the forest effects on the isotopic composition of … · foliar e com ciclagem de umidade modelada...

INSTITUTO DE QUÍMICA

PROGRAMA DE PÓS-GRADUAÇÃO EM GEOCIÊNCIAS – GEOQUÍMICA

ANGELA AMPUERO GRÁNDEZ

THE FOREST EFFECTS ON THE ISOTOPIC COMPOSITION OF RAINFALL IN THE NORTHWESTERN AMAZON BASIN

NITERÓI

2019

ANGELA AMPUERO GRÁNDEZ

THE FOREST EFFECTS IN THE ISOTOPIC COMPOSITION OF RAIN IN THE NORTHWESTERN AMAZON BASIN

Dissertação apresentada ao Curso de Pós-

Graduaçãoem Geociências da Universidade

Federal Fluminense, como requisito parcialpara a

obtenção do Grau de Mestre. Área de

Concentração: Geoquímica Ambiental.

Orientador:

Prof. Dr. Nicolás MisaílidisStrikis

Co-Orientador:

Prof. Dr. James Emiliano Apaéstegui Campos

NITERÓI

2019

Ficha catalográfica automática - SDC/BGQGerada com informações fornecidas pelo autor

Bibliotecária responsável: Catia Vasconcellos Marques - CRB7/5116

G751t Grández, Angela Ampuero THE FOREST EFFECTS ON THE ISOTOPIC COMPOSITION OF RAINFALLIN THE NORTHWESTERN AMAZON BASIN / Angela Ampuero Grández ;Nicolás Misaílidis Strikis, orientador ; James EmilianoApaéstegui Campos, coorientador. Niterói, 2019. 70 f. : il.

Dissertação (mestrado)-Universidade Federal Fluminense,Niterói, 2019.

DOI: http://dx.doi.org/10.22409/PPG-Geo.2019.m.06419394775

1. Amazon. 2. Stable isotopic composition of water. 3.Deuterium excess. 4. Speleothem. 5. Produção intelectual. I.Strikis, Nicolás Misaílidis, orientador. II. Campos, JamesEmiliano Apaéstegui, coorientador. III. Universidade FederalFluminense. Instituto de Química. IV. Título.

CDD -

ACKNOWLEDGMENTS

I thank my parents for all their love and for always encouraging me to do better.

I thank my family and friends for being a great support team, even though we were

far away.

I thank Rod for the time we spent together before I came here. I sure learnt some

survival skills that came out useful in this process. What a ride!

I thank my peruvian friends from the Instituto Geofísico del Perú for all the things you

thought me and the good times too!

I thank the class of 2017, you were with no doubt, the best class of my life.

I thank the professors, staff and fellow students from the Geoquimica Department for

making such an amazing and welcoming environment for learning and have fun.

I thank the climbers from Rio, specially the guys from Niteroi. I will never forget the

night climbs at Pracinha. I specially thank Kristy, my Colombian sister, climbing

partner and Python instructor.

I thank my roommates who were like a family to me, in the most complete sense.

I thank Abdel and Chico Bill, because it was you who set the base for the projects we

are currently working on.

I thank the community of the Palestina Cave in Peru, specially Graciela for collecting

the water samples, doing a wonderful job. Also, the Speleo Club Andino (ECA), the

Institute de Recherche pour le development (IRD) in Peru, for their support during the

field trips.

I thank Mathias Vuille, Renato Campello and Heitor Evangelista, for all your

comments and recommendations to improve this study. I also thank Delphine Zemp

and Jelena Maksic, for their disposition to solve my doubts and share their data.

I thank James for all the things you taught me, from driving to scientific writing, which

helped me along the way. Also, for showing me Peru and introducing me to

speleology, which is way cooler than I initially thought.

I thank Nicolás, in first place, for accepting me as his student. I know the whole

process was challenging but, in the end, it turned out ok. Who would have guessed! I

thank you for all the things you patiently taught me and for our long discussions.

Thank you for keeping me motivated, for making everything a little cooler and fun.

I thank CAPES for granting the scholarship over the period of this master's degree.

“I had nothing to offer anybody except my

own confusion.”

- Jack Kerouac,

On the road

ABSTRACT

Moisture recycling is a major process of the hydrologic cycle in the Amazon. Recent studies based on remote sensing tools and numerical modeling have shed light on the importance of this process, and even quantified it on a climatology perspective. Parallel efforts suggested the effects of recycled moisture in the isotopic composition of rainfall, although this subject is still controversial. Even more uncertain are the implications for the paleoclimate registers based on water stable isotopes (δ18O, δD). To assess temporal variations of water isotopic composition, we take an empirical approach and present four-year monitoring of isotopic composition of rainfall in the northwestern Amazon basin. We explore the effects of climate and vegetation on isotopic composition by establishing air mass history based on atmospheric back-trajectory analyses, satellite observations of precipitation, leaf area index and modeled moisture recycling along the transport pathway. The results suggest that all variables exert control on isotope variability and that control varies with prevailing atmospheric transport pathways. Observations agree that precipitation upwind is the main control on δ18Oin the northwestern Amazon basin. Furthermore, the results suggest that the forest exerts a significant control on the isotopic composition of precipitation, evidenced in dxs variations. In the light of these findings, we interpret Holocene dxs record based on fluid inclusions from well dated stalagmites from the Tigre Perdido cave. Results show that dxs points variations in the gradient of speleothem δ18Ocalcite from the east and west ends of the Amazon basin. Moreover, dxs follows shifts in rainforest cover inferred from lake pollen registers and global vegetation modelling. Finally, we suggest that the continental moisture contribution to precipitation in the northwestern Amazon involves the forest activity and has a significant imprint in the isotopic composition precipitation.

Keywords: Amazon. Stable isotopic composition of water. Deuterium excess. Speleothem. Fluid inclusions. Holocene.

RESUMO

A ciclagem de umidade é um processo importante do ciclo hidrológico na Amazônia. Estudos recentes baseados em ferramentas de sensoriamento remoto e modelagem numérica esclareceram a importância desse processo e até o quantificaram em uma perspectiva climatológica. Esforços paralelos sugeriram efeitos da umidade reciclada na composição isotópica da chuva, embora esse assunto ainda seja controverso. Ainda mais incertas são as implicações para os registros paleoclimáticos baseados em isótopos estáveis da água (δ18O, δD). Para avaliar as variações temporais dos isótopos da água, adotamos uma abordagem empírica e apresentamos um monitoramento de quatro anos da composição isotópica da precipitação no noroeste da bacia Amazônica. Nós exploramos os efeitos do clima e da vegetação na composição dos isótopos estabelecendo a historia das parcelas de ar com base na análise reretro-trajetórias, observações de precipitação por satélite, índice de área foliar e com ciclagem de umidade modelada no caminho das massas de ar. Os resultados sugerem que todas as variáveis exercem controle sobre a variabilidade isotópica e que o controle varia com as vias de transporte atmosféricas predominantes. Observações concordam que a precipitação a caminho dos fluxos de umidade é o principal controle no δ18O do noroeste da baciaAmazônica. Além disso, os resultados sugerem que a floresta exerce um controle significativo sobre a composição isotópica da precipitação, evidenciada nas variações de dxs. À luz dessas descobertas, nós interpretamos o registro do Holocene de dxs baseado em inclusões fluidas de estalagmites bem datadas da caverna do Tigre Perdido. Os resultados mostram que dxs aponta variações no gradiente δ18O de espeleotemas na borda leste e oeste da bacia Amazônica. Além disso, dxs segue mudanças na cobertura de floresta tropical inferidas a partir de registros de pólen de lagos e modelagem global de vegetação. Finalmente, sugerimos que a contribuição da umidade continental para a precipitação no noroeste da Amazônia envolve a atividade florestal e tem uma impressão significativa na composição isotópica das precipitações.

Palavras-chave: Amazônia. Composição isotopica estável da água. Excesso de deutério. Espeleotema. Inclusões fluidas. Holoceno.

LIST OF FIGURES

Figure 1. (a) Mean annual precipitation in mm for the period 1989-1995, based on

the average of the products CRU, GPCC, GPCP and CPC and(b) number of months

with less than 100 mm rainfall. Deforested (red dots) and non-forest (gray dots) areas

are indicated. ........................................................................................................... 19

Figure 2. Long term mean precipitation (shades) and wind circulation (vectors and

stream lines). Left panels show low level (950hPa) wind circulation for (a) January

and (b) July and right panels show high level (300hPa) wind circulation for (c)

January and (d) July. ................................................................................................ 20

Figure 3. (a) Mean annual evapotranspiration in mm for the period from 1989 to

1995 from forty products. including field observations, reanalysis and model outputs.

(b) Precipitation recycling defined as the fraction of rainfall that comes from

continental evapotranspiration and vertically integrated moisture fluxes (black

arrows). Deforested (red dots) and non-forest (gray dots) areas are indicated ......... 22

Figure 4. a) Schematic representation of tree transpiration recycling. On average

20% of rainfall in the Amazon has been transpired at least once. About half of this

transpiration (51%) is recycled in the first cycle, the remainder occurs after multiple

re-evaporation cycles (moisture cascading). b) Transpiration recycling ratio in the

Amazon against average rainfall from oceanic origin. 2005 and 2010 are labeled

because vast drought was registered in vast areas across the Amazon. .................. 23

Figure 5. a) Mean annual transpiration by trees that precipitate over land. b) Fraction

of mean annual rainfall that has been transpired by trees in the Amazon basin. The

Amazon basin is shown by black outline. ................................................................. 24

Figure 6. Schematic representation of cascading recycling effects in the vegetation-

rainfall system. a) System in equilibrium. b) Initial forest loss triggered by decreasing

oceanic moisture influx. c) Altered rainfall regime in another location, leading to

further forest loss and reduced moisture transport. .................................................. 25

Figure 7. Vegetation distribution map for different periods of the Holocene, from

numerical simulation. The model used from the Center for Weather Prediction and

Climate Studies (CPTEC) is the Potential Vegetation Model version 2 (CPTEC-

PVM2). Two experiments were performed for the 6K period, one with present SST

(6k-pvm veg type) and the other with mid Holocene SST (6k-pvm-MH-sst veg type).

................................................................................................................................. 29

Figure 8. Simplified Rayleigh distillation scheme in the water cycle. ........................ 30

Figure 9. Images of typical fluid inclusions in the speleothems analyzed in VAN

BREUKELEN et al., 2008. ........................................................................................ 32

Figure 10. Location of the Palestina station and precipitation climatology. Dark blue

shadows represent the Andes mountain range and the green line, the limit of the

Amazon basin taken from the HyBAM data base. Precipitation at the Palestina

station was obtained from TRMM 3B42 from 1998 to 2018. ..................................... 33

Figure 11. Records of δ18O (upper panel) and dxs (lower panel) from the stations

Palestina (black) and Pomacochas (red) on biweekly timestep. ............................... 34

Figure 12. Scheme of the rain collector used in this study. ...................................... 35

Figure 13. Schematic representation of one back-trajectory (black arrow). The back-

trajectory is divided in seven segments, each one covering one day. The beginning of

the back-trajectory is marked with a star at the Palestina station on the most recent

day (0), and then progresses back in time until completing 7 days (-7). Climate data

on precipitation, LAI and PR was selected from the tiles spanned by the back-

trajectory on the corresponding day. In this example, on the sixth day (between -5

and -6), the trajectory goes over the ocean (blue shadow), so information on those

tiles is excluded. The dashed line represents the coast line. .................................... 39

Figure 14. Clusters of seven-day back trajectories at 1500 m.a.g.l. from June 2012 to

May 2018.The bars under each map show the frequency of back-trajectory clusters

per month. ................................................................................................................ 42

Figure 15. Annual cycle of the degree of rainout upstream (DRU) and local

precipitation from 2012 to 2018. DRU is the accumulated precipitation along the

back-trajectories that initiate on precipitation days at the Palestina station. Local

precipitation is calculated from TRMM3B42 and GPM by averaging the nearest tiles

to the Palestina station. ............................................................................................ 44

Figure 16. Annual cycle of the degree of rainout upstream (DRU), Leaf area index on

the back-trajectories (LAI) and Precipitation Recycling on the back-trajectories (PR)

for the period from June 2012 to May 2018. ............................................................. 45

Figure 17. Precipitation recycling (PR) computed with WAM2lyr and data from ERA

Interim. a) Mean annual PR. b-e) Seasonal anomalies. ........................................... 46

Figure 18. The maps show mean moisture recycling (PR) per season in percentage

(%). Only values in the dominant back-trajectory areas are shown. Panels under the

maps show the meridional average PR. Clusters 1, 5 and 11 are frequent during

austral summer; clusters 2 and 3, during austral autumn and winter; and clusters 6

and 7, during austral winter. . .................................................................................. 46

Figure 19. Biweekly δ18O andδD of precipitation at the Palestina station. The GMWL

is represented by a solid red line and deviations of ±5‰ are represented with dashed

lines. ........................................................................................................................ 47

Figure 20. Correlation between δ18O and dxs of precipitation at the Palestina station

on biweekly timestep. ............................................................................................... 48

Figure 21. Correlation between of δ18O and dxs of precipitation at the Palestina

station on biweekly timestep for the seasons (a)JJA, (b)SON, (c)DJF and (d)MAM. 48

Figure 22. Panels on the left show the original records at the Paletina station and the

ones in the right, the records without seasonality (anomalies). (a) and (b) show δ18O

of precipitation and local precipitation accumulated along the water sampling period.

(c) and (d) show δ18O of precipitation and calculated degree of rainout upstream

(DRU), weighted by local precipitation for the water sampling period. (e) and (f) show

deuterium excess (dxs) of precipitation and Leaf area index accumulated along the

back-trajectories (LAI). LAI was weighted by local precipitation for the water sampling

period. ...................................................................................................................... 51

Figure 23. Time series of dxs, PR in the upper panels and LAI in the lower panels.

Lines represent the series in biweekly time step dots, the seasonal averages for (a,c)

wet season and (b,d) extended dry season. The gray shadow indicates the period

filled with dxs data from Pomacochas station. .......................................................... 52

Figure 24. Spatial correlation between LAI and VIDMF on monthly time step for the

period from June 2012 to May 2018. Seasonality was removed from both datasets.

Shadows show only significant grid cells (p<0.05).................................................... 53

Figure 25. Geographic location of the geochemical and palynological records

referenced in this study. ........................................................................................... 56

Figure 26. Comparison between stable isotope record from speleothems from the

western and eastern Amazon edges: a) dxs reconstruction from Tigre Perdido

speleothem record (VAN BREUKELEN et al., 2008); b) Tigre Perdido - Paraíso

δ18O; c) 20-yr interpolated δ18O record from Tigre Perdido cave speleothem (VAN

BREUKELEN et al., 2008). The δ18O values from Tigre Perdido are corrected by 1.4

‰ to account for temperature variations between the caves following the procedure

of Wang et al. (2017); d) as in (c) 20-yr interpolated δ18O record from Paraíso cave

speleothems (WANG et al., 2017). ........................................................................... 57

Figure 27. Comparison between calculated dxs from Tigre Perdido stalagmite (VAN

BREUKELEN et al., 2008) with the integrated record of rainforest pollen frequency

from five lakes: Iriri, Altamira - PA (SANTOS, 2019); Ilha Arapujá, Altamira - PA

(SANTOS, 2019);Laguna Chochos, Peru (BUSH et al. 2005);Saci, central-south

Amazon (FONTES et al., 2017) and Serra Sul Carajás Lake, CSS2 (ABSY et al.,

1991; SIFEDDINE et a., 2001). ................................................................................ 59

Figure 28. Normalized interpolated frequency of arboreal pollen from lakes used in

this study and average value. ................................................................................... 59

LIST OF TABLES

Table 1. Values of the quality layer for LAI/FPAR (8bit) in MODIS. .......................... 37

Table 2. Linear correlation coefficient (r) and p-value between isotope records and

potential climatic controls based on biweekly data. The r-values in bold (if p<0.05)

and in italics (if p<0.10). ........................................................................................... 50

Table 3. As in Table 1, but for dry months (JJAS) and wet months (ONDJFMAM).

