Impact of extreme weather and climate events on crop yields in the Tarim River Basin, China

doi:10.1007/s40333-025-0094-7

Journal of Arid Land

2025, Vol. 17

Issue (2): 200-223 DOI: 10.1007/s40333-025-0094-7 CSTR: 32276.14.JAL.02500947

Research article

Impact of extreme weather and climate events on crop yields in the Tarim River Basin, China

WANG Xiaochen¹^,², LI Zhi¹^,^*(

), CHEN Yaning¹, ZHU Jianyu¹^,³, WANG Chuan¹^,², WANG Jiayou¹^,², ZHANG Xueqi¹, FENG Meiqing¹^,², LIANG Qixiang¹^,²

¹State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
²University of the Chinese Academy of Sciences, Beijing 100049, China
³Chifeng Institute of Agricultural and Animal Husbandry Science, Inner Mongolia, Chifeng 024000, China

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Abstract

The Tarim River Basin (TRB) is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China. In the context of climate change, however, the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production. To better understand how unfavorable climatic conditions affect crop production, we explored the relationship of extreme weather and climate events with crop yields and phenology. In this research, ten indicators of extreme weather and climate events (consecutive dry days (CDD), min Tmax (TXn), max Tmin (TNx), tropical nights (TR), warm days (Tx90p), warm nights (Tn90p), summer days (SU), frost days (FD), very wet days (R95p), and windy days (WD)) were selected to analyze the impact of spatial and temporal variations on the yields of major crops (wheat, maize, and cotton) in the TRB from 1990 to 2020. The three key findings of this research were as follows: extreme temperatures in southwestern TRB showed an increasing trend, with higher extreme temperatures at night, while the occurrence of extreme weather and climate events in northeastern TRB was relatively low. The number of FD was on the rise, while WD also increased in recent years. Crop yields were higher in the northeast compared with the southwest, and wheat, maize, and cotton yields generally showed an increasing trend despite an earlier decline. The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices (TNx, Tn90p, TR, and FD), extreme daytime temperature indices (TXn, Tx90p, and SU), extreme precipitation indices (CDD and R95p), and extreme wind (WD). By using Random Forest (RF) approach to determine the effects of different extreme weather and climate events on the yields of different crops, we found that the importance of extreme precipitation indices (CDD and R95p) to crop yield decreased significantly over time. As well, we found that the importance of the extreme nighttime temperature (TR and TNx) for the yields of the three crops increased during 2005-2020 compared with 1990-2005. The impact of extreme temperature events on wheat, maize, and cotton yields in the TRB is becoming increasingly significant, and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.

Key words： extreme events extreme nighttime heat Tarim River Basin crop yield random forest model wheat maize cotton phenology

Received: 28 August 2024 Published: 28 February 2025

Corresponding Authors: *LI Zhi (E-mail: liz@ms.xjb.ac.cn)

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	WANG Xiaochen
	LI Zhi
	CHEN Yaning
	ZHU Jianyu
	WANG Chuan
	WANG Jiayou
	ZHANG Xueqi
	FENG Meiqing
	LIANG Qixiang

Cite this article:

WANG Xiaochen, LI Zhi, CHEN Yaning, ZHU Jianyu, WANG Chuan, WANG Jiayou, ZHANG Xueqi, FENG Meiqing, LIANG Qixiang. Impact of extreme weather and climate events on crop yields in the Tarim River Basin, China. Journal of Arid Land, 2025, 17(2): 200-223.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0094-7 OR http://jal.xjegi.com/Y2025/V17/I2/200

Fig. 1 Overview of the Tarim River Basin (TRB) in 2020. DEM, digital elevation model.

Table 1 Details of the 22 meteorological stations used in this study

Fig. 2 Flowchart of this study. CDD, consecutive dry days; TXn, min Tmax; TNx, max Tmin; TR, tropical nights; Tx90p, warm days; Tn90p, warm nights; SU, summer days; FD, frost days; R95p, very wet days; WD, windy days.

