Abstract/Summary

More frequent and stronger flood hazards in the last two decades have caused considerable environmental and socio-economic losses in many regions of the Amazon basin. It is therefore critical to advance predictions for flood levels, with adequate lead times, to provide more effective and earlier warnings to safeguard lives and livelihoods. Water-level variations in large, low-lying, free-flowing river systems in the Amazon basin, like the Negro River, follow large-scale precipitation anomalies. This offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at Manaus, occurring between May–July. We develop forecast models using multiple linear regression methods, to produce forecasts that can be issued in March, February and January. Potential predictors include antecedent catchment rainfall and water levels, large-scale modes of climate variability and the long-term linear trend in water levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than existing models at similar lead times. All models lose performance at longer lead times, as expected. However, our forecast models can issue skilful operational forecasts in March or earlier. We show the forecasts for the Negro River maximum water level at Manaus for 2020 and 2021.