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Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

1
State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering Science, Nanjing Hydraulic Research Institute, Nanjing 210029, China
2
Key Laboratory of Terrestrial Water Cycle and Surface Processes, Institute of Geographical Sciences and Resources, Chinese Academy of Sciences, Beijing 100101, China
3
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
4
Anhui Provincial Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(3), 579; https://doi.org/10.3390/w11030579
Received: 23 January 2019 / Revised: 11 March 2019 / Accepted: 12 March 2019 / Published: 20 March 2019
(This article belongs to the Section Hydrology)
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PDF [284 KB, uploaded 20 March 2019]
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Abstract

Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data. View Full-Text
Keywords: rainfall; spatial interpolation; radar; satellite; atmospheric reanalysis; rainfall merging rainfall; spatial interpolation; radar; satellite; atmospheric reanalysis; rainfall merging
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hu, Q.; Li, Z.; Wang, L.; Huang, Y.; Wang, Y.; Li, L. Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging. Water 2019, 11, 579.

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