II

The hydrologic cycle is a very important process in the proper functioning of the Amazon River basin. The most visible part of this cycle is the river itself. The river is 4,000mi in length, carrying approximately 20% of all of water discharged to the Earth’s oceans. The river originates in the high Andes Mountains, approximately 100mi west of the Pacific Ocean and travels east, terminating at the Atlantic Ocean. At the main outlet of the river, north of Marajo Island, the river is 40mi wide. At flood-stage, the river discharges 6,180,000ft³ at its mouth. The volume of water carried by the Amazon is so great that the salinity of the Atlantic Ocean is diluted within a 100mi radius from the terminus of the river.

The Amazon River is fed by a large network of over 1,000 tributaries. Seven of these tributaries are greater than 1,000mi in length, the largest of which is the Negro River. The Negro River alone carries 20% of the discharge of the Amazon River. The Amazon River’s tributaries can be roughly divided into three categories: blackwater, whitewater and clearwater (Saliot et al, 2001). Blackwater tributaries originate in the ancient crystalline highlands. Examples of blackwater rivers include the Jari, Trombetas, Negro, Tocantins-Araguaia and Xingu Rivers. These rivers are termed "blackwater" because they originate from acidic rains that are rich in humus and nutrient poor. Whitewater rivers, such as the Madiera are categorized by high sediment concentrations. Clearwater rivers like the Tapajos have slowed water rates where the sediment is allowed to settle.

The Amazon Basin rainforest covers an area of 2.3 million mi². At its widest part, it rainforest stretches 1,725mi. The basin includes parts of several nations, including Brazil, Peru, Columbia, Ecuador, Bolivia, and Venezuela. Brazil, which encloses 2/3 of the basin, was chosen to be the focus of the Project Amazonia class.

The Basin can also be roughly divided into two broad categories: lowland and upland. Lowland areas principally border the Amazon River itself and its tributaries and are 12-30mi wide. These areas are characterized by a yearly flooding cycle. The other 2/3 of the basin is considered upland. Upland regions are covered by immense rainforests that transition to dry forests and savannas in the West. Upland regions may also be described as "gently undulating hills." These areas are composed of layers of alluvial soil deposited as much as 2.5 million years ago and contain many shallow oxbow lakes[1] and wetlands. The average rainfall for upland regions is 60-120in/yr².

B. Rainfall

Precipitation arises from sources both within and outside the Amazon River basin. Sixty-four percent of water vapor flux into the Amazon comes through the eastern border of the basin. The remaining 36% enters through the northern border of the basin. Little water vapor enters the Amazon Basin from the west because the Andes Mountains serve as an effective barrier to storm systems moving eastward from the Pacific Ocean. At the same time, this also means that little water vapor escapes the basin through its western border. Water vapor entering the Amazon through the eastern and northern borders together account for approximately 50% of the precipitation in the Amazon. The remaining 50% comes from precipitation recycling within the Amazon Basin rainforest -- evaporation and evapotranspiration (“Amazon River”, 2002).

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 Amazonia any time of the year. During the winter, these systems are characterized by a sharp 15-20º temperature decrease, lasting 3-5 days. During the summer, these systems are generally NW-SE oriented and cross the coast at 15-25ºS. It is believed that Northern hemisphere frontal may also have similar effects(Molion, 1991).

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 Atlantic(Molion, 1991).

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 Atlantic Ocean Surface TemperaturesPrecipitation 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 Atlantic and Pacific oceans also plays a role in influencing the rainfall of the Amazon Basin region. When sea surface temperatures drop, floods result and when the temperatures increase, drought conditions are prevalent.

Three ocean regions affect the rainfall in the Amazon: 1. Atlantic Ocean 2. Eastern Pacific Ocean and 3. Western Pacific Ocean. The most influential of these three is actually the Western Pacific region, even though evaporation has to travel across the Andes Mountains. The Atlantic and eastern Pacific regions have similar, weaker effects on the rainfall of the Amazon(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(Costa et al, 1999).

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 North Atlantic high, the position of the ITCZA, and the sea surface temperature of Atlantic Ocean. 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(Costa et al, 1999).

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 Amazon Basin is not a closed system. Data suggest 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 implies that the outflux of atmospheric moisture from the basin may contribute important input to the hydrologic cycles of the surrounding regions. Furthermore, changes in the Amazon Basin evaporation levels may potentially affect the moisture supply and rainfall of surrounding regions(Eltahir et al, 1994).