The first two weeks of October correspond to the dry season and the last two, to the

wet season. .............................................................................................................. 50

NOMENCLATURE

δD Stable Hidrogen isotope ratio (2H/1H) of precipitation

δ18O Stable Oxigen isotope ratio (18O/16O) of precipitation

δ18Ocal Stable Oxigen isotope ratio (18O/16O) of calcite

DRU Degree of rainout upstream

dxs Deuterium excess

ENSO El Niño southern oscillation

GMWL Global meteoric water line

GPM Global precipitation mission

ITCZ Intertropical Convergence Zone

kyr BP Thousand years before present (1950)

LAI Leaf area index

LGM Last Glacial Maximum

LLJ Low Level Jet

m.a.g.l. Meters above ground level

m.a.s.l. Meters above sea level

PR Precipitation recycling or moisture recycling

SACZ South Atlantic Convergence Zone

SAMS South American Monsoon System

SST Sea surface temperature

TRMM Tropical rainfall measurement mission

CONTENTS

RESUMO ................................................................................................................... 6

LIST OF FIGURES .................................................................................................... 7

LIST OF TABLES .................................................................................................... 11

NOMENCLATURE................................................................................................... 12

1. INTRODUCTION ............................................................................................... 15

2. OBJECTIVES .................................................................................................... 18

2.1. SPECIFIC OBJECTIVES ................................................................................... 18

3. THEORETHICAL BASIS ................................................................................... 19

3.1. CLIMATE OF THE AMAZON AND GLOBAL IMPACTS ..................................... 19

3.2. THE AMAZON, A CONTINENTAL MOISTURE SOURCE ................................. 21

3.3. AMAZONIAN FOREST COVER SHIFTS: THE EVOLUTION OF THE FOREST

ALONG THE HOLOCENE ....................................................................................... 25

3.4. WATER STABLE ISOTOPOLOGUES IN THE WATER CYCLE ........................ 29

3.5. DEUTERIUM EXCESS: A PROXY FOR RECYCLED MOISTURE? .................. 30

3.6. SPELEOTHEMS AND FLUID INCLUSIONS ..................................................... 31

3.7. MONITORING SITE .................................................................................... 32

4. MATERIALS AND METHODS .......................................................................... 34

4.1. ISOTOPE MONITORING .................................................................................. 34

4.2. CLIMATE DATA ................................................................................................ 35

4.3. PALINOLOGYCAL RECORDS .......................................................................... 38

4.4. METHODS ........................................................................................................ 38

5. RESULTS .......................................................................................................... 41

5.1. PRECIPITATION AND BACK-TRAJECTORY ANALYSIS ................................. 41

5.2. FOREST MOISTURE FLUXES ......................................................................... 44

5.3. ISOTOPE MONITORING .................................................................................. 47

6. DISCUSSION .................................................................................................... 53

6.1. CONTROLS OF δ18O AND DXS IN PRECIPITATION ....................................... 53

7. CONCLUSION .................................................................................................. 60

8. REFERENCES .................................................................................................. 61

15

1. INTRODUCTION

The effects of rainforest evapotranspiration on the stable isotopic composition

of rainfall (δ18O, δD) is particularly important to reconstruct the hydroclimate history in

South America. In general, the isotopic composition of a precipitating air masses

moving over a continental area results from a combination of processes that mediate

the Rayleigh distillation. Raleigh distillation is the process where heavy isotope

species are progressively removed from the atmosphere through precipitation,

leaving the remaining vapor isotopically depleted (VUILLE; WERNER, 2005;

LACHNIET, 2008). While the progressive condensation of moisture leads to a

gradual lowering of heavy isotopes in the remaining vapor, re-evaporated moisture

from land compensates, at least partially, the loss of the heavy isotopes.

Several studies have shown that the Amazon basin itself constitutes a major

moisture source for the region, driven by the hydrologic regime of the rainforest

(SALATI, 1979, DRUMOND et al., 2014, MOLINA et al., 2019). Recently Staal et al.

(2018) estimated that about 32% of Amazonian rainfall originates from

evapotranspiration within the basin; two thirds of which correspond to tree

transpiration. Furthermore, the positive feedback between the forest and rainfall

promotes cascades of recycled moisture that precipitate across distant areas,

allowing for forest cover expansion. At the same time, the forest induces dry season

rainfall, necessary to sustain itself when oceanic moisture supply is limited (ZEMP et

al., 2014, STAAL et al., 2018).

Another proxy suitable to describe moisture activity is deuterium excess (dxs =

δD - 10 × δ18O), a second order parameter derived from kinetic fractionation of the

water stable isotopologues (DANSGAARD, 1964; MERLIVAR; JOUZEL, 1979). The

dxs value of an air mass is influenced by the physical conditions at the oceanic

moisture source, principally relative humidity and sea surface temperature (PFAHL;

SODEMANN, 2014). Moreover, prevailing conditions during advection across the

continent and interaction or mixing with different air masses further modify dxs

(FROEHLICH et al 2002). Particularly, over large continental areas, such as the

Amazon basin, variations in the dxs composition have the potential to serve as a

fingerprint of recycled air moisture (SALATI et al., 1979).

16

Water isotopologue data across the Amazon suggest that changes in forest

vegetation may affect the dxs values through changes in the rates of moisture

recycling associated with forest evapotranspiration (SALATI et al., 1979, ZEMP et al.,

2014 among others). Although progress has been made in understanding the major

drivers of δ18O and δD variability in the tropical region, the relationship between

moisture recycling and the rainfall isotopic composition in the Amazon basin is not

fully understood. Moreover, the climatic significance of dxs values over continental

areas in the tropics is still unclear (LANDAIS et al., 2010; RISI et al., 2013; VUILLE,

2018). In this sense, it is important to clarify the effect of land atmosphere-

interactions on the isotopic composition of rain to reliably interpret isotope-based

paleoclimate records. For instance, in the Amazon basin paleo-precipitation

reconstructions based on speleothem δ18Ocalcite records are controversial. Some

authors sustain the notion that during the Last Glacial Maximum (LGM, ~ 26.5 to 19

kyr BP) a strong east-west rainfall isotope gradient along the Amazon may have

resulted from reduced water recycling, as consequence of a widely drier Amazon,

leading to reduced plant transpiration (WANG et al., 2017). On the other hand, other

authors claim that the spatial variability in the isotope rainfall composition resulted

from an east-west precipitation dipole (CRUZ et al., 2009; SYLVESTRE et al., 1999;

CHENG et al., 2013) associated with an upper level wave response to enhanced

monsoon circulation, known as the Bolivian-High Nordeste Low system (LENTERS;

COOK, 1997; 1999, CHEN et al., 1999; SULCA et al., 2016). In this scenario, the

increased convective activity and precipitation over the western Amazon basin is

balanced by enhanced subsidence over Northeastern Brazil, leading to an

antiphased precipitation pattern between western and eastern Amazon, rather than

pervasive dry conditions.

In this scenario, the aim of this study is to clarify the role of the forest in the

Amazonian hydrologic cycle along the Holocene, capitalizing on dxs records. Based

on what is known so far about the climatic controls on the water stable isotopes and

current interpretations of the paleo-records, we proposed the following hypothesis:

H1: In continental forested areas, dxs of precipitation records recycled

precipitation.

H2: Large shifts in the forest cover modify the precipitation regimes across the

Amazon basin, leaving a distinct imprint in dxs.

17

To test the hypothesis, an empirical approach is taken, relying on δ18O and

dxs from rainfall monitored in the western Amazon basin. A description of transport

history of air masses using air parcel back-trajectory modeling is presented, together

with climatic and land data derived from satellite observations and reanalysis

products. Finally, a discussion of the paleoclimate implications of dxs variability along

the Holocene is proposed, based on published speleothem records, global vegetation

modeling and pollen from continental lake cores.

18

2. OBJECTIVES

The general objective of this thesis work is to identify possible effects of

recycled moisture on the isotopic composition of precipitation in order to improve the

climatic interpretations of speleothem records from the region.

2.1. SPECIFIC OBJECTIVES

The general objective of this work is approached in two phases. First, we

focus on modern observations of water stable isotopic composition of rainfall in the

north western Amazon basin, for what the following specific objectives were

establish:

• Constrain the effect of atmospheric circulation and regional precipitation on the

stable isotopic composition.

• Evaluate the effect of regionally recycled moisture on the stable isotopic

composition.

• Assess the possible influence of the regional forest cover on dxs.

• Evaluate the temporal variability of the relationship between δ18O and dxs.

On the second phase, we focus on published records of dxs in fluid inclusions

in speleothems from the northwestern Amazon and speleothem δ18Ocalcite across the

Amazon basin, for what the following specific objectives were established:

• Assess the dxs variability along the Holocene.

• Evaluate the variation of the regional land moisture contribution to precipitation in

the northwestern Amazon based on dxs variability.

• Evaluate the connection between δ18Ocalcite records across the Amazon basin

based on dxs variability.

19

3. THEORETHICAL BASIS

3.1. CLIMATE OF THE AMAZON AND GLOBAL IMPACTS

From the Andes cordillera to the Atlantic coast, the Amazon basin covers

approximately 6 200 000 km2, which is about one third of the continental mass of

south America (MARENGO et al., 2012). It hosts the largest tropical forest in the

planet and plays a major role in the global energy and hydrological budgets. The

potential energy derived from the large evapotranspiration through the forest drives

winds, affecting global Hadley and Walker circulations (MAKARIEVA et al., 2013;

BARICHIVICH et al., 2018). This implies that Amazonian precipitation responds to

the combination of large-scale atmospheric circulation and local water sources

(MARENGO; ESPINOZA, 2015).

Amazonian rainfall is large, about 2300 mm year-1 (DRUMOND et al., 2014).

The spatial distribution of rainfall varies largely in space, as shown in Figure 1a.

Rainfall regime is also heterogeneous across the basin, going from the ever-wet

north-western, with no distinct dry season, to the seasonal south-east, where dry

season last about five months (RESTREPO-COUPE et al., 2013). Figure 1b shows

the spatial distribution of the length of the dry season, given that in the Amazon, the

dry season is when precipitation falls below 100 mm month-1.

Figure 1. (a) Mean annual precipitation in mm for the period 1989-1995, based on the average of the products CRU, GPCC, GPCP and CPC and(b) number of months with less than 100 mm rainfall. Deforested (red dots) and non-forest (gray dots) areas are indicated.

Source: ZEMP, 2016.

Seasonal variations of precipitation in the Amazon basin are driven, in general, by the South American Monsoon Systems (SAMS). The SAMS is a seasonal mechanism that drives the moisture influx from the tropical north Atlantic to the continent, producing heavy rainfall over most of tropical South America during the

20

warm season (VERA et al., 2006). The features of the SASM in the lower atmosphere (~850 hPa) are the pervasive Low-Level Jet to the east of the Andes and the South Atlantic Convergence Zone (SACZ). In the upper troposphere (~300hPa) it appears the Bolivian High and the Nordeste Low. As a result, from late November through late February, the SASM sets the wet season for most of the Amazon basin. Contrasting conditions prevail during the dry season, from June to August. During this period, steady flow from East to West prevails in low and high atmospheric levels. The band of convection is restricted to the northern Hemisphere, reaching the northern tip of South America. Oceanic moisture input to the Amazon during the dry season is mainly from the tropical south Atlantic Ocean (DRUMOND et al., 2014), but it is less than the SAMS related influx. Climatological precipitation and atmospheric circulation are shown in Figure 2

Figure 2 for a mature phase SAMS month (January) and a month when SAMS

is absent (July).

Figure 2. Long term mean precipitation (shades) and wind circulation (vectors and stream lines). Left panels show low level (950hPa) wind circulation for (a) January and (b) July and right panels show high level (300hPa) wind circulation for (c) January and (d) July.

Source: GARREAUD et al., 2008.

Precipitation variability on interannual timescale is modulated by sea surface

temperature (SST) in the tropical Pacific and Atlantic oceans. In one hand, El Niño

Southern Oscillation (ENSO), rooted in the equatorial Pacific, affects precipitation in

21

the Amazon through atmospheric teleconnections (GARREAUD et al., 2008). Some

evidence of this are the severe droughts reported in 1912, 1926, 1983 and 1997-

1998, related to el Niño, and extreme floods in 2009, 2011 and 2012, related to la

Niña events (MARENGO; ESPINOZA, 2015). In the other hand, sea surface

temperature (SST) in the tropical Atlantic regulates directly the moisture influx to the

continent. For instance, the droughts of 1964 and 2005 were related to warm SST

anomalies in the tropical north Atlantic due to a northward displacement of the

Intertropical Convergence Zone (ITCZ) and the 2009 flooding, associated with warm

tropical South Atlantic SST (MARENGO; ESPINOZA, 2015, BARICHIVIC et al.,

2018).

Recent observations suggest that the impact of the Atlantic Ocean in the

Amazonian precipitation is even more important than the Pacific Ocean influence.

Wang et al. (2018) show the leading role of the Atlantic Ocean in the intensification of

the wet season precipitation over the equatorial Amazon for the last 30 years,

concomitant with increasing Atlantic’s SSTs. SST relatively warmer conditions

enhance moisture transport from the Atlantic Ocean to the Amazon, favoring

convergence, which results in intensified precipitation, especially in the equatorial

area. Furthermore, Barichivich et al. (2018) suggest that the severe floods in the

Amazon during the last decades, were driven by the strengthening of the Walker

circulation, which in turn is a response to the recent tropical Atlantic warming (LI et

al., 2016).

Mean annual low-level temperature in the Amazon basin is ~20°C, with a slight

thermal gradient from the Equator to the poles (GARREAUD et al., 2008). In general,

the annual cycle exhibits maximum temperatures in austral summer and minimum

temperatures in austral winter, with less seasonal variation near the Equator. The

exception is the southern Amazon, where the warmest temperatures appear during

austral winter, which is explained by the summer time cloud development that

shades the surface from sunlight (GARREAUD et al., 2008).

3.2. THE AMAZON, A CONTINENTAL MOISTURE SOURCE

Continental moisture is very important for the Amazonian water budget, as it

induces precipitation, especially during the dry season. The basic mechanism is

known as “moisture recycling” (Figure 3), which refers to the fraction of precipitation

22

originated form evaporation in land, within a determined region. Precipitated water

can re-evaporate and precipitate again downwind in a cycle that repeats along the

advection pathway, featuring what is referred to as “cascading moisture recycling”

(ZEMP et al., 2014; ZEMP et al., 2016; STAAL et al., 2018). A schematic

representation of moisture cascading in the Amazon basin is presented in Figure 6. It

also occurs that atmospheric moisture just flows across the continent towards areas

where convergence ensures that precipitation occurs, without exchange between

vegetation and atmosphere on the way (TRENBERTH, 1998). A combination of

these processes drives rainfall distribution in the Amazon.

