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LONGITUDINAL PATTERNS IN STREAM CHANNEL GEOMORPHOLOGY AND
AQUATIC HABITAT IN THE LUQUILLO MOUNTAINS OF PUERTO RICO
Andrew Stephen Pike
A DISSERTATION
in
Earth and Environmental Science
Presented to the faculties of the University of Pennsylvania in partial fulfillment of the
requirements for the degree of Doctor of Philosophy
2008
___________________________________
Supervisor of Dissertation – Dr. Frederick N. Scatena
___________________________________
Graduate Group Chairperson – Dr. Gomaa Omar
ACKNOWLEDGEMENTS
This dissertation would not have been possible with the help and support of
numerous mentors, colleagues, and friends, all of whom deserve recognition here. First
and foremost, I would like to thank my advisor, Fred Scatena. Although his expectations
were high and his demands were challenging, his perpetual guidance from the field to the
office has been unparalleled. With his iron drive to help me succeed, and his deep breadth
of knowledge of the natural world, it has truly been an honor to have studied under Fred.
Second, I would like to thank each member of my graduate committee. I extend special
thanks to Art Johnson, for first launching me onto the scientific research course, and for
his continued mentorship throughout the years; I most certainly would not have made it
this far without him. I thank Todd Crowl for arranging for me to spend an enjoyable and
beneficial semester in his laboratory at Utah State University. I thank Dana Tomlin for
his lucid intellect and always enlightening discussions. Lastly, I am deeply appreciative
of Gomaa Omar not only for his support in handling logistical issues, but also for sharing
his unique talent of bringing out the buried spiritual essence in scientific work.
The field work required to survey tropical mountain rainforest streams was
inherently enjoyable, but it was not without its challenges. Yet I had the pleasure of
working with people who knew how to get a difficult job done, and have a good time
doing it. The assistance from the following people made four summers of stream surveys
possible, and all the more pleasant: Erik Drew, Tamara Heartsill-Scalley, Emmanuelle
Humblet, Kris Johnson, Kunal Mandal, José Marcial, Amanda Moyer, Pablo Piña, and of
course, Jennifer Schiffner.
ii
Housing and logistical support while in the field was made possible by the El
Verde Field Station (University of Puerto Rico), the Sabana Research Center (United
States Forest Service), and the International Institute for Tropical Forestry (IITF).
My colleagues on the Biocomplexity project were critical in the formulation of
this dissertation. I especially thank Katie Hein for her hard work collecting ecological
data, her insight throughout the project, and for two excellent field seasons that made this
collaborative project work. I also thank Drs. Ellen Wohl, Jorge Ramirez, Alan Covich,
Felipe Blanco, and all others involved with the project for their intellectual contributions.
I would like to thank numerous people around the department of Earth and
Environmental Science at Penn. A special thanks for Karen Taylor for her support in all
matters of life. Also, thanks to all the fellow grad students for their camaraderie, and to
all the faculty who I’ve studied with and learned from over the past years.
Funding for this dissertation was provided from the National Science Foundation
Biocomplexity Grant (NSF #030414) —Rivers, Roads, and People: Complex Interactions
of Overlapping Networks in Watersheds. Additional support was provided by the
University of Pennsylvania, and the Long-Term Ecological Research (LTER) Network.
iii
ABSTRACT
LONGITUDINAL PATTERNS IN STREAM CHANNEL GEOMORPHOLOGY AND
AQUATIC HABITAT IN THE LUQUILLO MOUNTAINS OF PUERTO RICO
Andrew S. Pike
Frederick N. Scatena
The hydrologic, geomorphic, and ecological dynamics of tropical montane
streams are poorly understood in comparison to many temperate and/or alluvial rivers.
Yet as the threat to tropical freshwater environments increases, information on the
dynamics of relatively pristine streams is important for understanding landscape
evolution, managing and conserving natural resources, and implementing stream
restoration. This dissertation characterizes the geomorphology and hydrology of five
adjacent watersheds draining the Luquillo Experimental Forest (LEF) in northeastern
Puerto Rico, and discusses implications on aquatic habitat. I performed several
interrelated studies, including: 1) formulating a geographic information systems (GIS)
framework to estimate hydrologic parameters from topographic information and
hydrologic records, 2) developing a method to determine active stream channel
boundaries (“bankfull” stage) that allows for comparison of channel geometry on the
basis of flow-frequency, 3) decoupling the relative influences of lithologic and hydraulic
controls on channel morphology using an extensive field-based stream survey and
analysis of stream profiles, channel geometry, and sediment dynamics, 4) linking
network- and pool-scale geofluvial dynamics to the abundance of migratory fish and
iv
shrimp through a collaborative analysis combining geomorphic surveys and aquatic
faunal sampling. This research indicates that these streams have some properties
resembling both temperate montane and alluvial rivers. Similar to low-gradient rivers
where floodplains mark channel boundaries, the active channel stage in these streams is
defined by the incipient presence of woody vegetation and soil development. Systematic
basin-scale geomorphic patterns are well-developed despite apparent non-fluvial and
lithologic control on local channel morphology. This implies that strong fluvial forces are
sufficient to override channel boundary resistance; a feature common in self-forming
“threshold” alluvial channels. Migratory aquatic fauna abundances are influenced by a
variety of geomorphic factors such as barrier waterfalls and suitable headwater habitat,
and are consequently highly variable and patchy. These results stand in contrast to the
notion that aquatic communities mirror systematic geomorphic gradients, but rather
acknowledges the influences of multiscale geomorphic processes. Ultimately, this
research provides baseline information on physical and biological processes in relatively
unaltered tropical streams and can be used to inform further studies that document human
interactions with stream networks.
v
TABLE OF CONTENTS ACKNOWLEDGEMENTS ii
ABSTRACT iv
LIST OF TABLES x
LIST OF FIGURES xii
CHAPTER 1: General Introduction
Introduction 1
Chapter Outlines 6
References 9
CHAPTER 2: Application of Digital Terrain Analysis to Model Surface
Water Flow in the Luquillo Mountains of Northeastern Puerto Rico
Abstract 15
Introduction 16
Study Area 17
Digital Elevation Model (DEM) Construction 18
Stream Network Extraction 22
Rainfall, Runoff, and Discharge 24
Conclusion 27
Acknowledgements 27
References 28
CHAPTER 3: Defining a Bankfull Analog for Tropical Montane Streams
Using Riparian Features
Abstract 31
vi
Introduction 33
Study Area 38
Northeastern Puerto Rico 38
Regional Stream Gages 41
Riparian Vegetation 42
Methods 50
Field Surveys 50
Estimation of Flow-Frequency 51
Multivariate Regression Trees 55
Effective Discharge 56
Results 60
Riparian Vegetation 60
Bankfull Discharge and Effective Discharge 61
Multivariate Regression Trees 61
Discussion 70
Riparian Features 70
Bankfull and Effective Discharge 73
Applicability to other stream systems 76
Conclusion 77
Acknowledgements 78
References 79
CHAPTER 4: Longitudinal Patterns in Stream Channel Geomorphology in
the Tropical Mountain Streams of the Luquillo Mountains, Puerto Rico
vii
Abstract 88
Introduction 89
Study Area 97
Methods 105
Results 109
Longitudinal Profiles 109
Hydraulic Geometry 113
Grain Size 116
Stream Power 127
Discussion 130
Conclusion 136
Acknowledgements 137
References 138
CHAPTER 5: Multiscale Linkages Between Geomorphology and Aquatic
Habitat in a Tropical Montane Stream Network, Puerto Rico
Abstract 149
Introduction 151
Study Area 156
Stream Community 161
Methods 164
Field Methods 164
Pool Length and Spacing 168
Statistical Analyses 168
viii
Principal Components Analysis 168
Non-Parametric Multiplicative Regression 169
Stepwise Multiple Linear Regression 171
Results 171
Species Distribution in Geomorphic Space 171
Longitudinal Trends of Species 183
Influence of Reach and Pool-Scale Geomorphology 192
Pool Size and Spacing 195
Discussion 195
Landscape Scale Patterns 195
Reach and Pool-Scale Patterns 201
Conclusion 204
References 205
CHAPTER 6: Conclusions and Future Research
Summary and Conclusions 216
Future Research 218
APPENDIX 222
Site Information 227
Baseflow Channel Geometry 234
Active Channel Geometry 241
Grain Size and Pebble Count Data 248
Additional Biocomplexity Pool Information 255
INDEX 259
ix
LIST OF TABLES Table 3.1 Physiographic site information for the selected study gages.
Table 3.2 Riparian features (vegetation, substrate, and soil
characteristics) that were recorded at each survey point.
Table 3.3 For each study USGS gage, the time span of the discharge
record, flow parameters, and at station width and depth hydraulic
geometry coefficients and exponents.
Table 3.4 The median height above water table, unit discharge, flow
frequency, and recurrence interval of zones defined by the multivariate
regression tree analysis.
Table 3.5 The discharge, unit discharge, flow frequency, and
recurrence interval associated with the bankfull stage, the effective
discharge, and the bankfull analog at each study stream gage.
Table 4.1 Downstream hydraulic geometry coefficients and exponents
divided by watershed.
Table 5.1 Species identified during trapping and electrofish sampling.
Table 5.2 Principal component eigenvalues for 58 geomorphic
variables, using data from 113 pools throughout the study basins.
Table 5.3 Principal component eigenvalues for 20 geomorphic
variables measured for pools in the Quebrada Prieta.
Table 5.4 Landscape scale variables used to predict species
presence/absence using a NPMR 2-parameter model.
44
52
54
68
69
115
166
173-174
181
184
x
Table 5.5 Internal validation statistics for NPMR models of species
presence/absence.
Table 5.6 Stepwise Multiple Linear Regression model output and
goodness of fit showing the relative influence of geomorphic variables
on predicting decapod abundance at the reach-scale.
Table 5.7 Stepwise Multiple Linear Regression model output and
goodness of fit showing the relative influence of geomorphic variables
on predicting decapod abundance in the Quebrada Prieta.
190-191
193
194
xi
LIST OF FIGURES Figure 2.1 The process of extracting a stream network from contour
data.
