三江源区蒸散量的时空分布特征

doi:10.19657/j.geoscience.1000-8527.2020.075

摘要/Abstract

摘要：

三江源地区位于青海南部,是中国三大主要河流长江、黄河和澜沧江的源头汇水区,是中国海拔最高的天然湿地和面积最大的自然保护区。三江源地区生态环境脆弱,评估地下水蒸散量对该地区水循环和水资源量评价有重要作用,对生态环境保护也具有一定意义。基于中等分辨率的MODIS数据,利用表面能量平衡系统对三江源地区2001—2017年的区域蒸散量进行估算,并采用Sen+Mann-Kendall法分析其连续时间序列内的时空变化趋势,讨论其影响因素。结果表明:研究区蒸散量从2001年到2017年总体呈增长趋势;三个源区多年年平均蒸散量值表现为澜沧江源>黄河源>长江源的变化规律;三江源区超过62.62%的地区蒸散量变化呈显著增长趋势,轻微显著增长地区占28.03%,显著减小地区占比极少;蒸散量的变化主要受气候影响,与气温、降水量呈明显正相关关系,其确定系数分别为0.80、0.89;蒸散量与植被指数及土壤湿度也均呈明显正相关关系。

关键词: 蒸散量, 地表能量平衡系统, MODIS, Sen+Mann-Kendall, 三江源

Abstract:

Located in the southern Qinghai Province in China, the Sanjiangyuan region contains the headwaters of the three great rivers of Yangtze, Yellow and Lancang. The Sanjiangyuan region is the highest natural wetland and the largest nature reserve in China, with fragile eco-environment. Groundwater evapotranspiration (ET) assessment is important for evaluating the water cycle and water resource in the region, which is crucial for eco-environmental protection. Based on MODIS data with moderate resolution in this study, the regional ET of the Sanjiangyuan region was estimated for the period of 2001-2017 with the Surface Energy Balance System. The Sen+Mann-Kendall method was used to analyze the temporal-spatial variation trends, and the influencing factors were discussed. The results show that in the Sanjiangyuan region: (1) ET increased from 2001 to 2017; (2) the areas (from highest to lowest annual average ET) are the Lancang River, Yellow River, and the Yangtze River headwaters; (3) over 62.62% of the region has a major ET increasing trend, while 28.03% of the region has a minor increasing trend, and areas with significant ET decrease barely exist; (4) ET variation is positively correlated with precipitation and air temperature, with the correlation coefficients of 0.80 and 0.89, respectively; (5) variation of ET is positively correlated with NDVI and soil moisture.

Key words: evapotranspiration (ET), Surface Energy Balance System, MODIS, Sen+Mann-Kendall, Sanjiangyuan region

中图分类号:

P641.69
X143

甘海洪, 金晓媚, 张绪财, 朱晓倩. 三江源区蒸散量的时空分布特征[J]. 现代地质, 2021, 35(03): 665-674.

GAN Haihong, JIN Xiaomei, ZHANG Xucai, ZHU Xiaoqian. Temporal and Spatial Distribution of Evapotranspiration in the Sanjiangyuan Region[J]. Geoscience, 2021, 35(03): 665-674.

图/表 15

图1 研究区地理位置图

Fig.1 Geographic map of the study area

图2 研究区蒸散量年际变化趋势

Fig.2 Annual variation of actual ET in the study area

图3 研究区多年平均蒸散量年内变化趋势

Fig.3 Variation of the annual average ET in the study area

图4 2017年研究区年蒸散量空间分布图

Fig.4 Spatial distribution of annual ET in the study area in 2017

图5 研究区蒸散量空间变化趋势图

Fig.5 Spatial ET variation trend in the study area

图6 研究区蒸散量显著性变化分类图

Fig.6 Classification of significant ET change in the study area

表1 变化趋势分类

Table 1 Classification of variation trends

蒸散量变化趋势	程度
β<0且\|Z\|>1.96	显著减小
β<0且\|Z\|≤1.96	轻微减小
0≤β≤6	基本不变
β>6且\|Z\|≤1.96	轻微增长
β>6且\|Z\|>1.96	显著增长

表2 研究区蒸散量变化趋势面积统计

Table 2 Area statistics of ET variation trends in the study area

变化趋势	三江源		长江源		黄河源		澜沧江源
变化趋势	面积/km²	面积占比/%	面积/km²	面积占比/%	面积/km²	面积占比/%	面积/km²	面积占比/%
显著减小	002.31	0	2.05	0	0.26	0	0	0
轻微减小	1 288.86	0.37	524.31	0.31	266.04	0.26	237.58	0.63
基本不变	31 630.46	8.99	13 946.15	8.22	8 421.82	8.32	2 872.81	7.63
轻微增长	98 654.14	28.03	47 847.31	28.21	33 108.50	32.69	8 795.04	23.37
显著增长	220 383.42	62.62	107 261.67	63.25	59 472.94	58.73	25 735.95	68.37

