Variability In Rainfall Extremes Research Articles

Seasonal Dependence and Variability of Rainfall Extremes in a Tropical River Basin, South Asia

In recent years, rainfall extremes have increased significantly and have threatened the socio-economic development in Sri Lanka. This study investigates the rainfall extremes in the Mahaweli River Basin (MRB) of Sri Lanka with daily station datasets from 1985 to 2015. The extreme rainfall indices recommended by the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) have been used in this study. Results showed a strong seasonal variation of rainfall extreme events over the MRB and a significant interdecadal change of extreme rainfall indices between 1985–1999 and 2000–2015 in the wet region of MRB, which is coherent with the decadal shift of southwest monsoon (SWM) rainfall from wet to dry situations since the year 2000. Based on the General Extreme Value distributions, the long-term mean of SWM rainfall in the wet region (814 mm) moved leftward for the 2000–2015 period (704 mm) and rightward for the 1985–2000 period, suggesting that SWM rainfall is decreased in the late period. The observed above-average (below-average) rainfall and associated rainfall extremes during the SWM can be ascribed to strengthening (weakening) moisture flux from the Arabian Sea direction and strong (weak) convergence over the study domain. The results further identified the negative correlation between Dipole Mode Index and SWM rainfall and associated rainfall extremes in the wet region, signifying that the negative Indian Ocean Dipole phase can enhance the SWM rainfall over the MRB. The negative correlation between Nino3.4 and SWM rain and extreme indices suggested that high probability of wet rainfall extremes in the La Nina phase. The findings of this study can be used to understand precipitation extremes in the context of climate change at the river basin scale and benefit policymakers in building local adaptation strategies in response to long-term climate change.

Analysis of spatial patterns of daily precipitation and wet spell extremes in Southeast Asia

ABSTRACTThe study investigates the spatial distribution of extreme daily rainfall and wet spells in Southeast Asia (SEA) at annual, seasonal, and intraseasonal scales using 16 years of data from the Tropical Rainfall Measurement Mission. A detailed analysis is conducted using various indices that characterize exceedance probabilities and relative contribution of extremes to the total rainfall. The selected indices include the Gini index, which is a measure of overall inequality, and is employed for the first time to examine SEA rainfall characteristics. The favoured regions (hotspots) of rainfall extremes during each season are identified, and their linkages with terrain and seasonal winds discussed. The intraseasonal variability in rainfall extremes is investigated by focusing on different phases of the Madden–Julian Oscillation (MJO). At the annual scale, four main hotspots are observed, which are located off the coast of Myanmar, over eastern Indian Ocean, northwestern Borneo and over Philippines. A majority of indices shows considerable land–sea contrast, with lower values over land. Lower Gini index as obtained for land regions indicate that the rainfall distribution, and the relative contribution of each class to the total rainfall are more uniform over land compared to sea. The land–sea contrast in indices is stronger during boreal summer and winter, and weaker during boreal fall. During boreal spring, the Myanmar coast has high probability of extremes, which further increases during boreal summer. The most evident hotspot regions during boreal fall and winter are along the coast of Vietnam, and the eastern coast of Malay Peninsula, respectively. The wind‐terrain interactions play a crucial role in determining the location of most hotspots. In general, the impact of active (suppressed) MJO phases is to increase (decrease) the probability of extremes in the study domain. However, local scale departures from this general pattern are observed, which are discussed in this work.

Climate Change Impacts on Catchment-Scale Extreme Rainfall Variability: Case Study of Rize Province, Turkey

AbstractThis paper conducts a catchment-scale analysis of extreme rainfall events of the reference (1961–1990) and three future climate periods (2013–2039, 2040–2070, and 2071–2100) for Rize Province, Turkey. The extreme value theory (EVT) is applied to analyze observational and projected extreme rainfall data including regional climate model (RCM) outputs guided by two general circulation models (GCM) under SRES-A2 and RCP8.5 greenhouse gas scenarios. A new rapid and effective bias correction method is also developed and applied to adjust the climate models simulations. The EVT analysis results demonstrated significant differences between the model runs for both the reference and future periods with considerable spatial variability in rainfall extremes. Based upon the assembled mean results, approximately a 30% decrease in the median value of extreme rainfall events is projected over the study region for the near future, 2013–2039, and middle of the century. This change dramatically decreases to 15% of i...

