a case study on life in amazon river basin

• Life in the Amazon basin

Human beings are the most brained creatures. As humans, we excel in exploring the mother Earth and using it for our best purposes. This skill of ours has given us the advantage of interacting with the environment, and Amazon basin is the best living example of that. Amazon basin is the result of the many tributaries joining the Amazon river. Let’s explore the human capacity to interact with the environment.

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Life in amazon basin.

We humans, are dependent on nature and interact with it for various reasons. Our interaction with the environment is endless and so is our ability to use it. Being the best example of human interaction with the environment, the Amazon basin has been exploited since time immemorial. Amazon basin is located in  South America at 10° N and 10° S of the tropical region.

This region is also referred as the equatorial region. The Amazon river flows through the region and reaches the Atlantic Ocean through the mountains in the west.

Before reading further about Amazon basin it is important that we know the following definitions:

• River’s Mouth: The place where a river flows into another water body is called the river’s mouth.
• Tributaries: When a river or a stream flows into a larger river or lake then that river or lake is called the tributary of the larger river.

The Climate

Amazon basin is situated in the equatorial region which is hot and humid all through the year. Days and nights both are equally hot and wet. Nights are comparatively less hot but the humidity level remains unchanged. Due to the humid conditions here, it rains almost every day.

Browse more Topics under Human Environment Interactions

• Life in the Ganga – Brahmaputra Basin

Read more about Life in Ganga Brahmaputra Basin here in detail.

The Rainforest

As it rains throughout the year the forests here are dense. The trees form a dense roof of leaves that do not even let sunlight penetrate into the forest area. Also, the surface of the earth is damp and dark. So shade tolerant vegetation is present in abundance here. Prominent plant parasites found here are Bromeliads and Orchids. The rainforests of Amazon basin are flocked with a variety of fauna as well. As a result, you can find the rarest of species loitering in the forests here.

Hummingbird, Toucans, Amazon Kingfisher, Hyacinth Macaw, Blue-fronted Amazon are some of the bird species that are present here. And animals like Sloth, Capybara, monkeys, ant-eating tapirs, poison dart frogs are present all over the rainforests of Amazon. The list does not end here! You get a glimpse of the grandest of the reptiles as well.

Crocodiles and snakes like Pythons and Anacondas are common here. Apart from these aquatic animals like the Piranha and Giant Otter can be sighted in the river basin. The list of fauna and flora in Amazon Basin is endless.

People here cut a few trees and cultivate the land according to their needs and requirements. Men have occupations like fishing and hunting, while it’s the women who take care of the crops and fields. The land being near the Amazon river is very fertile which makes it a good for farming. People here generally grow crops like Pineapple, Tapioca, Sweet Potato, Cassava (manioc), Coffee, maize, and cocoa. We call them the Cash crops .

As already said, men practice fishing and hunting which are uncertain means of living. It is the women of Amazon basin who are the major bread earners of the family. From taking care of fields to feeding their families with the vegetables that they cultivate, women are responsible for their families well-being.

They practice slash and burn technique of agriculture. In slash and burn agriculture system, we clear the required forest land for farming. We slash or cut down the trees and bushes. As soon as the fertility of land degrades, women proceed to clear a new piece of land.

The old land eventually gains back its fertility with trees and bushes growing back on them. Manioc or Cassava is the staple food while queen ants and egg sacs are the other savouries of people near the Amazon river.

Settlements of People

People live in special kind of houses called the Maloca here. These houses have steep slant roofs and are large and apartment like in shape. People also reside in houses that are identical to beehives and have thatched roofs over them. Since the settlements here are in close proximity of forests, people find wood in abundance for their personal use.

With pacing technology and modernism, the life of people is slowly changing here. As it is the human nature to evolve and enjoy facilities to their best the advent of transportation has helped in easy navigation through the basin. An area which once was navigable only through the Amazon river can today be explored through Trans-Amazon Highway. Moreover, with aeroplanes and helicopters, it has become easier and faster to reach various places around the basin.

Human Interaction with environment benefits humans, but after an extent depletes the environment. The regular felling of trees in the Amazon basin has resulted in a change of ecology here. The developmental activities near the Amazon river, eventually, have resulted in the destruction of the rainforests. The effects may not be visible today, but in the near future, they surely will. The map below signifies the change human interaction has brought to the environment:

Amazon Basin is the richest example of human interaction with the environment. To let it stay rich with vegetation it has now become imminent to use the resources from the basin intelligently. We humans interact with the environment for our benefits but doing so at the cost of ecology can be fatal for our generations to come.

Solved Questions For You

Q1: Define the major difference between a hamlet and a village.

A) Population      B) Group of Houses      C) Pollution       D) None of the Above

Solution: A) Villages are larger than hamlets. They have a greater population than hamlets. Hamlets are small towns and community while as villages are mostly large suburbs and crossroads. Hence, Hamlet settlements are also less polluted as a result of a smaller population.

Q2: From where the Urban settlements have evolved?

A) Rural Settlements      B) Scattered Settlements      C)  Nucleated Settlements            D) None of the Above.

Solution: A) Rural settlements were the first step towards a stable life in the human history. Urban settlements have evolved through the growth and expansion of rural settlements. Increase in population of the rural settlements marks the beginning of urbanisation.

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Case Study: The Amazon Rainforest

The Amazon in context

Tropical rainforests are often considered to be the “cradles of biodiversity.” Though they cover only about 6% of the Earth’s land surface, they are home to over 50% of global biodiversity. Rainforests also take in massive amounts of carbon dioxide and release oxygen through photosynthesis, which has also given them the nickname “lungs of the planet.” They also store very large amounts of carbon, and so cutting and burning their biomass contributes to global climate change. Many modern medicines are derived from rainforest plants, and several very important food crops originated in the rainforest, including bananas, mangos, chocolate, coffee, and sugar cane.

In order to qualify as a tropical rainforest, an area must receive over 250 centimeters of rainfall each year and have an average temperature above 24 degrees centigrade, as well as never experience frosts. The Amazon rainforest in South America is the largest in the world. The second largest is the Congo in central Africa, and other important rainforests can be found in Central America, the Caribbean, and Southeast Asia. Brazil contains about 40% of the world’s remaining tropical rainforest. Its rainforest covers an area of land about 2/3 the size of the continental United States.

There are countless reasons, both anthropocentric and ecocentric, to value rainforests. But they are one of the most threatened types of ecosystems in the world today. It’s somewhat difficult to estimate how quickly rainforests are being cut down, but estimates range from between 50,000 and 170,000 square kilometers per year. Even the most conservative estimates project that if we keep cutting down rainforests as we are today, within about 100 years there will be none left.

How does a rainforest work?

Rainforests are incredibly complex ecosystems, but understanding a few basics about their ecology will help us understand why clear-cutting and fragmentation are such destructive activities for rainforest biodiversity.

High biodiversity in tropical rainforests means that the interrelationships between organisms are very complex. A single tree may house more than 40 different ant species, each of which has a different ecological function and may alter the habitat in distinct and important ways. Ecologists debate about whether systems that have high biodiversity are stable and resilient, like a spider web composed of many strong individual strands, or fragile, like a house of cards. Both metaphors are likely appropriate in some cases. One thing we can be certain of is that it is very difficult in a rainforest system, as in most other ecosystems, to affect just one type of organism. Also, clear cutting one small area may damage hundreds or thousands of established species interactions that reach beyond the cleared area.

Pollination is a challenge for rainforest trees because there are so many different species, unlike forests in the temperate regions that are often dominated by less than a dozen tree species. One solution is for individual trees to grow close together, making pollination simpler, but this can make that species vulnerable to extinction if the one area where it lives is clear cut. Another strategy is to develop a mutualistic relationship with a long-distance pollinator, like a specific bee or hummingbird species. These pollinators develop mental maps of where each tree of a particular species is located and then travel between them on a sort of “trap-line” that allows trees to pollinate each other. One problem is that if a forest is fragmented then these trap-line connections can be disrupted, and so trees can fail to be pollinated and reproduce even if they haven’t been cut.

The quality of rainforest soils is perhaps the most surprising aspect of their ecology. We might expect a lush rainforest to grow from incredibly rich, fertile soils, but actually, the opposite is true. While some rainforest soils that are derived from volcanic ash or from river deposits can be quite fertile, generally rainforest soils are very poor in nutrients and organic matter. Rainforests hold most of their nutrients in their live vegetation, not in the soil. Their soils do not maintain nutrients very well either, which means that existing nutrients quickly “leech” out, being carried away by water as it percolates through the soil. Also, soils in rainforests tend to be acidic, which means that it’s difficult for plants to access even the few existing nutrients. The section on slash and burn agriculture in the previous module describes some of the challenges that farmers face when they attempt to grow crops on tropical rainforest soils, but perhaps the most important lesson is that once a rainforest is cut down and cleared away, very little fertility is left to help a forest regrow.

What is driving deforestation in the Amazon?

Many factors contribute to tropical deforestation, but consider this typical set of circumstances and processes that result in rapid and unsustainable rates of deforestation. This story fits well with the historical experience of Brazil and other countries with territory in the Amazon Basin.

Population growth and poverty encourage poor farmers to clear new areas of rainforest, and their efforts are further exacerbated by government policies that permit landless peasants to establish legal title to land that they have cleared.

At the same time, international lending institutions like the World Bank provide money to the national government for large-scale projects like mining, construction of dams, new roads, and other infrastructure that directly reduces the forest or makes it easier for farmers to access new areas to clear.

The activities most often encouraging new road development are timber harvesting and mining. Loggers cut out the best timber for domestic use or export, and in the process knock over many other less valuable trees. Those trees are eventually cleared and used for wood pulp, or burned, and the area is converted into cattle pastures. After a few years, the vegetation is sufficiently degraded to make it not profitable to raise cattle, and the land is sold to poor farmers seeking out a subsistence living.

Regardless of how poor farmers get their land, they often are only able to gain a few years of decent crop yields before the poor quality of the soil overwhelms their efforts, and then they are forced to move on to another plot of land. Small-scale farmers also hunt for meat in the remaining fragmented forest areas, which reduces the biodiversity in those areas as well.

Another important factor not mentioned in the scenario above is the clearing of rainforest for industrial agriculture plantations of bananas, pineapples, and sugar cane. These crops are primarily grown for export, and so an additional driver to consider is consumer demand for these crops in countries like the United States.

These cycles of land use, which are driven by poverty and population growth as well as government policies, have led to the rapid loss of tropical rainforests. What is lost in many cases is not simply biodiversity, but also valuable renewable resources that could sustain many generations of humans to come. Efforts to protect rainforests and other areas of high biodiversity is the topic of the next section.

