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Advances in Meteorology - Volume 2017 2017, Article ID 3695285, 23 pages - https:-doi.org-10.1155-2017-3695285

Research Article

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China

Research Center of Fluid Machinery and Engineering, National Research Center of Pumps, Water Saving Irrigation, Jiangsu University, Zhenjiang 212013, China

Correspondence should be addressed to Alaa Alden Alazzy and Haishen Lü

Received 20 November 2016; Revised 26 January 2017; Accepted 1 February 2017; Published 30 March 2017

Academic Editor: Gwo-Fong Lin

Copyright © 2017 Alaa Alden Alazzy et al. 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.

Abstract

In the last few years, satellite-based precipitation datasets are believed to be a potential source for forcing inputs in driving hydrological models, which are important especially in complex terrain areas or ungauged basins where ground gauges are generally sparse or nonexistent. This study aims to comprehensively evaluate the satellite precipitation products, CMORPH-CRT, PERSIANN-CDR, 3B42RT, and 3B42 against gauge-based datasets and to infer their relative potential impacts on hydrological processes simulation using the HEC-HMS model in the Ganzi River Basin GRB of the Tibetan Plateau. Results from a quantitative statistical comparison reveal that, at annual and seasonal scales, both CMORPH-CRT and 3B42 perform better than PERSIANN-CDR and 3B42RT. The CMORPH-CRT and 3B42 tend to underestimate values at the medium and high precipitation intensities ranges, whereas the opposite tendency is found for PERSIANN-CDR and 3B42RT. Overall, 3B42 exhibits the best performance for streamflow simulations over GRB and even outperforms simulation driven by gauge data during the validation period. PERSIANN-CDR shows the worst overall performance. After recalibrating with input-specific precipitation data, the performance of all satellite precipitation forced simulations is substantially improved, except for PERSIANN-CDR. Furthermore, 3B42 is more suitable to drive hydrological models and can be a potential alternative source of sparse data in Tibetan Plateau basins.





Autor: Alaa Alden Alazzy, Haishen Lü, Rensheng Chen, Abubaker B. Ali, Yonghua Zhu, and Jianbin Su

Fuente: https://www.hindawi.com/



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