The hydrologic cycle is
a very important process in the proper functioning of the
The
The
The Basin can also be roughly divided into two
broad categories: lowland and upland. Lowland areas principally border the
B.
Rainfall
Precipitation arises from sources both
within and outside the
Rainfall-producing mechanisms can roughly be divided
into five scales -- continental, sinoptic, subsinoptic, meso-scale, and
micro-scales. Each of these mechanisms is responsible for a different type of
precipitation scheme. Our review of these mechanisms proceeds in the order of
diminishing scale (Molion, 1991).
At the continental scale, 80-90% of solar
radiation absorbed at the surface is used to evaporate water[2]. The remaining 10-20% is responsible for heating the air[3]. The intertropical convergence zone in the Atlantic (ITCZA)
as well as the convergence of Northern and Southern hemisphere trade winds
function at this scale (Molion, 1991).
The sinoptic scale (1000km) is the next smaller
scale. At this scale, Southern hemisphere cold fronts or frontal systems,
penetrate into
At the subsinoptic scale (500-1000km),
instabilities or squall lines in the atmosphere can cause precipitation. The
highest frequency of such instabilities occur in July. These squall lines
occasionally propagate inland, possibly due to convergence of sea breeze. These
instabilities may also be associated with waves in the trade wind field
triggered by the deep penetration of frontal systems over the subtropical
Meso-scale (100km) precipitation is caused by
convective cells and clusters of Cbs[4]. Precipitation caused by such cells is characterized by a
high intensity and short duration in scattered locations. Micro-scale (1-10km)
precipitation is caused by small convective cells that form during the morning
hours and precipitate around 14-15hrs local time (Molion, 1991).
Pacific
and
Precipitation in the
Amazon is affected by land alterations such as clear-cutting and farming.
Certain changes to the land and soil will cause flooding, others will cause
drought. Sea surface temperature of the
Three ocean regions affect the rainfall in the Amazon: 1.
Variations
in rainfall
On a decadal scale, water vapor input into the
On a yearly scale, precipitation variability may be attributed to the El
Niño-Southern Oscillation (ENSO) as well as several other secondary factors.
Secondary factors include the strength of the
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 (Costa
et al, 1999).
Rainfall
evolution
The simplest models of the flow of water through soil take advantage of the
fact that the surface soil can be divided into three major layers. 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. The relationship
between water saturation
and rainfall for each of the layers can be described by the following three
mathematical equations (Engman, 1991).
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 also produces more realistic evaporation
statistics, taking into consideration conservation of mass, momentum, energy,
and water vapor (Marengo et al, 1994).
The water budget equation for the atmosphere is also related to precipitation
(P), evapotranspiration (E), the vertically integrated moisture convergence
(C).
C. Evaporation
Evaporation can be indicated by a measure called
the precipitation recycling ratio (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 (Eltahir et al, 1994).
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 supposes 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 (Eltahir et
al, 1994).
Careful observation of evaporation data has led to the conclusion that the
atmosphere above the
The contribution to rainfall of precipitation
recycling is largest to the west and south. The maximum rate of recycling
occurs at the southwestern corner of the basin, where greater than 50% of
precipitation can be attributed to evaporation (Eltahir et al, 1994).
D. Evapotraspiration
Mechanisms controlling changes in
evapotranspiration are primarily driven by changes in albedo[5], surface roughness[6] and the depth of water available to plant roots. For
example, increased albedo inhibits absorption of the incoming solar radiation,
reducing the available energy for latent-heat exchanges (Roche,
1991).
The Amazon rainforest is highly efficient in
recycling water vapor back into the atmosphere. Measuring this parameter
however, is has proved extremely difficult. One reason for this is that
evapotranspiration levels are highly variable across the
Results of evapotranspiration are summarized
below, showing great variability due to great difficulty in making precise
measurements.
Table 1: Hydrologic cycle of the Amazon Region (Nobre, 1991)
Research |
Rainfall |
Transpiration |
Evapotranspiration |
Runoff |
|||||
|
mm |
mm |
% |
mm/day |
mm |
% |
mm/day |
mm |
% |
Marques et al. 1980 |
2328 |
|
|
|
1260 |
54.2 |
3.5 |
1068 |
45.8 |
|
2328 |
|
|
|
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 |
541 |
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 |
|
|
E. River Flow Volume
Introduction
Monitoring river volumes is an important method 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 Mission 2006 class decides to create industrial
zones along rivers. 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
o
Mean water velocity range: 21 - 95 cm/s
o
Amplitude of water level fluctuation: 2.2m
o
Flow rate range: 31,200 - 104,000 m3 / s
·
Almeirim (width = 6500m)
o
Mean water velocity range: 21 - 95 cm/s
o
Amplitude of water level fluctuation: 1.4m
o
Flow rate range: 28,700 - 122,000 m3 / s
·
Obidos
o
Mean water velocity range: 21 - 95 cm/s
o
Amplitude of water level fluctuation: 3.41m
o
Flow rate range: 104,000 - 112,000 m3 / s
Monitoring
One method for monitoring river flow rates 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 improve accuracy (Callede et al,
2000).
Data on river volumes can be best attained using remote sensing techniques[7]. These techniques promise vertical resolution of up
to 10cm. The most promising of these techniques for monitoring water
level changes is the interferometric synthetic aperture radar (SAR)[8]. This system however, is not applicable to bodies of
water less than 2km wide, meaning such a system could only apply to the
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).
