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Evaluation of the effects of temperature changes on fine timescale rainfall
Author(s) -
GyasiAgyei Yeboah
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/wrcr.20369
Subject(s) - skewness , environmental science , quantile , copula (linguistics) , climatology , return period , maximum temperature , statistics , atmospheric sciences , mathematics , geography , econometrics , geology , archaeology , flood myth
An enhanced copula‐based daily rainfall disaggregation model is presented. The rainfall data are grouped into maximum daily temperature quantiles instead of the usual monthly grouping in order to account for seasonal temperature shifts. Empirical and statistical evidence provided supports the conditioning of the model parameters on temperature. Linkage of the major model parameters to maximum daily temperature allows easy evaluation of temperature changes on fine timescale rainfall statistics. Rainfall data sets for three Australian capital cities located in different climatic regions were used to test the applicability of the presented model. It is observed that statistics such as the total wet periods' duration, storm event duration, and the autocorrelations decrease with increase in temperature, while the maximum wet period depth, variance, skewness, and the intensity‐duration‐frequency (IDF) show an increasing trend with temperature. Considering the 24 h wet day rainfall data sets, a 1°C rise in temperature could cause rates of change, depending on the climate (lowest rates for cool temperate zones and highest for the hot tropics), of 2%–14% in the total wet periods' duration, 5%–16% in variance, 10%–30% in skewness, 5%–9% in maximum period depth (including IDF), and 4%–25% in autocorrelations. Simulating single events of varying depths, and using temperature values spanning the spectrum of the recorded data for the capital cities, the average rates were 2%–12% for duration (and total wet periods' duration), 10%–26% for variance, 20%–32% for skewness and 1%–20% for the autocorrelations, all of which match the 24 h wet day results very well.