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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (10): 1774-1795    DOI: 10.1016/j.jaridl.2026.10.006     CSTR: 32276.14.JAL.20260060
Research article     
Lagged and cumulative vegetation responses to extreme climate and drought in arid and semi-arid ecosystems: Insights from the Yellow River Basin, China
WANG Meiying1, CHEN Yangbo1,*(), Shailesh Kumar SINGH2
1 School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2 National Institute of Water and Atmospheric Research, Christchurch 8011, New Zealand
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Abstract  

Climate extremes strongly influence vegetation dynamics in arid and semi-arid regions, but the mechanisms of vegetation responses remain unclear. Using the Yellow River Basin, China as a case study, we integrated 20 extreme climate indices (11 precipitation-related and 9 temperature-related) and a drought index to assess vegetation responses to climate extremes. An integrated framework combining random forest (RF) and structural equation modeling (SEM) was applied to quantify lagged and cumulative effects and separate the relative impacts of climate change and human activities. The results indicated that: (1) vegetation greenness in the basin increased by 0.004/a, with nearly three-quarters of the area showing a statistically significant upward trend; (2) the frequency of extreme precipitation events increased, while extreme high-temperature events became more frequent and intense, and extreme low-temperature events showed little change; (3) drought exhibited the strongest lagged effect on vegetation, lasting up to 6.7 months, whereas individual extreme event mainly induced cumulative effects on normalized difference vegetation index (NDVI); (4) drought and precipitation primarily affected the duration of vegetation growth, while temperature mainly controlled its disturbance intensity, with diurnal temperature range (DTR) and extreme temperature indices being the most important; and (5) climate change and human activities jointly dominated NDVI increases over 89.5% of the vegetated area, highlighting the high sensitivity and spatial heterogeneity of vegetation responses. These findings improve our understanding of vegetation-climate thresholds in dryland ecosystems and provide a transferable framework for environmental impact assessment and adaptive ecosystem management in arid regions.

Key words： extreme climate events      normalized difference vegetation index (NDVI)      lag and cumulative effects      structural equation modeling (SEM)      human activities     
Received: 03 February 2026      Published: 31 October 2026
Corresponding Authors: *CHEN Yangbo (E-mail: eescyb@mail.sysu.edu.cn)
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Conceptualization: WANG Meiying; Methodology: WANG Meiying, CHEN Yangbo; Formal analysis: WANG Meiying; Writing - original draft preparation: WANG Meiying; Writing - review and editing: WANG Meiying, CHEN Yangbo, Shailesh Kumar SINGH; Funding acquisition: CHEN Yangbo; Resources: CHEN Yangbo; Supervision: WANG Meiying, CHEN Yangbo. All authors approved the manuscript.
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WANG Meiying, CHEN Yangbo, Shailesh Kumar SINGH. Lagged and cumulative vegetation responses to extreme climate and drought in arid and semi-arid ecosystems: Insights from the Yellow River Basin, China. Journal of Arid Land, 2026, 18(10): 1774-1795.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.10.006     OR     http://jal.xjegi.com/Y2026/V18/I10/1774

Fig. 1 Geographic distribution of land cover types in the Yellow River Basin (YRB) in 2020
Fig. 2 Schematic diagram of normalized difference vegetation index (NDVI) response to extreme climate indices. CMA, China Meteorological Administration; PREC, precipitation; TEMP, temperature; RClimDex, R-based Climatic Extremes Index; SPEI, standardized precipitation evapotranspiration index; cum., cumulative; SEM, structural equation model.
