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地理研究, 2023, 42(7): 1921-1940 doi: 10.11821/dlyj020221029

三峡水库蓄水前后库区及周边区域降水变化及其影响因素

胡艳茹,1,2, 梁丽娇1, 何立平1, 许文锋1, 刘正学1, 兰波,1

1.重庆三峡学院环境与化学工程学院，重庆 404100

2.南京师范大学环境学院，南京 210023

Precipitation change and its influencing factors in the Three Gorges Reservoir area and its surrounding areas before and after impoundment

HU Yanru,1,2, LIANG Lijiao1, HE Liping1, XU Wenfeng1, LIU Zhengxue1, LAN Bo,1

1. College of Environmental and Chemical Engineering, Chongqing Three Gorges University, Chongqing 404100, China

2. School of Environment, Nanjing Normal University, Nanjing 210023, China

通讯作者: 兰波（1984-），男，四川遂宁人，博士，副教授，硕士生导师，主要从事环境演变和生态学方面的研究。E-mail: boblan2004@163.com

收稿日期: 2022-09-27   接受日期: 2023-03-6  

基金资助: 国家自然科学基金青年科学基金项目(41902024)
重庆市科委面上项目(cstc2019jcyj-msxmX0656)
重庆三峡学院校人才引进项目(17RC08)
重庆重点实验室开放基金项目(WEPKL2019ZD-02)
三峡库区可持续发展研究中心开放基金(18sxxyjd12)

Received: 2022-09-27   Accepted: 2023-03-6  

作者简介 About authors

胡艳茹（2000-），女，重庆江津人，硕士研究生，主要参与三峡库区水环境演变研究。E-mail: 2540845925@qq.com

摘要

本文利用三峡库区及周边地区共21个气象站点1955—2019年的器测气候资料，结合西南地区气候驱动因子（北极涛动指数AO、北大西洋涛动指数NAO、西南季风指数SWM、东南季风指数SEM、暖季均温WST）系统分析研究区65a来的降水特征及影响因素，对比了三峡水库2003年蓄水前后局地降水的变化并评估三峡水库修建对降水的影响。结果表明：SWM主要影响研究区夏秋季降水；SEM和WST主要影响研究区夏季降水；AO对研究区降水的影响集中在冷季；NAO对研究区降水的影响不显著。SWM、SEM、WST与降水存在较显著的1~4a短共振周期，而NAO与AO以7~11a的共振周期为主。库区西北部的年降水量在2003年存在突变现象，腹地和南部地区的年降水量无此突变，而前述气候驱动因子在蓄水当年均无突变，表明气候驱动因子不是导致近库区降水突变的原因。三峡库区蓄水可能直接或间接导致近库区西北部的年降水量明显增加，主要是2月、5—7月降水显著增加（P<0.05），3月、8—10月不显著增加（P>0.05），其原因可能是升温引起的更多的局地蒸发水汽被季风转移至西北部，导致西北部的降水量增加。水库蓄水后，腹地在8月、10月、12月降水量显著减少（P<0.05），南部在8月降水量显著减少（P<0.05），其可能的原因是水库蓄水后缓冲昼夜温差，增加水面大气下沉，抑制水面对流活动，从而抑制研究区腹地和南部形成降水。基于气候驱动因子和月降水的多元线性回归模型表明：三峡水库蓄水后，西北部的年降水比无水库的情形显著增加13.2%（P<0.05）；腹地、南部的年降水比无水库的情形分别增加0.1%和减少1.5%，但均不显著（P>0.05）。总的来说，三峡水库蓄水对降水存在局地效应，其长期影响还需要进一步评估。

关键词： 三峡库区; 降水量; 气候驱动因子; Mann-Kendall检验; 降水突变

Abstract

Based on the instrumental data of 21 meteorological stations in the Three Gorges Reservoir (TGR) area and its surrounding areas from 1955 to 2019, this paper systematically analyzes the characteristics of rainfall variation and the associated driving factors, further compares the precipitation variation before and after impounding of TGR in 2003, and evaluates the impacts of the TGR on local precipitation. The driving factors include Arctic Oscillation (AO), North Atlantic Oscillation (NAO), southwest monsoon (SWM), southeast monsoon (SEM) and warm season temperature (WST). Based on redundancy analysis, some conclusions can be drawn as follows. SWM mainly affected the precipitation in summer and autumn, SEM and WST mainly affected the precipitation in summer, AO may impose effects on precipitation in the cold season, and the effect of NAO on precipitation was not significant in the study area. 1~4a frequency bands are observed in Wavelet Transform Coherence spectrum between driving factors (e.g., SWM, SEM, WST) and precipitation, 7~11a frequency bands are characterized between driving factors (e.g., NAO, AO) and precipitation. Since the TGR impounding in 2003, an abrupt change of the AP was observed in the northwest part of the TGR area in 2003, the above climate driving factors had no such corresponding abrupt change after impoundment, implying that the TGR impounding may cause a significant increase of AP in the northwest part directly or indirectly. This may be due to the fact that more locally evaporated water vapor caused by warming was shifted to the northwest by the monsoon, resulting in increased rainfall in the northwest. The precipitation of hinterland in August, October and December decreased significantly (P<0.05) and the same as that of the southern part in August (P<0.05). The reason may be that the reservoir buffered the temperature difference between day and night after impoundment, which increased the atmospheric subsidence over the water surface and restrained the convective activity over the water surface, ultimately restraining the precipitation in the hinterland and south of the study area. The multiple linear regression model based on driving factors and monthly precipitation showed that compared with the simulated situation without the TGR, the AP in the northwest significantly increased by 13.2% (P<0.05), the AP in the hinterland and south increased by 0.1% and decreased by 1.5%, but they were not significant (P>0.05). Genarally speaking, the impoundment of the TGR has a local effect on precipitation, but its long-term impact of the impoundment of the TGR on local precipitation needs to be further evaluated.

Keywords： Three Gorges Reservoir area; precipitation; climate driving factors; Mann-Kendall test; abrupt change of precipitation

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本文引用格式

胡艳茹, 梁丽娇, 何立平, 许文锋, 刘正学, 兰波. 三峡水库蓄水前后库区及周边区域降水变化及其影响因素[J]. 地理研究, 2023, 42(7): 1921-1940 doi:10.11821/dlyj020221029

HU Yanru, LIANG Lijiao, HE Liping, XU Wenfeng, LIU Zhengxue, LAN Bo. Precipitation change and its influencing factors in the Three Gorges Reservoir area and its surrounding areas before and after impoundment[J]. Geographical Research, 2023, 42(7): 1921-1940 doi:10.11821/dlyj020221029

1 引言

全球气候变化和大型水库均可能对局地降水产生影响，相关的科学问题是最近十几年的研究焦点。局地气候变化主要受大气环流变化影响[1,2]。在全球气候变暖的背景下，增温以及大气环流指数变化对局地降水的影响依然存在不确定性[3⇓-5]。研究表明[6]，全球平均温度与全球极端降水量的变化成正比，即温度每升高1℃，极端降水的中等强度情形增加5.9%~7.7%。在中国西南地区，由快速升温引起的区域饱和水汽压快速增加，导致该地区小、中降水事件大幅减少而极端降水事件增加[7]。增温影响地表下垫面的蒸散发速率，亦是局地降水的重要水汽来源，重庆夏季近源局地蒸发水汽占比高达55.07%[8]。中国西南地区的降水主要出现在暖季（5—9月）[9]，由此可以推定暖季温度变化（Warm Season Temperature，WST）对局地降水可能产生重要影响。另一方面，大气环流指数变化对西南地区不同季节的降水产生了不同的影响。例如对于冷季降水，冬春季（1—3月）北极涛动（Arctic Oscillation，AO）影响了中国中西部地区低层湿度场分布，冬春季AO相位变化和中国西南地区同期冬季降水距平具有一致性，其活动增强时，会造成西南地区降水增加[10,11]。冬季北大西洋涛动（North Atlantic Oscillation，NAO）与中国西南地区冬季（12—次年2月）降水存在正相关关系，且这种关系在冬季NAO负相位时更为显著[12,13]。对于暖季降水，影响中国西南地区夏季降水的大气环流因素主要是西南季风（Southwest Monsoon，SWM）和东南季风（Southeast Monsoon，SEM），二者总共贡献了>60%的夏季降水水汽[14]，其强弱对西南地区暖季降水有着极其重要的影响[8,14,15]。综上所述，上述的气候驱动因子均可能对中国西南地区局地降水产生不同程度的影响，其综合效果还有待进一步研究与验证。