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
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
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
Over the 1960's and 1970's there was a general increase in
Deforestation
No one doubts that deforestation will have a
devastating effect on the hydrologic cycle of the
Research has also shown that deforestation of
the
In summary, 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. In
turn, deforestation will become self-perpetuating (Henderson-Sellers
et al, 1993)
Table 2: Model fields averaged over the simulation and over the
Field |
Control |
Deforested |
Change |
Daily 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 |
Table 3: Summary of Surface Variables for Control (C) and Deforested
(D) Simulations Averaged over 3 years for
Surface Variable |
Control |
Deforested |
Percent Difference |
Evapotranspiration
(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% |
G. Rainfall Monitoring
Trends in climate, like the ones described above, can be quantified by a number
of different methods. One such method
relies on river discharge records. River records however, may be skewed
by land use changes and artificial means of flow control[9]. The method does offer the advantage of integrating spatial
variability. An alternative method uses rain gauges. The effectiveness of this method is a
function of spatial density (Costa et al, 1999). One problem with traditional rain gauges is
that datasets created by such devices are extremely inefficient, as the devices
are programmed to record the amount of rainfall over a set interval of
time. Consequently, datasets are filled
with huge numbers of extraneous zeros, making the datasets difficult to
manipulate. One possible solution to
fixed-interval recording is fixed-event recording. Under this scheme, the device records the
amount of time over which a set amount of rain falls. This scheme eliminates the large amount of
extraneous zeros, yielding leaner and more manageable datasets (Tan et al, 1991).
The effectiveness of rain gauges
however, is limited by their spatial density and distribution. For rural areas such as the
The most effective method of
measuring rainfall is a combination of local and remote sensing. An example of this is the
H. Evapotranspiration Monitoring
Theory
Constructing a hydrologic budget for the Amazon is an extremely difficult and
imprecise task. In general, the three main factors to consider are
precipitation, evapotranspiration, and surface runoff. More precise
models also integrate zonal and meridional wind speed and specific humidity.
The underlying principle in constructing such balances is that the
long-term rate of precipitation (P) is equal to the sum of evapotranspiration
(E) and runoff (R). Some studies, however, have noticed a small imbalance
in this relationship, namely that P - (E + R) is -179mm/yr. The
explanation given to account for this phenomenon is that water was artificially
added to the basin during the reanalysis procedure.
Table 4 gives the water budget for the
Table 1: Mean water budget for
|
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 |
Using similar methods as those outlined above, one can estimate another
measure of the hydrologic cycle, namely the precipitation recycling ratio (p
). Estimates for the precipitation recycling ratio for the Amazon range
from 25 - 52%. The value is related to average evapotranspiration (E) and
water vapor input (I), though specific methods for calculating the ratio are
disagreed upon. One such method is shown below.
Another measure of the hydrologic
cycle is called convergence (C). This is simply the difference between
water vapor input (I) and output (O), such that C = I - O. Taking into
consideration the entire land-atmosphere water budget and the principle of mass
conservation, the long-term average convergence of should be equal to the discharge
of water out of the basin (Costa et al, 1999).
Methodology
Evapotranspiration can
be measured directly using a lysimeter. This device consists of a block of soil
covered with vegetation. The block of soil is initially removed from the
forest and placed into a container. Next
the block of soil is returned to its original location so that the container as
well as the soil is set into the ground. Over time, the input of
precipitation is measured via rain gauges and the drainage output is recorded.
During this same interval, the block of soil is frequently weighed to
estimate the amount of water loss via evapotranspiration.
Although lysimeters may be effective in accurately
determining evapotranspiration, on a large scale it would be impossible to implement
such a design. Researchers therefore have come to use large scale
measurements of rainfall to determine evapotranspiration levels.
Typically rainfall data is gathered from satellites. Evapotranspiration is then determined using
an algorithm.
Adding energy balance considerations, one can derive more accurate predictions
of evapotranspiration and evaporation. For specific plants, a simple
equation can be written to express the maximum evapotranspiration (ETM
) for that plant. This value is related to the maximum evapotranspiration
for a reference plant (ET0) such as green grass and a dimensionless
coefficient for the specific plant (KC).
where A, B, and C are
constants and Rg is the radiation that reaches the ground.
This however is just one
estimation of evapotranspiration. Countless other studies have developed
estimations based on similar principles. Another such equation relates
evapotranspiration to net radiation (Rn), surface temperature (Ts),
and air temperature (Ta).
where A and B are
constants (Engman et al, 1991).
Next: Aquatic Biota -->
[2] Latent heat
[3] Sensible heat
[4]
Cumulonimbus clusters are mesoscale cloud phenomena with an average diameter of
approximately 200km. They are often generated when the convection temperature
of the surface is reached. They dissolve during the evening. The highest frequency is in summertime over
land, but they also appear over sea during the whole year, at any time during
day or night (“Cumulonimus Cluster (CB),” 2000).
[5] Albedo fraction
of light that is reflected by a body or surface (Albedo, 2002).
[6] Surface roughness characteristics are described by the root mean square (rms) height difference s from a given datum, the correlation length l, and the correlation function (Cosyn, 2002).
[7]
Three types of differences between land and water can be detected by remote
sensing: 1) emissivity, 2) reflection of natural radiation, and 3) reflection
of satellite generated radiation. These
first two differences can be measured passively whereas the third is measured
actively. One problem with remote
sensing is that systems operating in the visible, near-infrared or thermal
infrared wavebands are incapable of penetrating cloud cover.
[8]
Data was acquired over the central Amazon by the Space Shuttle imaging radar
mission. This technique is used to measure subtle water level changes in an
area of flooded vegetation on the Amazon flood plain. The technique makes use of the fact that
flooded forests and floodplain lakes with emergent shrubs permit radar
double-bounce returns from water and vegetation surfaces.
[9] Dams, flow diversions, and river channelization