Variations and mechanisms of the Asian summer monsoon revealed by stalagmite δ18O records
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摘要:
亚洲夏季风（ASM，Asian Summer Monsoon）是全球气候系统的重要组成部分，也是目前地球系统科学的热点问题。亚洲石笋δ18O揭示的亚洲古季风变化，加深了我们对其时空演化机制的理解。然而，目前东亚夏季风区和印度夏季风区石笋δ18O记录在不同时间尺度上的相互关系和主控因素仍存在一些争议。针对这一问题，本文综述了东亚夏季风（EASM，East Asian Summer Monsoon）区和印度夏季风（ISM，Indian Summer Monsoon）区的高分辨率石笋δ18O记录，发现两者在轨道尺度上都受控于岁差驱动的北半球夏季日照量的变化，其机制涉及海陆热力差异强度、辐合强度和上升气流强度以及夏季时长的变化。这些机制共同导致了在北半球夏季日照高值期EASM和ISM区夏季降雨量增加以及降雨和石笋的δ18O值负偏。在千年尺度上，EASM区和ISM区的石笋δ18O记录则响应于北大西洋区的气候变化，具体响应机制主要有热带辐合带南移导致的印度洋水汽分馏减弱（主要影响ISM区和EASM区），以及西风带调节的水汽来源和降雨季节性变化（主要影响EASM区）。在百年及以下尺度，EASM区和ISM区石笋δ18O记录都受到与厄尔尼诺-南方涛动有关的大尺度大气环流影响。未来在关键区域构建更多高分辨率的石笋δ18O记录及相关的降雨指标，将有助于理解大气环流变化与东亚夏季风区降雨的关系。
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关键词:
- 石笋氧同位素 /
- 东亚夏季风 /
- 印度夏季风 /
- 北半球日照 /
- Heinrich事件
Abstract:The Asian summer monsoon is an important part of the global climate system and a hot issue of the earth system science. The variation of the Asian paleo-monsoon revealed by δ18O data from the Asian stalagmites deepens our understanding of the mechanisms of its spatiotemporal evolution. However, the main controlling factors of the stalagmite δ18O records in the East Asian and Indian summer monsoon regions in different time scales remain controversial. Aiming at this problem, we reviewed the high-resolution stalagmite δ18O records in the East Asian summer monsoon (EASM) and Indian summer monsoon (ISM) regions and revealed that they are in response to the variation in the Northern Hemisphere summer insolation driven by the precession on orbital scale. The specific mechanism involves the variation of zonal sea-land thermal contrast, seasonal humidity changes and cycles in low-level convergence, and the variation of summer length. These mechanisms jointly lead to increased summer rainfall with depleted δ18O in the EASM and ISM regions during high Northern Hemisphere summer insolation. On millennial scale, the stalagmite δ18O records in EASM and ISM regions are in response to the abrupt North Atlantic climate changes consistently, by mainly the weakening of water vapor fractionation in the Indian Ocean due to southward migration of the intertropical convergence zone (mainly influenced the ISM and EASM regions) and seasonal variation of water vapor sources and rainfall amount regulated by the westerly (mainly influenced the EASM region). Finally, at the centennial or shorter scale, the stalagmite δ18O records in both EASM and ISM regions are influenced by large-scale atmospheric circulation associated with EI Niño-Southern Oscillation. In the future, the development of more high-resolution stalagmite δ18O and other rainfall indicators records in key areas would facilitate our understanding of the relationship between atmospheric circulation and rainfall changes in the Asian summer monsoon region.
