3rd year project by james harper

7/31/2019 3rd Year Project by James Harper

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Land use types in the Amazon basin, their connection to precipitation and the implications of

land use change in the basin system

Written by

James Harper


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Abstract

This report looks at the rates of precipitation above different land use types in the Amazon basin

and shows that there is a significant relationship between the two variables. It is a small part of

the whole basin system but a very significant part none the less. Looking at pasture data sets and

data from the Tropical Rainfall Measurement Mission (TRMM) over the past 12 years from 1998

2009. Using each yearly average from different locations across the basin and statistical tests to

deem the significance of the rates of precipitation. There is a definite link between land use type

and the rates of precipitation, the possible implications these results have on the overall complex

basin system have been looked at and the impacts may be more far reaching that just the basin,

possibly the whole global circulation system.

Keywords

Amazon basin, Precipitation, Global circulation system, Land use change, Hydrological cycle


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Contents

Page 4 Section 1Introduction

Page 6 Section 2Method

Page 9 Section 3Results

Page 9 3.1Pasture data set

Page 11 3.212 year averaged data

Page 14 3.3Yearly averages of precipitation

Page 21 3.4Statistical Analysis

Page 24 Section 4Discussion

Page 27 Section 5Conclusion

Page 28 Section 6Acknowledgements

Page 29 Section 7References

Page 31 Section 8Appendix 1Project Proposal

Page 35 Section 9Appendix 2Progress Report


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Introduction (Section 1)

The Amazon basin is roughly located at latitude 5N15S and longitude 75W45W on the

South American continent. It contains the second largest river in the world with the largest

discharge of any river at over 200,000m-3

s-1

of water, due to a drainage basin of approximately

7,000,000km-2. It is the largest rainforest on our planet and one of the worlds major carbon

dioxide (CO2) reservoirs; this is not just looked at in terms of absorption by the trees but from all

the biomass and soil in the basin. It contains many valuable resources and has a large influence

on the global climate and is a dominant factor of the South American climate.

In this project I shall be looking at one aspect of the complex system that is the Amazon basin. I

want to concentrate on a small part of the whole, specifically the impact of the different types of

land use that occur within the Amazon basin. I want to see if there is a link between the rates of

precipitation over the different uses of land in the basin.

My hypothesis is this; there is a significant difference between the types of land use, and

precipitation rates above that land.

I decided to look at this aspect of the basin system because I will be able to justify my results

through quantitative and qualitative methods from the datasets that are available to me. There

have been many published articles on the Amazon Rainforest and the climates relationship to it,

and the link between the drier seasons and the increase in likelihood of forest fires (Arago et al,

2008). Also, how the different atmospheric phenomenon such as the El Nino Southern

Oscillation (ENSO) and the Atlantic Ocean Sea Surface Temperatures (SST) influence

precipitation and the climate across the Amazon basin (Nobre, 2009).

There are predictions about how the whole system may react to the changing global climate

through adaptation, for example by means of deep roots (Markewitz et al. 2010), or having more

negative effects on precipitation rates and the hydrological cycle where extreme events mayoccur for longer periods of time (Hiscock and Tanaka, 2006). There are also ideas on the further

deforestation of forest having an impact itself on the global climate (Bala et al, 2007).

The gap in the market that I am looking at specifically is land use linking to precipitation. I

want to place this work in the larger picture as a small part of the whole system, draw


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conclusions, and discuss the consequences of the whole system changing. Land use change has

been linked to forest fires (Cochrane and Laurance, 2008), deforestation and loss of bio diversity,

but I believe has not been linked specifically to rates of precipitation with the data set for land

use that I have used in this report (Ramankutty et al, 2008). Also, the availability of new data

sets for land use and the ever increasing volume of data gathered from the Tropical Rainfall

Measurement Mission (TRMM), I have been able to look at longer timescales for precipitation

than was previously available.

The main reasons I am looking at precipitation and land use is because I am interested in how

potentially such a small part of the whole system can have such large implications and feedback,

into not just the regional system of the Amazon basin but potentially the whole global

atmospheric system.


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Method (Section 2)

I first started by looking at where the precipitation data would come from. The best source for

this precipitation data in the tropics is from the TRMM satellite launched in 1997. Its purpose is

to observe and record precipitation rates and energy exchange in the tropics (Kummerow et al.