Moisture recycling derives from two processes. One is the physical

evaporation from open water, soil and canopy, which in this work is referred to as

evaporation. The other process is transpiration, which is the biological evaporation

trough the leave stomata. It is worth noting that there are different terms to refer to

these processes depending on the study line. For example, in the ERA Interim

atmospheric reanalysis, the variable "evaporation" accounts for the sum of

evaporation and transpiration, while in the Large-Scale Biosphere Atmosphere

Experiment in Amazonia (LBA) project, that same variable is referred to as

evapotranspiration or latent heat flux (LE) (DA ROCHA et al., 2009). Therefore, it is

important to clarify that in this work we use the term evapotranspiration to refer to the

sum of evaporation and transpiration.

Figure 3. (a) Mean annual evapotranspiration in mm for the period from 1989 to 1995 from forty products. including field observations, reanalysis and model outputs. (b) Precipitation recycling defined as the fraction of rainfall that comes from continental evapotranspiration and vertically integrated moisture fluxes (black arrows). Deforested (red dots) and non-forest (gray dots) areas are indicated

Source: ZEMP, 2016.

Mean annual evapotranspiration for the Amazon is shown in Figure 3.

Quantifications of evapotranspiration based in numerical modeling estimate in

23

average 3.9 mmd-1(~1420 mm yr-1), while estimations based on observations range

from 3.1 to 3.7 mm d-1(1130 to 1350 mm yr-1) (SHUTTLEWORTH, 1988; DA ROCHA

et al., 2004; VON RANDOW et al.,2004; HUTYRA et al., 2007). Evapotranspiration

rates vary across the Amazon basin, with mark differences expressed during the dry

season. While the areas south and east borders, show decreased evapotranspiration

due to soil moisture limitation during the dry season, towards the north and west,

evapotranspiration rates increase, mainly controlled by atmospheric evaporative

demand (DA ROCHA et al., 2009). The reason is that the broadleaf forest with deep

root systems in the northern and western Amazon is capable to extract water from

deep soil layers, maintaining high transpiration rates, unlike the south and east

Amazon, that is dominated by Transitional forest and Cerrado (NEPSTAD et al.,

1994; OLIVEIRA et al., 2005).This relation with vegetation highlights the importance

of tree transpiration in the Amazonian water budget. Indeed, tree transpiration is key

to maintain wet conditions when oceanic moisture supply is limited. This buffer effect

is well documented in Staal et al., (2018), where it determines that the transpiration

rate that produce precipitation in the Amazon, increases as oceanic moisture influx

diminishes (Figure 4). In the Amazon, tree transpiration is a notable portion of the

evapotranspiration flux, inducing in average 20% of the annual rainfall (Figure 4),

although it reaches values up to 50% in the western end of the basin (Figure 5).

Figure 4. a) Schematic representation of tree transpiration recycling. On average 20% of rainfall in the Amazon has been transpired at least once. About half of this transpiration (51%) is recycled in the first cycle, the remainder occurs after multiple re-evaporation cycles (moisture cascading). b) Transpiration recycling ratio in the Amazon against average rainfall from oceanic origin. 2005 and 2010 are labeled because vast drought was registered in vast areas across the Amazon.

Source: STAAL et al., 2018.

24

Figure 5. a) Mean annual transpiration by trees that precipitate over land. b) Fraction of mean annual rainfall that has been transpired by trees in the Amazon basin. The Amazon basin is shown by black outline.

Source: STAAL et al., 2018.

The forest-atmosphere interaction has been evidenced in different studies. An

evaluation of rainfall sensitivity to forest cover showed that air flowing over extensive

vegetated areas produce at least twice more precipitation after some days in

comparison with air flowing over less vegetated areas (SPARKLEN et al., 2012). In

addition, changes in rainfall might also affect forest stability. For instance, the

observed alteration of canopy structure and water content in southwestern Amazonia

and slow recovery after the drought in 2005, suggests that frequent droughts (every

5-10 years) might permanently alter the forest canopy (SAATCHI et al., 2013). In fact,

during the last decades, recurrent anomalous droughts and floods have been

reported. Furthermore, hydrological data show trends towards more extreme events

in the Amazon (MARENGO; ESPINOZA, 2015). Although all model projections don’t

agree, a possible scenario is that water stress over long periods will lead to

significant forest loss which, at the same time, could enhance the intensification of

the water cycle and further forest loss through feedbacks between forest and rainfall.

A schematic representation of this process is shown in Figure 6.

.

25

Figure 6. Schematic representation of cascading recycling effects in the vegetation-rainfall system. a) System in equilibrium. b) Initial forest loss triggered by decreasing oceanic moisture influx. c) Altered rainfall regime in another location, leading to further forest loss and reduced moisture transport.

Source: ZEMP et al., 2016.

3.3. AMAZONIAN FOREST COVER SHIFTS: THE EVOLUTION OF THE FOREST

ALONG THE HOLOCENE

The Holocene Epoch is the youngest subdivision of the Geological Time

Scale, representing the current interglacial Period. Different from other chrono-

stratigraphic divisions, the boundary of the Holocene defines a climate-

stratigraphically division. Fixed in the oxygen stable isotope record of the North

Greenland Ice Core Project (NGRIP) (SINHA et al., 2013), the Holocene starts

officially at 11.700 kyr BP (Before Present refers to 1950 CE) marked by a positive

δ18O excursion related to the end Younger Dryas cold event, the last cold period

recorded during the deglacial period (SINHA et al., 2013; RASMUSSEN et al., 2014).

The Holocene has been informally subdivided into three subseries/subepochs Early-

Middle- and Late Holocene. SINHA et al. (2013) proposed that the traditional

subdivision of the Holocene would represent major changes in the North Atlantic

climate associate to major changes in the thermohaline circulation, roughly

correspondent to the Bond events (BOND et al., 1997; BOND et al., 2001). On 2018

the International Subcommission on Quaternary Stratigraphy (ISQS) ratified the

26

subdivision of the Series/Epoch of the Holocene in three Stages/Ages (WALKER et

al., 2018):

▪ Stage/Age Greenlandian: equivalent to the Lower/Early

Subseries/Subepoch Holocene the Greenlandian spans from 11.653 ±

0.004 to 8.276 Kyr BP;

▪ Stage/Age Nortgrippian: equivalent to the Middle/Mid Holocene

Subseries/Subepoch the Nortgrippian spans from 8.276 to 4200 Kyr

BP;

▪ Stage/Age Meghalayan: equivalent to the Upper/Late

Subseries/Subepoch the Nortgrippian spans from 4200 Kyr BP to the

present;

Although the proposal coined by the ISQS is still a bit controversial, for

instance the Meghalayan lacks a global expression (VOOSEN, 2018), this new

subdivision offers a formal ratification whose boundaries roughly resemblance the

informal subdivision frequently used in the scientific publications. In face of that, in

this study is used the Subseries Early, Mid- and Late Holocene following the

boundaries defied by ISQS subdivisions of 2018 (WALKER et al., 2018).

In general, the Holocene is characterized as period of reduced climate

variability when compared with the glacial climate oscillations. However, at orbital

time-scale, changes in the hydrologic regime may have imprinted considerable

changes in the forest due to the close relation between the climate and vegetation.

Therefore, the evolution of the Amazonian forest is a major issue for paleoclimate

studies in South America.

Reconstructions of vegetation composition were inferred from variations in the

type of pollen present in the phases of well dated lake cores across the Amazon. The

information that pollen brings is the biomes present at one time, whether it is Rain

forest, Seasonal forest or open Savanna. The general principal is that some types of

vegetation can only exist in certain climate conditions. For instance, pollen records

from lake cores in the Colombian Andes suggest that, during glacial conditions,

vegetation belts migrated to lower altitudes, following warmer temperatures (VAN

DER HAMMEN, 1974; VAN DER HAMMEN; ABSY, 1994; WILLIE et al., 2004).

27

However, paleoenvironmental reconstructions are complicated and

controversial. In one hand, the early study made by Colinvaux et al. (2000) based on

pollen analysis from Serra dos Carajás and Pata Lake (eastern Amazon basin)

suggests that the Amazon lowlands remained under forest, with a remarkable

stability throughout the Pleistocene. That study argues that the forest didn´t expand

or regressed, but some plant species were replaced by others with better adaptation

to the warming climate. In the other hand, evidence based mostly on lake cores and

speleothem records suggest that the Amazon forest has undergone dramatic shifts

concomitant with wet and dry conditions along the Quaternary (FONTES et al.,

2017). Indeed, what observations suggest is the general expansion of the Amazon

rainforest throughout the Holocene, interrupted by regression periods.

The Early Holocene (~11.7 to 8.3 kyr BP) is recognized as a period of forest

expansion and increased monsoon precipitation in the Amazon basin when

compared with the LGM, conditions. Speleothem records form Paraíso cave (WANG

et al., 2017), located in the eastern edge of the Amazon basin, and lake records

based on pollen and biogeochemical proxies from the same region show wetter

conditions during the Early Holocene in comparison with the LGM (BEHLING et al.,

2002; CORDEIRO et al., 2011). At the same time, δ18O records from speleothems

from the western Amazon basin (El Condor and Cueva del Diamante) show the

opposite, indicating moderately dry conditions during the Early to Mid- Holocene int

that region (CHENG et al., 2013). The slight rainfall reduction is attributed to reduced

intensity of the SAMS during the Early Holocene.

During the Mid-Holocene (~ 8.3 – 4.2 kyr BP) the South Hemisphere summer

insolation (December, January and February) underwent an increase of near 3 %

departing from ~ 882 W/m2 at about 8 kyr BP to 913 W/m2 near 4 kyr BP. Several

evidences appoint to dry conditions in the Amazon. A gap in the sedimentation hiatus

in the Comprido Lake from 7.8 to 3.0 Kyr BP (MOREIRA et al., 2012). Furthermore,

in the eastern Amazon, at Serra dos Carajás lake records showing increase in

charcoal deposition between 7.4 to 4.7 kyr BP points to episodes of large biomass

burning which reinforces the notion of dry climate (CORDEIRO et al., 2008). Also,

lake records from southwestern Amazon basin show similar results from 7.2 to 3.3

kyr BP (BUSH et al., 2007). However, there is significant variability in the chronology

of these dry envents. Furthermore, the dryness observed in the lake records from

28

eastern (MOREIRA et al., 2012) and western (BUSH et al., 2007) Amazon basin,

contrasts with the expansion in the várzea/igapó forest at ca. 7.7 kyr BP associated

with the rise of the Amazon water level and the formation of Calado Lake at central

Amazon (BEHLING et al., 2001).

The Late-Holocene (from 4.2 kyr BP to the present) is well characterized as

period of increase in humidity and forest expansion. However, the geographic

patterns of climate change and vegetation variation along the Late-Holocene is still

controversial. Speleothems records from the Amazon basin suggests a high east-

west precipitation dipole during the Late-Holocene. While speleothems from Paraíso

cave (eastern Amazon) shows stepwise δ18O enrichment along the Late Holocene

(from 4.2 to the present) speleothem records from western Amazon basin from Tigre

Perdido cave shows the opposite trend (VAN BREUKELEN et al., 2008). The same

has been observed by Cheng et al. (2013) using speleothems from Peruvian

Amazon. They explain the east-west precipitation dipole with the intensification of the

SAMS, which results in enhanced convection and upward motion in the western

Amazon which, in turn, enhances the Nordeste low, resulting in subsidence and dry

conditions in the eastern side. Moreover, in the western Amazon basin, pollen

records from Parker, Gentry and Werth Lakes show an increase in arboreal pollen

(BUSH et al., 2007) after 3.7 kyr BP. Also, pollen records from the eastern Amazon

(CSS 2 Lake, Serra dos Carajás) (ABSY et al., 1991; SIFEDDINE et al., 2001) and

Saci Lake (FONTES et al., 2017) shows a remarkable expansion of the rainforest

during the Mid-Holocene, reaching their maximum after 4.2 kyr BP, during the Late-

Holocene.

A recent study by Maksic et al., (2018) assess the climate conditions and

vegetation shifts along the Holocene through numerical simulations with an

atmospheric model coupled with surface model. Simulations show that the tropical

Atlantic SST is the most important control on the Amazonian precipitation, rather than

the orbital forcing. They show reduced intensity of the SAMS during the Holocene

and gradual intensification the towards the present. Moreover, the results show that

vegetation changes follow moisture availability, mainly controlled by the SAMS, in an

heterogenous way across the Amazon. Thus, rainforest persisted in the western

Amazon throughout the Holocene, even with reduced precipitation compared with

present. At the same time, there was an expansion of savanna and seasonal forest in

29

the eastern Amazon, and fluctuations between different biomes in the ecotone areas,

as shown in Figure 7.

Figure 7. Vegetation distribution map for different periods of the Holocene, from numerical simulation. The model used from the Center for Weather Prediction and Climate Studies (CPTEC) is the Potential Vegetation Model version 2 (CPTEC-PVM2). Two experiments were performed for the 6K period, one with present SST (6k-pvm veg type) and the other with mid Holocene SST (6k-pvm-MH-sst veg type).

Source: MAKSIC et al. (2018).

3.4. WATER STABLE ISOTOPOLOGUES IN THE WATER CYCLE

Water stable isotopologues are widely used as tracers in the hydrologic cycle.

Their concentrations are given by the ratios 2H/1H and18O/16O and expressed in δ

notation with respect to a global standard value as δ=(Rsample/Rstandard)-1 (CRAIG,

1961). The accepted standard is Vienna Standard Mean Ocean Water (VSMOW).

Because relative differences between the isotopic ratios are small, δ values are

expressed in parts per thousand (‰).

In the hydrologic cycle, isotope fractionation occurs with transitions from one

physical state to another and transport processes. Most of the fractionation in the

hydrologic cycle is mass-dependent and is of two types: thermodynamic (when the

system is equilibrium) and kinetic (one-way reaction).The fractionation rate is given

by the fractionation factor α, which in turn is defined by the equilibrium constant of the

30

reaction (K) and diminishes exponentially with increasing temperature (MELANDER,

1960).

In the tropical region, Rayleigh distillation is a major driver of the isotopic

composition of precipitation over continental areas. Rayleigh distillation is the

process by which a moist air parcel progressively releases heavy isotopes through

precipitation, as it flows over the continent, leaving the remnant vapor isotopically

lighter (Figure 8). In the Amazon basin, the heavy isotope loss is compensated by

recharge from land evapotranspiration, isotopically heavier than the atmospheric

vapor originally present.

Figure 8. Simplified Rayleigh distillation scheme in the water cycle.

Source:

http://www.geo.cornell.edu/geology/classes/Geo656/656notes11/IsotopeGeochemistryChapter8.pdf

3.5. DEUTERIUM EXCESS: A PROXY FOR RECYCLED MOISTURE?

Another approach is based on the isotopic composition of rain, specifically

deuterium excess (dxs = δD – 8 × δ18O) (DANSGAARD, 1964), which is a second

order parameter, specifically sensitive to the conditions during evaporation from the

source (MERLIVAT; JOUZEL, 1979; JOHNSEN et al., 1989; PFAHL; WERNLI,

2008). Physically, dxs reflects the slower movement of the H218O molecule during

diffusion, leading to a relative enrichment of the H2HO molecules in the phase with

weaker bonds (e.g.: gas phase during evaporation). This slower movement could

lead to measurable differences when there is not enough time for the phases to

reach isotopic equilibrium. During evaporation, kinetic conditions result from a strong

relative humidity gradient over the water surface and advection before reaching

equilibrium between the two phases (PFAHL; SODEMANN, 2014).

