内蒙古各气候区主要作物生长季干旱特征及其与响应因子回归模型

doi:10.13866/j.azr.2022.05.07

Abstract

Abstract:

With the foundation of global climate change in recent years, droughts in various climatic areas in Inner Mongolia may bring unpredictable disaster risks to local agricultural production. The whole region was divided into five climatic areas, and monthly data was received from 46 meteorological stations from 1981 to 2012. Twenty meteorological stations from 2014 to 2020 were selected to calculate the different time scales of the standardized precipitation evapotranspiration index (SPEI) using precipitation and reference evapotranspiration from the Penman-Monteith method to reveal the drought characteristics and its dominant meteorological factors during the crop growing season (May-September) in Inner Mongolia. A monthly drought during the crop growing season was revealed. The high-incidence month and region of drought in the growing season in various climatic areas were identified by the SPEI of a 1-month scale, and a stepwise linear regression method was selected and verified to extract the dominant meteorological factors driving the drought in each month and the whole growing season in various climatic areas. Results indicated that the following: (1) from the interannual changes, the drought from 1998 to 2008 was more serious, and the drought in other years was less serious. (2) The largest drought area and degree appeared in May during the crop growing season. The probability of moderate drought in the moist and semi-humid area was 37% higher compared to the hyper-arid area, while the extreme drought probably occurred in the moist and semi-humid area and dry and semi-humid area. (3) Dominant meteorological factors driving the drought varied in the crop growth period of various climatic areas in Inner Mongolia. Precipitation and a minimum temperature were the main impact factors for a drought in the crop growing season. (4) Using limited meteorological data, the regression models in each climate region performance well can be used to estimate the monthly SPEI. The results provide a theoretical basis for scientific evaluation of drought characteristics in the spring maize growing season and formulates reasonable measures of response to the drought.

Key words: climate areas, drought characteristics, standardized precipitation evapotranspiration index, meteorological factors, regression models, growing season of crops, Inner Mongolia

GAO Xiaoyu,TANG Pengcheng,ZHANG Sha,QU Zhongyi,YANG Wei. Drought characteristics and regression models of drought characteristics and response factors of various climatic areas in Inner Mongolia during main crop growing season[J].Arid Zone Research, 2022, 39(5): 1410-1427.

Figures/Tables 13

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Tab. 2

Tab. 3

Regression coefficient of the best stepwise linear regression model from May to September of crop growing season in various climate areas"

