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GERMAN POVEDA published an article in August 2018.
Top co-authors See all
María Eugenia Solari

17 shared publications

Universidad Austral de Chile, Valdivia, Chile

Iván D Vélez

9 shared publications

Universidad de Antioquia, Programa de Estudio y Control de Enfermedades Tropicales PECET, Medellin, Colombia

Daniel Ruiz

7 shared publications

Pontificia Universidad Javeriana, Colombia

Mark Falvey

5 shared publications

Universidad de Chile, Chile

Juan Fernando Salazar

4 shared publications

GIGA, Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia

Publication Record
Distribution of Articles published per year 
(1979 - 2018)
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Article 0 Reads 0 Citations New Insights on Land Surface-Atmosphere Feedbacks over Tropical South America at Interannual Timescales Juan Mauricio Bedoya-Soto, Germán Poveda, David Sauchyn Published: 17 August 2018
Water, doi: 10.3390/w10081095
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We present a simplified overview of land-atmosphere feedbacks at interannual timescales over tropical South America as structural sets of linkages among surface air temperature (T), specific humidity at 925 hPa (q925), volumetric soil water content (Θ), precipitation (P), and evaporation (E), at monthly scale during 1979–2010. Applying a Maximum Covariance Analysis (MCA), we identify the modes of greatest interannual covariability in the datasets. Time series extracted from the MCAs were used to quantify linear and non-linear metrics at up to six-month lags to establish connections among variables. All sets of metrics were summarized as graphs (Graph Theory) grouped according to their highest ENSO-degree association. The core of ENSO-activated interactions is located in the Amazon River basin and in the Magdalena-Cauca River basin in Colombia. Within the identified multivariate structure, Θ enhances the interannual connectivity since it often exhibits two-way feedbacks with the whole set of variables. That is, Θ is a key variable in defining the spatiotemporal patterns of P and E at interannual time-scales. For both the simultaneous and lagged analysis, T activates non-linear associations with q925 and Θ. Under the ENSO influence, T is a key variable to diagnose the dynamics of interannual feedbacks of the lower troposphere and soil interfaces over tropical South America. ENSO increases the interannual connectivity and memory of the feedback mechanisms.
Article 0 Reads 0 Citations Atmosphere-Land Bridge between the Pacific and Tropical North Atlantic SST’s through the Amazon River basin during the 2... Alejandro Builes-Jaramillo, Antônio M. T. Ramos, Germán Pove... Published: 01 August 2018
Chaos: An Interdisciplinary Journal of Nonlinear Science, doi: 10.1063/1.5020502
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Article 0 Reads 0 Citations Conjoint Analysis of Surface and Atmospheric Water Balances in the Andes-Amazon System A. Builes-Jaramillo, G. Poveda Published: 01 May 2018
Water Resources Research, doi: 10.1029/2017wr021338
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Article 0 Reads 1 Citation Scaling properties reveal regulation of river flows in the Amazon through a forest reservoir Juan Fernando Salazar, Juan Camilo Villegas, Angela María Re... Published: 09 March 2018
Hydrology and Earth System Sciences, doi: 10.5194/hess-22-1735-2018
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Many natural and social phenomena depend on river flow regimes that are being altered by global change. Understanding the mechanisms behind such alterations is crucial for predicting river flow regimes in a changing environment. Here we introduce a novel physical interpretation of the scaling properties of river flows and show that it leads to a parsimonious characterization of the flow regime of any river basin. This allows river basins to be classified as regulated or unregulated, and to identify a critical threshold between these states. We applied this framework to the Amazon river basin and found both states among its main tributaries. Then we introduce the forest reservoir hypothesis to describe the natural capacity of river basins to regulate river flows through land–atmosphere interactions (mainly precipitation recycling) that depend strongly on the presence of forests. A critical implication is that forest loss can force the Amazonian river basins from regulated to unregulated states. Our results provide theoretical and applied foundations for predicting hydrological impacts of global change, including the detection of early-warning signals for critical transitions in river basins.
Article 1 Read 1 Citation Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy Hernán D. Salas, Germán Poveda, Oscar J. Mesa Published: 13 December 2017
Entropy, doi: 10.3390/e19120685
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We study spatial scaling and complexity properties of Amazonian radar rainfall fields using the Beta-Lognormal Model (BL-Model) with the aim to characterize and model the process at a broad range of spatial scales. The Generalized Space q-Entropy Function (GSEF), an entropic measure defined as a continuous set of power laws covering a broad range of spatial scales, Sq(λ)∼λΩ(q), is used as a tool to check the ability of the BL-Model to represent observed 2-D radar rainfall fields. In addition, we evaluate the effect of the amount of zeros, the variability of rainfall intensity, the number of bins used to estimate the probability mass function, and the record length on the GSFE estimation. Our results show that: (i) the BL-Model adequately represents the scaling properties of the q-entropy, Sq, for Amazonian rainfall fields across a range of spatial scales λ from 2 km to 64 km; (ii) the q-entropy in rainfall fields can be characterized by a non-additivity value, qsat, at which rainfall reaches a maximum scaling exponent, Ωsat; (iii) the maximum scaling exponent Ωsat is directly related to the amount of zeros in rainfall fields and is not sensitive to either the number of bins to estimate the probability mass function or the variability of rainfall intensity; and (iv) for small-samples, the GSEF of rainfall fields may incur in considerable bias. Finally, for synthetic 2-D rainfall fields from the BL-Model, we look for a connection between intermittency using a metric based on generalized Hurst exponents, M(q1,q2), and the non-extensive order (q-order) of a system, Θq, which relates to the GSEF. Our results do not exhibit evidence of such relationship.
CONFERENCE-ARTICLE 10 Reads 0 Citations <strong>New insights on land surface-atmosphere feedbacks over tropical South America at interannual timescales</strong> Juan Mauricio Bedoya-Soto, Germán Poveda Published: 10 November 2017
First International Electronic Conference on the Hydrological Cycle, doi: 10.3390/CHyCle-2017-04875
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Using monthly data for the period 1979-2010, we study the dynamics and strength of land surface-atmosphere feedbacks (LAFs) among variables involved in the heat and moisture fluxes, at interannual timescales for Tropical South America (TropSA). The variables include precipitation, surface air temperature, specific humidity at 925 hPa, evaporation, and estimates of volumetric soil water content. Using a dimensional reduction, we apply a Maximum Covariance Analysis (MCA) to rank the relative contributions to LAFs and group the time series into Maximum Covariance States (MCS) with common mechanisms among variables. We estimate linear (Pearson correlations) and non-linear (information transfer and causality) coupling metrics among pairs of variables to configure the structure of linkages. The main MCS associated with LAFs over TropSA are strongly influenced by ENSO, and the meridional and equatorial SSTs modes over the Atlantic and Indian Oceans. ENSO favors a unimodal behavior, with center of action in the Amazon River basin, while SSTs over the Tropical North Atlantic result in a dipole between northern and southern TropSA. Results show that soil moisture plays a leading role in regulating heat and water anomalies, and provides the memory of the atmosphere-driven processes and their subsequent influence. Thus, soil moisture is fundamental and leads up to 9 month-lags whereby ENSO enhances the interannual connectivity and memory of LAFs in 25% with respect to the mode influenced by TNA. Within the identified multivariate structure, evaporation and soil moisture enhance the interannual connectivity of the whole set of variables since both variables exhibit more frequent two-way feedbacks with the remaining variables.