1998). I will be using the data collected from the TRMM Precipitation Radar (TPR) and the

TRMM Microwave Imager (TMI) from on board the satellite; these are combined into in large

tables of data. The data is in 3 hourly averaged 0.25x.025 grid boxes, ~25km, from 50N to

50S in a band across the whole planet, therefore the Amazon basin is well within the satellites

coverage and a reliable data set to use for my research.

The data is displayed in mm hr-1 and is taken over the whole year, so the numbers may seem

small but that is because they are averaged over a total of 8760 hours. I also needed to have a

data set that would help me look at land use across the Amazon basin, so I will be using a land

use dataset focusing on pastures, having a resolution of 0.1x0.1, ~10km (Ramankutty et al.

2008). The data is from the year 2000 so it coincides well with my precipitation data that runs

from 19982009. As it is almost from the start of the precipitation dataset then I will be able to

look at the impacts, for nearly a decade after the data was created and hopefully be able to see

what impacts if any have occurred as a result. They are both very high resolution sets of data

which will help me to see if there are any small scale changes in the region.

Both data sets needed to be manipulated into manageable formats for me to be able to use them

effectively. I was helped and taught to use Unix and Python script of which I had no previous

experience. The data was in netcdf form, being a standard format for climate based data sets, and

so could be utilised in the special linked computers, called the escluster, we have at the

University of East Anglia. The cluster consists of ~100 computers linked together generating

sufficient computing power able to process and work with the large datasets I am using. The site

is escluster.uea.ac.uk.

Within the cluster I was using a program called Climate Data Analysis Tools (CDAT) - this

program can easily read the netcdf file format that my data uses. I also used a basic text writing

program called Emacs, this let me type in a script to look at the specific data I wanted to look at,


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it let me easily write what I needed and was able to be run as part of a longer script to let me see

my data in visual form.

Once my TRMM data had been sorted through for errors in the data and correctly sorted for use

in CDAT, I began to break it down into manageable sets, using Emacs, so I would be able to

look and compare the data. I decided to separate the data into yearly time intervals, as I deemed

it sensible to have an average for the whole year to start with, this would average out any

anomalies and help to highlight any annual patterns. My data ranged from 1998 2009, so I have

12 years of data and trends to look at. I also averaged the data over a 12 year period to see what a

complete average of the data would look like. After I had decided how to break the data down, I

wrote out the time scales, each being a year long, on Emacs and then ran it through scripts that I

had had help with creating (Section 6). Once the scripts had been run I had many individual data

sets for each year, and the multiple yearly averages that I wanted to use. If I needed other

averages or individual months I could still come back and write more into Emacs and run the

scripts I had again to produce what I needed.

The pasture data set was not as complex to work through as it was only from the year 2000. I

was not as confident using pure scripting code in CDAT so I using the Visual Climate Data

Analysis Tools (VCDAT) which was more interactive and easier for me to use, it had buttons

and sliders instead of having to write out the lines of code. I was able to zoom in on the Amazon

basin, by selecting specific latitude and longitude co-ordinates using the sliders on VCDAT, and

look at the areas in which there were the greatest concentrations of pasture. I deemed that

anything below 30% density of pasture was not of a significant impact on the landscape to

majorly alter the atmospheric system above. So I could look more closely at areas of higher

concentration that stood out and compare this to the TRMM data set to see if there were areas of

decreased precipitation that linked into areas of significant pasture outcrops in the basin that I

had found. I marked down those areas of the highest concentrations of pasture.

I began to look at areas of significantly high or low precipitation on the averages I had and mark

down those areas latitude and longitude as well. I created a new data set containing the wettest

season in the Amazon basin. This was December, January and February (DJF). As the wet

season overlaps into the next year I only had 11 data points for each grid box to compare. I did


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this as I wanted to see what the rates of precipitation would be at the forests wettest point and

what impact this had in the areas of significant pasture.

Taking the rough latitude and longitudes I had, I made 1x1 squares from these areas of pasture,

I have taken the average precipitation for each year from 19982009 in the grid box that I

looked at. I have then taken another 1x1 square of complete rainforest at a sensible distance

from the pasture box, and taken the precipitation average as with the first box. After collecting

the data from different areas across the basin, I wanted to see if the pairs of data were

significantly independent from each other and not just random coincidence.

I wanted to show that the precipitation falling in these areas was significantly different, and

helped to prove my hypothesis about precipitation being linked to land use (Section 1). I used a

Students T Test to test if the groups of data were significantly independent from each other

above a 99% significance level.


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Results (Section 3)

3.1- Pasture dataset

By looking at the data, I have been able to see some very interesting results relating to the land

use and precipitation in the Amazon basin and also some results I did not expect. The data below

is all that I deemed useful to my report as it shows the pasture data, wet season (DJF) data I was

using and the statistical analysis I conducted.