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湿地与海洋、森林并称地球的三大生态系统[1]，是珍贵的自然资源，具有不可替代的综合功能。湿地在蓄洪抗旱、促淤造陆、调节气候、降解污染等方面具有极为重要的作用，被称为“地球之肾”。湿地植物通过光合作用固定了大气中的二氧化碳，具有较强的存储二氧化碳和固碳能力，是重要的碳储库[2-3]，在全球碳循环中发挥了重要作用，其碳汇功能对于减缓全球变暖进程具有重要意义。
黄河以水少沙多、含沙量高而闻名于世，自1855年改道渤海以来，巨量的河流泥沙塑造了陆地面积约为5000余平方千米的现代黄河三角洲。黄河三角洲湿地地理位置优越，生态类型独特，是暖温带保存最完整、最典型、最年轻的湿地生态系统[4]。受到黄河频繁改道与入海水沙快速变化的影响，黄河三角洲河口湿地的分布格局、植被覆盖与群落结构等均随之快速变化。自1976年黄河三角洲改道清水沟入海以来，现行黄河三角洲河口湿地呈现阶段性演化的特征，发育演变不断调整。1996年黄河改道清8汊以来，原清水沟废弃河道遭受快速侵蚀，由之前的向海淤进状态快速逆转为向陆蚀退状态，而现行清8河口由于入海泥沙的堆积而不断淤积造陆。
因此，现行黄河口叶瓣兼具行水河口和废弃河口两类特征，为对比研究河口三角洲湿地变化提供了良好的试验场。研究黄河口湿地的时空变化过程，揭示其主要控制因素，阐明湿地植被时空分布特征，深化多因素影响下河口湿地发育演变的科学认识，可为黄河三角洲湿地管理与保护的相关政策制定提供有效的科学依据。
1. 研究区概况
研究区位于现代黄河三角洲东部。根据《国际湿地公约》中的湿地分类系统[5]，结合本文研究需要，将潮间带滩涂湿地及草甸沼泽作为湿地面积变化的主要研究对象，盐田、库塘、养殖池塘等人工湿地主要受人为因素的影响，对河流输入变化和海洋动力环境变化不敏感，本文不予重点分析。
黄河自1976年由刁口河道改道清水沟流路入海，清水沟流路行河以来先后经历了淤滩成槽、溯源冲刷和溯源淤积的阶段性演变过程。为有利于防洪安全和保障胜利油田的基础设施建设，1996年5月进行了一次小型人工改道，即清8改汊入海工程，将河道向北改道至清8航道。此后，清水沟河道被人工堤坝完全切断，与主干道断开，清水沟流路完全废弃，废弃河道长约20 km[6]。
本研究系统收集了1976—2020年期间覆盖现行黄河三角洲叶瓣区域的Landsat系列卫星遥感影像资料和黄河利津站水沙资料数据，对黄河口区域湿地演变的时空特征及影响因素进行了研究。为了对比研究清8汊（行水）和清水沟（废弃）不同流路湿地演化的差异及主控因素，将研究区以黄河改道处为基准点O（图1），以清8汊叶瓣与废弃清水沟叶瓣间“人”字区交点为固定点B，向南北两侧各呈60°进行分割，形成清8汊叶瓣湿地区（Ⅰ区）及废弃清水沟叶瓣湿地区（Ⅱ区）。
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图 1 现行黄河三角洲遥感影像及湿地研究分区示意图2020年10月24日 Landsat8 OLI标准假彩色影像，UTM投影。Figure 1. Image of the active Yellow River Delta and wetlandBase map: Landsat 8 false color image on October 24, 2020, UTM projection.2. 数据与方法
2.1 数据来源
本文所采用的数据包括卫星遥感数据及黄河入海水沙资料两部分。其中，卫星遥感数据为Landsat系列卫星影像，数据来源于地理空间数据云（http://www.gscloud.cn/），选取了1976—2020年每年影像获取时间相近、研究区云量少、植被状态较好的37幅遥感影像（表1）。首先，通过ENVI软件对遥感影像进行影像配准、辐射定标、大气校正等预处理；然后，在此基础上提取湿地面积、植被覆盖度、植被覆盖类型等信息。
表 1 Landsat遥感影像数据信息（1976—2020）Table 1. Information of Landsat imagery (1976—2020)日期 传感器 波段数 分辨率/m 日期 传感器 波段数 分辨率/m 1976-08-31 MSS 4 79 1998-09-10 TM 7 30 1978-08-30 MSS 4 79 1999-08-28 TM 7 30 1979-08-25 MSS 4 79 2000-09-15 TM 7 30 1980-07-14 MSS 4 79 2001-09-02 TM 7 30 1981-09-19 MSS 4 79 2004-09-10 TM 7 30 1983-07-07 MSS 4 79 2006-10-26 ETM+ 8 30 1984-09-11 MSS 4 79 2007-09-11 ETM+ 8 30 1985-09-06 TM 7 30 2008-09-05 TM 7 