Table 2 Typical extreme weather and climate event indices

Fig. 3 Temporal and spatial distribution of trends and index changes of typical extreme weather and climate events in the TRB from 1990 to 2020. (a), TNx; (b), TXn; (c), CDD; (d), TR; (e), Tx90p; (f), Tn90p; (g), SU; (h), FD; (i), R95p; (j), WD. In the left figures, positive triangles indicate increase, inverted triangles represent decrease, and the symbol "-" denotes no change. Hollow triangles indicate no significant trend, and red and green colors signify a significant increase and a significant decrease, respectively. In the right figures, the black horizontal line represents the median, the boundaries of the boxed line correspond to the 25^th and 75^th percentiles (quartiles), and black dots denote outliers. 1, Aksu City; 2, Xinhe County; 3, Awati County; 4, Wensu County; 5, Kuqa County; 6, Xayar County; 7, Korla City; 8, Hoxud County; 9, Luntai County; 10, Yuli County; 11, Ruoqiang County; 12, Qiemo County; 13, Zepu County; 14, Kashi City; 15, Yopurgha County; 16, Yengisar County; 17, Shache County; 18, Markit County; 19, Bachu County; 20, Hotan City; 21, Yutian County; 22, Akto County.

Fig. 4 Graphs of wheat (a), maize (b), and cotton (c) yields and their variation trends from 1990 to 2020. The yellow dashed line indicates the trend, and the gray area in the heat map represents missing data.

Fig. 5 Pearson correlation coefficients of wheat (a), maize (b), and cotton (c) yields with extreme weather and climate indices at 22 sites in the TRB from 1990 to 2020. ^*, significance at P≤0.05 level; ^**, significance at P≤0.01 level.

Fig. 6 Variable importance of typical extreme weather and climate events for different crops after standardization. (a), 1990-2005; (b), 2005-2020; (c), 1990-2020.

Fig. 7 Intra-annual distribution of the phenological periods for wheat (a), maize (b), and cotton (c) in Hotan City from 2016 to 2020

Fig. S1 Temporal and spatial distribution of trends and index changes of typical extreme weather and climate events during the wheat growing season in the TRB. (a), TNx; (b), TXn; (c), CDD; (d), TR; (e), Tx90p; (f), Tn90p; (g), SU; (h), FD; (i), R95p; (j), WD. In the left figures, positive triangles indicate an increase, inverted triangles represent a decrease. Hollow triangles indicate no significant trend, and red and green colors signify a significant increase and a significant decrease, respectively. In the right figures, the black horizontal line represents the median, the boundaries of the boxed line correspond to the 25^th and 75^th percentiles (quartiles), and black dots denote outliers.

Fig. S2 Temporal and spatial distribution of trends and index changes of typical extreme weather and climate events during the maize growing season in the TRB. (a), TNx; (b), TXn; (c), CDD; (d), TR; (e), Tx90p; (f), Tn90p; (g), SU; (h), FD; (i), R95p; (j), WD. In the left figures, positive triangles indicate an increase, inverted triangles represent a decrease, and the symbol "-" denotes no change. Hollow triangles indicate no significant trend, and red and green colors signify a significant increase and a significant decrease, respectively. In the right figures, the black horizontal line represents the median, the boundaries of the boxed line correspond to the 25^th and 75^th percentiles (quartiles), and black dots denote outliers.

Fig. S3 Temporal and spatial distribution of trends and index changes of typical extreme weather and climate events during the cotton growing season in the TRB. (a), TNx; (b), TXn; (c), CDD; (d), TR; (e), Tx90p; (f), Tn90p; (g), SU; (h), FD; (i), R95p; (j), WD. In the left figures, positive triangles indicate an increase, inverted triangles represent a decrease, and the symbol "-" denotes no change. Hollow triangles indicate no significant trend, and red and green colors signify a significant increase and a significant decrease, respectively. In the right figures, the black horizontal line represents the median, the boundaries of the boxed line correspond to the 25^th and 75^th percentiles (quartiles), and black dots denote outliers.


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