Spatial-temporal distribution characteristics and influencing factors of dry and wet in Guangdong Province based on CWSI

doi:10.11755/j.issn.1006-7639(2024)-04-0527

Abstract

Abstract:

Analyzing the evolution of dry and wet in Guangdong Province is of great significance for strengthening water resource utilization and maintaining ecological environment stability. Based on evapotranspiration product of MODIS (Moderate Resolution Imaging Spectroradiometer) and meteorological observation stations data, choosing CWSI (Crop Water Stress Index) as the index of drought, Mann-Kendall (M-K) trend analysis and contribution methods were adopted to spatial-temporal distribution characteristics of CWSI as well as dominant meteorological factors. The results were indicated as follows: the annual CWSI indicated a downward trend through M-K trend test (-2.8×10^-3 per year), season in year with the most significant downward trend was in winter (-7.0×10^-3per year), followed by autumn and spring, and the weakest was in summer. The annual CWSI was characterized by high (dry) in the southeast and low (wet) in the northwest. The CWSI in northern Guangdong, Pearl River Delta, and some areas in western Guangdong were at a lower degree of variation, while in Zhaoqing and the eastern coastal areas of Guangdong were relatively high. The CWSI in western Guangdong were all located at a lower degree of variation during the four seasons. The change of relative humidity was the overall dominant factor in the trend change of CWSI in Guangdong region, with spatial relative humidity being the dominant factor at 44.19% stations in Guangdong, followed by wind speed (22.09%), temperature (18.61%), and sunshine duration (15.11%).

Key words: MODIS, CWSI, dry and wet change, Guangdong Province

摘要：

分析广东省的干湿演变规律对加强水资源利用和维持生态环境稳定具有重要意义。本文基于2001—2021年中分辨率成像光谱仪（Moderate Resolution Imaging Spectroradiometer，MODIS）卫星蒸散遥感数据和86个国家气象站气温等观测数据，以作物缺水指数（Crop Water Stress Index， CWSI）为指标，采用Mann-Kendall趋势检验和贡献度分析方法，探讨广东省干湿时空分布特征及其主导气象因素。结果表明，广东省年CWSI呈显著下降趋势（-2.8×10^-3a^-1），四季中冬季下降趋势最明显（-7.0×10^-3 a^-1），其次为秋季和春季，夏季最弱。空间分布上，年CWSI东南高（干）、西北低（湿）。广东北部、珠三角和西部部分地区的CWSI变异程度较低，肇庆和广东东部沿海地区则较高。四季中，广东西部CWSI值的变异程度普遍较低。相对湿度的变化是广东省CWSI趋势变化的主要驱动因素，影响了44.19%的站点，其次是风速（22.09%）、气温（18.61%）和日照时数（15.11%）。

关键词: MODIS, 作物缺水指数, 干湿变化, 广东省

CLC Number:

P462.2

WANG Min, CHEN Xiaoyang, LIU Yanqun, LIANG Jian, HUANG Guanrong, LI Caoming, LI Xuan. Spatial-temporal distribution characteristics and influencing factors of dry and wet in Guangdong Province based on CWSI[J]. Journal of Arid Meteorology, 2024, 42(4): 527-535.

王敏, 陈晓旸, 刘艳群, 梁健, 黄观荣, 李曹明, 李轩. 基于CWSI的广东省干湿时空分布特征及影响因素分析[J]. 干旱气象, 2024, 42(4): 527-535.

Figures/Tables 9

Fig.1 Spatial distribution of meteorological observation stations in Guangdong Province

Fig.2 The inter-annual variation of CWSI and its anomaly in Guangdong Province during 2001-2021

Fig.3 Spatial distribution of multi-year average (a) and trend (b, Units: 10-3 a-1) of CWSI and their significance (c) in Guangdong Province during 2001-2021

Fig.4 Spatial distributions of the CV of CWSI in Guangdong Province during 2001-2021 (a) and in spring (b), summer (c), autumn (d) and winter (e)

Tab.1

时段	波动等级
时段	低波动	较低波动	中等波动	较高波动	高波动
春	0	0.28	5.97	47.01	46.74
夏	0	0.82	20.26	39.69	39.23
秋	0.03	12.17	46.46	29.92	11.42
冬	0	0.26	19.41	56.61	23.72
年	0.01	21.40	63.82	11.52	3.25

Fig.5 Spatial distributions of correlation coefficient of CWSI with wind speed (a), relative humidity (b), sunshine duration (c) and temperature (d) in Guangdong Province during 2001-2021 (The green circles indicate passed the significance test at α=0.05)

Fig.6 Scatter plots of TCWSI and CTCWSI in Guangdong Province during 2001-2021

Fig.7 Spatial distributions of CTCWSI (a) and contribution of wind speed (b), relative humidity (c), sunshine duration (d), temperature (e) to CWSI in Guangdong Province during 2001-2021 （The asterisks represent positive contribution, the triangles represent negative contribution）

Fig.8 Spatial distributions of dominant meteorological factors to CTCWSI in Guangdong Province during 2001-2021