大型水库通过湖泊效应可能会对局地气候产生一定的调节作用[16]。这是由于大型水库显著改变了下垫面类型，从而影响地表水汽的蒸散发速率。水库的存在会降低下垫面温度，减少气流的上升运动，增加低层水汽辐散从而导致降水减少；同时也会减少云量，增强净辐射量，下垫面升温，引起更多的水汽蒸发导致降水增加[17]。可见，大型水库对周边地区降水的影响效果是前述情况的综合结果，因地而异且影响范围和结果多样化。例如，林浩等[18]认为大型水库明显影响区域气候，总库容在10亿 m3以上，水面面积超过100 km2的大型水库的气候影响区域的面积大致同水库水面面积相等或略大，平均距离从几公里到数十公里。如安康水库蓄水后，除冬季外的其他各季、年、主汛期降水量都比蓄水前有所减少，且秋季、年、主汛期蓄水前后差异显著[19]。小浪底水库蓄水后，库区降水的下降趋势较蓄水前明显减弱，蓄水后库区内年降水量略有增加，而周边降水量略有减少[20]。基于数值模拟的结果显示：美国佛森大坝修建后，大坝下游地区的最大降水量相比于建坝前有所增加，而上游地区的最大降水量则有所减少[21]。也有研究表明，大型水库修建对局地降水几乎没有影响[22]。总而言之，水库蓄水后对区域降水的影响亦存在不确定性。目前的研究结果倾向性认为大型水利工程会改变周围地区的小气候，尤其在水库蓄水后对其局部气候影响较大[23]。

作为世界最大的河道型水库，三峡水库自2003年投入使用至今，库区水位由66 m上升至175 m，水域面积扩大至1084 km2[24]。目前，研究表明三峡水库蓄水后对库区及周边区域降水特征存在多方面的影响。有的观点认为三峡水库蓄水前后年降水量无明显变化，对气候仅存局地影响，范围不超过20 km[17,25]。另有观点与之相悖，即三峡水库的水域面积超过1000 km2，对区域气候的影响是区域尺度（100 km）而非局地尺度（10 km），三峡水库蓄水后导致库区以北的大巴山-秦岭区域降水增加而三峡库区降水减少[26]。还有观点认为三峡水库蓄水后影响区域降水量周期性，蓄水引起库区降水量的2a、4a的次长周期效应加强，蓄水初期库区降水1a的主周期性减弱，大坝附近区域降水的多周期性特征明显[27]。另外，基于不同的数据源得出的结论可能也不一致，例如三峡水库蓄水前后，基于器测数据的区域极端降水差异显著，且水库蓄水对远库区的影响要大于近库区；而基于热带测雨卫星的栅格数据则表明三峡水库修建前后对区域的极端降水无影响[24]。总之，目前关于三峡水库蓄水后对周边地区降水的影响研究尚无统一结论。

现有对特定区域降水特征的研究仅关注了年际和季节性降水量，而未提及降水变率特征。降水变率是表征特定时间内降水量变异性的重要指标，能够很好地反映降水的时间异质性[28]。为更好的总结三峡库区1955—2019年的器测降水特征，本研究首次计算并纳入降水变率对三峡库区及周边地区分区讨论以凸显降水的空间异质性，结合典型的序列分析方法对比三峡库区降水与气候驱动因子变化特征，探讨三峡水库蓄水前后局地降水变化及其可能原因，有助于深入理解大型水利工程蓄水对局地气候的影响，为保护长江中下游水生态安全提供参考依据。

2 研究区概况

三峡大坝是世界上最大的水利枢纽工程，同时是治理开发长江流域的关键性骨干工程，其具有防洪、发电以及航运等多方面功能。三峡大坝始建于1993年，于2003年蓄水位至139 m后开始发电，2009年蓄水位达到175 m，三峡库区总库容达393亿 m3，全长约600 km，平均宽度为1.1 km，总面积达1084 km2[24]。三峡库区的范围大约在29°16′N~31°25′N、106°50′E~110°51′ E之间，库区位于四川盆地和长江中下游平原的结合部，跨越鄂中山区峡谷及川东岭谷地带，西起重庆江津，东至湖北宜昌，包括重庆市22个县（区）以及湖北省的4个县，总面积约5.8万km2。三峡库区属于亚热带湿润季风气候，气候表现为冬短夏长，降水充沛但季节分配不均，具冬干、夏雨、伏旱、秋淋的特征[29]。库区地处于大巴山褶皱带、川东平行岭谷和川鄂湘黔隆起褶皱带交汇处，背倚大巴山，南抵云贵高原[30]。库区内地形较为复杂，奉节以东属川东鄂西山地，奉节以西属川东平行峡谷低山丘陵区，山高坡陡，高差悬殊，河谷深切。河谷平坝约占库区总面积的4.3%，丘陵占21.7%，山地占74%。库区内物种资源丰富，主要植被类型为常绿阔叶林、落叶阔叶混交林、落叶阔叶林与常绿针叶混叶林、针叶林和灌草丛等[31]。研究区地理位置、气象站点分布及三峡水库相对水位、水域面积情况[32]18,25见图1。

图1

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图1   研究区地理位置、气象站点分布及三峡水库相对水位、水域面积情况

Fig. 1   Location of the study area and distribution of weather stations, water level and water area of the TGR


3 研究方法

3.1 数据来源

本研究资料来源于国家气象科学数据中心（https://data.cma.cn），收集1955—2019年三峡库区以及周边地区21个气象站点的降水和温度数据，计算得出不同时间尺度下的降水量和降水变率、各月温度T月均值以及暖季均温WST。

北极涛动指数AO、北大西洋涛动指数NAO均来自美国海洋和大气管理局（NOAA）（https://www.noaa.gov/），且均为标准化指数；季风指数（西南季风指数SWM和东南季风指数SEM）采用风场资料与向外长波辐射资料（OLR）整合得出（计算公式见后文），风场资料和向外长波辐射资料（OLR）来源于NCEP再分析资料，空间分辨率均为2.5°×2.5°。

3.2 降水分区

区域降水的表征中，降水量的大小和变率都至关重要。本研究引入香农熵（SE）（即香农多样性指数）对降水量转换计算，后得到标准化变异系数（SVI），将SVI应用于17个时间序列（12个月时间序列，4个季节性时间序列和1个年度时间序列）来评估各时间尺度上的年际（内）变化。香农熵（SE）代表了随机变量的平均信息量，可用于计算数据分布的可变性，在信息学、生态学等领域有极广泛的运用[33⇓-35]。同样的，它也被用于降水序列的变异分析[36]。因此，基于香农熵（SE）的指标SVI能表征特定时间段降水序列变异大小，其取值范围为0~1，0表示无变异性，1表示高变异性[28]。根据年、季和月降水量时间序列得到的SVI，有助于了解年际内不同时间尺度降水量的波动情况。将多个时间尺度上的平均降水量和相应的SVI值以及经纬度、海拔数据按lg（100x+1）转换以消除量纲和极端值的影响后，用主成分分析（PCA），得到本研究区降水的空间聚类结果。

