Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

被引:9
作者
Hasan, Md Masud [1 ]
Croke, Barry F. W. [2 ]
Liu, Shuangzhe [3 ]
Shimizu, Kunio [4 ]
Karim, Fazlul [5 ]
机构
[1] Australian Natl Univ, ANU Coll Asia & Pacific, Crawford Sch Publ Policy, Canberra, ACT 2601, Australia
[2] Australian Natl Univ, Fenner Sch Environm & Soc, Canberra, ACT 2601, Australia
[3] Univ Canberra, Fac Sci & Technol, Bruce, ACT 2617, Australia
[4] Inst Stat Math, Sch Stat Thinking, Tokyo 1900014, Japan
[5] CSIRO, Land & Water, Canberra, ACT 2601, Australia
关键词
sub-daily rainfall; ungauged catchment; statistical modelling; probability distribution; CLIMATE-CHANGE; VARIABILITY; IMPACTS;
D O I
10.3390/geosciences10020043
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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).
引用
收藏
页数:11
相关论文
共 27 条
[1]   Classification of Regional Climate Variability in the State of California [J].
Abatzoglou, John T. ;
Redmond, Kelly T. ;
Edwards, Laura M. .
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2009, 48 (08) :1527-1541
[2]  
[Anonymous], 2012, WATER RESOUR RES, DOI [10.1029/2011WR010490, DOI 10.1029/2011WR010490]
[3]  
[Anonymous], 2014, J SOIL SEDIMENT, DOI DOI 10.1007/s11368-013-0809-9
[4]   Impacts of climate change on rainfall extremes and urban drainage systems: a review [J].
Arnbjerg-Nielsen, K. ;
Willems, P. ;
Olsson, J. ;
Beecham, S. ;
Pathirana, A. ;
Gregersen, I. Bulow ;
Madsen, H. ;
Nguyen, V. -T. -V. .
WATER SCIENCE AND TECHNOLOGY, 2013, 68 (01) :16-28
[5]   Development and evaluation of a stochastic daily rainfall model with long-term variability [J].
Chowdhury, A. F. M. Kamal ;
Lockart, Natalie ;
Willgoose, Garry ;
Kuczera, George ;
Kiem, Anthony S. ;
Manage, Nadeeka Parana .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2017, 21 (12) :6541-6558
[6]   Evaluating regional climate models for simulating sub-daily rainfall extremes [J].
Cortes-Hernandez, Virginia Edith ;
Zheng, Feifei ;
Evans, Jason ;
Lambert, Martin ;
Sharma, Ashish ;
Westra, Seth .
CLIMATE DYNAMICS, 2016, 47 (5-6) :1613-1628
[7]   A new empirical model of sub-daily rainfall intensity and its application in a rangeland biophysical model [J].
Fraser, G. W. ;
Carter, J. O. ;
McKeon, G. M. ;
Day, K. A. .
RANGELAND JOURNAL, 2011, 33 (01) :37-48
[8]  
Hasan MM, 2013, 20TH INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2013), P380
[9]   Seasonal rainfall totals of Australian stations can be modelled with distributions from the Tweedie family [J].
Hasan, Md Masud ;
Dunn, Peter K. .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2015, 35 (10) :3093-3101
[10]   Entropy, consistency in rainfall distribution and potential water resource availability in Australia [J].
Hasan, Md Masud ;
Dunn, Peter K. .
HYDROLOGICAL PROCESSES, 2011, 25 (16) :2613-2622