Living World - Amazon Case Study

The Amazon is the largest tropical rainforest on Earth. It sits within the Amazon River basin, covers some 40% of the South American continent and as you can see on the map below includes parts of eight South American countries: Brazil, Bolivia, Peru, Ecuador, Colombia, Venezuela, Guyana, and Suriname. The actual word “Amazon” comes from river.

Amazing Amazon facts; • It is home to 1000 species of bird and 60,000 species of plants • 10 million species of insects live in the Amazon • It is home to 20 million people, who use the wood, cut down trees for farms and for cattle. • It covers 2.1 million square miles of land • The Amazon is home to almost 20% of species on Earth • The UK and Ireland would fit into the Amazon 17 times!

The Amazon caught the public’s attention in the 1980s when a series of shocking news reports said that an area of rainforest the size of Belgium was being cut down and subsequently burnt every year. This deforestation has continued to the present day according to the Sao Paulo Space Research Centre. In 2005 they had lost 17% of Amazon rainforest or 650000 square kilometres. Their satellite data is also showing increased deforestation in parts of the Amazon. The process of deforestation The Amazon helps a Newly Emerging Economy(NEE), Brazil, to make money. They build roads into the forest, logging firms then go in and take out valuable hard woods such as mahogany and cedar, worth thousands of pounds in richer economies like Europe. Then farmers, often cattle ranchers from big companies, burn the rest to make way for cattle pasture. 75% of cleared areas are used in this way. This is clearly shown on the map on figure 22 in red. Many of the deforested areas follow roads and branch off from there.  Deforestation is also worse in the South and South East of the Amazon basin, closer to major centres of population in Brazil.

© WWF   Source  Used with permission.

The causes of deforestation 1. Subsistence and commercial farming – subsistence farming is where poor farmers occupy plots of the forest to grow food to feed themselves and their families. They clear forest and then burn it, hence the name slash and burn.  They grow crops until the soil is exhausted and then move on.  This contributes to deforestation but not as much as commercial farming (Farming to sell produce for a profit to retailers or food processing companies). The Brazilian region of Mato Grosso was affected by deforestation in the 1980s and 1990s. 43% of rainforest losses were in this region, and area almost ½ the size of France. It has been replaced by fields for grain and cattle. This has allowed Brazil to overtake Australia as the largest exporter of beef in the world. The land is also flat and easy to farm. It also has high temperatures and lots of rainfall.

2. Logging – This involves cutting down trees for sale as timber or pulp.  The timber is used to build homes, furniture, etc. and the pulp is used to make paper and paper products.  Logging can be either selective or clear cutting. Selective logging is selective because loggers choose only wood that is highly valued, such as mahogany. Clear-cutting is not selective.  Loggers are interested in all types of wood and therefore cut all of the trees down, thus clearing the forest, hence the name- clear-cutting.

3. Road building – trees are also clear for roads.  Roads are an essential way for the Brazilian government to allow development of the Amazon rainforest.  However, unless they are paved many of the roads are unusable during the wettest periods of the year.  The Trans Amazonian Highway has already opened up large parts of the forest and now a new road is going to be paved, the BR163 is a road that runs 1700km from Cuiaba to Santarem. The government planned to tarmac it making it a superhighway. This would make the untouched forest along the route more accessible and under threat from development.

4. Mineral extraction – forests are also cleared to make way for huge mines. The Brazilian part of the Amazon has mines that extract iron, manganese, nickel, tin, bauxite, beryllium, copper, lead, tungsten, zinc and gold! 

The Belo Monte dam site under construction, copyright  Used with the kind permission of Phil Clarke-Hill  - His website is amazing, click here to see it.

5. Energy development – This has focussed mainly on using Hydro Electric Power, and there are 150 new dams planned for the Amazon alone.  The dams create electricity as water is passed through huge pipes within them, where it turns a turbine which helps to generate the electricity.  The power in the Amazon is often used for mining.  Dams displace many people and the reservoirs they create flood large area of land, which would previously have been forest.  They also alter the hydrological cycle and trap huge quantities of sediment behind them. The huge Belo Monte dam started operating in April 2016 and will generate over 11,000 Mw of power.  A new scheme the 8,000-megawatt São Luiz do Tapajós dam has been held up because of the concerns over the impacts on the local Munduruku people.

6. Settlement & population growth – populations are growing within the Amazon forest and along with them settlements.  Many people are migrating to the forest looking for work associated with the natural wealth of this environment. Settlements like Parauapebas, an iron ore mining town, have grown rapidly, destroying forest and replacing it with a swath of shanty towns. The population has grown from 154,000 in 2010 to 220,000 in 2012. The Brazilian Amazon’s population grew by a massive 23% between 2000 and 2010, 11% above the national average.

Impacts of deforestation – economic development, soil erosion, contribution to climate change. • Every time forest is cleared species are lost – so we lose BIODIVERSITY • Climate Change - Burning the forest releases greenhouse gasses like CO2.  This contributes to the warming of our planet via climate change and global warming.  In addition, the loss of trees prevents CO2 being absorbed, making the problem worse. The Amazon also helps to drive the global atmospheric system. There is a lot of rainfall there and changes to the Amazon could disrupt the global system. • Economic development – Brazil has used the forests as a way to develop their country.  The forest has many natural riches that can be exploited.  In addition, Brazil has huge foreign debt and lots of poor people to feed, so they want to develop the forest. May Brazilians see deforestation as a way to help develop their country and improve people’s standard of living. • Soil erosion - the soils of the Amazon forest are not fertile and are quickly exhausted once the forest is cleared. The farmers now artificially fertilise the soil when in the past the nutrient cycle would have done this naturally.  In addition, the lack of forest cover means that soils are exposed to the rainfall.  This washes huge amounts of soil into rivers in the process of soil erosion.

NEXT TOPIC - Living World - Sustainable Forest Management

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• Open access
• Published: 01 September 2020

Amplified seasonal cycle in hydroclimate over the Amazon river basin and its plume region

• Yu-Chiao Liang   ORCID: orcid.org/0000-0002-9347-2466 1 ,
• Min-Hui Lo   ORCID: orcid.org/0000-0002-8653-143X 2 ,
• Chia-Wei Lan   ORCID: orcid.org/0000-0002-9650-8460 2 ,
• Hyodae Seo   ORCID: orcid.org/0000-0002-4352-5080 1 ,
• Caroline C. Ummenhofer   ORCID: orcid.org/0000-0002-9163-3967 1 ,
• Stephen Yeager   ORCID: orcid.org/0000-0003-0268-9895 3 ,
• Ren-Jie Wu 2 &
• John D. Steffen 1  

Nature Communications volume  11 , Article number:  4390 ( 2020 ) Cite this article

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• Climate sciences

The Amazon river basin receives ~2000 mm of precipitation annually and contributes ~17% of global river freshwater input to the oceans; its hydroclimatic variations can exert profound impacts on the marine ecosystem in the Amazon plume region (APR) and have potential far-reaching influences on hydroclimate over the tropical Atlantic. Here, we show that an amplified seasonal cycle of Amazonia precipitation, represented by the annual difference between maximum and minimum values, during the period 1979–2018, leads to enhanced seasonalities in both Amazon river discharge and APR ocean salinity. An atmospheric moisture budget analysis shows that these enhanced seasonal cycles are associated with similar amplifications in the atmospheric vertical and horizontal moisture advections. Hierarchical sensitivity experiments using global climate models quantify the relationships of these enhanced seasonalities. The results suggest that an intensified hydroclimatological cycle may develop in the Amazonia atmosphere-land-ocean coupled system, favouring more extreme terrestrial and marine conditions.

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Introduction.

The Amazon river basin (delineated by the black contour in Fig.  1a ) receives ~2000 mm of rainfall annually 1 . This vast amount of precipitation feeds the Amazon river, ranked as the world’s largest river in terms of annual discharge 2 . The Amazon river discharge contributes ~17% of global river freshwater input to the ocean 2 , significantly affecting the physical and biogeochemical upper ocean properties in the coastal and neighboring oceans 3 , 4 , 5 , 6 , 7 . One prominent feature is the so-called Amazon plume region (APR), which is characterized by relatively low ocean salinity 3 , 4 , 8 and high nutrients brought by the Amazon river discharge 3 , 4 , 9 , 10 . Observational evidence suggests that high nutrient contents from the Amazon river discharge help sustain high marine productivity in the APR, with the maximum chlorophyll content concentrated in the upper 5-m ocean 3 . In addition, the mixing of supersaturated Amazon freshwater with undersaturated surface ocean water results in a net sink of atmospheric carbon dioxide within the APR 11 , 12 . Therefore, the variability of Amazon river discharge can impact marine biogeochemistry, productivity, and the carbon cycle within the APR and surrounding areas 13 .

a The geographic domain of Amazon river basin (black contour line) and APR (red box). The color shading over the ocean represents annual mean 5-m ocean salinity with 34.5 PSU countered as magenta, and the black arrows denote annual mean 5-m ocean current velocities. The magenta and blue stars denote the location of the Obidos and Ciudad Bolivar gauge stations, where Amazon and Orinoco river discharges were recorded, respectively. b Long-term mean (1979–2018) of observed precipitation averaged over the Amazon river basin in each month. Note that the mean TRMM precipitation is averaged over 1998–2018. c Long-term mean of Amazon river discharge (1979–2018) at Obidos and Dai and Trenberth river discharge (1979–2014) in each month. d Long-term mean of 5-m ECCO4 (1992–2017), GECCO2 (1979–2016), ORAS5 (1979–2018), SODA3.3.1 (1980–2015), and EN4 (1979–2018) ocean salinity averaged over the APR in each month. The error bars in b , c , and d indicate the standard deviations of each month throughout the analysis period. The geographic map is produced by Python Cartopy package 77 .

The low salinity water in the APR also significantly increases the upper ocean stratification, creating a thick barrier layer that inhibits the mixing of cold thermocline water into the surface waters 12 . As a consequence of reduced mixing, more heat is trapped in the upper ocean 4 , 8 , 14 , 15 , 16 . Due to the APR’s areal extent spanning from the Amazon river mouth near the Equator to the East Caribbean Sea (red box in Fig.  1a , see “Methods” for the box definition), the enhanced near-surface heat storage in the APR associated with the barrier layer dynamics provides favorable surface conditions for hurricane genesis over the broad regions of the tropical western Atlantic 8 , 15 . Moreover, the variability of Amazon freshwater and resultant ocean salinity changes have been suggested to affect tropical Atlantic air–sea interactions 17 , 18 and the variability of the Atlantic intertropical convergence zone (ITCZ) 19 , regional sea-level height changes 20 , 21 , 22 , as well as to exert potentially far-reaching impacts on the Atlantic meridional overturning circulation (AMOC) 23 . Thus, understanding the mechanisms for changes in the Amazon river discharge and the associated upper ocean stratification in the APR is important not only for the Atlantic hurricane forecasts but also for improved understanding of basin-scale climate variability.

