关中地区土壤干湿变化及对气候的响应

doi:10.13866/j.azr.2024.02.09

摘要/Abstract

摘要：

关中地区是陕西省主要的农业生产基地，但频发的旱灾严重阻碍了社会经济的发展。土壤湿度作为反映旱灾的一个重要指标，进行土壤湿度对气候因子响应的研究，可为科学认识干旱规律及制定政策提供依据。以关中地区为研究区，采用2001—2020年的MODIS-NDVI与MODIS-LST长时间序列数据，构建了关中地区地表土壤干湿状况（TVDI）特征空间，采用线性趋势法、相关性分析、敏感性分析等统计方法对关中地区土壤干湿状况的空间分布特征及其对气候的响应进行分析。结果表明：（1）TVDI能够较为准确的反演出关中地区的土壤湿度状况。近20 a来，关中地区土壤干湿状况存在变干趋势；其中，春季最旱、冬季次之。（2）土壤干湿状况的空间分布存在明显的空间异质性，整体上呈现西南向东北干旱递增的趋势。（3）土壤干湿状况与降水和气温存在相关性。与降水呈正相关关系，随着降水量增加，土壤湿度增加；与气温呈负相关关系，随着气温升高，土壤湿度降低。（4）降水对土壤干湿状况的敏感性较高，而气温对土壤干湿状况的变化程度起着较大的影响。降水决定了土壤湿度值的增加或减少的方向，而气温则决定了增加或减少的程度。土壤干湿状况是一个综合指标，其值受降水和气温的影响。降水是决定增减趋势的主要因素，而气温则决定了增减的幅度。因此，仅研究气温和降水的影响时，降水是控制土壤干湿状况增减趋势的主要因素，而气温则调节了这种增减的幅度。

关键词: 土壤干湿状况, 地表温度-植被指数, 降水, 气温, 关中地区

Abstract:

The Guanzhong region serves as the main agricultural production base in Shaanxi Province. However, frequent droughts severely impede socioeconomic development in the area. Soil moisture, a vital drought indicator, can offer valuable insights into understanding drought laws and formulating policies to address them by studying the response of soil moisture to climate factors. Within the Guanzhong region, long-term MODIS-NDVI and MODIS-LST series data from 2001 to 2020 were used to establish the characteristic spatial distribution of surface soil moisture and dryness (TVDI). The linear trend method, correlation analysis, sensitivity analysis, and other mathematical statistical methods were used to scrutinize the spatial distribution patterns of soil moisture and dryness in response to climate conditions. Key results are as follows: (1) TVDI can accurately reflect soil moisture status in the Guanzhong region, revealing a trend of both soil drying and wetting over the past two decades, with spring being the driest, followed by winter. (2) There is significant spatial heterogeneity in soil moisture distribution and dryness, with an overall trend of increasing drought from the southwest to the northeast. (3) Soil moisture exhibits correlations with precipitation and temperature: it is positively correlated with precipitation (as precipitation increases, soil moisture increases) and negatively correlated with temperature (as temperature increases, soil moisture decreases). (4) Precipitation has a high sensitivity to soil moisture and dryness, while temperature significantly impacts the degree of changes in soil moisture and dryness. Precipitation determines the direction in which soil moisture values increase or decrease, whereas temperature determines the degree of increase or decrease. Soil dryness and wetness act as comprehensive indicators, influenced by both precipitation and temperature. Precipitation predominantly determines the trend of value increase or decrease, while temperature determines the magnitude of the increase or decrease. Hence, when studying the impacts of temperature and precipitation, precipitation emerges as the main factor controlling soil moisture and dryness trends, while temperature regulates the extent of these changes.

Key words: dry and wet soil conditions, surface temperature vegetation index, precipitation, temperature, Guanzhong region

杨雅青, 张翀, 张婕, 王玉丹. 关中地区土壤干湿变化及对气候的响应[J]. 干旱区研究, 2024, 41(2): 261-271.

YANG Yaqing, ZHANG Chong, ZHANG Jie, WANG Yudan. Changes in soil moisture and dryness and their response to climate change in the Guanzhong region[J]. Arid Zone Research, 2024, 41(2): 261-271.

