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	A merging framework for rainfall estimation at high spatiotemporal resolution for distributed hydrological modeling in a data-scarce area
	Long, Yinping1,2; Zhang, Yaonan1,3; Ma, Qimin1,2
刊名	Remote sensing
	2016-07-01
卷号	8期号:7页码:18
关键词	Downscaling Hydrological modeling Indicator kriging Merging Optimization Trmm
ISSN号	2072-4292
DOI	10.3390/rs8070599
通讯作者	Zhang, yaonan(yaonan@lzb.ac.cn)
英文摘要	Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. however, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. the objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. in the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25 degrees daily rainfall field derived from the tropical rainfall measuring mission (trmm) multisatellite precipitation analysis (tmpa) precipitation product version 7. the nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled trmm and gauged rainfall with minimum cross-validation error. an indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. the framework was applied to estimate daily precipitation at a 1 km resolution in the qinghai lake basin, a data-scarce area in the northeast of the qinghai-tibet plateau. the final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (gbhm). the proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.
WOS关键词	SATELLITE RAINFALL ; GAUGE OBSERVATIONS ; TIBETAN PLATEAU ; PRECIPITATION ; RADAR ; CHINA ; TRMM ; BASIN ; VALIDATION ; AUSTRALIA
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000382224800069
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2375377
专题	中国科学院大学
通讯作者	Zhang, Yaonan
作者单位	1.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Gansu Resources & Environm Sci Data Engn Technol, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Long, Yinping,Zhang, Yaonan,Ma, Qimin. A merging framework for rainfall estimation at high spatiotemporal resolution for distributed hydrological modeling in a data-scarce area[J]. Remote sensing,2016,8(7):18.
APA	Long, Yinping,Zhang, Yaonan,&Ma, Qimin.(2016).A merging framework for rainfall estimation at high spatiotemporal resolution for distributed hydrological modeling in a data-scarce area.Remote sensing,8(7),18.
MLA	Long, Yinping,et al."A merging framework for rainfall estimation at high spatiotemporal resolution for distributed hydrological modeling in a data-scarce area".Remote sensing 8.7(2016):18.

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