Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting
Changes in the spatial and temporal rainfall pattern affected by the climate change need to be investigated as its significant characteristics are often used for managing water resources. In this study, the impacts of climate change on rainfall variability in Johor was investigated by using General...
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| Format: | Thesis |
| Language: | English English English |
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2020
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| Online Access: | http://eprints.uthm.edu.my/982/ http://eprints.uthm.edu.my/982/1/24p%20%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI.pdf http://eprints.uthm.edu.my/982/2/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/982/3/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20WATERMARK.pdf |
| _version_ | 1848887355862155264 |
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| author | Ahmad Tarmizi, ‘Aainaa Hatin |
| author_facet | Ahmad Tarmizi, ‘Aainaa Hatin |
| author_sort | Ahmad Tarmizi, ‘Aainaa Hatin |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Changes in the spatial and temporal rainfall pattern affected by the climate change need to be investigated as its significant characteristics are often used for managing water resources. In this study, the impacts of climate change on rainfall variability in Johor was investigated by using General Circulation Model (GCM) on the availability of daily simulation for three representative concentration pathways (RCP) scenarios, RCP2.6, RCP4.5 and RCP8.5 for interval year of Δ2030, Δ2050 and Δ2080. In addition, the annual future rainfall trend and harvested rainwater volume estimation for the first interval year of Δ2030 were also made. Daily rainfall series from eight (8) stations in Johor capturing 30 years period (1988-2017) with less than 10% missing data were chosen. Of all 26 predictors, only five (5) were chosen for each station to form a rainfall equation at each station for prediction analyses. It can be observed that the temperature (nceptemp), surface specific humidity (ncepshum) and near-surface relative humidity (nceprhum) had a strongest influence in the local weather formations with R values ranged from 0.5 to 0.7. The annual mean rainfall for RCP 2.6, 4.5 and 8.5 was predicted increase by of 17.5%, 18.1% and 18.3%, respectively as compared to historical data. Kluang was predicted to receive the highest amount of rainfall, and the lowest was in Segamat. Moreover, the Mann-Kendall test was used to detect the trend and resulted in no trend for RCP 2.6. Even so, RCP 4.5 showed a significant upward trend in Muar and Kota Tinggi, and for RCP 8.5, all regions were detected to have an upwards trend except for Pontian and Kluang. Volume harvested rainwater ( |
| first_indexed | 2025-11-15T19:53:05Z |
| format | Thesis |
| id | uthm-982 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:53:05Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-9822021-09-19T07:31:07Z http://eprints.uthm.edu.my/982/ Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting Ahmad Tarmizi, ‘Aainaa Hatin QA75-76.95 Calculating machines Changes in the spatial and temporal rainfall pattern affected by the climate change need to be investigated as its significant characteristics are often used for managing water resources. In this study, the impacts of climate change on rainfall variability in Johor was investigated by using General Circulation Model (GCM) on the availability of daily simulation for three representative concentration pathways (RCP) scenarios, RCP2.6, RCP4.5 and RCP8.5 for interval year of Δ2030, Δ2050 and Δ2080. In addition, the annual future rainfall trend and harvested rainwater volume estimation for the first interval year of Δ2030 were also made. Daily rainfall series from eight (8) stations in Johor capturing 30 years period (1988-2017) with less than 10% missing data were chosen. Of all 26 predictors, only five (5) were chosen for each station to form a rainfall equation at each station for prediction analyses. It can be observed that the temperature (nceptemp), surface specific humidity (ncepshum) and near-surface relative humidity (nceprhum) had a strongest influence in the local weather formations with R values ranged from 0.5 to 0.7. The annual mean rainfall for RCP 2.6, 4.5 and 8.5 was predicted increase by of 17.5%, 18.1% and 18.3%, respectively as compared to historical data. Kluang was predicted to receive the highest amount of rainfall, and the lowest was in Segamat. Moreover, the Mann-Kendall test was used to detect the trend and resulted in no trend for RCP 2.6. Even so, RCP 4.5 showed a significant upward trend in Muar and Kota Tinggi, and for RCP 8.5, all regions were detected to have an upwards trend except for Pontian and Kluang. Volume harvested rainwater ( 2020-09 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/982/1/24p%20%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI.pdf text en http://eprints.uthm.edu.my/982/2/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/982/3/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20WATERMARK.pdf Ahmad Tarmizi, ‘Aainaa Hatin (2020) Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | QA75-76.95 Calculating machines Ahmad Tarmizi, ‘Aainaa Hatin Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title | Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title_full | Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title_fullStr | Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title_full_unstemmed | Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title_short | Forecasting of rainfall using statistical downscaling model (SDSM) – general circulation model (GCM) for future estimation of rainwater harvesting |
| title_sort | forecasting of rainfall using statistical downscaling model (sdsm) – general circulation model (gcm) for future estimation of rainwater harvesting |
| topic | QA75-76.95 Calculating machines |
| url | http://eprints.uthm.edu.my/982/ http://eprints.uthm.edu.my/982/1/24p%20%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI.pdf http://eprints.uthm.edu.my/982/2/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/982/3/%E2%80%98AAINAA%20HATIN%20AHMAD%20TARMIZI%20WATERMARK.pdf |