具体计算公式如下：

SE(X)=-∑i=1nxiXlog2xiX
SVI=SEmax-SESEmax

式中：n表示降水时间序列的年数，本研究中n=65；xi为各气象站点的降水数据，如月降水、季降水、年降水；X为对应的降水量之和；SEmax是已知序列的理论最大值。

3.3 季风指数

对于季风指数的确定，有许多学者提出不同的方法[37]。本文运用梁建茵等[38]提出的使用西南风分量与OLR相结合的方法定义西南季风指数ISWM，计算方法如下：

ISWM=(VSW-1.0)/a+(235-VOLR)/b

同理定义东南季风指数ISEM：

ISEM=(VSE-1.0)/a+(235-VOLR)/b

式中：VSW和VSE分别为西南季风、东南季风的月平均风速（m/s）；VOLR为向外长波辐射月平均值（OLR,W/m2）；a和b均为常数，a=1m/s，b=10W/m2。由于OLR资料时间范围仅为1974年6月至今，因此季风指数时间范围为1975年1月—2019年12月。

3.4 统计方法

本研究主要采用单因素方差分析法、配对样本t检验、Mann-Kendall趋势及突变检验法（M-K检验）、滑动t检验法、Kendall′s tau-b非参数相关性分析法[39]、冗余分析（ReDundancy Analysis，RDA分析）、小波相干变化（Wavelet Transform Coherence, WTC）、多元线性回归分析等统计方法对三峡库区及周边地区的降水量特征进行分析。单因素方差分析法和配对样本t检验用于检验区域降水参数的显著性。M-K检验常用于判断气候序列中是否存在气候突变并确定突变发生的时间节点[40]。在M-K检验中，需将时间序列标准化得到UF曲线，与经转化的逆时间序列标准化得到的UB曲线一起作图，若UF曲线与UB曲线在置信区间之内相交且交点唯一时，说明时间序列在该点存在突变；若交点不唯一时，则用滑动t检验法逐点验证其突变的显著性。滑动t检验法的统计量t与显著性水平为α的临界值tα相比较，若t≥tα，即拒绝原假设，临界点前后两个子序列的均值存在显著差异，该点认定为突变点；若t<tα，不能拒绝原假设，两子序列的均值不存在显著差异，认定该临界点非突变点[41,42]。Kendall′s tau-b非参数相关性分析法主要用于分析区域降水序列和气候驱动因子的秩相关性。RDA分析用于量化各气候驱动因子对研究区降水量及降水SVI的贡献。WTC是表征时频空间中两个时间序列共同变化的方法，通过两个序列频带共存的时间间隔识别两者可能的关系[43,44]。多元线性回归分析用于模拟三峡水库蓄水前后研究区的理论降水量，与实测降水量进行对比，从而量化水库蓄水对研究区降水的影响。多元线性回归的预测结果采用平均绝对百分比误差（MAPE）评估[45]，MAPE被广泛应用于评估模型预测的准确性[46,47]。当MAPE≤10%时模拟效果为优，10%≤MAPE≤20%为良好，20%≤MAPE≤50%为合理，MAPE≥50%为不合理[48]。其计算方法如下：

MAPE=1n∑i=1n|xi-yixi|×100%

式中：xi，yi，n分别为实测值、预测值、样本量。

单因素方差分析、配对样本t检验、Kendall′s tau-b非参数相关性分析、多元线性回归在SPSS 22中完成，M-K检验、滑动t检验以及WTC在Matlab 2020b完成，PCA和RDA分析在软件Canoco 5.0中完成，作图在ArcMap 10和Origin 2017pro中完成。

3.5 基于气候驱动因子的降水重建

考虑到气候驱动因子（WST、SWM、SEM、AO、NAO）对降水的影响存在季节性特征，本研究选取各区域月降水以及与区域月降水关系显著的气候驱动因子，按lg（100x+1）转换上述指标的数据矩阵以消除量纲和极端值的影响，应用RDA分析将月降水和气候驱动因子的多元对应关系降维处理，在二维排序图上直观呈现月降水参数和气候驱动因子的关系，同时得到气候驱动因子对RDA排序轴的解释度和显著性。采用与RDA排序轴具显著关系（P<0.05）的气候驱动因子作自变量，以三峡水库2003年蓄水前的年降水量作因变量构建线性回归方程，并基于回归方程计算2003年以后的理论年降水（即无三峡水库情形下的理论降水）。经配对样本t检验验证水库蓄水后理论年降水与实测降水的显著性差异以评估非气侯影响因素（如水库效应）对降水的影响。

4 结果与讨论

4.1 降水分区

基于PCA结果，可将研究区大致分为3个区域：西北部地区、腹地地区以及南部地区（图2）。西北部地区包括安康、达县、高坪区、巴中、遂宁5个站点；腹地地区包括荆州、宜昌、巴东、奉节、万州5个站点；南部地区包括恩施、来凤、铜仁、酉阳、思南、彭水、湄潭、桐梓、沙坪坝、江津、宜宾11个站点。

图2

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图2   基于降水量、降水SVI、地理空间参数的PCA聚类结果

Fig. 2   PCA results based on precipitation amount, SVI of precipitation and geospatial parameters for spatial cluster of weather stations


4.2 区域降水特征

4.2.1 月降水

结合各区域多年月平均降水量及月时间尺度下的变异性（SVI）分析得出（图3），各区域月平均降水量特征：均为夏多冬少、南部>腹地>西北部。西北部与南部在多个月份（1—6月、9月、11—12月）均存在显著性差异（P<0.05），西北部与腹地在2—4月以及11月的降水量存在显著性差异，而腹地与南部仅在6月和12月存在显著性差异。三个区域在7—10月的差异不大，表明随季风深入各区域形成稳定强降水。各区域在月时间尺度下的变率特征：1月、2月、12月大，3—9月变率小；西北部>腹地>南部。不难看出，3—9月降水量主导全年降水，1月、2月、12月降水量恰为全年最小，即降水量多的月份对该地区降水量变率影响不大，而降水量较少的月份对该地区降水量变率影响较大。

图3

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图3   各区域多年月平均降水量及对应的月降水SVI月值

Fig. 3   Multi-year averaged monthly mean precipitation and its SVI in each region


4.2.2 年降水

各区域年降水量大多超过1000 mm（图4，见第1928页），南部地区年降水量多年平均最高（1201.9±126.6 mm），腹地次之（1115.4±155.1 mm），西北部最少（1019.7±143.3 mm）。在过去65a中，西北部年均降水量超过1000 mm的有37a，腹地有53a，南部地区最多为61a。各区域的降水量年际变化较大，西北部1956年为历年最多，为1300.4 mm，1997年最少，为695.1 mm；腹地1983年最多，为1522.7 mm，1966年最少，为756.0 mm；南部2016年最多，为1456.7 mm，2011年最少，为912.3 mm。西北部降水量在1985年以前上升趋势较为平缓，1985—2000年显著减少，其后呈增加趋势但增势平缓；腹地地区的年降水量持续在均值附近波动，总体变化趋势不大；南部地区年降水量在1980年以前呈增加趋势，随后年降水量波动变化，在2010年后降水呈明显增加的趋势。

图4

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图4   各区域逐年降水特征及年降水突变情况

Fig. 4   The characteristics of annual rainfall and the abrupt change of annual precipitation in each region


为探究1955—2019年研究区各区域降水量是否存在突变现象，经M-K检验及10年滑动t检验得知：尽管库区西北部存在UF和UB的多个交点（即潜在的M-K突变点），但是仅有1987年、2003年的降水突变点通过了显著性水平检验（P<0.05），即认为西北部降水在1987年、2003年存在突变，其中，西北部2003年的降水突变是否与三峡水库蓄水有关需进一步探究。同理，腹地年降水未通过显著性水平检验（P>0.05），认为其降水不存在突变点；南部仅在1994年存在一个降水突变点。

4.3 气候驱动因子对降水的贡献

为研究气候驱动因子对研究区降水的贡献，本文分析了气候驱动因子在不同时间尺度下与降水量的相关性（表1，见第1929页）。可见，AO和NAO指数主要影响冷季降水，均呈显著正相关（P<0.05）；SWM、SEM与降水极显著正相关而T月均值与研究区暖季降水极显著负相关（P<0.01），这3个驱动因子对研究区的南部地区相关性最为显著，与武茜茜等[8]的观点一致。