Class Abbreviation Indicator name Definition Unit
PREC RX1day Max 1-d precipitation amount Maximum daily precipitation per year mm
RX5day Max 5-d precipitation amount Maximum 5-d consecutive precipitation per year mm
CWD Consecutive wet days Maximum consecutive wet days per year (precipitation ≥1 mm) d
CDD Consecutive dry days Maximum consecutive dry days per year (precipitation <1 mm) d
R10 Rainy days with PREC ≥10 mm Number of days per year with precipitation ≥10 mm d
R20 Rainy days with PREC ≥20 mm Number of days per year with precipitation ≥20 mm d
R25 Rainy days with PREC ≥25 mm Number of days per year with precipitation ≥25 mm d
R95p Very wet days Annual total precipitation on days exceeding the 95th percentile mm
R99p Extremely wet days Annual total precipitation on days exceeding the 99th percentile mm
SDII Simple daily intensity index Annual mean precipitation per wet day (wet days with precipitation ≥1 mm) mm/d
PRCPTOT Annual total wet-day precipitation Annual total precipitation on wet days (days with precipitation ≥1 mm) mm
TEMF TXx Max Tmax Annual maximum of daily maximum temperature °C
TNx Max Tmin Annual maximum of daily minimum temperature °C
TXn Min Tmax Annual minimum of daily maximum temperature °C
TNn Min Tmin Annual minimum of daily minimum temperature °C
DTR Diurnal temperature range Monthly average diurnal temperature range °C
TX10p Cold days Number of days per year when daily maximum temperature falls below the 10th percentile d
TN10p Cold nights Number of days per year when daily minimum temperature falls below the 10th percentile d
TX90p Warm days Number of days per year when daily maximum temperature exceeds the 90th percentile d
TN90p Warm nights Number of days per year when daily minimum temperature exceeds the 90th percentile d
Table 1 Expert Team on Climate Change Detection and Indices (ETCCDI)
Slope(NDVIobs) Driving factor Criteria for classifying driver Contribution of driver
Slope(NDVICC) Slope(NDVIHA) CC HA
>0 CC&HA >0 >0 $\frac{\text{slope}\left({\text{NDVI}}_{\text{CC}}\right)}{\text{slope}\left({\text{NDVI}}_{\text{obs}}\right)}$ $\frac{\text{slope}\left({\text{NDVI}}_{\text{HA}}\right)}{\text{slope}\left({\text{NDVI}}_{\text{obs}}\right)}$
CC >0 <0 100 0
HA <0 >0 0 100
<0 CC&HA <0 <0 $\frac{\text{slope}\left({\text{NDVI}}_{\text{CC}}\right)}{\text{slope}\left({\text{NDVI}}_{\text{obs}}\right)}$ $\frac{\text{slope}\left({\text{NDVI}}_{\text{HA}}\right)}{\text{slope}\left({\text{NDVI}}_{\text{obs}}\right)}$
CC <0 >0 100 0
HA >0 <0 0 100
Table 2 Criteria for identifying normalized difference vegetation index (NDVI) change drivers and calculation of their contributions
Item Upstream Midstream Downstream YRB
Mean 0.290 0.410 0.480 0.340
Magnitude of change 0.003 0.006 0.005 0.004
Table 3 Multi-year mean and variation amplitude of NDVI in different sub-regions of the Yellow River Basin (YRB) from 2001 to 2020
Fig. 3 Spatial variation and significance analysis of NDVI in the YRB from 2001 to 2020. (a), mean NDVI; (b) Sen's-based NDVI trend; (c), significance levels and their percentages. ESI, SI, SSI, NSI, NC, NSD, SSD, SD, and ESD represent extremely significant increase, significant increase, slightly significant increase, non-significant increase, no change, non-significant decrease, slightly significant decrease, significant decrease, and extremely significant decrease, respectively; (d), sub-regions I-VIII indicate areas experiencing a highly significant decline in NDVI.
Fig. 4 Spatial trend slopes and corresponding annual time series of 11 extreme precipitation indices in the YRB from 2001 to 2020. (a), maximum daily precipitation per year (RX1day); (b), maximum 5-d consecutive precipitation per year (RX5day); (c), consecutive wet days (CWD); (d), consecutive dry days (CDD); (e), number of days per year with precipitation ≥10 mm (R10); (f), number of days per year with precipitation ≥20 mm (R20); (g), number of days per year with precipitation ≥25 mm (R25); (h), annual total precipitation on days exceeding the 95th percentile (R95p); (i), annual total precipitation on days exceeding the 99th percentile (R99p); (j), simple daily intensity index (SDII); and (k), annual total wet-day precipitation (PRCPTOT). The upper sub-panels show the annual time series and linear trends, with shaded areas representing 95.0% confidence intervals, whereas the lower subpanels show the spatial distribution of trend slopes for each index.
