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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (5): 523-544    DOI: 10.1007/s40333-023-0059-7
Research article     
Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China
BAI Miao1,2, LI Zhanling1,2,3,*(), HUO Pengying1,2, WANG Jiawen1,2, LI Zhanjie4
1School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China
2MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, Beijing 100083, China
3State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
4College of Water Sciences, Beijing Normal University, Beijing 100875, China
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Abstract  

In the context of global warming, drought events occur frequently. In order to better understanding the process and mechanism of drought occurrence and evolution, scholars have dedicated much attention on drought propagation, mainly focusing on drought propagation time and propagation probability. However, there are relatively few studies on the sensitivities of drought propagation to seasons and drought levels. Therefore, we took the Heihe River Basin (HRB) of Northwest China as the case study area to quantify the propagation time and propagation probability from meteorological drought to agricultural drought during the period of 1981-2020, and subsequently explore their sensitivities to seasons (irrigation and non-irrigation seasons) and drought levels. The correlation coefficient method and Copula-based interval conditional probability model were employed to determine the drought propagation time and propagation probability. The results determined the average drought propagation time as 8 months in the whole basin, which was reduced by 2 months (i.e., 6 months) on average during the irrigation season and prolonged by 2 months (i.e., 10 months) during the non-irrigation season. Propagation probability was sensitive to both seasons and drought levels, and the sensitivities had noticeable spatial differences in the whole basin. The propagation probability of agricultural drought at different levels generally increased with the meteorological drought levels for the upstream, midstream, and southern downstream regions of the HRB. Lesser agricultural droughts were more likely to be triggered during the irrigation season, while severer agricultural droughts were occurred mostly during the non-irrigation season. The research results are helpful to understand the characteristics of drought propagation and provide a scientific basis for the prevention and control of droughts. This study is of great significance for the rational planning of local water resources and maintaining good ecological environment in the HRB.

Key words： meteorological drought      agricultural drought      drought propagation time      drought propagation probability      Copula function      interval conditional probability      Heihe River Basin     
Received: 21 October 2022      Published: 31 May 2023
Corresponding Authors: *LI Zhanling (E-mail: zhanling.li@cugb.edu.cn)
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BAI Miao, LI Zhanling, HUO Pengying, WANG Jiawen, LI Zhanjie. Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China. Journal of Arid Land, 2023, 15(5): 523-544.

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http://jal.xjegi.com/10.1007/s40333-023-0059-7     OR     http://jal.xjegi.com/Y2023/V15/I5/523

Fig. 1 Overview of the Heihe River Basin (HRB) and locations of hydrological stations. Black solid line is the basin dividing line of the upstream, midstream, and downstream. DEM, digital elevation model.
Fig. 2 Comparison of precipitation data derived from GLDAS-2.0 and GLDAS-2.1 during the period of 2000-2014. GLDAS, Global land data assimilation system.
Fig. 3 Spatial distributions of multi-year average annual precipitation (a), annual average air temperature (b), soil moisture (c), and land use and land cover change (d) in the HRB
Data Unit Spatial and temporal scales Data source
Precipitation kg/(m2•s2) 0.25°×0.25°
Monthly		 https://earthdata.nasa.gov/
Air temperature K
Soil moisture kg/m2
Land use and land cover change / 30 m https://landsat.gsfc.nasa.gov/satellites/landsat-8/
Table 1 Key information of the data used in this study
Fig. 4 Framework of this study. SPEI, standardized precipitation evapotranspiration index; SWDI, soil water deficit index. SPEI-1, SPEI-3, SPEI-6, and SPEI-12 mean SPEI at the 1-, 3-, 6-, 12-month time scales, respectively.
Level Meteorological drought category SPEI values
0 Non-drought SPEI> -0.5
1 Mild drought -1.0<SPEI≤ -0.5
2 Moderate drought -1.5<SPEI≤ -1.0
3 Severe drought -2.0<SPEI≤ -1.5
4 Extreme drought SPEI≤ -2.0
Table 2 Classification of meteorological drought categories based on standardized precipitation evapotranspiration index (SPEI)
Level Agricultural drought category SWDI values
0 Non-drought SWDI>0
1 Mild drought -2<SWDI≤0
2 Moderate drought -5<SWDI≤ -2
3 Severe drought -10<SWDI≤ -5
4 Extreme drought SWDI≤ -10
Table 3 Classification of agricultural drought categories based on soil water deficit index (SWDI)
Copula function type C(u, v) r value range
Archimedean Copula Clayton ( u − r + v − r − 1 ) − 1 r r >0
Gumbel r ≥ 1
Frank − 1 r ln ( e − r u − 1 ) ( e − r v − 1 ) e − r − 1 r ≠ 0
Joe r ≥ 1
Elliptical Copula t ∫ − ∞ t k − 1 ( u ) ∫ − ∞ t k − 1 ( v ) 1 2 π 1 − r 2 exp [ 1 + s 2 − 2 r s t + t 2 k ( 1 − r 2 ) ] − k + 2 2 d s d t r > 0
Table 4 Cumulative density functions and parameter ranges of Copula functions
Fig. 5 Spatial distributions of SPEI-1 (a), SPEI-3 (b), SPEI-6 (c), SPEI-12 (d), and SWDI (e) in the HRB
Fig. 6 Spatial distributions of propagation time from meteorological drought to agricultural drought for the year-round (a), and during the irrigation season (b) and non-irrigation season (c) in the HRB
Fig. 7 Spatial distributions of the Sen's slope estimator values (P<0.05) of propagation time from meteorological drought to agricultural drought for the year-round (a), and during the irrigation season (b) and non-irrigation season (c) in the HRB
Fig. 8 Percentages of different marginal distribution functions for the SPEI and SWDI series. GEV, General extreme value; P3, Pearson-III.
Drought index Optimal marginal distribution function Percentage of grids that passed the K-S test (%)
SPEI-1 GEV 99.95
SPEI-3 GEV 99.98
SPEI-6 GEV 98.13
SPEI-12 GEV 94.07
SWDI P3 81.98
Table 5 Percentages of grids that passed the Kolmogorov-Smirnov (K-S) test for the optimal marginal distribution function
Fig. 9 Percentages of Copula functions for the combinations of SPEI and SWDI series among all grids in the HRB under the Akaike information criterion
Fig. 10 Percentages of Copula functions for the combinations of SPEI and SWDI series among all grids in the HRB under the Bayesian information criterion
Fig. 11 Spatial distributions of the propagation probability values of different agricultural drought levels (i.e., mild, moderate, severe, and extreme drought levels from the top to bottom) under various meteorological drought levels (i.e., mild, moderate, severe, and extreme drought levels from the left to right) at the year-round scale in the HRB
Fig. 12 Spatial distributions of the propagation probability values of different agricultural drought levels (i.e., mild, moderate, severe, and extreme drought levels from the top to bottom) under various meteorological drought levels (i.e., mild, moderate, severe, and extreme drought levels from the left to right) during the irrigation season in the HRB
Fig. 13 Spatial distributions of the propagation probability values of different agricultural drought levels (mild, moderate, severe, and extreme drought levels from the top to bottom) under various meteorological drought levels (i.e., mild, moderate, severe, and extreme drought levels from the left to right) during the non-irrigation season in the HRB
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