Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia
The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and perfor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-11-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/13/11/3830 |
| _version_ | 1811185839506456576 |
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| author | Aina Izzati Mohd Esa Syafrina Abdul Halim Norhaslinda Ali Jing Xiang Chung Mohd Syazwan Faisal Mohd |
| author_facet | Aina Izzati Mohd Esa Syafrina Abdul Halim Norhaslinda Ali Jing Xiang Chung Mohd Syazwan Faisal Mohd |
| author_sort | Aina Izzati Mohd Esa |
| collection | DOAJ |
| description | The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skill in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014.
HIGHLIGHTS
BCM-QMskewed with LGNORM and GUM was considered to capture the extreme values.;
The linear covariate model was considered to capture the nonstationary trend in extreme temperatures series.;
Results indicate the model's ability to correct the biases of extreme temperatures data for both RCPs in the study area with decent accuracy.;
Higher mortality rate of CVD is consistent with higher extreme temperatures.; |
| first_indexed | 2024-04-11T13:36:06Z |
| format | Article |
| id | doaj.art-069c0ebc083e4107adcb22845181e251 |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-11T13:36:06Z |
| publishDate | 2022-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-069c0ebc083e4107adcb22845181e2512022-12-22T04:21:28ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542022-11-0113113830385010.2166/wcc.2022.215215Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular MalaysiaAina Izzati Mohd Esa0Syafrina Abdul Halim1Norhaslinda Ali2Jing Xiang Chung3Mohd Syazwan Faisal Mohd4 Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Serdang 43400, Selangor, Malaysia Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Serdang 43400, Selangor, Malaysia Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Serdang 43400, Selangor, Malaysia Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia National Water Research Institute of Malaysia (NAHRIM), Seri Kembangan 43300, Selangor, Malaysia The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skill in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014. HIGHLIGHTS BCM-QMskewed with LGNORM and GUM was considered to capture the extreme values.; The linear covariate model was considered to capture the nonstationary trend in extreme temperatures series.; Results indicate the model's ability to correct the biases of extreme temperatures data for both RCPs in the study area with decent accuracy.; Higher mortality rate of CVD is consistent with higher extreme temperatures.;http://jwcc.iwaponline.com/content/13/11/3830bias correctioncardiovascular diseaseextreme temperaturelinear covariatemortalityskewed distribution |
| spellingShingle | Aina Izzati Mohd Esa Syafrina Abdul Halim Norhaslinda Ali Jing Xiang Chung Mohd Syazwan Faisal Mohd Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia Journal of Water and Climate Change bias correction cardiovascular disease extreme temperature linear covariate mortality skewed distribution |
| title | Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia |
| title_full | Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia |
| title_fullStr | Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia |
| title_full_unstemmed | Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia |
| title_short | Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia |
| title_sort | optimizing future mortality rate prediction of extreme temperature related cardiovascular disease based on skewed distribution in peninsular malaysia |
| topic | bias correction cardiovascular disease extreme temperature linear covariate mortality skewed distribution |
| url | http://jwcc.iwaponline.com/content/13/11/3830 |
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