时空克里金评估河套灌区土壤盐分时空格局

doi:10.13866/j.azr.2023.02.03

干旱区研究 ›› 2023, Vol. 40 ›› Issue (2): 182-193.doi: 10.13866/j.azr.2023.02.03 cstr: 32277.14.j.azr.2023.02.03

时空克里金评估河套灌区土壤盐分时空格局

孙贯芳¹(),高照良¹,朱焱²(),杨金忠²,屈忠义³

1.西北农林科技大学水土保持研究所，陕西杨凌 712100
2.武汉大学水资源与水电工程科学国家重点实验室，湖北武汉 430072
3.内蒙古农业大学水利与土木建筑工程学院，内蒙古呼和浩特 010018

收稿日期:2022-08-12 修回日期:2022-10-12 出版日期:2023-02-15 发布日期:2023-03-08
通讯作者: 朱焱. E-mail:zyan0701@163.com
作者简介:孙贯芳（1989-），男，助理研究员，主要研究方向为农业水土资源与环境. E-mail: gfsun1990@126.com
基金资助:
国家重点研发计划项目(2021YFD1900805);中央高校基本科研业务费专项资金(2452021081);内蒙古自治区科技成果转化专项资金(2021CG0022)

Spatio-temporal patterns of soil salinity in Hetao Irrigation District based on spatio-temporal Kriging

SUN Guanfang¹(),GAO Zhaoliang¹,ZHU Yan²(),YANG Jinzhong²,QU Zhongyi³

1. Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, Shaanxi, China
2. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, Hubei, China
3. College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China

Received:2022-08-12 Revised:2022-10-12 Published:2023-02-15 Online:2023-03-08

摘要/Abstract

摘要：

区域土壤盐分时空变异性大，采用经典统计和地统计方法无法准确判断取样时间不规则、空间位置不一致的土壤盐分的时空变化趋势。本文以内蒙古河套灌区隆胜研究区68个监测点0~1.8 m土壤剖面4582个土壤盐分数据为基础，利用时空地统计方法分析区域土壤盐分时空变化特征，比较时空克里金较传统空间克里金插值的精度提升效果，并验证时空地统计方法在监测点减少50%情况下预测区域盐分时空动态的能力。结果表明：（1）该研究区土壤盐分空间变异系数的变化范围是0.43~1.14，为中强变异，0~0.6 m根系层生育期积盐、非生育期脱盐，0.6~1.8 m土壤剖面生育期脱盐、非生育期积盐、农田土壤盐分有明显的季节性规律。（2）和度量模型能较好拟合盐分时空经验半方差，各层土壤盐分预测值和观测值间的均方根误差RMSE均小于0.21 dS·m^-1，较传统空间克里金的RMSE小0.02~0.09 dS·m^-1。（3）采用该方法在减少50%监测点情况下确定的土壤盐渍化分布与所有取样点确定的结果一致性较高，0~0.6 m和0.6~1.2 m土壤盐分面积间的相对误差MRE分别为-13.20%和-8.35%，RMSE为466.67 hm²和494.43 hm²，决定系数R²为0.79和0.72。时空克里金同时利用了土壤盐分时间和空间上的更多信息，实现了稀疏盐分监测点数据集土壤盐分时空动态的精确估计，可极大提高区域土壤盐分时空格局监测的效率。

关键词: 土壤盐分, 时空克里金, 河套灌区, 时空格局, 和度量模型

Abstract:

For maintaining crop yield in salt-affected dry agricultural settings, monitoring and analyzing spatio-temporal dynamics of soil salinity over broad areas is crucial yet challenging due to its high variability. The most popular techniques for evaluating spatial distribution patterns and temporal trends are classical statistical analysis and traditional geostatistical analysis, but they are not suitable for accurately capturing spatio-temporal trends of soil salinity due to irregular sampling time and inconsistent spatial position during sampling time. Spatio-temporal Kriging is an extension of spatial geostatistics to space-time geostatistics and may overcome this problem effectively because its model covariance/variance is a function of both space and time. However, its application in spatio-temporal modeling and prediction of regional soil salinity is still unclear. Based on 4582 soil salinity data of 0-1.8 m soil profiles from 68 monitoring locations in the Longsheng study area of Hetao Irrigation District, Inner Mongolia, spatio-temporal variation characteristics of regional soil salinity using a spatio-temporal geostatistical method, and spatio-temporal Kriging interpolation accuracy was compared with traditional spatial Kriging interpolation. Furthermore, the ability of spatio-temporal Kriging to obtain regional soil salinity dynamics was verified using less than half of the original monitoring locations. The results showed that the spatial variation coefficient of soil salt in the study area ranged from 0.43 to 1.14, which was categorized as medium to strong variability. Regional averaged soil salinity dynamics had obvious seasonal variation characteristics, and the root zone (0-0.6 m) soil salinity accumulated in the crop growing season and desalted in the fallow season, while the deep soil salinity (0.6-1.8 m) was the opposite. The sum-metric model can fit the temporal and spatial experience semi-variance of soil salinity well, and the root mean square error (RMSE) between the predicted value and observed value of soil salinity in each layer was less than 0.21 dS·m^-1, which was 0.02-0.09 dS·m^-1 less than that of traditional spatial Kriging. The areas of different soil salinity determined by 32 sparse monitoring locations were in good agreement with those determined by all sampling sites, and the mean relative error between areas of different soil salinity for 0-0.6 m and 0.6-1.2 m were -13.20% and -8.35%, respectively. Similarly, the respective RMSE were 466.67 hm² and 494.43 hm² and the determination coefficient (R²) were 0.79 and 0.72, indicating that spatial distribution of soil salinity obtained by sparse monitoring locations is consistent with the results of all sampling locations. Spatio-temporal Kriging significantly improves the prediction accuracy of soil salinity compared with ordinary Kriging, since it uses more information on soil salinity in time and space. The accurate estimation of spatio-temporal dynamics of soil salinity in the data set of sparse monitoring points was realized, which can greatly improve the monitoring efficiency of the spatio-temporal pattern of soil salinity in the region.

Key words: soil salinity, spatio-temporal Kriging, Hetao Irrigation District, spatio-temporal pattern, sum-metric model

孙贯芳, 高照良, 朱焱, 杨金忠, 屈忠义. 时空克里金评估河套灌区土壤盐分时空格局[J]. 干旱区研究, 2023, 40(2): 182-193.

SUN Guanfang, GAO Zhaoliang, ZHU Yan, YANG Jinzhong, QU Zhongyi. Spatio-temporal patterns of soil salinity in Hetao Irrigation District based on spatio-temporal Kriging[J]. Arid Zone Research, 2023, 40(2): 182-193.

图/表 11

图1

表1

4次取样时间土壤盐分统计特征"

