• Energy Research

AbstractAnomalous conditions in the oceans and atmosphere have the potential to be used to enhance the predictability of flood events, enabling earlier warnings to reduce risk. In the Amazon basin, extreme flooding is consistently attributed to warmer or cooler conditions in the tropical Pacific and Atlantic oceans, with some evidence linking floods to other hydroclimatic drivers such as the Madden–Julian Oscillation (MJO). This review evaluates the impact of several hydroclimatic drivers on rainfall and river discharge regimes independently, aggregating all the information of previous studies to provide an up‐to‐date depiction of what we currently know and do not know about how variations in climate impact flooding in the Amazon. Additionally, 34 major flood events that have occurred since 1950 in the Amazon and their attribution to climate anomalies are documented and evaluated. This review finds that despite common agreement within the literature describing the relationship between phases of climate indices and hydrometeorological variables, results linking climate anomalies and flood hazard are often limited to correlation rather than to causation, while the understanding of their usefulness for flood forecasting is weak. There is a need to understand better the ocean–atmosphere response mechanisms that led to previous flood events. In particular, examining the oceanic and atmospheric conditions preceding individual hydrological extremes, as opposed to composite analysis, could provide insightful information into the magnitude and spatial distribution of anomalous sea surface temperatures required to produce extreme floods. Importantly, such an analysis could provide meaningful thresholds on which to base seasonal flood forecasts.

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world's population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

Abstract. 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.

Abstract. 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.