Figure 1 Pasture data from the year 2000 across the Amazon basin at < 30% density per 0.1 x 0.1 pixel

The pasture data (Figure 1) shows the spread of deforested areas that contain exposed pasture

used for grazing. The colour indicates the level of density of pasture in that area, red meaning the

highest concentrations at 100% coverage and blue meaning 30% coverage and white is the a

density lower than 30% per pixel. There are significant concentrations of pastures on the exterior

of the Amazon basin, but a lower concentration on the interior of the rainforest. The reason for

this is because there are fewer areas of concentrated population. The inhabitants will mostly only

live on the edge of the Amazon River, so any areas that have been deforested for pasture will be

Land use data over the Amazon

Pasture Density (%)


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smaller than those more accessible areas on the edges of the forest. It is easier for people to fell

trees and use slash and burn techniques on the fringes of the forest and work inwards, this is

easier than trying to log from the interior of the forest and trying to transport the wood cut down

along the river where there is lower accessibility for a large scale operation.


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3.212 year averaged data

Figure 2 12 year average (19982009) of precipitation (mm hr-1) over the whole Amazon basin

The precipitation data averaged over the 12 years (Figure 2) shows some results I was not

expecting to find, primarily that there are two main areas of more concentrated precipitation in

the Amazon basin.

One is in the West and I know that this is topographically related to the Andes mountain range.

The increasing height above sea level leading up to the mountain range forces the moisture to

ascend but this causes the clouds to form and cool and hold less moisture leading to forced

precipitation in the mountains. This is the reason for the higher levels of precipitation occurringon the western side of the Amazon basin. The rates of precipitation are also influenced in some

part by ENSO during its different phases.

In the area to the East of basin there is a dense concentration of pasture in the approximate area

2S by 49W (Figure 1) that correlates to an anomalously higher area of precipitation in the

19982009 averaged precipitation

Precipitation (mm hr-1)


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similar location (Figure 2), however this may not be the only cause. As this is a coastal area there

would be the influence of warmer moist air coming off the South Atlantic Ocean due to varying

warmer SST. The ocean acts as a thermal reservoir and there may be increased moisture

available compared to that available on average further inland. Also this area has a high density

of population so the urbanized areas contribute to the modification of the local environment.

Figure 3 12 year average (1998 - 2009) precipitation (mm hr -1) with the Amazon river and its main arteries highlighted

The final interesting piece of information that I found was that purely from averaged

precipitation data the main arteries of the Amazon River itself can clearly be seen. This can be

shown as I have highlighted its outline (Figure 3). I did not expect to see the river as an area of

lower precipitation; I would have expected to see an area of increased precipitation as it is above

a large source of moisture, and would have caused increased rates provided by convective

precipitation in the afternoons in the Amazon. However, I can infer that it is the trees themselves

that provide a greater source from evapotranspiration than the moisture provided the river. Hence

it clearly being defined (Figure 2), approximately 34% of the precipitation in the basin is directly


Amazon River highlighted through precipitation


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from recycled water from within the basin (Trenberth, 1999). As there is greater volume of trees

compared to area of river, the lower rate of precipitation of the river compared to that of the trees

makes it clearly defined on my graph (Figure 3) when the precipitation rates are averaged over

the 12 years of data I have.


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3.3Yearly averages of precipitation

I have looked at each year of precipitation (Figure 415) to see if there are any significant

patterns in the precipitation across the basin. Looking at the yearly averaged data I can compare

this to global meteorological events that have occurred but I do not want to speculate in this

section, as I am leaving that for my discussion (Section 4).

From the graphs below (Figure 415) it can be seen that over the year the highest volume of

precipitation is consistently within the same two areas in the basin. On the Western side of the

basin at the base of the Andes and on the East coast, I have discussed these initial results already

(Section 3.2) but it is interesting to note that even in the years of severe drought (Figure 11) and

regardless of any other atmospheric influences they remain areas of higher intensity

precipitation, also in the North an area of consistently lower precipitation.

