Climate variability and extended range flood forecasting for the Amazon basin

The aim of this research is to investigate how large-scale climate variability affects flooding in the Amazon basin, using this assessment to demonstrate the potential predictability that these modes can provide to enable earlier warning of impactful floods. To address this a multi-stage approach is adopted; first to understand the gaps and confidence in the state of current knowledge on how climate variability affects both rainfall and river discharge in the Amazon basin, secondly, to understand the skill of global hydrological models for undertaking further assessment, and thirdly to undertake a robust assessment of the impact of climate variability on different flood characteristics while considering different methodological approaches in more detail. An assessment of the robustness in the results of previous studies suggests the need to explore in detail the physical mechanisms leading to flood events on an individual basis. While composite analysis of several floods identified a particular response associated with La Niña conditions, investigation into individual events show it is unknown if the same response would be identified for all events individually. The performance of eight large-scale hydrological models are evaluated for their ability to capture previous peak river flows. The choice of precipitation input is found to be the dominant component of the hydrometeorological modelling chain, with improvement found when ERA5 is the chosen meteorological forcing. Calibration of the Lisflood routing model is identified to have no impact on the ability to capture flood peaks, stressing the need to use an objective function that fits the purpose of the model. Examination of how climate variability impacts flood characteristics in the Amazon basin identified significant changes for both flood magnitude and duration during the negative ENSO phase, particularly in the north-eastern Amazon. This response was not identified for eastern Pacific ENSO events, highlighting how results can differ between ENSO types, while no notable impact or pattern is observed for flood timing. This thesis has provided important information on how climate variability impacts less studied flood characteristics (flood timing and duration) which are associated with important flood types (e.g. early or long floods). Future work should focus on the improvement of climate reanalysis to produce a longer-term dataset consistent with observations to extend climate analysis. This would allow the examination on the impact of climate phases at a more granular scale (e.g. analysing the strength or combination of climate phases

Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin

Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.Campus Lima Centr

Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin

Extreme flooding impacts millions of people that live within the Amazon floodplain. Global hydrological models (GHMs) are frequently used to assess and inform the management of flood risk, but knowledge on the skill of available models is required to inform their use and development. This paper presents an intercomparison of eight different GHMs freely available from collaborators of the Global Flood Partnership (GFP) for simulating floods in the Amazon basin. To gain insight into the strengths and shortcomings of each model, we assess their ability to reproduce daily and annual peak river flows against gauged observations at 75 hydrological stations over a 19-year period (1997–2015). As well as highlighting regional variability in the accuracy of simulated streamflow, these results indicate that (a) the meteorological input is the dominant control on the accuracy of both daily and annual maximum river flows, and (b) groundwater and routing calibration of Lisflood based on daily river flows has no impact on the ability to simulate flood peaks for the chosen river basin. These findings have important relevance for applications of large-scale hydrological models, including analysis of the impact of climate variability, assessment of the influence of long-term changes such as land-use and anthropogenic climate change, the assessment of flood likelihood, and for flood forecasting systems

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. Archives of Virology (2021) 166:3567–3579. https://doi.org/10.1007/s00705-021-05266-wIn March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through Laulima Government Solutions, LLC prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N91019D00024, Task Order No. 75N91019F00130 to I.C., who was supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research. This work was also funded in part by Contract No. HSHQDC-15-C-00064 awarded by DHS S&T for the management and operation of The National Biodefense Analysis and Countermeasures Center, a federally funded research and development center operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowledges partial support from the Special Research Initiative of Mississippi Agricultural and Forestry Experiment Station (MAFES), Mississippi State University, and the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project 1021494. Part of this work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001030), the UK Medical Research Council (FC001030), and the Wellcome Trust (FC001030).S

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

Attribution of Amazon floods to modes of climate variability: A review

Anomalous conditions in the oceans and atmosphere have the potential to beused to enhance the predictability of flood events, enabling earlier warnings toreduce risk. In the Amazon basin, extreme flooding is consistently attributedto warmer or cooler conditions in the tropical Pacific and Atlantic oceans, withsome evidence linking floods to other hydroclimatic drivers such as theMadden–Julian Oscillation (MJO). This review evaluates the impact of severalhydroclimatic drivers on rainfall and river discharge regimes independently,aggregating all the information of previous studies to provide an up-to-datedepiction of what we currently know and do not know about how variationsin climate impact flooding in the Amazon. Additionally, 34 major flood eventsthat have occurred since 1950 in the Amazon and their attribution to climateanomalies are documented and evaluated. This review finds that despite com-mon agreement within the literature describing the relationship betweenphases of climate indices and hydrometeorological variables, results linkingclimate anomalies and flood hazard are often limited to correlation rather thanto causation, while the understanding of their usefulness for flood forecastingis weak. There is a need to understand better the ocean–atmosphere responsemechanisms that led to previous flood events. In particular, examining the oce-anic and atmospheric conditions preceding individual hydrological extremes,as opposed to composite analysis, could provide insightful information into themagnitude and spatial distribution of anomalous sea surface temperaturesrequired to produce extreme floods. Importantly, such an analysis could pro-vide meaningful thresholds on which to base seasonal flood forecast

Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin

Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive/negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979-2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative ENSO phases when the SST anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing however does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods

Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones

International humanitarian organisations increasingly turn to forecast teams to support the coordination of efforts to respond to disasters caused by hazards such as tropical cyclones and large-scale fluvial floods. Such disasters often occur where there is limited local capacity or information available to support decision making and so global forecasting capacity is utilised to provide impact-based flood forecast bulletins. A multidisciplinary team joined together to provide forecast bulletins and expertise for such events through the UK Foreign and Commonwealth Development Office (FCDO). This paper captures the successes and challenges from two cyclones: Hurricane Iota in Central America (November 2020) and Cyclone Eloise in Mozambique (January 2021). Recommendations to improve global forecasting systems are made which will benefit the international community of researchers and practitioners involved in disaster prediction, anticipatory action and response. These include the requirement for additional data and expertise to support the interpretation of global models, clear documentation to support decision makers faced with multiple sources of information, and the development of user relevant metrics to assess the skill of global models. We discuss the value of effective partnerships and improving synergies between global models and local contexts, highlighting how global forecasting can help build local forecasting capability