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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
- Source :
- Journal of Water and Climate Change. 11:322-342
- Publication Year :
- 2020
- Publisher :
- IWA Publishing, 2020.
-
Abstract
- 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.
- Subjects :
- Atmospheric Science
Global and Planetary Change
geography
geography.geographical_feature_category
010504 meteorology & atmospheric sciences
Flood myth
0207 environmental engineering
Drainage basin
02 engineering and technology
Management, Monitoring, Policy and Law
Satellite precipitation
01 natural sciences
Climatology
PERSIANN
Environmental science
020701 environmental engineering
0105 earth and related environmental sciences
Water Science and Technology
Subjects
Details
- ISSN :
- 24089354 and 20402244
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Journal of Water and Climate Change
- Accession number :
- edsair.doi...........c0b25fc8ecf01f7ad97b31e17aebca9d