Figure 4 Average precipitation (mm hr-1) in 1998 over the Amazon basin


Rainfall in the Amazon basin in 1998


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Figure 7 Average Precipitation (mm hr-1) in 2001 over the Amazon basin







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3.4Statistical Analysis

Table 1 Statistical analysis of precipitation rates (mm hr-1) of different land use types in the Amazon basin over the wet season

(DJF) from each year of the dataset at each given latitude and longitude

Latitude/Longitude3.625N2.625N / 61.875W

60.875W1.625S2.625S / 61.875W

60.875W

Year Pasture Rainforest

98/99 0.1837 0.4217

99/00 0.1446 0.3431

00/01 0.0413 0.3094

01/02 0.0378 0.3662

02/03 0.0283 0.2831

03/04 0.0306 0.2222

04/05 0.1114 0.3371

05/06 0.1536 0.4022

06/07 0.0141 0.2098

07/08 0.0787 0.4303

08/09 0.2309 0.4745

Average 0.0959 0.3454

Std Deviation 0.074 0.085

T Test 0.000Table 1a.

Latitude/Longitude0.375S1.375S / 49.625W

48.625W0.125S1.125S / 50.375W

49.375W


98/99 0.4392 0.4616

99/00 0.5697 0.5223

00/01 0.5391 0.5626

01/02 0.3840 0.3815

02/03 0.3765 0.3931

03/04 0.5446 0.5263

04/05 0.3736 0.4260

05/06 0.4738 0.531706/07 0.3747 0.3895

07/08 0.5520 0.5978

08/09 0.5685 0.6315

Average 0.4723 0.4931


T Test 0.071Table 1b.


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62.125W7.125S8.125S / 63.125W

62.125W


98/99 0.3987 0.4926

99/00 0.3164 0.3938

00/01 0.4074 0.5923

01/02 0.4118 0.5919

02/03 0.3966 0.4598

03/04 0.3905 0.4095

04/05 0.3478 0.3794

05/06 0.4613 0.5466

06/07 0.3516 0.372607/08 0.3739 0.4016

08/09 0.3874 0.4732

Average 0.3858 0.4648


T Test 0.000Table 1c.

Latitude/Longitude

1.125S2.125S / 53.875W

52.875W

1.125S2.125S / 51.875W

50.875W


98/99 0.2293 0.3354

99/00 0.4112 0.3705

00/01 0.2606 0.3873

01/02 0.2107 0.3388

02/03 0.2204 0.3008

03/04 0.2827 0.3674

04/05 0.2587 0.4612

05/06 0.3296 0.493006/07 0.1512 0.2858

07/08 0.2486 0.3893

08/09 0.3537 0.4909

Average 0.2688 0.3837


T Test 0.000Table 1d.


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47.125W1.875S2.875S / 48.125W

47.125W

Year Pasture Rainforest98/99 0.2327 0.4317

99/00 0.4118 0.5012

00/01 0.3638 0.524201/02 0.3847 0.3619

02/03 0.3825 0.4606

03/04 0.4650 0.5026

04/05 0.3008 0.365105/06 0.3234 0.4863

06/07 0.3475 0.4006

07/08 0.2517 0.5236

08/09 0.2787 0.4704

Average 0.3402 0.4571


T Test 0.000Table 1e.

The pairs of data are made up of one complete area of pasture and one complete area of

rainforest. Four out of my five pairs of data (Table 1a, c, d, e), shown in blue are the results that

passed at a 99% significance level, to 3 decimal places. This proves that these locations are

independent and the data sets are from different locations. My statistical analysis (Table 1a-e)

has helped to show that there is a significant difference in the rates of precipitation, and proves

that my initial hypothesis (Section 1) that land use type affects the rate of precipitation above that

land use. It is the quantitative result I wanted to see and to show that there is a difference

between the types of land use and from this to discuss the implications of my findings. There was

one data pair that was not significant at either the 99% or 95% significance level (Table 1b),

which was the exception to the rest of my data. This pair was taken in the East of the basin in an

area of anomalously high average precipitation (Figure 2), which I have discussed previously in

my report. With further time and more detailed datasets on more land use types I believe I would

find results of a similar nature.


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Discussion (Section 4)

From my results (Section 3) I have shown that there is a statistically significant difference

between the rates of precipitation above the different types of land use in the Amazon basin. I

have also found that the Amazon River itself is a land use type that influences the rates of

precipitation (Figure 23). The averaged data from each year (Figure 415) can be seen to

have been influenced from factors external to the basin. Although, it is hard to discern any real

seasonal pattern of rainfall from my graphs due to these external factors that vary from year to

year. It can still be seen throughout each graph that there are areas of consistently higher and

lower precipitation (Section 3.3). In 2001 (Figure 7) there is a higher precipitation rate further

into the Amazon basin than in the rest of my yearly averages. I believe this is due to a stronger

ENSO and a warmer set of SST in the South Atlantic which have both been seen to influence therates of precipitation in the basin (Liebmann, 2001).

