区域气候变化下洞里萨湖表面水温时空变化的归因

doi:10.13249/j.cnki.sgs.2022.04.019

地理科学 ›› 2022, Vol. 42 ›› Issue (4): 739-750.doi: 10.13249/j.cnki.sgs.2022.04.019

• 黄河流域城市高质量发展专栏 • 上一篇

区域气候变化下洞里萨湖表面水温时空变化的归因

潘梅娥¹^,²(), 杨昆¹^,²^,^*(), 邹天乐¹, 孙润¹, 张锡花¹, 张煜¹

1.云南师范大学地理学部，云南昆明 650500
2.西部资源环境地理信息技术教育部工程研究中心，云南昆明 650500

收稿日期:2021-06-12 修回日期:2021-10-01 出版日期:2022-04-10 发布日期:2022-06-07
通讯作者: 杨昆 E-mail:pme123@126.com;kmdcynu@163.com
作者简介:潘梅娥（1986-），女，云南曲靖人，讲师，博士研究生，主要从事水环境遥感监测与评价。E-mail: pme123@126.com
基金资助:
国家863计划(2012AA121402);国家自然科学基金项目(41761084)

Determining Factors in Spatio-temporal Variation of the Tonle Sap Lake’s Surface Water Temperature Under Regional Climate Change

Pan Meie¹^,²(), Yang Kun¹^,²^,^*(), Zou Tianle¹, Sun Run¹, Zhang Xihua¹, Zhang Yu¹

1. Faculty of Geograghy, Yunnan Normal University, Kunming 650500, Yunnan, China
2. The Engineering Research Center of GIS Technology in Western China, Ministry of Education, Kunming 650500, Yunnan, China

Received:2021-06-12 Revised:2021-10-01 Online:2022-04-10 Published:2022-06-07
Contact: Yang Kun E-mail:pme123@126.com;kmdcynu@163.com
Supported by:
China High Technology Research and Development Program 863(2012AA121402);National Natural Science Foundation of China(41761084)

摘要/Abstract

摘要：

基于2001—2019年洞里萨湖白天的湖泊表面水温（LSWT_day）、近地表气温（AT）、太阳辐射（SR）、地表气压（SP）、降水量（TP）、相对湿度（RH）、风速（WS）、总云量（TCC）以及水位（WL）数据，采用趋势分析、稳定性分析、持续性分析和突变分析等方法，研究了LSWT_day及各气候因素的时空分布及变化特征；利用相关性分析、回归分析和Z-Score标准化方法，厘清了各气候因素变化对LSWT_day的影响。结果表明：① 时间尺度上，区域气候以AT、WS、TCC显著升高，WL显著下降为主要特征；LSWT_day的平均升温速率为0.372℃/10a，且具有持续变暖趋势，与气候因素的突变点具有时间一致性。空间尺度上，LSWT_day和各气候因素的空间分布及变化趋势具有明显的空间异质性。其中，LSWT_day除西部湖区有微小降温外，其余湖区表现出明显的升温趋势。气候因素中，AT、SP、TP、TCC和RH的变化率大致呈现为南高值北低值的分布，而SR和WS的变化率分布为南低值北高值。② 不同时间尺度上，驱动洞里萨湖LSWT_day变化的气候因素不同。年均尺度上，LSWT_day主要受到AT、WS和WL的驱动；月均尺度上，AT变化仍是驱动LSWT_day变化的主要因素；季节尺度上，TP、WL和TCC升高为春夏LSWT_day降温的主要原因，RH是秋季LSWT_day波动的主要影响因素，冬季LSWT_day随同AT的升温而变暖。

关键词: 湖泊表面水温, 气候因素, 洞里萨湖, MODIS

Abstract:

