1. bookVolumen 70 (2022): Heft 2 (June 2022)
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License
Format
Zeitschrift
eISSN
1338-4333
Erstveröffentlichung
28 Mar 2009
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

Correlation between climate and flood indices in Northwestern Italy at different temporal scales

Online veröffentlicht: 19 May 2022
Volumen & Heft: Volumen 70 (2022) - Heft 2 (June 2022)
Seitenbereich: 178 - 194
Eingereicht: 17 Sep 2021
Akzeptiert: 13 Jan 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1338-4333
Erstveröffentlichung
28 Mar 2009
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Abstract

The occurrence of river floods is strongly related to specific climatic conditions that favor extreme precipitation events leading to catchment saturation. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the relationship between peak discharges and standard Climate Change Indices (ETCCDI) of precipitation and temperature, widely used in climate research. It is of interest to evaluate whether these indices are relevant for characterizing and predicting floods in the Alpine area. In this study, a correlation analysis of the ETCCDI indices annual time series and annual maximum flows is presented for the Piedmont Region, in North-Western Italy. Spearman’s rank correlation is used to determine which ETCCDI indices are temporally correlated with maximum discharges, allowing to hypothesize which climate drivers better explain the interannual variability of floods. Moreover, the influence of climate (decadal) variability on the tendency of annual maximum discharges is examined by spatially correlating temporal trends of climate indices with temporal trends of the discharge series in the last twenty years, calculated using the Theil-Sen slope estimator. Results highlight that, while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, with different indices that are consistent with catchment size, the decadal tendencies of extreme discharges may be better explained by the decadal tendencies of the total annual precipitation over the study area. This suggests that future projections of the annual precipitation available from climate models simulations, whose reliability is higher compared to precipitation extremes, may be used as covariates for non-stationary flood frequency analysis.

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