Recent studies have found that the Amazonia hydroclimatological cycle, manifested as the seasonality changes in precipitation and Amazon river discharge, has become intensified during the past few decades 24 , 25 , 26 and resulted in increased likelihood of extreme terrestrial events, such as droughts and floods 24 , 26 , 27 . Ocean salinity in the APR has also been affected by the seasonality changes in the Amazonia hydroclimatological cycle 4 , 7 , 28 . However, it remains unclear if the enhanced precipitation seasonal cycle has intensified the seasonalities of the river discharges and APR ocean salinity by increasing the peaks and deepening the troughs. This study aims to examine the causal link between the changes in the amplitude of the seasonality of the Amazonia hydroclimatological system and APR ocean salinity during the period 1979–2018, using observations, reanalysis, and ocean-state estimate products. The effect of seasonality changes is further quantified using a hierarchical global climate modeling approach.

Enhanced seasonalities in observations and reanalysis data

We first examine the monthly climatological values of precipitation, Amazon river discharge, and APR ocean salinity during the period 1979–2018 (Fig.  1b–d ) to illustrate their seasonal cycle linkage. The area-averaged Amazonia precipitation shows the highest values during January–February–March and the lowest during July–August–September (Fig.  1b ). Only small differences appear among different observational precipitation datasets (Fig.  1b ), indicating the robustness of the estimated annual cycle for the Amazonia precipitation reported in this study.

Following the peak precipitation in March, the Amazon river discharge (expressed as volume transport in m 3  s −1 ), observed at Obidos station (magenta star in Fig.  1a ), reaches its highest value in June (Fig.  1c ). Similarly, the lowest precipitation in August leads to the lowest level of river discharge in November. This 3-month delayed response of the river discharge to precipitation is a prominent hydrological feature in the Amazon river basin 2 .

The seasonal cycle of the near-surface (~5 m below the sea surface) ocean salinity in the APR from multiple ocean-state estimate products shows the freshest values in May–June–July and the saltiest in December–January–February (Fig.  1d ), which largely follows the seasonal cycle of the Amazon river discharge (Fig.  1c ). Although the magnitude of the seasonal cycle varies with the chosen datasets, these ocean-state estimate datasets agree that the APR ocean salinity exhibits a similar seasonal evolution to the Amazon river discharge with no apparent lag. The seasonal cycles of APR ocean near-surface salinity from the 6-year soil moisture and ocean salinity (SMOS, 2011–2016) 29 and 3-year Aquarius (2012–2014) 30 satellite observations have similar characteristics (Supplementary Fig.  1 ).

The above results suggest that the atmosphere, land, and ocean within and near the Amazon river basin are closely connected through their seasonal cycle characteristics and lag relationships. The annual maximum and minimum values of Amazon precipitation during 1979–2018 indicate that the wet seasons have become wetter and the dry seasons’ drier (Fig.  2a ). A significant increase in the maximum values and decrease in the minimum values over the analysis period result in overall significant (at 5% level) increasing trends in the precipitation seasonality (Fig.  2b ). The increasing trend of the seasonality averaged across the Global Precipitation Climatology Project (GPCP) 31 , Global Precipitation Climatology Centre (GPCC) 32 , and Precipitation Reconstruction over Land (PREC/L) 33 observational datasets is +0.35 (±0.05) mm day −1 decade −1 . This represents ~6% of the mean seasonality in the Amazon precipitation (~6 mm/day, Fig.  1b ). Similar increasing trends of seasonality can be found using Tropical Rainfall Measuring Mission (TRMM) 34 and Climate Prediction Center Merged Analysis of Precipitation (CMAP) 35 datasets (Supplementary Fig.  2a–d ).

a Amazonia precipitation during the period 1979–2018. b The seasonality of Amazonia precipitation (maximum minus minimum values) during the period 1979–2018. c , d , e , f , and g , h are similar to a , b , but for Amazon river discharge and APR ocean salinity, respectively. Note that ECCO4 salinity data only cover the period 1992–2017; GECCO2 the period 1979–2016; and SODA3.1.3 the period 1980–2015. The solid (dashed) lines are the linear fits used to determine the trends.

Similarly, increased seasonality in the Amazon river discharge, due to the increased maximum and decreased minimum values, is also found (Fig.  2c, d ). The trend of the increased seasonality in Amazon river discharge is ~1.3 × 10 4  m 3  s −1  decade −1 , representing about 9% of its mean seasonality (~1.4 × 10 5  m 3  s −1 , Fig.  1c ). We also examine thirteen other river discharge datasets available within the Amazon river basin (Supplementary Fig.  3 ), and ten of them show increasing trends throughout the Amazon sub-basins (Supplementary Fig.  4 ), although the temporal coverages of some river discharge data are too short of providing robust trend estimates (e.g., Supplementary Fig.  4e, i ).

Following the enhanced seasonality in the Amazon river discharge, the seasonality trends in APR 5-m ocean salinity (averaged over the red box in Fig.  1a ) have also increased by ~2.89 × 10 −2 , 1.82 × 10 −2 , and 1.23 × 10 −1 PSU decade −1 for the German contribution to ECCO version 2 (GECCO2) 36 , Estimating the Circulation and Climate of the Ocean project version 4 (ECCO4) 37 , and Simple Ocean Data Assimilation version 3.1.1 (SODA3.3.1) 38 products, respectively (Fig.  2f, h ). However, we find decreasing trends in the Ocean Reanalysis/analysis version 5 (ORAS5) 39 and EN4 40 products (Fig.  2h ). The discrepancy among ocean salinity products is likely related to different data-processing or assimilation procedures, and quality and sampling biases of input data (see discussion in “Methods”). The trend averaged over five products is ~1.44 × 10 −2 PSU decade −1 , which accounts for only ~1% of the mean seasonality (~1.38 PSU, Fig.  1d ). However, this trend increases to 5.66 × 10 −2 PSU decade −1 , ~4% of the mean seasonality, when the three products that show an increasing seasonality trend are averaged (though this estimate is dominated by the increasing trend in the SODA3.3.1 product, red line in Fig.  2h ). We also used five gridded ARGO products 41 to determine recent (post-2001) changes in APR ocean salinity seasonality (see Supplementary Fig.  5b ); the average of the five available gridded ARGO datasets shows an unambiguous increasing trend (rightmost bar in Supplementary Fig.  5b ).

To understand the underlying mechanisms for the seasonal cycle changes, we consider the vertically integrated atmospheric moisture budget (see “Methods”), averaged over the Amazon river basin using multiple reanalysis products. The net increasing trend in precipitation seasonality (Fig.  2b ) is found in all the reanalysis datasets (Fig.  3a ), which are largely attributed to the enhanced vertical moisture advection (i.e., \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) , Fig.  3d ) and secondarily to the enhanced horizontal moisture advection (i.e., \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) , Fig.  3c ). In contrast, the evapotranspiration (i.e., E , Fig.  3b ) and residuals (i.g., δ , Fig.  3e ) counteract the advection effects. The trend toward increasing seasonality in vertical moisture advection is ~0.52 ± 0.15 mm day −1 decade −1 , which contributes to precipitation of ~0.36 ± 7.15 mm day −1 decade −1 and is larger than the horizontal moisture advection of 0.16 ± 0.06 mm day −1 decade −1 . Further decomposition of the vertical moisture advection shows that the dynamical component ( \(- \left\langle {\omega^{ \prime} \frac{{\partial q}}{{\partial p}}} \right\rangle\) , 0.45 ± 0.15 mm day −1 decade −1 , Fig.  3g ) contributes more strongly than the thermodynamic component ( \(- \left\langle {\omega \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) , 0.07 ± 0.03 mm day −1 decade −1 , Fig.  3f ). Such increased seasonality in the dynamic component is related to the vertical motion (Fig.  3i ) that favors increased seasonality in convective activity above the Amazon river basin, while that in the thermodynamic component is associated with the increasing atmospheric moisture content (Fig.  3j ).

The seasonality of reanalysis precipitation ( a ), evapotranspiration ( E ) ( b ), horizontal moisture advection ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ) ( c ), vertical moisture advection ( \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) ) ( d ), residual ( δ ) ( e ), thermodynamic component ( \(- \left\langle {\bar \omega \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) ) ( f ), dynamical component ( \(- \left\langle {\omega ^{\prime} \frac{{\partial \bar q}}{{\partial p}}} \right\rangle\) ) ( g ), nonlinear component ( \(- < \omega ^{\prime} \frac{{q^\prime }}{{\partial p}}\) ) ( h ), vertical velocity ( ω ) ( i ), and total moisture ( q ) ( j ) over time. The blue shadings are the range among reanalysis products, and the red lines are the linear fits used to determine trends. Note different y -axis ranges.

The precipitation and evapotranspiration seasonality changes over the APR region should also affect the local ocean salinity. Thus, we perform the same moisture budget analysis over the APR (red box in Fig.  1a ), but find no robust and consistent trends in seasonalities of precipitation and evapotranspiration compared to that of the APR ocean salinity (Supplementary Fig.  6 ). The precipitation seasonality has a negative trend, while that of evapotranspiration has weakly increased, neither of which can account for the observed robust increasing trend in the APR ocean salinity seasonality. This leaves the increased seasonality in river discharge as a sole source for the observed change in ocean salinity seasonality, although it could be modulated by ocean advection and vertical mixing processes. In addition, the Orinoco river discharge may influence the APR ocean salinity 8 ; however, we find its effect is less important than that of the Amazon river discharge, as the seasonality trend in the Orinoco river discharge has decreased before 2000 and weakly increased (2.1 × 10 3  m 3  s −1  decade −1 ) after mid-2000 (Supplementary Fig.  7 ).

