Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia
Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall us...
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| Format: | Article |
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MDPI AG
2020-01-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/10/2/43 |
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| author | Md Masud Hasan Barry F. W. Croke Shuangzhe Liu Kunio Shimizu Fazlul Karim |
| author_facet | Md Masud Hasan Barry F. W. Croke Shuangzhe Liu Kunio Shimizu Fazlul Karim |
| author_sort | Md Masud Hasan |
| collection | DOAJ |
| description | Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles). |
| first_indexed | 2024-12-10T21:04:59Z |
| format | Article |
| id | doaj.art-d8a8cc3bf2d14e548fa173f99a5e3c9b |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-12-10T21:04:59Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-d8a8cc3bf2d14e548fa173f99a5e3c9b2022-12-22T01:33:39ZengMDPI AGGeosciences2076-32632020-01-011024310.3390/geosciences10020043geosciences10020043Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in AustraliaMd Masud Hasan0Barry F. W. Croke1Shuangzhe Liu2Kunio Shimizu3Fazlul Karim4Crawford School of Public Policy, ANU College of Asia and the Pacific, The Australian National University, Canberra ACT 2601, AustraliaFenner School of Environment & Society, The Australian National University, Canberra ACT 2601, AustraliaFaculty of Science and Technology, University of Canberra, Bruce ACT 2617, AustraliaSchool of Statistical Thinking, The Institute of Statistical Mathematics, Tokyo 190-0014, JapanCSIRO Land and Water, Commonwealth Scientific and Industrial Research Organisation, Canberra ACT 2601, AustraliaProbabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles).https://www.mdpi.com/2076-3263/10/2/43sub-daily rainfallungauged catchmentstatistical modellingprobability distribution |
| spellingShingle | Md Masud Hasan Barry F. W. Croke Shuangzhe Liu Kunio Shimizu Fazlul Karim Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia Geosciences sub-daily rainfall ungauged catchment statistical modelling probability distribution |
| title | Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia |
| title_full | Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia |
| title_fullStr | Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia |
| title_full_unstemmed | Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia |
| title_short | Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia |
| title_sort | using mixed probability distribution functions for modelling non zero sub daily rainfall in australia |
| topic | sub-daily rainfall ungauged catchment statistical modelling probability distribution |
| url | https://www.mdpi.com/2076-3263/10/2/43 |
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