30 1986-08-08 TM 7 30 2009-08-23 TM 7 30 1987-08-11 TM 7 30 2010-09-11 TM 7 30 1988-06-10 TM 7 30 2011-09-22 ETM+ 8 30 1989-09-01 TM 7 30 2013-09-03 OLI 11 30 1991-09-23 TM 7 30 2015-06-05 OLI 11 30 1992-08-24 TM 7 30 2016-08-26 OLI 11 30 1993-09-28 TM 7 30 2017-09-30 OLI 11 30 1994-10-17 TM 7 30 2018-09-17 OLI 11 30 1995-09-18 TM 7 30 2019-08-19 OLI 11 30 1996-08-19 TM 7 30 2020-10-24 OLI 11 30 1997-09-07 TM 7 30 黄河入海水沙数据来源于黄河水利委员会公开发布的黄河利津站水沙观测资料，包括1976—2019年逐年径流量、输沙量等数据。最近70年来黄河入海水沙的变化过程及控制机制、河口沉积效应等，可参见Wang[7-8]和Wu[6, 9]等研究。
2.2 湿地面积信息提取
湿地面积提取采用分层次分类的方法[10]，建立解译标志（表2），利用监督分类提取滩涂湿地面积，利用归一化植被指数（normal difference vegetation index, NDVI）提取草甸沼泽面积，利用改进的归一化水体指数（modified normal difference water index, MNDWI）提取人工水体面积。
2.2.1 滩涂及沼泽湿地信息提取
本研究区内的滩涂主要为潮间带滩涂，滩涂湿地面积较大且与除水体外其他地物在颜色方面有明显差别，可在ENVI中采用最大似然法进行监督分类提取，在ArcGIS中结合目视解译对异常区域剔除。
研究区内的沼泽湿地覆有植被，以草本沼泽和沼泽化草甸为主，可采用NDVI进行提取，沼泽湿地的NDVI值为大于0.2的正值。
NDVI值可指示影像像元中绿色植被的数量，较高的NDVI值对应较多的绿色植被，可用来监测区域的植被覆盖变化。通过比值处理，NDVI可以部分消除与太阳高度角、卫星观测角、地形、云及阴影和大气有关的条件变化影响，增强了对植被的响应能力[11]。
计算公式如下：
$$ {\rm{NDVI}}=\frac{{\rho({\rm{NIR}})}-{\rho({\rm{RED}})}}{{\rho({\rm{NIR}})+\rho({\rm{RED}})}} $$ (1) 其中，
$ \mathrm{\rho }\left(\text{NIR}\right) $ 表示遥感影像中的近红外波段的反射值，$ \mathrm{\rho }\left(\text{RED}\right) $ 表示红光波段的反射值，在Landsat系列影像中各波段对应如表3所示。表 3 不同传感器中NDVI各波段对应值Table 3. Band of NDVI in different Landsat sensors传感器波段 MSS TM ETM+ OLI RED 3 3 3 4 NIR 4 4 4 5 2.2.2 人工湿地信息提取
研究区域内存在大量的人工湿地，如盐田、库塘、养殖池等，其含水量较多，可通过MNDWI进行提取。MNDWI表达式为：
$$ {\text{MNDWI}}=\frac{{\rho({\rm{GREEN}})}-{\rho({\rm{MIR}})}}{{\rho({\rm{GREEN}})+\rho({\rm{MIR}})}} $$ (2) 其中，
$ \mathrm{\rho }\left(\text{MIR}\right) $ 表示遥感影像中的中红外波段的反射值，$ \mathrm{\rho }\left(\text{GREEN}\right) $ 表示绿光波段的反射值，在Landsat系列影像中各波段对应如表4所示。表 4 不同传感器中MNDWI各波段对应值Table 4. Band of MNDWI in different Landsat sensors传感器波段 TM ETM+ OLI GREEN 2 2 3 MIR 5 5 6 2.2.3 非湿地区域信息剔除
研究区内的非湿地区主要为北部的孤东油田。在1987年孤东海堤建成后，该区域不再被海水周期性覆盖，因此可归为非湿地区域。由于孤东油田形状比较规则，可直接通过目视解译将其进行裁剪剔除。
2.3 植被覆盖度信息提取
像元二分模型是一种简单实用、应用广泛的遥感估算模型[12]。该模型假设单个影像像元内的地表由无植被覆盖与有植被覆盖组成，即纯土壤与纯植被。通过遥感传感器观测到的光谱信息也由这两个组分因子线性加权合成，各组分因子的权重是各自面积在像元中所占的比例，其中植被的权重可表征该像元的植被覆盖度[13]。
根据像元二分模型，在一个像元中NDVI值可以表达为由无植被覆盖部分所贡献的信息