气候区	月份	模型变量	R²	未标准化系数	标准化系数	显著性
湿润半湿润气候区	5	（常量）	0.148	-1.875		0
		T_min		0.096	0.34	0
		P		0.006	0.237	0.005
	6	（常量）	0.635	-0.012		0.995
		RHU		0.063	0.319	0
		SSD		-0.166	-0.206	0.02
		T_min		0.199	0.39	0
		T_max		-0.21	-0.449	0
	7	（常量）	0.879	-1.962		0
	7	P	0.879	0.022	0.937	0
	8	（常量）	0.861	-1.111		0
		P		0.021	0.921	0
		T_max		-0.027	-0.076	0.024
	9	（常量）	0.845	-0.939		0
	9	P	0.845	0.026	0.919	0
	生长季平均	（常量）	0.72	3.692		0.023
		SSD		-0.158	-0.243	0.001
		RHU		0.043	0.251	0.001
		T_min		0.469	1.514	0
		T_mean		-0.585	-1.55	0
		WS		-0.369	-0.24	0.003
干旱半湿润气候区	5	（常量）	0.272	24.174		0.003
		T_min		0.223	0.664	0
		WS		-0.588	-0.294	0.001
		APS		-0.027	-0.463	0.002
	6	（常量）	0.71	-2.524		0.222
		RHU		0.084	0.531	0
		T_min		0.234	0.445	0
		T_max		-0.231	-0.481	0
	7	（常量）	0.806	-1.431		0
		P		0.017	0.882	0
		T_min		-0.03	-0.092	0.048
	8	（常量）	0.872	-1.668		0
	8	P	0.872	0.021	0.934	0
	9	（常量）	0.801	-1.108		0
	9	P	0.801	0.03	0.895	0
	生长季平均	（常量）	0.726	10.932		0.082
		RHU		0.069	0.496	0
		T_min		0.517	1.641	0
		T_mean		-0.522	-1.243	0
		APS		-0.013	-0.276	0.041
半干旱气候区	5	（常量）	0.097	0.105		0.643
		WS		-0.191	-0.251	0
		RHU		-0.011	-0.161	0
		SSD		-0.057	-0.114	0.002
	6	（常量）	0.543	0.896		0
		RHU		0.047	0.447	0
		WS		-0.252	-0.167	0
		T_max		-0.231	-0.586	0
		T_min		0.188	0.481	0.036
		SSD		-0.011	-0.049	0
	7	（常量）	0.864	-1.938		0
	7	P	0.864	0.021	0.93	0
	8	（常量）	0.794	-1.574		0
	8	P	0.794	0.022	0.891	0
	9	（常量）	0.785	-0.812		0
		P		0.024	0.882	0
		WS		-0.066	-0.058	0
	生长季平均	（常量）	0.605	1.616		0
		RHU		0.055	0.638	0
		WS		-0.21	-0.203	0
		APS		-0.003	-0.23	0
		T_min		0.137	0.532	0
		T_max		-0.139	-0.481	0
		SSD		-0.043	-0.101	0
干旱气候区	5	（常量）	0.02	-1.535		0
	5	Tmin	0.02	0.031	0.14	0.036
	6	（常量）	0.511	0.762		0.133
		RHU		0.029	0.45	0
		SSD		-0.053	-0.115	0.04
		T_max		-0.163	-0.639	0
		T_min		0.132	0.485	0
	7	（常量）	0.908	-3.557		0
		P		0.021	0.945	0
		T_min		0.081	0.073	0.001
		WS		0.084	0.046	0.048
	8	（常量）	0.861	3.789		0
		P		0.022	0.926	0
		APS		-0.006	-0.071	0.005
	9	（常量）	0.862	-0.623		0
		P		0.021	0.928	0
		WS		-0.047	-0.055	0.03
	生长季平均	（常量）	0.504	0.593		0.167
		RHU		0.026	0.519	0
		SSD		-0.05	-0.094	0.08
		T_max		-0.138	-0.668	0
		T_min		0.112	0.496	0
特干旱气候区	5	（常量）	0.097	-1.186		0
		T_min		0.061	0.256	0.001
		WS		-0.148	-0.317	0
		RHU		-0.008	-0.173	0.037
	6	（常量）	0.543	-5.092		0
		RHU		0.022	0.574	0
		APS		0.004	0.375	0
		SSD		-0.059	-0.21	0.017
	7	（常量）	0.864	-1.843		0
	7	P	0.864	0.02	0.955	0
	8	（常量）	0.794	-1.537		0
	8	P	0.794	0.023	0.949	0
	9	（常量）	0.785	1.957		0.59
		P		0.024	0.837	0
		APS		-0.004	-0.144	0
		T_min		0.038	0.083	0.034
	生长季平均	（常量）	0.605	-2.776		0
		RHU		0.016	0.531	0
		SSD		-0.119	-0.441	0
		APS		0.003	0.362	0
全区	5	（常量）	0.125	1.336		0
		T_min		0.089	0.553	0
		APS		-0.001	-0.084	0.004
		WS		-0.12	-0.182	0
		RHU		-0.014	-0.262	0
		T_mean		-0.073	-0.405	0
		SSD		-0.049	-0.107	0.001
	6	（常量）	0.478	0.241		0.561
		RHU		0.041	0.595	0
		T_min		0.183	0.601	0
		T_max		-0.158	-0.512	0
		SSD		-0.02	-0.07	0
		APS		-0.001	-0.062	0.037
	7	（常量）	0.877	-1.958		0
	7	P	0.877	0.021	0.936	0
	8	（常量）	0.834	-0.281		0.337
		P		0.022	0.901	0
		APS		-0.002	-0.057	0
		T_min		0.016	0.034	0.002
	9	（常量）	0.803	-0.1		0.59
		P		0.024	0.894	0
		APS		-0.001	-0.048	0
		WS		-0.045	-0.043	0
	生长季平均	（常量）	0.529	0.156		0.55
		RHU		0.039	0.798	0
		T_min		0.103	0.51	0
		APS		-0.002	-0.173	0
		SSD		-0.057	-0.141	0
		T_max		-0.057	-0.266	0

