Selecting a probability distribution for extreme rainfall series in Malaysia
This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson...
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Veröffentlicht in: | Water science and technology 2002, Vol.45 (2), p.63-68 |
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description | This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia. |
doi_str_mv | 10.2166/wst.2002.0028 |
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D ; DESA, M. N. M ; NGUYEN, V-T-V ; KASSIM, A. H. M</creator><contributor>Burlando, P</contributor><creatorcontrib>ZALINA, M. D ; DESA, M. N. M ; NGUYEN, V-T-V ; KASSIM, A. H. M ; Burlando, P</creatorcontrib><description>This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.</description><identifier>ISSN: 0273-1223</identifier><identifier>ISBN: 1843394111</identifier><identifier>ISBN: 9781843394112</identifier><identifier>EISSN: 1996-9732</identifier><identifier>DOI: 10.2166/wst.2002.0028</identifier><identifier>PMID: 11890166</identifier><identifier>CODEN: WSTED4</identifier><language>eng</language><publisher>London: IWA Publishing</publisher><subject>Annual rainfall ; Correlation coefficient ; Correlation coefficients ; Datasets ; Discharge measurement ; Distribution ; Earth, ocean, space ; Environmental Monitoring ; Exact sciences and technology ; External geophysics ; Extreme values ; Extreme weather ; Gaging stations ; Investigations ; Malaysia ; Maximum likelihood method ; Mean square errors ; Meteorology ; Models, Statistical ; Parameter estimation ; Precipitation ; Probability distribution ; Probability theory ; Rain ; Rainfall ; Resampling ; Stream discharge ; Water in the atmosphere (humidity, clouds, evaporation, precipitation)</subject><ispartof>Water science and technology, 2002, Vol.45 (2), p.63-68</ispartof><rights>2002 INIST-CNRS</rights><rights>Copyright IWA Publishing Jan 2002</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c411t-8e60570839c94bdd57d55a1350bef61d2a46573c768154b7d4fa1e83677a03673</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,4024,4050,4051,23930,23931,25140,27923,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14052311$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11890166$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Burlando, P</contributor><creatorcontrib>ZALINA, M. D</creatorcontrib><creatorcontrib>DESA, M. N. M</creatorcontrib><creatorcontrib>NGUYEN, V-T-V</creatorcontrib><creatorcontrib>KASSIM, A. H. M</creatorcontrib><title>Selecting a probability distribution for extreme rainfall series in Malaysia</title><title>Water science and technology</title><addtitle>Water Sci Technol</addtitle><description>This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.</description><subject>Annual rainfall</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Datasets</subject><subject>Discharge measurement</subject><subject>Distribution</subject><subject>Earth, ocean, space</subject><subject>Environmental Monitoring</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Extreme values</subject><subject>Extreme weather</subject><subject>Gaging stations</subject><subject>Investigations</subject><subject>Malaysia</subject><subject>Maximum likelihood method</subject><subject>Mean square errors</subject><subject>Meteorology</subject><subject>Models, Statistical</subject><subject>Parameter estimation</subject><subject>Precipitation</subject><subject>Probability distribution</subject><subject>Probability theory</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Resampling</subject><subject>Stream discharge</subject><subject>Water in the atmosphere (humidity, clouds, evaporation, precipitation)</subject><issn>0273-1223</issn><issn>1996-9732</issn><isbn>1843394111</isbn><isbn>9781843394112</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp90c1r2zAYBnCxdixpt-OuQzBaenH2vnr1YR9HWD8gpYdtZyPb8lBw7FSyafPfTyGBQA89SDrox8MrPYx9RVgI1PrHSxwXAkAs0so_sDkWhc4KQ-KMXWAuiQqJiOdsDsJQhkLQjF3EuAYAQxI-sRliXkCKmrPVb9e5evT9P275NgyVrXznxx1vfByDr6bRDz1vh8Dd6xjcxvFgfd_aruPRBe8i9z1_tJ3dRW8_s4_pJrovx_OS_b399Wd5n62e7h6WP1dZnQYbs9xpUAZyKupCVk2jTKOURVJQuVZjI6zUylBtdI5KVqaRrUWXkzbGQtrpkl0fctPAz5OLY7nxsXZdZ3s3TLHEnFABUYI370MQUktttEz0-xu6HqbQp2eUWKQ_RZCkk8oOqg5DjMG15Tb4jQ27FFXuyylTOeW-nHJfTvLfjqlTtXHNSR8LSODqCGysbdcG29c-npwEJQiR_gNifJMh</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>ZALINA, M. 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D ; DESA, M. N. M ; NGUYEN, V-T-V ; KASSIM, A. H. 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D</au><au>DESA, M. N. M</au><au>NGUYEN, V-T-V</au><au>KASSIM, A. H. M</au><au>Burlando, P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Selecting a probability distribution for extreme rainfall series in Malaysia</atitle><jtitle>Water science and technology</jtitle><addtitle>Water Sci Technol</addtitle><date>2002</date><risdate>2002</risdate><volume>45</volume><issue>2</issue><spage>63</spage><epage>68</epage><pages>63-68</pages><issn>0273-1223</issn><eissn>1996-9732</eissn><isbn>1843394111</isbn><isbn>9781843394112</isbn><coden>WSTED4</coden><abstract>This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.</abstract><cop>London</cop><pub>IWA Publishing</pub><pmid>11890166</pmid><doi>10.2166/wst.2002.0028</doi><tpages>6</tpages></addata></record> |
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subjects | Annual rainfall Correlation coefficient Correlation coefficients Datasets Discharge measurement Distribution Earth, ocean, space Environmental Monitoring Exact sciences and technology External geophysics Extreme values Extreme weather Gaging stations Investigations Malaysia Maximum likelihood method Mean square errors Meteorology Models, Statistical Parameter estimation Precipitation Probability distribution Probability theory Rain Rainfall Resampling Stream discharge Water in the atmosphere (humidity, clouds, evaporation, precipitation) |
title | Selecting a probability distribution for extreme rainfall series in Malaysia |
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