. doi: 10.1038/ngeo2182
[12] Waliser D E, Gautier C. A satellite-derived climatology of the ITCZ [J]. Journal of Climate, 1993, 6(11): 2162-2174. doi: 10.1175/1520-0442(1993)006<2162:ASDCOT>2.0.CO;2
[13] Yang S, Zhang T T, Li Z N, et al. Climate variability over the maritime continent and its role in global climate variation: a review [J]. Journal of Meteorological Research, 2019, 33(6): 993-1015. doi: 10.1007/s13351-019-9025-x
[14] Wurster C M, Rifai H, Zhou B, et al. Savanna in equatorial Borneo during the late Pleistocene [J]. Scientific Reports, 2019, 9(1): 6392. doi: 10.1038/s41598-019-42670-4
[15] Konecky B, Russell J, Bijaksana S. Glacial aridity in central Indonesia coeval with intensified monsoon circulation [J]. Earth and Planetary Science Letters, 2016, 437: 15-24. doi: 10.1016/j.jpgl.2015.12.037
[16] Wicaksono S A, Russell J M, Holbourn A, et al. Hydrological and vegetation shifts in the Wallacean region of central Indonesia since the Last Glacial Maximum [J]. Quaternary Science Reviews, 2017, 157: 152-163. doi: 10.1016/j.quascirev.2016.12.006
[17] Aldrian E, Dwi Susanto R. Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature [J]. International Journal of Climatology, 2003, 23(12): 1435-1452. doi: 10.1002/joc.950
[18] Chang C P, Wang Z, Ju J H, et al. On the relationship between western maritime continent monsoon rainfall and ENSO during northern winter [J]. Journal of Climate, 2004, 17(3): 665-672. doi: 10.1175/1520-0442(2004)017<0665:OTRBWM>2.0.CO;2
[19] Yang Z B, Li T G, Lei Y L, et al. Vegetation evolution-based hydrological climate history since LGM in southern South China Sea [J]. Marine Micropaleontology, 2020, 156: 101837. doi: 10.1016/j.marmicro.2020.101837
[20] DiNezio P N, Tierney J E. The effect of sea level on glacial Indo-Pacific climate [J]. Nature Geoscience, 2013, 6(6): 485-491. doi: 10.1038/ngeo1823
[21] Gibbons F T, Oppo D W, Mohtadi M, et al. Deglacial δ18O and hydrologic variability in the tropical Pacific and Indian Oceans [J]. Earth and Planetary Science Letters, 2014, 387: 240-251. doi: 10.1016/j.jpgl.2013.11.032
[22] Griffiths M L, Drysdale R N, Gagan M K, et al. Increasing Australian-Indonesian monsoon rainfall linked to early Holocene sea-level rise [J]. Nature Geoscience, 2009, 2(9): 636-639. doi: 10.1038/ngeo605
[23] Partin J W, Cobb K M, Adkins J F, et al. Millennial-scale trends in west Pacific warm pool hydrology since the Last Glacial Maximum [J]. Nature, 2007, 449(7161): 452-455. doi: 10.1038/nature06164
[24] Tierney J E, Oppo D W, Rosenthal Y, et al. Coordinated hydrological regimes in the Indo-Pacific region during the past two millennia [J]. Paleoceanography, 2010, 25(1): PA1102.
[25] Kershaw A P, Van Der Kaars S, Flenley J R. The quaternary history of far Eastern rainforests[C]//Tropical Rainforest Responses to Climatic Change. Berlin, Heidelberg: Springer, 2011: 85-123.
