Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins
The unprecedented flood disaster that had caused massive damages and losses striking the eastern coast region of Peninsular Malaysia in December 2014 had necessitated for an improvement in the flood forecasting and warning system (FFWS). Comprehensive effort had been concerted in the enhancement of...
Saved in:
Main Authors: | , , , , , |
---|---|
Other Authors: | |
Format: | Article |
Published: |
IAEME Publication
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-23733 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-237332023-05-29T14:51:21Z Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins Wardah T. Suzana R. Sazali O. Hafiz A. Lariyah M.S. Sharmy J. 42862377800 57195067562 57203661689 57203655814 35070506500 57203657953 The unprecedented flood disaster that had caused massive damages and losses striking the eastern coast region of Peninsular Malaysia in December 2014 had necessitated for an improvement in the flood forecasting and warning system (FFWS). Comprehensive effort had been concerted in the enhancement of the FFWS including the use of radar rainfall as input to the system. This study focused on radar rainfall calibration to improve radar rainfall estimates to be used as inputs to National Flood Forecasting and Warning System (NaFFWS) for Kelantan and Terengganu river basins. The reflectivity data (Z) for period between 1st. November 2014 until 30st November 2015 from Kota Bharu (KB) radar together with the hourly rainfall (R) depths at 88 rainfall stations located in the basins for the same periods were used. Correlation analysis between derived radar rainfall using Marshall Palmer Z-R relation and gauged rainfall indicates that the further distance from the radar, the weaker the R2-coefficient value. The Z-R climatological calibration was subsequently done for four divisions of the river basin based on the distances from KB radar. The resulted optimized Z-R relations are Z=20R1.8 for distance 0-50 km and Z=10R1.9 for distance between 51-100 km, Z= 2R1.9 for distance 101-150 km and Z= 0.25R1.4 for distance above 150 km from KB radar. The performance measurement shows improved radar rainfall estimates for Kelantan and Terengganu river basins when using the calibrated Z-R equations. In addition, calibration using the ratio of gauged over radar rainfall was also shown to improve the radar rainfall estimates. � IAEME Publication. Final 2023-05-29T06:51:21Z 2023-05-29T06:51:21Z 2018 Article 2-s2.0-85052602396 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052602396&partnerID=40&md5=7e6f6d7506ea43af1bf1600bcb1b8729 https://irepository.uniten.edu.my/handle/123456789/23733 9 8 27 36 IAEME Publication Scopus |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
description |
The unprecedented flood disaster that had caused massive damages and losses striking the eastern coast region of Peninsular Malaysia in December 2014 had necessitated for an improvement in the flood forecasting and warning system (FFWS). Comprehensive effort had been concerted in the enhancement of the FFWS including the use of radar rainfall as input to the system. This study focused on radar rainfall calibration to improve radar rainfall estimates to be used as inputs to National Flood Forecasting and Warning System (NaFFWS) for Kelantan and Terengganu river basins. The reflectivity data (Z) for period between 1st. November 2014 until 30st November 2015 from Kota Bharu (KB) radar together with the hourly rainfall (R) depths at 88 rainfall stations located in the basins for the same periods were used. Correlation analysis between derived radar rainfall using Marshall Palmer Z-R relation and gauged rainfall indicates that the further distance from the radar, the weaker the R2-coefficient value. The Z-R climatological calibration was subsequently done for four divisions of the river basin based on the distances from KB radar. The resulted optimized Z-R relations are Z=20R1.8 for distance 0-50 km and Z=10R1.9 for distance between 51-100 km, Z= 2R1.9 for distance 101-150 km and Z= 0.25R1.4 for distance above 150 km from KB radar. The performance measurement shows improved radar rainfall estimates for Kelantan and Terengganu river basins when using the calibrated Z-R equations. In addition, calibration using the ratio of gauged over radar rainfall was also shown to improve the radar rainfall estimates. � IAEME Publication. |
author2 |
42862377800 |
author_facet |
42862377800 Wardah T. Suzana R. Sazali O. Hafiz A. Lariyah M.S. Sharmy J. |
format |
Article |
author |
Wardah T. Suzana R. Sazali O. Hafiz A. Lariyah M.S. Sharmy J. |
spellingShingle |
Wardah T. Suzana R. Sazali O. Hafiz A. Lariyah M.S. Sharmy J. Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
author_sort |
Wardah T. |
title |
Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
title_short |
Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
title_full |
Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
title_fullStr |
Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
title_full_unstemmed |
Radar rainfall calibration for improved quantitative precipitation estimates in Kelantan and Terengganu river basins |
title_sort |
radar rainfall calibration for improved quantitative precipitation estimates in kelantan and terengganu river basins |
publisher |
IAEME Publication |
publishDate |
2023 |
_version_ |
1806427835500331008 |
score |
13.214268 |