基于三维识别的中国干旱事件演变特征分析

doi:10.13866/j.azr.2023.06.01

Abstract

Abstract:

Globally, China is one of the country’s most frequently and severely affected by drought. These frequent drought events have subsequently caused serious economic, social development, and ecological environment losses. Drought simultaneously leads to alterations in both space and time, and this paper aims to identify drought events and quantify their spatial and temporal dynamic evolution using a three-dimensional clustering algorithm for mainland China, from 1981-2020. The three-dimensional clustering algorithm can be used to effectively identify drought events and describe their dynamic processes. From 1981-2020 there were 102 drought events lasting 2 months or more in mainland China. Spatially, the trajectory of drought events was found to show a tendency to develop from east to west. Temporally, there are high time overlaps between different drought events. In addition, drought events with wide coverage and high severity were concentrated in the period from 2005-2010. The findings of this paper will help to elucidate the spatial and temporal evolution patterns of drought events and provide scientific references for drought monitoring and drought risk management in mainland China.

Key words: three-dimensional clustering algorithm, drought event, three-dimensional evolutionary features, dynamic evolution, SPEI, China

LYU Xiaoyu, GUO Hao, MENG Xiangchen, BAO Anming, TIAN Yunfei, ZHU Li. Characterization of the evolution of drought events in China based on 3D identification[J].Arid Zone Research, 2023, 40(6): 849-962.

Figures/Tables 11

Fig. 1

Tab. 1

The calculation steps of SPEI"

步骤	公式	备注
1	$D j = P j - P E T j$	j表示月份； $D j$ 表示第j月的降水（ $P j$ ）与该月潜在蒸散发（PET_j）的差值
2	$X i, j k = ∑ l = 13 - k + j 12 D i - 1, l + ∑ l = 1 j D i, ? l, ? j < k X i, j k = ∑ l = j - k + j j D i - 1, l, ? ? j ≥ k$	$X i, j k$ 表示降水（ $P j$ ）与潜在蒸散发（PET_j）在k个月时间尺度上第i年j月的累加值
3	$F (X) = 1 + α X - γ β - 1$	$F (X)$ 是根据log-logistic分布得出的 $D j$ 序列的概率分布函数，其中α、β和γ分别代表尺度系数、形状系数和 origin 参数
4	$β = 2 w 1 - w 0 6 w 1 - w 0 - 6 w 2 α = w 0 - 2 w 1 β τ 1 + 1 β τ 1 - 1 β γ = w 0 - α τ 1 + 1 β τ 1 - 1 β$	$w 0$ 、 $w 1$ 、 $w 2$ 表示log-logistic分布的3个矩比参数，采用线性矩方法（L-moment）拟合获得； $τ$ 是Gamma分布函数
5	$p = 1 - F (x)$ $w = - 2 l n (p) - 2 l n (1 - p), ? ? p ≤ 0.5, ? ? p > 0.5$ $S P E I = w - c 0 + c 1 w + c 2 w 2 1 + d 1 w + d 2 w 2 + d 3 w 3$	p是标准化的分布函数值；w是一个参数； $c 0$ =2.515517， $c 1$ =0.802853， $c 2$ =0.010328， $d 1$ =1.432788， $d 2$ =0.189269和 $d 3$ =0.001308，相关参数来自于Vicente-Serrano等^[32]

Tab. 1

Fig. 2

Tab. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

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序号	起始时间/年-月	干旱历时 /个月	干旱烈度	干旱严重度 /10⁷ km²	干旱面积 /10⁶ km²	干旱质心			干旱质心路径/km	备注
序号	起始时间/年-月	干旱历时 /个月	干旱烈度	干旱严重度 /10⁷ km²	干旱面积 /10⁶ km²	纬度（N）	经度（E）	时间/年-月	干旱质心路径/km	备注
1	2008-08—2009-09	14	0.44	4.91	8.00	38.13	103.13	2009-04	5553.09	干旱事件d
2	2010-11—2011-11	13	0.51	3.67	5.52	32.13	109.88	2011-04	4377.83
3	2018-10—2020-07	22	0.40	3.32	3.78	41.88	120.63	2019-07	8027.61	干旱事件f
4	1998-10—1999-06	9	0.61	3.30	6.01	33.38	105.13	1999-02	3278.50	干旱事件b
5	2001-02—2002-04	15	0.36	2.89	5.40	40.63	112.88	2001-09	6381.09
6	2004-02—2004-11	10	0.40	2.66	6.70	39.38	106.38	2004-06	7401.21
7	2009-09—2010-07	11	0.44	2.58	5.37	31.63	101.63	2010-01	4907.81
8	2017-03—2018-06	16	0.31	2.37	4.82	41.38	108.63	2017-12	7257.69
9	2013-07—2014-09	15	0.28	2.30	5.57	36.38	103.63	2013-12	7550.17
10	2015-07—2016-06	12	0.40	2.19	4.53	34.88	91.63	2015-12	3549.72
11	1996-10—1997-12	15	0.28	2.07	4.94	35.13	111.13	1997-08	4298.00
12	2012-12—2013-05	6	0.68	2.05	5.04	35.38	100.63	2013-03	2246.02	干旱事件e
13	1984-03—1985-05	15	0.51	2.01	2.64	34.88	89.13	1984-10	3309.17
14	1986-08—1987-09	14	0.32	1.74	3.89	32.63	98.38	1987-03	4501.10	干旱事件a
15	2007-06—2008-05	12	0.90	1.74	1.61	46.88	123.13	2007-11	1444.02	干旱事件c
16	2006-03—2006-10	8	0.48	1.70	4.39	34.38	106.63	2006-07	2247.29
17	2014-07—2015-05	11	0.27	1.44	4.93	38.88	110.38	2015-01	6247.83
18	1991-04—1991-12	9	0.37	1.35	4.02	33.38	108.13	1991-09	3548.70
19	2019-09—2020-08	12	0.33	1.34	3.37	40.63	94.63	2020-03	6040.55
20	2017-11—2018-07	9	0.38	1.34	3.93	31.13	98.63	2018-02	3783.17

Characterization of the evolution of drought events in China based on 3D identification

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