表1   各区域不同时间尺度下降水与驱动因子的Kendall′s tau-b相关性

Tab. 1  Kendall′s tau-b correlation between precipitation and driving factors at different time scales in each region

地区月份AONAOSWMSEMT月均值（℃）
西北部











10.178*0.175*0.103-0.014-0.114
20.189*0.1320.001-0.0070.017
30.169*0.066-0.0360.091-0.055
40.151-0.0110.012-0.1030.116
50.1530.135-0.041-0.1550.170*
6-0.001-0.1500.174-0.0570.068
70.002-0.0480.210*0.121-0.092
8-0.007-0.1530.291**0.156-0.487***
90.0640.0770.319**-0.044-0.138
10-0.029-0.0160.152-0.242*0.146
110.194*0.194*0.154-0.125-0.039
120.0130.0140.133-0.143-0.041
年均值0.028-0.0250.091-0.057-0.115
腹地











1
0.215*0.231**0.172-0.036-0.089
20.231**0.210*0.158-0.1390.207*
30.1220.0120.0830.0400.044
40.123-0.0260.111-0.1410.130
50.1540.1570.053-0.051-0.014
6-0.009-0.0630.228*0.0100.012
7-0.010-0.0090.271**0.291**-0.313***
80.012-0.1560.426***0.242*-0.441***
90.0910.0290.267**0.097-0.233**
100.1350.1560.396***-0.0180.078
110.0360.1140.303**-0.1480.022
120.014-0.0340.416***-0.1760.032
年均值0.0630.0270.0970.161-0.087
南部











10.256**0.196*0.0850.141-0.081
20.207*0.175*0.014-0.0240.044
30.1380.002-0.261*0.0080.122
40.122-0.0930.073-0.0060.041
50.1000.0880.1680.097-0.151
60.0850.0310.335***0.323**-0.145
7-0.054-0.1020.388***0.352***-0.442***
8-0.014-0.1300.347***0.366***-0.479***
90.0090.0660.380***0.295**-0.363***
100.1570.185*0.301**0.0480.155
110.1050.0440.225*0.074-0.048
120.032-0.0780.379***0.013-0.013
年均值-0.012-0.0240.252*0.264*-0.166

注：*** P<0.001；** P<0.01；* P<0.05。

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为进一步量化各气候驱动因子对研究区降水量及降水SVI的贡献，基于各气候驱动因子对各区域降水的相关性（表1，见第1929页）对其进行RDA分析。考虑到气候驱动因子的季节影响特征，基于表1结果，AO、NAO为1、2、12月指数均值，SWM、SEM为6—9月指数均值，WST为5—9月指数均值。结果显示，前两个轴解释了25.92%的变异，第一轴解释了20.52%的变异，第二轴解释了5.40%的变异（图5，见第1930页）。各驱动因子对研究区降水的贡献由大到小依次为SWM、WST、AO、SEM和NAO（表2，见第1930页）。SWM与WST对研究区降水的贡献最大，其对轴的解释度分别达到了14.4%和4.3%。这说明研究区的降水主要受SWM、WST的影响，其次受AO和SEM的影响，NAO的贡献不显著。

图5

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图5   气候驱动因子对研究区降水量及降水SVI的贡献

注：图中SVI1~ SVI12为标准化变异系数（下角标1~12为月份）； P1~ P 12为月平均降雨量（下角标1~12为月份）。

Fig. 5   Contribution of climate driving factors to precipitation amount and its SVI in the study area


表2   基于RDA的气候驱动因子贡献统计汇总

Tab. 2  Summary information for the contribution of climatic driving factors based on RDA results

解释度（%）贡献值（%）P
SWM14.450.10.002
WST4.315.10.036
AO3.913.50.046
SEM3.813.40.040
NAO2.38.00.306

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4.4 气候驱动因子突变

库区西北部在1987年、2003年存在降水突变点，库区南部在1994年存在一个降水突变点，而腹地地区年降水无突变点。上述的降水突变是否是气候驱动因子引起的，可以根据气候驱动因子是否具有相同的突变点进行推测，即降水和气候驱动因子若突变的时间节点一致，则表明降水的变异很可能是气候驱动因子引起的；反之，若突变的时间节点不一致，这说明降水可能是由其他因素引起的。经检验发现AO、NAO、SWM和SEM在1955—2019年间不存在突变年，WST在2004年存在突变现象（图6，见第1931页），即WST在2004年后增加但相对于2004年之前无显著性差异。需要指出的是，WST的M-K检验UF和UB有唯一交点，可忽略滑动t检验而认定为真实突变点[41]20。通过分析气候驱动因子（AO、NAO、SWM、SEM、WST）在水库蓄水（2003年）前后的显著性差异，发现上述气候驱动因子在水库蓄水前后不存在显著性差异（P>0.05）。因此，可初步判定西北部在水库蓄水后降水量的突变并非由气候驱动因子引起。

图6

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图6   气候驱动因子年际变化及其M-K检验、滑动t检验

Fig. 6   Interannual variations of climatic driving factors and the associated abrupt changes based on M-K test and moving t-test


4.5 降水与气候驱动因子的周期关系

小波相干谱（WTC谱）基于时频谱分析研究区的年平均降水与气候驱动因子两组时间序列的共振周期及滞后关系，用于代表周期性“变化趋势”的一致性[44]。锥形实线外侧因受边际效应影响而导致不确定性增加[49]，后文不再讨论其外侧区域。图7（见第1932页）显示：

图7

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图7   驱动因子与年降水的小波相干(WTC)谱

注：图中右（左）向箭头为正（负）相关，上（下）箭头为降水滞后（超前）于驱动因子；锥形实线内的闭合曲线表示超过95%置信检验水平。

Fig. 7   WTC spectra based on driving factors and annual precipitation


（1）AO与三个区域在1970—1990年期间存在显著的7~11a的周期性。此外，AO与西北部年降水存在3~5a（1986—1993年），与腹地年降水存在16~20a（1967—1990年）的共振周期。

（2）NAO与西北部年降水的周期相关性最强，离散分布如下共振周期：1~2a（1983—1988年）、2~4a（2010—2015年）、4~6a（1984—1995年）、7~11a（1970—1990年）。NAO与腹地存在8~11a（1970—1990年）的周期性，与南部的年降水周期相关性弱。

（3）SWM与研究区年降水呈显著的正相关性，其中SWM与西北部年降水存在1~2a（1979—1982年、1999—2005年）、3~7a（1994—2012年）的共振周期。SWM与腹地年降水显著存在3个共振周期，分别为1~2a（1978—1983年、1991—1995年）、3~5a（1981—1990年、1995—2005年）、7~10a（1984—1990年、2002—2006年）。SWM与南部年降水在存在1~2a（1978—1981年、1994—1996年）、3~4a（1995—2006年、2007—2013年）以及6a（1984—1988年）的共振周期。

（4）SEM与研究区年降水显著正相关，其中与西北部年降水显著性周期较其他区域长，存在4~7a（1988—2000年）的共振周期；与腹地、南部年降水存在显著的1~2a短周期相关性，且与腹地的周期性更明显。

（5）WST与三个区域年降水显著负相关，均存在1~4a的共性共振短周期。需要注意的是，2003年前后，西北部年降水1a的周期滞后于WST但未通过95%显著水平，表明二者无显著的时滞相关性。而WST在2004年的突变点虽与西北部在2003年的突变点临近，但二者无显著1a周期的时滞相关性，表明西北部2003年降水的突变并非由WST引起而很可能是由其他因素引起的。

总的来说，SWM、SEM、WST与年降水存在较显著的1~4a短共振周期，而NAO与AO类似，与年降水的共振周期以7~11a为主，且NAO对南部年降水影响较弱。综合前述研究区降水和气候驱动因子突变情况，可判定库区西北部于蓄水后的降水突变并非由气候驱动因子引起。