Climate model sensitivity experiments

We conduct two sets of historical global climate model experiments during 1979–2009 (the availability of the Coordinated Ocean-ice Reference Experiments Phase 2 project, CORE-II, forcing 42 limits the simulation period, see “Methods”) to single out the effects of Amazonia precipitation and Amazon river discharge seasonality changes. The experiments also help address the causality. The first set uses a global land model with increased seasonality in the precipitation forcing by a factor of 1.5 and 1.75 during the 1979–2009 period over the Amazon river basin (Fig.  4a and see “Methods”). In response to the enhanced precipitation seasonality trend, we find a nearly linear response in the seasonality trend of the Amazon river discharge (Fig.  4b ). The relative change in the river discharge seasonality is slightly higher than that in precipitation seasonality; that is, the precipitation seasonality increases by a factor of 1.75, whereas that of the river discharge increases by a factor of 1.87 (Fig.  4b ). This runoff intensification effect is likely attributed to nonlinear river discharge responses to precipitation intensity identified in a previous study 27 .

a The seasonality in the precipitation forcing used in the global land model control and experimental simulations. b Seasonality changes in Amazon river discharge in the land model experiments. c similar to a , but for Amazon river runoff forcing used to force the ocean model experiments. In a , c , the dashed lines are the linear fits used to determine trends. d similar to b , but for seasonality changes in APR ocean salinity in the ocean model experiments. The black star is the APR salinity seasonality trend, averaged over five spin-up cycles.

To test the robustness of the ocean salinity seasonality response, given the discrepancy shown in the ocean-state estimate products (Fig.  2f, h ), we conduct the second set of experiments using a global ocean model forced with varied seasonality in Amazon river discharge (Fig.  4c and see “Methods”). The APR 5-m ocean salinity seasonality (averaged over the red box in Fig.  1a ) also increases nearly linearly as the river discharge seasonality increases (Fig.  4d ). It should be noted that the total amount of Amazon freshwater input in each experiment remains the same by our experimental design (see “Methods”); that is, no additional freshwater is added to the model when changing its seasonality. We further analyze 12 CORE-II ocean-only hindcast simulations forced with the same CORE-II forcings, including precipitation and river discharge from 1979 to 2009 (see “Methods”), and obtain overall increased APR ocean salinity seasonality trends (Supplementary Fig.  5a ). The above hierarchical historical climate model experiments and CORE-II simulations, therefore, lend support to the increased APR salinity seasonality trends found in GECCO2, ECCO4, and SODA3.1.1 ocean-state estimate products.

The enhanced seasonality in the ocean salinity can affect the ocean physics and dynamics within the APR. Thus, we further examine the trends in the seasonalities of the APR area (Fig.  5a , defined as the area where ocean salinity are less than 34.5 as denoted by the magenta contour line in Fig.  1a ), the 5-m ocean temperature (Fig.  5b ), the upper ocean stratification (i.e., buoyancy frequency,  N 2 , which also indicates the strength of vertical mixing, Fig.  5c ), and the barrier-layer thickness (Fig.  5d ) from the ocean model experiments. In order to better characterize the localized changes, the averaged values shown in Fig.  5 are taken over only the regions where the 5-m ocean salinity is <34.5 PSU (an alternative definition of APR area) rather than averaged over the APR box as those presented in Fig.  4 ). However, both the areal metrics produce very similar results (c.f., Supplementary Fig.  8 and Fig.  4d ). The seasonality trends of the APR area and vertical mixing strength again respond nearly linearly to that of the Amazon river discharge, whereas the trends of the 5-m ocean temperature and barrier-layer thickness seasonalities show more sensitivity, with larger trend increases from ×1.5 to ×1.75 experiments. These features indicate that the vertical mixing process is dominated by the effect of ocean salinity change associated with the Amazon river freshwater change, manifested as more linear behaviors, but the ocean temperature and barrier-layer thickness are also affected by other factors. It is possible that the temperature responses are more affected by the variability of internal ocean dynamics (e.g., baroclinic eddies or the bifurcation of the North Brazilian Current) 43 , 44 manifested as the larger spreads among five ensembles in each experiment (Fig.  5b ). In addition, the Amazon river discharge temperature is not accounted for in the ocean model configuration, which may also contribute to the APR ocean temperature response.

a Seasonality changes in the size of APR, defined as the region where 5-m ocean salinity is <34.5 PSU. b Seasonality changes in the 5-m ocean temperature averaged over the region where 5-m ocean salinity is <34.5 PSU. c , d similar to b , but for seasonality changes in buoyancy frequency and barrier-layer thickness.

This study finds that an amplified seasonal cycle of Amazonia precipitation during the period 1979–2018 leads to enhanced seasonalities in both Amazon river discharge and APR ocean salinity, using a combination of observations and reanalysis datasets. Hierarchical climate model experiments support the observed seasonality changes and shed light on the sole effects of changing seasonalities in the Amazonia precipitation and Amazon river discharge. While previous studies mainly focused on specific dry or wet seasons without taking into account the seasonality changes in a comprehensive fashion 24 , 25 , 26 , our results provide a new route to further study the Amazonia hydroclimatology and the occurrence of extreme events in the Amazon river basin and APR.

Within the APR, we find the enhanced seasonality of ocean salinity is tied closely with the enhanced seasonalities in the plume area, upper ocean stratification, near-surface ocean temperature, and barrier-layer thickness in our ocean model experiments (Fig.  5 ). These changing ocean properties are important in that they could affect the carbon cycle and marine biogeochemistry within the APR more significantly as a consequence of increased seasonality of Amazon river discharge 7 . It is noted that the increasing precipitation trend in the wet season (mostly January–February–March) contributes more than that in the dry season (mostly July–August–September) to the increasing seasonality trend (Fig.  2a ), which is also the case for Amazon river discharge (Fig.  2c ) and APR ocean salinity (Fig.  2e, g ). Presumably, warmer near-surface ocean temperature and thicker barrier layer in the APR in the “fresher” season (mostly May–June–July) could offer favorable surface ocean conditions for hurricane genesis via barrier layer dynamics 4 , 8 , 14 , 15 . Previous studies using statistical and dynamical hurricane forecast framework showed that the inclusion of upper ocean heat content provides longer and better predictability of hurricane intensity 45 , 46 , 47 . The results presented in this study, therefore, have important implications for hurricane forecasting. However, the near-linear relationship in our modeling results does not include the atmosphere–land–ocean feedback processes. There may exist limitations to tie the seasonality changes of Amazon river discharge and APR ocean salinity, and unrealistic seasonality responses. In addition, previous studies have shown that the Amazon river discharge can affect tropical Atlantic air–sea interactions 17 , regional sea-level height 20 , 21 , 22 , and have potentially far-reaching impacts on the AMOC 23 .

The atmospheric moisture budget analysis reveals that the “wet-get-wetter-and-dry-get-drier” phenomenon in the tropical precipitation due to anthropogenic global warming 48 may contribute to the seasonality change. The “wet-get-wetter-and-dry-get drier” precipitation signal can result in a host of consistent seasonality responses in the Amazonia atmosphere–land–ocean coupled system. The enhanced vertical velocity seasonality (Fig.  3i ) may reflect changes in the location and strength of the Atlantic ITCZ that are related to changes in the local sea-surface temperature gradient in the tropical Atlantic 49 .

It should be noted that the decadal and multidecadal natural modes of variability, such as Pacific decadal variability (PDV), interdecadal Pacific variability (IPV), or Atlantic multidecadal variability (AMV), are possible large-scale drivers of the precipitation seasonality changes at longer timescales 50 , 51 , which have been shown by some studies to be more influential than anthropogenic forcing in the Amazon river basin in observations and climate model simulations 52 , 53 . For example, recent dry season droughts across the Amazon river basin have been attributed to the AMV 54 , 55 . We find that the AMV, PDV, and IPV indices and the seasonality of the Amazon river discharge time series, after applying 11-year running average, are correlated after 1970 ( R  = 0.58 for AMV index, R  = −0.81 for PDV index, R  = −0.64 for IPV, all of which are larger than 0.41, critical value at 99% significance level, see Supplementary Fig.  9 ), indeed suggesting that part of the increased Amazon river discharge seasonality trend in the past 30 years can be attributed to low-frequency Atlantic and Pacific sea-surface temperature variations. In addition, changes in hydropower dam construction 56 , deforestation 57 , and groundwater dynamics 58 may have also affected the hydroclimatology of Amazonia and consequently river discharge seasonality. All these effects are not considered in this study. To quantify the relative and combined effect of all local versus remote forcings and natural versus anthropogenic factors would require significant modeling efforts, including a series of well-designed global climate model experiments. This will be left for a future study.

APR and the Amazon river basin

The APR is chosen according to the extent to which the effect of Amazon river freshwater can reach in terms of overall mean state 8 . Previous studies showed that the size of the freshwater plume is determined by the combination of the region of relatively low salinity extending from the Amazon river mouth (magenta contour line in Fig.  1a ) and strength of the prevailing North Brazilian Current (denoted by black arrows near coasts in Fig.  1a ); it carries the freshwater released at the Amazon river mouth northwestward to the Caribbean 4 , 7 , though sometimes the North Brazilian Current turns eastward to bring freshwater eastward 43 . The APR we used in this study (red box in Fig.  1a ) covers not only the near-coastal area but also this bifurcation branch of ocean circulation. The APR is used to calculate the area-averaged near-surface ocean salinity time series. We also consider the region with ocean salinity <34.5 PSU following a previous study 8 to define APR (magenta contour line in Fig.  1a ), which produces similar results (c.f., Fig.  4d and Supplementary Fig.  8 ).

We define the Amazon river basin (black contour line in Fig.  1a ) as the catchment upstream of the Obidos station based on ArcGISV10.1, which is different from the conventional Amazon river basin based on the Amazon river mouth near the Equator (black contour in Supplementary Fig.  3 ). Because the conventional definition includes river discharge downstream of the Obidos station, this adjusted river basin is used to more accurately calculate the area-averaged Amazonia precipitation time series consistent with river discharge observed at the Obidos station. We compare area-averaged precipitation annual cycles using this adjusted and conventional river basin (black contour line in Supplementary Figs.  3 and 10a ). Slightly less precipitation using the adjusted river basin occurs in the wet season, and more precipitation from spring to fall. We also compare the absolute difference ratio between them and find the ratio ranges from 2.5% to 21% (Supplementary Fig.  10b ). The correlation coefficient between their monthly time series during 1979–2018 is as high as 0.99. These results using the adjusted river basin largely capture the amount and variability of those using the conventional Amazon river basin.

The observational, reanalysis, and ocean-state estimate datasets

The monthly Amazon river discharge observed at Obidos gauge station (#17050001, 1.9225°S, and 55.6753°W, magenta star in Fig.  1a ) for 1968–2018 and the Orinoco river discharge data observed at Ciudad Bolivar gauge station (#408000000, 08.1536°N, and 063.5361°W, blue star in Fig.  1a ) for 2003–2018 are obtained from SO HYBAM material transport datasets (formerly Environmental Research Observatory, http://www.ore-hybam.org/ ). Other river discharge data within the Amazon river basin are also obtained from the HYBAM website (Supplementary Figs.  3 and 4 ). We also use the Amazon river and Orinoco river discharge data from the Global River Flow and Continental Discharge Data Set during 1979–2018 ( http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/ ) 59 , which provides river discharge data for the world’s 925 largest rivers primarily based on gauge observations with the assistance of model simulations.

For the observational monthly precipitation datasets, we use GPCP version 6 ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ) 31 , 60 , GPCC ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ) 32 , and Precipitation Reconstruction over Land (PREC/L, https://www.esrl.noaa.gov/psd/data/gridded/data.precl.html ) 33 for 1979–2019. We also use observational precipitation datasets from the TRMM version 7 ( https://pmm.nasa.gov/data-access/downloads/trmm ) 34 and the Climate Prediction CMAP ( https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html ) 35 . Multiple reanalysis products during 1979–2018 are used when calculating the atmospheric water moisture budget (see Supplementary Tables  1 and 4 – 8 ). We have compared the variability of reanalysis precipitation datasets with the observed ones. Their characteristics are very similar (c.f., Figs.  2b and 3a ).

When calculating the effect of evapotranspiration 61 , 62 , we use multiple reanalysis products (Supplementary Table  4 ). We also use global monthly evapotranspiration fields from the Global Land Evaporation Amsterdam Model (GLEAM, during 1980–2018, https://www.gleam.eu/ ) 63 , 64 , which assimilates a series of land surface and satellite observations. Their results are largely similar (c.f. Fig.  3b and Supplementary Fig.  2f ).

Several monthly observational and ocean-state estimate products for the salinity field at the surface and 5 m below the ocean surface used in this study include the SMOS (during 2011–2016, https://www.esa.int/Our_Activities/Observing_the_Earth/SMOS ) 29 and the U.S./Argentina Aquarius/SACD (during 2012–2014, https://aquarius.oceansciences.org/cgi/index-noflash.htm ) 30 , Estimating the Circulation and Climate of the Ocean project version 4 (ECCO4, during 1992–2017, www.ecco-group.org ) 37 and the German contribution to ECCO version 2 (GECCO2, during 1979–2016, https://icdc.cen.uni-hamburg.de/1/daten/reanalysis-ocean/gecco2.html ) 36 , EN4.2.1 (EN4 hereafter, during 1979–2018, https://www.metoffice.gov.uk/hadobs/en4/en4-0-2-profile-file-format.html ) 40 , Ocean Reanalysis/analysis version 5 (ORAS5, during 1979–2018, https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis ) 39 , and Simple Ocean Data Assimilation version 3.1.1 (SODA3.1.1, during 1980–2015, http://www.soda.umd.edu/ ) 38 . We also use five interpolated ARGO products (see the first four bars from the left in Supplementary Fig.  5b , http://www.argo.ucsd.edu/Gridded_fields.html ) 41 .

We examine the data quality of EN4 according to its salinity observation weight and uncertainty in the APR (Supplementary Fig.  11 ). Despite the increasing observation weight and decreasing salinity uncertainty after the late 1990s in the APR; low observation weight and large uncertainty before the mid-1990s may contribute to the discrepancy of APR salinity seasonality between EN4 and other products. In addition, different assimilation process in generating the products may also be a factor.

Before calculating the area-averaged values, we only regrid the field from ORAS5 and GECCO2 product to ~1° × 1° using nearest interpolation because the former is output in T-grid and the latitude grid of the latter varies in different variables. We do not perform interpolation for other datasets.

Atmospheric moisture budget analysis

We utilize a vertically integrated moisture budget analysis to explore the mechanisms behind the enhanced precipitation seasonality. A similar analysis has been performed in many studies to examine global and regional precipitation changes 27 , 65 on various timescales (i.e., daily, monthly, and interannually). The moisture budget is formulated as

where P denotes precipitation, \({\vec{\mathbf{v}}}\) the horizontal velocity field, q specific humidity, E evapotranspiration, δ the residual term, ω is the pressure velocity, and <> mass integration throughout the atmospheric layers (surface to model top). The first term on the right-hand side ( \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) ) represents the vertical moisture advection, while the second term ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ) represents the horizontal moisture advection ( \(- \left\langle {{\vec{\mathbf{v}}} \cdot \nabla q} \right\rangle\) ). When vertical integration is performed on \(- \left\langle {\omega \frac{{\partial q}}{{\partial p}}} \right\rangle\) , the pressure velocities at the surface and at the model top are assumed to be zero. Note that the residual term ( δ ) includes transient eddy and nonlinear effects.

The vertical moisture advection can be further divided into:

where \(\overline {()}\) indicates seasonal averaging from 1980 to 2018 and ()′ denotes the seasonal anomaly from the seasonal mean in the wet and dry seasons. We disregard the nonlinear term \(- \left\langle {\omega ^{\prime} \frac{{\partial q^\prime }}{{\partial p}}} \right\rangle\) . This decomposition allows us to examine the dynamical and thermodynamical contributions to precipitation changes. The first term of the above equation on the right-hand side represents the thermodynamical term, while the second term, the dynamical term respectively follows previous studies 65 , 66 , 67 . The unit of each term is kg s −1  m −2 , which is equivalent to ml s −1 .

Seasonality calculation and enhancement

Since this study focuses on the seasonal averages of precipitation, river discharge, and APR ocean salinity, we take 3-month averages before calculating their seasonality. The seasonality of the time series with 3-monthly averaging is defined as the difference of its maximum value minus its minimum value within 1 year in this study. Similar results can be obtained without taking a 3-month average. We also calculate seasonality with the difference between fixed wet and dry seasons (e.g., January–February–March averaged precipitation minus July–August–September averaged precipitation based on the climatological seasonal cycle, Fig.  1b ), and obtain very similar results.

To enhance the seasonality for a given forcing field in climate model experiments, we use a fast Fourier transformation (FFT) approach. We first apply FFT on a target time series, and before applying inverse FFT to retrieve the resultant time series, we multiply a targeted factor to enhance its amplitude. To double the amplitude, for example, we choose the factor as 2. To demonstrate, we consider a simple combined sine wave, \(\sin (\frac{{2\pi }}{{360}}x) + \sin \left( {\frac{{4\pi }}{{360}}x} \right) + 5\) , which is shown as blue line in Supplementary Fig.  12 , while the time series that has been amplified by a factor of 2 is shown as the red line. It is noted that the mean of the two time series is exactly the same.

Global land model historical experiments

To test the sensitivity and quantify the seasonality of Amazon river discharge changes in response to Amazonia precipitation seasonality changes, we use the Community Land Model version 4.5 (CLM4.5) 63 under the Community Earth System Model (CESM) framework to conduct land-only experiments with varying precipitation forcing seasonality. The atmospheric conditions used to force the CLM4.5 hindcast experiments are constructed following a previous study 64 using observational and reanalysis datasets from 1948 to 2009. For the control simulation, we conduct a 62-year simulation from 1948 to 2009 with corresponding forcings prescribed. We then repeat four cycles with the same forcings to generate a total of five ensemble members. For the “x1.5” (“x1.75”) experiment, we conduct another 62-year simulation from 1948 to 2009 cycling five times using the precipitation forcing seasonality increased by a factor of 1.5 (1.75) above the Amazon river basin. We only analyze the results from 1979 to 2009.

When we construct precipitation forcing with enhanced seasonality, due to the fact that precipitation forcing is given in 6-h time intervals, we first take the monthly average from the 6-h precipitation field and then perform FFT on the monthly field to enhance the seasonality. We then add increased or decreased values in 1 month back to the original 6-hourly precipitation forcing for the experimental simulations. However, some resultant 6-h values can be less than zero, which is not reasonable, so we set all negative values to zero. Although in this way, the mean of the resultant precipitation forcing is not exactly the same as that of the original precipitation forcing, we compare their mean values and find only a small difference.

Global ocean model historical experiments

We conduct an ocean-only experiment similar to the land-only experiment described above, but with the river runoff forcing seasonality changed at the Amazon river mouth, to examine the sensitivity of ocean salinity in the APR. We use the Parallel Ocean Program version 2 (POP2) under the CESM framework. The boundary conditions used to force POP2 are prepared according to the Coordinated Ocean-ice Reference Experiments Phase 2 project (CORE-II) 42 , which spans from 1948 to 2009. In the global river runoff forcing field to drive the ocean model, we increase the river runoff at the grid, where largest annual mean river runoff occurs in the South American continent, by a factor of 1.5 and 1.75 to construct runoff forcings for the “x1.5” experiment and the “x1.75” experiment, respectively, using the FFT approach as well. It is noted that the global river runoff forcing is constructed based on the Dai-Trenberth’s dataset 59 , which is analyzed in Figs.  1 and 2 .

Due to the fact that the ocean model requires a longer time to reach quasi-equilibrium and to effectively reduce model drift in the historical ocean-only simulations, a five-cycle spin-up simulation was suggested by previous studies 68 , 69 . Therefore, we conduct five-cycle spin-up simulations with repeating boundary conditions from 1948 to 2009 (black star in Fig.  4d ). The control simulation is continued from the spin-up run for another five more cycles to generate five ensemble members, whereas the “x1.5” and the “x1.75” experiments are continued from the spin-up runs for another five more cycles given river runoff forcings with Amazon river runoff forcing seasonalities increased by a factor of 1.5 and 1.75, respectively. We only analyze results from 1979 to 2009.

The original runoff and amplified seasonalities of river runoff are shown in Fig.  4c . We choose the amplification factors in ocean model experiments in order to prevent the amplified runoff value from being smaller than zero. It is noted that the total river runoff released into the ocean is exactly the same in each experiment because the monthly mean of runoff fields is the same, which is a direct result of the FFT approach described above.

Coordinated Ocean-ice Reference Experiments Phase 2

Coordinated Ocean-ice Reference Experiments Phase 2 (CORE-II) entails a set of coordinated historical global ocean model experiments using different state-of-the-art global ocean models developed by different modeling groups. The models are prescribed with common forcings, including precipitation and river runoff from 1948 to 2009 42 , 70 . The boundary fluxes are computed following the same bulk formulae 42 . CORE-II simulations provide a framework to investigate mechanisms of significant ocean phenomena and their seasonal and decadal variabilities, including both forced and internal variability. Therefore, CORE-II fits well for this study to examine whether the increasing seasonality trend of near-surface ocean salinity in the APR is a common feature in response to the observed precipitation and subsequent river runoff seasonality changes in the Amazon river basin.