31

In one hand, the main application for dxs is the identification of oceanic

moisture sources. For instance, Araguas-Araguas et al. (1998) found a difference

around 5‰ between dxs values of rain in Southeast Asia associated to moisture

fluxes from the Pacific and Indian monsoons. Moreover, Masson-Delmotte al., (2005)

identified southward shifts in Greenland oceanic moisture source during cold events,

based on the relation between dxs and SST at millennial time scale.

In the other hand, observations over the continent point that dxs is related to

moisture recycling across the forest (SALATI et al., 1979). In fact, several studies

have used dxs to estimate the fraction of recycled moisture (e.g. KONG et al., 2012).

However, for practical reasons, only few models consider vegetation effect in its

simulations of the water cycle (LAI; EHLERINGER; 2010). Although there are

significant advances in the understanding of dxs, it is still necessary to clarify what if

implies for the water cycle in tropical regions.

3.6. SPELEOTHEMS AND FLUID INCLUSIONS

Speleothems present several advantages that make them remarkable tools in

paleo-climate studies. For instance, they form in stable cave environments, are

geographically widely distributed and can be precisely dated by U-series mass

spectrometry. As rainfall infiltrates the soil and epikarst, it uptakes trace elements and

dissolved calcite that will later form the speleothems through dripping and calcite

precipitation in the cave environment. Thereby, the isotopic composition of rainfall

incorporates in the calcite as the speleothem grows. Oxygen and carbon isotope

records from speleothem calcite (δ18Ocalcite, δ13Ccalcite) have been extensively used in

Paleoclimate and paleoenvironmental reconstruction of tropical areas (e.g. BURNS

et al., 2001; GRIFFITHS et al., 2010; CHENG et al., 2013; APAÉSTEGUI et al., 2014

WANG et al., 2017).

Another technique to work with speleothems is the analysis of the fossil drip

water trapped in the speleothem calcite as “fluid inclusions”, as shown in Figure 9.

The advantage of fluid inclusions is that they provide δ18O and δD composition of the

dripping water that formed the stalagmite, providing a unique opportunity to access

the past rainfall water. Since drip water isotopic composition is believed to reflect that

of rainfall recharging the cave aquifer, fluid inclusions in stalagmites provide temporal

records of rainfall isotope variations which can be related to changing rainfall patterns

32

through time (MCDERMOTT et al., 2006; VONHOF et al., 2007). Fluid inclusion

analysis of δD (in combination with corresponding δ18Ocalcite) has been used to

reconstruct paleo-temperatures (VAN BREUKELEN et al., 2008) and changes in the

source and amount of rainfall (e.g. FLEITMANN et al., 2003b; HARMON et al., 1979;

MCGARRY et al., 2004; SCHWARCZ et al., 1976).

Fluid inclusion analysis also offer the possibility to explore past dxs variability

which, as mentioned above, was suggested as a moisture recycling tracer over the

Amazon, although this interpretation has not yet been tested. Therefore, this thesis

work will attempt to interpret this isotopic tracer in fluid inclusions along the Holocene

in the northwestern Amazon.

Figure 9. Images of typical fluid inclusions in the speleothems analyzed in VAN BREUKELEN et al., 2008.

Source: VAN BREUKELEN et al., 2008

3.7. MONITORING SITE

As part of the paleoclimate reconstructions in South America, van Breukelen

et al. (2008) analyzed fluid inclusions in two precisely dated stalagmites, covering the

Holocene. These stalagmites were collected in the Tigre Perdido cave (Rioja, San

Martín, Perú), located in northeast Perú at ~1000 m.a.s.l., in the transition region

between the Amazon lowlands and the Andes. In the same region, near the Palestina

cave (5.92°S, 77.35°W, 870 m. a. s. l.), a rain collector for was placed in 2011 by

Apaéstegui et al. (2014) and reactivated for the present study. The location of the

monitoring site is presented in Figure 10.

The present-day climate is tropical humid with a mean annual temperature of

22.8°C and mean precipitation of ~1570 mm year-1, measured at the meteorological

33

station of Rioja (APAÉSTEGUI et al., 2014), located 15 KM from the Palestina

station. Precipitation regime features a long, wet season (precipitation higher than

100 mm/month) from September to May, with peaks in November and March (Figure

10).

Figure 10. Location of the Palestina station and precipitation climatology. Dark blue shadows represent the Andes mountain range and the green line, the limit of the Amazon basin taken from the HyBAM data base. Precipitation at the Palestina station was obtained from TRMM 3B42 from 1998 to 2018.

Source: AMPUERO, 2019

J F M A M J J A S O N D0

50

100

150

200

250

Local pre

cip

itation (

mm

month

-1)

34

4. MATERIALS AND METHODS

4.1. ISOTOPE MONITORING

The monitoring was made in three stages at the Palestina station. The first

stage of the monitoring goes from 06/2012 to 09/2013 by Apaéstegui et al. (2014).

The second and third stages on the monitoring were performed in the frame mark of

this study and go from 10/2013 to 05/2014 and from 10/2016 to 05/2018. To fill the

gap from July to September 2016, data from the Pomacochas station (5.84°S,

77.97°W, 2257 m.a.s.l.) was used, only for the analysis on interannual timestep.

Although there is a significant height difference, the stations are located close to

each other and the records are remarkably similar (Figure 11).

Figure 11. Records of δ18O (upper panel) and dxs (lower panel) from the stations Palestina (black) and Pomacochas (red) on biweekly timestep.

-20

-16

-12

-8

-4

0

18O

(‰

, V

SM

OW

)

Palestina

Pomacochas

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/05

/201

6

1/08

/201

6

1/11

/201

6

1/02

/201

7

1/05

/201

7

1/08

/201

7

1/11

/201

7

1/02

/201

8

1/05

/201

8

0

4

8

12

16

20

dxs (

‰, V

SM

OW

)

Source: AMPUERO, 2019.

The rainwater sampling followed the guidelines given by the Global Network of

Isotopes in Precipitation (GNIP) from the International Atomic Energy Agency (IAEA).

A tube-dip-in-water collector with pressure equilibration was used (Figure 12). This

35

collector works as follows: Rain enters the collector trough the funnel on top and

passes through a tube that goes to the bottom of the container. As water fills the

collector, only the area inside the tube is exposed, which limits evaporation. Pressure

equilibration with the atmosphere is attained with a 2 mm hole on top of the

container. The hole is connected to a 6 m long hose, long enough to avoid

exchanges with the atmosphere. This system permits accumulation and preservation

of rain fallen within the sampling period. The samples were taken twice a month

using 8mL HDPE and amber glass bottles.

Figure 12. Scheme of the rain collector used in this study.

Source: Adapted from the GNIP manual.

Water analysis were performed at the CEPAS - Centro de Pesquisas de

Águas Subterrâneas at the University of São Paulo (IGc-USP), using a PICARRO

L2130i water analyzer and processed by LIMS for Lasers software. Values are

reported with an analytical precision of 0.09‰ for δ18O and 0.9‰ for δD relative to

Vienna Standard Mean Ocean Water (VSMOW). In total values from 86 samples

were obtained and used in this study.

4.2. CLIMATE DATA

We used satellite derived precipitation from two products to cover the

monitoring period. Precipitation data for the period from 05/2012 to 03/2014 is from

the Tropical Rainfall Measuring Mission (TRMM; http://trmm.gsfc.nasa.gov/) multi-

satellite3B42 V7 daily product, available at 0.25° x 0.25° resolution. The 3B42

http://trmm.gsfc.nasa.gov/

36

product combines data from TRMM and other satellites to estimate precipitation

(HUFFMAN et al., 2007). In the beginning of April 2014, the instruments on TRMM

were turned off, so another precipitation product was necessary to complete the

monitoring period. For this reason, precipitation data for the period from 04/2014 to

05/2018 from the Integrated Multi-satellitE Retrievals for GPM (IMERG)(GPM;

https://pmm.nasa.gov/data-access/downloads/gpm) was used. Daily data is available

in 0.1º x 0.1º resolution with full coverage over 60ºN - 60ºS. GPM data was upscaled

spatially applying linear interpolation in order to match TRMM resolution (from 0.1° to

0.25°).

Data on Leaf Area Index (LAI) was used to represent the structure of the forest

canopy. In general, LAI is a dimensionless variable that refers to the area of

photosynthetic tissue per unit ground surface area. However, the exact definition

depends on the technique used to measure it (JONCKHEERE et al., 2004). In this

study, LAI is derived from measurements made by the Moderate Resolution Imaging

Spectroradiometer (MODIS), from the National Aeronautics and Space

Administration’s (NASA). Therefore, in LAI is defined as the one-sided green leaf

area per unit ground area in broadleaf canopies. The product used is MCD15A2H

version 6 MODIS Level 4 with 500 m pixel size

(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd15a2h_

v006) (MYNENI et al., 2015). The algorithm chooses the best pixel available from all

the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua

satellites. The retrievals are obtained every 8 days, by averaging the best quality

values. For this study, we minimized biases by screening out pixels produced with

low confidence using the quality layer included in the product. Specifically, pixels

where bits from 5 to 7 were 011 and 100 were ruled out. Details on the bit coding are

presented in Table 1. The accepted pixels were spatially interpolated to a 0.25° grid.

There is still debate about the interpretation of LAI regarding the apparent

greening in the Amazon during the dry season. Morton et al. (2014) argues that the

apparent greening observed in optical remote sensing data is an artifact of variations

in the sun-sensor geometry and concludes that moisture availability governs

photosynthetic activity in the Amazon. However, evidence that includes modeling and

field observations appoint that, in the Amazon, LAI seasonality identified with MODIS

is real, but the amplitude of the seasonal changes might not yet be precise

https://pmm.nasa.gov/data-access/downloads/gpm

https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd15a2h_v006

https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd15a2h_v006

37

(SALESKA et al., 2016). These evidences support that sunlight is the limiting control

on photosynthetic activity in the Amazon. A comprehensive analysis of LAI is

reported in Myneni et al. (2007) where all the biases are analyzed, showing the

consistency of the database and appointing a good relationship with

evapotranspiration.

Table 1. Values of the quality layer for LAI/FPAR (8bit) in MODIS.

Source: MODISCollection6(C6) LAI/FPAR Product User’s Guide.

Precipitation recycling (PR) was calculated using the Eulerian atmospheric

moisture tracking model WAM-2layers (Water Accounting Model – two layers)

version 2.4.8 (VAN DER ENT, 2014). The model was run with precipitation, land

evaporation and wind data from the European Centre for Medium-Range Weather

Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) (DEE et al., 2011), on a 1.5°

grid. The the first year from the results was omitted due to model spin-up. The result

in each grid cell is the ratio of precipitation that originates from land evaporation.

38

Data on Vertically Integrated Divergence of the Moisture Flux (VIDMF) from

ERA Interim was used for complementary analysis. This variable is computed with

the specific humidity (q), which is a measure of the atmospheric moisture, and the

orthogonal components of the wind field (u and v), integrated from 1000 hPa to 300

hPa levels in the atmosphere. VIDMF is often used to identify atmospheric moisture

sources and sinks (e.g. DRUMOND et al., 2014), so that the positive values point

moisture sources or surface evaporation regions, and the negative values, moisture

sinks or precipitation regions.

4.3. PALINOLOGYCAL RECORDS

A rain forest index was calculated based on available pollen frequency records

from five lakes in the eastern and western edges of the Amazon: Iriri, Altamira - PA

(SANTOS, 2019); Ilha Arapujá, Altamira - PA (SANTOS, 2019); Laguna Chochos,

Peru (BUSH et al. 2005); Saci, central-south Amazon (FONTES et al., 2017) and

Serra Sul Carajás Lake, CSS2 (ABSY et al., 1991; SIFEDDINE et a., 2001). The

records are shown in Figure 28 and the geographic location of the lakes in shown in

Figure 25. The records were interpolated in time in order to calculate the average

(Figure 27).

4.4. METHODS

Local precipitation on daily time step was estimated based on TRMM and

GPM products. To obtain the local precipitation values, the average of 25 tiles was

calculated, considering the tile over the Palestina station in the center and the

adjacent tiles around it.

Air parcel back-trajectories were modeled with the HYbrid Single-Parcel

Lagrangian Integrated Trajectory 4 (HySPLIT 4) model (STEIN et al., 2015; ROLPH

et al., 2017). The model was run with wind fields from Era Interim on native spatial

resolution of 0.75°. Trajectories were tracked back in time for seven days. As ten

days is the mean residence time of water vapor in the atmosphere (NUMAGUTI,

1999), it was considered that seven-day back trajectories cover an air mass pathway

back to the point of the last saturation, hence, the relevant isotopic fractionation

processes along the atmospheric moisture transport (HURLEY et al., 2012). Back-

trajectories were initiated at the Palestina station at 500, 1000, 1500 and 2000

39

meters above ground level (m.a.g.l.), representing the boundary layer and the lower

troposphere, where the main moisture transport to our study site takes place. Back-

trajectories were computed at 0, 6, 12 and 18 UTM from May 2012 to May 2018.

All the climate data (precipitation, LAI and PR) were summed up along the

path of the back-trajectories, excluding the grid cells over the ocean. The schematic

representation of the selection of tiles along the back-trajectories is presented in

Figure 13. Thereby, the daily indices of degree of rainout upstream (DRU),

accumulated LAI upstream and Precipitation recycling upstream (PR) were

computed. The resulting units for DRU are mm traj-1 while LAI and PR are

dimensionless. Similar approach has been used in other studies (FIORELLA et al.,

2015; BAKER et al., 2016).

Figure 13. Schematic representation of one back-trajectory (black arrow). The back-trajectory is divided in seven segments, each one covering one day. The beginning of the back-trajectory is marked with a star at the Palestina station on the most recent day (0), and then progresses back in time until completing 7 days (-7). Climate data on precipitation, LAI and PR was selected from the tiles spanned by the back-trajectory on the corresponding day. In this example, on the sixth day (between -5 and -6), the trajectory goes over the ocean (blue shadow), so information on those tiles is excluded. The dashed line represents the coast line.


Daily indexes were averaged across the four initialization times and vertical

levels mentioned above. To better assess the effect of these indexes in the isotopic

signature, all values were weighted by local precipitation on bi-weekly timestep,

which is the frequency of the water isotopologue sampling. This approach permits us

to better estimate the extent by which the indices affect the isotope values of rainfall

at the monitoring site. For instance, by weighting DRU by the local precipitation, a

high value of DRU associated with an event of reduced local precipitation will have a

minor importance given that it corresponds to a small fraction of the total water that

reached the study site. Hence, this approach allows us to estimate how much of the

40

isotopic signals can be related to DRU, LAI and PR of our monitoring site. From here

on, DRU, LAI and PR will be used to refer to the bi-week weighted values of the

accumulated data along back-trajectories. Moreover, as we know that our isotopic

composition data and calculated parameters (DRU, LAI and PR), exhibit a seasonal

cycle, seasonality was removed by means of calculating anomalies with respect to

the monitoring period from June 2012 to May 2018. For example, within that period,

there are six values of each calculated parameter for the 1st of July and four values

of the isotopic composition due to the monitoring gaps. The average of those six or

four values, corresponding to the same day and month every year, was calculated for

each parameter and then subtracted from each value to get the anomalies.

To assess atmospheric transport variability, cluster analysis of the back-

trajectories was performed with the package included in HySPLIT4. This cluster

analysis technique is more accurate than the monthly clustering because low-level

large-scale circulation in the Amazon vary on intra-seasonal time scale (PACCINI et

al., 2017). This means that, although a circulation pattern may be more frequent

during one season, it can appear during other periods of the year as well. Thereby,

with the cluster analysis it is possible to group and visualize the back-trajectories

more clearly than with the monthly approach. Moreover, as part of the clustering

process, each back-trajectory is tagged with a cluster number, which permits to

calculate the frequency of occurrence of each cluster. It is also possible to relate

each trajectory with the amount of local rainfall that fell on the starting day of the

back-trajectory. Based on this association, the amount of local rainfall that co-occur

with each cluster was estimated. This study presents only clusters of back-

trajectories initiated at 1500 m.a.g.l. at 12:00 UTM, as considering other heights and

time lead to similar results.

41

5. RESULTS

5.1. PRECIPITATION AND BACK-TRAJECTORY ANALYSIS

Local precipitation accounts for ~1570 mmyear-1 and corresponds to a tropical

regime with a marked peak in March and dry season from July to August

(precipitation <100 mm month-1). Because dry season precipitation is significant at

our site, it is important to consider its influence on the mean annual δ18O and dxs

composition of rainfall.

Trade winds dominate large-scale atmospheric transport, varying from

northeast during austral summer to southeast during austral winter. Eleven clusters

of back-trajectories summarize the atmospheric transport (Figure 14). Air masses

crossing the Amazon basin (clusters 2, 3, 4, 6 and 7) are associated with 40% of

annual rainfall at the collecting site, while 34% result from northeast trajectories

(clusters 1, 5 and 11) and 14.5% from short-length trajectories (cluster 8) that appear

to be related to regional-scale transport. Although trajectories originating over the

Pacific Ocean (cluster 10) account for 8.5% of the annual rainfall, it is unlikely that

those air masses transport moisture across the Andes. Instead, associated

precipitation likely results from local moisture. Precipitation associated with

extratropical cold fronts, known to significantly affect precipitation in the south-central

Peruvian Andes (HURLEY et al., 2015) are associated with the remaining 3% of local

rainfall (cluster 9).

Based on daily data from back-trajectories and precipitation it is possible to

compute the degree of rainout upstream (DRU) associated with each rain event. In

general, the DRU annual cycle is similar with the one of local precipitation, although

there is an offset from March to July (Figure 15). The differences become larger on

shorter time scales; in fact, at biweekly time steps, DRU and local precipitation are

correlated with r=0.51, p<0.01. Those differences arise because DRU integrates

areas with different precipitation regimes across the continent, while local

precipitation reflects only the local regime.

42

Figure 14. Clusters of seven-day back trajectories at 1500 m.a.g.l. from June 2012 to May 2018.The bars under each map show the frequency of back-trajectory clusters per month.

(Continue)

J F MA M J J A S ON D

0

10

20

30

40

50 C1


0

10

20

30

40

50 C2


0

10

20

30

40

50 C3


0

10

20

30

40

50 C4


0

10

20

30

40

50 C5


0

10

20

30

40

50 C6


43

Figure 14. Clusters of seven-day back trajectories at 1500 m.a.g.l. from June 2012 to May 2018.The bars under each map show the frequency of back-trajectory clusters per month.

(End)


0

10

20

30

40

50 C7


0

10

20

30

40

50 C8


0

10

20

30

40

50 C9


0

10

20

30

40

50 C10


0

10

20

30

40

50 C11


44

Figure 15. Annual cycle of the degree of rainout upstream (DRU) and local precipitation from 2012 to 2018. DRU is the accumulated precipitation along the back-trajectories that initiate on precipitation days at the Palestina station. Local precipitation is calculated from TRMM3B42 and GPM by averaging the nearest tiles to the Palestina station.


200

400

600

800

1000

1200

1400D

RU

(m

m tra

j-1

)

0

50

100

150

200

250

Lo

ca

l p

recip

ita

tio

n (

mm

mo

nth

-1)


5.2. FOREST MOISTURE FLUXES

Accumulated Leaf area Index values along the back-trajectories (LAI) present

the lowest values in February and increase continuously until reaching a peak around

August and September (Figure 16). Although cloud cover during the rainy season

might lead to lower than expected LAI, leading to a negative relationship with

precipitation, LAI is not consistently correlated with DRU. This could be related to the

fact that the main moisture transport to our monitoring site does not consistently

cross the region of most intense convection upstream. For instance, during austral

summer, strongest convection locates over the central and southern part of the

Amazon basin, while advection to our site occurs farther north. The opposite is true

during austral winter. These observations, derived from the back-trajectory approach,

suggest that LAI in the back-trajectories pathways to the monitoring site in this study

is not significantly affected by cloud cover.

45

Figure 16. Annual cycle of the degree of rainout upstream (DRU), Leaf area index on the back-trajectories (LAI) and Precipitation Recycling on the back-trajectories (PR) for the period from June 2012 to May 2018.


200

400

600

800

1000

1200

1400

DR

U (

mm

tra

j-1

)

0

200

400

600

800

1000

1200

LA

I

3

4

5

6

7

PR


Moisture recycling along trajectories (PR) was also analyzed. It is worth noting

that PR display similar annual cycle with LAI from March to December, and similar

with DRU from January to April (Figure 16). Based in this observation, it appears that

evapotranspiration is the most important control on PR during the dry season and the

beginning of the wet season and precipitation is most important during the end of the

wet season. Hence, it makes sense that the lowest PR values appear during austral

summer and the higher values at the end of the dry season. It is worth noting that the

most intense precipitation recycling rates are in the western and southern Amazon

and are higher from June to November (Figure 17).

Analogously, during austral summer, the main moisture influx comes from

tropical north Atlantic (DRUMOND et al., 2014), consistent with the highest frequency

of north east back-trajectories observed during austral that period. Moreover, during

austral winter, evapotranspiration in the central, south and eastern Amazon is

maximum (DA ROCHA et al. 2009), concomitant the prevalence of south-easterly

back-trajectories (Figure 18).

46

Figure 17. Precipitation recycling (PR) computed with WAM2lyr and data from ERA Interim. a) Mean annual PR. b-e) Seasonal anomalies.

Source: AMPUERO 2019

Figure 18. The maps show mean moisture recycling (PR) per season in percentage (%). Only values in the dominant back-trajectory areas are shown. Panels under the maps show the meridional average PR. Clusters 1, 5 and 11 are frequent during austral summer; clusters 2 and 3, during austral autumn and winter; and clusters 6 and 7, during austral winter.


a)

d) e)

b) c)

47

5.3. ISOTOPE MONITORING

The isotopic composition of the local precipitation fits well with the Global

Meteoric Water Line (GMWL) (Figure 19), presenting a slope of 8.40 and an intercept

(dxs) of16.74. The δ18O values range from -18‰ to 0‰, with the most depleted

values in March-April-May. Dxs values range from 8.4‰ to 20.4‰ with the highest

values in July-August-September and higher seasonal amplitude than its analytical

uncertainty (0.94‰). During the study period, dxs and δ18O show a positive

correlation (r=0.50, p<0.01, Figure 20). dxs increases with δ18O at about

∂dxs/∂δ18O=0.40, on two-week time step. This slope is high compared with the global

average, although that value is based on long term annual averages from GNIP

stations (0.1-0.2) (FROEHLICH et al., 2002). It also appears that the relation

between dxs and δ18O changes with the seasons, with the best covariation during the

austral winter months (Figure 21).

Figure 19. Biweekly δ18O andδD of precipitation at the Palestina station. The GMWL is represented by a solid red line and deviations of ±5‰ are represented with dashed lines.

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2

-140

-120

-100

-80

-60

-40

-20

0

20

D

(‰

, V

SM

OW

)

18

O (‰, VSMOW)

Palestina station

GMWL


48

Figure 20. Correlation between δ18O and dxs of precipitation at the Palestina station on biweekly timestep.

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4

6

8

10

12

14

16

18

20

22

24

dxs (

‰, V

SM

OW

)

18

O (‰, VSMOW)

r = 0.50

p < 0.01


Figure 21. Correlation between of δ18O and dxs of precipitation at the Palestina station on biweekly timestep for the seasons (a)JJA, (b)SON, (c)DJF and (d)MAM.

-20 -16 -12 -8 -4 0

8

12

16

20

dxs

18

O

dxs=0.41*18

O+17.78

R=0.48

-20 -16 -12 -8 -4 0

8

12

16

20

a)

c)

b)dxs=0.24*

18O+16.36

R= 0.13

dxs

18

O

a)

-20 -16 -12 -8 -4 0

8

12

16

20

d)dxs=0.22*

18O+14.90

R=0.03

dxs

18

O

-20 -16 -12 -8 -4 0

8

12

16

20 dxs=0.24*18

O+14.60

R=0.30

dxs

18

O


Although δ18O and dxs are correlated, significant differences arise in

response to distinct land-atmosphere processes. Table 2 summarizes the linear

49

correlation coefficients (r-values) between the isotope values of δ18O and dxs with

the local precipitation at the monitoring site and calculated parameters in the back-

trajectories (DRU, LAI and PR). Linear correlations were calculated directly with the

original data and with data with the seasonality removed by means of calculating

anomalies. Timeseries for local precipitation, DRU and LAI are shown in Figure 22. It

is important to make this distinction because the parameters used follow seasonal

cycles that may induce apparent correlations which do not represent causality

relations.

The main control on δ18O is the precipitation upstream the monitoring site.

When the complete original records are considered, the best correlation is with DRU

(r=-56, p<0.05), followed by the local precipitation (r=-0.31, p<0.05). δ18O also

presents a positive correlation with LAI (r=0.29, p<0.05), possibly related to the fact

that higher LAI values in the Amazon represent more leaf area, with the potential to

transpire more water from the soil that is enriched in heavy isotopes compared with

the atmospheric vapor. However, when seasonality is removed, only DRU is

significantly correlated with δ18O (r=-0.21, p<0.10). This result confirms that DRU,

which represents the air mass precipitation history, is the most accurate metric of the

physical processes affecting the isotopic fractionation compared with the other

parameters evaluated.

The analysis of the original records of dxs shows the best positive correlation

with LAI (r=0.43, p<0.05) and weaker but still positive correlation with PR (r=0.31,

p<0.05). There are also negative correlations with precipitation-based parameters,

but these are weaker than with LAI. However, when seasonality is removed, only PR

exhibit a significant correlation with dxs (r=0.28, p<0.05).

Correlations during the wet (ONDJFMAM) and the extended dry season

(JJAS) were also calculated separately and are presented in Table 3. During the dry

season, strong correlation between δ18O and DRU based on the original data (r=-

0.81, p<0.05) an on the data with seasonality removed (r=-0.67, p<0.05) were found.

Weaker correlations than with DRU, but still significant, were found with local

precipitation based on the original data (r=-0.40, p<0.10) and with removed

seasonality (r=-0.39, p<0.10). Significant correlation with PR only appears in the

original data (r=0.41, p<0.05). Moreover, during the same period, only LAI and PR

presented significant correlations with dxs in both the original data (both r=0.57,

50

p<0.05) and when seasonality is removed (r=0.49, p<0.05 and r=0.71, p<0.05

respectively). During the wet season significant correlations only appear when the

original data is considered; both δ18O and dxs are correlated with DRU (r=-0.44 and

r=-0.29, p<0.05 respectively).

Since the focus of this study is dxs, the interannual variability was assessed,

focusing on the most relevant parameters (LAI and PR) related with dxs, and

separating the wet and dry seasons as shown in Figure 23. Although there are only

four values per year representing the seasonal averages, it is worth noting the

consistent positive relations during the dry season.

Table 2.Linear correlation coefficient (r) and p-value between isotope records and potential climatic controls based on biweekly data. The r-values in bold (if p<0.05) and in italics (if p<0.10).

Original data Seasonality removed

δ18O Dxs δ18O dxs

Local precipitation -0.31 -0.20 -0.10 -0.01

DRU -0.56 -0.38 -0.21 -0.18

LAI 0.29 0.43 0.06 0.15

PR 0.16 0.31 0.04 0.28


Table 3.As in Table 1, but for dry months (JJAS) and wet months (ONDJFMAM). The first two weeks of October correspond to the dry season and the last two, to the wet season.

Original data Seasonality removed

δ18O dxs δ18O dxs

Dry

Local precipitation -0.40 -0.18 -0.39 -0.33

DRU -0.81 -0.36 -0.67 -0.34

LAI 0.22 0.57 -0.14 0.49

PR 0.41 0.57 0.07 0.71

Wet

Local precipitation -0.12 0.01 -0.05 0.05

DRU -0.44 -0.29 -0.10 -0.14

LAI -0.13 0.05 0.10 0.08

PR -0.06 0.10 0.03 0.19


51

Figure 22. Panels on the left show the original records at the Paletina station and the ones in the right, the records without seasonality (anomalies). (a) and (b) show δ18O of precipitation and local precipitation accumulated along the water sampling period. (c) and (d) show δ18O of precipitation and calculated degree of rainout upstream (DRU), weighted by local precipitation for the water sampling period. (e) and (f) show deuterium excess (dxs) of precipitation and Leaf area index accumulated along the back-trajectories (LAI). LAI was weighted by local precipitation for the water sampling period.

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

0

-5

-10

-15

-20

18O

(‰

, V

SM

OW

)

a)

0

50

100

150

200

Lo

ca

l pre

cip

itatio

n (m

m)

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

10

5

0

-5

-10

Local p

recip

itatio

n (m

m)

18O

(‰

, V

SM

OW

)

b)

-100

-50

0

50

100

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

0

-5

-10

-15

-20

18O

(‰

, V

SM

OW

)

0

500

1000

1500

2000

2500

3000

3500

DR

U

c)

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

10

5

0

-5

-10

18O

(‰

, V

SM

OW

)

d)

-2000

-1500

-1000

-500

0

500

1000

1500

2000

DR

U

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

8

12

16

20

dxs (

‰,

VS

MO

W)

0

200

400

600

800

1000

1200

1400

LA

I

e)

1/05

/201

2

1/08

/201

2

1/11

/201

2

1/02

/201

3

1/05

/201

3

1/08

/201

3

1/11

/201

3

1/02

/201

4

1/05

/201

4

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

-6

-4

-2

0

2

4

6

dxs (

‰,

VS

MO

W)

f)

-400

-200

0

200

400

LA

I


52

Figure 23. Time series of dxs, PR in the upper panels and LAI in the lower panels. Lines represent the series in biweekly time step dots, the seasonal averages for (a,c) wet season and (b,d) extended dry season. The gray shadow indicates the period filled with dxs data from Pomacochas station.

1/06

/201

2

1/09

/201

2

1/12

/201

2

1/03

/201

3

1/06

/201

3

1/09

/201

3

1/12

/201

3

1/03

/201

4

1/06

/201

4

1/09

/201

4

1/12

/201

4

1/03

/201

5

1/06

/201

5

1/09

/201

5

1/12

/201

5

1/03

/201

6

1/06

/201

6

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

1/09

/201

8

1/12

/201

8

6

8

10

12

14

16

18

20

22

Annual mean: r = 0.77

15 days sampling: r = 0.13

dxs

Mean annual dxs

Precipitation Recylcing (PR)

Mean annual PR

dxs (

‰)

Wet season (ONDJFMAM)

0

1

2

3

4

5

6

7

8

9

Pre

cip

itatio

n R

ecylc

ing (P

R)

a)

1/12

/201

1

1/03

/201

2

1/06

/201

2

1/09

/201

2

1/12

/201

2

1/03

/201

3

1/06

/201

3

1/09

/201

3

1/12

/201

3

1/03

/201

4

1/06

/201

4

1/09

/201

4

1/12

/201

4

1/03

/201

5

1/06

/201

5

1/09

/201

5

1/12

/201

5

1/03

/201

6

1/06

/201

6

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

10

12

14

16

18

20

22



dxs

Mean annual dxs

Precipitation Recylcing (PR)

Mean annual PR

dxs (

‰)

Dry season (JJAS)

3

4

5

6

7

8

9

Pre

cip

itatio

n R

ecylc

ing

(PR

)

b)

1/01

/201

2

1/04

/201

2

1/07

/201

2

1/10

/201

2

1/01

/201

3

1/04

/201

3

1/07

/201

3

1/10

/201

3

1/01

/201

4

1/04

/201

4

1/07

/201

4

1/10

/201

4

1/01

/201

5

1/04

/201

5

1/07

/201

5

1/10

/201

5

1/01

/201

6

1/04

/201

6

1/07

/201

6

1/10

/201

6

1/01

/201

7

1/04

/201

7

1/07

/201

7

1/10

/201

7

1/01

/201

8

1/04

/201

8

1/07

/201

8

1/10

/201

8

1/01

/201

9

6

8

10

12

14

16

18

20

22

Annual mean: r = -0.56


dxs

Mean annual dxs

LAI

Mean annual LAI

dxs (

‰)

Wet season (ONDJFMAM)

200

400

600

800

1000

LA

I

c)

1/12

/201

1

1/03

/201

2

1/06

/201

2

1/09

/201

2

1/12

/201

2

1/03

/201

3

1/06

/201

3

1/09

/201

3

1/12

/201

3

1/03

/201

4

1/06

/201

4

1/09

/201

4

1/12

/201

4

1/03

/201

5

1/06

/201

5

1/09

/201

5

1/12

/201

5

1/03

/201

6

1/06

/201

6

1/09

/201

6

1/12

/201

6

1/03

/201

7

1/06

/201

7

1/09

/201

7

1/12

/201

7

1/03

/201

8

1/06

/201

8

1/09

/201

8

1/12

/201

8

10

12

14

16

18

20

22



dxs

Mean annual dxs

LAI

Mean annual LAId

xs (

‰)

Dry season (JJAS)

600

800

1000

1200

1400

LA

I

d)

Source: AMPUERO, 2019

53

6. DISCUSSION

The Amazon basin leaf area observations during our monitoring period (2012-

2018) show a consistent seasonality, presenting an increment of about 20% during

the dry season, relative to the wet season. These results are consistent with previous

findings (MYNENI et al., 2007). The forest leaf area exerts a strong control on

moisture exchange between the forest and the atmosphere, with the potential to

modulate the Rayleigh distillation of water isotopologues over the Amazon basin.

Indeed, the positive spatial correlation between LAI and the Vertically Integrated

Divergence of Moisture Flux (VIDMF) (Figure 24) suggests that the moisture input

from the forest to the atmosphere, increases with denser forest canopy. Moreover,

both LAI and PR on the back-trajectories present a consistent increase of about 140

% and 44 % respectively during the dry season, which might result from a

combination of larger routes over the continent and denser forest canopy.

Figure 24. Spatial correlation between LAI and VIDMF on monthly time step for the period from June 2012 to May 2018. Seasonality was removed from both datasets. Shadows show only significant grid cells (p<0.05).


6.1. CONTROLS OF δ18O AND DXS IN PRECIPITATION

The correlations of δ18O and dxs with the original calculated parameters

showed higher coefficients than the ones based on the data without seasonality. The

apparent high correlations might result in part from the similar seasonal cycles, rather

than from physical processes. However, the correlations found when seasonality is

54

removed are significant and consistent with the mechanisms documented in previous

studies.

The negative correlation between δ18O and DRU reflects the removal of heavy

isotopic species through precipitation during air mass transport as in a Rayleigh

model. The correlation with DRU is stronger than with local precipitation, suggesting

that at our monitoring site, a strong local influence on the isotopic composition of

rainwater can be ruled out. This result is consistent with previous studies from the

eastern flank of the Andes, where regional precipitation and large-scale atmospheric

transport are considered the main drivers on δ18O (VIMEUX et al., 2005; VILLACÍS et

al., 2008; INSEL et al., 2012; WINDHORST et al. 2013; FIORELLA et al., 2015;

SAMUELS-CROW et al. 2014; HURLEY et al. 2015). We also assume that below

clouds, secondary evaporation has a negligible effect in our study site, given that

frequent heavy rainfall, typical for tropical rain forests, would suppress that effect

(PENG et al. 2007). Meanwhile, dxs is best correlated with LAI along the air parcel

trajectories, however, the relationship is only significant when the seasonality is

included, while correlation with PR is significant in both series.

When wet and dry months are analyzed separately (Table 3) the time series

without seasonality do not show significant correlations with δ18O or dxs during the

wet months. From back trajectory analysis it is known that the atmospheric transport

from the tropical north Atlantic dominates during the wet season months and

precipitation is mostly of oceanic in origin. As shown in Figure 14, during austral

summer, the tropical North Atlantic pathways (Clusters 1, 5 and 11) feature the

lowest moisture recycling and contributes nearly 34% of the total annual precipitation

falling over the western Amazon basin. During this period, there is reduced re-supply

of moisture through evaporation, thereby, precipitation is not significantly affected by

land processes.

The opposite is true for the extended dry season. Advection across open

forest permits land-atmospheric interactions, including moisture recycling and large-

scale transport. In fact, correlations of dxs with LAI and PR are only significant during

the dry months. The positive relations of dry season dxs with LAI and PR are also

consistent on interannual timescale (Figure 23). These results, together with the high

moisture recycling observed, supports the idea of forest influence on dxs. Since LAI

is a good index for evapotranspiration in the Amazon basin (MYNENI et al., 2007), it

55

is possible to infer that high values of dxs imply an increased contribution of recycled

moisture from the forest to precipitation at the monitoring site. At the same time, the

high negative correlation between δ18O and DRU suggests that, despite the moisture

recharge from land to the atmosphere, the effect of Rayleigh distillation on the

isotopic composition of water is remarkable. It is worth noting that, even though

rainfall is enriched in heavy isotopes compared with the wet season, the fractionation

process related to precipitation upstream is still dominant. Indeed, the larger

exposure to vegetation during this season could favor both land-atmosphere

interactions and heavy isotope loss due to rainout upstream.

The analysis of the relation between δ18O and dxs points toward significant

seasonal differences (Figure 21). The relationship is best during JJA, which might be

explained by a more constant moisture source placed in the Amazon forest and more

stable atmospheric conditions compared with the wet season. In contrast, during

DJF, a combination of moisture sources from the North Atlantic and from the Amazon

forest might complicate the relation between δ18O and dxs, resulting in a weak

correlation, with large scatter of the values.

6.2. PALEOCLIMATE IMPLICATIONS

The anti-phased behavior observed in the Holocene δ18O records from

speleothems from the eastern and western edges of the Amazon basin (Figure 25)

point to a see-saw effect in the monsoon precipitation at orbital time scale. In one

hand, this pattern is consistent with the notion that variations in the intensity of the

Bolivian-High - Nordeste low system serve as the main driver of precipitation

variability in the Amazon basin at orbital time-scale (CHENG et al., 2013). On the

other hand, Wang et al. (2017) argue that plant transpiration may bias the

paleoclimate interpretation based on the degree of rainout upstream. In this sense,

the Holocene and glacial rainfall stable isotopic composition over the western

Amazon basin was largely modulated by the forest evapotranspiration, which buffer

the effect of Rayleigh fractionation.

56

Figure 25. Geographic location of the geochemical and palynological records referenced in this study.


The isotopic records, based on fluid inclusions of speleothems from the

western Amazon, provide important clues regarding the moisture flux pathways and

recycling processes. The calculated dxs of speleothem fluid inclusions from Tigre

Perdido cave, shows an antiphase behavior through the Holocene, when compared

with the δ18Ocalcite (VAN BREUKELEN et al., 2008). The antiphase response between

dxs and δ18Ocalcite suggests that other processes besides rainout upstream drove

changes in the isotopic composition variability during the Holocene. It is noteworthy

that the Holocene dxs variations are similar with variations in the difference between

δ18O from the western and eastern edges of the Amazon basin, δ18OTigrePerdido-Paraíso

(Figure 26).

A negative δ18OTigrePerdido-Paraíso implies more negative rainfall δ18O over the

western Amazon compared with its eastern border. Assuming a common moisture

flux pathway for both sites, the observed δ18Ocalcite gradient cannot be explained by

the Rayleigh fractionation processes alone. Indeed, if moisture reaching the western

Amazon basin were the same as the one that passes over the central Amazon, the

57

δ18Ocalcite values from the western border should always be a slightly more negative

than those from the eastern edge. Instead, the east and west edges show similar

isotopic compositions from the early to the mid- Holocene, followed by the increasing

of the east-west gradient towards the present.

Figure 26. Comparison between stable isotope record from speleothems from the western and eastern Amazon edges: a) dxs reconstruction from Tigre Perdido speleothem record (VAN BREUKELEN et al., 2008); b) Tigre Perdido - Paraíso δ18O; c) 20-yr interpolated δ18O record from Tigre Perdido cave speleothem (VAN BREUKELEN et al., 2008). The δ18O values from Tigre Perdido are corrected by 1.4 ‰ to account for temperature variations between the caves following the procedure of Wang et al. (2017); d) as in (c) 20-yr interpolated δ18O record from Paraíso cave speleothems (WANG et al., 2017).

-9

-8

-7

-6

-5

1

0

-1

-2

-3

-4

0 1 2 3 4 5 6 7 8 9 10 11

-9

-8

-7

-6

-5c)

d)

b)

Less

PR

Age (kyr BP)

Tig

re P

erd

ido

1

8O

(te

mpe

ratu

re c

orr

ecte

d)

More

PR

gradient

increase

Para

íso

18O

(‰

, VPD

B)

Tig

re P

erd

ido-P

ara

íso

18O

(‰

, VPD

B)

6

8

10

12

14

a)

Flu

id I

nclu

sio

n D

Excess (

‰, V

SM

OW

)


From the early to the mid- Holocene, dxs values are low compared with the

present, ranging from 7 to 10 ‰. The low values suggest reduced moisture recycling

along the atmospheric transport pathway, consistent with the notion of reduced forest

cover during this period. Since the forest is the main moisture source for present

winter precipitation, it is likely that from the early to the mid- Holocene, precipitation

was restricted to the wet season. This implies that the dominant atmospheric

transport was along northeast pathways, as in the present austral summer back-

trajectory clusters observed in this study. In this scenario, it is likely that parallel

58

atmospheric transport routes for Tigre Perdido and Paraiso were dominant, with

precipitation mostly of oceanic origin, resulting in similar isotopic compositions over

the western and eastern edges of the Amazon basin. Moreover, since these

atmospheric fluxes travel short distances over the continent until the precipitation

sites, they are subject to only limited Rayleigh fractionation.

As we move towards the late Holocene, δ18OTigrePerdido-Paraíso becomes more

negative as a result of more negative rainfall δ18O over the western Amazon,

concomitant with increased δ18O values in the eastern Amazon. This pattern

suggests enhanced Rayleigh distillation at that time. The dxs values from Tigre

Perdido follow the same trend (Figure 26), pointing to an increase in moisture

recycling associated with forest expansion, which allows for a greater contribution of

winter precipitation feed by forest evapotranspiration. The results are also consistent

with the establishment of a Rayleigh distillation mechanism as observed when

analyzing winter isotopic composition in present-day rainfall.

As shown in Figure 27, the increasing trend in dxs along the Holocene is

concomitant with the increased rainforest pollen frequency recorded in lakes from

western, south-central and eastern Amazon, supporting the notion of forest

expansion during the mid- and late Holocene (BUSH et al 2015; SANTOS, 2019;

FONTES et al., 2017; ABSY et al., 1991; SIFEDDINE et al., 2001). The complete

available palynological records referenced in this study is shown in Figure 28.

Furthermore, the dxs increase observed from about 6 KBP to present agrees with

numerical simulations of Amazon vegetation (MAKSIC et al., 2018), which show a

clear expansion of the evergreen vegetation in the Amazon basin towards the

present.

59

Figure 27. Comparison between calculated dxs from Tigre Perdido stalagmite (VAN BREUKELEN et al., 2008) with the integrated record of rainforest pollen frequency from five lakes: Iriri, Altamira - PA (SANTOS , 2019); Ilha Arapujá, Altamira - PA (SANTOS, 2019);Laguna Chochos, Peru (BUSH et al. 2005);Saci, central-south Amazon (FONTES et al., 2017) and Serra Sul Carajás Lake, CSS2 (ABSY et al., 1991; SIFEDDINE et a., 2001).

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-2

-1

0

1

2

Arboreal Pollen

Fluid Inclusion dxs

Age (kyr BP)

Arb

ore

al P

olle

n

6

7

8

9

10

11

12

13

14

Flu

id In

clu

sio

n D

Excess (‰

, VS

MO

W)


Figure 28. Normalized interpolated frequency of arboreal pollen from lakes used in this study and average value.


-3

-2

-1

0

1

2

3

4

5

0 2 4 6 8 10 12 14 16 18

Freq

uen

cy

Age (kyr BP)

Lago Iriri

Lago Arapujá

Lago Chochos

Serra Sul Carajas

Lago Saci

MEDIA

60

7. CONCLUSION

This study investigated the role of vegetation in driving changes in the rainfall

isotopic composition. The tropical North Atlantic and the Amazon forest are the main

moisture sources for precipitation at the monitoring site. Their moisture contributions

vary seasonally with air parcel transport patterns, identified by means of cluster

analysis. The main driver on the isotopic composition of precipitation is remote

precipitation upstream, along atmospheric transport pathways. Precipitation derived

from oceanic moisture with minimum exchange with surface evaporation has heavier

isotopic composition than precipitation originating from air parcels that travel over

land and are affected by DRU through the Rayleigh distillation mechanism.

Furthermore, dxs at the monitoring site is a good indicator of the remote contribution

to precipitation from forest evapotranspiration. Although the dry season isotopic

composition is heavier than the wet season and there is major moisture contribution

from the forest through evapotranspiration, there is a well stablished Rayleigh

distillation mechanism resulting from long travel distance over the continent. The

relationship of dxs with δ18O is sensitive variability of moisture sources, showing the

best coupling between them during austral winter, when the main moisture source is

the Amazon forest. Paleo-records show that during the early to the mid- Holocene,

the main moisture source to the monitoring site was located over the tropical North

Atlantic with limited moisture contribution from land due to the reduced forest cover at

the time. As a result, the water supply was limited during the dry season. After the

mid-Holocene, the contribution from winter precipitation gains importance as the

expansion of the forest permits more moisture recycling.

61

8. REFERENCES

ABSY, M.L.; CLEEF, A.M.; FOURNIER, M.; MARTIN, L.; SERVANT, M.; SIFEDDINE, A.; FERREIRA DASILVA, M.; SOUBIÈS, F.; SUGUIO, K.; TURCQ, B.; VAN DER HAMMEN, T., Mise en evidence de quatre phases d’ouverture de la foret dense dans le sudest de l'Amazonie au cours de 60,000 dernieres annees. Premiere comparaison avec dautres regions tropicales, C. R. Acad. Sci., v. 312, p. 673-678, 1991.

APAÉSTEGUI, J.; CRUZ, F.W.; SIFEDDINE, A.; VUILLE, M.; ESPINOZA, J.C.; GUYOT, J.L.; KHODRI, M.; STRIKIS, N.; SANTOS, R.V.; CHENG, H.; EDWARDS, L.; CARVALHO, E.; SANTINI, W., Hydroclimate variability of the northwestern Amazon Basin near the Andean foothills of Peru related to the South American Monsoon System during the last 1600 years, Clim. Past., v. 10, p. 1967–1981, 2014, doi: 10.5194/cp-10-1967-2014.

ARAGUAS-ARAGUAS, L.; FROEHLICH, K.; ROZANSKI, K., Stable isotope composition of precipitation over Southeast Asia. J. Geophys. Res., v. 103, n. d22, p. 721 – 724, 1998, doi: https://doi.org/10.1029/98JD02582.

BAKER, J.C.A.; GLOOR, M.; SPRACKLEN, D.V.; ARNOLD, S.R.; TINDALL, J.C.; CLERICI, S.J.; LENG, M.J.; BRIENEN R.J.W., What drives interannual variation in tree ring oxygen isotopes in the Amazon? Geophys. Res. Lett., v. 43, p. 11, 831–11, 840, 2016, doi: 10.1002/2016GL071507.

BARICHIVICH, J.; GLOOR, E.; PEYLIN, P.; BRIENEN, R.J.W.; SCHÖNGART, J.; ESPINOZA, J.C.; PATTNAYAK, K.C., Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation, Science Advances, p. 1-7, 2018, doi: 10.1126/sciadv.aat8785.

BEHLING, Hermann et al., Holocene environmental changes in the central amazon basin inferred from Lago Calado (Brazil), Palaeogeography, Palaeoclimatology, Palaeoecology, v. 173, p. 87–101, 2001, doi: 10.1016/S0031-0182(01)00321-2.

BEHLING, H., Carbon storage increases by major forest ecosystems in tropical South America since the Last Glacial Maximum and the early Holocene, Global and Planetary Change, v. 33, n. 1-2, p. 107–116, 2002, doi: https://doi.org/10.1016/S0921-8181(02)00065-6.

BOND, G. et al., A Pervasive Millennial-Scale Cycle in North Atlantic Holocene and Glacial Climates, Science, v. 278, n. 5341, p. 1257–1266, 1997, doi: 10.1126/science.278.5341.1257.

BOND, G. et al. Persistent Solar Influence on North Atlantic Climate During the Holocene. Science. v. 294, n. 5549, p. 2130-2136, 2001, doi: 10.1126/science.1065680.

62

BURNS, S.J. et al. Speleothem evidence from Oman for continental pluvial events during interglacial periods. Geological Society of America. v. 29, n. 7, p. 623-626, 2001, doi: 10.1130/0091-7613(2001)029<0623:SEFOFC>2.0.CO;2.

BUSH, M.B.; SILMAN, M.R.; LISTOPAD, C.M.C.S., A regional study of Holocene climate change and human occupation in Peruvian Amazonia, Journal of Biogeography, v. 34, p. 1342–1356, 2007.

CHEN, T.; WENG, S.; SCHUBERT, S., Maintenance of austral summertime upper-tropospheric circulation over tropical South America: The Bolivian high–Nordeste low system, J. Atmos. Sci., v. 56, p. 2081–2100, 1999, doi: https://doi.org/10.1175/1520-0469(1999)056<2081:MOASUT>2.0.CO;2.

CHENG, Hai et al., Climate change patterns in Amazonia and biodiversity, Nature communications, v. 4, p. 1-6, 2013, doi: 10.1038/ncomms2415.

COLINVAUX, P.A.; DE OLIVEIRA, P.E.; BUSH, M.B., Amazonian and neotropical plant communities on glacial time-scales: The failure of the aridity and refuge hypotheses, Quaternary Science Reviews, v. 19, n. 1–5, p. 141–169, 2000.

CORDEIRO, R.C. et al., Holocene fires in East Amazonia (Carajás), new evidences, chronology and relation with paleoclimate, Global and Planetary Change, v. 61, n. 1–2, p. 49–62, 2008, doi: https://doi.org/10.1016/j.gloplacha.2007.08.005.

CORDEIRO, R. C. et al., Biogeochemical indicators of environmental changes from 50 Ka to 10 Ka in a humid region of the Brazilian Amazon, Palaeogeography, Palaeoclimatology, Palaeoecology, v. 299, n. 3–4, p. 426–43C, 2011, doi: https://doi.org/10.1016/j.palaeo.2010.11.021.

CRAIG, H. Standard for Reporting Concentrations of Deuterium and Oxygen-18 in Natural Waters, Science, v. 133, p. 1833-1834,1961.

CRUZ, F.W.; VUILLE, M.; BURNS, S.J.; WANG, X.; CHENG, H.; WERNER, M.; EDWARDS, R.L.; KARMANN, I.; AULER, A.S.; NGUYEN, H., Orbitally driven east–west antiphasing of South American precipitation, Nat. Geosci, v. 2, n. 3, p. 210–214, 2009, doi: 10.1038/NGEO444.

DANSGAARD, W., Stable isotopes in precipitation, Tellus, v. 16, p. 436–468, 1964.

DA ROCHA, H. R.; GOULDEN, M.; MILLER, S.; MENTON, M.; PINTO, L.; FREITAS, H.; FIGUEIRAA. S., Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia, Ecol. Appl., v. 1 n. 4, p. 22–32, 2004, doi: https://doi.org/10.1890/02-6001.

DA ROCHA, H. R., MANZI A. O.; CABRAL, O. M.; MILLER, S. D.; GOULDEN, M. L.; SALESKA, S. R.; COUPE, N. R. et al., Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil, Journal of Geophysical Research, v. 114, n.G00B12, p. 1-8, 2009, doi: 10.1029/2007JG000640.

63

DEE, D. et al., The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., v. 137, p. 553–597, 2011, doi: 10.1002/qj.828.

DRUMOND, A.; MARENGO, J.; AMBRIZZI, T.; NIETO, R.; MOREIRA L.; GIMENO, L., The role of the Amazon Basin moisture in the atmospheric branch of the hydrological cycle: a Lagrangian analysis, Hydrol. Earth Syst. Sci., v. 18, p. 2577–2598, 2014, doi: 10.5194/hess-18-2577-2014.

FIORELLA, R.P.; POULSEN, C.J.; PILLCOZOLÁ, R.S.; BARNES, J.B.; TABOR, C.R.; EHLERS, T.A., Spatiotemporal variability of modern precipitation δ18O in the central Andes and implications for paleoclimate and paleoaltimetry estimates, J. Geophys. Res. Atmos., v. 120, p. 4630–4656, 2015, doi: 10.1002/2014JD022893.

FLEITMANN, D.; BURNS, S.J.; NEFF, U.; MANGINI, A.; MATTER, A., Changing moisture sources over the last 330,000 years in Northern Oman from fluid-inclusion evidence in speleothems, Quat. Res., v. 60, p. 223-232, 2003b.

FONTES, D. et al., Paleoenvironmental dynamics in South Amazonia, Brazil, during the last 35,000 years inferred from pollen and geochemical records of Lago do Saci, Quaternary Science Review, v. 176, p. 161-180, 2017, doi: http://dx.doi.org/10.1016/j.quascirev.2017.08.021.

FROEHLICH, K.; GIBSON, J.J.; AGGARWAL, P., Deuterium excess in precipitation and its climatological significance, in: Study of Environmental Change Using Isotope Techniques, International Atomic Energy Agency, Vienna, p. 54–66, 2002.

GARREAUD, R.D.; VUILLE, M.; COMPAGNUUCCI, R.; MARENGO, J., Present day South American climate, Palaeogeography, Palaeoclimatology, Palaeoecology, v. 281, p. 180-195, 2008, doi: 10.1016/j.palaeo.2007.10.032.

GRIFFITHS, M.L. et al., Younger Dryas–Holocene temperature and rainfall history of southern Indonesia from δ18O in speleothem calcite and fluid inclusions, Earth and Planetary Science, v. 295, p. 30-36, 2010, doi: 10.1016/j.epsl.2010.03.018.

HARMON, R.S.; SCHWARCZ, H.P.; ONEIL, J.R., D-H ratios in speleothem fluid inclusions – guide to variations in the isotopic composition of meteoric precipitation, Earth Planet. Sc. Lett., v. 42, p. 254–266, 1979, doi:10.1016/0012-821x(79)90033-5.

HUFFMAN, G.J. et al., The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, Journal of Hydrometeorology, v. 8, p. 38-55, 2007, doi: 10.1175/JHM560.1.

HURLEY, J.V.; GALEWSKY, J.; WORDEN, J.; NOONE, D.A., Test of the advection-condensation model for subtropical water vapor using stable isotopologue observations from Mauna Loa Observatory, Hawaii, J. Geophys. Res, v. 117, n. D19118, 2012, doi; https://doi.org/10.1029/2012JD018029.

64

HURLEY, J.V.; VUILLE, M.; HARDY, D.R.; BURNS, S.; THOMPSONL.G., Cold air incursions, δ18O variability and monsoon dynamics associated with snow days at Quelccaya Ice Cap, Peru, J. Geophys. Res., v. 120, p. 7467-7487, 2015, doi: https://doi.org/10.1002/2015JD023323.

HUTYRA, L.; MUNGER, J.; SALESKA, S.; GOTTLIEB, E.; DAUBE, B.; DUNN, A.; AMARAL, D.; CAMARGO, P.; WOFSY, S., Seasonal controls on the exchange of carbon and water in an Amazonian rain forest, J. Geophys. Res., v. 112, n. G03008, p. 1-16, 2007, doi: 10.1029/2006JG000365.

INSEL, N.; POULSEN, C.J.; STURM, C.; EHLERS, T.A., Climate controls on Andean precipitation δ18O interannual variability, J. Geophys. Res. Atmos, v. 118, p. 9721–9742, 2013, doi: 10.1002/jgrd.50619.

JONCKHEERE, I.; FLECK S.; NACKAERTS, K.; MUYSA, B.; COPPIN P.; WEISS, M.; BARET, F., Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography, Agricultural and Forest Meteorology, v. 121, p. 19-35, 2004, doi: 10.1016/j.agrformet.2003.08.027.

JOHNSEN, S.J.; DANSGAARD, W.; WHITE, J.W.C., The origin of Arctic precipitation under present and glacial conditions, Tellus B, v. 41, p. 452-468, 1989. doi: 10.1111/j.1600-0889.1989.tb00321.x.

KONG, Y.; PANG, Z.; FROEHLICH, K., Quantifying recycled moisture fraction in precipitation of an arid region using deuterium excess, Tellus B, v. 65, p. 19251, 2012, doi: http://dx.doi.org/10.3402/tellusb.v65i0.19251.

LACHNIET, M.S., Climatic and environmental controls on speleothem oxygen-isotope values, Quaternary Science Reviews, v. 28, n. 5-6, p.412–432, 2008, doi: https://doi.org/10.1016/j.quascirev.2008.10.021.

LAI, C.; EHLERINGER, J.R., Deuterium excess reveals diurnal sources of water vapor in forest air, Ecosystem Ecology, v. 165, p. 213-23, 2010, doi: 10.1007/s00442-010-1721-2.

LANDAIS, A.; RISI, C.; BONY, S.; VIMEUX, F.; DESCROIX, L.; FALOURD, S.; BOUYGUES, A., Combined measurements of 17O-excess and d-excess in African monsoon precipitation: Implications for evaluating convective parameterizations. Earth and Planetary Science Letters, v. 298, p. 104-112, 2010, doi: 10.1016/j.epsl.2010.07.033.

LI, X.; XIE, S.-P., GILLE, S.T.; YOO, C., Atlantic-induced pan-tropical climate change over the past three decades. Nat. Clim. Change, v. 6, p. 275–279, 2016, doi: https://doi.org/10.1038/nclimate2840.

LENTERS, J.D.; COOK, K.H., On the origin of the Bolivian high and related circulation features of the south American climate, J. Atmos. Sci., v. 54, p. 656-678, 1997, doi: https://doi.org/10.1175/1520-0469(1997)054<0656:OTOOTB>2.0.CO;2.

65

LENTERS, J.D.; COOK, K.H., Summer time precipitation variability over South America: role of the large-scale circulation, Mon. Weather Rev., v. 127, p. 409–431, 1999, doi: https://doi.org/10.1175/1520-0493(1999)127<0409:SPVOSA>2.0.CO;2.

MAKARIEVA, A.M.; GORSHKOV, V.G.; SHEIL, D.; NOBRE, A.D.; LI, B.-L., Where do winds come from? A new theory on how water vapor condensation influences atmospheric pressure and dynamics, Atmos. Chem. Phys., v. 13, p. 1039–1056, 2013, doi: https://doi.org/10.5194/acp-13-1039-2013.

MAKSIC, J. et al., Simulation of the Holocene climate over South America and impacts on the vegetation, The Holocene, p. 1-13, 2018, doi: 10.1177/0959683618810406.

MARENGO, J. A.; ESPINOZA, J.C., Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts, Int. J. Climatol., v. 36, p. 1033–1050, 2015, doi: 10.1002/joc.4420.

MARENGO, J. A.; TOMASELLA, J.; SOARES, W. G; ALVES, L. M.; NOBRE, C.A. Extreme climatic events in the Amazon basin. Theor Appl Climatol., v. 107, p. 73–85, 2012, doi: 10.1007/s00704-011-0465-1.

MASSON-DELMOTTE, V.; JOZUEL, J.; LANDAIS, A.; STIEVENARD, M.; JOHNSEN, S. J.; WHITE, J. W. C.; WERNER, M.; SVEINBJORNSDOTTIR, A.; FUHRER, K., GRIP Deuterium Excess Reveals Rapid and Orbital-Scale Changes in Greenland Moisture Origin, Science, v. 309, p. 118-121, 2005. doi: 10.1126/science.1108575.

MCDERMOTT, F.; SCHWARCZ, H.P.; HARMON, R.S.; THOMPSON, P.; FORD, D.C. Isotopes in speleothems. In: Leng, M.J. (Ed.), Isotopes in Palaeoenvironmental Research. Developments in Paleoenvironmental Research. Dordrecht, the Netherlands: Springer, 2006. p. 185–225. doi: 10.1007/1-4020-2504-1_05.

MCGARRY, S.; BAR-MATTHEWS, M.; MATTHEWS, A.; VAKS, A.; SCHILMAN, B.; AYALON, A., Constraints on hydrological and paleotemperature variations in the Eastern Mediterranean region in the last 140ka given by the δD values of speleothem fluid inclusions, Quaternary Science Reviews, v. 23, p. 919-934, 2004, doi: 10.1344/105.000001646.

MELANDER, L., Isotope Effects on Reaction Rates: Modern concepts in chemistry, Michigan, Ronald Press, 1960, p. 181.

MERLIVAT, L.; JOUZEL, J., Global climatic interpretation of the deuterium–oxygen-18 relationship for precipitation, Journal of Geophysical Research-Oceans and Atmospheres, v. 84, n. C8, p. 5029–5033, 1979, doi: https://doi.org/10.1029/JC084iC08p05029.

