“State of the Water of the Amazon”
Mission 2006 Water Group
Ryan Allard / Jonathan KarrLauren Cooney / MinJi Kim
Katrina Cornell / Melanie Michalak
November 21, 2002
Table of Contents
I. Introduction
This document attempts to summarize the most useful research done on the Amazon Basin Rainforest's water system. Deforestation, which affects all abiotic and biotic aspects of the Amazon, has a negative effect on the hydrology in the following ways: 1) In deforested areas, the rain washes away the topsoil, which contains critical nutrients, 2) Deforested areas may flood, which cuts off oxygen supplies to the soil, and washes away microorganisms, and 3) Rainfall, evapotranspiration and total runoff will decrease.
II. Water Cycle
The hydrologic cycle is a very important mechanism in the proper function of the Amazon River basin.
A. Rainfall
Sources of Rainfall
64% of water vapor enters the Amazon basin from the eastern border. The remaining 34% enters through the northern border of the basin.
Pacific and Atlantic Ocean Surface Temperatures
It is known that precipitation patterns in the Amazon Basin are affected when the land is changed by clear-cutting and farming. Some areas suffer drought while other areas flood. Research has recently shown that the sea surface temperature of the Atlantic and Pacific oceans surrounding South America has as much of an influence on rainfall as do changes in land cover.
Rong Fu, an atmospheric scientist at the Georgia Institute of Technology has linked rainfall patterns over the Amazon with sea surface temperatures in the tropical Atlantic and Pacific oceans using a computer climate model. Plugging El Niño data his model, he has found that the rainfall pattern in the eastern equatorial Amazon region of Brazil is extremely sensitive to temperature changes on the sea's surface. More specifically, drought conditions appear as sea surface temperatures rise and conversely, flooding results from a decrease in sea surface temperatures.
Not all regions of the Amazon follow this pattern however. To the west of Brazil, rainfall in Peru and Columbia is relatively unaffected by El Niño. This demonstrates that Pacific Ocean sea surface temperatures are actually a better determinant of Amazon precipitation than that of the Atlantic Ocean. This is surprising because moisture from the Pacific Ocean has to travel over the Andes Mountains before reaching the Amazon.
To determine which ocean had the greatest effect on rainfall changes Fu removed Atlantic sea surface temperature readings from the model. Spring, normally Brazil's dry season, registered as its wettest. When Fu removed the eastern Pacific Ocean, sea surface temperature showed a similar, if weaker, effect on rainfall.
It wasn't until Fu removed western Pacific sea surface temperature readings that an unexpected result occurred. Water evaporating from the Atlantic Ocean and Brazil returns to Earth in the form of rain. Thus, Fu expected the Atlantic to have a greater impact on rainfall patterns in the Amazon. But the Pacific influence proved stronger even though evaporation from the Pacific must travel over mountains to reach Brazil. (Shaw, 1999)
Variations in rainfall
On a decadal scale, water vapor input into the Amazon River basin has been experiencing a decreasing trend since the 1960's. This trend is believed to be associated with relaxed southeasterly trade winds, a decreasing east-to-west pressure gradient, and a general warming of the sea surface temperatures in the equatorial South Atlantic.
On a yearly scale, precipitation variability may be attributed to the El Niño-Southern Oscillation (ENSO) as well as several other secondary factors which include the strength of the North Atlantic high, the position of the intertropical convergence zone, and the surface temperatures of Atlantic. Precipitation lags behind ENSO by 3-4 months, with river discharge lagging an additional 3 months. This additional lag is likely due to the contribution from subsurface drainage since surface runoff tends to occur at a much shorter timescale. Soil water storage similarly follows precipitation by approximately 1-2 months.
On a season cycle, precipitation has been observed to vary up to 5mm / day, with runoff vary up to 2mm / day and evapotranspiration remaining constant.
Rainfall evolution
Since the surface soil can be divided into three major layers, there exist three distinct relationships between the water saturation of those layers and rainfall. The first of these layers includes the top soil. The second layer extends to rooting depth (d2) and the third layer extends to the total soil depth (d3). The sum of the water saturation of the three components is equal to the total rainfall to reach the land surface. Each of the layers can be described by the following three mathematical equations.
A more physically realistic general circulation model (GCM) developed at the NASA / Goddard Institute for Space Science (GISS) introduces a canopy resistance and a six-layer soil system. This new scheme also allows runoff to travel from a river's headwater to its mouth according to topography and other channel characteristics. This model produces more realistic evaporation statistics. The new model takes into consideration conservation of mass, momentum, energy, and water vapor.
The water budget equation for the atmosphere is also related to precipitation (P), evapotranspiration (E), the vertically integrated moisture convergence (C).
