Abstract
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).
Highlights
Rainfall data have been used as basic inputs in hydrological, agricultural, ecological and other environmental models and have applications in planning, design and management of water resources [1,2,3,4,5]
Among the three models considered in the study, log-normal distribution fits better to the sub-daily rainfall data from the case study stations located at east coast and Tasmania
The results indicate that the Gamma and Lognormal (G-L) model is better than the W-L model in sub-daily rainfall intensities
Summary
Rainfall data have been used as basic inputs in hydrological, agricultural, ecological and other environmental models and have applications in planning, design and management of water resources [1,2,3,4,5]. Better estimates of runoff and soil infiltration and drainage were achieved when sub-daily, rather than daily rainfall intensities were used as model inputs [6,7]. Studying characteristics of sub-daily rainfall helps understanding incidence of extremely intense events causing flash-flood [8,9]. Sub-daily rainfall is important in biogeochemical and nutrient cycle modelling [10]. Because of inadequate data availability, the use of sub-daily rainfall is limited. The rain gauge network lacks in continuous long-term records, spatial representativeness and climate homogeneity [11,12]
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