Figure 2.2 Comparison of the USGS stream network to the DEM
generated stream network at 6ha drainage area threshold for the Río
Mameyes.
Figure 2.3 Spatial distribution of mean annual rainfall, mean annual
runoff, and mean annual discharge within the Río Mameyes drainage
basin according to elevation-based regression equations.
Figure 3.1 Location map of the selected study USGS gages in and
around the Luquillo Experimental Forest in Northeastern Puerto Rico.
Figure 3.2 Photographs of study USGS gages.
Figure 3.3 Illustration of the vertical zonation of vegetation types at a
cross section at Río Mameyes near Sabana, alongside a hydrograph
representative of the flow regime and flood disturbance.
Figure 3.4 The flow duration, sediment transport, and relative
effectiveness curves using data from the Río Mameyes near Sabana
gage, and the corresponding discharge at which different vegetation
types occur.
Figure 3.5 Box plots of the flow frequency for surveyed data points at
alluvial sites that have been partitioned into clusters based on
vegetation, substrate, and soil characteristics using a multivariate
regression tree technique.
20
23
26
43
45-46
48
59
63-64
xii
Figure 3.6 Box plots of the flow frequency for surveyed data points at
mid-elevation and steepland sites that have been partitioned into
clusters based on vegetation, substrate, and soil characteristics using a
multivariate regression tree technique.
Figure 4.1 Location map of the 238 surveyed reaches in the study
watersheds and the regional topography, geology, and land cover in
northeastern Puerto Rico.
Figure 4.2 Longitudinal profiles of the main stem of each river
highlighting the relationship between local profile shape and lithology.
Figure 4.3 Downstream hydraulic geometry relationships between
active channel discharge, width, depth, and velocity using data from
all surveyed reaches.
Figure 4.4 Upstream views of typical reaches throughout the basins.
Figure 4.5 Grain size distributions for all measured clasts, grouped by
lithology.
Figure 4.6 The percentage of megaboulders in the channel as a
function of the adjacent hillslope steepness.
Figure 4.7 Relationship between active channel discharge and
dimensionless shear stresses and critical shear stresses.
Figure 4.8 Relationship between median grain size, drainage area, and
slope.
65-66
98-99
110-111
114
117-118
120
121
123-124
126
xiii
Figure 4.9 Downstream changes in elevation, drainage area, median
grain size, stream power, and the ratio of stream power to coarse grain
size along the main stem profile of each river.
Figure 5.1 Location map of 33 sample sites located in the Rio
Mameyes, Rio Espiritu Santo, and Rio Fajardo.
Figure 5.2 Plot of pools in geomorphic space, with species presence
indicated.
Figure 5.3 Plot of pools in geomorphic space, with decapod relative
abundance and proportional abundance indicated.
Figure 5.4 Plot of Quebrada Prieta pools in geomorphic space, with
decapod relative abundance and proportional abundance indicated.
Figure 5.5 An example of a non-parametric multiplicative regression
response curve applied to map the distribution of species.
Figure 5.6 Longitudinal profiles of estimated probability of
occurrence for each sampled species.
Figure 5.7 Map showing the location of 7 stream segments where
pools length and spacing were measured, as well as relationships
between drainage area, pool length, and pool spacing.
128-129
157-158
175-176
178-179
182
185-186
187-188
196-197
xiv
CHAPTER 1
GENERAL INTRODUCTION
INTRODUCTION
A common theme in river research and aquatic and riparian ecology is the role of
self-organizing principles that give rise to systematic basin-scale patterns. Decades of
research have identified several similar morphologic features in alluvial rivers worldwide
that are formed in response to the flow regime and sediment transport capacity. These
include a floodplain defining the bankfull discharge that occurs with a recurrence interval
of approximate 1-3 years (Wolman and Miller 1960), well-graded concave upward
longitudinal profiles (Hack 1957), well-developed downstream hydraulic geometry
(Leopold and Maddock 1953), and systematic changes in grain size (Leopold et al. 1964).
These features are a result of the tendency of rivers to adjust to the frequency and
magnitude of flows, and to create gradients that most effectively route water and
sediment through the network. Furthermore, some theory in aquatic ecology is based on
the observation that aquatic habitats, populations, and communities often change
systematically downstream in response to imposed gradients in channel morphology and
consequent changes in canopy openness, light, substrate size, and stream flow (Vannote
et al. 1982). A careful investigation of these morphologic and ecological patterns can
yield insight into landscape evolution, inform management of natural resources, and
provide guidelines for stream restoration.
However, these established linkages between geomorphic patterns and fluvial
processes may not hold in mountainous and bedrock streams. Recent research on the
morphology of mountain streams demonstrates their complexity and implicates several
1
different controls on their morphology, including: tectonic and structural influences
(Whipple 2004, VanLaningham et al. 2006), bedrock (Snyder et al. 2003), storm pulses
(Gupta 1988), non-fluvial processes such as landslides/debris flows (Brummer and
Montgomery 2003, Stock and Dietrich 2006) and glaciers (Wohl et al. 2004). Other
studies have demonstrated the characteristic morphology of mountain streams (Grant et
al. 1990, Montgomery and Buffington 1997, Wohl and Merritt 2001), the lack of
consistent bankfull indicators (Radecki-Pawelick 2002), their complicated hydraulic
geometry (Wohl 2004), the complexity of sediment transport (Blizard and Wohl 1998,
Lenzi et al. 2006, Torizzo and Pitlick 2004), as well as the distribution of sediment within
mountain drainages (Pizzuto 1995, Golden and Spring 2006).
Of all mountain streams in the world, drainages in humid tropical montane areas
are among the most extreme fluvial environments (Gupta 1988). The high rates of erosion
and dramatically dissected landscapes prevalent in the world’s tropical mountainous
regions are testament to the power of these rivers. In comparison to the many temperate
montane and/or alluvial rivers and streams that have been studied worldwide, tropical
montane streams have several unique characteristics that may structure their morphology:
A combination of steep slopes, high mean annual rainfall, and intense tropical storms
generate an energetic and powerful flow regime (Gupta 1995). The absence of past and
present glaciation excludes glacial landforms, such as U-shaped valleys and coarse
moraine deposits, that are prevalent in some temperate montane basins. Relatively high
rates of chemical and physical weathering rapidly denude tropical landscapes and may
affect rates of channel-sediment diminution and patterns of downstream fining (Brown et
al. 1995, White et al. 1998, Rengers and Wohl 2007). Frequent landslides triggered by
2
heavy rains introduce pulses of coarse sediment to the channels and strongly link fluvial
and colluvial forces (Larsen et al. 1999). Large woody debris that is common in
temperate streams is rapidly decomposed in the tropics, despite frequent inputs from
surrounding mature forests and hurricanes (Covich and Crowl 1990). Periodic high-
magnitude floods associated with hurricanes and other tropical disturbances effectively
rework boulder channels (Gupta 1975, Scatena and Larsen 1991). Yet the channel
morphology that is sculpted by fluvial and non-fluvial processes in tropical montane
environments is generally unknown. The relatively few studies that have specifically
addressed the morphology of tropical mountain streams have shown that the conflicting
lithologic and hydraulic controls complicate the development of expected downstream
morphologic patterns (Lewis 1969, Ahmad et al. 1993, Wohl 2005).
Montane streams are also an integral part of the ecological web of many humid
tropical islands. Biodiversity in tropical island streams is generally low in comparison to
continental streams due to physical isolation (Covich 1988, Smith et al. 2003). The
species that do inhabit tropical island streams have distinct habitat preferences and most
fish and decapods in these streams have a diadromous life cycle, requiring direct linkages
between freshwater and salt water to breed and feed. Consequently, the geomorphology
of the river channel, imposed by the interplay between fluvial and tectonic processes, is
critical to understanding habitat formation and the consequent distribution of aquatic
fauna throughout the stream network. Several theories have been proposed to describe the
linkages between geomorphology and aquatic organisms in streams, emphasizing the
roles of systematic longitudinal gradients (Vannote et al. 1982), patchiness and
heterogeneity (Pringle et al. 1988, Townsend 1989), hydraulics (Statzner and Higler
3
1986), geomorphic disturbance (Montgomery 1999), multiscale habitat formation (Wu
and Loucks 1995, Poole 2002), and river network structure (Benda et al. 2004). Although
these theories apply to many stream systems worldwide, it is not known whether their
predictions of community distributions hold in tropical island streams where migratory
aquatic fauna interact with short, steep-gradient, frequently-flooded, bedrock and
boulder-lined channels that are punctuated by waterfalls.
As the threat to tropical freshwater streams increases through dam-building and
landuse changes (Holmquist et al. 1997, Pringle and Scatena 1998, Gupta and Ahmad
1999, Pringle et al. 2000, March et al. 2003, Anderson-Olivas et al. 2006, Greathouse et
al. 2006), it is critical to understand the dynamics of geomorphic processes and the
consequent response on stream channel morphology and aquatic biota in relatively
unaltered streams. Toward this end, this dissertation focuses on such geomorphic and
ecological patterns and processes in tropical mountain streams.
Specifically, I investigate the longitudinal variations in channel morphology and
aquatic biota in bedrock and alluvial streams draining the Luquillo Mountains of
northeastern Puerto Rico; a relatively old, subtropical island landscape that is subject to a
high frequency of atmospheric and hillslope disturbances, as well as steep elevational and
climatic gradients (Scatena 1995). Five adjacent watersheds are considered in this
research: Río Blanco, Río Espiritu Santo, Río Fajardo, Río Mameyes, and Río Sabana.
The watersheds are similar physiographically, but are different in size, geology, and land
cover. A combination of high-quality geographic information systems (GIS) coverages,
long-term hydrologic records, and extensive field-based survey data allows for high-
resolution spatial analyses of geomorphic patterns and processes.
4
This dissertation is also an integral part of a National Science Foundation-
supported Biocomplexity study to understand complex interactions between roads, rivers,
and people (NSF #030414). This Biocomplexity project is a multidisciplinary
collaboration addressing linkages between fluvial geomorphology, aquatic biology, and
human recreation in Luquillo streams. Researchers on this project have conducted
specialized yet complementary studies at common field sites (road-river crossings) with
the intent of generating holistic conclusions on how the structure of both river and road
networks facilitates flows of both aquatic organisms and human recreation. In this light,
this research provides both a conceptual and quantitative understanding of the hydrology,
geomorphology, and physical processes of the stream network of northeastern Puerto
Rico that can be used as a baseline for complementary studies addressing aquatic biota
and human influences.