Tab. 3

Tab. 4

Fig. 8

Tab. 5

References 27

[1]	Dai A, Trenberth K E, Qian T. A global dataset of Palmer Drought Severity index for 1870-2002: Relationship with soil moisture and effects of surface warming[J]. Journal of Hydrometeorology, 2004, 5(6): 1117-1130. doi: 10.1175/JHM-386.1
[2]	郑大玮, 李茂松, 霍治国. 农业灾害与减灾对策[M]. 北京: 中国农业大学出版社, 2013.
	[Zheng Dawei, Li Maosong, Huo Zhiguo. Agricultural Disasters and Countermeasures for Disaster Reduction[M]. Beijing: China Agricultural University Press, 2013. ]
[3]	马柱国, 符淙斌. 1951—2004 年中国北方干旱化的基本事实[J]. 科学通报, 2006, 51(20): 2429-2439.
	[Ma Zhuguo, Fu Congbin. Characteristics of aridification over northern China during 1951-2004[J]. Chinese Science Bulletin, 2006, 51(20): 2429-2439. ]
[4]	王娜, 王靖, 冯利平, 等. 华北平原冬小麦-夏玉米轮作区采用“两晚”技术的产量效应模拟分析[J]. 中国农业气象, 2015, 36(5): 611-618.
	[Wang Na, Wang Jing, Feng Liping, et al. Modeling the impact of “Double-Delay” technology on yield of wheat-maize cropping system in the North China Plain[J]. Chinese Journal of Agrometeorology, 2015, 36(5): 611-618. ]
[5]	周扬, 李宁, 吉中会, 等. 基于SPI 指数的1981—2010年内蒙古地区干旱时空分布特征[J]. 自然资源学报, 2013, 28(10): 1694-1706.
	[Zhou Yang, Li Ning, Ji Zhonghui, et al. Temporal and spatial patterns of droughts based on standard precipitation index(SPI) in Inner Mongolia during 1981-2010[J]. Journal of Natural Resource, 2013, 28(10): 1694-1706. ]
[6]	刘荣花, 朱自玺, 方文松, 等. 华北平原冬小麦干旱灾损风险区划[J]. 生态学杂志, 2006, 25(9): 1068-1072.
	[Liu Ronghua, Zhu Zixi, Fang Wensong, et al. Risk regionalization of yield loss caused by drought for winter wheat in North China Plain[J]. Chinese Journal of Ecology, 2006, 25(9): 1068-1072. ]
[7]	Palmer W C. Meteorology Drought[R]. Washington: No.30 US Department of Commerce, Weather Bureau Washington, 1965.
[8]	周丹, 张勃, 罗静, 等. 基于SPEI 的华北地区近50 年干旱发生强度的特征及成因分析[J]. 自然灾害学报, 2014, 23(4): 192-202.
	[Zhou Dan, Zhang Bo, Luo Jing, et al. SPEI-based intensity characteristics and cause analysis of drought in North China during recent 50 years[J]. Journal of Natural Disasters, 2014, 23(4): 192-202. ]
[9]	Hayes M J, Svoboda M D, Wilhite D A, et al. Monitoring the 1996 drought using the standardized precipitation index[J]. Bulletin of the American Meteorological Society, 1999, 80(3): 429-438. doi: 10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2
[10]	周磊, 武建军, 吕爱峰, 等. 华北不同地表覆盖类型区干旱演变特征[J]. 地理研究, 2012, 31(4): 597-607. doi: 10.11821/yj2012040003
	[Zhou Lei, Wu Jianjun, Lv Aifeng, et al. Drought evolution of different land cover regions in North China[J]. Geographical Research, 2012, 31(4): 597-607. ] doi: 10.11821/yj2012040003
[11]	吴英杰, 李玮, 王文君, 等. 基于降水量距平百分率的内蒙古地区干旱特征[J]. 干旱区研究, 2019, 36(4): 943-952.
	[Wu Yingjie, Li Wei, Wang Wenjun, et al. Drought characteristics in Inner Mongolia based on precipitation anomaly percentage[J]. Arid Zone Research, 2019, 36(4): 943-952. ]
[12]	朱烨, 靳鑫桐, 刘懿, 等. 基于短时间尺度自适应帕尔默干旱指数的中国干旱演变特征分析[J]. 水资源保护, 2022, 38(4): 124-130.
	[Zhu Ye, Jin Xintong, Liu Yi, et al. Drought characteristics analysis in China based on self-calibrating Palmer drought severity index in a short time scale[J]. Water Resources Protection, 2022, 38(4): 124-130. ]
[13]	王林, 陈文. 标准化降水蒸散指数在中国干旱监测的适用性分析[J]. 高原气象, 2014, 33(2): 423-431. doi: 10.7522/j.issn.1000-0534.2013.00048
	[Wang Lin, Chen Wen. Applicability analysis of standardized precipitation evapotranspiration index in drought monitoring in China[J]. Plateau Meteorology, 2014, 33(2): 423-431. ] doi: 10.7522/j.issn.1000-0534.2013.00048
[14]	Vicente-Serrano S M, Begueria S, Lopez-Moreno J I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index[J]. Journal of Climate, 2010, 23(7): 1696-1718. doi: 10.1175/2009JCLI2909.1