References 31

[1]	陈洁, 刘玉洁, 潘韬, 等, 2019. 1961—2010年中国降水时空变化特征及对地表干湿状况影响[J]. 自然资源学报, 34(11): 2 440-2 453.
[2]	褚荣浩, 李萌, 谢鹏飞, 等, 2021. 安徽省近20年地表蒸散和干旱变化特征及其影响因素分析[J]. 生态环境学报, 30(6): 1 229-1 239.
[3]	邓兴耀, 刘洋, 刘志辉, 等, 2017. 中国西北干旱区蒸散发时空动态特征[J]. 生态学报, 37(9): 2 994-3 008.
[4]	李召旭, 王建国, 黄伟杰, 等, 2023. 从水旱灾害风险普查谈广东地区抗旱减灾能力建设[J]. 中国防汛抗旱, 33(5): 42-46.
[5]	马梓策, 孙鹏, 张强, 等, 2022. 基于MODIS数据的华北地区遥感干旱监测研究[J]. 地理科学, 42(1): 152-162. DOI
[6]	石欣荣, 佘敦先, 夏军, 等, 2022. 1960—2019年三北地区潜在蒸散发的变化及归因[J]. 武汉大学学报(工学版), 55(10): 973-984.
[7]	史丽, 张柳红, 伍红雨, 2021. 1994—2018年广东主要气象灾害特征分析[J]. 广东气象, 43(2): 54-57.
[8]	王敏, 尹义星, 陈晓旸, 等, 2022. 基于SPEI的近百年天津地区气象干旱时空演变特征[J]. 干旱气象, 40(1): 11-21. DOI
[9]	王莹, 张舒, 徐永清, 等, 2023. 近50 a黑龙江省5—9月气象干旱及大气环流异常特征[J]. 干旱气象, 41(4): 540-549. DOI
[10]	吴天晓, 李宝富, 郭浩, 等, 2023. 基于优选遥感干旱指数的华北平原干旱时空变化特征分析[J]. 生态学报, 43(4): 1 621-1 634.
[11]	武荣盛, 侯琼, 杨玉辉, 等, 2021. 多时间尺度气象干旱指数在内蒙古典型草原的适应性研究[J]. 干旱气象, 39(2): 177-184.
[12]	许敏, 江鹏, 2020. 基于MODIS和ERA-Interim的安徽省地表蒸散发及其受植被覆盖度影响研究[J]. 水资源与水工程学报, 31(2): 253-260.
[13]	余慧倩, 张强, 孙鹏, 等, 2019. 干旱强度及发生时间对华北平原五省冬小麦产量影响[J]. 地理学报, 74(1): 87-102. DOI
[14]	余兴湛, 蒲义良, 康伯乾, 2022. 基于SPEI的广东省近50 a干旱时空特征[J]. 干旱气象, 40(6): 1 051-1 058.
[15]	周惜荫, 李谢辉, 2021. 1978—2017年西南地区干湿时空变化特征[J]. 干旱气象, 39(3): 357-365.
[16]	IPCC, 2012. Managing the risks of extreme events and disasters to advance climate change adaptation: A special report of the Intergovernmental Panel on Climate Change[M]. Cambridge and New York: Cambridge University Press:582.
[17]	JACKSON R D, IDSO S B, REGINATO R J, et al, 1981. Canopy temperature as a crop water stress indicator[J]. Water Resources Research, 17(4): 1 133-1 138.
[18]	KENDALL M G, 1957. Rank correlation methods[J]. Biometrika, 44(1/2): 298-298.
[19]	LI M, CHU R H, SHEN S H, et al, 2018. Quantifying climatic impact on reference evapotranspiration trends in the Huai River Basin of eastern China[J]. Water, 2018, 10(2): 144-167.
[20]	MA Z C, SUN P, ZHANG Q, et al, 2021. Characterization and evaluation of MODIS-derived crop water stress index (CWSI) for monitoring drought from 2001 to 2017 over inner Mongolia[J]. Sustainability, 13(2): 916-932.
[21]	MANN H B, 1945. Nonparametric tests against trend[J]. Econometrica, 13(3): 245-259.
[22]	MILICH L, WEISS E, 2000. GAC NDVI interannual coefficient of variation (CoV) images: Ground truth sampling of the Sahel along north-south transects[J]. International Journal of Remote Sensing, 21(2): 235-260.
[23]	MU Q Z, HEINSCH F A, ZHAO M S, et al, 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data[J]. Remote Sensing of Environment, 111(4): 519-536.
[24]	MU Q Z, ZHAO M S, RUNNING S W, 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 115(8): 1 781-1 800.
[25]	SALINGER M J, STIGTER C J, DAS H P, 2000. Agrometeorological adaptation strategies to increasing climate variability and climate change[J]. Agricultural and Forest Meteorology, 103(1/2): 167-184.
[26]	SEN P K, 1968. Estimates of the regression coefficient based on Kendall’s tau[J]. Journal of the American Statistical Association, 63(324): 1 379-1 389.
[27]	SHIRU M S, SHAHID S, CHUNG E S, et al, 2019. Changing characteristics of meteorological droughts in Nigeria during 1901-2010[J]. Atmospheric Research, 223: 60-73.
[28]	THEIL H, 1992. A rank-invariant method of linear and polynomial regression analysis[M]. Advanced studies in theoretical and applied econometrics. Dordrecht: Springer Netherlands: 345-381.
[29]	VICENTE-SERRANO S M, BEGUERÍA S, LÓPEZ-MORENO J I, 2010. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index[J]. Journal of Climate, 23(7): 1 696-1 718.
[30]	WANG Y, LIU Y B, JIN J X, 2018. Contrast effects of vegetation cover change on evapotranspiration during a revegetation period in the Poyang Lake basin, China[J]. Forests, 9(4): 217-231.
[31]	WEST H, QUINN N, HORSWELL M, 2019. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities[J]. Remote Sensing of Environment, 232: 111291. DOI: 10.1016/J.RSE.2019.111291.