Also, during 2005 (Figure 11) there was a major drought that plagued the Amazon basin (Zeng,

et al. 2008). It was very close to pushing the Amazon Rainforest past a tipping point, where it

would not have been able to fully recover from the damage caused. The reason that this was of

such concern was because, the longer the drought continued the lower the water table in the soil

became, this forced deep roots to seek out water further into the soil (Markewitz et al, 2010). If

the drought had continued any further without any significant period of precipitation the treeswould not have had enough water to survive till the next rainy season, even with the adaptations

of deep roots that draw water from deep sources (Zeng et al, 2009) throughout the dry season. So

with an increase in the change in land use across the basin, which we know has an influence on

the rates of precipitation (Table 1) Also from other papers, that show different land use types

have different varying on the atmosphere such as Pielke et al (2007). I can infer that there may

be more exposed soil, less land containing large tropical trees, which are able to contain

significant volumes of water, and more areas of pasture. Thus overall a decreased rate of

moisture entering the basin system through evapotranspiration in the long term would begin to

cause wide spread problems throughout the system.

It has been discussed byMalhi et al. (2009) that in the short term the Amazon may adapt its

overall system due to a change in land use and increased temperatures, but it would most likely


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push the Amazon towards a seasonal forest instead of a rainforest. Even though land use change

is a small part of the whole it can have a large impact on the overall system. The lower

precipitation rates mean that there is more chance of prolonged large scale droughts throughout

the basin. If there were longer periods of drought there would be a higher chance of forest fires

occurring (Arago et al, 2008) due to the drying out of vegetation. The outside forcing of a

potential increase in global temperature, may also be another major factor that may influence the

length of the wet and dry seasons in the Amazon basin. With the chance of increased areas of

pasture it is much easier for this land use and similar types to dry out compared to tropical forest,

so more fires may occur and spread across the basin, making the rest of the basin vulnerable to a

much greater extent of damage. Also from the increased amount of fires occurring more burnt

carbon would be put into the atmosphere, leading to a higher proportion of cloud condensational

nuclei (CCN). In the short term this may be advantageous meaning there are more particles for

rain droplets to form around, but as the proportion increased then the spread of water droplets

would become thinned out across the area instead of more concentrated rainfall, and with less

moisture entering the system this would only help to further weaken the intensity of

precipitation. If this occurred or if there was less precipitation due to the changing land use in the

basin then the forest could be pushed past a tipping point (Nepstad et al, 2008) where it would be

almost impossible to restore the forest to its original state.

After the tipping point the forest will either readjust or collapse to become a seasonal forest

rather than a rainforest as mentioned above in the paper byMahli et al, (2009). If this were to

occur it would mean a massive loss in bio diversity in the basin. The forest would rapidly die

back to a much smaller size than it currently is and this has more implications. It would be a loss

of a large CO2

sink and storage area (Botta et al, 2002), although the scale of the sink is a subject

of discussion, as with all of the major carbon sinks and the volumes of CO2

they are able to hold.

But regardless of its scale the fact that we may lose large parts of the forest does not just mean a

loss of carbon sink it means more CO

2

would be released from the soil and biomass decay intothe atmosphere. The volume of carbon released may alter the atmospheric chemistry or increase

pressure on other carbon sinks, like the worlds oceans, which are undergoing acidification from

carbonic acid, which is causing a bleaching of coral reefs due to the oceans trying to process the

increased intake of CO2 from the atmospheric part of the system (Erez et al.2011).


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With an increasing demand for renewable energies and cheaper sources of fuel I believe that

there is a danger posed by rapid land use change, for bio-fuels created from crops. As the

demand for them increases, more pressure will be put on creating space for these crops and for

more food. So the change in land use may occur more rapidly due to industrial pressure as they

want to press ahead and take advantage of a potentially lucrative opportunity, however this will

lead to the vulnerable systems such as the vast Amazon Rainforests, that will be exploited to the

point where in 50 years we may not have an Amazon Rainforest to protect.

The ramifications of the loss of the Amazon Rainforest through change in land use or forcing

from other factors are unknown, as we do not know what kinds of impact the loss of the forest

will have on the global atmospheric system. Also these are only some of my basic potential ideas

of branches of scenarios that have the potential for occurring, but it has been well documented by

recent journal articles such as the ones I have used in this discussion that there is a cause for

concern about the impact of a change in the volume of areas in the Amazon basin that are

undergoing land use change from humans.