$ {\text{NDVI}}_{\text{soil}} $ 和绿色植被所贡献的信息$ {\text{NDVI}}_{\text{veg}} $ 两部分组成，表达式为：$$ {{F}}_{\text{vc}}\text=\frac{{\text{NDVI}}-{{\text{NDVI}}_{\text{soil}}}}{{\text{NDVI}}_{\text{veg}}-{\text{NDVI}}_{\text{soil}}} $$ (3) 其中，
$ {\text{NDVI}}_{\text{veg}} $ 和$ {\text{NDVI}}_{\text{soil}} $ 分别为纯植被像素和纯裸地像素的NDVI值。纯裸土区域的NDVI值理论上应该为0或接近于0，纯植被区域的NDVI值应该接近于1，但在实际环境中由于受各种因素影响，$ {\text{NDVI}}_{\text{soil}} $ 和$ {\text{NDVI}}_{\text{veg}} $ 的值会因地理位置和植被类型不同而改变，因此一般情况下无固定值，需结合本区域NDVI概率分布值而确定。结合前人在黄河三角洲地区的研究，本文将NDVI统计数据中频率为5%的NDVI值作为$ {\text{NDVI}}_{\text{soil}} $ ，把植被覆盖区的内取频率95%的NDVI值作为$ {\text{NDVI}}_{\text{veg}} $ [11]。$$ {{F}}_{\text{vc}}\text=\frac{{\text{NDVI}}-{{\text{NDVI}}_{\text{min}}}}{{\text{NDVI}}_{\text{max}}-{\text{NDVI}}_{\text{min}}} $$ (4) 利用ENVI 软件的快速统计功能可提取各时相影像数据的NDVI统计值，获得频率在5%和95%时对应的
$ {\text{NDVI}}_{\text{min}} $ 及$ {\text{NDVI}}_{\text{max}} $ ，对各时相影像进行波段运算并对异常值进行处理。根据现行黄河三角洲叶瓣植被覆盖度的变化，将植被大致划分为5个等级（表5）。表 5 植被覆盖度等级划分标准Table 5. Rank of fractional vegetation cover序号 植被覆盖度 覆盖等级 1 Fvc＜10% 裸土或无植被覆盖 2 10%≤Fvc≤30% 低植被覆盖度 3 30%≤Fvc≤50% 中植被覆盖度 4 50%≤Fvc≤80% 中高植被覆盖度 5 Fvc＞80% 高植被覆盖度 2.4 植被覆盖类型信息提取
本文以黄河三角洲湿地较为典型的四种植物：芦苇、互花米草、碱蓬和柽柳为研究对象，选择了1980、1995、2000、2004、2011、2016、2020年共7期影像进行分类研究。首先结合谷歌地球历史图像及图新地球软件的高分辨图像进行先验学习，建立湿地植被的解译标志（表6）；在此基础上，使用最大似然法和支持向量机两种监督分类方法进行分类，利用地面真实感兴趣区或真实分类图像进行混淆矩阵分析生成精度评价表，选择总体精度和Kappa系数都较高的支持向量机分类结果。
3. 结果与讨论
3.1 现行黄河三角洲叶瓣湿地时空变化特征及影响因素
根据卫星遥感影像提取现行黄河三角洲湿地面积信息，建立了自1976年黄河改道清水沟以来的河口不同类型湿地面积的时间序列数据（图2），其时序变化总体分为以下4个阶段（图3）。
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图 2 现行黄河三角洲叶瓣湿地面积时序变化Figure 2. Temporal variation of the wetland area in the active Yellow River Delta lobe![]()
图 3 黄河口湿地面积随黄河累积入海输沙量（a）及累积径流量（b）的阶段性变化Qsa为累积输沙量，Qa为累积径流量。Figure 3. Stepwise variations of wetland area in the active Yellow River delta with cumulative sediment load （a） and cumulative water discharge （b） from the Yellow RiverQsa is cumulative sediment load, Qa is cumulative water discharge.（1）快速淤进阶段（1976—1981年）：湿地面积整体呈现快速增长趋势，平均增长速率约为77.42 km2/a。湿地面积与黄河累积入海水沙量均呈现显著正相关，决定系数R2分别为0.92和0.86（图3）。在黄河改道初期，入海流路尚未成槽，频繁分汊摆动，河流漫流沉积明显[14]，沉积物向外海的输送能力较弱，湿地呈扇形分布向海淤进（图4a）。陆地面积快速增长为植被生长提供了栖息地，且低流速的淡水补给为植被提供了良好的生长环境，植被覆盖面积随之增加，植被类型以碱蓬等抗盐性植被为主，在河道周边多覆盖喜淡水植物芦苇（图5a）。
