LAUSR

Although satellite precipitation products (SPPs) increasingly provide an alternative means to groundbased 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. doi: 10.2166/wcc.2020.180 om http://iwaponline.com/jwcc/article-pdf/11/S1/322/816916/jwc0110322.pdf er 2021 Eugene Zhen Xiang Soo Wan Zurina Wan Jaafar (corresponding author) Sai Hin Lai Faridah Othman Ahmed Elshafie Hazlina Salehan Othman Hadi Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia E-mail: [email protected] Tanvir Islam Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Prashant Srivastava Hydrological Sciences, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA