Hydroclimatology of the Upper Madeira River basin: spatio-temporal variability and trends

Molina, Jorge; Espinoza, Jhan Carlo; Vauchel, Philippe; Ronchail, Josyane; Caloir, Beatriz Gutierrez; Guyot, Jean‐Loup; Noriega, Luis

doi:10.1080/02626667.2016.1267861

Cited by 63 publications

(78 citation statements)

References 55 publications

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“…It is not unusual to have dry periods during the rainy season corresponding to the "break phase" of the SAMS [31,74], and even droughts and floods are part of the natural variability of the system [75,76] and often occur in response to sea-surface temperature (SST) anomalies. However there has been indication of an increasing tendency to extreme seasonal drought or flood occurrences in the Amazon basin [19,39,77,78], including in the southern Bolivian Amazon [40]. An increase in the intensity of the rainfall has also been observed [39] and is predicted by multi-model climate projections [18].…”

Section: Discussion and Future Outlookmentioning

confidence: 87%

“…This seasonal behavior is mainly driven by synoptic atmospheric patterns, i.e., (1) the high pressure of the Brazilian and South Atlantic anticyclone from May to September; and (2) the interactions between the Intertropical Convergence Zone and the South Atlantic Convergence Zone during austral summer [37]. Beyond these seasonal mechanisms, rainfall is influenced by: (1) the atmospheric flow of water vapor from the Atlantic Ocean and connections with the Atlantic and Pacific sea surface temperatures [21,[38][39][40]; and (2) a large hydrological recycling process above the forests [41][42][43][44]. The large uncertainty of the ocean and continent surface coupling [39,45,46] drives regional and interannual rainfall variability, while the strong heterogeneity of the structure and intensity of the convection related to evapotranspiration influences fine/local spatio-temporal variability.…”

Section: Study Areamentioning

confidence: 99%

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Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

et al. 2017

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Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) is a relatively new product (released in 2013) but that contains data since 1983, thus enabling long-term rainfall analysis. In this work, we used three decades (1983-2014) of PERSIANN-CDR daily rainfall data to characterize precipitation patterns in the southern part of the Amazon basin, which has been drastically impacted in recent decades by anthropogenic activities that exacerbate the spatio-temporal variability of rainfall regimes. We computed metrics for the rainy season (onset date, demise date and duration) on a pixel-to-pixel basis for each year in the time series. We identified significant trends toward a shortening of the rainy season in the southern Amazon, mainly linked to earlier demise dates. This work thus contributes to monitoring possible signs of climate change in the region and to assessing uncertainties in rainfall trends and their potential impacts on human activities and natural ecosystems.

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Section: Discussion and Future Outlookmentioning

confidence: 87%

Section: Study Areamentioning

confidence: 99%

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

et al. 2017

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“…Across the record period, Q varies from by 29% (relative standard deviation calculated over all 10 day periodicity data points available) for OBI and by up to 80% for RUR and CAI stations. Some northern Andean stations exhibit a relatively low variability in Q (e.g., around 35% for FOR or BOR), whereas at some of the southern downstream stations, such as PVE of FAZ, Q is much more variable (relative standard deviation > 65%) reflecting the difference in precipitation variability between the two regions (Espinoza et al, ; Molina‐Carpio et al, ).…”