Supplementary fig.  5a shows that the near-surface ocean seasonalities in 12 CORE-II simulations are overall enhanced during 1979–2007 (we drop the last two years because some models do not provide simulated results in 2008 and 2009), which is consistent with the result found in the GECCO4, ECCO, and SODA3.1.1 ocean-state estimate products. The multi-model mean trend is about 0.054 PSU per decade. The values of the increased seasonality trend are comparable to those in the ocean state estimate products (~0.04 PSU per decade for GECCO2 and ~0.03 PSU per decade for ECCO4), indicating that CORE-II simulations reasonably capture the increased trends of ocean salinity seasonality and support our findings based on ocean-state estimate products. For a comparison of interannual variability between simulated long-term mean seasonal cycles, one is referring to a previous study 42 . CORE-II simulation results are downloaded from the NCAR/NCEP Research Data Archive ( https://rda.ucar.edu/datasets/ds262.0/ ).

Natural variability indices

To assess potential linkages between the seasonality in the Amazonia hydroclimatological system and natural variability, we consider the AMV 71 , PDV 72 , and IPV 73 , as shown in Supplementary Fig.  9 . The time series are shown as 11-year running average to illustrate their decadal variability. The AMV index and a tripole index representing IPV are downloaded from ESRL Physical Science Division ( https://www.esrl.noaa.gov/psd/data/timeseries/AMO/ , and https://www.esrl.noaa.gov/psd/data/timeseries/IPOTPI/ ), and the PDV index is obtained from the Joint Institute for the Study of the Atmosphere and Ocean ( http://research.jisao.washington.edu/pdo/ ).

Near-surface ocean mixing and barrier-layer calculations

In order to examine the responses of near-surface ocean physics and dynamics to Amazon river discharge in the APR, we calculate the barrier-layer thickness and potential energy following a previous study 74 . The barrier-layer thickness is defined as the isothermal layer depth (ILD) minus the mixed layer depth (MLD) when the former is deeper than the latter. If ILD is shallower than MLD, the barrier-layer thickness at this grid is not considered. The MLD is calculated as σ ref  + Δ σ , where σ ref is chosen as 5 m and Δ σ 0.1 kg m −3 . The ILD is computed using the temperature difference equivalence to 0.1 kg m −3 of density increase from the reference depth with the salinity at the reference 5-m depth. We only consider grid points where the 5-m ocean salinity is <35.4 PSU within the APR to better characterize the freshwater plume following a previous study 8 , but similar results can be obtained without this constraint. To characterize the near-surface mixing processes, we also calculate the squared buoyancy frequency (in the unit of s −2 ), defined as:

where g is the gravitational constant, ρ 0 is a reference density (1025 kg m −3 ), ρ is density, and z is depth.

Statistical significance test

For a given time series, the statistical significance of its trend is determined based on a Student’s t test with a null hypothesis that the trend is zero 75 . If the P value is <0.05, the null hypothesis can be rejected with 5% significance, and the trend is considered significant at the 5% level. We consider the effective sample size when performing the t test to take into account the effect of serial correlation. The effective sample size (ESS) is given as:

where N is the length of time series, and R x and R y are the lag-1 autocorrelations of time series x and y , respectively 76 .

Data availability

The monthly Amazon river discharge observed at Obidos gauge station and the Orinoco river discharge data observed at Ciudad Bolivar gauge station are obtained from SO HYBAM material transport datasets (formerly Environmental Research Observatory, http://www.ore-hybam.org/ ). We also use the Amazon river and Orinoco river discharge data from the Global River Flow and Continental Discharge Data Set ( http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/ ). For the observational monthly precipitation datasets, we use Global Precipitation Climatology Project version 6 ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ), Global Precipitation Climatology Centre ( https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html ), and Precipitation Reconstruction over Land ( https://www.esrl.noaa.gov/psd/data/gridded/data.precl.html ). We also use observational precipitation datasets from the TRMM version 7 ( https://pmm.nasa.gov/data-access/downloads/trmm ) and the Climate Prediction Center Merged Analysis of Precipitation ( https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html ). Multiple reanalysis datasets are used: ERAI is obtained from ECMWF ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ), MERRA from NASA ( https://gmao.gsfc.nasa.gov/reanalysis/MERRA/ ), JRA and JRA-55 from the Japan Meteorological Agency and the Central Research Institute of Electric Power Industry ( https://jra.kishou.go.jp/JRA-55/index_en.html ), NCEP_R2 from ESRL ( https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html ), and NCEP_CFSR from NCAR/UCAR ( https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr ). The Global Land Evaporation Amsterdam Model data are downloaded from https://www.gleam.eu/ . Several monthly observational and ocean-state estimate products for the salinity field at the surface and 5 m below the ocean surface used in this study include the soil moisture and ocean salinity ( https://www.esa.int/Our_Activities/Observing_the_Earth/SMOS ) and the U.S./Argentina Aquarius/SACD ( https://aquarius.oceansciences.org/cgi/index-noflash.htm ), Estimating the Circulation and Climate of the Ocean project version 4 ( www.ecco-group.org ) and the German contribution to ECCO version 2 ( https://icdc.cen.uni-hamburg.de/1/daten/reanalysis-ocean/gecco2.html ), EN4.0.2 ( https://www.metoffice.gov.uk/hadobs/en4/en4-0-2-profile-file-format.html ), Ocean Reanalysis/analysis version 5 ( https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis ), and Simple Ocean Data Assimilation version 3.1.1 ( http://www.soda.umd.edu/ ). We also use five interpolated ARGO products ( http://www.argo.ucsd.edu/Gridded_fields.html ). Ocean-only simulations of Coordinated Ocean-ice Reference Experiments Phase 2 are obtained from NCAR/UCAR ( https://rda.ucar.edu/datasets/ds262.0/ ). The AMV index and a tripole index are downloaded from ESRL Physical Science Division ( https://www.esrl.noaa.gov/psd/data/timeseries/AMO/ , and https://www.esrl.noaa.gov/psd/data/timeseries/IPOTPI/ ), and the PDV index is obtained from the Joint Institute for the Study of the Atmosphere and Ocean ( http://research.jisao.washington.edu/pdo/ ). CORE-II simulation results are downloaded from the NCAR/NCEP Research Data Archive ( https://rda.ucar.edu/datasets/ds262.0/ ). ERAI and ERA5 datasets are downloaded from ECMWF ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ). JRA-55 data are downloaded from JRA Project ( https://jra.kishou.go.jp/JRA-55/index_en.html#download ). NCEP_R1 and NCEP_R2 are downloaded from NOAA’s Physical Sciences Laboratory ( https://psl.noaa.gov/data/gridded/index.html ). The sensitivity climate model simulations are compiled on the Zenodo data repository ( https://doi.org/10.5281/zenodo.3939611 ). The geographic maps in Fig.  1a and Supplementary Fig.  3 are produced by Python Cartopy package ( https://scitools.org.uk/cartopy/docs/latest/# ) 77 .

Code availability

The codes that analyze the data and make figures are available on Y.-C. L.’s GitHub website ( https://github.com/yuchiaol/Amazon_river_seasonality ).

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Acknowledgements

M.-H.L., C.-W.L., and R.-J.W. are supported by the Ministry of Science and Technology in Taiwan under grant 106-2111-M-002-010-MY4. H.S. and J.D.S. are grateful for support from NOAA NA19OAR4310376 and NA17OAR4310255. C.C.U. acknowledges support from the U.S. National Science Foundation under grant OCE-1663704. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. We thank Dr. Young-Oh Kwon at Woods Hole Oceanographic Institution and Dr. Who Kim at NCAR for discussions about the ocean model experiment design. We thank Dr. Mehnaz Rashid at National Taiwan University and Wen-Yin Wu at the University of Texas at Austin in helping generate the high-resolution Amazon river mask. We also thank Dr. Gael Forget at Massachusetts Institue of Technology for comments on using ECCO and other ocean-state estimate products.

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Min-Hui Lo, Chia-Wei Lan & Ren-Jie Wu

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The paper was conceived and written by Y.-C.L. and M.-H.L. with inputs from C.-W.L., H.S., C.C.U., S.Y., R.-J.W., and J.D.S. The global ocean and land model experiments were conducted by Y.-C.L. and R.-J.W. C.-W.L. performed the atmospheric moisture budget analysis. J.D.S. performed the analyses on surface ocean properties and barrier-layer thickness.

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Liang, YC., Lo, MH., Lan, CW. et al. Amplified seasonal cycle in hydroclimate over the Amazon river basin and its plume region. Nat Commun 11 , 4390 (2020). https://doi.org/10.1038/s41467-020-18187-0

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Case Study: The Amazonian Road Decision

The proposed Pucallpa–Cruzeiro do Sul will connect the Amazon’s interior to urban centers and export markets in Peru and Brazil. However, critics are worried that the road will also create new opportunities for illegal logging and infringe on the territory of indigenous communities and wildlife.

Biology, Geography, Human Geography

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A proposed road along the western edge of the Amazon River has sparked a great debate. This road would connect the remote town of Cruzeiro do Sul, Brazil, with the larger city of Pucallpa, Peru.

Proponents of the road claimed that it would provide an efficient way for rural farmers and tradesmen to get their goods to city markets. They claim it would also allow loggers to more easily transport timber from the depths of the Amazon rainforest to sawmills. The wood could then be easily shipped from Peru's Pacific coast to international buyers.

However, critics argue that the Pucallpa-Cruzeiro do Sul road would cut through traditional territories of the Ashéninka, an indigenous people of eastern Peru. Many leaders fear the road will increase access to previously undeveloped rainforest , threatening the ecosystem and the Ashéninka way of life. Large trees, such as mahogany, would catch the eye of illegal loggers because of their high value.

The great mahogany trees also serve to protect the Ashéninka from the outside world and are essential for the health of the rainforest . The trees provide shelter, food, and nesting grounds that sustain the vast biodiversity within the ecosystem . The Ashéninka have come to depend on nature for food and shelter.

A Snapshot of the Geography

The Amazon River Basin is located in South America, covering an area of 4.3 million square kilometers (2.7 million square miles). That's about the size of four Alaskas combined. Nearly 70 percent of the basin falls within Brazil. The remaining areas stretch into parts of Peru, Ecuador, Bolivia, Colombia, Venezuela, and Guyana.

