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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (10): 1361-1377    DOI: 10.1007/s40333-025-0029-3     CSTR: 32276.14.JAL.02500293
Research article     
Spatiotemporal variation of drought and its influential factors in the Yellow River Basin, China based on vegetation health index
Haoriwa1,2, Zhalagahu3, ZHOU Ruiping1,2,*()
1College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
2Inner Mongolia Land Use and Improvement Project Research Center, Hohhot 010022, China
3College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010022, China
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Abstract  

Drought is a natural disaster that significantly impacts the Earth's ecological environment, especially in arid and semi-arid areas. However, drought at a large watershed scale, which plays an important role in sustainable environmental development, has received limited attention. In this study, we analyzed the spatial and temporal variations in drought in the Yellow River Basin, China from 2002 to 2022 and its driving factors using a vegetation health index (VHI). Results showed that average VHI in the Yellow River Basin from 2002 to 2022 was 0.581, with the most severe drought occurring in summer and autumn. The basin showed a slow decreasing trend in drought during the study period. Regarding spatial distribution of monthly drought frequency and trend of VHI, the mean of the frequency was 13.00%, and 78.00% had a drought frequency of 10.00%-20.00%, with moderate drought generally prevailing. Regarding land use types, forest land, grassland, agricultural land, construction land, water body, and wasteland showed a descending order for the annual average VHI. VHI of each land use type was the lowest in summer and autumn, with pronounced seasonal characteristics. The uneven distribution of drought in the Yellow River Basin was primarily influenced by annual precipitation, solar-induced chlorophyll fluorescence, and relative humidity. VHI effectively quantified drought conditions at a regional scale and proved to be highly applicable in the Yellow River Basin. The results clarify the effectiveness of VHI for drought monitoring in the Yellow River Basin and can provide a reference for drought monitoring across the basin.

Key words： aridity index      drought frequency      land use      humidity      precipitation      Geodetector     
Received: 17 December 2024      Published: 31 October 2025
Corresponding Authors: *ZHOU Ruiping (E-mail: 20041310@imnu.edu.cn)
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Haoriwa
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Haoriwa, Zhalagahu, ZHOU Ruiping. Spatiotemporal variation of drought and its influential factors in the Yellow River Basin, China based on vegetation health index. Journal of Arid Land, 2025, 17(10): 1361-1377.

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http://jal.xjegi.com/10.1007/s40333-025-0029-3     OR     http://jal.xjegi.com/Y2025/V17/I10/1361

Fig. 1 Topography (a) and land use cover (b) of the Yellow River Basin, China. The figure of the Yellow River Basin is based on the standard map (GS(2024)0650) of the National Geomatics Center of China (https://www.ngcc.cn/), and the boundary of the standard map has not been modified. DEM, digital elevation model.
Classification of drought TCI VCI VHI
Extreme drought 0.000-0.100 0.000-0.100 0.000-0.100
Severe drought 0.100-0.200 0.100-0.200 0.100-0.200
Moderate drought 0.200-0.300 0.200-0.300 0.200-0.300
Mild drought 0.300-0.400 0.300-0.400 0.300-0.400
General drying 0.400-0.500 0.400-0.500 -
No drought 0.500-1.000 0.500-1.000 0.400-1.000
Table 1 Classification of drought levels based on different indices
Basis of judgement Interaction
q(X1∩X2)<min(q(X1), q(X2)) Nonlinear attenuation
min(q(X1), q(X2))<q(X1∩X2)<max(q(X1), q(X2)) Single-factor nonlinear attenuation
q(X1∩X2)<max(q(X1), q(X2)) Two-factor enhancement
q(X1∩X2)<q(X1)+q(X2) Separate
q(X1∩X2)>q(X1)+q(X2) Nonlinear enhancement
Table 2 Categorization of interactions among factors influencing drought index
Influencing factor Abbreviation Data description Source
Digital elevation model DEM Generated based on Latest SRTM v.4.1 data resampling https://www.resdc.cn
Wind erosion - Estimated based on erosion equation and wind erosion equation
Water conservation - Obtained based on moisture regulation effect equation, modified generalized erosion equation, and modified wind erosion equation model
Soil conservation -
Wind protection and sand fixation -
Actual evaporation ET Obtained based on MODIS surface evapotranspiration, ET, and PET (MOD16A2)
Potential evaporation PET
Relative humidity RH Obtained based on station daily observation and interpolation by ANUSPLIN v.3.2 software
Average annual temperature TEM
Annual precipitation PRE
Solar-induced chlorophyll fluorescence GOSIF Obtained by reanalysis based on Orbiting Carbon Observatory-2 discrete GOSIF data and MODIS data, with a resolution of 0.05° https://globalecology.unh.edu
Table 3 Description of factor information
Fig. 2 Changes in TCI (Temperature Condition Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) at different time scales in the Yellow River Basin from 2002 to 2022. (a), spring; (b), summer; (c), autumn; (d), winter; (e), annual scale; (f), monthly scale. The abbreviations are the same as in the following figures.
Fig. 3 Annual trends of TCI, VCI, and VHI across different sub-basins in the Yellow River Basin from 2002 to 2022. (a), Lanzhou Basin; (b), Lanzhou-Hekou Basin; (c), middle reaches of the Yellow River Basin; (d), lower reaches of the Yellow River Basin.
Fig. 4 TCI (a1-a4), VCI (b1-b4), and VHI (c1-c4) distribution in the Yellow River Basin in different seasons from 2002 to 2022
Fig. 5 Drought trends (a-c) and frequency (d-f) distributions of TCI, VCI, and VHI in the Yellow River Basin from 2002 to 2022
Fig. 6 Changes in annual (a) and monthly (b) VHI for each land use type in the Yellow River Basin from 2002 to 2022
Fig. 7 Factors affecting VHI in the Yellow River Basin. (a), water conservation; (b), wind erosion; (c), soil conservation; (d), wind protection and sand fixation; (e), GOSIF (solar-induced chlorophyll fluorescence); (f), ET (evaporation); (g), PET, (potential evaporation); (h), RH (relative humidity); (i), TEM (temperature); (j), PRE (precipitation). The abbreviations are the same as in the following table.
Factor code Factor name Explanatory power (q-value)
X1 DEM 0.110
X2 Water conservation 0.199
X3 Wind erosion 0.150
X4 Soil conservation 0.045
X5 Wind protection and sand fixation 0.157
X6 GOSIF 0.290
X7 ET 0.123
X8 PET 0.236
X9 RH 0.263
X10 TEM 0.121
X11 PRE 0.364
Table 4 Detection results of factors affecting VHI
Factor A Factor B Interaction between factor A and factor B
X1 X2, 3, X5, X7, X8, X9, X10, and X11 Nonlinear enhancement
X4 and X6 Two-factor enhancement
X2 X3, X4, X5, X6, X7, X8, X9, X10, and X11 Nonlinear enhancement
X3 X4, X5, X6, X7, X8, X9, X10, and X11 Nonlinear enhancement
X4 X5, X6, X8, X9, and X10 Nonlinear enhancement
X7 and X11 Two-factor enhancement
X5 X6, X7, X8, X9, X10, and X11 Nonlinear enhancement
X6 X7, X8, X9, X10, and X11 Nonlinear enhancement
X7 X8, X9, X10, and X11 Nonlinear enhancement
X8 X9, X10, and X11 Nonlinear enhancement
X9 X10 and X11 Nonlinear enhancement
X10 X11 Nonlinear enhancement
Table 5 Interaction detection results
Fig. 8 Interaction effects of influence factors on VHI in the Yellow River Basin
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