土层/m	时间	观测数	均值 / (dS·m^-1)	中位数 / (dS·m^-1)	标准差 / (dS·m^-1)	变异系数	峰度	偏度	最小值 / (dS·m^-1)	最大值 / (dS·m^-1)
0~0.2	Y1705	65	0.34	0.23	0.39	1.14	18.52	4.18	0.13	2.34
	Y1709	66	0.35	0.23	0.28	0.78	5.1	2.12	0.1	1.55
	Y1805	67	0.28	0.23	0.13	0.45	3.34	1.69	0.14	0.77
	Y1809	66	0.28	0.2	0.21	0.77	11.34	3.18	0.08	1.29
0.2~0.4	Y1705	65	0.3	0.25	0.18	0.6	3.84	1.87	0.12	0.99
	Y1709	66	0.33	0.24	0.24	0.74	3.39	1.97	0.12	1.19
	Y1805	67	0.29	0.23	0.15	0.52	1.39	1.42	0.13	0.76
	Y1809	66	0.3	0.23	0.21	0.71	9.18	2.87	0.08	1.23
0.4~0.6	Y1705	65	0.31	0.27	0.21	0.66	10.29	2.74	0.11	1.35
	Y1709	66	0.32	0.26	0.2	0.63	1.99	1.57	0.07	0.96
	Y1805	67	0.32	0.24	0.2	0.63	2.58	1.64	0.12	1.02
	Y1809	66	0.32	0.26	0.2	0.63	3.14	1.77	0.1	1.03
0.6~0.8	Y1705	65	0.33	0.26	0.2	0.6	4.3	1.88	0.1	1.18
	Y1709	66	0.29	0.26	0.15	0.53	1.27	1.21	0.07	0.74
	Y1805	67	0.33	0.26	0.2	0.61	2.22	1.62	0.12	1.03
	Y1809	66	0.31	0.24	0.2	0.64	3.73	1.87	0.09	1.06
0.8~1.0	Y1705	65	0.33	0.27	0.19	0.57	5.64	2.06	0.1	1.18
	Y1709	66	0.28	0.27	0.13	0.47	1.94	1.17	0.07	0.73
	Y1805	67	0.33	0.27	0.18	0.54	2.17	1.53	0.12	0.92
	Y1809	66	0.29	0.22	0.17	0.61	3.3	1.8	0.09	0.89
1.0~1.2	Y1705	64	0.31	0.26	0.16	0.52	3.7	1.76	0.06	0.9
	Y1709	66	0.28	0.25	0.13	0.46	0.71	1.1	0.09	0.63
	Y1805	67	0.33	0.27	0.17	0.53	2.26	1.53	0.11	0.9
	Y1809	66	0.28	0.23	0.18	0.62	8.86	2.63	0.09	1.13
1.2~1.4	Y1705	51	0.29	0.24	0.15	0.51	5.79	1.98	0.12	0.92
	Y1709	66	0.26	0.24	0.12	0.44	1.83	1.24	0.09	0.68
	Y1805	65	0.31	0.25	0.15	0.48	0.78	1.26	0.11	0.72
	Y1809	66	0.27	0.23	0.15	0.56	4.2	1.85	0.1	0.91
1.4~1.6	Y1705	45	0.28	0.23	0.15	0.54	4.49	1.82	0.12	0.88
	Y1709	66	0.25	0.24	0.11	0.43	0.89	1.05	0.1	0.55
	Y1805	64	0.29	0.24	0.13	0.46	2.4	1.53	0.14	0.78
	Y1809	66	0.26	0.22	0.13	0.51	6.12	2.17	0.08	0.86
1.6~1.8	Y1705	29	0.32	0.27	0.19	0.61	5.14	2.04	0.12	1.01
	Y1709	66	0.26	0.24	0.12	0.48	5.72	1.76	0.07	0.81
	Y1805	58	0.27	0.22	0.12	0.45	3.16	1.52	0.11	0.77
	Y1809	66	0.25	0.23	0.12	0.47	2.99	1.64	0.08	0.65

表1

图2

表2

图3

图4

图5

表3

图6

图7

表4

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土层/m	C₀	空间参数		时间参数		联合参数
土层/m	C₀	C_S	a_S	C_T	a_T	C_ST	a_ST	α
0~0.2	0.0063	0.0240	957.00	0.0282	292.70	0.0032	235.01	0.1000
0.2~0.4	0.0081	0.0143	1438.69	0.0201	287.37	0.0092	1217.14	0.6647
0.4~0.6	0.0186	0.0152	1001.61	0.0073	289.24	0.0162	1868.67	0.1000
0.6~0.8	0.0080	0.0249	1615.07	0.0147	227.55	0.0074	1003.57	0.1856
0.8~1.0	0.0055	0.0259	1916.53	0.0188	209.64	0.0009	1003.37	0.4790
1.0~1.2	0.0028	0.0257	1323.09	0.0158	220.30	0.0009	955.91	3.0303
1.2~1.4	0.0069	0.0169	863.06	0.0061	147.70	0.0093	1930.13	0.2737
1.4~1.6	0.0061	0.0187	1127.09	0.0063	175.33	0.0061	1879.02	1.1730
1.6~1.8	0.0042	0.0010	1863.53	0.0132	293.88	0.0241	1827.54	0.0978
0~0.6	0.0089	0.0280	1250.41	0.0046	246.15	0.0017	1861.18	0.0244
0.6~1.2	0.0054	0.0290	1945.91	0.0090	244.73	0.0013	1259.28	0.0391

时空克里金评估河套灌区土壤盐分时空格局

Spatio-temporal patterns of soil salinity in Hetao Irrigation District based on spatio-temporal Kriging

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参考文献 31

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土层/m	ASML			LSML
土层/m	MRE/%	RMSE/(dS·m^-1)	R²	MRE/%	RMSE/(dS·m^-1)	R²
0~0.6	3.60	0.13	0.64	2.52	0.12	0.73
0.6~1.2	3.11	0.10	0.65	2.54	0.09	0.70

土层/m	时空克里金			普通克里金
土层/m	MRE/%	RMSE/hm²	R²	MRE/%	RMSE/hm²	R²
0~0.6	-13.20	466.47	0.79	26.14	412.99	0.78
0.6~1.2	-8.35	494.43	0.72	120.09	731.13	0.48

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