图/表 12

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图2

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图5

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图7

表1

土壤干湿状况趋势显著性等级划分"

$θ s l o p e$	Th
$θ s l o p e$	\|Th\|<4.41	\|Th\|>4.41
$θ s l o p e$ <-0.001	不显著变湿	显著变湿
-0.001< $θ s l o p e$ <0.001	稳定不变	稳定不变
$θ s l o p e$ >0.001	不显著变干	显著变干

表1

图8

表2

表3

表4

参考文献 17

[1]	陈维英, 肖乾广, 盛永伟. 距平植被指数在 1992 年特大干旱监测中的应用[J]. 环境遥感, 1994, 36(2): 106-112.
	[Chen Weiying, Xiao Qianguang, Sheng Yongwei. Application of the anomaly vegetation index to monitoring heavy drought in 1992[J]. Remote Sensing of Environment, 1994, 36(2): 106-112.]
[2]	Kogan F N. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data[J]. Bulletin of the American Meteorological Society, 1995, 76(5): 655-668. doi: 10.1175/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2
[3]	郑兴明, 赵凯, 李晓峰, 等. 利用微波遥感土壤水分产品监测东北地区春涝范围和程度[J]. 地理科学, 2015, 35(3): 334-339.
	[Zheng Xingming, Zhao Kai, Li Xiaofeng, et al. Moisture derived from microwave remote sensing in Northeast China[J]. Scientia Geographica Sinica, 2015, 35(3): 334-339.]
[4]	王鹏新, 蔡健雅, 李晓文. 条件植被温度指数及其在干旱监测中的应用[J]. 武汉大学学报, 2001, 26(5): 413-417.
	[Wang Pengxin, Cai Jianya, Li Xiaowen. Vegetation-temperature conditional index and its application for drought monitoring[J]. Geomatics and Information Science of Wuhan University, 2001, 26(5): 413-417.]
[5]	张晶言, 白建军, 于茜. 基于TVDI模型的关中地区春旱时空动态监测[J]. 兰州大学学报, 2017, 53(6): 800-806.
	[Zhang Jingyan, Bai Jianjun, Yu Qian. Spatial-temporal dynamic monitoring of spring drought based on TVDI model in the Guanzhong region[J]. Journal of Lanzhou University, 2017, 53(6): 800-806.]
[6]	何慧娟, 卓静, 李红梅, 等. 基于MOD16产品的陕西关中地区干旱时空分布特征[J]. 干旱地区农业研究, 2016, 36(1): 236-241.
	[He Huijuan, Zhuo Jing, Li Hongmei, et al. Spatial-temporal distribution characteristics of drought in Guanzhong region of Shaanxi Province based on MOD16 products[J]. Agricultural Research in the Arid Areas, 2016, 36(1): 236-241.]
[7]	齐述华, 王长耀, 牛铮. 利用温度植被旱情指数(TVDI)进行全国旱情监测研究[J]. 遥感学报, 2003, 7(5): 421-427.
	[Qi Shuhua, Wang Changyao, Niu Zheng. Evaluating soil moisture status in China using the temperature/vegetation dryness index (TVDI)[J]. Journal of Remote Sensing, 2003, 7(5): 421-427.]
[8]	刘英, 马保东, 吴立新, 等. 基于NDVI-ST 双抛物线特征空间的冬小麦旱情遥感监测[J]. 农业机械学报, 2012, 43(5): 55-63.
	[Liu Ying, Ma Baodong, Wu Lixin, et al. Drought remote sensing for winter wheat based on double parabola NDVI-ST space[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(5): 55-63.]
[9]	王汉文, 原喜忠, 雷胜友, 等. 基于TVDI的不同土地类型土壤湿度趋势研究[J]. 河南理工大学学报(自然科学版), 2020, 39(5): 50-60.
	[Wang Hanwen, Yuan Xizhong, Lei Shengyou, et al. Study on soil moisture trend of different land types based on TVDI[J]. Journal of Henan University of Polytechnic (Natural Science), 2020, 39(5): 50-60.]