The chapters presented in this dissertation are organized as journal articles. This
dissertation research is organized into four interrelated studies with the following goals:
1) Develop a Geographic Information Systems (GIS) template to quantify topographic
features, map the stream network, and estimate hydrologic parameters across the
landscape, 2) Define an active channel boundary in tropical montane streams, analogous
to bankfull in alluvial streams, based on characteristics of the riparian zone to be used as
a basis to compare channel geometry, 3) Decouple lithologic and hydraulic controls on
channel morphology through analysis of downstream patterns in channel form, and 4)
Determine the influence of local and network-scale geomorphology on the distribution of
migratory aquatic fauna.
5
CHAPTER OUTLINES
Chapter 2 develops a Geographic Information Systems (GIS) template to analyze
topographic features, map the stream network, and estimate hydrologic parameters
(rainfall, runoff, and discharge) in the region. This chapter also generates independent
variables (drainage area, slope, discharge) to be used in subsequent chapters. The process
involves (1) creation of a hydrologically correct Digital Elevation Model (DEM), (2)
defining the stream network using flow accumulation models using a drainage area
threshold, and (3) developing relationships to estimate mean annual rainfall, runoff, and
discharge from topographic factors. The result is a spatial framework to describe
hydrological variables for 10 m × 10 m cells within the stream networks.
Chapter 3 develops a method to define an active channel boundary for tropical
montane stream channels that is analogous to the bankfull stage in alluvial rivers. The
bankfull stage of a river channel is an important geomorphologic boundary, as it marks
the channel-forming discharge that occurs at a consistent flow-frequency throughout the
stream network, yet it is rarely identifiable in steep mountain channels that lack
floodplains. However, other features along the channel margins may be used to mark
bankfull stage. For example, assuming that there is a functional relationship between the
frequency of flooding and the establishment of vegetation, the characteristics of near-
channel vegetation and associated substrate and soil development may indicate
hydrogeomorphic conditions. By correlating the relative elevation of different types of
vegetation and other riparian features with the known magnitude and frequency of flows
that inundate that elevation, we can determine whether or not these features are a reliable
indicator of the channel-forming discharge. This chapter quantifies such relationships
6
between flow-frequency and riparian features at a series of long-term stream gages to
find the characteristics of vegetation, soil, and substrate that mark the active channel and
correspond to the bankfull stage in adjacent alluvial channels. The active channel
boundary determined here is used in the next chapter as the basis to compare cross-
sectional channel geometry at a known flow-frequency throughout the study basins.
Chapter 4 investigates potential lithologic and hydraulic controls on stream
channel morphology. Using a comprehensive dataset of surveyed stream cross-sections
capturing reaches along the entire longitudinal gradient from the headwaters to the
estuary, I analyze channel profiles and subsequent longitudinal changes in channel cross-
sectional geometry, grain size, stream power, and shear stresses. Such analyses of
downstream geomorphic trends yield insight into the evolution and self-organization of
the stream network. If local lithologic factors such as multiple rock types, resistant
channel boundaries, and coarse sediment delivery from landslides dominate the form of
the river, the river profile should be segmented, display poorly developed hydraulic
geometry, and a have seemingly random pattern of grain sizes. Conversely, if the high
unit discharge and associated stream power of the energetic tropical flow regime are
sufficient to overcome lithologic resistance and mobilize coarse sediment, the
downstream changes in channel morphology should have systematic trends similar to
those in many alluvial rivers.
Chapter 5 links the geomorphology of the stream network and local-scale physical
habitat with the distribution of migratory fish and decapods. Extensive geomorphic
surveys of pools, complemented with intensive biological sampling at the same sites,
were used to correlate geomorphic features of the stream channel to the presence and
7
abundances of fish and decapods. At the landscape scale, spatial patterns of the presence
and absence of all major macrofauna were analyzed. We expect that the steepness of the
stream channel may hinder the upstream migration of some species so that they display
distinct yet discontinuous longitudinal patterns. At the reach-scale and pool-scale, where
primarily decapod species are known to be present, we correlate local geomorphic
features of the pool with abundance. We expect that the decapods seek out optimal
habitat, but that the natural variability in the geomorphic environment at these scales
gives rise to patchiness in their abundances.
Lastly, Chapter 6 summarizes the key results of the previous chapters, synthesizes
the relationships between hydrologic, geomorphic, and ecologic flows, and discusses
further avenues of research based on additional questions that this research poses.
8
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riverine macrobiota in the New World: Tropical-temperate comparisons.
BioScience 50: 807-823.
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Radecki-Pawlik A. 2002. Bankfull discharge in mountain streams: theory and practice.
Earth Surface Processes and Landforms 27: 115-123.
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Panama. Geomorphology 83: 282-293.
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Biotropica 23: 317-323.
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14
CHAPTER 2
APPLICATION OF DIGITAL TERRAIN ANALYSIS TO MODEL SURFACE WATER FLOW IN THE LUQUILLO MOUNTAINS OF NORTHEASTERN PUERTO
RICO*
A.S. Pike
ABSTRACT
Digital terrain analysis was applied to estimate hydrologic parameters in basins
draining the Luquillo Mountains of Northeastern Puerto Rico. A 10m resolution Digital
Elevation Model (DEM) was interpolated from 10m elevation contour lines and used as
the template for hydrologic analysis. A high drainage density stream network,
representing perennial streams, including previously unmapped 1st order streams, was
extrapolated from the DEM. Similarly, for each 10m grid cell within the DEM, rainfall,
runoff, and mean annual discharge were estimated using regression equations derived
from long-term rainfall and stream flow gages. The result is a simple, reproducible spatial
framework that researchers and managers can use to estimate hydrologic conditions in the
region.
* Published as: Pike AS. 2006. Application of digital terrain analysis to estimate hydrological variables in the Luquillo Mountains of Puerto Rico. In: Climate Variability and Change–Hydrological Impacts (Proceedings of the Fifth FRIEND World Conference held at Havana, Cuba, November 2006), IAHS Publ. 308, 81-85.
15
INTRODUCTION
Digital terrain analysis can be used to derive a wealth of information about the
morphology and hydrodynamics of a land surface. When coupled with the spatial
distribution of basic hydrologic variables, such as rainfall and runoff, digital terrain
analysis is a powerful tool for estimating stream network parameters and analyzing
drainage basin characteristics (Montgomery et al. 1998). However, digital terrain analysis
is often underutilized in tropical drainage basins due to a scarcity of appropriate data.
This paper details how digital terrain analysis is used to model surface water flow in the
region draining the Luquillo Experiment Forest (LEF), a montane subtropical rainforest
in northeastern Puerto Rico.
Extensive research has been conducted in the LEF in the fields of fluvial
geomorphology (Scatena 1989), aquatic biology (Covich et al. 1996), and human-river
interactions (González-Cabán and Loomis 1998). Each of these disciplines demand
spatially explicit information on annual rainfall and streamflow. In mountain drainage
basins, hydrologic and geomorphic processes are key drivers of biological processes and
ecological integrity, which in turn influence the human and economic valuation of the
river.
While there has been work incorporating Geographic Information Systems (GIS)
to predict drought and low-flows in the LEF (García-Martinó et al. 1996a), a simple
organized framework to estimate hydrologic parameters that is reproducible between
researchers in the area is lacking. Unfortunately, estimates of elevation, slope, and
drainage areas from topographic maps may not always be accurate. The river network
portrayed on United States Geological Survey (USGS) maps do not represent all streams
16
of research value; many studied 1st and even 2nd order streams in the region are not
plotted on drainage basin scale maps.
Therefore, this paper aims to develop an initial organized framework to estimate
hydrologic parameters for all 10m x 10m cells within a stream network that can act as a
template for future research on the stream in the area. The process involves 1) creation of
a hydrologically correct Digital Elevation Model (DEM), 2) extraction of the stream
network using a drainage area threshold, and 3) estimation of mean annual rainfall,
runoff, and discharge from elevation data.
STUDY AREA
The Luquillo Mountains in northeastern Puerto Rico are characterized by rugged
terrain and steep gradients in elevation and climate. Over a distance of 10 to 20km, the
mountain range rises from sea level to an elevation of 1075m. Mean annual rainfall
increases with elevation from approximately 1500mm/yr at the coast to >4500mm/yr at
the highest elevations (García-Martinó et al. 1996b).
The climate is characterized as humid tropical maritime, and is influenced by both
northeasterly trade winds and local orographic effects. The principal weather systems
affecting climate are convective storms, easterly waves, cold fronts, and tropical storms
(van der Molen 2002). Rainfall events at mid-elevations are generally small (median
daily rainfall 3mm/day) but numerous (267 rain days per year) and of relatively low
intensity (<5mm/hr) (Schellekens et al. 1999). At mid-upper elevations, the majority of
streamflow results from direct surface runoff in the form of saturated overland flow or
17
through shallow (>30cm depth) soil macropores, as no significant groundwater sources
exist on the steep slopes (Schellekens et al. 2004).
Five principal basins drain the Luquillo Experimental Forest: Río Blanco, Río
Espiritu Santo, Río Fajardo, Río Mameyes, and Río Sabana. Streams draining the mid-
upper elevations (>100m) are relatively pristine, surrounded by protected forest, and are
laterally confined by steep valley walls. In contrast, streams flowing across the broad
alluvial coastal plain characterizing the lower elevations readily migrate laterally, and
many are physically altered to attend to local needs. Intensive agriculture and
urbanization has resulted in many lowland 1st order streams being rerouted for irrigation
canals or artificially channelized (Clark & Wilcock 2000). Similarly, the main stems of
all major rivers in the region have either a dam or water intake device to withdraw water
for municipal use (March et al. 2003).
DIGITAL ELEVATION MODEL (DEM) CONSTRUCTION
The main principle of digital terrain analysis is that an abundance of topographic
information is contained within elevation contour lines (elevation, geomorphic position,
slope, etc.) such that a continuous landscape surface can be generated from these
contours. Surface water flow can be routed across this surface under the assumption that
water flows downslope according to principles of least energy, i.e. water follows the path
of steepest descent (Jenson and Domingue 1988). Using this simple rule, the drainage
network of a landscape can be extracted.
