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.

This study detects climate trends and variability from precipitation and temperature observations in Ecuador and assesses their links to the El Niño Southern Oscillation (ENSO) for the period 1966–2011, using the El Niño 1+2 and El Niño 3.4 indices. Excluding the Amazonian region (for which there is a lack of data), two main regions were distinguishable in terms of variability and trends among climate variables, especially for precipitation. In general, there was no trend in precipitation for the coastal region, and a very close relationship between the magnitude and seasonal distribution of precipitation and the El Niño 1+2 variability was found. In contrast, for the mountainous region (the Andes), there was an increase of precipitation during the study period, and a signal of El Niño 3.4 influence was detected. Temperatures were spatially homogeneous and showed an intense warming trend, except for maximum temperatures in the coastal region. The El Niño 1+2 influence on temperature was large from January to July. The results provide evidence of the close control exerted by the ENSO, especially in the coast of Ecuador, as well as for the occurrence of significant warming across the country independent of the ENSO phenomenon.

In recent years, malaria (Plasmodium vivax and Plasmodium falciparum) has been successfully controlled in the Ecuador–Peru coastal border region. The aim of this study was to document this control effort and to identify the best practices and lessons learned that are applicable to malaria control and to other vector-borne diseases. A proximal outcome evaluation was conducted of the robust elimination programme in El Oro Province, Ecuador, and the Tumbes Region, Peru. Data collection efforts included a series of workshops with local public health experts who played central roles in the elimination effort, review of epidemiological records from Ministries of Health, and a review of national policy documents. Key programmatic and external factors are identified that determined the success of this eradication effort. From the mid 1980s until the early 2000s, the region experienced a surge in malaria transmission, which experts attributed to a combination of ineffective anti-malarial treatment, social-ecological factors (e.g., El Niño, increasing rice farming, construction of a reservoir), and political factors (e.g., reduction in resources and changes in management). In response to the malaria crisis, local public health practitioners from El Oro and Tumbes joined together in the mid-1990s to forge an unofficial binational collaboration for malaria control. Over the next 20 years, they effectively eradicated malaria in the region, by strengthening surveillance and treatment strategies, sharing of resources, operational research to inform policy, and novel interventions. The binational collaboration at the operational level was the fundamental component of the successful malaria elimination programme. This unique relationship created a trusting, open environment that allowed for flexibility, rapid response, innovation and resilience in times of crisis, and ultimately a sustainable control programme. Strong community involvement, an extensive microscopy network and ongoing epidemiologic investigations at the local level were also identified as crucial programmatic strategies. The results of this study provide key principles of a successful malaria elimination programme that can inform the next generation of public health professionals in the region, and serve as a guide to ongoing and future control efforts of other emerging vector borne diseases globally.

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.