Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5752398 | Journal of Hydrology: Regional Studies | 2017 | 17 Pages |
â¢We investigated step and monotonic trends in sub-daily precipitation data from six Global Climate Model across Tasmania.â¢We have evaluated trends by adopting a smoothing algorithm process.â¢We extracted daily sub-daily data by split-apply-combine approach and low pass filter process.â¢Monotonic and step trends were present in sub-daily precipitation data across the state. The significance of trends was found to increase with decreased event duration.
Study regionIsland state of Tasmania, Australia.Study focusThis study detected monotonic and step trends in maximum sub-daily precipitation for durations ranging from 3 to 24Â h over the period 1961-2100. It also looked at whether or not there is agreement between six dynamically downscaled global circulation models (GCMs) in terms of the extent and magnitude of monotonic and step trends in the dataset. This was done using a split-apply-combine approach for data manipulation. The study included trend evaluation, application of a smoothing algorithm, and the application of non-parametric and parametric statistical tests on low pass filtered series.New hydrological insightsMonotonic and step trends in maximum sub-daily precipitation occurring in each month were identified across the state. Decreasing trends were found to become more evident in the Central Plateau region. There was reasonable agreement between GCMs on the sign and the magnitude of the precipitation changes, with the exception of the Central Plateau region of Tasmania, where the GCMs disagreed as to the spatial extent of the decreasing in trends.The duration and intensity (percentile) of maximum sub-daily precipitation were found to influence trends in sub-daily precipitation. Evidence of spatial patterns in monotonic and step trends for the data between the baseline period (1961-1990) and future climates (2010-2039, 2040-2069, and 2070-2099) have been evaluated.