[15]	刘珂, 姜大膀. 基于两种潜在蒸散发算法的SPEI对中国干湿变化的分析[J]. 大气科学, 2015, 39(1): 23-36.
	[Liu Ke, Jiang Dabang. Analysis of dryness/wetness over China using standardized precipitation evapotranspiration index based on two evapotranspiration algorithms[J]. Chinese Journal of Atmospheric Sciences, 2015, 39(1): 23-36. ]
[16]	庄少伟, 左洪超, 任鹏程, 等. 标准化降水蒸发指数在中国区域的应用[J]. 气候与环境研究, 2013, 18(5): 617-625.
	[Zhuang Shaowei, Zuo Hongchao, Ren Pengcheng, et al. Application of standardized precipitation evapotranspiration index in China[J]. Climatic and Environmental Research, 2013, 18(5): 617-625. ]
[17]	刘宇, 李雯晴, 刘招, 等. 基于SPEI渭北黄土台塬区干旱时空演变特征[J]. 水土保持研究, 2021, 28(1): 109-117.
	[Liu Yu, Li Wenqing, Liu Zhao, et al. Spatial and temporal evolution characteristics of the drought in Weibei loess tableland area based on SPEI[J]. Research of Soil and Water Conservation, 2021, 28(1): 109-117. ]
[18]	张煦庭, 潘学标, 徐琳, 等. 基于降水蒸发指数的1960—2015年内蒙古干旱时空特征[J]. 农业工程学报, 2017, 33(15): 190-199.
	[Zhang Xuting, Pan Xuebiao, Xu Lin, et al. Analysis of spatio-temporal distribution of drought characteristics based on SPEI in Inner Mongolia during 1960-2015[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(15): 190-199. ]
[19]	任晓东. 内蒙古不同气候区ET₀估算方法(FAO-PM温度法和Hargreaves-Samani法)的适用性评价[D]. 呼和浩特: 内蒙古农业大学, 2014.
	[Ren Xiaodong. Assessment of ET₀ Estimation Methods (FAO-PM Temperature and Hargreaves-Samani Methods) in Different Climates of Inner Mongolia[D]. Hohhot: Inner Mongolia Agricultural University, 2014. ]
[20]	王潇潇, 潘学标, 顾生浩, 等. 内蒙古地区参考作物蒸散变化特征及其气象影响因子[J]. 农业工程学报, 2015, 31(增刊1): 142-152.
	[Wang Xiaoxiao, Pan Xuebiao, Gu Shenghao, et al. Trend in reference crop evapotranspiration and meteorological factors affecting trends in Inner Mongolia[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(Suppl.1): 142-152. ]
[21]	Allen R G, Pereira L S, Raes D, et al. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements[M]. Roma Italy: FAO Irrigation and Drainage, 1998: 56.
[22]	李伟光, 易雪, 侯美亭, 等. 基于标准化降水蒸散指数的中国干旱趋势研究[J]. 中国生态农业学报, 2012, 20(5): 643-649. doi: 10.3724/SP.J.1011.2012.00643
	[Li Weiguang, Yi Xue, Hou Meiting, et al. Standardized precipitation evapotranspiration index shows drought trends in China[J]. Chinese Journal of Eco-Agriculture, 2012, 20(5): 643-649. ] doi: 10.3724/SP.J.1011.2012.00643
[23]	李崇瑞, 游松财, 武永峰. 东北地区干旱特征与春玉米生长季干旱主导气象因子[J]. 农业工程学报, 2020, 36(19): 97-106.
	[Li Chongrui, You Songcai, Wu Yongfeng. Drought characteristics and dominant meteorological factors driving drought in spring maize growing season in Northeast China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(19): 97-106. ]
[24]	Liu S, Kang W, Wang T. Drought variability in Inner Mongolia of northern China during 1960-2013 based on standardized precipitation evapotranspiration index[J]. Environmental Earth Sciences, 2016, 75(2): 1-14. doi: 10.1007/s12665-015-4873-x
[25]	Huang J, Sun S L, Xue Y, et al. Changing characteristics of precipitation during 1960-2012 in Inner Mongolia, northern China[J]. Meteorology and Atmospheric Physics, 2015, 127(3): 257-271. doi: 10.1007/s00703-014-0363-z
[26]	那音太. 基于SPI 指数的近50 a内蒙古地区干旱特征分析[J]. 干旱区资源与环境, 2015, 29(5): 161-166.
	[Na Yintai. Drought characteristics in Inner Mongolia based on the SPI index in the last 50 years[J]. Journal of Arid Land Resources and Environment, 2015, 29(5): 161-166. ]
[27]	李秋月, 潘学标, 王丽, 等. 内蒙古地区气候资源变化趋势分析[J]. 中国农业气象, 2011, 32(增刊): 19-23.
	[Li Qiuyue, Pan Xuebiao, Wang Li, et al. Analysis of the trend of climate resources change in Inner Mongolia[J]. Chinese Journal of Agrometeorology, 2011, 32(Suppl.): 19-23. ]