图7 研究区降水量年际变化趋势

Fig.7 Annual variation of precipitation in the study area

图8 降水(a)和气温(b)对蒸散量的影响

Fig. 8 Effect of precipitation (a) and air temperature (b) on evapotranspiration

图9 研究区年均气温 (a)、月均气温 (b)变化趋势

Fig.9 Annual (a) and monthly (b) variation of air temperature in the study area

图10 植被指数对蒸散量的影响

Fig.10 Effect of NDVI on evapotranspiration

图11 研究区不同用地类型蒸散量柱状图

Fig.11 Histogram of ET of different land-use types in the study area

图12 研究区土壤湿度与蒸散量年际变化趋势

Fig.12 Annual variation of soil moisture and ET in the study area

图13 土壤湿度对蒸散量的影响

Fig.13 Effect of soil moisture on evapotranspiration

参考文献 20

[1]	BURMAN R, POCHOP L. Evaporation,Evapotranspiration and Climatic Data[M]. Amsterdam:Elsevier Science, 1994.
[2]	金晓媚, 万力, SU Z. 遥感与区域地面蒸散量 [M]. 北京: 地质出版社, 2008:62-95.
[3]	张立锋, 张继群, 张翔, 等. 三江源区退化高寒草甸蒸散的变化特征[J]. 草地学报, 2017, 25(2): 273-281.
[4]	裴超重, 钱开铸, 吕京京, 等. 长江源区蒸散量变化规律及其影响因素[J]. 现代地质, 2010, 24(2): 362-368.
[5]	周秉荣, 李凤霞, 肖宏斌, 等. 三江源区潜在蒸散时空分异特征及气候归因[J]. 自然资源学报, 2014, 29(12): 2068-2077.
[6]	SEGUIN B, ITIER B. Using midday surface temperature to estimate daily evaporation from satellite thermal IR data[J]. International Journal of Remote Sensing, 1983, 4(2): 371-383. DOI URL
[7]	SU Z B. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes[J]. Hydrology and Earth System Sciences, 2002, 6(1): 85-89. DOI URL
[8]	SU Z B. Remote sensing of land use and vegetation for mesoscale hydrological studies[J]. International Journal of Remote Sensing, 2000, 21(1): 213-233. DOI URL
[9]	金晓媚, 万力, 梁继运. 水均衡法验证蒸散量计算的可靠性:以张掖盆地为例[J]. 现代地质, 2008, 22(2): 299-303.
[10]	郭任宏, 金晓媚, 王晓林, 等. 基于中等分辨率遥感数据的柴达木盆地实际蒸散量的计算[J]. 地学前缘, 2014, 21(4): 107-114.
[11]	金晓媚, 郭任宏, 夏薇. 基于MODIS数据的柴达木盆地区域蒸散量的变化特征[J]. 水文地质工程地质, 2013, 40(6): 8-13.
[12]	王凯霖, 金晓媚, 郭任宏, 等. 柴达木盆地土壤湿度的遥感反演及对蒸散发的影响[J]. 现代地质, 2016, 30(4): 834-841.
[13]	杨笑天. 基于SEBS模型的格尔木河流域地表蒸散量遥感反演及其时空格局分析[D]. 西安:长安大学, 2017.
[14]	张晓玉. 基于SEBS模型的艾比湖流域蒸散发估算及时空变化研究[D]. 乌鲁木齐:新疆大学, 2018.
[15]	王春敏. 基于NDVI的三江源植被变化及影响因素分析 [D]. 北京:中国地质大学(北京), 2018.
[16]	SU Z B, JACOBS C. Advanced Earth Observation-Land Surface Climate[M]. Amsterdam:Publications of the Netherlands Remote Sensing Board (BCRS), 200l:183.
[17]	BEUTSAERT W. Aspects of bulk atmospheric boundary layer similarity under free-convective conditions[J]. Reviews of Geophysics, 1999, 37(4): 439-451. DOI URL
[18]	MENENTI M, CHOUDHURY B J. Parametrization of land surface evapotranspiration using a location-dependent potential evapotranspiration and surface temperature range[M]//BOLLE H J. Exchange Processes at the Land Surface for a Range of Space and Time Scales.Walling Ford:IAHS Publication, 1993:561-568.
[19]	王佃来. 基于遥感图像分析的北京植被状态与变化研究[D]. 北京:北京林业大学, 2013.
[20]	代子俊, 赵霞, 李冠稳, 等. 基于GIMMS NDVI 3g.v1的近34年青海省植被生长季NDVI时空变化特征[J]. 草业科学, 2018, 35(4): 713-725.