基于站点观测和模式模拟的北京市土壤湿度

doi:10.31497/zrzyxb20190116

摘要/Abstract

摘要：

为了获取全面的地面表层的时空变化信息,研究行政区域尺度内的水循环和能量循环,必需结合模型模拟和站点观测。基于国家气象局开发的时空连续的CLDAS土壤湿度产品,结合已获取的北京市区域内82个监测站点的逐日土壤湿度监测数据,评估CLDAS土壤湿度产品在行政区域尺度内的准确性与一致性,进一步获取北京市行政区域内的精确、全面、连续的土壤湿度时空变化信息,并在此基础上分析北京市土壤墒情时空变异特征。对比分析CLDAS产品和站点观测两种土壤湿度数据显示,北京市2013年10月1日至12月31日范围内CLDAS产品具有以下特点：CLDAS产品基本与观测数据具有一致变化的趋势,除顺义外CLDAS产品均高于观测数据。当日20∶00的土壤湿度均高于8∶00的土壤湿度,在平均气温降为0 ℃后,土壤湿度波动剧烈,20∶00与8∶00的土壤湿度出现显著差异。在时间尺度上,随着降水的减少,北京市的土壤湿度在逐渐降低。在空间尺度上,北京市干旱范围在逐渐扩大,并且呈现以昌平为中心的极旱逐渐蔓延至多个区。由于顺义区土壤相对湿度较高,呈现另外一个以顺义为中心的土壤相对湿度逐渐变小的区域,但空间范围变化较小。

关键词: 土壤湿度, CLDAS, 站点观测, 模式模拟, 北京市

Abstract:

It is of great significance to accurately and objectively evaluate the spatial and temporal variation characteristics of soil moisture in studies on the biochemical evolution process of the earth surface. Thus, the combination of model simulation and site observation is necessary to studying water and energy recycling at a regional scale especially. In this paper, based on daily soil surface (0-10 cm) moisture data of 82 monitoring stations in Beijing and GLDAS soil moisture products, the accuracy and consistency of CLDAS was evaluated and the spatio-temporal variability of soil moisture content in Beijing was examined. The root mean square error (RMSE) was used to identify the consistency of CLDAS products and observation values. Specifically, we compared and analyzed the accumulated values of CLDAS products and observation data in 1 day (8∶00, 20∶00), 7 days (8∶00, 20∶00), 15 days (8∶00, 20∶00), 31 days (8∶00, 20∶00), 61 days (8∶00, 20∶00), and 92 days (8∶00, 20∶00). The results showed that: (1) Although the data from CLDAS products were generally slightly higher than the measured data, CLDAS products and site observation data show a good consistency that RMSE in various countries was almost around 10; (2) CLDAS products could reflect the distribution characteristics of soil moisture in Beijing when the temperature was higher than 0 ℃, while there existed a bias in GLDAS products when the temperature was lower than 0 ℃; (3) Based on drought identification based on soil moisture data, we found that the drought area in Beijing was gradually expanding and one extreme drought center was spreading from Changping (soil relative moisture content < 30%) to other districts. Meanwhile, the other area with high soil relative humidity (soil relative moisture content > 50%), centered in Shunyi District, gradually decreased at a spatial scale; (4) In terms of temporal scale, soil moisture in Beijing gradually decreased with the decrease of precipitation that soil humidity at 20∶00 was all higher than that at 8∶00 in the same day (ME_obs >0). When the temperature was lower than 0 ℃, soil humidity at 20∶00 and 8∶00 became significantly different. These conclusions would help us make effective use of GLDAS products in soil moisture related studies and would help us know the spatial and temporal variability of soil moisture content in Beijing.

Key words: soil moisture, CLDAS, site observation, model simulation, Beijing

秦道清, 赵岩, 王红瑞, 邓彩云, 赵勇. 基于站点观测和模式模拟的北京市土壤湿度[J]. 自然资源学报, 2019, 34(1): 191-204.

Dao-qing QIN, Yan ZHAO, Hong-rui WANG, Cai-yun DENG, Yong ZHAO. Soil moisture in Beijing based on site observation and model simulation[J]. JOURNAL OF NATURAL RESOURCES, 2019, 34(1): 191-204.

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区县	实测	10月	0 ℃之前	0 ℃之后
朝阳	0.10	0.15	0.14	-6.18
丰台	-0.07	0.50	0.45	-6.70
海淀	0.09	0.29	0.24	-6.38
通州	0.13	0.43	0.31	-7.33
大兴	0.07	0.40	0.01	-8.62
顺义	0.06	0.31	0.16	-6.83
房山	0.06	0.38	0.19	-7.77
门头沟	0	0.41	-0.12	-6.89
昌平	0.06	0.32	0.23	-6.27
延庆	0.07	0.25	-1.07	-5.97
怀柔	0.07	0.29	-0.22	-6.15
密云	0.01	0.28	0.12	-6.53
平谷	0.07	0.24	0.15	-6.89

干旱类型	土壤相对湿度范围	干旱影响程度
无旱	>60%	地表湿润或正常
轻旱	50%~60%	地表蒸发量小,近地表空气干燥
中旱	40%~50%	土壤表面干燥,地表植物叶片有萎蔫出现
重旱	30%~40%	干土层较厚,地表植物萎蔫,叶片干枯
特旱	<30%	基本无土壤蒸发,地表植物干枯、死亡

时间范围	时刻	土壤相对湿度
所有时长	整体	0.3906
	8∶00	0.3901
	20∶00	0.3911
分期前	整体	0.4101
	8∶00	0.4096
	20∶00	0.4105
分期后	整体	0.3617
	8∶00	0.3608
	20∶00	0.3621
10月	整体	0.4247
	8∶00	0.4245
	20∶00	0.4250
11月	整体	0.3889
	8∶00	0.3885
	20∶00	0.3892
12月	整体	0.3582
	8∶00	0.3573
	20∶00	0.3590