The Amazon's massive drainage basin is made up of dozens of smaller watersheds . A watershed is an area of land that separates water flowing through the basin. In a drainage basin, the water drains into the ground and creates a rich habitat. The Ashéninka people have lived near a watershed at the border of Peru and Brazil for centuries. They survive on hunting, fishing and crops, such as sweet potato, corn, coffee, and sugar cane.

A Variety of Plants, Animal Life and Mahogany Trees

The rainforest surrounding the Amazon River is the largest on the entire planet. In addition to 33 million human inhabitants, including 385 distinct Indigenous groups, it hosts the greatest diversity of plant and animal life in the world. More than two million species of insects are native to the region, including hundreds of spiders and butterflies. Howler and spider monkeys are abundant, along with sloths, snakes, and iguanas. Brightly colored birds like parrots and toucans live there too. Many of these species are only found in the Amazon rainforest.

Mahogany is one of the most valuable resources from the Amazon forest. The tree's rich, red color and durability make it one of the most desired building materials in the world. A single mahogany tree can fetch thousands of dollars.

Logging is prohibited in much of the Amazon River Basin, but it is still legal in some areas because the wood is so valuable. The high demand for mahogany has left many of Peru's watersheds stripped of these important trees. Without large trees, and their roots, the soil in the watershed is at risk of being worn away or washed out by heavy flooding.

A Conflict of Values

The Pucallpa-Cruzeiro do Sul road is part of a larger project to connect South America's more remote areas. Yet, there is tension between communities because of this project. Some want to make the countryside of the Amazon basin better suited for trade and business. Others want to preserve its forested areas.

Supporters of the Pucallpa-Cruzeiro do Sul road say that, since Amazonian resources are so valuable, they could help bring jobs and businesses to the basin. They also say the road will allow members of rural communities to access better healthcare, education, and welfare.

Conservationists are concerned that buildings and infrastructure such as the Pucallpa-Cruzeiro do Sul road will destroy an already weakened Amazonian ecosystem . There is a known link between roads and deforestation , or the cutting down of trees. In Brazil, for instance, 80 percent of deforestation occurs within 48.3 kilometers (30 miles) of a road. Critics argue that a road along the Brazil-Peru corridor will provide easier access for illegal loggers to reach mahogany and other trees.

Major Stakeholders

Indigenous Communities: Members of the Ashéninka community are trying to protect the forest and their native lands. However, opinions are largely divided between those favoring conservation and those seeking greater job and business opportunities. Indigenous communities like the Ashéninka have largely maintained a traditional way of life. Yet, while the Ashéninka want to preserve their culture and connections to the forest, they also need access to clothes, soap, and medicine. The road could establish trade routes that make these goods more accessible. Then again, these communities could be exposed to diseases they never had before. Giving easier access to their land would also increase the risk of unscrupulous companies taking it from them.

Wildlife: The Pucallpa-Cruzeiro do Sul road would run through Serra do Divisor National Park, Brazil, and other nature reserves. Threatened and rare species live here. For some of these species, such as the spider monkey, the construction of the road could make their population scatter and more visible to hunters. As large trees are removed, any wildlife that relies on the trees for shelter or food will need to relocate.

Amazonian Ecosystem: In addition to the negative effects on the area's plants and animals, the construction of the Pucallpa-Cruzeiro do Sul road could wear away land, reduce water quality, and increase deforestation . Fewer forests means larger amounts of greenhouse gases entering the atmosphere. This in turn could worsen global warming.

Logging Companies: If a road is constructed, loggers will have easier access to mahogany and other trees. This will allow them to make more money for their families and communities. This could mean better education, healthcare, and the chance to participate in political debate.

Residents of Rural Communities: The Pucallpa-Cruzeiro do Sul road would allow local farmers and business people to transfer goods from the Amazon to Peru's Pacific coast. Right now, merchants who want to travel between Cruzeiro do Sul and Pucallpa must do so by plane. A reliable road would improve the social and business relations between Peru and Brazil.

International Consumers: The global demand for mahogany makes it a multimillion dollar business. Mahogany is used to create bedroom sets, cabinets, and flooring throughout the world, mostly in the United States and Europe.

Seeking a Compromise

Groups are seeking to lower tension in the Pucallpa-Cruzeiro do Sul road conflict through dialogue. They are trying to come up with alternate infrastructure plans.

Environmental conservation groups have suggested that the Pucallpa-Cruzeiro do Sul road be delayed until the community discusses two key pieces of the project. First, conservationists want more information on the environmental impact of the construction. This discussion involves local environmental groups, government representatives and businesses. Second, conservationists are seeking full permission for the project from indigenous communities.

Some critics of the Pucallpa-Cruzeiro do Sul road argue that roads are not the only option for the Pucallpa community to extend its business. Traditional river systems are already in place. These critics think the river network should be explored as an alternative to road construction.

The Upper Amazon Conservancy (UAC) is a group that protects the environment and cultures of southern Peru. It is working with indigenous peoples to help protect their land. One plan involves members of indigenous groups staying on patrol at national parks to keep illegal loggers out.

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October 19, 2023

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Related Resources

Case Study: The Amazon Basin

• Cattle ranching in Brazil
• Highway construction e.g Marshal Rondon Highway Mineral mining in Carajas
• Since 1970, over 232,000 square miles of Amazon rainforest have been destroyed
• 1995-1998 government granted land in the Amazon to150,000 families
• The Balbina dam flooded some 920 square miles of rainforest when it was completed

Question and Answer forum for K12 Students

Life in the Amazon Basin: The Amazon River, Climate, People, Examples

The compilation of these Human Environment Interactions  Notes makes students exam preparation simpler and organised.

Life in the Amazon Basin

Human beings are the most brained creatures. As humans, we excel in exploring the mother Earth and using it for our best purposes. This skill of ours has given us the advantage of interacting with the environment, and the Amazon basin is the best living example of that. Amazon basin is the result of the many tributaries joining the Amazon river. Let’s explore the human capacity to interact with the environment.

Life in Amazon Basin

We humans, are dependent on nature and interact with it for various reasons. Our interaction with the environment is endless and so is our ability to use it. Being the best example of human interaction with the environment, the Amazon basin has been exploited since time immemorial. Amazon basin is located in South America at 10° N and 10° S of the tropical region.

This region is also referred to as the equatorial region. The Amazon river flows through the region and reaches the Atlantic Ocean through the mountains in the west.

Before reading further about the Amazon basin it is important that we know the following definitions:

• River’s Mouth: The place where a river flows into another water body is called the river’s mouth.
• Tributaries: When a river or a stream flows into a larger river or lake then that river or lake is called the tributary of the larger river.

Amazon basin is the result of the many tributaries that flow into the Amazon river. Amazon basin passes through some parts of Brazil, parts of Peru, Bolivia, Ecuador, Columbia, and a tiny part of Venezuela. The Amazon River is home to thousands of unique species of flora and fauna. As a result, the Amazon river has been home to many wonderful civilizations like ancient Mayans and the Incas, etc. Let’s throw light on the various features of the Amazon basin.

The Climate Amazon basin is situated in the equatorial region which is hot and humid all through the year. Days and nights both are equally hot and wet. Nights are comparatively less hot but the humidity level remains unchanged. Due to the humid conditions here, it rains almost every day.

The Rainforest As it rains throughout the year the forests here are dense. The trees form a dense roof of leaves that do not even let sunlight penetrate into the forest area. Also, the surface of the earth is damp and dark. So shade-tolerant vegetation is present in abundance here. Prominent plant parasites found here are Bromeliads and Orchids. The rainforests of the Amazon basin are flocked with a variety of fauna as well. As a result, you can find the rarest species loitering in the forests here.

Hummingbird, Toucans, Amazon Kingfisher, Hyacinth Macaw, Blue-fronted Amazon are some of the bird species that are present here. And animals like Sloth, Capybara, monkeys, ant-eating tapirs, poison dart frogs are present all over the rainforests of the Amazon. The list does not end here! You get a glimpse of the grandest of the reptiles as well.

Crocodiles and snakes like Pythons and Anacondas are common here. Apart from these aquatic animals like the Piranha and Giant Otter can be sighted in the river basin. The list of fauna and flora in the Amazon Basin is endless.

People People here cut a few trees and cultivate the land according to their needs and requirements. Men have occupations like fishing and hunting, while it’s the women who take care of the crops and fields. The land being near the Amazon river is very fertile which makes it good for farming. People here generally grow crops like Pineapple, Tapioca, Sweet Potato, Cassava (manioc), Coffee, maize, and cocoa. We call them the Cash crops.

As already said, men practice fishing and hunting which are uncertain means of living. It is the women of the Amazon basin who are the major bread earners of the family. From taking care of fields to feeding their families with the vegetables that they cultivate, women are responsible for their family’s well-being.

They practice the slash and burn technique of agriculture. In the slash and burn agriculture system, we clear the required forest land for farming. We slash or cut down the trees and bushes. As soon as the fertility of land degrades, women proceed to clear a new piece of land.

The old land eventually gains back its fertility with trees and bushes growing back on them. Manioc or Cassava is the staple food while queen ants and egg sacs are the other savouries of people near the Amazon river.

The indigenous people have been shifted to newer places so that they practice their distinctive skill of farming.

Settlements of People People live in special kinds of houses called the Maloca here. These houses have steep slant roofs and are large and apartment-like in shape. People also reside in houses that are identical to beehives and have thatched roofs over them. Since the settlements here are in close proximity to forests, people find wood in abundance for their personal use.

With pacing technology and modernism, the life of people is slowly changing here. As it is human nature to evolve and enjoy facilities to their best the advent of transportation has helped in easy navigation through the basin. An area that once was navigable only through the Amazon river can today be explored through Trans-Amazon Highway. Moreover, with aeroplanes and helicopters, it has become easier and faster to reach various places around the basin.

Human Interaction with the environment benefits humans, but to an extent depletes the environment. The regular felling of trees in the Amazon basin has resulted in a change of ecology here. The developmental activities near the Amazon river, eventually, have resulted in the destruction of the rainforests. The effects may not be visible today, but in the near future, they surely will. The map below signifies the change human interaction has brought to the environment:

Amazon Basin is the richest example of human interaction with the environment. To let it stay rich with vegetation it has now become imminent to use the resources from the basin intelligently. We humans interact with the environment for our benefit but doing so at the cost of ecology can be fatal for our generations to come.