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图 4 不同时期河口湿地植被覆盖度空间分布Figure 4. Spatial distribution of wetland fractional vegetation cover at different stages![]()
图 5 不同时期河口湿地植被覆盖类型空间分布Figure 5. Spatial distribution of wetland vegetation types at different stages（2）慢速淤进阶段（1983—1995年）：湿地面积增长相对缓慢，平均增长速率约为31.61 km2/a。在此期间，滩涂湿地与草甸沼泽呈现出同步变化的趋势。湿地面积与黄河入海累积输沙量和径流量亦呈现显著正相关，其线性关系的决定系数R2分别为0.68和0.72（图3）。1983—1986年期间，黄河清水沟河道逐渐归一成槽，呈单河道入海，径流泥沙向海输运效率明显提高，湿地随河道延伸而不断向东淤进，河流并汊后淡水的补给主要集中于河道两侧。在远离河道处，淡水补给缺少不利于植被发育，植被生长速度较慢，植被覆盖度分布呈现出由河道两侧向南北两岸递减，南北两岸出现裸地或无植被覆盖区（图4b）。1987年湿地北侧由于孤东海堤的阻隔而不再被海水周期性覆盖，湿地面积下降，湿地呈现“楔”状（图4c），河道北岸植被覆盖度较低（图4d），淡水补给主要在河道附近，芦苇沿河道两岸分布，柽柳、碱蓬等抗盐类植物生长在芦苇外侧（图5b）。
（3）快速蚀退阶段（1996—2001年）：1996年5月黄河入海流路改道清8汊河，形成现行河口和废弃河口并存的格局。区域湿地面积出现逐年减小趋势，平均蚀退速率约为27.4 km2/a。该阶段湿地面积与黄河入海累积输沙量和径流量之间呈现显著的负相关关系，而同时段的清8汊叶瓣湿地面积却与入海累积水沙无显著相关关系（图6a、6b）。上述反差表明，在该阶段河口湿地面积变化受控于南部废弃清水沟河口湿地的强烈蚀退，其蚀退速率远高于北部清8河口新生湿地的发育速率。究其原因，在该时段黄河入海水沙量快速减少，下游多次出现断流情况（1997年黄河断流时间高达226天），导致清8河口湿地发育缓慢，难以弥补海洋动力的侵蚀破坏；同时，废弃清水沟河口水沙供应断绝，湿地面积减小（图6c、6d），河口湿地受到强烈的海洋动力侵蚀，成为该阶段河口湿地时空变化的主导因素。
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图 6 湿地面积与黄河累积入海输沙量及径流量相关性分析Figure 6. Stepwise variation of wetland area along with the cumulative sediment load and cumulative water discharge from the Yellow River（4）稳定发育阶段（2004—2020年）：研究区湿地面积处于相对稳定增加的状态，但湿地面积与黄河累积入海水沙量无显著相关关系，R2仅为0.04和0.02（图3），而同期清8河口湿地面积变化与黄河累积入海水沙量则呈现显著正相关，这表明南侧废弃清水沟河口区域的侵蚀速率与现行河口新生湿地发育的速率基本相当。在此阶段，伴随着南部废弃清水沟河口的持续蚀退，北部清8河口向北淤进且堆积速率加快。其根本原因是2002年以来黄河实施调水调沙，入海水沙以脉冲方式集中在6—7月期间入海，期间黄河入海径流量增加，下游河道冲刷导致大量粗粒级泥沙被输送入海并迅速在河口处沉降，促进了清8河口湿地面积增加[9]。2007年河口向北出汊（图4g），在北部生成新的沙嘴，而原东向沙嘴发生侵蚀向陆后退，湿地冲淤共存，导致清8河口湿地面积呈现波动性变化。此外，在此阶段黄河口南部区域潮滩湿地及小部分草甸沼泽湿地被人为开发，盐田、养殖池、库塘等人工湿地明显增加，使自然湿地面积减小，也可能对湿地发育造成一定影响。
3.2 清8汊湿地时空变化及影响因素
现行清8河口区自1996年黄河改道后持续向海淤进，并伴随着河口湿地的快速扩展。在清8河口区，无论是湿地面积还是湿地植被面积均与黄河累积入海输沙量及径流量呈现显著正相关性（图6—7），尤其是2002年黄河调水调沙以来，入海粗颗粒泥沙量增加，河口造陆速率加快，为植被生长提供了栖息地，而入海径流增加则为植被生长提供了充足的淡水。