干旱等级	SPEI范围
正常	>-0.5
轻旱	-1.0<SPEI≤-0.5
中旱	-1.5<SPEI≤-1.0
重旱	-2.0<SPEI≤-1.5
特旱	≤-2.0

区域	项目	月份					生长季平均
区域	项目	5	6	7	8	9	生长季平均
湿润半湿润区	Kolmogorov-Smirnov显著性	0.605	0.990	0.566	0.945	0.620	0.523
湿润半湿润区	是否满足正态分布	是	是	是	是	是	是
干旱半湿润区	Kolmogorov-Smirnov显著性	0.154	0.692	0.502	0.740	0.801	0.944
干旱半湿润区	是否满足正态分布	是	是	是	是	是	是
半干旱区	Kolmogorov-Smirnov显著性	0.724	0.760	0.56	0.676	0.225	0.988
半干旱区	是否满足正态分布	是	是	是	是	是	是
干旱区	Kolmogorov-Smirnov显著性	0.666	0.697	0.350	0.433	0.666	0.934
干旱区	是否满足正态分布	是	是	是	是	是	是
特旱区	Kolmogorov-Smirnov显著性	0.830	0.911	0.963	0.251	0.101	0.420
特旱区	是否满足正态分布	是	是	是	是	是	是

气候区	SPEI-1值与主导气象因子回归方程	气候区	SPEI-1值与主导气象因子回归方程
湿润半湿润气候区	SPEI-1₅=-1.875+0.096T_min+0.006P	干旱气候区	SPEI-1₅=-1.535+0.031T_min
	SPEI-1₆=-0.012+0.063RHU-0.166SSD+0.199T_min-0.210T_max		SPEI-1₆=0.762+0.029 RHU-0.053SSD-0.163T_max+0.132T_min
	SPEI-1₇=-1.962+0.022P		SPEI-1₇=-3.557+0.021 P+0.081 T_min+0.084WS
	SPEI-1₈=-1.111+0.021P-0.027T_max		SPEI-1₈=3.789+0.022 P-0.006APS
	SPEI-1₉=-0.939+0.026P		SPEI-1₉=-0.623+0.021P-0.047WS
	SPEI-1_mean=3.692-0.158SSD+0.043RHU+0.469T_min-0.585T_mean-0.369WS		SPEI-1_mean=0.593+0.026RHU-0.050SSD-0.138T_max+0.112T_min
干旱半湿润气候区	SPEI-1₅=24.174+0.223T_min-0.588WS-0.027APS	特干旱气候区	SPEI-1₅=-1.186+0.061T_min-0.148WS-0.008RHU
	SPEI-1₆=-2.524+0.084RHU+0.234T_min-0.231T_max		SPEI-1₆=-5.092+0.022 RHU+0.004APS-0.059SSD
	SPEI-1₇=-1.431+0.017P-0.030T_min		SPEI-1₇=-1.843+0.020 P
	SPEI-1₈=-1.668+0.021P		SPEI-1₈=-1.537+0.023 P
	SPEI-1₉=-1.108+0.030P		SPEI-1₉=1.957+0.024P-0.004APS+0.038T_min
	SPEI-1_mean=10.932+0.069RHU+0.517T_min-0.522T_mean-0.013APS		SPEI-1_mean=-2.776+0.016RHU-0.119SSD+0.003APS
半干旱气候区	SPEI-1₅=0.105-0.191WS-0.011RHU-0.057SSD	全区	SPEI-1₅=1.336+0.089T_min-0.01APS-0.12WS-0.014RHU-0.073T_mean+0.049SSD