[26] Farquhar G D, Ehleringer J R, Hubick K T. Carbon isotope discrimination and photosynthesis [J]. Annual Review of Plant Physiology and Plant Molecular Biology, 1989, 40(1): 503-537. doi: 10.1146/annurev.pp.40.060189.002443
[27] Collins J A, Schefuß E, Govin A, et al. Insolation and glacial-interglacial control on southwestern African hydroclimate over the past 140000 years [J]. Earth and Planetary Science Letters, 2014, 398: 1-10. doi: 10.1016/j.jpgl.2014.04.034
[28] Eglinton T I, Eglinton G. Molecular proxies for paleoclimatology [J]. Earth and Planetary Science Letters, 2008, 275(1-2): 1-16. doi: 10.1016/j.jpgl.2008.07.012
[29] Gratton C, Forbes A E. Changes in δ13C stable isotopes in multiple tissues of insect predators fed isotopically distinct prey [J]. Oecologia, 2006, 147(4): 615-624. doi: 10.1007/s00442-005-0322-y
[30] Wurster C M, McFarlane D A, Bird M I. Spatial and temporal expression of vegetation and atmospheric variability from stable carbon and nitrogen isotope analysis of bat guano in the southern United States [J]. Geochimica et Cosmochimica Acta, 2007, 71(13): 3302-3310. doi: 10.1016/j.gca.2007.05.002
[31] Zahn A, Haselbach H, Güttinger R. Foraging activity of central European Myotis myotis in a landscape dominated by spruce monocultures [J]. Mammalian Biology, 2005, 70(5): 265-270. doi: 10.1016/j.mambio.2004.11.020
[32] Wurster C M, Bird M I, Bull I D, et al. A protocol for radiocarbon dating tropical subfossil cave guano [J]. Radiocarbon, 2009, 51(3): 977-986. doi: 10.1017/S0033822200034056
[33] Wurster C M, Rifai H, Haig J, et al. Stable isotope composition of cave guano from eastern Borneo reveals tropical environments over the past 15, 000 cal yr BP [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2017, 473: 73-81. doi: 10.1016/j.palaeo.2017.02.029
[34] Eglinton G, Hamilton R J. Leaf epicuticular waxes [J]. Science, 1967, 156(3780): 1322-1335. doi: 10.1126/science.156.3780.1322
[35] Vogts A, Schefuß E, Badewien T, et al. n-Alkane parameters from a deep sea sediment transect off southwest Africa reflect continental vegetation and climate conditions [J]. Organic Geochemistry, 2012, 47: 109-119. doi: 10.1016/j.orggeochem.2012.03.011
[36] Chikaraishi Y, Naraoka H, Poulson S R. Hydrogen and carbon isotopic fractionations of lipid biosynthesis among terrestrial (C3, C4 and CAM) and aquatic plants [J]. Phytochemistry, 2004, 65(10): 1369-1381. doi: 10.1016/j.phytochem.2004.03.036
[37] Niedermeyer E M, Sessions A L, Feakins S J, et al. Hydroclimate of the western Indo-Pacific Warm Pool during the past 24, 000 years [J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(26): 9402-9406. doi: 10.1073/pnas.1323585111
[38] Hu J F, Peng P A, Fang D Y, et al. No aridity in Sunda Land during the Last Glaciation: Evidence from molecular-isotopic stratigraphy of long-chain n-alkanes [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2003, 201(3-4): 269-281. doi: 10.1016/S0031-0182(03)00613-8
[39] 崔子恒. 末次冰期以来巽他陆架东北部地区古环境与古植被变化[D]. 同济大学硕士学位论文, 2020.
CUI Ziheng. Paleo-vegetation and Paleo-environment in the Northeast of the Sunda Shelf since the Last Glacial Period[D]. Master Dissertation of Tongji University, 2020.
[40] 杨莹. 末次冰期以来巽他陆架植被与水文演化: 南海南部沉积物生物标志物记录[D]. 同济大学硕士学位论文, 2020.
YANG Ying. Vegetation and hydrological evolution on the Sunda Shelf since the last glaciation: sedimentary biomarker records from the Southern South China Sea[D]. Master Dissertation of Tongji University, 2020.
[41] Wicaksono S A, Russell J M, Bijaksana S. Compound-specific carbon isotope records of vegetation and hydrologic change in central Sulawesi, Indonesia, since 53, 000 yr BP [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2015, 430: 47-56. doi: 10.1016/j.palaeo.2015.04.016
[42] Russell J M, Vogel H, Konecky B L, et al. Glacial forcing of central Indonesian hydroclimate since 60, 000 y B. P [J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(14): 5100-5105. doi: 10.1073/pnas.1402373111
[43] Hope G. Environmental change in the late Pleistocene and later Holocene at wanda site, soroako, South Sulawesi, Indonesia [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2001, 171(3-4): 129-145. doi: 10.1016/S0031-0182(01)00243-7
[44] Ruan Y M, Mohtadi M, Van Der Kaars S, et al. Differential hydro-climatic evolution of East Javanese ecosystems over the past 22, 000 years [J]. Quaternary Science Reviews, 2019, 218: 49-60. doi: 10.1016/j.quascirev.2019.06.015
[45] 孙湘君, 罗运利, 陈怀成. 中国第四纪深海孢粉研究进展[J]. 科学通报, 2003, 48(20):2155-2164 doi: 10.1007/BF03182842
SUN Xiangjun, LUO Yunli, CHEN Huaicheng. Deep-sea pollen research in China [J]. Chinese Science Bulletin, 2003, 48(20): 2155-2164. doi: 10.1007/BF03182842
[46] 赵辰辰, 王永波, 胥勤勉. 2.5Ma以来中国陆地孢粉记录反映的古气候变化[J]. 海洋地质与第四纪地质, 2020, 40(4):175-191
ZHAO Chenchen, WANG Yongbo, XU Qinmian. Climate changes on Chinese continent since 2.5 Ma: Evidence from fossil pollen records [J]. Marine Geology & Quaternary Geology, 2020, 40(4): 175-191.