B. Evapotranspiration
Mechanism
Mechanism controlling changes in evapotranspiration are primarily driven by changes in albedo, roughness and the depth of water available to plant roots. Increased albedo inhibits absorption of the incoming solar radiation, reducing the available energy for latent-heat exchanges.
Data
The Amazon rainforest is highly efficient in recycling water vapor back into the atmosphere. Measuring this parameter however, is has proved extremely difficult. Evapotranspiration levels are highly variable across the Amazon basin as evidenced by the following data:
- 610mm in the semi-arid Rio Grande basin
- 1520mm in the Orthon River basin
- 780mm in Andean part of Beni River basin
- 1220mm in oriental basins of Mamoré River
- 800mm in the Bolivian Andean part of the upper Madeira River basin
- 63-68% of precipitation , 33-37% is runoff
Results of evapotranspiration are summarized below:
Table 1: Hydrologic cycle of the Amazon Region
mm / mm / % / mm/day / mm / % / mm/day / mm / %
Marques et al. 1980 / 2328 / 1260 / 54.2 / 3.5 / 1068 / 45.8
23289 / 1000 / 43.0 / 2.7 / 1328 / 57.0
2328 / 1330 / 57.1 / 3.6 / 998 / 42.9
Villa Nova et al. 1976 / 2000 / 1460 / 73.0 / 4.0 / 540 / 27.0
1168 / 58.4 / 3.2 / 832 / 41.6
2105 / 1569 / 73.4 / 4.3 / 532 / 26.6
Molion 1975 / 2379 / 1146 / 48.2 / 3.2 / 1233 / 51.8
Ribeiro et al. 1979 / 2478 / 1536 / 62.2 / 4.2 / 942 / 38.0
1508 / 60.8 / 4.1 / 970 / 39.2
Ipean 1978 / 2179 / 1475 / 67.5 / 4.0 / 704 / 32.5
1320 / 60.6 / 3.6 / 859 / 39.4
Dmet 1978 / 2207 / 1452 / 65.8 / 4.0 / 755 / 34.2
1306 / 59.2 / 3.6 / 901 / 40.8
Jordan et al. 1981 / 3664 / 1722 / 47.0 / 4.7 / 1905 / 52.0 / 5.2 / 1759 / 48.0
Leopolo et al. 1981 / 2089 / 1014 / 48.5 / 2.7 / 1542 / 74.1 / 4.1 / 5441 / 25.9
Leopolo et al. 1982 / 2075 / 1287 / 62.0 / 3.5 / 1675 / 80.7 / 4.6 / 400 / 19.3
Shuttleworth 1988 / 2636 / 992 / 37.6 / 2.7 / 1320 / 50.0 / 3.6
Able-2B 1987 (1 month) / 290 / 157 / 54.1 / 5.2
Table 2: Summary of Surface Variables for Control (C) and Deforested (D) Simulations Averaged over 3 years for Amazonia
Surface Variable / Control / Deforested / Precent DifferenceEvapotranspiration (m/d) / 3.12 / 2.27 / -27.2%
Precipitation (m/d) / 6.60 / 5.26 / -20.3%
Soil Moisture (cm) / 16.13 / 6.66 / -58.7%
Runoff (m/d) / 3.40 / 3.00 / -11.9%
Net Radiation (W/m^2) / 147.29 / 125.96 / -14.4%
Temperature (C) / 23.55 / 25.98 / 10.3%
Sensible Heat (W/m^2) / 57.19 / 60.15 / 5.2%
Bowen Ratio / 0.85 / 1.50 / 76.5%
Table 3: Mean water budget for Amazonia. The data re 12-month mean (January to December) values
Total Precipitation (P) (mm/year) / Evapotranspiration (E) (mm/year) / E-P / E/P / Precipitable Water (mm)Control / 2464 / 1657 / -807 / 0.67 / 37.7
Deforestation / 1821 / 1161 / -661 / 0.63 / 35.4
Difference / -642 / -496 / +146 / -0.04 / -2.3
Change (%) / -26.1 / -30.0 / +18.0 / -5.9 / -6.1
C. Evaporation
Evaporation can be indicated by a measure called the precipitation recycling ration (p). This ratio is the contribution of evaporation within a region to precipitation in the same region. A high precipitation recycling ratio estimate is not sufficient to conclude a strong role for land surface hydrology in the regional climate. Rather, it suggests a strong potential for significant changes in surface hydrology to impact regional climate.
The following model makes two assumptions: 1) atmospheric water vapor is well-mixed, and 2) the rate of change of storage of water vapor is negligible compared with water vapor fluxes at the time-scale for which the model is applicable. The model gives two distinct relationships for water vapor evaporation, that within the region, and that outside the region, yielding the equation,
where inflow is represented by I, evaporation is represented by E, and the subscripts o and w represent outside the region and inside the region respectively.
