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

Journal Article · Mon Dec 19 04:00:00 EST 2016 · Journal of Hydrometeorology

DOI:https://doi.org/10.1175/JHM-D-16-0045.1· OSTI ID:1361986

Zhang, Xuejun ^[1]; Tang, Qiuhong ^[2]; Liu, Xingcai ^[2]; Leng, Guoyong ^[3]; Li, Zhe ^[2]

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1. Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences, Beijing (China)
2. Chinese Academy of Sciences (CAS), Beijing (China)
3. Pacific Northwest National Lab., College Park, MD (United States). Joint Global Change Research Inst.

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. Furthermore, 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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: National Natural Science Foundation of China (NSFC); National Basic Research Program of China; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1361986
• Report Number(s): PNNL-SA--117141; KP1703030
• Journal Information: Journal of Hydrometeorology, Journal Name: Journal of Hydrometeorology Journal Issue: 1 Vol. 18; ISSN 1525-755X
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Ensembles
Hydrologic models
Nowcasting
Operational forecasting
Seasonal forecasting