Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall
The Multiplicative Discrete Random Cascade (MDRC) class of model is used to temporally disaggregate rainfall volumes through multiplying the volumes by random weights, which is repeated through multiple disaggregation levels. The model development involves the identification of probability density f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/9/7/496 |
| _version_ | 1811295709081632768 |
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| author | Neil McIntyre András Bárdossy |
| author_facet | Neil McIntyre András Bárdossy |
| author_sort | Neil McIntyre |
| collection | DOAJ |
| description | The Multiplicative Discrete Random Cascade (MDRC) class of model is used to temporally disaggregate rainfall volumes through multiplying the volumes by random weights, which is repeated through multiple disaggregation levels. The model development involves the identification of probability density functions from which to sample the weights. The parameters of the probability density functions are known to be dependent on the rainfall volume. This paper characterises the volume dependency over the scarcely observed extreme ranges of rainfall, introducing the concept of volume-bounded MDRC models. Probable maximum precipitation (PMP) estimates are used to define theoretically-based points and asymptotes to which the observation-based estimates of the MDRC model parameters are extrapolated. Alternative models are tested using a case study of rainfall data from Brisbane, Australia covering the period 1908 to 2015. The results show that moving from a baseline model with constant parameters to incorporating the volume dependency of the parameters is essential for acceptable performance in terms of the frequency and magnitude of modelled extremes. As well as providing better estimates of parameters at each disaggregation level, the volume dependency provides an in-built bias correction when moving from one level to the next. A further, relatively small performance gain is obtained by extrapolating the observed dependency to the theoretically-based bounds. The volume dependency of the parameters is found to be reasonably time-scaleable, providing opportunity for advances in the generalisation of MDRC models. Sensitivity analysis shows that the subjectivities and uncertainties in the modelling procedure have mixed effects on the performance. A principal uncertainty, to which the results are sensitive, is the PMP estimate. Therefore, in applications of the bounded approach, the PMP should ideally be described by a probability distribution function. |
| first_indexed | 2024-04-13T05:37:30Z |
| format | Article |
| id | doaj.art-7aeb287309b4458e9b23ddf3b051a644 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-13T05:37:30Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-7aeb287309b4458e9b23ddf3b051a6442022-12-22T03:00:14ZengMDPI AGWater2073-44412017-07-019749610.3390/w9070496w9070496Using Probable Maximum Precipitation to Bound the Disaggregation of RainfallNeil McIntyre0András Bárdossy1Sustainable Minerals Institute, The University of Queensland, St Lucia, Brisbane, Queensland 4072, AustraliaInstitute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Pfaffenwaldring 61, 70550 Stuttgart, GermanyThe Multiplicative Discrete Random Cascade (MDRC) class of model is used to temporally disaggregate rainfall volumes through multiplying the volumes by random weights, which is repeated through multiple disaggregation levels. The model development involves the identification of probability density functions from which to sample the weights. The parameters of the probability density functions are known to be dependent on the rainfall volume. This paper characterises the volume dependency over the scarcely observed extreme ranges of rainfall, introducing the concept of volume-bounded MDRC models. Probable maximum precipitation (PMP) estimates are used to define theoretically-based points and asymptotes to which the observation-based estimates of the MDRC model parameters are extrapolated. Alternative models are tested using a case study of rainfall data from Brisbane, Australia covering the period 1908 to 2015. The results show that moving from a baseline model with constant parameters to incorporating the volume dependency of the parameters is essential for acceptable performance in terms of the frequency and magnitude of modelled extremes. As well as providing better estimates of parameters at each disaggregation level, the volume dependency provides an in-built bias correction when moving from one level to the next. A further, relatively small performance gain is obtained by extrapolating the observed dependency to the theoretically-based bounds. The volume dependency of the parameters is found to be reasonably time-scaleable, providing opportunity for advances in the generalisation of MDRC models. Sensitivity analysis shows that the subjectivities and uncertainties in the modelling procedure have mixed effects on the performance. A principal uncertainty, to which the results are sensitive, is the PMP estimate. Therefore, in applications of the bounded approach, the PMP should ideally be described by a probability distribution function.https://www.mdpi.com/2073-4441/9/7/496rainfallPMPstochasticcascadefloodsdisaggregation |
| spellingShingle | Neil McIntyre András Bárdossy Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall Water rainfall PMP stochastic cascade floods disaggregation |
| title | Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall |
| title_full | Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall |
| title_fullStr | Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall |
| title_full_unstemmed | Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall |
| title_short | Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall |
| title_sort | using probable maximum precipitation to bound the disaggregation of rainfall |
| topic | rainfall PMP stochastic cascade floods disaggregation |
| url | https://www.mdpi.com/2073-4441/9/7/496 |
| work_keys_str_mv | AT neilmcintyre usingprobablemaximumprecipitationtoboundthedisaggregationofrainfall AT andrasbardossy usingprobablemaximumprecipitationtoboundthedisaggregationofrainfall |