Precision of raw and bias-adjusted satellite precipitation estimations (TRMM, IMERG, CMORPH, and PERSIANN) over extreme flood events: case study in Langat river basin, Malaysia

被引:15
作者
Soo, Eugene Zhen Xiang [1 ]
Jaafar, Wan Zurina Wan [1 ]
Lai, Sai Hin [1 ]
Othman, Faridah [1 ]
Elshafie, Ahmed [1 ]
Islam, Tanvir [2 ]
Srivastava, Prashant [3 ]
Hadi, Hazlina Salehan Othman [1 ]
机构
[1] Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur, Malaysia
[2] CALTECH, Jet Prop Lab, Pasadena, CA USA
[3] NASA, Hydrol Sci, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
关键词
bias correction; extreme floods; HEC-HMS; Malaysia; satellite precipitation; REGIONAL CLIMATE MODEL; RAINFALL PRODUCTS; ANALYSIS TMPA; SPATIAL INTERPOLATION; HIGH-RESOLUTION; STREAMFLOW SIMULATION; HYDROLOGIC PREDICTION; CHANGE IMPACT; GPM IMERG; GAUGE;
D O I
10.2166/wcc.2020.180
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.
引用
收藏
页码:322 / 342
页数:21
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