Careful observation of evaporation data has led to the conclusion that the atmosphere above the Amazon basin is not a closed system. Data suggests that there is a significant migration of moisture out of the basin. Furthermore, this flux out of the basin accounts for only 68% of the flux into the system. This means that the outflow of atmospheric moisture from the basin may contribute important input to the hydrologic cycles of the surrounding regions. Further, changes in the Amazon basin evaporation may potentially affect the moisture supply and rainfall of surrounding regions.
The contribution to rainfall of precipitation recycling increases westward and southward. The maximum rate of recycling occurs at the south-western corner of the basin, where more than 50% of the precipitation is contributed to by evaporation.
D. River Flow Volume
Introduction
Monitoring of river volume is important as a means of calibrating hydrologic cycle models. The same techniques used to monitor river volumes may also be used to monitor vegetations densities. From this information, friction coefficients may be derived and used to further improve hydrologic models. Secondly, it is important to monitor river volumes in order to predict and give advance warning for floods further downstream. In particular, if the class decides to create industrial zones along rivers, for example to take advantage of the readily available natural transportation network provided by the river, it will be important to know which areas are and are not susceptible to floods. Further, if frequently flooded cites are chosen, it will be important to be able to predict floods for those areas (Alsdorf, et al, 2000).
Data
The following measurements were carried out on November 23 and 30, 1998:
- Gurupa
- Mean water velocity range: 21 - 95 cm/s
- Amplitude of water level fluctuation: 2.2m
- Flow rate range: 31,200 - 104,000 m3 / s
- Almeirim (width = 6500m)
- Mean water velocity range: 21 - 95 cm/s
- Amplitude of water level fluctuation: 1.4m
- Flow rate range: 28,700 - 122,000 m3 / s
- Obidos
- Mean water velocity range: 21 - 95 cm/s
- Amplitude of water level fluctuation: 3.41m
- Flow rate range: 104,000 - 112,000 m3 / s
Monitoring
One method for monitoring river volumes uses an ultrasonic device called an Acoustic Doppler Current Profiler (ADCP). The most frequent problem with this technique is that it ignores a non-negligible river bottom displacement when calculating river flow. This uniformly leads to an underestimation in flow volume measurements. This error is commonly referred to as "moving bottom error." Recent studies into the problem have developed promising solutions which should be able to correct it easily (Cobby et al, 2001).
Data on river volumes can be best attained using remote sensing techniques[1]. These techniques promise vertical resolution of up to 10cm. The most promising of these techniques for monitoring of water level changes is the interferometric synthetic aperture radar (SAR)[2]. This system however, is not applicable to bodies of water of less than 2km wide, meaning such a system could only apply to the parts of the Amazon River itself and its major tributaries. An alternative approach uses a technique called airborne scanning laser altimetry or LiDAR to detect water level changes. This technique has already proven to be highly useful for measuring vegetation height and so data taken from such a system would be particularly useful in modeling runoff (Cobby et al, 2001).
The two techniques have particular advantages over the Landsat, ERS-1, JERS-1 and Radarsats systems because of the frequency at which they can monitor rivers. These systems have the capability to monitor water changes up to every six hours, which is necessary for quickly detecting floods (Cobby et al, 2001).
E. Groundwater
Threats
One major source of threats is the mining processes and their side effects. Acid mine dragains (or AMD) is a solution originating at a mine site and carried off in rain or surface water. It is deposited in nearby water sources including the groundwater and is often extremely acidic with high concentrations of toxic metals. During the mining process the groundwater is depleted (along with surface water). “Heap leaching using cyanide or sulphuric acid poisons rivers, streams, and groundwater and gills fish and wildlife.” Finally, tailings, the ground up waste from the mined rock, can leak from where they are stored polluting the surrounding water and soil. (The Relevance of the OECD Guidelines for Multinational Enterprises to the Mining Sector and the Promotion of Sustainable Development).
Another threat comes as a side effect to law enforcement. In attempt to control drug production, the US has undertaken projects to spray the coca plants “with chemicals that not only destroy the coca plant but contaminate the groundwater and legal subsistence crops. (
Monitoring
Part of SIVAM’s data collection system includes thermal imaging cameras on the planes that can be used to locate groundwater flows. This is really only a mapping tool and is not useful for determining chemical composition. ( slide number 20) Hydrolab, however, has something called a “Quanta-G water quality instrument” which is designed specifically for ground water monitoring. It measured temperature specific conductance, salinity, total dissolved solids, dissolved oxygen, pH, oxygen reduction potential, depth, and vented level. IT monitors up to depths of 100m. According to the advertisement, new users can be trained in 30 minutes or less, and advantage is we want to train indigenous people to get them involved. (
F. Trends
Over the past twenty years, the hydrologic cycle has experienced a number of trends, which are likely to be indicators of the effect of deforestation on the whole Amazon River basin region. If changes in water vapor transport continue into the future, combined with decreases in evapotranspiration, all of the sources of water vapor into the Amazonian atmosphere will be significantly altered. In turn, this will have huge ramifications on the entire Amazon River basin ecosystem.