[47] 戴璐, Yeok F S. 末次冰期时暴露的巽他大陆架可能被热带稀树草原覆盖吗?[J]. 地球科学进展, 2017, 32(11):1147-1156 doi: 10.11867/j.issn.1001-8166.2017.11.1147
DAI Lu, Yeok F S. Was there savanna corridor on the exposed Sunda Shelf during the last glacial period? [J]. Advances in Earth Science, 2017, 32(11): 1147-1156. doi: 10.11867/j.issn.1001-8166.2017.11.1147
[48] Wang X M, Sun X J, Wang P X, et al. Vegetation on the Sunda Shelf, South China Sea, during the Last Glacial Maximum [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2009, 278(1-4): 88-97. doi: 10.1016/j.palaeo.2009.04.008
[49] Sun X J, Li X, Luo Y L. Vegetation and climate on the Sunda shelf of the South China Sea during the last glaciation-Pollen results from station 17962 [J]. Acta Botanica Sinica, 2002, 44(6): 746-752.
[50] 李逊, 孙湘君. 南海南部末次冰期以来的孢粉记录及其气候意义[J]. 第四纪研究, 1999(6):526-535 doi: 10.3321/j.issn:1001-7410.1999.06.005
LI Xun, SUN Xiangjun. Palynological records since Last Glacial Maximum from a deep sea core in Southern South China Sea [J]. Quaternary Sciences, 1999(6): 526-535. doi: 10.3321/j.issn:1001-7410.1999.06.005
[51] 王晓梅. 巽他陆架近四万年以来的古植被及其古环境意义[D]. 同济大学博士学位论文, 2006.
WANG Xiaomei. Paleovegetation on the Sunda Shelf over the Last 40 ka and Its Paleoenvironmental Significance[D]. Doctor Dissertation of Tongji University, 2006.
[52] Luo C X, Haberle S, Zheng Z, et al. Environmental changes in the north-east Sunda region over the last 40 000 years [J]. Journal of Quaternary Science, 2019, 34(3): 245-257. doi: 10.1002/jqs.3093
[53] 杨再宝. 南海南部孢粉分布特征及其对周边地区4万年来气候环境演化历史的指示[D]. 中国科学院大学博士学位论文, 2019.
YANG Zaibao. Distribution characteristics of sporopollen in the Southern South China Sea and its implications for regional climate and environmental evolution since 40 ka[D]. Doctor Dissertation of University of Chinese Academy of Sciences, 2019.
[54] Hunt C O, Gilbertson D D, Rushworth G. A 50, 000-year record of late Pleistocene tropical vegetation and human impact in lowland Borneo [J]. Quaternary Science Reviews, 2012, 37: 61-80. doi: 10.1016/j.quascirev.2012.01.014
[55] Jones S E, Hunt C O, Reimer P J. A Late Pleistocene record of climate and environmental change from the northern and southern Kelabit Highlands of Sarawak, Malaysian Borneo [J]. Journal of Quaternary Science, 2014, 29(2): 105-122. doi: 10.1002/jqs.2682
[56] Anshari G, Kershaw A P, Van Der Kaars S, et al. Environmental change and peatland forest dynamics in the Lake Sentarum area, West Kalimantan, Indonesia [J]. Journal of Quaternary Science, 2004, 19(7): 637-655. doi: 10.1002/jqs.879
[57] Maloney B K, McCormac F G. A 30, 000-year pollen and radiocarbon record from highland sumatra as evidence for climate change [J]. Radiocarbon, 1995, 37(2): 181-190. doi: 10.1017/S0033822200030629
[58] Maloney B K. Pollen analytical evidence for early forest clearance in North Sumatra [J]. Nature, 1980, 287(5780): 324-326. doi: 10.1038/287324a0
[59] Taylor D, Yen O H, Sanderson P G, et al. Late quaternary peat formation and vegetation dynamics in a lowland tropical swamp; Nee Soon, Singapore [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2001, 171(3-4): 269-287. doi: 10.1016/S0031-0182(01)00249-8
[60] White J C, Penny D, Kealhofer L, et al. Vegetation changes from the late Pleistocene through the Holocene from three areas of archaeological significance in Thailand [J]. Quaternary International, 2004, 113(1): 111-132. doi: 10.1016/j.quaint.2003.09.001
[61] Bian Y P, Jian Z M, Weng C Y, et al. A palynological and palaeoclimatological record from the southern Philippines since the Last Glacial Maximum [J]. Chinese Science Bulletin, 2011, 56(22): 2359-2365. doi: 10.1007/s11434-011-4573-1
[62] Flenley J R. Tropical forests under the climates of the last 30, 000 years [J]. Climatic Change, 1998, 39(2-3): 177-197.