Fig. 5 Spatial trend slopes and annual time series of 9 extreme temperature indices in the YRB from 2001 to 2020. (a), annual maximum of daily maximum temperature (TXx); (b), annual maximum of daily minimum temperature (TNx); (c), annual minimum of daily maximum temperature (TXn); (d), annual minimum of daily minimum temperature (TNn); (e), diurnal temperature range (DTR); (f), cold days (TX10p); (g), cold nights (TN10p); (h), warm days (TX90p); and (i), warm nights (TN90p). The upper subpanels show the annual time series and linear trends, with shaded areas representing 95.0% confidence intervals, whereas the lower subpanels show the spatial distribution of trend slopes for each index.
Fig. 6 Time-lagged and cumulative effects of extreme precipitation indices on NDVI in the YRB from 2001 to 2020. (a), RX1day&NDVI; (b), RX5day&NDVI; (c), CWD&NDVI; (d), CDD&NDVI; (e), R10&NDVI; (f), R20&NDVI; (g), R25&NDVI; (h), R95p&NDVI; (i), R99p&NDVI; (j), SDII&NDVI; (k), PRCPTOT&NDVI. TLAi-j denotes an i-month lag and a j-month cumulative period. The pie charts show the area proportions of different temporal response types and the first three values are shown.
Fig. 7 Time-lagged and cumulative effects of extreme temperature indices on NDVI in the YRB from 2001 to 2020. (a), TXx&NDVI; (b), TNx&NDVI; (c), TXn&NDVI; (d), TNn&NDVI; (e), DTR&NDVI; (f), TX10p&NDVI; (g), TN10p&NDVI; (h), TX90p&NDVI; (i), TN90p&NDVI. TLAi-j denotes an i-month lag and a j-month cumulative period. The pie charts show the area proportions of different temporal response types and the first three values are shown.
Fig. 8 Area percentage and spatial distribution of lagged and cumulative effects of drought on vegetation in the YRB. (a1-a4), area percentages of optimal lagged months for the whole, upstream, midstream, and downstream regions, respectively; (b1-b4), area percentages of optimal cumulative months for the whole, upstream, midstream, and downstream regions, respectively; (c), spatial distribution of lagged month; (d), spatial distribution of cumulative month; (e), maximum lagged correlation coefficient (Rmax_lag); (f), maximum cumulative correlation coefficient (Rmax_cum). The red dashed lines in Figure a1-a4 and b1-b4 indicate the mean optimal lagged and cumulative months for the whole region and sub-regions, respectively.
Fig. 9 Radar plots showing the cumulative (a) and lagged (b) effects of extreme climate indices on NDVI and effects of Standardized Precipitation Evapotranspiration Index (SPEI) on NDVI across different land cover types (c)
Fig. 10 Impacts of extreme climate indices on vegetation across different land cover types based on random forest (RF) model and structural equation modelling (SEM). (a1-a5), RF-based feature importance rankings for cropland, forest, grassland, desert, and the whole region, respectively; (b1-b5), SEM results for cropland, forest, grassland, desert, and the whole region, respectively. %IncMSE, percentage increase in mean squared error. In SEM diagrams, numbers on arrows represent standardized path coefficients, and solid and dashed arrows indicate positive and negative effects, respectively. df, degrees of freedom; p, probability; RMSEA, root mean square error of approximation. * and ** indicate significance at P<0.05 and P<0.01 levels, respectively; ns, no significance.
Fig. 11 Spatial distribution and contribution rate of climate change (CC) and human activity (HA) to NDVI in the YRB from 2001 to 2020. (a), spatial distribution of driving factors to NDVI. Table in Figure 11a categorizes NDVI trends into increase and decrease, exploring the impacts of CC&HA, CC, and HA on NDVI changes and their area percentages; (b), contribution rate of HA to NDVI; (c), contribution rate of CC to NDVI.
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