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图 7 清8汊湿地植被面积与黄河入海输沙量（a）及径流量（b）相关性分析Figure 7. Stepwise variation of vegetation area in the active river mouth （Q8） along with the cumulative sediment load （a） and cumulative water discharge （b） from the Yellow River清8河口湿地植被覆盖面积总体上呈现出阶段性变化特征（图8）：第一阶段（1996—2001年），清8河口发育初期，湿地状态不稳定，加之该阶段黄河断流频繁，淡水供应不足，植被覆盖面积相对较小；第二阶段（2004—2010年），黄河实施调水调沙，入海径流增加同时入海泥沙颗粒粗化，促进了河口湿地的快速发育；第三阶段（2011—2020年），湿地植被覆盖面积明显增加，高植被覆盖区和中植被覆盖区面积增加尤为突出。清水沟流路自2008年起在每年6—7月的调水调沙期进行河口三角洲湿地生态补水调度，通过洪水满溢和侧渗增加湿地水面面积，2010年后每年累积补水量增加至2000万m3以上[15]，淡水的补充为河口湿地植被提供了良好的生长环境。
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图 8 清8汊叶瓣区各等级植被覆盖区面积阶段变化Figure 8. Temporal distribution of Q8 wetland fractional vegetation cover at different stages1996—2020年间7期影像的解译数据反映了河口湿地植被覆盖的空间变化（图9），总体来看，低植被覆盖度区与高植被覆盖度区变化差异最为明显。1996年低植被覆盖区域较为分散，分布在靠改道点处以及潮滩外侧，中高、高植被覆盖度区集中在改道点处；2001—2008年期间低植被覆盖区域主要沿河道向海延伸至河口处，河道两侧有少部分中、中高植被度覆盖区；至2011年，低河道两侧中高、高植被覆盖度区域增加明显，同时在潮滩外侧区域发育；2016—2020年中高、高植被覆盖度区“包围”式覆盖于低植被覆盖度区外侧，且在沙嘴处有高植被覆盖度区域显著发育。
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图 9 不同时期清8汊湿地植被覆盖度空间分布Figure 9. Spatial distribution of Q8 wetland Fractional Vegetation Cover during different periods从植被覆盖类型来看（图10），2000年低植被覆盖区域主要覆盖有碱蓬及少量柽柳，在改道处有芦苇分布；2004年河口处碱蓬覆盖面积明显增大，北侧出现少量互花米草；2011年河道两岸有芦苇分布，且叶瓣东岸覆盖有大量互花米草；2020年河道两岸芦苇发育较好，柽柳与碱蓬混生，被互花米草“包围”其中。
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图 10 不同时期清8汊湿地植被覆盖类型空间分布Figure 10. Spatial distribution of Q8 wetland vegetation types during different periods总体而言，现行河口湿地的植被覆盖度和植被覆盖类型的空间分布均呈现明显的阶段性变化，其主要原因有两个方面：① 黄河淡水补给增加为植被生长提供了良好的繁殖环境，尤其是2008年起进行的清水沟流路生态补水扩大了淡水的覆盖面积，河道两侧湿地为喜淡水的植被（例如芦苇）提供了良好的栖息环境；②互花米草的大量繁殖增加了植被覆盖面积（图10），尤其是2016年以来，中高、高植被覆盖度区域面积占比明显增加，其中互花米草扩展速度最快。互花米草是一种光合效率高、种群密度大的盐生植物，生长繁殖迅速[16]，且由于是外来物种，在河口沙嘴及潮间带处的爆发增加了植被的覆盖度和覆盖面积。
3.3 废弃清水沟湿地时空变化及影响因素
废弃清水沟叶瓣位于研究区东南侧，1997年南部清水沟河道被人工堤坝完全切断，与主干道断开。本部分研究中选取的时间段为1989—2020年，跨越清水沟河道废弃前、中、后三个阶段。
该区域湿地面积经历了先上升后下降再趋于稳定的阶段性变化：第1阶段为20世纪80年代到1996年河道废弃前，湿地面积随着黄河累积入海泥沙量的增加而不断向海淤进。第2阶段为1996年清水沟河道废弃后，黄河入海水沙供应断绝，湿地持续萎缩，海洋动力导致的侵蚀成为影响湿地的主控因素。第3阶段为2006年后，废弃清水沟叶瓣湿地面积趋于稳定，平均面积为108.58 km2，湿地逐步趋于相对平衡状态。
从植被覆盖面积构成来看（图11），各年份湿地植被覆盖面积构成差别较大，但总体来看，低植被覆盖度区域在湿地中占比最大，中高、高植被覆盖度区域面积占比较小。1989年，中高、高植被覆盖度区在黄河河道两侧零散分布；1996年，随着河道继续向东南方向延伸，中高、高植被覆盖度区域向河道两侧靠拢，集中于河道两侧呈长条状分布；2001年，在河道顶端植被茂盛，中高、高植被覆盖度区域在此处聚集，在河道两侧集中覆盖。2020年，中高、高植被覆盖区覆盖范围趋于稳定，主要分布于废弃河道顶端及两侧（图12）。
