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Evaluation of the performance of gridded precipitation products over balochistan province, pakistan

Gauge-based gridded precipitation estimates are emerged as a supplementary source of precipitation data where in-situ precipitation data are not readily available. In this study, four widely used gauge-based gridded precipitation products, namely Global Precipitation Climatology Centre (GPCC), Clima...

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Main Authors: Ahmed, K., Shahid, S., Ali, R. O., Harun, S. B., Wang, X. J.
Format: Article
Published: Desalination Publications 2017
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/76092/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027108765&doi=10.5004%2fdwt.2017.20859&partnerID=40&md5=a0b4d8f7b529f30225c6aa22621de07c
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spelling my.utm.760922018-05-30T04:19:58Z http://eprints.utm.my/id/eprint/76092/ Evaluation of the performance of gridded precipitation products over balochistan province, pakistan Ahmed, K. Shahid, S. Ali, R. O. Harun, S. B. Wang, X. J. TA Engineering (General). Civil engineering (General) Gauge-based gridded precipitation estimates are emerged as a supplementary source of precipitation data where in-situ precipitation data are not readily available. In this study, four widely used gauge-based gridded precipitation products, namely Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), and Center for Climatic Research – University of Delaware (UDel) are compared with in-situ precipitation at three stations located in semi-arid, arid, and hyper-arid regions of Balochistan province, Pakistan. The assessment is carried out at monthly scale and at 0.5° resolutions during 1961–2007. The performance of the data products is evaluated using various statistical approaches including root mean square error (RMSE), bias, non-parametric Kendall rank correlation, and the Mann-Kendall’s trend test. The results reveal that the performance of different products varies at different stations. However, GPCC is found considerably better than other products showing high agreement with annual and seasonal precipitation. GPCC also showed lower errors and higher correlations than other products. Albeit with the lack of spatially dense precipitation data in the study domain, this study suggests GPCC precipitation estimates as the most suitable product for the climatic and hydrological studies in a predominantly arid region like Balochistan. Desalination Publications 2017 Article PeerReviewed Ahmed, K. and Shahid, S. and Ali, R. O. and Harun, S. B. and Wang, X. J. (2017) Evaluation of the performance of gridded precipitation products over balochistan province, pakistan. Desalination and Water Treatment, 79 . pp. 73-86. ISSN 1944-3994 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027108765&doi=10.5004%2fdwt.2017.20859&partnerID=40&md5=a0b4d8f7b529f30225c6aa22621de07c
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ahmed, K.
Shahid, S.
Ali, R. O.
Harun, S. B.
Wang, X. J.
Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
description Gauge-based gridded precipitation estimates are emerged as a supplementary source of precipitation data where in-situ precipitation data are not readily available. In this study, four widely used gauge-based gridded precipitation products, namely Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), and Center for Climatic Research – University of Delaware (UDel) are compared with in-situ precipitation at three stations located in semi-arid, arid, and hyper-arid regions of Balochistan province, Pakistan. The assessment is carried out at monthly scale and at 0.5° resolutions during 1961–2007. The performance of the data products is evaluated using various statistical approaches including root mean square error (RMSE), bias, non-parametric Kendall rank correlation, and the Mann-Kendall’s trend test. The results reveal that the performance of different products varies at different stations. However, GPCC is found considerably better than other products showing high agreement with annual and seasonal precipitation. GPCC also showed lower errors and higher correlations than other products. Albeit with the lack of spatially dense precipitation data in the study domain, this study suggests GPCC precipitation estimates as the most suitable product for the climatic and hydrological studies in a predominantly arid region like Balochistan.
format Article
author Ahmed, K.
Shahid, S.
Ali, R. O.
Harun, S. B.
Wang, X. J.
author_facet Ahmed, K.
Shahid, S.
Ali, R. O.
Harun, S. B.
Wang, X. J.
author_sort Ahmed, K.
title Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
title_short Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
title_full Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
title_fullStr Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
title_full_unstemmed Evaluation of the performance of gridded precipitation products over balochistan province, pakistan
title_sort evaluation of the performance of gridded precipitation products over balochistan province, pakistan
publisher Desalination Publications
publishDate 2017
url http://eprints.utm.my/id/eprint/76092/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027108765&doi=10.5004%2fdwt.2017.20859&partnerID=40&md5=a0b4d8f7b529f30225c6aa22621de07c
_version_ 1643657215701155840
score 13.226497

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