4.6 蓄水前后降水差异

由研究区各区域蓄水前后（2003年）的月平均降水量对比（图8）可知：西北部蓄水后在2月及5—7月降水显著增加（P<0.05），3月、8—10月降水不显著增加（P>0.05）；腹地在8月、10月、12月的降水显著减少（P<0.05），3月、5—7月不显著减少（P>0.05）；南部地区仅8月降水显著减少（P<0.05），4—7月、10—12月不显著减少（P>0.05）。考虑到气候驱动因子在水库蓄水前后不存在显著性差异（P>0.05），因此推测三峡库区蓄水后对库区周边降水可能有直接或间接的影响，主要表现为：库区西北部暖季降水增加，而库区腹地和南部降水存在一定程度的减少。

图8

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图8   各区域蓄水前后平均月降水量变化

注：*表示蓄水前后具有显著性差异（P<0.05）。

Fig. 8   Variation of the month-averaged precipitation before and after reservoir impounding in each region


为量化水库蓄水对研究区降水的影响，基于气候驱动因子（AO、SWM、SEM、WST）和月降水构建的多元线性回归曲线（表3），以研究各区域蓄水前后降水量变化并用MAPE评估拟合效果。西北部回归方程方程预测模型MAPE=11.46%，腹地回归方程预测模型MAPE=10.30%，模拟效果均达到良好；南部回归方程预测模型MAPE=8.07%，模拟效果优异。结果表明：三峡水库蓄水后，西北部的年降水显著增加（P<0.05），比无水库的情形增加13.2%；腹地年降水仅比无水库的情形增加0.1%，但无显著差异（P>0.05）；南部年降水比无水库情形减少1.5%（P>0.05）。综上，三峡水库蓄水后库区西北部年降水显著增加，而腹地及南部地区无明显变化（图9）。

表3   各区域降水的多元线性回归曲线

Tab. 3  Multiple linear regression curve of precipitation in each region

多元线性回归方程Sig.R2FMAPE（%）
西北部y=-7.026x1+71.006x2-30.520x3-133.991x4+4173.5590.0880.2982.32911.46
腹地y=9.573x1+54.892x2+70.162x3-57.042x4+2641.2530.0720.3132.50310.30
南部y=-2.048x1+52.975x2+68.659x3-12.702x4+1637.2220.0300.3733.2768.07

注：x1、x2、x3、x4分别代表1月、2月、12月AO均值、6—9月SWM均值、6—9月SEM均值、5—9月WST均值。

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图9

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图9   各区域蓄水前后实测年降水与理论降水比对

Fig. 9   Theoretical and measured annual precipitation before and after impoundment in each region


4.7 水库蓄水影响降水的可能机理

总的来说，三峡库区及周边地区的降水变化是由海洋、陆地、自循环综合作用的结果[50]，而大范围的气候驱动因素在蓄水前后无显著变化，表明自循环（如水库引起的局地水汽循环）可能是引起研究区降水变化的重要因素。由于水文循环的复杂性，目前已知方法无法真正完成单要素分析[51]，因此三峡水库蓄水前后的降水变化机理仍需大量推测。

Wen等[52]利用WRF-CLM模型发现鄂陵湖和扎陵湖会促进湖面低层水平辐合和上升气流，并提供了能量和水汽，有利于湖区和附近地区7—10月的降水。三峡水库蓄水后，坝前水位较建坝前抬升60 m以上，汛期（7—9月）水域面积由2002年的526.5 km2增至2003年的610.6 km2[32]18。从2001年至2015年，三峡水库表面积由884.4 km2增至1106.2 km2[53]，其蒸散量也相应增加[54,55]。崔豪[56,57]的研究表明：三峡库区流域蒸发量与同期温度和降水均呈正相关关系，三峡水库蓄水后年平均实际蒸散发量及植被蒸腾量、植被蒸发量较蓄水前有所增加且夏季最高，其主要影响因素是气温。本研究中，WST在2004年后存在正向突变，据此推测WST的升高能一定程度促进夏季降水。李强等[58]通过加入水体的敏感性试验对三峡地区的下垫面效应进行研究，发现局地水体下垫面能通过增加地表热通量和水汽输送对降水起促进作用。根据美国佛森大坝蓄水前后降水的差异及影响机理[59]类比推理得出：研究区暖季主盛行西南季风和东南季风，在三峡水库蓄水后暖季季风强度未明显变化的背景条件下（图6），相对稳定的季风向将库区蒸发的水汽带至下风区（西北部），从而利于西北部降水增加。

黄亚[60]模拟了三峡库区水体面积为960 km2（方案1，与现今情形较接近）和4800 km2（方案2）时对库区降水的影响，发现两种模拟方案均出现库区各季节降水及年降水减少的现象，其中方案1的库区年降水仅减少约7%，方案2的减少22%，这说明三峡水库的局地气候效应存在且受水体面积的影响。局地尺度的环流受下垫面变化影响，会再分配局地水汽，但方案1中近地表的白天水汽辐散和夜间水汽辐合仅十分微弱地增强，换言之，三峡水库蓄水未显著改变库区水面垂向散度场格局。此外，水库蓄水后水面积的增加缓冲昼夜温差，气层稳定，抑制水平和垂向对流活动，导致对流降水减少。综上，这可能是腹地和库区南部地区在月降水不显著变化的原因。

本研究数据得出驱动因子与降水变率SVI之间的相关性不显著（P>0.05，未列出），猜测可能是由季风频率[61]引起的。未来该区域的降水特征及影响机理还有待更全面的观测、更长时间尺度的验证及更深层次的探究。

5 结论

本研究分析了三峡库区不同时间尺度下的降水特征，结合气候驱动因子，评估了三峡水库蓄水对降水的影响，得到如下结论：

（1）研究区降水夏多冬少，月份、年际的降水南部>腹地>西北部；降水多的月份降水SVI低，降水少的月份降水SVI高。西北部降水在三峡水库蓄水当年（2003年）存在突变，腹地和南部均无2003年的突变。

（2）气候驱动因子在不同时间尺度下显著影响研究区降水，AO与研究区冷季降水正相关，SWM主要影响研究区夏、秋季的降水，SEM和WST主要影响研究区夏季降水，WST与降水呈显著的负相关关系。WTC结果表明，SWM、SEM、WST与降水存在较显著的1~4a短共振周期，而NAO、AO与降水以7~11a的共振周期为主。基于气候驱动因子、降水量及降水SVI的RDA第一轴解释了20.52%的变异，第二轴解释了5.40%的变异。驱动因子对降水特征轴的贡献由大到小依次为SWM（14.4%）、WST（4.3%）、AO（3.9%）、SEM（3.8%），NAO的贡献不显著。上述气候驱动因子在水库蓄水前后（2003年）无显著性差异（P>0.05），表明西北部降水2003年的突变可能与三峡水库蓄水有关。

（3）三峡水库蓄水后，库区西北部2月、5—7月平均降水显著增加（P<0.05），3月、8—10月不显著增加（P>0.05）；库区腹地8月、10月、12月的降水显著减少（P<0.05）；南部8月降水量显著减少（P<0.05）。蓄水后，相对于无水库情形，西北部的年降水显著增加13.2%（P<0.05）；腹地年降水增加0.1%、南部年降水减少1.5%，但二者变化不显著（P>0.05）。表明三峡水库蓄水后可能导致库区西北部降水增加，而对库区腹地及南部影响较小。

（4）三峡水库蓄水后对周边地区降水的可能影响机理如下：西北部降水量的增加可能是由于三峡水库蓄水后，由于水体面积以及WST的增加，导致蒸散量相应增加。研究区相对稳定的风向将蒸发水汽带至向下风区（西北部），利于西北部降水增加。三峡水库蓄水几乎对库区水面垂向散度场格局无影响，但蓄水引起的水面积的增加缓冲昼夜温差，气层稳定，抑制对流，减少对流降水，可能是腹地和南部地区在月降水不显著变化的原因。未来该区域的降水特征及影响机理还需深入探究。

致谢

衷心感谢匿名评审专家在论文评审中所付出的时间和精力，评审专家对文章逻辑、结果验证、文字表述等方面的修改意见，使本文受益匪浅。

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Acta Geographica Sinica, 2011, 66(9): 1179-1190.]. DOI: 10.11821/xb201109003.