A high-resolution Digital Elevation Model (DEM) is critical for terrain and
hydrologic analysis. While a 30m for the entire island of Puerto Rico exists, this is not
18
sufficient resolution to model the complex topographic structure of the Luquillo
mountains, in particular for 1st order streams that typically have an active channel width
of < 10m. Therefore a 10m x 10m grid cell resolution DEM was constructed for the
region of northeastern Puerto Rico for the purpose of digital terrain analysis.
Many Geographical Information Systems (GIS) packages are available that
provide the necessary tools and algorithms to generate a hydrologically correct DEM
from contour data. These include the ArcGIS Spatial Analyst, ArcHydro (Maidment
2002), TauDEM (Tarboten 2000), and GRASS open-source GIS (Neteler and Mitasova
2002). The general procedure employed by these packages is as follows (Fig. 2.1):
a. Conversion of Contour Lines to Triangulated Irregular Network (TIN)
b. Conversion of TIN to Digital Elevation Model (DEM) raster grid
c. Fill sinks in DEM to create a hydrologically correct surface
d. Calculation of Flow Direction Grid
e. Calculation of Flow Accumulation Grid
f. Designation of Stream Channel Threshold from Flow Accumulation Grid
As the basis for the DEM, 10m elevation contours from the United States
Geological Survey (Seiders 1971) were used. Contours were converted to a Triangulated
Irregular Network (TIN) surface, which is constructed by triangulating a set of vertices
that are connected with a series of edges to form a network of triangles. The edges of
TINS form contiguous, non-overlapping triangular facets, and can be used to capture the
position of linear features that play an important role in a surface, such as ridgelines or
stream courses (Wise 1998). TIN surfaces are advantageous for representing a landscape
in that they are variable resolution, with more triangles where the topography is more
19
Figure 2.1 The process of extracting a stream network from contour data. Contour
TIN DEM Flow Direction Flow Accumulation Vector Stream Network. The
area illustrated is known locally as ‘Puente Roto’, on the Río Mameyes.
10m ContoursTriangulated Irregular
Network (TIN)10m Digital Elevation
Model (DEM)
Flow Direction Grid Flow Accumulation Grid Vector Stream Network0 500 m
¯
20
complex. However, algorithms to route flow and extract stream networks from TINs are
less widely available than raster surface algorithms, given the complex structure of the
TIN.
For the purpose of flow routing, the TIN was interpolated to a raster DEM at 10m
x 10m grid cell resolution using ArcGIS Spatial Analyst. Because interpolation of the
input TIN surface occurs at regular intervals, some loss of information in the output raster
should be expected. How well the raster represents the TIN is dependent on the resolution
of the raster, and the degree and interval of the TIN surface variation (Wise 1998).
Generally, as the resolution is increased, the output raster more closely represents the
TIN surface. In the Luquillo Mountains, 10m resolution is sufficient to capture many
elements of the TIN, and is an appropriate resolution for digital terrain analysis (Pike
2001).
The resulting raw DEM generated from contour lines often contains topographic
sinks that create problems in simple hydrologic models. These include possible negative
(below sea-level) values, and pits and depressions that ultimately act as sinks for flow
(Tarboten et al. 1991). They are the product of both digital interpolation errors and
natural features of the landscape (lakes, depressions, etc.), and are easily corrected by
raising negative values and filling sinks to ensure continuous hydrologic flow. Similarly,
in flat terrain, fine-scale features such as river meanders and river course may not be well
constrained by the contour lines. If uncorrected, rivers flowing over low-relief surfaces
may have overly straight flow paths, or may appear as a series of banded lines. For better
accordance with known river paths, the river can be forced into a DEM such that river
21
cells are lowered by several meters to ensure that they are topographically lower than
surrounding cells (Maidment 2002).
Flow direction was calculated according to the simple D8 algorithm, whereby
flow is routed to the adjacent cell of steepest descent, or greatest elevation drop (Jenson
and Domingue 1988). A flow direction matrix is computed where each grid cell is
assigned a value (1-8), corresponding to the eight cardinal directions, that routes that flow
to the appropriate adjacent cell.
The flow accumulation grid was computed using the flow direction grid to sum
the total number of upslope cells contributing to a given cell. Cells can be weighted so
that the accumulated surface represents the sum of upslope weights. For example, if the
weight is a constant 100m2 area for each 10m cell, then the accumulation represents the
drainage area. Similarly, if the weight for each cell is the yearly runoff, then the
accumulated surface will represent mean annual discharge.
STREAM NETWORK EXTRACTION
To extract a stream network from a DEM, a drainage area threshold must be
applied to the flow accumulation surface (Tarboten et al. 1998). The threshold represents
the critical drainage area that distinguishes perennial from ephemeral streams; grid cells
that exceed the threshold represent streams with year-round flow. The threshold for
streams in the lower elevations, or tabonuco forest zone, of the Luquillo Experimental
Forest has been determined to be approximately 6ha (Scatena 1989).
The resulting map of the perennial stream network shows a much higher drainage
density than the USGS stream network (Fig. 2.2). For the Río Mameyes, the resulting
22
±
2,000
Meters
±
2,000
Meters
Figure 2.2 Comparison of the USGS stream network (a) to the DEM generated stream
network at 6ha drainage area threshold (b) for the Río Mameyes. Lines widths are scaled
according to stream order. While only the Río Mameyes is illustrated (to show fine scale
features), other drainage basins show a similar comparison between USGS and DEM
generated stream networks.
a) b)
23
total length of the stream network at 6ha drainage area is 133km, compared to a total
length of 70km for the USGS stream network. This increased length is due to the
inclusion of a large number of previously unmapped 1st and 2nd order streams that do not
appear at the resolution of the 1:20,000 scale USGS map. Similarly, the large amount of
1st order streams changes the total stream ordering of the network. The mouth of the Río
Mameyes is a 4th order stream according to the USGS map, but is a 5th order stream
according to the derived stream network.
While the resulting stream network accurately represents the path of major stream
channels (as they were forced to follow the mapped lines), some of the smaller streams
may not be accurately represented. This is generally a problem in the lowland areas;
stream paths in flat terrain may follow artifacts of the surface interpolation procedures
rather than real topographic features. Similarly, small streams have been diverted in the
lowland agricultural fields and urbanizations, so that even if the flow paths were
represented correctly by stream network, the digital estimation and reality may not agree.
Therefore, for the purpose of mapping small 1st and 2nd order streams, the digitally
extracted stream network should be used with caution when outside of natural, valley
confined upland streams.
RAINFALL, RUNOFF, AND DISCHARGE
The high spatial variability of rainfall in the LEF, especially its relationship with
elevation, suggests that simple scaling of drainage area with discharge may not apply to
this landscape. A small drainage in the uplands will have distinctly more runoff than a
comparable sized drainage basin in the lowlands. To estimate the spatial distribution in
24
rainfall, runoff and discharge, the following regression equations estimated from long-
term rain and stream gages were used (García-Martinó et al. 1996b):
P = 2300 + 3.8h -.0016h2 n = 17, r2 = 0.91, P < 0.001 Eq. 2.1
R = 4.26havg + 360 n = 9, r2 = 0.77, P = 0.002 Eq. 2.2
Discharge can be estimated from runoff by multiplying by drainage area:
Q = 3.17x10-5DA(4.26havg + 360) n = 9, r2 = 0.97, P < 0.001 Eq. 2.3
where: P = mean annual rainfall (mm/yr), R = mean annual runoff (mm/yr), Q = mean
annual discharge (m3/s), h = elevation (masl), havg = weighted average elevation (m), and
DA = drainage area (km2).
The resulting maps of mean annual rainfall, runoff, and discharge for the Río
Mameyes are shown (Fig. 2.3). Note that rainfall and runoff very closely resemble the
elevation structure, while discharge shows a similar pattern as the flow accumulation
grid. This is due to the fact that both rainfall and runoff are based on elevation, while
discharge is a strong function of drainage area.
The “weighted upstream elevation”, used in calculating runoff, is an accumulated
function. That is, it is the sum the elevation of all upslope cells, divided by the number of
accumulated cells. This accounts for the fact that basins at higher elevations have greater
mean annual runoff than corresponding basins of equal area at lower elevations.
More complex models exist to estimate the spatial distribution of rainfall and
runoff, such as PRISM (Daly et al. 2003), a rainfall models incorporating aspect,
windward/leeward orographic affects, and coastal advection, and TOPMODEL (Bevin et
al. 1995), a rainfall-runoff model based on topographic properties. While these models
are a better predictor of rainfall and runoff on the scale of the island of Puerto Rico, the
25
±
2,000
Meters
Dischargem3/s
5.0
0.0
±
2,000
Meters
Runoffmm/yr
4300
800
±
2,000
Meters
Rainfallmm/yr
4500
2300
Figure 2.3 Spatial distribution of a) mean annual rainfall, b) mean annual runoff, and c)
mean annual discharge within the Río Mameyes drainage basin according to elevation-
based regression equations.
a) b) c)
26
simple regression-based approach mentioned here is sufficient to make accurate
predictions of rainfall and runoff on the windward steep slopes of the Luquillo
Mountains.
CONCLUSIONS
The stream network and rainfall, runoff, and mean annual discharge can be
accurately estimated at 10m spatial resolution according to a simple DEM-based process
for basins draining the Luquillo Experimental Forest (LEF). The estimates are best
applied to stream in the forested upland regions, as anthropogenic activity on the lowland
rivers have altered stream channel courses and hydrologic budgets. However, the
simplicity of this DEM-based approach allows any researcher knowledgeable in GIS and
working in the regional area to estimate key hydrological parameters.
ACKNOWLEDGEMENTS
The author thanks Dr. Fred Scatena for advice on the manuscript, and Dr. Lena
Tallaksen for strengthening comments. Funding for this study was provided by the
National Science Foundation Biocomplexity Grant (NSF #030414)—Rivers, Roads, and
People: Complex Interactions of Overlapping Networks in Watersheds.
