Searching. Please wait…
1582
37
170
29108
4409
2600
347
389
Abstract: In hydrology, extreme value analysis is normally applied at stationary yearly maxima. However, climate variability can bias the estimation of extremes by partially invalidating the stationary assumption. Extreme value analysis for sub-yearly data may depart from stationarity (since maxima from one month may not be exchangeable with maxima from another) in terms of requiring to include it in the analysis. Here, we analyse the non-stationary structure of extreme monthly rainfall in Spain using two approaches: a parametric approach and an approach based on autoregressive time series models. Our analysis considers seasonality, climate variability and long-term trends for both approaches, and it compares both including their goodness of fit and complexity. The approach uses maximum likelihood estimation and Bayesian techniques. Our results show that autoregressive models outperform parametric models, providing a more accurate representation of extreme events when extrapolating outside of the period of fit.
Congress: International Association of Hydrological Sciences: STAHY (11ª : 2021 : En Linea)
Publisher: Taylor and Francis Ltd.
Year of publication: 2023
No. of pages: 17
Publication type: Conference object
DOI: 10.1080/02626667.2023.2193294
ISSN: 0262-6667,2150-3435
Spanish project: RTI2018-096449-B-I00
Publication Url: https://www.tandfonline.com/doi/full/10.1080/02626667.2023.2193294
Consult in UCrea Read publication
DIEGO ARMANDO URREA MENDEZ
MANUEL DEL JESUS PEÑIL
Back