The first of these trends is decreasing atmospheric transport of water vapor both into and out of the system. This trend is believed to be associated with relaxed southeasterly trade winds, a decreasing east-to-west pressure gradient, and a general warming of the sea surface temperatures in the equatorial South Atlantic.
The second of these trends is increasing internal recycling of precipitation and basin-wide precipitation. This is occurring even as evapotranspiration and runoff have remained at a constant level across the entire basin. Annual mean atmospheric trends do exist for the eastern part of the basin. On a yearly scale, precipitation variability may be attributed to the El Niño-Southern Oscillation (ENSO) as well as several other secondary factors which include the strength of the North Atlantic high, the position of the intertropical convergence zone, and the surface temperatures of Atlantic. On the decadal scale, these factors are still important, but less so.
Over the 1960's and 1970's there was a general increase in Amazon River basin precipitation and river discharge. The precipitation and river discharge 1970's and 1980’s however were average. One explanation for this decrease is changes in the frequency and duration of the positive phases of the Southern Oscillation (Costa et al, 1999).
Deforestation
No one doubts that deforestation will have a devastating effect on the hydrologic cycle of the Amazon Basin. Research has shown that deforestation of the Amazon will cause a decrease in precipitation of 25% or 1.4mm / day (Dickinson et al, 1992). In addition, from 1990-1993 rainfall decreased in almost every month. However, reductions in rainfall do not occur uniformly across the Amazon region. At some locations rainfall may decrease by up to 65%, whereas other locations (typically the mountainous regions of Peru and Ecuador) will experience increases in rainfall. Furthermore, changes in precipitation are not confined to the Amazon River basin itself. For example, during the southern summer and autumn there are large fluctuations in precipitation in eastern Brazil which seem to correlate with precipitation changes over deforestation areas (Lean et al, 1992).
Research has also shown that deforestation of the Amazon basin will cause an increase in evapotranspiration of 0.7 mm / day. Similarly, total runoff will decrease by 0.7 mm / day (Dickinson et al, 1992). Surface runoff however, will increase substantially, primarily as a result of decreased soil infiltration capacity and changes in the spatial distribution and intensity of rainfall (Lean et al, 1992). Temperature will increase 1-4°C. This results from a decrease in the energy used in evaporating water at the canopy and soil surface, and a decrease in roughness (Dickinson et al, 1992).
These changes in the hydrologic cycle will be caused by
1)Decreased surface roughness
2)Increased surface albedo
3)Changing soil properties
4)Decreased rooting depths, and
5)Decreased infiltration rates (Dickinson et al, 1992).
One conclusion that may be drawn from the observation that the reduction in precipitation is larger than the reduction in evapotranspiration, is that the length of the dry season will increase, thereby making deforestation self-perpetuating (Henderson-Sellers et al, 1993).
Table 4: Model fields averaged over the simulation and over the Amazon Forest (Dickinson et al, 1992)
Field / Control / Deforested / ChangeDaily Maximum Temperature (K) / 304.1 / 306.7 / 2.6
Daily Minimum Temperature (K) / 294.8 / 294.6 / -0.2
Mean Surface Soil Temperature (K) / 298.8 / 299.4 / 0.6
Precipitation (mm / day) / 5.5 / 4.1 / -1.4
Runoff (mm / day) / 2.0 / 1.3 / -0.7
Evapotranspiration (mm / day) / 3.5 / 2.8 / -0.7
Interception (mm / day) / 1.3 / 0.8 / -0.5
Sensible Flux (W / m2) / 54.0 / 56.0 / 2.0
Absorbed Solar Radiation (W / m2) / 215.0 / 212.0 / -3.0
Net Longwave Radiation (W / m2) / 59.0 / 74.0 / 15.0
Fractional Cloud Cover / .53 / .46 / -0.07
Relative Soil Moisture / 0.7 / 0.4 / -0.3
G. Rainfall Monitoring
Trends in climate, like the ones described above can be indicated by a number of different measures. One method relies on river discharge records. River records however, may be skewed by land use changes and artificial means of flow control (i.e. damns). The method does offer the advantage of integrating spatial variability. An alternative, which is increasingly effective with increasing spatial density, is rain gauges (Costa et al, 1999).