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Conclusion (Section 5)

Through the statistical analysis of the data that I have used (Section 3) to look at the relationship

between the type of land use and the precipitation rates above it, I have seen that there is a

statistically significant difference between the rates of precipitation in different areas across the

basin. Even though this is a small part of the Amazon basin system, from looking at the whole

system I can theorise that this small part has the potential to have large implications on the basin

system as I have discussed previously (Section 4). We cannot know the future implications as

trying to model all of the response of the basin system linking to the Global system is nearly

impossible to do. We have few long term averages or predictions for different parts of the

Amazon basin system, like the total impact of long term droughts or increases in local

temperature rise or the impact of more CCN being present in the atmosphere above the Amazon

due to increasing intensity of forest fires. We can only look to limit the potential anthropogenic

changes of land use in the basin, by trying to reduce the level of non-renewable logging and

illegal felling of trees in the basin and the clearing of large areas for use as pasture or cropland

for more food. As other climate forcing are out of our hands, we may already be at a point where

another long term drought in the Amazon basin may cause irreparable damage to the forest,

biodiversity and overall climate system. As this is yet to happen we may be able to limit these

manmade impacts.

The most important piece of information I have gained from doing this report is that the Amazon

basin is a dynamic system and even the smallest change, like a percentage increase in the area of

anthropogenically changed land use in the system, can have large knock on effect to the whole

system and potentially the whole global climate system.


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Acknowledgements (Section 6)

I would like to thank Dr Adrian Matthews for all his help with the processing of the large

volumes of data I had to go through and helping to teach me the basics of python scripting, also

for helping to steer all my ideas into a cohesive project.


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References (Section 7)

Arago, L.E.O.C., Mahli, Y., Barbier, N., Lima, A., Shimabukuro, Y., Anderson, L., Saatchi, S.,

2008, Interactions between rainfall, deforestation and fires during recent years in the Brazilian

Amazonia, Philosophical Transactions of the Royal Society Biological Sciences, 363, 1779

1785.

Balla, G., Caldeira, K., Wickett, M., Phillips, T.J., Lobell, D.B., Delire, C., Mirin, A., 2007

Combined climate and carbon-cycle effects of large-scale deforestation, PNAS, 104, No 16, 6550

6555.

Botta, A., Ramankutty, N., Foley, J.A., 2002, Long-term variations of climate and carbon fluxes

over the Amazon basin, Geophysical Research Letters, 29, No 9, 10.1029/2001GL013607.

Cochrane, M.A., Laurance, W.F., 2008, Synergisms among Fire, Land Use, and Climate Change

in the Amazon,Ambio: A Journal of the Human Environment, 37, No 78, 522527.

Erez, J., Reynaud, S., Silverman, J., Schneider, K., Allemand, D., 2011, Coral Calcification

Under Ocean Acidification and Global Change, Coral Reefs: An Ecosystem in Transition, Part

3, 151-176,DOI: 10.1007/978-94-007-0114-4_10.

Hiscock, K., Tanaka, Y., 2006, Potential impacts of Climate Change on Groundwater

Rescources: From the High Plains of the U.S. to the Flatlands of the U.K, National Hydrolody

Seminar 2006, 1926.

Kummerow, C., Barnes, W., Kozu, T., Shiue, J., Simpson, J., 1998, The Tropical Rainfall

Measuring Mission (TRMM) Sensor Package,Journal of Atmospheric and Oceanic Technology,

15,809817.

Liebmann, B., 2001, Interannual variability of the rainy season and rainfall in the

Brazilian Amazon basin,Journal of Climate, 14, 43084318.

Malhi, Y., Arago, L.E.O.C., Galbraith, D., Huntingford, C., Fisher, R., Zelazowski, P., Sitch, S.,

McSweeney, C., Meir, P., 2009, Exploring the likelihood and mechanism of a climate-change-

induced dieback of the Amazon rainforest, PNAS, 106, No. 49, 2061020615.


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Markewitz, D., Devine, S., Davidson, E.A., Brando, P., Nepstad, D.C., 2010, Soil moisture

depletion under simulated drought in the Amazon: impacts on deep root uptake, New

Phytologist, 187, 592607.

Nepstad, D.C., Stickler, C.M., Soares-Filho, B., Merry, F., 2008, Interactions among Amazon

land use, forest and climate: prospects for a near-term forest tipping point, Philosophical

Transactions of the Royal Society Biological Sciences, 363, 1737-1746.