[本文引用: 1]

Based on the daily data of temperature and precipitation of 108 meteorological stations in Southwest China from 1960 to 2009, we calculated the surface humid indexes of months and years, as well as the extreme drought frequency. According to the data, the temporal and spatial characteristics of the extreme drought frequency in inter-annual, inter-decadal, summer monsoon and winter monsoon has been analyzed. The results are indicated as follows: (1) In general, the southwest of Sichuan Basin, the southern part of Hengduan Mountains, southern coast of Guangxi and north of Guizhou are the areas in which the extreme drought frequency has significantly increased in the past 50 years. For the decadal change, from the 1960s to the 1980s the extreme drought frequency has presented a decreasing trend, and the high frequency area appeared alternately in the southeast - northwest - east of Southwest China; the 1990s is the most wettest decade and it is wet over the whole area. In the 2000s, the extreme drought frequency rises quickly, but the regional differences reduce. (2) In summer monsoon, the extreme drought frequency is growing, which generally happens in the high mountains around the Sichuan Basin, most parts of Guangxi and "the broom-shaped mountains". It is obvious that the altitude has impacts on the extreme drought frequency of summer monsoon; in winter monsoon, the area is relatively wet and the extreme drought frequency is decreasing. (3) In summer monsoon, the abrupt change happened in 2003, while the abrupt change of winter monsoon happened in 1989, and the joint abrupt changes in different periods lead to the extreme drought frequency variation. The departure sequence vibration of annual extreme drought frequency is quasi 5 years and quasi 12 years.

郑祚芳, 任国玉, 王耀庭, 等.

大型人工湖气候效应观测研究: 以密云水库为例

地理科学, 2017, 37(12): 1933-1941.

DOI:10.13249/j.cnki.sgs.2017.12.018      [本文引用: 1]

应用近5 a自动气象站观测资料,分析了华北地区最大人工湖——密云水库的局地气候效应。结果表明：① 密云水库库区相比于附近平原地带具有气温偏低、湿度偏高、风速偏弱、降水量偏大等特点。水库对区域气候的影响范围约在10 km内,离水库越近的地方,受影响越大。② 密云水库的气候效应主要体现在夏半年,尤以气温和降水最为明显。③ 水库南、北两侧受到的局地环流的影响具有明显的差异,库区东西方向的年平均局地风速约为0.14 m/s,南北方向约为 0.10 m/s。下垫面属性的热力差异及特殊地形条件使得密云水库附近同时存在山谷风和湖陆风现象,其叠加效应是导致区域内不同位置间气象要素出现季节性及日变化差异的主要原因。

[Zheng Zuofang, Ren Guoyu, Wang Yaoting, et al.

Observational study on climate effect of large artificial lake: Taking Miyun Reservoir as an example

Scientia Geographica Sinica, 2017, 37(12): 1933-1941.]. DOI: 10.13249/j.cnki.sgs.2017.12.018.

[本文引用: 1]

There exists lake-land breeze that wind blowing onshore from lake to land during the day and offshore in the evening around lake area, due to differences in air pressure mainly caused by different heat capacities between lake and land. Generally speaking, the closer to the lake, the more remarkable the lake climate effects. For studying the lake climate effects, it is not only important for understanding the characteristics of local climate and atmospheric circulation, but also helpful for analyzing and forecasting meso-and micro-scale weather processes. Miyun Reservoir (MYR), which located at 15 km north of Miyun District of Beijing city, is the largest artificial lake in North China. Up to now, there are seldom reports on the influence of MYR on local weather and climate. Based on hourly observation data obtained by 77 automatic weather stations surrounding MYR from 2011 to 2015, local climate effects of MYR were explored and discussed in the present work. The results showed that: 1) comparing with peripheral plains, climate effects of MYR were characterized by the facts that lower air temperature, higher humidity, slightly weaker wind speed and larger rainfall. As far as regional climate was concerned, the MYR had a modulate effect on the local climate and the spatial extent of the effect is about 10 km. The closer to the MYR, the more notable the MYR climate effects. 2) The MYR climate effects were mainly occurred in the summer, especially for temperature and rainfall. In detail, averaged air temperature was lower of 0.96℃ and averaged rainfall amount was higher of 13.3% in MYR than in the adjacent plains, where has the same elevation with MYR away from 10km. In addition, there were significant differences in diurnal variations of meteorological factors between the south and north regions of the MYR. 3) Excluding the impact of the large-scale background wind field, local wind presented the characteristics of monthly variation, i.e., mountain breeze was dominant from April to September, especially in summer; while in other months, valley breeze was more prevailing, especially in the wintertime. In general, annual averaged local wind speed was about 0.14 m/s in the east-west direction, which was slightly larger than that of 0.10 m/s in the north-south direction around the MYR. 4) In summer, there existed significant differences in wind vectors at the south and north regions of the MYR, due to the influence of local circulation. In most time of the whole day, component anomalies were usually in the same phase, while component anomalies presented out of phase at the south and north regions of the MYR. Lake-land breeze and mountain-valley breeze had the same/opposite directions at the north/south regions of the MYR, and thus these correspondingly formed the overlaying/counteractive effects. Due to lake-land differences in heat capacities and topography effects, lake-land breeze and mountain-valley breeze in the areas around MYR usually existed at the same time, which mainly caused the differences in seasonal and diurnal variations of meteorological elements at different locations around the MYR region.

Miller N L, Jin J M, Tsang C F.

Local climate sensitivity of the Three Gorges Dam

Geophysical Research Letters, 2005, 32(16): L16704. DOI: 10.1029/2005GL022821.

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[Lin Hao, Zhang Guojun. Analysis of influence of large reservoir on climate and environment. In:Proceedings of the National Symposium on Outstanding Young Meteorological Science and Technology Workers. Beijing, Changsha: Chinese Meteorological Society, Hunan Meteorological Bureau, 2006: 453-456.]

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王娜, 孙娴, 蔡新玲, 等.

安康水库蓄水前后上游气候变化特征

气象科技, 2010, 38(5): 649-654.

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[Wang Na, Sun Xian, Cai Xinling, et al.

Characteristics of climate change in Ankang Reservoir upstream basin before and after impoundment

Meteorological Science and Technology, 2010, 38(5): 649-654.]. DOI: 10.19517/j.1671-6345.2010.05.026.

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Ma Z R.

Analysis on the variation trend of evaporation and precipitation in Xiaolangdi Reservoir

IOP Conference Series: Earth and Environmental Science, 2020, 558: 042001. DOI: 10.1088/1755-1315/558/4/042001.

[本文引用: 1]

Based on the monthly evaporation and precipitation data from 1982 to 2013 of 13 meteorological stations in and around the Xiaolangdi Reservoir, the linear tendency estimation method, cumulative anomaly, M-K mutation detection method and ARIMA model are used to study and predict the changing characteristics of evaporation and precipitation. The results show that the annual evaporation and precipitation in the reservoir area both show a downward trend. The evaporation has an abrupt decline in 2001, and the precipitation decreases suddenly in 2003 and 2011. After impoundment, the annual evaporation volume changed from the upward trend to a weak downward trend, and the downward trend of annual precipitation is weakened. The impact of impoundment on evaporation and precipitation is limited, and the impact intensity decreases with increasing distance from the reservoir. The reservoir area’s evaporation and precipitation have significant seasonal variation characteristics, and the future evaporation will show a slight downward trend, while the precipitation will show a slight upward trend.

