Evaluating the double-kernel smoothing technique of blending TRMM and gauge data to identify flood events in the Xiangjiang River Basin, China

Abstract

In the recent decades, mass casualties and property losses are caused by flood events. A combination of satellite and hydrological methods has been promoted owing to the advantages of satellite precipitation products (SPPs), such as their wide coverage and high spatiotemporal resolution; these benefits may make up for the deficiencies of gauge data and make SPPs a good supplementary data source, especially for sparsely gauged areas. The present study employs the double-kernel smoothing technique (DS) to integrate TRMM precipitation data and gauge data and evaluates the performance of this method in identifying extreme flood events via the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) in the humid Xiangjiang River Basin, China. Results show that the HEC-HMS driven by TRMM precipitation data can capture the 9 selected flood events accurately both before and after the merging process despite some deficiencies in the TRMM precipitation data. In addition, the merging method generally improved the consistency (CC increased from 0.04 to 0.24) and rainfall-detection capability of the TRMM precipitation data (FAR decreased by 9.12%, POD increased by 56.91% and HSS increased by 15.43%) and also promoted the overall hydrological simulation accuracy and reliability (the average CC increased from 0.86 to 0.96, the average NSE increased from 0.58 to 0.73). However, the blended TRMM precipitation data did not always outperform the nonblended data in terms of certain flood feature simulation details, such as the flood volume, flood peak and peak time.

Keywords:

• TRMM
• precipitation evaluation
• double-kernel smoothing technique
• flood events
• HEC-HMS

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

TRMM precipitation data are collected from Goddard Earth Sciences Data and Information Services Center (GES DISC), https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_7/summary, where you can find several options for downloads. DEM data is provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences. (http://www.gscloud.cn). Land use and land cover(LULC) data is derived from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Soil data is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn). Rain gauge observations are collected from National Meteorological Information Centre(http://data.cma.cn/). Hydrological station records are collected from Hunan Bureau of Hydrological and Water Resources. Derived data supporting the findings of this study are available from the corresponding author [J.D] on request.

Additional information

Funding

This research was funded by the Program of Introducing Talent to Universities, 111 Project, grant number BP0820003