Nobre,P., Malagutti, M., Urbano, D.F., de Almeida, R.A.F., Giarolla, E., 2009, Amazon

Deforestation and Climate Change in a Coupled Model Simulation,Journal of Climate, 22, 5686

5697.

Pielke, R.A., Adegoke, J., Beltrn-Przekurat, A., Hiemstra, C.A., Lin, J., Nair, U.S., Niyogi, D.,

Nobis, T.E., 2007, An overview of regional land-use and land-cover impacts on rainfall, Tellus,

59B, 587601.

Ramankutty, N., T. Evan, A.T., Monfreda, C.,Foley, J.A., Farming the planet: 1. Geographic

distribution of global agricultural lands in the year 2000, Global Biogeochemical Cycles, 22,

GB1003.

Trenberth, K.E., 1999, Atmospheric moisture recycling: Role of advection and local evaporation,

Journal of Climate, 12, 13681381.

Zeng, N., Yoon, J., Marengo, J.A., Subramaniam, A., Nobre, C.A., Mariotti, A., Neelin, J.D.,

2008, Causes and impacts of the 2005 Amazon drought,Environmental Research Letters, 3,

014002.

http://www.geog.mcgill.ca/~nramankutty/Datasets/Datasets.html - pasture data set

http://trmm.gsfc.nasa.gov/data_dir/data.html - TRMM dataset
http://www.geog.mcgill.ca/~nramankutty/Datasets/Datasets.htmlhttp://www.geog.mcgill.ca/~nramankutty/Datasets/Datasets.htmlhttp://trmm.gsfc.nasa.gov/data_dir/data.htmlhttp://trmm.gsfc.nasa.gov/data_dir/data.htmlhttp://trmm.gsfc.nasa.gov/data_dir/data.htmlhttp://www.geog.mcgill.ca/~nramankutty/Datasets/Datasets.html


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Appendix 1 (Section 8)

Project Proposal

Background and Rationale

The use of satellites in observing the environment has revolutionized science in the last few

decades. They are used for a variety of measurements, the Tropical Rainfall Measuring

Mission (TRMM) measures precipitation rates over 3 hourly periods (Huffman et al. 2007)

across the planets tropical regions. Measuring changes in diurnal cycles, monthly averages and

yearly patterns can help us to better predict areas of intense rainfall and track small scale changes

across specific areas.

With advances in technology we are able to view and analyse parts of the environment that we

have never been able to or even thought of before. Deforestation is starting to become a major

factor influencing conditions in the rainforests of the world. The rates of precipitation across the

world can vary to the extremes; typical values in the tropical rainforests are from 2000

2500mm per year. However, with the large scale removal of forested areas is this having an

effect on the local climate (Nobre et al. 1991) and these rates may be changing. With the use of

the near real time data from TRMM there is data readily available to analyse these cycles (Negri.

2002) however, it is still not clear how data gathered by TRMM can be used to look at diurnal

cycles in the smallest spatial regions, so it is unknown if for example if the land use in an area

can effect rates of precipitation. Looking to find a gap in the science it seems that the data

gathered from TRMM has not been used when overlaid with the land use in specific areas, for

example the Amazon rainforest, where large swathes of trees are being cut down and used for

agriculture and seeing if rate of precipitation are affected by this.

Hypothesis

To explore whether changes in land use across the Amazon basin have an impact on precipitation

rates across different temporal periods.


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The Study System

The reason that I will be using the Amazon basin is because over the past decades the area has

undergone significant changes in land use. Deforestation has destroyed large swathes of land in

the basin and much of this has been used for agriculture, other areas have just been left as empty

areas of loose soil and tree stumps. This change in land use and with large areas still as original

rainforest I will be able to compare the rates of precipitation, between these areas to see if there

is any significant difference spatially over the whole basin and over time as areas of

deforestation have increased over the decades has there been a more dramatic change in the rates

of precipitation in those areas. The TRMM data has been gathered for a long time so I will be

able to have accurate averaged measurements over the whole time period I will be looking at.

Design and Methodology

The test my hypothesis I will be using the data gathered from the TRMM satellites over the past

years and averaging it over different temporal periods to see if there is a change in the average

precipitation rates over the Amazon basin. I will be using analysing software such as CDAT to

gather the data into a manageable format so I will be able to over lay the data with a map of the

Amazon basins land use. I will be comparing the types of land use also to see if each type has a

different impact on precipitation rates, where in theory untouched rainforest should see the

highest amounts of rainfall and deforested areas the least.

