Forecasting annual maximum water level for the Negro River at Manaus. Issue 1 (21st October 2021)

• Record Type: Journal Article
• Authors: Chevuturi, Amulya; Klingaman, Nicholas P.; Rudorff, Conrado M.; Coelho, Caio A. S.; Schöngart, Jochen
• Other Names: Brown Foster guestEditor.; Peters Wouter guestEditor.; Koren Gerbrand guestEditor.; Koven Charlie guestEditor.
• Is Part Of: Climate resilience and sustainability. Volume 1:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-21
• Subjects: flood level -- Manaus -- multiple linear regression -- Negro River -- seasonal forecasts; Climatic changes -- Periodicals; Sustainability -- Periodicals; Climatic changes; Sustainability; Climate change mitigation$2fast$0(OCoLC)fst01749583; Periodicals; 338.927
• DOI: 10.1002/cli2.18
• Languages: English
• ISSNs: 2692-4587

Abstract

Abstract: 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, 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. Abstract : Water‐level variations in free‐flowing river systems in the Amazon basin, such as the Negro River, follow large‐scale precipitation anomalies, which offers an opportunity to predict water levels using observed antecedent rainfall. We develop statistical forecasts, that can be issued in March, February and January, for annual maximum water level of the Negro River at Manaus, occurring between May and July. Our statistical models gain one month of lead time against existing operational forecasts for the same skill level.