Yigzaw W, Hossain F, Kalyanapu A.

Impact of artificial reservoir size and land use/land cover patterns on probable maximum precipitation and flood: Case of Folsom Dam on the American river

Journal of Hydrologic Engineering, 2013, 18(9): 1180-1190. DOI: 10.1061/(ASCE)HE.1943-5584.0000722.

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On the climate impact of the local circulation in the Itaipu Lake Area

Climatic Change, 2005, 72(1-2): 103-121. DOI: 10.1007/s10584-005-5923-2.

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符坤, 张六一, 任强.

蓄水前后三峡库区气候时空变化特征

环境影响评价, 2018, 40(3): 82-86+96.

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[Fu Kun, Zhang Liuyi, Ren Qiang.

Spatial and temporal characteristics of climate change in Three Gorges Reservoir Area before and after impoundment

Environmental Impact Assessment, 2018, 40(3): 82-86+96.]. DOI: 10.14068/j.ceia.2018.03.019.

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Lü M Q, Jiang Y, Chen X L, et al.

Spatiotemporal variations of extreme precipitation under a changing climate in the Three Gorges Reservoir Area (TGRA)

Atmosphere, 2018, 9(1): 24. DOI: 10.3390/atmos9010024.

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陈鲜艳, 宋连春, 郭占峰, 等.

长江三峡库区和上游气候变化特点及其影响

长江流域资源与环境, 2013, 22(11): 1466-1471.

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[Chen Xianyan, Song Lianchun, Guo Zhanfeng, et al.

Climate change over the Three Gorges Reservoir and upper Yangtze with its possible effect

Resources and Environment in the Yangtze Basin, 2013, 22(11): 1466-1471.]. DOI: CNKI:SUN:CJLY.0.2013-11-013.

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Wu L G, Zhang Q, Jiang Z H.

Three Gorges Dam affects regional precipitation

Geophysical Research Letters, 2006, 33(13): L13806. DOI: 10.1029/2006GL026780.

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谢萍, 张双喜, 汪海洪, 等.

利用交叉小波技术分析三峡水库蓄排水过程对库区降雨量的影响

武汉大学学报(信息科学版), 2019, 44(6): 821-829+907.

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[Xie Ping, Zhang Shuangxi, Wang Haihong, et al.

Cross Wavelet analysis on the influence of the Three Gorges Dam impounding on the reservoir precipitation

Geomatics and Information Science of Wuhan University, 2019, 44(6): 821-829+907.]. DOI: 10.13203/j.whugis20180410.

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Guntu R K, Maheswaran R, Agarwal A, et al.

Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory

Journal of Hydrology, 2020, 590: 125236. DOI: 10.1016/j.jhydrol.2020.125236.

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张静, 刘增进, 肖伟华, 等.

三峡水库蓄水后库区气候要素变化趋势分析

人民长江, 2019, 50(3): 113-116+165.

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[Zhang Jing, Liu Zengjin, Xiao Weihua, et al.

Analysis on variation trend of climate factors in Three Gorges Reservoir area after impoundment

Yangtze River, 2019, 50(3): 113-116+165.]. DOI: 10.16232/j.cnki.1001-4179.2019.03.020.

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[Liu Xiangmei. Climate assessment and climate changes during the last 54 years in Three Gorges Reservoir. Chongqing: Master Dissertation of Southwest University, 2007: 9-11.]

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周小英. 1955-2015年三峡库区腹地气候变化特征分析:以重庆万州区为例. 重庆: 西南大学硕士学位论文, 2018: 4-5.

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[Zhou Xiaoying. The characteristic analysis of climate change in the Hinterland of Three Gorges Reservoir from 1955 to 2015:A case study of Wanzhou in Chongqing. Chongqing: Master Dissertation of Southwest University, 2018: 4-5.]

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徐其勇. 基于遥感影像三峡大坝蓄水对库区水域面积的变化分析:以坝前至江津段为例. 成都: 四川师范大学硕士学位论文, 2018.

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[Xu Qiyong. The analysis of the change of water area in reservoir area based on the remote sensing image of the three gorges dam:A case study of pre-dam to Jiangjin section. Chengdu: Master Dissertation of Sichuan Normal University, 2018.]

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Lan B, Li H L, Xiang X F, et al.

Spatial-temporal characteristics of epilithic algae succession on artificial substrata in relation to water quality in Erhai Lake, Yunnan province, China

Biologia, 2018, 73(9): 821-830. DOI: 10.2478/s11756-018-0100-z.

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Idowu D, Zhou W.

Land use and land cover change assessment in the context of flood hazard in Lagos State, Nigeria

Water, 2021, 13(8): 1105. DOI: 10.3390/w13081105.

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Incessant flooding is a major hazard in Lagos State, Nigeria, occurring concurrently with increased urbanization and urban expansion rate. Consequently, there is a need for an assessment of Land Use and Land Cover (LULC) changes over time in the context of flood hazard mapping to evaluate the possible causes of flood increment in the State. Four major land cover types (water, wetland, vegetation, and developed) were mapped and analyzed over 35 years in the study area. We introduced a map-matrix-based, post-classification LULC change detection method to estimate multi-year land cover changes between 1986 and 2000, 2000 and 2016, 2016 and 2020, and 1986 and 2020. Seven criteria were identified as potential causative factors responsible for the increasing flood hazards in the study area. Their weights were estimated using a combined (hybrid) Analytical Hierarchy Process (AHP) and Shannon Entropy weighting method. The resulting flood hazard categories were very high, high, moderate, low, and very low hazard levels. Analysis of the LULC change in the context of flood hazard suggests that most changes in LULC result in the conversion of wetland areas into developed areas and unplanned development in very high to moderate flood hazard zones. There was a 69% decrease in wetland and 94% increase in the developed area during the 35 years. While wetland was a primary land cover type in 1986, it became the least land cover type in 2020. These LULC changes could be responsible for the rise in flooding in the State.

Kharazmi O, Balakrishnan N.

Jensen-information generating function and its connections to some well-known information measures

Statistics & Probability Letters, 2021, 170: 108995. DOI: 10.1016/j.spl.2020.108995.

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Mishra A K, Özger M, Singh V P.

An entropy-based investigation into the variability of precipitation

Journal of Hydrology, 2009, 370(1-4): 139-154. DOI: 10.1016/j.jhydrol.2009.03.006.

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高辉, 梁建茵.

南海夏季风建立日期的确定和东亚夏季风强度指数的选取

热带气象学报, 2005, 21(5): 525-532.

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[Gao Hui, Liang Jianyin.

Definition of South China Sea summer monsoon's onset date and East Asian summer monsoon's Index

Journal of Tropical Meteorology, 2005, 21(5): 525-532.]. DOI: 10.3969/j.issn.1004-4965.2005.05.009.

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梁建茵, 吴尚森.

南海西南季风多时间尺度变化及其与海温的相互作用

应用气象学报, 2000, 11(1): 95-104.

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[Liang Jianyin, Wu Shangsen.

The multi-time scale variations of summer monsoon over South China Sea and its interaction with SST anomaly

Journal of Applied Meteorological Science, 2000, 11(1): 95-104.]. DOI: 10.3969/j.issn.1001-7313.2000.01.012.

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李世宽. 基于Kendall秩相关系数的沙漠地震噪声性质研究及应用. 长春: 吉林大学硕士学位论文, 2020: 8-15.

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[Li Shikuan. Research and application of desert seismic noise properties based on Kendall rank correlation coefficient. Changchun: Master Dissertation of Jilin University, 2020: 8-15.]. DOI: 10.27162/d.cnki.gjlin.2020.006289.

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Mallick J, Talukdar S, Alsubih M, et al.

Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis

Theoretical and Applied Climatology, 2020, 143(1-2): 823-841. DOI: 10.1007/s00704-020-03448-1.

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倪楠. 中国降水的时空变化特征研究. 北京: 对外经济贸易大学博士学位论文, 2020.