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图 11 废弃清水沟叶瓣区湿地植被覆盖度时序变化Figure 11. Temporal distribution of Qingshuigou wetland Fractional Vegetation Cover![]()
图 12 不同时期清水沟湿地植被覆盖度空间分布Figure 12. Spatial distribution of Qingshuigou wetland Fractional Vegetation Cover during different periods从植被覆盖类型来看（图13），1995年南部河道尚未废弃，植被自改道点O向海呈扇形延伸，由河道向两侧依次分布芦苇-柽柳-碱蓬，河道两侧芦苇分布茂盛，2000年以后芦苇分布区呈长条状集中在废弃河道两侧，而柽柳和碱蓬在靠近海的外侧。相较于北侧清8河口湿地植被，南侧废弃河口湿地仅在2011年以后零星分布少量互花米草，以芦苇为代表的喜淡水、高植被覆盖度的湿生植被面积变化较小，而以柽柳、碱蓬为代表的植被覆盖度较低的盐生植被面积变化较大，受到海洋动力环境影响，面积持续下降。
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图 13 不同时期废弃清水沟湿地植被覆盖类型空间分布Figure 13. Spatial distribution of abandoned Qingshuigou wetland vegetation types during different periods综上所述，在1996年之前，废弃清水沟湿地时空变化主要受控于黄河入海水沙以及河口的向海淤积延伸；而在1996年之后，由于入海水沙供应断绝，海洋动力引起的侵蚀成为湿地时空变化的主控因素。同时，由于潮汐不对称而引起的废弃河道再充填以及海水入侵加强，改变了废弃河道区域湿地植被的生境，由废弃前的淡水环境逐渐转变为废弃后的海水环境，喜淡水的芦苇植被受到逆境胁迫而导致面积减小。
4. 结论
（1）基于1976—2020年的长时间序列卫星遥感影像研究表明，黄河口湿地发育演变呈现显著的阶段性和空间差异性变化，经历了快速增加、稳定增长、快速蚀退、相对稳定四个阶段：① 1976—1981年，河水分汊漫流，湿地以“扇形”扩张，面积快速增加；② 1983—1995年，河道逐渐归一成槽，沙嘴先向东后向东南延伸，湿地形态沿河道方向呈现典型的西北宽东南窄的三角“楔形”，不断向东南方向稳定淤进；③ 1996—2001年，黄河改道至清8汊入海，清8汊湿地开始发育，废弃清水沟湿地出现蚀退，北部增长速度不及南部蚀退速度，湿地面积不断减小；④ 2004—2020年，北部清8汊叶瓣湿地向海淤进，南部废弃清水沟叶瓣原突出的沙嘴逐渐被侵蚀，面积变化较小，空间上呈现清8汊叶瓣湿地与废弃清水沟叶瓣湿地“双扇形”的形态。
（2）河口湿地植被的空间分布表现出集群特征：以中高、高植被覆盖度类型为主的植被主要分布在河道两侧淡水供应充足的区域，典型植被为喜淡水的湿生植被芦苇；低植被覆盖度类型植被主要分布于潮滩湿地处，距离河道越远植被覆盖度越低，受海洋动力改造作用较大，面积具有明显的年际变化，典型植被为盐生植被碱蓬。
（3）黄河口湿地时空变化与黄河来水来沙、海洋侵蚀、人类活动等因素密切相关，但影响北部清8河口湿地与废弃清水沟湿地变化的主控因素截然不同：① 清8河口湿地时空变化主要受控于黄河入海径流量和输沙量，湿地面积和植被覆盖与累积入海水沙量呈显著正相关，植被覆盖度与人类活动高度相关，如黄河人为改道、调水调沙、生态补水等；② 废弃清水沟叶瓣湿地的时空变化主要受控于海洋动力的侵蚀改造作用，同时潮汐不对称作用下废弃河道再充填以及海水入侵在一定程度上改变了河道两侧湿地的植被生境，致使植被面积逐渐减小，植被群落结构发生演替。
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图 2 1981—2020年1、4、7月和10月平均的垂直综合水汽输送图
紫色、绿色和蓝色的点分别表示三宝洞、葫芦洞和董哥洞的位置。
Figure 2. Average vertical synthesis of atmospheric moisture flux in January, April, July, and October from 1981 to 2020
The purple, green, and blue dots indicate the locations of Sanbao Cave, Hulu Cave, and Dongge Cave, respectively.