Question 1. Define the major difference between a hamlet and a village. A) Population B) Group of Houses C) Pollution D) None of the Above Solution: A) Villages are larger than hamlets. They have a greater population than hamlets. Hamlets are small towns and communities while villages are mostly large suburbs and crossroads. Hence, Hamlet’s settlements are also less polluted as a result of a smaller population.

Question 2. From where the Urban settlements have evolved? A) Rural Settlements B) Scattered Settlements C) Nucleated Settlements D) None of the Above Solution: A) Rural settlements were the first step towards a stable life in human history. Urban settlements have evolved through the growth and expansion of rural settlements. An increase in the population of the rural settlements marks the beginning of urbanisation.

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• Latin America and Caribbean
• Land and Food

Conflict over Land in the Amazon Basin, Ecuador

As is true for the entire Amazon region, the Ecuadorian Amazon region has undergone different phases of colonisation. This has created land and territorial conflict between settlers from the highlands, and indigenous peoples of the Amazon region. Meanwhile, rising interest for productive land and other natural resources, as well as development plans on the national scale, have further fueled conflict. The tensions between families of the Shuar people and settlers over access to land in the Province of Morona Santiago, Ecuador shows how current infrastructure projects in the Amazon region worsen the conflicts generated during homesteading settlement and reflect the complexity of resolving them in intercultural contexts.

Conceptual Model

Conflict history.

By the early 1970s, approximately 43,000 agricultural colonists from the highlands had moved to the Ecuadorian Amazon Basin, also known as the Oriente, in a state-led effort to integrate the Oriente through settlement. Most migrants settled in Morona Santiago and Zamora Chinchipe in the southern region, where direct routes to Cuenca and Loja in the nearby highlands existed ( Southgate et al. 2009 ). This homesteading changed the social and political structure of the Amazon region and led in some cases to peaceful coexistence, to violence in others, between different ethnic and cultural groups. Meanwhile, the biodiversity of the Amazon Basin - which covers 44% of the land area of the South American continent - is threatened by deforestation, changes in the hydrologic circle associated with changes in the global climate and water pollution ( OAS 2005 ).

Disputed property rights

Two decades ago, farmers settled in the parish of Shaimi, who formed the Puma Association (ASOPUMA, some 80 members) and got property titles. In the parish of Shaimi, up to 2008 the farmers and Shuar people lived peacefully together. The conflict began when, starting in 2011, there were the first irregular Shuar settlements in the urban zone of the parish. The four Shuar families involved were demanding their right to settle that land, because they felt that the land distribution done during colonisation times was illegal, and they never received the payment that had been agreed to at the time. There is no documentary support to confirm or contradict their version.

Rising speculative land values

This conflict developed in the context of rising speculative land values in the urban zone of the parish, because the Méndez – Morona highway linking the parish with the urban centers of the Province was completed. There were also rumors that a port was to be built, with the idea of making a connection by river and overland between Ecuador’s Pacific Ocean port of Manta with Manaus in Brazil, and from there to the Atlantic coast of South America. This strategic project is part of the Initiative to Integrate South American Regional Infrastructure (IIRSA) which includes improving regional transport, energy and telecommunications infrastructure.

Worsening of the conflict

Beginning in 2012, Shuar families made new squatting settlements, denouncing illegal tenure and ownership of the land in the Shaimi sector, arguing that they had ancestral rights to the territory. These families squatted on several farms, leading to verbal and physical aggression and death threats between the two population groups. In May 2012, the conflict increased, resulting in the death of a youth. This caused more mistrust and an environment of insecurity. For the first time, regular police and army stations were set up.

Conflict resolution

Because of the scale of the conflict, an Inter-Ministry Commission intervened, led at the time by the Ministry to Coordinate Policy, and later by the Ministry of the Interior, the National Secretariat to Manage Policy and the Ministry of Agriculture, Livestock, Aquaculture and Fishing (MAGAP) of the Province, to help organise urban planning under the leadership of the Municipality of Tiwintza, to legalise land tenure, help validate their Intercultural Life Plan, and establish lasting order.

Conflict mediation committee

A conflict mediation committee was formed with the Shuar representative (President of the Interprovincial Federation of Shuar Centers - FICSH), the Municipality of Tiwintza, and three societal leaders. Under the mediating committee, the parties prepared different proposals but no agreement was reached among the stakeholders in the conflict. Therefore, the Inter-Ministry Commission and FICSH prepared a proposal for final resolution, which was presented in July and August 2014. This proposal was based on the findings of the report on land tenure in the area, prepared by the MAGAP Conflict Management Unit, which had gathered information since mid-2012 and updated it to June 2014; and proceedings from the dialogues which had lasted nine months (17 December 2013 to 02 September 2014). This proposal recognised titles to 38 properties (26 farmer families and two Shuar families) and proposed to grant the other eight properties to the Shuar people, totaling 1772 hectares, which were basically land with no owner and no one in possession; or lots that the mestizos had allegedly abandoned.

The Shuar families' rejection of the proposed agreement

The farmers accepted the proposal in writing. The Shuar families rejected it and have stated that they would sign a peace and coexistence agreement when the ownership titles by the farmers for properties where the Shuars are currently settled have been canceled. In view of this situation, the Governor's Office, on behalf of the Inter-Ministry Commission, has stated that this proposal had already been analyzed by government agencies and is not sustainable, and anyway it would require a series of legal actions that would require time. In this context, government institutions – members of the Inter-Ministry Commission – have exhausted all administrative options for dialogue, concluded their intervention, and will let any other administrative or judicial bodies take over and resolve these disagreements.

Resilience and Peace Building

Mediation & arbitration.

Several mediation attempts were made by way of a conflict mediation committee, which included representatives from all stakeholder groups, as well as an Inter-Ministry Commission tasked with preparing a proposal for final resolution. However, these interventions have failed to resolve the disagreements between the farmers and the Shuar families. The Inter-Ministry Commission has concluded its mediation efforts.

Social inclusion & empowerment

Shuar families have been represented in dialogues by the President of the Interprovincial Federation of Shuar Centers (FICSH) and have had an opportunity to present their own proposals for review. The latest proposed agreement prepared jointly by the Inter-Ministry Commission and FICSH granted eight properties (1772 hectares) to the Shuar people. Nonetheless, the Shuar families have rejected this proposal and are demanding rights to land where they are currently settled.

Resources and Materials

• Southgate, D., Wasserstrom, R. and Reider, S. (2009) Oil Development, Deforestation, and Indigenous Populations in the Ecuadorian Amazon. Latin American Studies Association in Rio de Janeiro, Brazil, 11 - 14 June 2009
• Organization of American States: Office for sustainable Development & Environment. (2005). Amazon River Basin – Integrated and sustainable management of transboundary water resources in the Amazon River Basin. (Water project series, Number 8 - October 20

Inspired Geography 6

Orient blackswan.

• Mapping the Earth
• Landforms of the Earth
• Water Bodies of the Earth
• Agriculture
• North America: Location, Political Divisions and Physical Features
• South America: Location, Political Divisions and Physical Features

Case Studies: Lumberjacks of Canada, Life in the Amazon Basin

Available answers.

Fill in the blanks:

• The eastern states of Canada where lumbering is carried out are ____________ and __________.
• Skilled forest workers who cut and transport trees for further processing are known as ____________.
• When a bundle of logs get entangled, they create a ___________.
• The Amazon Basin lies in the continent of ______________.
• Hunter-gatherers use tools such as ___________, poison-tipped arrows and darts to fish and hunt.
• The main method of agriculture practiced in the Amazon is __________ _____ ___________ agriculture.

Tick the sentences that are true. Correct the others.

• The third stage of lumbering involves the felling of trees.
• High riggers use dynamite to fell trees.
• Lumbering is an activity done in the winter.
• The Amazon Basin contains more than two-thirds of the world's plant species.
• A majority of the population in the Amazon Basin lives in the interiors of the rainforest.
• The indigenous tribal groups have distinct cultures, languages and territories.

Multiple choice questions (MCQs).

• economic exploitation
• transportation
• a loading point
• collection point
• High riggers
• terra firms
• terra preta
• terra burna

Almost 80% of the trees that are cut in the world are softwoods. Give reasons.

High riggers use cables. Give reasons.

The form of agriculture practised by the indigenous people of the Amazon Basin does not harm the rainforest. Give reasons.

Article  

• Volume 20, issue 2
• HESS, 20, 589–603, 2016
• Peer review
• Related articles
• Catchment co-evolution: space–time patterns and functional...

A scaling approach to Budyko's framework and the complementary relationship of evapotranspiration in humid environments: case study of the Amazon River basin

A. m. carmona, g. poveda, m. sivapalan, s. m. vallejo-bernal, e. bustamante.

Abstract. This paper studies a 3-D state space representation of Budyko's framework designed to capture the mutual interdependence among long-term mean actual evapotranspiration ( E ), potential evapotranspiration ( E p ) and precipitation ( P ). For this purpose we use three dimensionless and dependent quantities: Ψ  =   E ⁄ P , Φ  =   E p ⁄ P and Ω  =   E ⁄ E p . This 3-D space and its 2-D projections provide an interesting setting to test the physical soundness of Budyko's hypothesis. We demonstrate analytically that Budyko-type equations are unable to capture the physical limit of the relation between Ω and Φ in humid environments, owing to the unfeasibility of E p ⁄ P   =  0 when E ⁄ E p   →  1. Using data from 146 sub-catchments in the Amazon River basin we overcome this inconsistency by proposing a physically consistent power law: Ψ  =   k Φ e , with k   =  0.66, and e   =  0.83 ( R 2   =  0.93). This power law is compared with two other Budyko-type equations. Taking into account the goodness of fits and the ability to comply with the physical limits of the 3-D space, our results show that the power law is better suited to model the coupled water and energy balances within the Amazon River basin. Moreover, k is found to be related to the partitioning of energy via evapotranspiration in terms of Ω. This suggests that our power law implicitly incorporates the complementary relationship of evapotranspiration into the Budyko curve, which is a consequence of the dependent nature of the studied variables within our 3-D space. This scaling approach is also consistent with the asymmetrical nature of the complementary relationship of evapotranspiration. Looking for a physical explanation for the parameters k and e , the inter-annual variability of individual catchments is studied. Evidence of space–time symmetry in Amazonia emerges, since both between-catchment and between-year variability follow the same Budyko curves. Finally, signs of co-evolution of catchments are explored by linking spatial patterns of the power law parameters with fundamental characteristics of the Amazon River basin. In general, k and e are found to be related to vegetation, topography and water in soils.

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