Section: Data Source and Processingmentioning

confidence: 99%

River Mixing in the Amazon as a Driver of Concentration‐Discharge Relationships

Bouchez

Moquet

Espinoza

et al. 2017

Water Resources Research

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Large hydrological systems aggregate compositionally different waters derived from a variety of pathways. In the case of continental‐scale rivers, such aggregation occurs noticeably at confluences between tributaries. Here we explore how such aggregation can affect solute concentration‐discharge (C‐Q) relationships and thus obscure the message carried by these relationships in terms of weathering properties of the Critical Zone. We build up a simple model for tributary mixing to predict the behavior of C‐Q relationships during aggregation. We test a set of predictions made in the context of the largest world's river, the Amazon. In particular, we predict that the C‐Q relationships of the rivers draining heterogeneous catchments should be the most “dilutional” and should display the widest hysteresis loops. To check these predictions, we compute 10 day‐periodicity time series of Q and major solute (Si, Ca2+, Mg2+, K+, Na+, Cl‐, SO42−) C and fluxes (F) for 13 gauging stations located throughout the Amazon basin. In agreement with the model predictions, C‐Q relationships of most solutes shift from a fairly “chemostatic” behavior (nearly constant C) at the Andean mountain front and in pure lowland areas, to more “dilutional” patterns (negative C‐Q relationship) toward the system mouth. More prominent C‐Q hysteresis loops are also observed at the most downstream stations. Altogether, this study suggests that mixing of water and solutes between different flowpaths exerts a strong control on C‐Q relationships of large‐scale hydrological systems.

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“…Transitional periods occur at the onset of the rainy season as discharge increases (flood—December to January) and as the flood retreats (ebb—June to July; Barthem et al., ). The flood pulse produces marked effects, with large changes in water level (ranging from 15.4 at low water to 21.8 m at high water—Molina‐Carpio et al., ; Torrente‐Vilara et al., ). Mean annual discharge (1967–2013) at the Porto Velho station is 18,500 m 3 /s with discharge varying between 2,322 and 47,236 m 3 /s, comprising nearly 10% of the discharge of the Amazon River into the Atlantic Ocean (Molina‐Carpio et al., ; Torrente‐Vilara et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…hydrological period, Jirau Hydroelectric Power Plant, Madeira River, run-of-river dam, zooplankton community structure to 21.8 m at high water- Molina-Carpio et al, 2017;Torrente-Vilara et al, 2008). Mean annual discharge (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Damming interacts with the flood pulse to alter zooplankton communities in an Amazonian river

et al. 2019

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Flow modification of lotic ecosystems is one of the main threats to global freshwater biodiversity. Commonly, and in the river studied here, modification results from hydroelectric dam installation. We evaluated the impacts of damming on zooplankton communities in the Amazonian floodplain of the Madeira River (Porto Velho, Rondônia, Brazil) following construction in 2012 of the run‐of‐river dam of Jirau Hydroelectric Power Plant. Using data sampled between 2009 and 2015, we tested for discontinuities in zooplankton community composition attributable to damming and the naturally occurring flood pulse. The flood pulse remained the main predictor explaining variation in zooplankton community structure even with the installation of the dam on the Madeira River. Despite this, discontinuities for the entire zooplankton community and for the main compositional groups (testate amoebae, rotifers, cladocerans, and copepods) were detected in relation to the dam (pre‐/post‐dam periods), mainly in ebb and low water, and with weaker evidence of dam effects during flood and highwater hydrological periods. A multivariate regression tree explained 9.6% of the variation in zooplankton communities and identified four groups: (1) flood and high‐water periods; (2) low water post‐dam; (3) low water pre‐dam; and (4) ebb hydrological periods. The deviance in each multivariate regression tree node was attributable to variation in eight rotifer, three testate amoeba, and three copepod taxa. Our study demonstrates that the flood pulse, dam construction, and interaction between both of these factors affect zooplankton community structure in the Madeira River. While for many zooplankton community variables, effects occurred mainly during ebb and low‐water periods, some effects were also observed during high water and flood periods. We thus recommend the establishment of a permanent environmental monitoring programme during all hydrological periods in tropical floodplain rivers and the addition of sampling sites downstream from dams. Many rivers in the world are increasingly disrupted by multiple dams, yet little is known of their effects, especially for run‐of‐river dams. Our study identified short‐term impacts of only one run‐of‐river dam on zooplankton communities. More research is needed on the effects of multiple run‐of‐river dams on zooplankton and other biota, especially in tropical floodplain rivers, so that negative effects can be understood and ameliorated.

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Hydroclimatology of the Upper Madeira River basin: spatio-temporal variability and trends

Abstract: International audienc

Cited by 63 publications

References 55 publications

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

River Mixing in the Amazon as a Driver of Concentration‐Discharge Relationships

Damming interacts with the flood pulse to alter zooplankton communities in an Amazonian river

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