MOLINA, R.D.; SALAZAR, J.F; MARTÍNEZ, J.A.; VILLEGAS, J.C.; ARIAS, P.A., Forest‐induced exponential growth of precipitation along climatological wind

66

streamlines over the Amazon, J. Geophys. Res., 2019, doi: https://doi.org/10.1029/2018JD029534.

MOREIRA, L.S. et al., Holocene paleoenvironmental reconstruction in the Eastern Amazonian Basin: Comprido Lake, Journal of South American Earth Sciences, v. 44, p. 55–62, 2012, doi: http://dx.doi.org/10.1016/j.jsames.2012.12.012.

MORTON, D.C. et al., Amazon forests maintain consistent canopy structure and greenness during the dry season, Nature, v. 506, p. 221-224, 2014, doi: 10.1038/nature13006.

MYNENI, R. B.; YANG, W.; NEMANI, R. R.; HUETE, A. R.; DICKINSON, R. E.; KNYAZIKHIN, Y. et al., Large seasonal swings in leaf area of Amazon rainforests, Proceedings of the national academy of sciences of the united states of America, v. 104, n. 12, p. 4820–4823, 2007, doi: https://doi.org/10.1073/pnas.0611338104.

MYNENI, R.; KNYAZIKHIN, Y.; PARK, T., MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC, 2015, doi: http://doi.org/10.5067/MODIS/MCD15A2H.006.

NEPSTAD, D.C.; DE CARVALHO, C.R.; DAVIDSON, E.A.; JIPP, P.H.; LEFEBVRE, P.A.; NEGREIROS, G.H.; DA SILVA, E.D.; STONE, T.A.; TRUMBORE, S.E.; VIEIRA, S., The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures, Nature, v. 372, p. 666–669, 1994, doi: https://doi.org/10.1038/372666a0DO

NUMAGUTI, A., Origin and recycling processes of precipitating water over the Eurasian continent: Experiments using an atmospheric general circulation model, J. Geophys. Res., v. 104, p. 1957- 1972, 1999, doi: https://doi.org/10.1029/1998JD200026.

OLIVEIRA, R.S.; DAWSON, T.E.; BURGESS, S.S.O.; NEPSTAD, D.C., Hydraulic redistribution in three Amazonian trees, Oecologia, v. 145, p. 354–363, 2005, doi: 10.1007/s00442-005-0108-2.

PACCINI, L.; ESPINOZA., J. C.; RONCHAIL, J.; SEGURA, H., Intra‐seasonal rainfall variability in the Amazon basin related to large‐scale circulation patterns: a focus on western Amazon–Andes transition region, nt. J. Climatol., v. 38, p. 2386–2399, 2017, doi: https://doi.org/10.1002/joc.5341.

PENG, H.; MAYER, B.; HARRIS, S.; H. KROUSE, R., The influence of below-cloud secondary effects on the stable isotope composition of hydrogen and oxygen in precipitation at Calgary, Alberta, Canada, Tellus B: Chemical and Physical Meteorology, v. 59, n. 4, p. 698-704, 2007, doi: https://doi.org/10.1111/j.1600-0889.2007.00291.x.

PFAHL, S.; SODEMANN, H., What controls deuterium excess in global precipitation? Clim. Past., v. 10, p. 771–781, 2014, doi: 10.5194/cp-10-771-2014.

67

PFAHL, S.; WERNLI, H., Air parcel trajectory analysis of stable isotopes In water vapor in the eastern Mediterranean, Journal of Geophysical Research, v. 113, p. 1-16, 2008, doi: 10.1029/2008JD009839.

RASMUSSEN, S.O. et al., Stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy, Quaternary Science Reviews, v. 106, p. 14-28, 2014, doi: https://doi.org/10.1016/j.quascirev.2014.09.007.

RESTREPO-COUPE et al., What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network, Agricultural and Forest Meteorology, v. 182-183, p. 128–144, 2013, doi: https://doi.org/10.1016/j.agrformet.2013.04.031.

RISI, C.; LANDAIS, A.; WINKLER, R.; VIMEUX, F., Can we determine what controls the spatio-temporal distribution of d-excess and 17O-excess in precipitation using the LMDZ general circulation model? Climate of the Past, v. 9, p. 2173-2193, 2013, doi: https://doi.org/10.5194/cp-9-2173-2013.

ROLPH, G.; STEIN, A.; STUNDER, B., Real-time Environmental Applications and Display sYstem: READY, Environmental Modelling and Software, v. 95, p. 210-228, 2017, doi: http://dx.doi.org/10.1016/j.envsoft.2017.06.025.

SAATCHI, S.; ASEFI-NAJAFABADY, S.; MALHI, Y.; ARAGÃO, L.E.O.C.; ANDERSON, L.O.; MYNENI, R.B.; NEMANI, R., Persistent effects of a severe drought on Amazonian forest canopy, Proceedings of the National Academy of Sciences, v. 110, p. 565–570, 2013, doi: https://doi.org/10.1073/pnas.1204651110.

SALATI, E.; DALL’OLIO, A.; MATSUI, E.; GAT, J.R., Recycling of water in the Amazon Basin: An Isotopic Study, Water resources research, Res., v. 15, p. 1250–1258, 1979, doi: https://doi.org/10.1029/WR015i005p01250.

SALESKA, S.R.; WU, J; GUAN, K.; ARAUJO A.C.; HUETE, A.; NOBRE, A.D.; RESTREPO-COUPE, N., Dry-season greening of Amazon forest, Nature, v. 531, 2016, doi: https://doi.org/10.1038/nature16457.

SAMUELS-CROW, K.E.; GALEWSKY, J; HARDY, D.R.; SHARP, Z.; WORDEN, J.; BRAUN, C., Upwind convective influences on the isotopic composition of atmospheric water vapor over the tropical Andes, Journal of Geophysical Research, v. 119, n. 12, p. 7051-7063, 2014, doi: https://doi.org/10.1002/2014JD021487.

SANTOS, R.A., Relações paleoclimáticas e Paleoambientais durante o Holoceno no lesta da Amazônia na Região da volta Grande do Rio Xingu, São Paulo, 2019, 119 p., Tese (doutorado em Geociências) - Universidade de São Paulo, São Paulo, 2019.

SCHWARCZ, H.P.; HARMON, R.S.; THOMPSON, P.; FORD, D.C., Stable isotope studies of fluid inclusions in speleothems and their paleoclimatic significance,

68

Geochimica et Cosmochimica Acta, v. 40, n. 6, p. 657-665, 1976, doi: https://doi.org/10.1016/0016-7037(76)90111-3.

SHUTTLEWORTH, W.J., Evaporation from Amazonian rain forest, Proc. R. Soc. London, v. 233, p. 321–346, 1988, doi: https://doi.org/10.1098/rspb.1988.0024.

SIFEDDINE, A.; MARTIN, L.; TURCQ, B.; RIBEIRO, C.V.; SOUBIES, F.; CORDEIRO, R.C.; SUGUIO, K., Variations of the Amazonian rainforest environment: a sedimentological record covering 30,000 years, Palaeogeogr. Palaeoclimatol. Palaeoecol., v. 168, p. 221-235, 2001, doi: https://doi.org/10.1016/S0031-0182(00)00256-X.

SINHA, D.K.; SINGH, A.K.; MALLICK, K.; SINGH, V.P., Holocene: Definition and current stratigraphic status in the Geological time-scale. In: Holocene, Bahadur Singh Kotlia, 2013.

SPRACKLEN, D.V.; ARNOLD, S.R.; TAYLOR, C.M., Observations of increased tropical rainfall preceded by air passage over forests, Nature, v. 489, p. 282-285, 2012, doi: https://doi.org/10.1038/nature11390.

STAAL, A.; TUINENBURG, O.A.; BOSMANS, J.H.C.; HOLMGREN, M.; VAN NES, E. H.; SCHEFFER, M.; ZEMP, D.C.; DEKKER, S.C., Forest-rainfall cascades buffer against drought across the Amazon, Nature Climate Change, v. 8, p. 539–543, 2018, doi: https://doi.org/10.1038/s41558-018-0177-yDO.

STEIN, A.F. et al., NOAA’s HySPLIT atmospheric transport and dispersion modeling system, American Meteorological Society, p. 2059-2077, 2015, doi: 10.1175/BAMS-D-14-00110.1.

SULCA, J.; VUILLE, M.; SILVA, Y; TAKAHASHI, K., Teleconnections between the Peruvian central Andes and Northeast Brazil during extreme rainfall events in austral

summer, J. Hydrometeorol., v. 17, p. 499-515, 2016, doi: https://doi.org/10.1175/JHM-D-15-0034.1.

SYLVESTRE, F.; SERVANT, M.; SERVANT-VILDARY, S.; CAUSSE, C.; FOURNIER, M.; YBERT, J-P., Lake-Level Chronology on the Southern Bolivian Altiplano (18°–23°S) during Late-Glacial Time and the Early Holocene, Quaternary Research, v. 51, n. 1, p. 54-66, 1999, doi: https://doi.org/10.1006/qres.1998.2017.

TRENBERTH, K.E., Atmospheric Moisture Recycling: Role of Advection and Local Evaporation, Journal of Climate, v. 12, p. 1368-1381, 1998, doi: https://doi.org/10.1175/1520-0442(1999)012<1368:AMRROA>2.0.CO;2.

VAN BREUKELEN, M.R. et al., Fossil drip water in stalagmites reveals Holocene temperature and rainfall variation in Amazonia, Earth Planet. Sci. Lett., v. 275, n. 1-2, p. 54-60, 2008, doi: https://doi.org/10.1016/j.epsl.2008.07.060.

VAN DER ENT, R. J., A new view on the hydrological cycle over continents. Delft, 2014, 106 p., Thesis (Doctor in Civil Engineering and Geosciences-Water Management) - Delft University of Technology, Delft, 2014.

https://www.ncbi.nlm.nih.gov/pubmed/?term=Spracklen%20DV%5BAuthor%5D&cauthor=true&cauthor_uid=22951966

https://www.ncbi.nlm.nih.gov/pubmed/?term=Arnold%20SR%5BAuthor%5D&cauthor=true&cauthor_uid=22951966

https://www.ncbi.nlm.nih.gov/pubmed/?term=Taylor%20CM%5BAuthor%5D&cauthor=true&cauthor_uid=22951966

https://www.ncbi.nlm.nih.gov/pubmed/22951966

69

VAN DER HAMMEN, T., The Pleistocene changes of vegetation and climate in tropical South America, J. Biogeogr, v.1, p. 3-26, 1974, doi: 10.2307/3038066.

VAN DER HAMMEN, T.; ABSY, M. L., Amazonia during the last glacial. Palaeogeogr. Palaeoclimatol. Palaeoecol., v. 109, p. 247-261, 1994, doi: https://doi.org/10.1016/0031-0182(94)90178-3.

VERA, C et al., Toward a Unified View of the American Monsoon Systems, Journal of climate, v. 19, p. 4977-5000, 2006, doi: https://doi.org/10.1175/JCLI3896.1

VILLACÍS, M.; VIMEUX, F.; TAUPIN, J. D. Analysis of the climate controls on the isotopic composition of precipitation (δ18O) at Nuevo Rocafuerte, 74.5°W, 0.9°S, 250m, Ecuador.C. R. Geosci, v. 340, n. 1, p. 1–9, 2008, doi: https://doi.org/10.1016/j.crte.2007.11.003.

VIMEUX, F.; GALLAIRE, R.; BONY, S.; HOFFMANN, G.; CHIANG, J.C.H, What are the climate controls on δD in precipitation in the Zongo Valley (Bolivia)? Implications

for the Illimani ice core interpretation, Earth Planet. Sci. Lett., 240, p. 205–220, 2005, doi: 10.1016/j.epsl.2005.09.031.

VONHOF, H.B. et al., Fluid inclusion hydrogen and oxygen isotope analyses using the “Amsterdam Device”: a progress report, Geophysical Research Abstracts, v. 9, 2007.

VON RANDOW, C. et al., Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in southwest Amazonia, Theor. Appl. Climatol., v. 78, p. 5-26, 2004, doi: 10.1007/s00704-004-0041-z

VOOSE, P., New geological age comes under fire, Science, v. 361, p. 537-538, 2018, doi: 10.1126/science.361.6402.537.

VUILLE, M.; WERNER, M., Stable isotopes in precipitation recording South American summer monsoon and ENSO variability - observations and model results, Climate Dyn., v. 25, n. 4, p. 401-413, 2005, doi: https://doi.org/10.1007/s00382-005-0049-9.

VUILLE, M., Current state and future challenges in stable isotope applications of the tropical hydrologic cycle (Invited Commentary), Hydrological Processes, v. 32, p. 1313-1317, 2018, doi: https://doi.org/10.1002/hyp.11490.

WANG, X.; EDWARDS, R. L.; AULER, A.S.; CHENG, H.; KONG, X.; WANG, Y.; CRUZ, F. W.; DORALE, J. A.; CHIANG H., Hydroclimate changes across the Amazon lowlands over the past 45,000 years, Nature, v. 541, p. 201-207, 2017, doi: https://doi.org/10.1038/nature20787.

WANG, X.; LI, W; ZHU, J.; TANAJURA, C.A.S., The strengthening of Amazonian precipitation during the wet season driven by tropical sea surface temperature forcing, Environ. Res. Lett., v. 13, p. 1-12, 2018, doi: https://doi.org/10.1088/1748-9326/aadbb9.

70

WALKER M. et al., Formal ratification of the subdivision of the Holocene Series/Epoch (Quaternary System/Period): two new Global Boundary Stratotype Sections and Points (GSSPs) and three new stages/subseries, Episodes, v.41, n. 4, p. 213–223, 2018, doi: https://doi.org/10.18814/epiiugs/2018/018016.

WILLE, M.; HOOGHIEMSTRA, H.; VAN GEEL, B.; BEHLING, H.; DE JONG, A.; VAN DER BORG, K., Submillennium-scale migrations of the rainforest-savanna boundary inColombia: 14C wiggle-matching and pollen analysis of core Las Margaritas, Palaeogeogr. Palaeoclimatol. Palaeoecol., v. 193, n. 2, p. 201-223, 2001, doi: https://doi.org/10.1016/S0031-0182(03)00226-8.

WINDHORST, D.; WALTZ, T.; TIMBE, E.; FREDE, H.-G.; BREUER, L., Impact of elevation and weather patterns on the isotopic composition of precipitation in a tropical montane rainforest, Hydrol. Earth Syst. Sci., v. 17, p. 409–419, 2013, doi: https://doi.org/10.5194/hess-17-409-2013.

ZEMP, D.C.; SCHLEUSSNER, C.-F.; BARBOSA, H.M.J.; VAN DER ENT, R.J.; DONGES, J. F.; HEINKE, J.; SAMPAIO, G.; RAMMIG, A., On the importance of cascading moisture recycling in South America, Atmos.Chem.Phys., v. 14, n. 23, p. 13337–13359, 2014, doi: https://doi.org/10.5194/acp-14-13337-2014.

ZEMP, D.C., Feedbacks between vegetation and rainfall in the Amazon basin. Berlin, 2016, 160 p. Dissertation (Doctor in Geography) - Humboldt-Universität zu Berlin, Berlin., 2016.

ZEMP, D.C. et al., Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks, Nature Communications, v. 8, p. 1-10, 2016, doi: 10.1038/ncomms14681.

the forest effects on the isotopic composition of … · foliar e com ciclagem de umidade modelada...

Documents