[63] Dam R A C, Fluin J, Suparan P, et al. Palaeoenvironmental developments in the Lake Tondano area (N. Sulawesi, Indonesia) since 33, 000 yr B. P [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2001, 171(3-4): 147-183. doi: 10.1016/S0031-0182(01)00244-9
[64] Van Der Kaars S, Bassinot F, De Deckker P, et al. Changes in monsoon and ocean circulation and the vegetation cover of southwest Sumatra through the last 83, 000years: The record from marine core BAR94-42 [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2010, 296(1-2): 52-78. doi: 10.1016/j.palaeo.2010.06.015
[65] Van Der Kaars S, Penny D, Tibby J, et al. Late quaternary palaeoecology, palynology and palaeolimnology of a tropical lowland swamp: Rawa Danau, West-Java, Indonesia [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2001, 171(3-4): 185-212. doi: 10.1016/S0031-0182(01)00245-0
[66] Van Der Kaars S, Dam R. Vegetation and climate change in West-Java, Indonesia during the last 135, 000 years [J]. Quaternary International, 1997, 37: 67-71. doi: 10.1016/1040-6182(96)00002-X
[67] Wang X, Van Der Kaars S, Kershaw P, et al. A record of fire, vegetation and climate through the last three glacial cycles from Lombok Ridge core G6-4, eastern Indian Ocean, Indonesia [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 1999, 147(3-4): 241-256. doi: 10.1016/S0031-0182(98)00169-2
[68] Van Der Kaars S, Wang X, Kershaw P, et al. A late quaternary palaeoecological record from the Banda Sea, Indonesia: Patterns of vegetation, climate and biomass burning in Indonesia and northern Australia [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2000, 155(1-2): 135-153. doi: 10.1016/S0031-0182(99)00098-X
[69] Raes N, Cannon C H, Hijmans R J, et al. Historical distribution of Sundaland’s Dipterocarp rainforests at Quaternary glacial maxima [J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(47): 16790-16795. doi: 10.1073/pnas.1403053111
[70] Ratnam J, Tomlinson K W, Rasquinha D N, et al. Savannahs of Asia: Antiquity, biogeography, and an uncertain future [J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2016, 371(1703): 20150305. doi: 10.1098/rstb.2015.0305
[71] Denton G H, Alley R B, Comer G C, et al. The role of seasonality in abrupt climate change [J]. Quaternary Science Reviews, 2005, 24(10-11): 1159-1182. doi: 10.1016/j.quascirev.2004.12.002
[72] Shakun J D, Clark P U, He F, et al. Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation [J]. Nature, 2012, 484(7392): 49-54. doi: 10.1038/nature10915
[73] Denniston R F, Wyrwoll K H, Asmerom Y, et al. North Atlantic forcing of millennial-scale Indo-Australian monsoon dynamics during the Last Glacial period [J]. Quaternary Science Reviews, 2013, 72: 159-168. doi: 10.1016/j.quascirev.2013.04.012
[74] Moerman J W, Cobb K M, Adkins J F, et al. Diurnal to interannual rainfall δ18O variations in northern Borneo driven by regional hydrology [J]. Earth and Planetary Science Letters, 2013, 369-370: 108-119. doi: 10.1016/j.jpgl.2013.03.014
[75] Sadekov A Y, Ganeshram R, Pichevin L, et al. Palaeoclimate reconstructions reveal a strong link between El Niño-Southern Oscillation and Tropical Pacific mean state [J]. Nature Communications, 2013, 4(1): 2692. doi: 10.1038/ncomms3692
[76] De Deckker P, Tapper N J, Van Der Kaars S. The status of the Indo-Pacific Warm Pool and adjacent land at the Last Glacial Maximum [J]. Global and Planetary Change, 2003, 35(1-2): 25-35. doi: 10.1016/S0921-8181(02)00089-9
[77] Sun X J, Li X, Luo Y L, et al. The vegetation and climate at the last glaciation on the emerged continental shelf of the South China Sea [J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2000, 160(3-4): 301-316. doi: 10.1016/S0031-0182(00)00078-X
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