[10]	程伟, 辛晓平. 基于 TVDI 的内蒙古草地干旱变化特征分析[J]. 中国农业科学, 2020, 53(13): 2728-2742. doi: 10.3864/j.issn.0578-1752.2020.13.020
	[Cheng Wei, Xin Xiaoping. Analysis of spatial-temporal characteristics of drought variation in grassland area of Inner Mongolia based on TVDI[J]. Scientia Agricultura Sinica, 2020, 53(13): 2728-2742.] doi: 10.3864/j.issn.0578-1752.2020.13.020
[11]	Hu Ling, Fan Wenjie, Ren Huazhong, et al. Spatiotemporal dynamicsin vegetation GPP over the great Khingan Mountains usingGLASS products from 1982 to 2015[J]. Remote Sensing, 2018, 10(3): 488-500. doi: 10.3390/rs10030488
[12]	李双双, 杨赛霓, 刘宪锋, 等. 1960—2014年北京感知温度变化特征及其敏感性分析[J]. 资源科学, 2016, 38(1): 175-184. doi: 10.18402/resci.2016.01.19
	[Li Shuangshuang, Yang Saini, Liu Xianfeng, et al. Changes in outdoor thermal sensation and sensitivity to climate factors in Beijing from 1960 to 2014[J]. Resources Science, 2016, 38(1): 175-184.] doi: 10.18402/resci.2016.01.19
[13]	薛天翼, 白建军. 基于 TVDI 和气象数据的陕西省春季旱情时空分析[J]. 水土保持研究, 2017, 24(4): 240-246.
	[Xue Tianyi, Bai Jianjun. Spatiotemporal variations of spring drought based on TVDI and meteorological index in Shaanxi Province[J]. Research of Soil and Water Conservation, 2017, 24(4): 240-246.]
[14]	孙灏, 陈云浩, 孙洪泉. 典型农业干旱遥感监测指数的比较及分类体系[J]. 农业工程学报, 2012, 28(14): 147-154.
	[Sun Hao, Chen Yunhao, Sun Hongquan. Comparisons and classification system of typical remote sensing indexes for agricultural[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(14): 147-154.]
[15]	张翀, 雷田旺, 宋佃星. 黄土高原植被覆盖与土壤湿度的时滞关联及时空特征分析[J]. 生态学报, 2018, 38(6): 2128 - 2038.
	[Zhang Chong, Lei Tianwang, Song Dianxing. Analysis of temporal and spatial characteristics of time lag correlation between the vegetation cover and soil moisture in the Loess Plateau[J]. Acta Ecologica Sinica, 2018, 38(6): 2128-2038.]
[16]	李俊霖, 延军平, 孙虎, 等. 关中平原东、中、西部气候干旱化程度比较分析[J]. 干旱区资源与环境, 2005, 19(1): 131-134.
	[Li Junlin, Yan Junping, Sun Hu, et al. All analysis on the climatic changes in east, middle, and west areas of Guanzhong Plain[J]. Journal of Aria Land Resources and Environment, 2005, 19(1): 131-134.]
[17]	庞素菲, 魏伟, 郭泽呈, 等. 基于 TVDI 的甘肃省农业旱情特征及其影响因素[J]. 生态学杂志, 2019, 38(6): 1849-1860.
	[Pang Sufei, Wei Wei, Guo Zecheng, et al. Agricultural drought characteristics and its influencing factors in Gansu Province based on TVDI[J]. Chinese Journal of Ecology, 2019, 38(6): 1849-1860.]

敏感性系数S	敏感性等级
0 < \|S\| <0.05	不敏感
0.05 < \|S\| < 0.2	中度敏感
0.2 < \|S\| < 1	高度敏感
\|S\| > 1	极度敏感

地区	降水敏感系数	气温敏感系数
关中地区	-0.4252	0.2788
西安	-0.557	0.316
铜川	-0.463	0.305
宝鸡	-0.624	0.419
咸阳	-0.302	0.184
渭南	-0.28	0.17

时间	降水敏感系数	气温敏感系数	降水贡献率/%	气温贡献率/%
全年	-0.4252	0.2788	-25.2	84.9
春季	-0.377	0.32	5	20.9
夏季	-0.338	0.397	-14.9	15.2
秋季	-0.539	0.282	-32.3	12.7
冬季	-0.444	0.086	17	36.1