Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

doi:10.1175/JHM-D-16-0045.1

	Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China
	Zhang, Xuejun 1,2; Tang, Qiuhong 1; Liu, Xingcai 1; Leng, Guoyong 3; Li, Zhe 1
刊名	JOURNAL OF HYDROMETEOROLOGY
	2017
卷号	18 期号:1 页码:5-23
ISSN号	1525-755X
DOI	10.1175/JHM-D-16-0045.1
通讯作者	Tang, Qiuhong(tangqh@igsnrr.ac.cn)
英文摘要	In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. A quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.
WOS关键词	DATA ASSIMILATION SYSTEM ; LAND-SURFACE MODEL ; SEASONAL HYDROLOGIC PREDICTION ; CONTERMINOUS UNITED-STATES ; PRECIPITATION ; STREAMFLOW ; FLUXES ; SKILL ; WATER ; REGIONS
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	AMER METEOROLOGICAL SOC
WOS记录号	WOS:000393014500002
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/64951]
专题	中国科学院地理科学与资源研究所
通讯作者	Tang, Qiuhong
作者单位	1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Pacific Northwest Natl Lab, Joint Global Change Res Inst, College Pk, MD USA
推荐引用方式 GB/T 7714	Zhang, Xuejun,Tang, Qiuhong,Liu, Xingcai,et al. Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China[J]. JOURNAL OF HYDROMETEOROLOGY,2017,18(1):5-23.
APA	Zhang, Xuejun,Tang, Qiuhong,Liu, Xingcai,Leng, Guoyong,&Li, Zhe.(2017).Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China.JOURNAL OF HYDROMETEOROLOGY,18(1),5-23.
MLA	Zhang, Xuejun,et al."Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China".JOURNAL OF HYDROMETEOROLOGY 18.1(2017):5-23.