27
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Computer Models of Watershed Hydrology. Singh VP (ed). Water Resources
Publications: Highlands Ranch, CO; 627-668.
Clark JJ, Wilcock PR. 2000. Effects of land-use change on channel morphology in
northeastern Puerto Rico. Geological Society of American Bulletin 112: 1763-
1777.
Covich AP, Crowl TA, Johnson SL, Pyron M. 1996 Distribution and abundance of
tropical freshwater shrimp along a stream corridor: response to disturbance.
Biotropica 28: 484-492.
Daly C, Helmer EH, Quiñones M. 2003. Mapping the climate of Puerto Rico,
Vieques, and Culebra. International Journal of Climatology 23: 1359-1381.
García-Martinó AR, Scatena FN, Warner GS, Civco DL. 1996a. Statistical low flow
estimation using GIS analysis in humid montane regions in Puerto Rico. Water
Resources Bulletin 32: 1259-1271.
García-Martinó A.R, Warner GS, Scatena FN, Civco DL. 1996b. Rainfall, runoff, and
elevation relationships in the Luquillo Mountains of Puerto Rico. Caribbean
Journal of Science 32: 413-424.
González-Cabán A., Loomis J. 1998. Economic benefits of maintaining ecological
integrity of Río Mameyes, in Puerto Rico. Ecological Economics 21: 63- 75.
28
Jenson SK, Domingue JO. 1988. Extracting topographic structure from digital
elevation data for geographic information system analysis. Photogrammetric
Engineering and Remote Sensing. 54: 1593-1600.
Maidment DR. 2002. ArcHydro: GIS for water resources. ESRI Press: Redlands, CA.
March JG, Benstead JP, Pringle CM, Scatena FN. 2003 Damming tropical island
streams: problems, solutions, and alternatives. BioScience 53: 1069-1078.
Montgomery DR, Dietrich WE, Sullivan K. 1998. The role of GIS in watershed
analaysis. In: Landform Monitoring, Modelling, and Analysis. Lane SN,
Richards KS, and Chandler JH (eds). Wiley: West Sussex, England; 241-261.
Neteler M, Mitasova H. 2002. Open Source GIS: A GRASS GIS Approach. Kluwer
Academic Press: Boston, Dordrecht.
Pike RJ. 2001. “Topographic fragments” of geomorphometry, GIS, and DEMs. In:
DEMS and Geomorphology, Geographic Information Systems Asssociation
(Japan) Special Publication. 5th International Conference on Geomorphology,
Chuo University: Tokyo, Japan; 1: 34-35.
Scatena FN. 1989. An introduction to the physiography and history of the Bisley
Experimental Watersheds in the Luquillo Mountains of Puerto Rico. United
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Schellekens J, Scatena FN, Bruijnzeel LA, Wickel AJ. 1999. Modelling rainfall
interception by a lowland tropical rain forest in north eastern Puerto Rico. Journal
of Hydrology 225: 168-184.
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Schellekens J, Scatena FN, Bruijnzeel LA, van Dijk AIJM, Groen MMA, van
Hoogezand RJP. 2004. Stormflow generation in a small rain-forest catchment
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503-530.
Seiders VM. 1971. Geologic map of the El Yunque quadrangle, Puerto Rico. United
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England; 139-164.
30
CHAPTER 3
DEFINING A BANKFULL ANALOG FOR TROPICAL MONTANE STREAMS USING RIPARIAN FEATURES*
A.S. Pike and F.N. Scatena
ABSTRACT
The bankfull stage of a river channel is an important geomorphologic and
ecological boundary, but is rarely identifiable in steep mountain channels. This study
defines a ‘bankfull’ zone in tropical mountain streams that is based on statistically
defined combinations of riparian features that occur at the same flood frequency as the
bankfull stage and the effective discharge in adjacent alluvial channels. The relative
elevation of riparian vegetation, soil, and substrate characteristics were surveyed at nine
(9) stream gages in and around the Luquillo Experimental Forest in Northeastern Puerto
Rico. The corresponding discharge, flow frequency, and recurrence intervals associated
with these features was determined from a partial duration series analysis using long-term
15-minute resolution discharge records. Survey data indicate that mosses and short
grasses dominate at a stage often inundated by sub-effective flows. Herbs first occur at
elevations along the channel margin associated with intermediate discharges that
correspond to the threshold for sediment mobilization. Near-channel woody shrubs and
trees establish at elevations with a less frequent discharge that is coincident with the
effective discharge of bedload sediment transport. A multivariate regression tree
technique was then used to identify the characteristic features within the riparian zone
that are inundated at similar flow frequencies as the bankfull stage in alluvial channels. * Submitted to the Journal of Hydrology in January 2008. All data analysis, interpretation of results, and manuscript writing were done by author of this dissertation.
31
Our data demonstrate that in alluvial channels, both the bankfull stage (as marked by a
floodplain) and the channel-forming (effective) discharge are associated with the
presence of fine-grained substrate and soil, and tall, mature woody vegetation. In
montane reaches that lack a floodplain, a zone that is characterized by the incipient
presence of soil, woody shrubs, and trees has the same flow frequency as the bankfull
discharge of the alluvial channels. The bankfull discharge based on these riparian features
in steepland sites has an average exceedance probability between 0.09% and 0.30%, and
a recurrence interval between 40 and 90 days (based on 15-min resolution discharge
data). We conclude that flows with similar frequencies influence the establishment of
riparian vegetation, soil development, and substrate characteristics along channel margins
in similar ways. Thus, riparian features can be used as an indicator of hydrogeomorphic
site conditions to identify active-channel boundaries that occur at a constant flow
frequency throughout the study basins.
32
INTRODUCTION
The bankfull discharge and morphology of river channels are fundamental
concepts in fluvial geomorphology, hydrology, and stream ecology. Bankfull stage is
loosely defined as where stream water flow begins to flow out of the main channel and
into the adjacent floodplain (Williams 1978). It is the most common morphologic
measure used in comparing spatial variations in channel morphology for downstream
hydraulic geometry studies (Leopold and Maddock 1953) and is thought to correspond to
the effective, or channel-forming, discharge in alluvial reaches (Wolman and Miller
1960, Andrews 1980). Bankfull stage is also an important hydrologic and ecological
boundary that can mark the extent of flood zones and riparian forests (Williams 1978,
Andrews 1980, Radecki-Pawlick 2002). Consequently, it is also used as a central design
parameter in stream restoration projects and land use planning (Rosgen 1994). Although
the utility of bankfull conditions is widely acknowledged, the difficulty of consistently
identifying bankfull conditions across streams and rivers is equally recognized, even in
alluvial channels where a floodplain is present (Navratil et al. 2006). The task of
identifying bankfull flows is even more difficult in mountainous environments that do not
have readily distinguishable alluvial floodplains. This study describes a field based
statistical technique that can be used to characterize a bankfull analog in tropical montane
stream channels that has the same flow frequency as the bankfull discharge in adjacent
alluvial channels.
Many definitions have been coined to capture the range and complexity of
bankfull conditions (see Radecki-Pawlick 2002 for a comprehensive review). The
multitude of definitions are based on features in alluvial channels that fall into four
33
general categories: 1) morphologic features, 2) geometric features, 3) bank and sediment
features, and 4) riparian features (Harrelson et al. 1994). Morphologic indicators are
marked by the elevation of the top of depositional surfaces that correspond to the
boundary in the channel between net sediment transport and sediment deposition
(Wolman and Leopold 1957, Woodyer 1968, Williams 1978). Geometric features are
primarily based on the change in slope that occurs along the cross-section from the
channel to the banks (Wolman 1955, Riley 1972). Bank and sediment features include
changes in particle size or the extent of undercutting under dense root mats (Leopold and
Skibitzke 1967). Lastly, riparian features such as stain lines and vegetation are often used
to mark bankfull in steep streams where other features are not present. Stain lines on
large boulders that are marked by fine sediment may indicate either bankfull or the last
large flood (Harrelson et al. 1994), and a sharp break in the vegetation density, a change
in the type of vegetation, or the lower limit perennial vegetation (usually trees) can also
approximate bankfull (Williams 1978).
Although the bankfull concept is widely used in alluvial streams, it does not
necessarily apply to channels in mountain landscapes (Radecki-Pawelik 2002). In these
environments, the morphologic and geometric features typically used to mark bankfull
conditions rarely exist in the steep, confined, ‘v’-shaped valleys. Many researchers have
estimated ‘bankfull conditions’ in mountain streams using field observations of high-flow
features in riparian zones, including: the boundary of the active-scour zone (Montgomery
et al. 2001), flow-deposited organic debris and changes in the grain size of surface
sediment (Wohl and Wilcox 2005), changes in bank-gradient and channel geometry
(Wohl et al. 2004), the presence of perennial vegetation (Radecki-Pawlik 2002), or a
34
combination of all factors. However, the flow-frequency associated with these various
reference features is rarely quantified in mountain stream studies and many of them may
reflect the influence of the last large flood rather than the steady forcing from more
frequent bankfull conditions. Consequently, it is more appropriate to refer to these high-
water marks as a ‘reference’ discharge (Wohl and Wilcox 2005), rather than using the
term ‘bankfull discharge’—a term that implies a discharge with a specific recurrence
interval (Andrews 1980). Yet there is no common consensus on what either ‘bankfull
discharge’ or ‘reference discharge’ are in mountain streams, nor how often these high-
flow events occur.
In the absence of traditional depositional and morphologic bankfull indicators in
mountain streams, the occurrence of riparian vegetation may be used as a marker of flow
frequency. There is often a consistently observable vertical zonation in the type and
structure of riparian vegetation along a gradient from the active channel to the banks. It
has been shown that such patterns in riparian vegetation are strongly influenced and
maintained by the natural flow regime of a river (Poff et al. 1997). The active channel is
physically the harshest environment for terrestrial vegetation because it has the highest
frequency of flooding and scouring (Naiman et al. 1998, Swanson et al. 1998). Woody
plants may be mechanically broken by the force of floodwaters, uprooted by erosion of
the substrate in which they are rooted, or unable to establish in their seedling stage
(Bendix and Hupp 2000). Since periodic flooding is an important physical determinant of
the establishment and growth of many riparian plants, there should be a functional
relationship between flood hydrology and riparian plant community patterns (Chopin et
al. 2002). In aseasonal humid tropical environments where vegetation is abundant and
35
rapidly colonizes disturbed surfaces, streamside vegetation should closely reflect the
flood disturbance regime. Therefore, vegetation and corresponding surficial features
(substrate type, degree of soil development, organic matter) can presumably be used as
indicators for particular hydrogeomorphic site conditions (Hupp and Osterkamp 1985).
