Dam Hydrological Risk and the Design Flood Under Non-stationary Conditions

• Published in: Water resources management Vol. 35; no. 5; pp. 1499 - 1512
• Main Authors: Isensee, Leandro José, Pinheiro, Adilson, Detzel, Daniel Henrique Marco
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2021; Springer Nature B.V
• Subjects: Atmospheric Sciences; Case studies; Civil Engineering; Dam design; Dam engineering; Dam safety; Dams; Design; Earth and Environmental Science; Earth Sciences; Environment; Extreme values; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology/Water Resources; Model testing; Risk; Risk taking; Stream discharge; Stream flow; Time series; Trends; Non-stationary; Hydrological risk; Dam safety; Design flood; Time series; Extreme values
• DOI: 10.1007/s11269-021-02798-3
• ISSN: 0920-4741

Increasing global trends in time series of annual maximum daily streamflow (AMX) raise the concern that the safety of dams and other sensitive structures is compromised. There is no defined methodology to estimate the design flood (DF) under non-stationarity; thus, the objective of this work is to evaluate the behavior of the hydrological risk of Brazilian dams due to the non-stationary nature of the AMX time series and the implications of the non-stationary nature of the AMX time series in the design of new dams. For this, the hydrological risk of 108 AMX time series was evaluated, comparing the time intervals between 1954–1984 and 1954–2014. A case study was also executed, where the DF was estimated in a non-stationary time series. The generalized distribution of extreme values (GEV) was applied in the time series analyses. The results indicate that the hydrological risk of Brazilian dams increased, and safety may have been reduced. Regarding the ranking of models, the use of physical covariates in the estimate of the DF makes the estimates more reliable. Finally, although significant trends are good indicators, they alone do not guarantee a reduction or increase in risk. It was also observed that using non-stationary models is less important than updating the estimates with newly observed data.