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Research Article

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 1301-1316 | Received 11 Oct 2023, Accepted 29 May 2024, Published online: 10 Jul 2024
 

ABSTRACT

The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

Editor S. Archfield; Associate Editor M. Batalini de Macedo

Editor S. Archfield; Associate Editor M. Batalini de Macedo

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Daily streamflow series: https://snih.hidricosargentina.gob.ar/Inicio.aspx

Sea surface temperature in the Equatorial Pacific Ocean: Monthly ERSSTv5 (1991–2020 base period) Niño 1+2 (0–10°S)(90–80°W) Niño 3 (5°N–5°S)(150–90°W) Niño 4 (5°N–5°S) (160°E–150°W) Niño 3.4 (5°N–5°S)(170–120°W)

https://www.cpc.ncep.noaa.gov/data/indices/ersst5.nino.mth.91-20.ascii

PDO index: http://research.jisao.washington.edu/pdo/PDO.latest

TPI index: https://psl.noaa.gov/data/timeseries/IPOTPI/Tripole

Additional information

Funding

This work was supported by the “Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación” Mincyt-Argentina, under Grant [PICT- 2019-03430]; Agencia Nacional de Promoción Científica y Tecnológica.

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