Effect of warming climate on extreme daily rainfall depth using non-stationary Gumbel model with temperature co-variate

In this study, non-stationary frequency analysis was carried out to apply non-stationarity of extreme rainfall driven by climate change using the scale parameter of two parameters of the Gumbel distribution (GUM) as a co-variate function. The surface air temperature (SAT) or dew-point temperature (DPT) is applied as the co-variate. The optimal model was selected by comparing AICs, and 17 of 60 sites were found to be suitable for the non-stationary GUM model. In addition, SAT was chosen as the more appropriate co-variate among 13 of the 17 sites. As a result of estimating changes in design rainfall depth with future SAT rises at 13 sites, it is likely to increase by 10% in 2040 and 18% in 2070.

Simple method for estimating daily and total COVID-19 deaths using a Gumbel model

The purpose of the present paper is to introduce a method for forecasting daily and total numbers of COVID-19-associated deaths. We apply the Gumbel distribution function for the analysis of time series data of the first wave. The Gumbel distribution function F(t) has a notable property of F(t) = 1/2.718 at the node (peak) point of the distribution. Therefore, we can forecast the number of total deaths N. In the present study, the Gumbel model gives the estimate N ≈ 2.718N1, where N1 is the partial sum of the daily numbers of deaths until the day of the peak. The proposed model can also forecast the daily numbers after the peak. We investigated the data of New York City, Belgium, Switzerland, Sweden, and the United Kingdom. The Gumbel model gives reasonable results for New York City, Belgium, and Switzerland. On the other hand, the proposed method underestimates N for Sweden and the United Kingdom. The proposed approach is very simple, and carrying out the analysis is easy. This method uses spreadsheet software for most of the calculations, and no special program is needed.

Rainfall Intensity-Duration-Frequency Relationships for Risk Analysis in the Region of Matopiba, Brazil

The region of study was MATOPIBA, located in the north of Brazilian Savanna biome (Cerrado), encompassing part of north/northeast of Brazil. The region has been gaining prominence in the last years due to the expansion of agricultural over this area. The aims of this study were: to adjust parameters for rainfall intensity-duration-frequency; and to identify the most vulnerable agricultural areas to erosion based on erosivity and erodibility. The rainfall intensity-duration-frequency function were adjusted using series of maximum annual rainfall event from 105 rainfall gauges. Gumbel model was the most efficient to simulate the maximum rainfall intensity, where these data were used to adjusted the rainfall intensity-duration-frequency model based on K, a, b and c parameters. The most rainfall gauges showed intensity between 51 and 80 mm h-1 and 81 and 120 mm h-1, respectively, for return period of 2 and 100 years with rainfall duration of 30 minutes. The higher rainfall intensity was observed mainly in the central-north of the region associated with rainfall systems. The rainfall intensity showed a huge capacity to cause soil erosion based on the erosivity energy, while the moderate erodibility was observed for areas with Ferralsols and Leptosols and low erodibility for areas with Arenosols.

Analysis and Modelling of Extreme Rainfall: A Case Study for Dodoma, Tanzania

The analysis of climate change, climate variability and their extremes has become more important as they clearly affect the human society and ecology. The impact of climate change is reflected by the change of frequency, duration and intensity of climate extreme events in the environment and on the economic activities. Climate extreme events, such as extreme rainfall threaten to environment, agricultural production and loss of people’s lives. Dodoma daily rainfall data exported from R-Instat software were used after being provided by Tanzania Meteorological Agency. The data were recorded from 1935 to 2011. In this essay, we used climate indices of rainfall to analyse changes in extreme rainfall. We only used 6 rainfall indices related to extremes to describe the change in rainfall extremes. Extreme rainfall indices did not show statistical evidence of a linear trend in Dodoma rainfall extremes for 77 years. Apart from the extreme rainfall indices, this essay utilized two techniques in extreme value theory namely the block maxima approach and peak over threshold approach. The two extreme value approaches were used for univariate sequences of independent identically distributed (iid) random variables. Using Dodomadaily rainfall data, this essay illustrated the power of the extreme value distributions in modelling of extreme rainfall. Annual maxima of Dodoma daily rainfall from 1935 to 2011 were fitted to the Generalized Extreme Value (GEV) model. Gumbel was found to be the best fit of the data after likelihood ratio test of GEV and Gumbel models. The Gumbel model parameters were considered to be stationary and non-stationary in two different models. The stationary Gumbel model was found to be good fit of Dodoma maximum rainfall. Later, the levels at which maximum Dodoma rainfall is expected to exceed once, on average, in a given period of time T = 2, 5, 10, 20, 30, 50 and 100 years, were obtained using stationary Gumbel model. Lastly, the data of exceedances were fitted to the Generalized Pareto (GP) model under stationary climate assumption.

Efficient Fitting Procedure for Corrosion Rate Distribution in View of Reliability Analysis

The Gumbel model is widely used for the theoretical distribution of the corrosion rate. In applying the reliability analysis, the parameters of the distribution must be estimated from the inspected data. The estimation of parameters is done by using some fitting procedures. However, it is not necessarily clear which fitting procedure is suitable in view of reliability analysis. Especially, the fitting accuracy around tail region is possibly influence the reliability analysis. In this study, the efficient fitting procedure for the corrosion rate distribution in view of reliability analysis was investigated using Monte Carlo Simulation together with reliability analysis.