Climatic driving mechanisms of the propagation from meteorological drought to agricultural and ecological droughts

被引:0
作者
Li, Chong [1 ,2 ,3 ]
Fu, Yongshuo [1 ]
Zhao, Qianzuo [1 ]
Zhang, Xuan [1 ]
Ding, Ruiqiang [2 ,3 ]
Hao, Fanghua [1 ]
Yin, Guodong [4 ]
机构
[1] Beijing Normal Univ, Coll Water Sci, Beijing Key Lab Urban Hydrol Cycle & Sponge City, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Key Lab Environm Change & Nat Disasters Chinese, Minist Educ, Beijing 100875, Peoples R China
[4] China Renewable Energy Engn Inst, Beijing 100120, Peoples R China
基金
中国国家自然科学基金;
关键词
Drought propagation; Vegetation response; Climatic factors; Random forest; Jinsha river basin; RIVER-BASIN; WATER; VEGETATION; MODEL; RESPONSES; DYNAMICS; INDEXES; TRENDS; CHINA; NDVI;
D O I
10.1016/j.jenvman.2025.125445
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Droughts significantly impact terrestrial vegetation ecosystems. Understanding the mechanisms by which drought affects ecosystems under different hydrogeological conditions is crucial for ecosystem protection. The aim of this study was to investigate the characteristics and mechanisms of propagation from meteorological drought (MD) to agricultural drought (AD) and ecological drought (ED) in the Jinsha River Basin from 2000 to 2014. The monthly standardized precipitation evapotranspiration index (SPEI), soil moisture index (SSMI), normalized difference vegetation index (SNDVI), and solar-induced chlorophyll fluorescence (SSIF) data were used to investigate the responses of AD and ED to MD. On the basis of the maximum correlation coefficients (MCCs), the differences in the drought propagation times of MD to AD and ED were explored in positively and negatively correlated areas. A random forest algorithm was used to identify the impacts of climatic factors driving drought propagation. The results revealed that AD was mainly positively correlated with MD, whereas the correlation coefficients between ED and MD ranged from negative to positive. The propagation time from MD to AD was relatively short in summer and autumn. In positively correlated areas, the propagation time from MD to ecological drought indicated by NDVI (EDndvi) was longer than that indicated by SIF (EDsif), and the opposite was true in negatively correlated areas. The random forest algorithm results indicated that temperature (T), solar radiation (S) and precipitation (P) were key factors influencing ED in positively correlated areas and that T was an important factor in controlling the occurrence of ED in negatively correlated areas. Solar-induced chlorophyll fluorescence (SIF) was more sensitive to MD and had a shorter response time in positively correlated areas, suggesting its potential for monitoring vegetation growth responses to drought. We found that MD was not the main factor influencing vegetation growth in negatively correlated areas. The findings of this study had significant implications for understanding the mechanisms of the response of vegetation growth to MD and offered scientific guidance for maintaining terrestrial ecosystem health.
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页数:12
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共 85 条
[1]   A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model [J].
Abbaspour, K. C. ;
Rouholahnejad, E. ;
Vaghefi, S. ;
Srinivasan, R. ;
Yang, H. ;
Klove, B. .
JOURNAL OF HYDROLOGY, 2015, 524 :733-752
[2]   Understanding the Role of Climate Characteristics in Drought Propagation [J].
Apurv, Tushar ;
Sivapalan, Murugesu ;
Cai, Ximing .
WATER RESOURCES RESEARCH, 2017, 53 (11) :9304-9329
[3]   Large area hydrologic modeling and assessment - Part 1: Model development [J].
Arnold, JG ;
Srinivasan, R ;
Muttiah, RS ;
Williams, JR .
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 1998, 34 (01) :73-89
[4]   Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India [J].
Banerjee, Abhishek ;
Kang, Shichang ;
Meadows, Michael E. ;
Xia, Zilong ;
Sengupta, Dhritiraj ;
Kumar, Vinod .
ENVIRONMENTAL RESEARCH, 2023, 234
[5]   From meteorological to hydrological drought using standardised indicators [J].
Barker, Lucy J. ;
Hannaford, Jamie ;
Chiverton, Andrew ;
Svensson, Cecilia .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2016, 20 (06) :2483-2505
[6]   Propagation of Meteorological to Hydrological Droughts in India [J].
Bhardwaj, Kunal ;
Shah, Deep ;
Aadhar, Saran ;
Mishra, Vimal .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (22)
[7]   Temperate forest trees and stands under severe drought:: a review of ecophysiological responses, adaptation processes and long-term consequences [J].
Breda, Nathalie ;
Huc, Roland ;
Granier, Andre ;
Dreyer, Erwin .
ANNALS OF FOREST SCIENCE, 2006, 63 (06) :625-644
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   Climate legacy and lag effects on dryland plant communities in the southwestern US [J].
Bunting, Erin L. ;
Munson, Seth M. ;
Villarreal, Miguel L. .
ECOLOGICAL INDICATORS, 2017, 74 :216-229
[10]   On the relation between NDVI, fractional vegetation cover, and leaf area index [J].
Carlson, TN ;
Ripley, DA .
REMOTE SENSING OF ENVIRONMENT, 1997, 62 (03) :241-252