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[Ni Nan. Spatial and temporal variation of precipitation in China. Beijing: Doctoral Dissertation of University of International Business and Economics, 2020.]. DOI: 10.27015/d.cnki.gdwju.2020.000024.

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Du R S, Shang F H, Ma N.

Automatic mutation feature identification from well logging curves based on sliding t test algorithm

Cluster Computing-The Journal of Networks Software Tools and Applications, 2018, 22(S6): 14193-14200. DOI: 10.1007/s10586-018-2267-z.

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Huang H B, Huang X R, Yang M L, et al.

Identification of vehicle interior noise sources based on wavelet transform and partial coherence analysis

Mechanical Systems and Signal Processing, 2018, 109: 247-267. DOI: 10.1016/j.ymssp.2018.02.045.

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尼格娜热·阿曼太, 丁建丽, 葛翔宇, 等.

1960-2017年艾比湖流域实际蒸散量与气象要素的变化特征

地理学报, 2021, 76(5): 1177-1192.

DOI:10.11821/dlxb202105010      [本文引用: 2]

传统估算蒸散发的方法大都基于局地尺度,而在生态水文发生剧烈变化的资料稀缺流域背景下,充分考虑流域下垫面的空间变异性的陆面过程模型为流域长时序、大尺度及连续模拟实际蒸散量提供了新途径。以艾比湖流域为研究区,应用可变下渗能力模型（VIC）模拟1960—2017年艾比湖流域的水文过程,探讨研究区值实际蒸散发量的年、月、日时空变化规律,并运用小波分析方法对5个气象要素及研究区实际蒸散发量的模拟值进行多尺度特征分析,结果表明：① VIC在温泉和博乐的径流纳什效率系数（NSE）分别为0.09和0.23,模拟效果较为满意;VIC实际蒸散量的模拟值与理论计算值,R<sup>2</sup>达0.80,均方根误差（RMSE）为31.76 mm a<sup>-1</sup>,NSE为0.32,模拟效果相对较好;② 时间尺度上,艾比湖流域58 a来年际实际蒸散量呈上升趋势,年均实际蒸散量以1.03 mm a<sup>-1</sup>的速率递增;月值和日值蒸散量均呈单峰趋势;且年代际变化中5—7月的实际蒸散量在20世纪90年代和21世纪呈现下降趋势,20世纪70年呈现上升趋势,而其余月份无明显变化;③ 空间分布上,艾比湖流域内实际蒸散发量总体上呈现高海拔及其附近地区蒸散强烈,从春季到夏季,强蒸散区由西北向东南转移,年实际蒸散量空间分布与春夏季分布一致;④ 艾比湖流域实际蒸散发量与各气象要素在时频域中均存在1~4个显著性周期,且在一定尺度的周期上,平均风速、平均温度以及日照时数超前于实际蒸散量变化,而年降水量和相对湿度滞后于实际蒸散量变化,受降水影响实际蒸散发1965年和2003年发生1 a周期的“强—弱”转换,受相对湿度影响实际蒸散量在1965年和2008年发生2~4.5 a周期的“强—弱”转换。

[Amantai Nigenare, Ding Jianli, Ge Xiangyu, et al.

Variation characteristics of actual evapotranspiration and meteorological elements in the Ebinur Lake basin from 1960 to 2017

Acta Geogrphica Sinica, 2021, 76(5): 1177-1192.]. DOI: 10.11821/dlxb202105010.

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Negi B B, Aliveli M, Behera S K, et al.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization

Environmental Research, 2023, 219: 115073. DOI: 10.1016/j.envres.2022.115073.

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Guo X, He J H, Wang B, et al.

Prediction of sea surface temperature by combining interdimensional and self-attention with neural networks

Remote Sensing, 2022, 14(19): 4737. DOI: 10.3390/rs14194737.

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Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation.

李良伟. 基于机器学习的海表温度对中国降水影响的时空信息挖掘及预测研究. 北京: 国家海洋环境预报中心硕士学位论文, 2021: 45-59.

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[Li Liangwei. Spatio-temporal information mining and prediction of the influence of SST on precipitation in China based on machine learning. Bejing: Master Dissertation of National Marine Environmental Forecasting Center, 2021: 45-59.]. DOI: 10.27810/d.cnki.ghyhz.2021.000005.

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Sustainable Cities and Society, 2022, 84: 103995. DOI: 10.1016/j.scs.2022.103995.

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胡胜, 邱海军, 宋进喜, 等.

气候变化对秦岭北坡径流过程的影响机制研究: 以灞河流域为例

干旱区地理, 2017, 40(5): 967-978.

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[Hu Sheng, Qiu Haijun, Song Jinxi, et al.

Influencing mechanisms of climate change on runoff process in the north slope of Qinling Mountains: A case of the Bahe River Basin

Arid Land Geography, 2017, 40(5): 967-978.]. DOI: 10.13826/j.cnki.cn65-1103/x.2017.05.006.

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. Precipitation changes in the Three Gorges Reservoir\nRegion (TGRR) play a critical role in the operation and regulation of the\nThree Gorges Dam (TGD) and the protection of residents and properties. The\npotential impacts of the TGD on local and regional circulation patterns,\nespecially the precipitation patterns, have received considerable attention\nsince its construction. However, how the moisture transport affects\nprecipitation changes in the TGRR spatially and temporally remains obscure.\nIn this study, we investigate the long-term moisture sources of\nprecipitation and their contributions to precipitation changes over\nthe TGRR using an atmospheric moisture tracking model. Results suggest that\nalthough there is seasonal variation, the moisture contributing to the TGRR\nprecipitation primarily originates from the areas southwest of the TGRR\ndominated by the Indian summer monsoon. In particular, the sources with the\nhighest annual moisture contribution are the southwestern part of the\nYangtze River basin and the southeastern tip of the Tibetan Plateau (TP). On average, 41 %, 56 %, and 3 % of the TGRR precipitation originates from ocean, land, and local recycling, respectively. In addition, the decreased precipitation over the TGRR during 1979–2015 is mainly attributed to the significantly decreased moisture contribution from the source regions southwest of the TGRR (especially around the southeastern tip of the TP). Compared to dry years, the higher precipitation in the TGRR during wet years is contributed by the extra moisture from the southwestern source regions that is delivered by the intensified southwesterly monsoon winds.\n

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崔豪, 王丽川, 王贺佳, 等.

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李强, 李永华, 周锁铨, 等.

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基于WRF模式， 在模式下垫面中嵌入长江水体以及改变地形高度， 研究了三峡地区长江水体和局地地形在有降水和无降水事件中的局地效应\.结果表明, 在有降水事件中， 水体下垫面为降水提供了局地水汽， 同时在局地喇叭口和峡谷地形动力强迫作用下， 近地面河谷风增加， 偏南气流携带水汽北上， 三峡地区下游段在峡谷地形的“狭管”效应作用下， 河谷风偏东风增加， 增加水汽向西输送并与偏南风北上水汽在喇叭口地形末端汇合， 下垫面变化的同时也为降水提供了充分的潜热能量， 夹带水汽气流在局地地形的强迫抬升作用下， 促使水汽辐合上升， 与高空的干冷空气造成了层结不稳定， 潜热不稳定能量的释放， 导致了该地区的降水量增加； 在无降水事件中， 长江水体在白天（夜晚）具有相应的降温（保温）作用， 进而呈现出冷（暖）湖效应， 在水体下垫面和地形作用下， 潜热在喇叭口末端聚集， 没有地形对气流阻挡作用， 偏南气流、 偏东气流加强； 在有\, 无降水事件中， 环流特征变化表明局地水体下垫面和局地地形对三峡地区天气均有不可忽视的重要性。

[Li Qiang, Li Yonghua, Zhou Suoquan, et al.

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程龙, 刘海文, 周天军, 等.

近30余年来盛夏东亚东南季风和西南季风频率的年代际变化及其与青藏高原积雪的关系

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