	SPEI-1₆=0.896+0.047RHU-0.252WS-0.231T_max+0.188T_min-0.011SSD		SPEI-1₆=0.24+0.041RHU+0.183T_min-0.158T_max-0.02SSD-0.001APS
	SPEI-1₇=-1.938+0.021P		SPEI-1₇=-1.958+0.021P
	SPEI-1₈=-1.574+0.022P		SPEI-1₈=-0.281+0.022P-0.002APS+0.016T_min
	SPEI-1₉=-0.812+0.024P-0.066WS		SPEI-1₉=-0.1+0.024P-0.001APS-0.045WS
	SPEI-1_mean=1.616+0.055RHU-0.210WS-0.003APS+0.137T_min-0.139T_max-0.043SSD		SPEI-1_mean=0.156+0.039RHU+0.103T_min- 0.002APS-0.057SSD-0.057T_max

Drought characteristics and regression models of drought characteristics and response factors of various climatic areas in Inner Mongolia during main crop growing season

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 27

Related Articles 15

Metrics

Comments

Recommended 0

气候区	月份	RMSE	NSE	R²	b	MRE
湿润半湿润气候区	5	0.36	0.52	0.53	0.93	19%
	6	0.57	0.68	0.65	0.65	-11%
	7	0.77	0.77	0.93	0.95	-0.1%
	8	0.67	0.79	0.86	1.12	0.4%
	9	0.54	0.66	0.92	0.84	11%
	生长季	0.29	0.69	0.7	0.66	-11%
干旱半湿润气候区	5	0.43	0.05	0.21	0.98	8%
	6	0.73	0.61	0.63	0.64	-27%
	7	0.39	0.91	0.96	0.76	-32%
	8	0.29	0.97	0.97	0.98	-12%
	9	0.24	0.93	0.93	0.89	-15%
	生长季	0.26	0.51	0.68	0.99	-6%
半干旱气候区	5	0.41	-0.17	0	0.97	17%
	6	0.59	0.36	0.44	0.92	49%
	7	0.37	0.91	0.92	0.85	-25%
	8	0.44	0.86	0.86	0.80	-19%
	9	0.45	0.72	0.74	0.71	-15%
	生长季	0.43	-0.36	0.42	1.23	31%
干旱气候区	5	0.27	-0.34	0.42	0.91	-3%
	6	0.21	0.62	0.80	0.92	-7%
	7	0.21	0.84	0.86	1.00	8%
	8	0.15	0.97	0.97	0.93	-11%
	9	0.28	0.84	0.94	0.72	-7%
	生长季	0.23	0.21	0.54	1.09	28%
特干旱气候区	5	0.23	0.83	0.85	0.90	-6%
	6	0.07	0.63	0.77	0.99	-0.4%
	7	0.21	0.84	0.95	1.14	15%
	8	0.18	0.85	0.85	0.95	25%
	9	0.35	0.61	0.69	0.6	-1%
	生长季	0.18	0.55	0.79	0.88	6%
全区	5	0.53	-0.42	0.01	0.85	17%
	6	0.68	0.38	0.21	0.53	-39%
	7	0.54	0.82	0.83	0.86	-18%
	8	0.59	0.78	0.85	1.00	-5%
	9	0.48	0.69	0.33	0.86	-10%
	生长季	0.38	0.42	0.2	0.51	-41%