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图 3 东亚和南亚石笋δ18O与北半球夏季日照量
a：石笋δ18O数据合成自三宝洞、葫芦洞和董哥洞[10], b：Bittoo洞石笋δ18O数据[18]。 橙色线为7月21日北半球65°N的日照量变化[10,18]。
Figure 3. Stalagmite δ18O records in eastern and southern Asia, and the Northern Hemisphere summer insolation
a: Composited stalagmite δ18O data synthesized from Sanbao Cave, Hulu Cave, and Dongge Cave[10]; b: stalagmite δ18O data of Bittoo Cave[18]. The orange lines show the variation in insolation at 65°N in the Northern Hemisphere on July 21.
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图 4 岁差驱动北半球热带降雨变化的两种机制示意图[61]
a：棕色区域表示陆地，蓝色区域表示海洋；黑色线代表平均径向环流，较粗的线表示较高的输送强度；b：蓝色阴影表示大气湿度，较深的阴影表示较大的湿度。蓝色雨滴代表降水，雨滴越多表示降水越多。
Figure 4. Mechanisms of precession driving tropical rainfall change in the Northern Hemisphere[61]
a: Brown areas indicate land and blue areas indicate sea; the black lines represent the mean meridional circulation; the thicker lines indicate higher transport intensity; b: blue shadows indicate atmospheric humidity, and darker shadows indicate greater humidity; Blue raindrops represent precipitation, and more raindrops mean more precipitation.
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图 5 末次冰期亚洲季风区洞穴石笋δ18O记录所在位置图
Xl：兴隆洞[17]，Dr：Dragon洞/龙洞[16]，Zz：珍珠洞[65]，Sj：宋家洞[66]，Hl：葫芦洞[48]，Sb：三宝洞[9]，Yx：永兴洞[15]，Bi：Bittoo洞[18]，Yz：扬子洞[67]，Fr：芙蓉洞[68]，Yk：羊口洞[69-70]，Sx：三星洞[71]，Wl：Wulu/五路洞[72]，Qx：七星洞[23, 73]，Maw：Mawmluh洞[74]，Dg：董哥洞[49]，Xs：响水洞[75]，Xy：仙云洞[19-20]，Xbl：小白龙洞[76]，Fy：风雨洞[77]。
Figure 5. Locations of stalagmite δ18O records during the last glacial period from different caves in the Asian monsoon region
Xl: Xinglong Cave[17], Dr: Dragon Cave[16], Zz: Zhenzhu Cave[65], Sj: Songjia Cave[66], Hl: Hulu Cave[48], Sb: Sanbao Cave[9], Yx:Yongxing Cave[15], Bi: Bittoo Cave[18], Yz: Yangzi Cave[67], Fr: Furong Cave[68], Yk: Yangkou Cave[69-70], Sx: Sanxing Cave[71], Wl: Wulu Cave[72], Qx: Qixing Cave[23, 73], Maw: Mawmluh Cave[74], Dg: Dongge Cave[49], Xs: Xiangshui Cave[75], Xy: Xianyun Cave[19-20], Xbl: Xiaobailong Cave[76], Fy: Fengyu Cave[77].
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图 6 亚洲季风区石笋δ18O记录的末次冰期千年尺度事件波动
a: NGRIP表示北格陵兰冰芯的δ18O记录[78]，b-h:各个洞穴数据的引用文献与图5中相同。 浅黄色条带指示了Heinrich事件1-7和Younger Dryas事件的持续时间[48,79]。
Figure 6. Millennial-scale events during the last glacial period recorded by stalagmite δ18O in the Asian monsoon region
a: NGRIP represents the δ18O record of the North Greenland ice core[78], b-h: citations of the references are the same as in Fig. 5. The light yellow bands indicate the duration of Heinrich stadials 1-7 and Younger Dryas stadials[48,79].
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