Furthermore, it is likely that some combination of riparian vegetation and corresponding
features can approximate bankfull on the basis of flow-frequency.
Bankfull discharge has been shown to correspond to the effective, or channel-
forming, discharge in alluvial streams (Wolman and Miller 1960, Andrews 1980). The
effective discharge is defined as the discharge that transports the most sediment over time
(Leopold et al. 1964). The concept of effective discharge assumes a balance between the
frequency and magnitude of flows and the corresponding amount of sediment transport.
Flows of low magnitude are common, but transport little to no bed sediment. Conversely,
large floods exert tremendous fluvial energy, transport large amounts of sediment,
deposit sediment on floodplains, and have the potential to completely reform the stream
channel. However, these high-magnitude flows are infrequent so that their effective
contribution to geomorphic work over time is negligible. Between the tranquility of
baseflow and the ferocity of large floods, there exists a relatively frequent, moderately
high magnitude channel-maintaining flow that effectively transports sediment through the
channel. The effective discharge often corresponds with the bankfull stage in alluvial
channels, where the morphology of the channel depends on the magnitude and frequency
of sediment-transporting flows (Wolman and Miller 1960).
Textbook scenarios of bankfull recurrence intervals assert that the bankfull stage
is exceeded by a flood occurring every 1-3 years (Leopold et al. 1964, Harrelson et al.
36
1994, Knighton 1998). This number has been used as the basis for many stream
restoration projects to construct channels with dimensions in balance with the natural
flow regime (Rosgen 1994). This recurrence interval has also been used in many
downstream hydraulic geometry studies (Wohl et al. 2004, Wohl and Wilcox 2005),
where longitudinal changes in bankfull channel width, depth, and velocity are driven by
the magnitude of this channel-forming discharge (Leopold and Maddock 1953).
Furthermore, analysis of downstream hydraulic geometry requires that both the bankfull
and effective discharge occur at a constant frequency throughout a basin. Identifying a
common marker of the channel-forming discharge with equal flow-frequency along the
course of a river is essential to make meaningful comparisons of channel cross-sectional
geometry.
This study investigates relationships between the flow regime and riparian
features in the tropical montane streams of Northeastern Puerto Rico to ultimately find a
consistent indicator of bankfull stage. We have four primary objectives. First, we
quantify the flow-frequency associated with the first occurrence of different types of
riparian vegetation, soil development, organic debris, and substrate sizes at a series of
long-term stream gages. Second, we determine combinations of these features that are
inundated at similar flow frequencies throughout the stream network. Third, we compare
these riparian features in montane reaches to bankfull stage and effective discharge in
adjacent alluvial channels. Lastly, we characterize the properties of those features that
mark an analog of bankfull stage in these tropical steepland streams.
37
STUDY AREA
Northeastern Puerto Rico
This study was conducted in the streams draining the Luquillo Mountains in
Northeastern Puerto Rico. The Luquillo Mountains rise steeply from sea-level to over
1000m in elevation over a distance of 15km-20km. The climate is maritime subtropical
and is influenced by convective storms, easterly waves, cold fronts, tropical storms and
hurricanes (Scatena 1995). Mean annual temperatures at mid-elevations are 26ºC, and
range from an average of 22ºC in the winter to 30ºC in the summer (Ramirez and
Melendez-Colom 2003). Mean annual rainfall increases with elevation from
approximately 1500mm per year at the coast to >4500mm/yr at elevations above 1000 m
(Garcia-Martino et al. 1996). Rainfall events at mid-elevations are generally small
(median daily rainfall 3mm/day) and numerous (267 rain days per year) (Schellekens et
al. 1999). High-intensity rainfall events and floods can occur in any given month.
Hurricanes are common between August through October and typically bring high daily
rainfall in excess of 200mm/day (Heartsill-Scalley et al. 2007). The maximum recorded
daily rainfall and runoff in the region are in excess of 600mm/day (Scatena and Larsen
1991).
Luquillo streams drain a landscape that is tectonically active, disturbed by
periodic tropical storms and hurricanes, and prone to massive landsliding (Larsen and
Torres-Sanchez 1998). These streams, as well as montane streams in the Greater Antilles
in general, have steep gradients, channels lined with coarse boulder-sized sediment,
numerous bedrock cascades, and abrupt waterfalls. Their morphologies have been called
“flood dominated” and like many mountain streams, traditional depositional forms built
38
by sand, pebbles, cobbles, and boulders are found sporadically or only at the lowest
gradient reaches (Gupta 1975, Gupta 1988, Ahmad et al. 1993). It has also been
cautioned that the standard descriptions of alluvial channel form and behavior are not
necessarily adequate for these rivers (Ahmad et al. 1993). Nevertheless, the following
areas are considered to have a similar combination of geological, tectonic, and climatic
conditions that influence channel morphology (Gupta 1988): 1) River valleys of East
Asia, especially Taiwan and the Philippines, 2) Upland areas of Vietnam, Sumatra, Java,
and Burma, 3) Humid areas of the Indian subcontinent, 4) Madagascar and neighboring
parts of coastal East Africa, 5) North and northeastern Australia, 6) Central and South
American highlands and other Caribbean islands.
The intense tropical rains, steep slopes, and rapid runoff generation in the
Luquillo Mountains create an extremely flashy flow regime (Schellekens et al. 2004).
High-magnitude, but short-lived, floods occur sporadically throughout the year. Peak
discharges can be approximately 1000 times greater than baseflow in Luquillo and other
Caribbean streams (Gupta 1995). The average unit discharge of baseflow is
0.02m3/s/km2, whereas the highest peak unit discharge measured by regional USGS
stream gages is 19.7m3/s/km2. However, high-flow hydrographs are short and stormflow
runoff is quickly flushed through the system such that the streams return to baseflow
within hours, even after the largest events. These large floods are primarily driven by
atmospheric disturbances that occur throughout the year rather than by seasonal events
(such as snowmelt) that are common in temperate basins. Consequently, flood discharges
that are close to the annual peak are often experienced independently several times in a
year (Scatena et al. 2004).
39
The morphology of Luquillo stream channels, as well as the composition of the
channel bed, are directly related to the underlying lithology (Ahmad et al. 1993). There
are three dominant lithologies in the study region: volcaniclastics, granodiorite, and
coastal plain alluvium (Seiders 1971). The streams draining volcaniclastics are steep and
typically have a bed composed of large boulders (up to several meters in diameter),
interspersed with finer cobbles and gravels, as well as sporadic bedrock outcrops.
Although the volcaniclastic rocks weather to deep clayey saprolite, the channels are
relatively devoid of sands, silts and clays because these sediments are quickly transported
out of the mountains as suspended load in floods. These channels are generally situated at
the bottom of steeply-walled bedrock valleys and lack floodplains along the channel
margins.
In contrast to areas underlain by the volcaniclastic rocks, streams draining
granodiorite are almost entirely composed of sand and large case-hardened boulders that
can be several meters in diameter. This granodiorite bedrock has one of the highest
documented weathering rates in the world (White et al. 1998). The sand-beds of these
channels are constantly mobile, even at low flows, and are readily reworked during high
flows. These reaches do have some bars and depositional surfaces within the channels,
but they still generally lack a continuous or well-defined floodplain. Like the streams
draining the volcaniclastic bedrock, they are lined with large immobile boulders, even
though the fluvial transport capacity is considered to exceed the sediment supply (i.e. a
‘supply limited’ environment), (Larsen 1997). Many of the larger boulders in both
volcaniclastic and granodioritic channels were apparently delivered to the channels by
landslides and are not readily transported by fluvial processes. Furthermore, some of the
40
largest boulders have not moved in the years of modern observation, and have been
estimated to only be mobile in a 500-year flood or larger.
Streams flowing across the coastal plain alluvium typically have a comparatively
gentle gradient compared to channels on bedrock, and have beds composed of cobbles
and gravels. These lowland streams have several overflow and bench surfaces that are
typical of many alluvial channels, including a morphological bankfull surface (Gupta
1975). The lowest alluvial surface is often an in-channel partially vegetated inset deposit
or bar composed of cobbles (Clark 1997). A slightly higher inset floodplain surface
marks the bankfull stage, and is generally between 1m to 1.5m above the baseflow water
level, and approximately 1.5 to 3 m above the channel thalweg. A discontinuous terrace
at an elevation between 1.75 and 3 m above the floodplain, occurs throughout the coastal
plain, and is most evident on the cutbank side of the channel. There is also a distinctive
higher terrace approximately 9 m above the river that occurs sporadically along the
length of the channel. These terraces are thought to be remnant alluvial surfaces from
Puerto Rico’s pre-agricultural period before 1830 A.D. (Clark and Wilcock 2000).
Regional Stream Gages
Nine long-term stream gaging stations in and around the Luquillo Mountains that
are maintained by the United States Geological Survey (USGS) were selected as sites for
this study (Figure 3.1). Selection criteria included: 1) currently operating gages, with 2)
greater than 10 years of record, and 3) instantaneous (i.e. 15 min) discharge records. The
gages are located in five (5) adjacent watersheds: Río Blanco, Río Espiritu Santo, Río
Fajardo, Río Mameyes, and Río Sabana (Figure 3.1). The sites range from small 1st order
headwater streams to larger 3rd and 4th order streams, with corresponding drainage basin
41
areas ranging from 0.3 km2 to 39 km2. Basic site information is available in Table 3.1.
The reaches near the stream gages are pictured in Figure 3.2.
