Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

Abstract

The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

Non‐stationary frequency analysis of extreme streamflow disturbance in a typical ecological function reserve of China under a changing climate

“…Nonstationary model has been widely applied for frequency analysis of extreme events such as rainstorm and flood. The covariates of the probability distribution parameters can be time (Cheng et al, 2014;Xavier et al, 2020), or some other physical variables (e.g., climate indices) (Gao & Zheng, 2018;Gu et al, 2016;Renard & Lall, 2014;Vasiliades et al, 2015). Since the probability of hydrological extremes is strongly influenced by climatic conditions (expressed as climate indices), it is helpful to improve the simulation by adding climate indices to a non-stationary model (Lee & Ouarda, 2010).…”

Evaluation of Quantile Delta Mapping as a bias‐correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil)

“…The bias corrected climate model of block maxima datasets may serve as the input for extreme distribution probabilities, for example, Generalized Extreme Values distribution (Fisher and Tippett, 1928). The practical sense of this distribution is to estimate exceedance probabilities of extreme quantiles series (block maxima) (Silva‐Dias et al ., 2013; De Paola et al ., 2018; Xavier et al ., 2019).…”

“…The quality of such a dataset has already been assessed in Xavier et al . (2019). The missing data were filled through the method proposed by Hirsch et al .…”

“…The list of sites is available in Data S1. The quality of such a dataset has already been assessed in Xavier et al (2019). The missing data were filled through the method proposed by Hirsch et al (1992) which is based in days with no data or error code, the method based on linear regression.…”

Chaos arising from the hydrological behaviour of a floodplain river during the last century

“…Assessing the occurrence of non-stationary patterns allows a meaningful understanding of the hydrological regime in the frame of changes in the Paraná basin such as meteorological extremes (Xavier et al , 2020) and land uses (Lee et al , 2018). It is also an opportunity to evolve in recognising the non-stationarity of many ecological systems (Poff et al , 2017) from which the water regime is the driving factor (Thomaz et al , 2007;Webb, Stewardson & Koster, 2010;Arthington, Naiman, Mcclain & Nilsson, 2010;Devercelli, Scarabotti, Mayora, Schneider & Giri, 2016).…”