Surface and atmospheric water balances analysis for Amazon River basin at monthly time scale are performed with an emphasis on the linkages between the Andes and low‐lying Amazonia. Analyses are performed using observational and reanalysis data covering five different periods of analysis (depending on length of evapotranspiration data sets) for the entire basin, major subcatchments, and for the Andes and low‐lying Amazonia. Results for the entire basin show a state close to balance within tolerance, while the spatial disaggregation produced a less clear picture, presenting major discrepancies between observations and reanalysis data sets. The atmospheric budget exhibits no closure and positive residuals regardless of data set, with the magnitude of residuals for the entire Amazon basin highly dependent on evapotranspiration data source. The imbalance between the two water budgets (14%–16%) is driven by higher values of runoff and by an abrupt change in runoff when changing from dry to wet seasons in the Amazon. We unveil two shortcomings of the available data, namely poor quality in the representation of surface processes by reanalysis, and flaws and scarcity of information in the high Andes that induce uncertainties and errors in both water budgets. The results of the present study highlight the importance of the Andean region for the hydrological integrity of the entire Amazon River basin. Our results confirm the paramount importance of the joint analysis of atmospheric and surface water budgets at the basin level with respect to achieving a complete understanding of the whole hydrological cycle at the continental scale.

We investigated the relationship between the frequency of occurrence of the Orinoco low-level jet (OLLJ) and hydroclimatic variables over northern South America. We use data from the ERA5 atmospheric reanalysis to characterize the spatial and temporal variability of the OLLJ in light of the low-level jet (LLJ) classification criteria available in the literature. An index for the frequency of occurrence of an LLJ was used, based on the hourly maxima of wind speed. The linkages among the OLLJ, water vapor flux, and precipitation were analyzed using a composite analysis. Our results show that during December–February (DJF), the OLLJ exhibits its maximum wind speed, with values around 8–10 m s⁻¹. During DJF, the analysis shows how the OLLJ transports atmospheric moisture from the tropical North Atlantic Ocean. During this season, the predominant pathway of the OLLJ is associated with an area of moisture flux divergence located over northeastern South America. During June–August (JJA), an area of moisture flux convergence associated with the northernmost location of the ITCZ inhibits the entrance of moisture from northerlies. We also show that the occurrence of the OLLJ is associated with the so-called cross-equatorial flow. During DJF, the period of strongest activity of the OLLJ is associated with the northerly cross-equatorial flow and dry season, whereas during JJA the southerly cross-equatorial flow from the Amazon River basin predominates and contributes to the rainy season over the Orinoco region.

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.

Introduction: Leptospirosis is a zoonotic disease caused by a bacteria of the genus Leptospira. Climate is key in order to understand leptospirosis dynamics. El Niño Southern Oscillation is the main modulator of climate in Colombia. Our goal was to analyze the changes that occurred in number of cases and incidence rate of leptospirosis during La Niña and El Niño episodes in Colombia at three spatial scales in the period between 2007 and 2015. Methodology: A cross-sectional retrospective study was performed. Data analysis: correlation and lagged cross correlation between time series of Oscillation Niño Index and time series of standardized number of leptospirosis cases; construction of annual cycle of leptospirosis; comparison of changes of number of cases between Neutral, periods with El Niño and Neutral periods with La Niña. Results: At the national level, monthly number of cases raised a 25% during La Niña and decreased of 17% during El Niño. At departmental level, increase of cases in both phases of ENSO, depending on the location in the country, was found. At the municipal level, 17 have a rise in the number of cases during La Niña months. Of those, seven presented also an increase of cases during El Niño months and eight have a significant negative correlation with ONI. Conclusions: In Colombia, there exists a relationship between leptospirosis and the excess and lack of rainfall related with ENSO. The contrasting results from each spatial scale, reinforce that leptospirosis is a multidimensional disease with high complex interactions among its determinants.

This study presents the implementation of a methodology for the formulation of a vulnerability indicator for low-income urban territories in flood-prone areas, for two flood types: Sudden and slow. The methodology developed a computational assessment tool based on the Multiple Correspondence Analysis and the framework for vulnerability analysis in sustainable science. This approach uses participatory mapping and on-site data. The data collection was easily implemented with free software tools to facilitate its use in low-income urban territories. The method combines the evaluation of experts using the of the traditional approach for the qualification of the variables of vulnerability in its three components (exposure, susceptibility, and resilience), and incorporates a computational method of the correspondence analysis family to formulate the indicators of vulnerability. The results showed that the multiple correspondence analysis is useful for the identification of the most representative variables in the vulnerability assessment, used for the construction of spatial disaggregated vulnerability indicators and therefore the development of vulnerability maps that will help in the short term in disaster risk management, urban planning, and infrastructure protection. In addition, the variables of the susceptibility component are the most representative regardless of the type of flooding, followed by the variables of the exposure component, for sudden flood-prone territories, and the resilience component for slow flood-prone territories. Our findings and the computational tool can facilitate the prioritization of improvement projects and flood risk management on a household, neighborhood, and municipal level.