The gaged reaches were divided into three physiographic categories: lowland (0-
50m), mid-elevation (50-150m), and steepland (>150m). The lowland reaches are low-
gradient, slightly meandering channels that flow across an alluvial coastal plain. These
reaches are unconfined by valley walls, and are typically accompanied by an adjacent
floodplain, high flow channels, and/or terrace deposits. The mid-elevation reaches are
moderate gradient channels that are located upstream of where the mountains grade to the
coastal plain, but downstream of the major cascades and waterfalls. They flow typically
within confining steep valley walls, and have a coarse boulder and bedrock channels. The
steepland reaches are high gradient channels located in the bottom of deeply incised “v-
notched” shaped valleys, and are often confined by nearly vertical valley walls. Cascades,
waterfalls, and long step-pool sequences composed of large boulders are common in
these steepland streams.
Riparian Vegetation
The vegetation of the Luquillo Mountains is typical of a humid tropical rainforest,
with greater tree species diversity, vegetation density, and productivity than most
temperate forests. There are over 225 tree species in the Luquillo Experimental Forest,
and four major forest types (Lugo and Scatena 1995). Although tree species diversity is
higher than in montane temperate forests, the diversity of understory herbs, ferns, grasses,
and shrubs is typically less (Arnold 1996).
Luquillo vegetation is dense and productive due to the tropical climate and year-
round growing season, and covers virtually any surface that is not frequently disturbed.
42
Figu
re 3
.1 L
ocat
ion
map
of t
he se
lect
ed st
udy
gage
s in
and
arou
nd th
e Lu
quill
o Ex
perim
enta
l For
est i
n N
orth
east
ern
Puer
to R
ico.
98
2
6
4
5
3
1 7
04
2Km
Luqu
illo
Expe
rimen
tal F
ores
t
Rio
Esp
iritu
San
to
Rio
Mam
eyes
Rio
Sab
ana
Rio
Faj
ardo
Rio
Bla
nco
Puer
to R
ico
670’W
6630’W
660’W
6530’W
180’N1830’N
43
Tab
le 3
.1 P
hysi
ogra
phic
info
rmat
ion
for t
he se
lect
ed st
udy
gage
s.
U
SGS
Gag
e #
Gag
e N
ame
Cha
nnel
Ty
pe
Geo
logy
B
ed S
ubst
rate
El
evat
ion
(m)
Dra
inag
e A
rea
(km
2 )
Rea
ch
Slop
e (m
/m)
Med
ian
Gra
in
Size
, d50
(m
)
Mea
n D
isch
arge
(m
3 /s)
1 50
0638
00
Rio
Esp
iritu
San
to n
r Rio
Gra
nde
Low
land
A
lluvi
um
Cob
ble,
Gra
vel,
Bed
rock
12
2
2.4
0.01
1 0.
127
1.69
2 50
0710
00
Rio
Faj
ardo
nr F
ajar
do
Low
land
A
lluvi
um
Cob
ble,
Gra
vel
4
2
38.
8 0.
008
0.09
0
2.
05
3 50
0642
00
Rio
Gra
nde
nr E
l Ver
de
Low
land
A
lluvi
um
Cob
ble,
Gra
vel,
Bed
rock
50
1
9.0
0.01
2 0.
177
1.10
4 50
0657
00
Rio
Mam
eyes
at M
amey
es
Low
land
A
lluvi
um
Gra
vel,
San
d, S
ilt
5
34.
9 0.
002
0.03
8
2.
43
5 50
0655
00
Rio
Mam
eyes
nr S
aban
a M
id-
Ele
vatio
n V
olca
nicl
astic
B
ould
er, C
obbl
e,
Bed
rock
84
1
7.9
0.01
5 0.
159
1.55
6 50
0670
00
Rio
Sab
ana
at S
aban
a M
id-
Ele
vatio
n V
olca
nicl
astic
C
obbl
e, G
rave
l
79
1
0.3
0.01
3 0.
068
0.59
7 50
0634
40
Que
brad
a S
onad
ora
nr E
l Ver
de
Ste
epla
nd
Vol
cani
clas
tic
Bou
lder
375
2.6
0.
233
0.48
3
0.
22
8 50
0749
50
Que
brad
a G
uaba
nr N
agua
bo
Ste
epla
nd
Gra
nodi
orite
S
and,
Bou
lder
640
0.3
0.
102
0.01
9
0.
02
9 50
0750
00
Rio
Icac
os n
r Nag
uabo
S
teep
land
G
rano
dior
ite
San
d, B
ould
er
61
6
3
.3
0.02
0 0.
019
0.39
44
Figure 3.2 Photographs of study gages. Note the abundance of vegetation along the
channel margins, the bankfull forms of the alluvial sites (#1-4), and the absence of
bankfull forms in mid-elevation and steepland sites (#5-9).
45
Figu
re 3
.2 c
ont.
46
Moreover, the active stream channels are one of the only geomorphic features
consistently devoid of vegetation. A vegetation transect along a fluvial disturbance
gradient from the middle of the channel into the adjacent forest follows a consistent
pattern. Cushion mosses colonize in-channel boulders, whereas herbs, ferns, and grasses
grow along channel margins, and woody shrubs and trees establish on higher, less
frequently flooded surfaces (Figure 3.3). Vegetation stature similarly increases with
stage. Short-stature vegetation grows along the channel and tall closed-canopy woody
vegetation and tall grasses grow on the banks and hillslopes.
Although there are consistent vegetation patterns in every reach, not all the types
of vegetation are present everywhere, because the abundance of certain species can also
be influenced by land-use legacies and light availability (Heartsill-Scalley and Aide 2003,
Brown et al. 2006). In areas surrounded by forests, the riparian understory vegetation is
mainly composed of shrubs, herbs, and ferns (Scatena 1990, Heartsill-Scalley 2005).
Riparian zones surrounded by pastures and mixed land-uses are commonly dominated by
grasses, vines, and bare soil. Mosses and lichens that require shade are more common in
steepland streams having ample canopy cover. Conversely, wider lowland channels have
a greater amount of incident light and consequently have a greater abundance of grasses.
Furthermore, unlike many arid and semi-arid riparian forests, there is no distinct riparian
forest community in the headwater streams of the Luquillo Mountains (Heartsill-Scalley
2005). Riparian forests along many alluvial streams in arid and semi-arid regions often
have a unique composition and greater productivity than the surrounding vegetation due
to increased availability of water. Yet in the continually humid climate of the Luquillo
Mountains, both riparian and non-riparian forests have ample moisture availability and
47
Figu
re 3
.3 C
ross
sec
tion
at R
ío M
amey
es n
ear
Saba
na, a
mid
-ele
vatio
n si
te. T
here
is a
ver
tical
zon
atio
n of
veg
etat
ion
type
s, fr
om
mos
ses
to h
erbs
to g
rass
es to
shr
ubs
to tr
ees.
The
vege
tatio
n re
flect
s th
e flo
w re
gim
e, a
nd h
ydro
grap
h on
the
right
is u
sed
to v
isua
lly
com
pare
the
inu
ndat
ion
perio
ds o
f ea
ch v
eget
atio
n ty
pe.
Not
e th
e se
vera
l in
tra-a
nnua
l flo
ods
reac
hing
eac
h su
rfac
e. H
ydro
grap
h
disc
harg
e da
ta is
15-
min
ute
reso
lutio
n fo
r the
yea
r 200
3.
48
are consequently similar in composition, but can be different in structure and biomass
(Scatena and Lugo 1995).
Although there is no distinct Luquillo riparian forest community, some tree
species are more abundant along the streams. Valley floors are typically dominated by
palms, herbs, and by light-gap colonizing species, whereas the dominant hardwoods are
confined to more stable ridges (Scatena 1990). Native species commonly found alongside
the steepland streams include: Guarea glabra (alligatorwood), Pterocarpus officinalis
(dragonsblood tree), Inga vera (river koko), and Prestoea montana (sierra palm). Non-
native tree species are common along lowland to mid-elevation streams and are generally
associated with reforesting former agricultural land (O’Connor et al. 2000, Brown et al.
2006). Common non-natives found alongside the streams are: Syzygium jambos (rose
apple), Spathodea campanulata (african tulip tree), Mangifera indica (mango), and
Bambusa spp. (bamboo).
Following a disturbance (flood, treefall gap, hurricane), grasses and herbs can
begin colonizing within days and are typically well established within weeks to months
(Scatena et al. 1996). Likewise, early successional trees can become established within a
year. Given this rapid establishment of vegetation, it is assumed in this study, and
supported by our observations over the years, that there is a general balance between the
frequency and magnitude of floods and the vegetation and soil features adjacent to the
stream channel. Small floods frequently cover in-channel and side-channel boulders that
are habitat for cushion mosses and lichen. Intermediate-magnitude floods inundate
channel bars and low-lying benches, mobilize coarse sediment, and disturb the substrate
occupied by herbs and grasses. Larger floods can have sufficient power to flatten in-
49
channel vegetation, particularly grasses and shrubs, but rarely uproot trees. It is only the
rarest and largest floods, like those observed during Hurricane Hugo (Scatena and Larsen
1990), that uproot mature riparian trees, scour the banks, and completely rework the
channel morphology. These observations indicate that vegetation structure is a highly
sensitive indicator of flow-frequency and that differences in vegetation near the active
channel can be used to define flow regimes and flow frequencies.
METHODS
Our general method involved surveying the relative elevation of different
vegetation types and riparian features at a series of long-term stream gages, and relating
the elevation of each survey point to the corresponding discharge and flow-frequency for
that gage. Multivariate regression techniques were then used to statistically partition the
survey points into groups that maximized the difference in average flow-frequency. This
created several “zones” of equal flow-frequency that are identified by distinct soil,
substrate, and vegetation characteristics. Lastly, it was determined which of these zones
was analogous to the bankfull stage and effective discharge of adjacent alluvial channels,
based on a comparison of flow-frequency.
Field Surveys
Approximately 8 to 10 transects spanning from the channel into the adjacent
forest were surveyed in the immediate vicinity of each USGS gage. Along each transect
we surveyed the elevation of the moss, herb, grass, shrub, and tree closest to the water
level. This allowed us to capture the boundary of incipient vegetation growth and the
minimum flow-frequency associated with the establishment of each type of vegetation.
50
At each survey point we also noted the vegetation height and the accompanying substrate,
soil development, leaf litter abundance, and degree of canopy cover (Table 3.2).
