A climate informed model for nonstationary flood risk prediction: Application to Negro River at Manaus, Amazonia

Historically, flood risk management and flood frequency modeling have been based on assumption of stationarity, i.e., flood probabilities are invariant across years. However, it is now recognized that in many places, extreme floods are associated with specific climate states which may recur with non-uniform probability across years. Conditional on knowledge of the operating climate regime, the probability of a flood of a certain magnitude can be higher or lower in a given year. Here we explore nonstationary flood risk for the streamflow series of the Negro River at the city of Manaus in Brazil by investigating climate teleconnections associated with the interannual variability of the peak flows. We evaluate attributes and the fit of a generalized extreme value (GEV) distribution with nonstationary parameters to the annual peak series of the Negro River stages. The annual peak flood occurs between May and July and its magnitude depends on the Negro River stage at the beginning of the year and on the previous December sea surface temperature (SST) of a region in the tropical Pacific Ocean. A statistically significant monotonic trend is also observed in the peak level series. The indexing of the parameters of a GEV distribution to the NINO3 index and to the observed river stage at the beginning of the year reveals a changing flood hazard for the city, with the joint occurrence of high values associated with La Nina conditions in the previous December and-high river stages in January preceding the flood season. The proposed model is shown to be useful for quantifying the changing flood hazard several months in advance for Manaus, thus providing an early flood alert system for the city and may be an important tool for the dynamic flood risk management for the region. (C) 2015 Elsevier B.V. All rights reserved.