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 Amazon Basin as evidenced by the following data:

610mm/yr in the semi-arid Rio Grande basin
1520mm/yr in the Orthon River basin
780mm/yr in Andean part of Beni River basin
1220mm/yr in oriental basins of Mamoré River
800mm/yr in the Bolivian Andean part of the upper Madeira River basin(Roche, 1991).

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 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).

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(Costa et al, 1999).

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 (Costa et al, 1999).

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 (Costa et al, 1999).

Over the 1960's and 1970's there was a general increase in Amazon River basin precipitation and river discharge. However, precipitation and river discharge over 1970's and 1980’s were average. One explanation for this decrease is the changes in the frequency and duration of the positive phases of the ENSO (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 clearly shown that deforestation of the Amazon will cause a decrease in precipitation of 25% or 1.4mm/day(Dickinson et al, 1992). From 1990-1993 rainfall decreased in almost every month, as evidence to this trend. However, reductions in rainfall will not occur uniformly across the basin. 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. Evidence for this comes from the observation that during the southern summer and autumn there are large fluctuations in precipitation in eastern Brazil which seem to correlate with precipitation changes over deforested areas (Lean et al, 1993).

Research has also shown that deforestation of the Amazon Basin will cause an increase in evapotranspiration of 0.7mm/day. Similarly, total runoff will decrease by 0.7mm/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, 1993). In addition, temperature will increase 1-4°C. This results from a decrease in surface roughness and a decrease in the amount of energy used to evaporate water at the canopy and soil surface levels (Dickinson et al, 1992).

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 Amazon Forest(Dickinson et al, 1992)

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 Amazonia(Nobre et al, 1991)

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 Amazon Basin rainforest, this density is particularly low. A more effective method of measuring rainfall takes advantage of satellites to monitor the entire Amazon Basin rainforest. Two types of satellite monitoring include infrared and microwave monitoring. The former determines rainfall amounts from cloud-top temperatures. The advantage of these systems that they are able to continuously monitor given region. The latter is more accurate in determining instantaneous rainfalls. The problem with microwave-based systems is that they are only capable of monitoring any given location two times per day. This method determines rainfall amounts from the distribution of hydrometeors within clouds (Sorooshian et al, 2000).

The most effective method of measuring rainfall is a combination of local and remote sensing. An example of this is the Climate Prediction Center merged analysis of precipitation (CMAP). This merged analysis is composed of two kinds of data: standard precipitation (STD) and enhanced precipitation (ENH). STD consisted of gauge observations, where as ENH consists of five kinds of satellite estimates. Specifically these estimates are:

Outgoing longwave radiation (OLR)-based precipitation index,
Infrared-based Geostationary Operational Environmental Satellite (GOES) precipitation index,
Microwave sounding unit,
Microwave scattering from Special Sensor Microwave/Imager (SSM/I), and
Microwave emission from SSM/I (Matsuyama et al, 2002).

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 Amazon Basin rainforest.

Table 1: Mean water budget for Amazonia. The data are 12-month mean (January to December) values (Nobre et al, 1991)

	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 (ET_M ) 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 (K_C).

where A, B, and C are constants and R_g 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 (R_n), surface temperature (T_s), and air temperature (Ta).

where A and B are constants (Engman et al, 1991).

Next: Aquatic Biota -->

[1] Oxbow lakes are generally formed from mature rivers which begin to curve, cutting and eroding the outside of the curve. At the same time, sediment deposits on the inside of the curve. These two mechanisms arise from the fact that the flow of water is more rapid toward the outside of the curve. Over time, as erosion and deposition continue, the curve becomes larger and more circular. Ultimately, the loop of the curve reaches a diameter approximately five times the width of the stream and the river begins to cut the loop off by eroding its neck. Finally, the river breaks through a cutoff and forms a new streambed. The result is a crescent-shaped lake that looks like an abandoned river meander. Such lakes are called oxbow lakes because they look like the bow part of the yoke used with teams of oxen (Rosenberg, 2002). Eventually, oxbow lakes are silted up to form marshes and finally meander scars, marked by different vegetation or the absence of cultivation. The lakes commonly are filled with clay-sized sediment that is less easy to erode than surrounding material and thus may cause a more complex meandering system in its parent stream (Oxbow lake, 2002).

[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