Data Summaries and Analyses

I intend to display my data as a set of graphs with precipitation data, which will be coloured by

intensity so it is easily readable, overlaid by the Amazon basin and its land use types. I will be

showing numerous graphs that will display different temporal scales of averaged precipitation

and also possibly the Amazon at different times to show if there has been significant changes in

the land use across the basin.


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Relevance

I will be helping to contribute to our understanding of the changes to micro scale and regional

scale climate, and how the intensity of meteorological events change as the land use changes

over time over an area of significant precipitation. I will be adding to our understand of the

impact humans are having as we change the environment around us and what far reaching

impacts that can have for the environmental norm as we disrupt the natural cycles that occur

within the rainforests.

Planning Schedule

References

Huffman, G. J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y.,

Stocker, E.F., Wolff, D.B., 2007, The TRMM Multisatellite Precipitation Analysis (TMPA):

Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales.Journal of

Hydrometeorology, 8, 38-55

Negri, A.J., Bell, T.L., 2002, Sampling of the Diurnal Cycle of Precipitation Using TRMM.

Journal of Atmospheric and Oceanic Technology, 19, 1333-1344


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Nobre, C.A., Sellers,P.J., Shukla, J., 1991, Amazonian Deforestation and Regional Climate

Change,Journal of Climate, 4, 957-988


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Appendix 2 (Section 9)

Progress report

Work completed to date

At this current stage I have gathered and collected the data necessary to have started looking to

see if there was any link between the land use in the Amazon and precipitation rates across the

basin. I have processed and manipulated this data into graphical form and have been started to

prepare and make notes on how to write up my finding and researching the implications of a

change in precipitation rates and its effect on the Amazon as a whole

The methods used and further analyses to do

With the data I had gathered and sourced from many places I began by using the UEA cluster to

manipulate the data into manageable pieces that could be looked at and easily compare year by

year. After this I have began to use some land use data and overlain it on the precipitation data to

see if there are any changing trends.

However because I have been using a different type of computer code to deal with the vast

quantities of data I have been limited in the amounts of available land use data that I can find to

overlay on the precipitation data. So I have been thinking about finding other sources of land use

data and not overlaying it but comparing it side by side, I believe this is a viable way of

comparing the data I have and being able to see if there are changes throughout the basin over

different temporal periods. I have been using yearly averages so far but may try to use monthly

averages for each year to see if the seasonal variations affect the overall averages.

Results so far


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Work remaining

Over the coming weeks is shall be continuing to improve on the graphs looking at more specific

areas within the Amazon and making them presentable and easy to understand

Dissertation structure

Title

Changes in land use across the Amazon basin and how precipitation rates vary across the basin

temporally and spatially

Abstract

A brief summary of my results and what the report is about

Keywords

A few words that can be looked at to get an idea of what the project relates to

Introduction

Outlining what my research is about, how and why I have investigated it and what relevance is has

to the broader area of climate change and land use impacts

Method


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I will be detailing how I put the data into an easily understandable format, explaining how I edited

and manipulated the data, and the processes used to get the data into the correct format

Results

In this section I am going to be displaying all of my graphs from the average yearly rainfalls,

overlays of the land use data that was available to me and the zoomed areas of the Amazon that do

have significant changes in precipitation with regards the land use over time. I will attempt to

explain my findings from the data I have gathered and try to get across the significance of them if

there is any major revelation that I have found

Discussion

I believe this is going to be my largest section in my report as I will be discussing the implications

of my findings, if I do find significant evidence of decreasing precipitation with respect to change

of land use then I can go into detail about the impacts. For example, the implications on the

regional climatology, potentially global impacts to do with carbon sequestration and changing

climate for the whole of the South American continent. Changes in rainfall patterns in the area with

less precipitation and if these areas are now urbanised then the potential for more concentrated acid

rain and its impact on the forest. Also with the decrease in rain fall the more potential for forest

fires and talked about in other journal articles that I can cite. I believe there are other further areas I

can discuss if I find other evidence and significance to the changing patterns of rainfall linked to

land use

Conclusion

I will be summarising all that I have found while carrying out this scientific report and what it may

mean in the wider context of regional changes linked to changing land use over time and any other

significant findings that I have made over the due course of the time spent doing this report

Acknowledgements

I shall be thanking all those who have helped my with report and giving them fair credit where it is

due


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References

This will be a comprehensive list of all the journals I have cited in my report and other sources of

data such as these sites I have found my land use data

3rd year project by james harper

Documents