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Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China
Zhang, Xuejun1,2; Tang, Qiuhong1; Liu, Xingcai1; Leng, Guoyong3; Li, Zhe1
2017
Source PublicationJOURNAL OF HYDROMETEOROLOGY
ISSN1525-755X
Volume18Issue:1Pages:5-23
Corresponding AuthorTang, Qiuhong(tangqh@igsnrr.ac.cn)
AbstractIn 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.
DOI10.1175/JHM-D-16-0045.1
WOS KeywordDATA ASSIMILATION SYSTEM ; LAND-SURFACE MODEL ; SEASONAL HYDROLOGIC PREDICTION ; CONTERMINOUS UNITED-STATES ; PRECIPITATION ; STREAMFLOW ; FLUXES ; SKILL ; WATER ; REGIONS
Indexed BySCI
Language英语
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000393014500002
PublisherAMER METEOROLOGICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64952
Collection中国科学院地理科学与资源研究所
Corresponding AuthorTang, Qiuhong
Affiliation1.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
Recommended Citation
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.
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