Lake surface water temperature (LSWT) is an important factor affecting the aquatic ecosystem. This study aimed to reveal the influence of regional climate change on the surface water temperature of Tonle Sap Lake. For this purpose, based on the datasets of the Tonle Sap Lake’s daytime surface water temperature (LSWT_day), near-surface atmospheric temperature (AT), solar radiation (SR), surface pressure (SP), total precipitation (TP), relative humidity (RH), wind speed (WS), total cloud cover (TCC) and water level (WL) from 2001 to 2019, the temporal-spatial distribution and variation characteristics of LSWT_day and climate factors were studied by trend analysis, stability analysis, persistence analysis and mutation analysis. Then, through correlation analysis, regression analysis and Z-score standardization method, the impact of climate factors on LSWT_day was clarified. Results showed that: 1) At the time scale, the Tonle Sap Lake region climate was characterized by a significant increase in AT, WS and TCC, and a significant decrease in WL. The average warming rate of LSWT_day was 0.372℃/10a, and showed a continuous warming trend. LSWT_day had time consistency with the abrupt change point of climatic factors. At the spatial scale, the spatial distribution and change trend of LSWT_day and climate factors had obvious spatial heterogeneity. Among them, except for the slight cooling trend in the western lake area, the LSWT_day of other lake areas showed an obvious warming trend. Among climatic factors, the distribution of the rate of change of AT, SP, TP, TCC, and RH was roughly high values in the south and low values in the north, while the distribution of change rates of SR and WS was low in the south and high in the north. 2) In different time scales, the factors driving LSWT_day change were different. On the average annual scale, LSWT_day was mainly driven by AT, WS and WL. At the monthly mean scale, the change of AT was still the main driving factor of LSWT_day. At the seasonal scale, the increase of TP, WL and TCC was the main reason for the cooling of LSWT_day in spring and summer, RH was the main influencing factor for the fluctuation of LSWT_day in autumn, and LSWT_day was warming in winter along with the warming of AT.

Key words: lake surface water temperature (LSWT), climate factors, the Tonle Sap Lake, MODIS

中图分类号:

K909

潘梅娥, 杨昆, 邹天乐, 孙润, 张锡花, 张煜. 区域气候变化下洞里萨湖表面水温时空变化的归因[J]. 地理科学, 2022, 42(4): 739-750.

Pan Meie, Yang Kun, Zou Tianle, Sun Run, Zhang Xihua, Zhang Yu. Determining Factors in Spatio-temporal Variation of the Tonle Sap Lake’s Surface Water Temperature Under Regional Climate Change[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(4): 739-750.

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变量	含义	变量	含义
$ X $	WL观测值、LSWT_day及各气象因素整景影像年均值	$ H $	Hurst指数
$ {x}_{t} $	LSWT_day及各气象因素第 $ t $ 年影像像元值	$ r $	相关系数，绝对值越大表明相关性越强
$T,t$	时间系列， $T,t=1, 2, \cdots ,19 ; 1 \leqslant t \leqslant T$	${\overline{LSWT} }$	LSWT_day时间系列内的均值
$ a $	截距	$ \stackrel{-}{Y} $	各气候因素时间系列内的均值
$ b $	年均总变化率	$ {LSWT}_{t} $	第t年的LSWT_day值
$ e $	残差	$ {Y}_{t} $	第t年的各气候因素值
$ slope $	各像元变化率	$ {b}_{1},{b}_{2}\cdots $ , $ {b}_{8} $	回归系数
$ CV $	变异系数，值越大说明 $ X $ 的年际波动性越大	$ {LSWT}_{m} $	LSWT_day模拟值
$ SD\left(X\right) $	$ X $ 的标准差	$ N $	样本总数19
$ \stackrel{-}{X} $	19 a $ X $ 的总均值	$ Z $	Z-Score标准化结果
$ {X}_{t} $	第 $ t $ 年的 $ X $ 值

时间尺度		回归模型	显著性检验(置信水平)	R²	RMSE/℃
注：LSWT_day为白天的湖泊表面水温、AT为近地表气温、WS为风速、WL为水位、SP为地表气压、TP为降水、RH为相对湿度、TCC为总云量。
年均		$ {LSWT}_{day}=0.843AT+2.987 $	99%	0.80	0.18
		$ {LSWT}_{day}=0.867AT-1.133WS+4.855 $	95%	0.85	0.15
		$ {LSWT}_{day}=0.665AT-1.033WS-0.162WL+ $ 10.922	90%	0.88	0.14
月均		$ {LSWT}_{day}=1.577AT-17.754 $	99%	0.77	0.91
季节均值	春	$ {LSWT}_{day}=-0.421TP+30.855 $	99%	0.54	0.60
	夏	$ {LSWT}_{day}=-0.639WL+28.892 $	99%	0.47	0.61
		$ {LSWT}_{day}=-0.658WL-8.04TCC+35.687 $	95%	0.65	0.50
		$ {LSWT}_{day}=-0.647WL-8.054TCC-0.29TP+37.031 $	95%	0.74	0.43
	秋	$ {LSWT}_{day}=-0.186RH+40.259 $	99%	0.40	0.40
	冬	$ {LSWT}_{day}=0.462AT+12.347 $	99%	0.35	0.34

区域气候变化下洞里萨湖表面水温时空变化的归因

Determining Factors in Spatio-temporal Variation of the Tonle Sap Lake’s Surface Water Temperature Under Regional Climate Change

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