Long-term trends and variability of rainfall extremes in the Philippines

Owing to the increasing concerns about climate change due to the recent extreme rainfall events in the Philippines, long-term trends and variability in rainfall extremes in the country are investigated using 60-year (1951–2010) daily rainfall data from 35 meteorological stations. Rainfall extremes are described using seven extreme precipitation indices (EPI) that characterize daily rainfall in terms of intensity, accumulation, and duration on a seasonal perspective. The nonparametric Mann–Kendall test is employed in combination with the moving blocks bootstrapping technique to detect significant trends in EPI. The results suggest a tendency toward a drying condition for the dry season, January–March (JFM), as indicated by statistically significant decreasing trends in seasonal wet days total rainfall (PCPTOT) associated with increasing trends in maximum length of dry spell (LDS). In contrast, statistically significant increasing trends in maximum 5-day rainfall (RX5day) and decreasing trends in LDS denote a wetting condition during the July–September (JAS) season, particularly at stations located in the northwest and central Philippines. The trends obtained are further assessed by examining the longer time series of EPI at four meteorological stations (Aparri, Dagupan, Iloilo, and Masbate) that have rainfall data from 1911 to 2010. The longer historical data revealed that the trends obtained in the shorter period (1951–2010) could either be consistent with the continuous long-term trends, as observed in RX5day during JAS at Aparri and Masbate, or represent interdecadal variability as was observed at Dagupan and Iloilo. The long-term (1911–2010) southwestward extension of the western North Pacific subtropical high associated with a weakening of the 850-hPa westerly wind over the South China Sea partly provides a possible cause of the trends in EPI during JAS, whereas the weakening of the East Asian winter monsoon contributed somewhat to the trends obtained during JFM. Furthermore, interannual variations in EPI are found to be influenced greatly by the El Niño–Southern Oscillation (ENSO). Composite analyses suggest that El Niño (La Niña) events are associated with statistically significant drier (wetter) conditions over the Philippines, especially during the seasons close to ENSO mature stage.

The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia

AbstractA 0.05° × 0.05° gridded dataset of daily observed rainfall is compared with high‐quality station data at 119 sites across Australia for performance in capturing extreme rainfall characteristics. A range of statistics was calculated and analysed for a selection of extreme indices representing the frequency and intensity of heavy rainfall events, and their contribution to total rainfall. As is often found for interpolated data, we show that the gridded dataset tends to underestimate the intensity of extreme heavy rainfall events and the contribution of these events to total annual rainfall as well as overestimating the frequency and intensity of very low rainfall events. The interpolated dataset captures the interannual variability in extreme indices. The spatial extent of significant trends in the frequency of extreme rainfall events is also reproduced to some degree. An investigation into the performance of this gridded dataset in remote areas reveals issues, such as the appearance of spurious trends, when stations come in and out of use. We recommend masking over areas of low station density for this particular gridded data. It is likely that in areas of low station density, gridded datasets will, in general, not perform as well. Therefore, caution should be exercised when examining trends and variability in these regions. We conclude that this gridded product is suitable for use in studies on trends and variability in rainfall extremes across much of Australia. The methodology employed in this study, to examine extreme rainfall over Australia in a gridded dataset, may be applied to other areas of the world. While our study indicates that, in general, gridded datasets can be used to investigate extreme rainfall trends and variability, the data should first be subjected to tests similar to those employed here.

Seasonal variability of rainfall extremes

Abstract Monthly rainfall extremes have been analyzed for three stations in Southern Ontario. The double exponential probability distribution was fitted to the extreme values for each month considered, each duration selected, and sets of annual extremes. A station‐year approach yielded monthly and annual extreme value distributions for the lumped region of Southern Ontario. The analysis has revealed a pronounced seasonal pattern in the rainfall extremes – the amount of rain expected with a selected probability of occurrence during the summer being considerably greater than the rainfall that might be expected to be exceeded at the same probability level during the spring or fall. The extent of the seasonal variability was found also to vary with duration. The implications of the variability are seen to be significant for the estimation of the magnitude and frequency of floods.