Abstract
AbstractMore 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, such as 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 and 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.
Highlights
The Amazon is the largest river basin in the world, draining about one-sixth of global freshwater to the ocean (Callède et al, 2010)
The main objective of this study is to develop and evaluate statistical seasonal forecast models for annual maximum water level for Negro River at Manaus, using a multiple linear regression approach
We investigate the possibility of improving existing statistical forecasts, using observed antecedent rainfall as a predictor and leveraging the teleconnections from large-scale modes of variability represented by climate indices, to develop a model that can be implemented operationally at the current earliest forecast date (March; which is three months prior to the average timing of the maximum water level around June; Figure 1b)
Summary
The Amazon is the largest river basin in the world, draining about one-sixth of global freshwater to the ocean (Callède et al, 2010). We investigate the possibility of improving existing statistical forecasts, using observed antecedent rainfall as a predictor and leveraging the teleconnections from large-scale modes of variability represented by climate indices, to develop a model that can be implemented operationally at the current earliest forecast date (March; which is three months prior to the average timing of the maximum water level around June; Figure 1b). If this method is successful for Manaus, it should be further tested to develop statistical forecast models for other location in the Amazon basin. We evaluated our models against benchmark persistence and climatological forecasts, calculated as the previous years’ observed maximum water level and climatological mean of maximum water level from 1903 up to the previous year, respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