[1]	YI Nana, SU Lijuan, ZHENG Xucheng, XIN Yue, CAI Min, LI Hui, JIN Yuchen. Environmental parameters and forecast models of hail events [J]. Arid Zone Research, 2024, 41(1): 13-23.
[2]	LI Hong, LI Zhongqin, CHEN Puchen, PENG Jiajia. Spatio-temporal variation of snow cover in Altai Mountains of Xinjiang in recent 20 years and its influencing factors [J]. Arid Zone Research, 2023, 40(7): 1040-1051.
[3]	HU Yanan, PEI Hao, JIANG Yanfeng, MIAO Bailing, JIA Chengzhen. Spatiotemporal variation characteristics of precipitation pH in Inner Mongolia from 1991 to 2021 [J]. Arid Zone Research, 2023, 40(4): 552-562.
[4]	DANG Hui, RONG Lihua, LI Yitong, ZHAO Mingjun. Spatiotemporal evolution characteristics and influencing factors of production-living-ecological spaces in the farming-pastoral ecotone: Taking Hohhot of Inner Mongolia as an example [J]. Arid Zone Research, 2023, 40(10): 1698-1706.
[5]	HUANG Xiaolu,LI Ruiqing,LI Linhui,LIN Hongjie,YAO Lebao. Various characteristics of the mesoscale convection system of a convective rainstorm in the Hetao area of Inner Mongolia [J]. Arid Zone Research, 2022, 39(6): 1728-1738.
[6]	ZHANG Haochen,SA Chula,MENG Fanhao,LUO Min,WANG Mulan,GAO Hongdou,ADIYA Saruulzaya. Dynamic changes and driving factors of the surface freeze-thaw index in Inner Mongolia [J]. Arid Zone Research, 2022, 39(6): 1996-2008.
[7]	SANG Jing,WANG Yingbin,QIAN Lianhong,WANG Haimei,WANG Qiyu. Analysis of the relationship between the dynamic snowmelt process of meadow grassland and meteorological factors: Ergun City [J]. Arid Zone Research, 2022, 39(5): 1428-1436.
[8]	YU Shuiyan,BI Lige,SU Lijuan,LIU Jidong,SHI Jinli,YI Na’na,FAN Ruxia,XU Zhili. Movement paths and characteristics of hail clouds in Bayannur, Inner Mongolia [J]. Arid Zone Research, 2022, 39(4): 1047-1055.
[9]	QIANG Yuquan,XU Xianying,ZHANG Jinchun,LIU Hujun,GUO Shujiang,DUAN Xiaofeng. Characteristics of stem sap flow of Haloxylon ammodendron and its response to environmental factors in Qingtu Lake, Minqin [J]. Arid Zone Research, 2022, 39(4): 1143-1154.
[10]	GAO Bingli,GONG Jie,LI Yan,JIN Tiantian. Analysis of multi-scalar characteristics of dry and wet conditions in the Yellow River Basin based on SPEI [J]. Arid Zone Research, 2022, 39(3): 723-733.
[11]	ZHANG Yaozong,ZHANG Bo,ZHANG Duoyong,LIU Yanyan. Spatio temporal patterns of pan evaporation from 1960 to 2018 over the Loess Plateau: Changing properties and possible causess [J]. Arid Zone Research, 2022, 39(1): 1-9.
[12]	LI Ruiqing,SONG Guiying,YIN Chun. Characteristics of summer climate change and its response to El Niño in Inner Mongolia during the past 60 years [J]. Arid Zone Research, 2021, 38(6): 1601-1613.
[13]	YANG Qi,LI Shuheng,LI Jiahao,WANG Jiachuan. Phenology of forest vegetation and its response to climate change in the Qinling Mountains [J]. Arid Zone Research, 2021, 38(4): 1065-1074.
[14]	SONG Yuxin,ZUO Qiting,MA Junxia. Variation and dynamic drivers of drought in Kaidu River Basin based on the SWAT model [J]. Arid Zone Research, 2021, 38(3): 610-617.
[15]	HONG Guangyu,WANG Xiaojiang,LIU Guohou,ZHANG Lei,GAO Xiaowei,LI Zhuofan,LIU Tieshan,LIU Chenming,LI Zihao. Characteristics of Salix psammophila sap flow and its response to environmental factors in Mu Us Sandy Land [J]. Arid Zone Research, 2021, 38(3): 794-801.