Although vegetation type and vegetation height are not strictly independent, they were
separated because both short and tall communities of grasses and herbs were prevalent at
some lowland reaches.
The surveys were made during baseflow conditions in June 2006. The elevation
of each survey point was measured relative to the USGS stream gage, using a Sokkia
Total Station and reflector prism. A total of 309 points were surveyed at the nine stream
gages, or approximately 34 points per reach. Water surface slope was also surveyed in the
field by surveying the height of the water level throughout the length of the reach.
Estimation of Flow-Frequency
For each of the surveyed points, the corresponding discharge was determined
using the gage’s most current stage-discharge rating curve. The corresponding flow-
frequency of each discharge value was determined using two different metrics: flow-
duration and recurrence intervals. Flow-duration is the amount of time that a given
discharge threshold is met or exceeded and is a metric of the total duration that a surface
is inundated by water. The recurrence interval is the average number of individual
occurrences that exceed a threshold discharge and indicates the average time between
events. Both measures are needed to understand both the extent and regularity of high-
flows. Within this manuscript, flow-frequency and exceedance probability are
synonymous with flow-duration, and corresponding recurrence intervals are given where
applicable.
51
Table 3.2 Riparian features that were recorded at each survey point. Vegetation,
substrate, and soil characteristics were divided into numerical categories for multivariate
statistical analysis.
Riparian Features
Vegetation Type 0 - mosses 1 - herbs, ferns 2 - grasses 3 - shrubs 4 - trees (continuous cover
on boulder or bedrock)
(saplings included)
(both short and tall)
(woody stem, <2.5cm dbh)
(woody stem, >2.5cm dbh)
Vegetation Height 0 - short 1 - short/medium 2 - medium 3 - medium/tall 4 - tall
(<30cm) (30-60cm) (60-90cm) (90-120cm) (>120cm, includes some grasses)
Substrate 0 - soil, clay 1 - sand, silt 2 - gravel 3 - cobble, boulder 4 - bedrock
(0 - 1/256mm) (1/256 - 2mm) (2 - 64mm) (> 64mm)
Soil 0 - none 1 - discontinuous 2 - continuous (bare rock and/or
no soil) (some soil and/or some bare rock)
(stable, developed accumulation of soil)
Leaf Litter 0 - none 1 - discontinuous 2 - continuous (no litter) (litter present in
small patches) (continuous litter present)
Canopy Cover 0 - none 1 - partial shade 2 - full shade 3 - canopy tree (full light, no
canopy cover) (under canopy, but receives direct incident light)
(under closed canopy)
(a canopy dominant tree)
52
Flow-duration curves were constructed from the USGS approved discharge
records for each gage. While both daily records and instantaneous (15-minute) discharge
records were available, the instantaneous data were used because they capture the
magnitude and timing of peak flows in the flashy hydrologic regime of the Luquillo
Mountains. The Log-Pearson Type III (LP3) distribution was fit to the flow-duration
curves in this analysis (Water Resources Council 1981, Goodwin 2004). The LP3
distribution is given by the following probability density function and cumulative
distribution functions:
( )ε)λ(yexpΓ(β)
ε)(yλpdf(y)1ββ
−−−
=−
Eq. 3.1
( )dyε)λ(yexpΓ(β)
ε)(yλcdf(y)y
ε
1ββ
∫ −−−
=−
Eq. 3.2
where: y = ln(Q), Q = discharge (m3/s), and:
yσβ
=λ 2
yC2⎟⎟⎠
⎞⎜⎜⎝
⎛=β
λβ
−μ=ε y (Eqs. 3.3, 3.4, 3.5)
LP3 distribution parameters (λ, β, ε) for each gage were estimated from the
sample mean (μy), standard deviation (σy), and skew coefficient (Cy) of the natural
logarithm-transformed discharge records (Table 3.3).
Recurrence intervals were calculated using partial duration flood series (where the
entire hydrograph is considered) rather than annual maximum series (one peak discharge
value per year). This approach was preferred because of the abundance of intra-annual
floods that can modify riparian vegetation. Intra-annual floods were counted as long as
they were independent events (i.e. not part of the same rainfall event or influenced by
53
Tab
le 3
.3 T
he ti
me
span
of t
he d
isch
arge
reco
rd, t
he s
ampl
e m
ean
(μ),
stan
dard
dev
iatio
n (σ
), an
d sk
ew c
oeff
icie
nt (C
) of t
he n
atur
al
loga
rithm
-tran
sfor
med
dis
char
ge r
ecor
d (in
m3 /s
) us
ed f
or th
e Lo
g-Pe
arso
n Ty
pe I
II d
istri
butio
n, a
nd th
e co
effic
ient
s an
d ex
pone
nts
for a
t-sta
tion
wid
th (w
) and
dep
th (h
) hyd
raul
ic g
eom
etry
rela
tions
hips
of t
he fo
rm (w
=c1Q
b and
h=c
2Qf ).
D
istr
ibut
ion
Stat
istic
s (1
5-m
inut
e da
ta in
m3 /s
)
Hyd
raul
ic G
eom
etry
Mom
ents
Coe
ffici
ents
Ex
pone
nts
Gag
e N
ame
Star
t En
d
µy
σy
Cy
c 1
c 2
b
f
Rio
Esp
iritu
San
to n
r Rio
Gra
nde
7/27
/199
4 8/
20/2
006
-0
.21
0.94
1.
24
11
.4
0.36
0.
28
0.35
R
io F
ajar
do n
r Faj
ardo
10
/1/1
986
9/30
/200
6
-0.1
3 1.
10
0.74
14.4
0.
24
0.34
0.
22
Rio
Gra
nde
nr E
l Ver
de
8/16
/199
0 8/
20/2
006
-0
.68
1.00
1.
16
11
.4
0.33
0.
19
0.30
R
io M
amey
es a
t Mam
eyes
8/
1/19
97
6/1/
2006
0.23
0.
94
1.04
15.4
0.
32
0.17
0.
24
Rio
Mam
eyes
nr S
aban
a 10
/1/1
990
6/1/
2006
0.00
0.
76
1.24
12.7
0.
34
0.19
0.
34
Rio
Sab
ana
at S
aban
a 10
/1/1
990
8/20
/200
6
-1.3
8 1.
08
0.73
12.3
0.
33
0.17
0.
29
Que
brad
a S
onad
ora
nr E
l Ver
de
10/1
/199
4 8/
20/2
006
-2
.42
1.17
0.
73
4.
3 0.
58
0.28
0.
32
Que
brad
a G
uaba
nr N
agua
bo
6/23
/199
2 6/
1/20
06
-4
.65
0.86
1.
47
5.
5 0.
36
0.33
0.
29
Rio
Icac
os n
r Nag
uabo
7/
21/1
992
6/1/
2006
-1.3
3 0.
70
1.64
8.4
0.35
0.
34
0.31
54
saturation from previous storms). We followed general guidelines for identifying
independent peaks set forth by Lang et al. (2002), which suggest that independent flows
must be separated by a minimum of 5 days and accompanied by a drop below 75% of the
lower peak. However, due to the short duration of floods in these flashy streams, we
considered events to be independent if they were separated by at least 24 hours, and also
had a 75% drop between peaks.
Multivariate Regression Trees
Multivariate regression trees (MRT) are a statistical technique that can be used to
predict relationships between a response variable and multiple environmental
characteristics (De’ath 2002). MRT forms clusters or groups by repeated splitting of the
data, with each split defined by a simple rule based on the predictor variables. The splits
are chosen to minimize the dissimilarity of data within clusters, or maximize the
differences between clusters. The groups or clusters formed by MRT are defined by a
simple splitting of one environmental variable at a time, generating an intuitive and easily
interpreted decision tree. We used MRT to define groups of equal flow frequencies based
on the environmental variables measured in the field (Table 3.2). The flow-frequency of
each surveyed point was used as the response variable, after a logarithm-transformation
was used to reduce skewness and achieve a normal distribution. Splits were based on the
vegetation type, vegetation height, substrate size, soil development, leaf litter, canopy
cover, and reach location. This procedure ultimately generated clusters of riparian
features that occur at distinct flow frequencies.
Separate regression trees were performed for the data collected at the alluvial sites
and the mid-elevation/steepland sites. One reach, Quebrada Guaba near Naguabo, was
55
removed from the MRT analysis due to anomalous flow-frequencies. Further supporting
its removal from this analysis, this gage also has the smallest drainage area, a closed
canopy that reduces the regeneration of channel side vegetation, the most flashy
hydrograph, subsurface drainage through sandy substrate, and riparian features that occur
above the boundary of apparent common floods.
Effective Discharge
The effective, or channel-forming discharge, and its flow frequency, was
estimated for the alluvial and mid-elevation reaches so that this frequency could be
compared to frequencies of the clusters formed by MRT in the steepland channels. The
effective discharge is defined as the discharge that transports the most sediment over time
and is quantified as the discharge where the product of the frequency of discharge and the
magnitude of sediment transport (the relative effectiveness curve) is a maximum
(Wolman and Miller 1960). Sediment transport here is quantified by the bedload
discharge, because the gravel-, cobble-, and boulder-bedded channel form of these rivers
is fundamentally determined by the bedload rather than suspended sediment (Knighton
1998). Bedload discharge is usually estimated using either a bedload sediment rating
curve or similar threshold-based function of fluvial discharge (Emmett and Wolman
2001, Torizzo and Pitlick 2004). This approach has been challenged by some authors
(Lenzi et al. 2006a) because treating sediment transport as a continuous function of water
discharge does not consider the variation in sediment supply over time, the impulsive and
pulsating nature of sediment discharge, and the dramatic increase in transport when some
discharge thresholds are passed. However, empirical bedload transport formulas based on
56
discharge have been shown to accurately predict sediment yields in these and other
steepland drainages in Puerto Rico (Simon and Guzman-Rios 1990), and were used here.
The bedload sediment transport curve for each alluvial reach was estimated
according to the Meyer-Peter and Muller (1948) relationship. The Meyer-Peter & Muller
sediment transport equation relates sediment yield to channel width, unit boundary shear
stress, and critical shear stress:
2/3c2/3s )τ(τ
)gρ1(s8wQ −
−= for )cτ(τ − > 0, Qs = 0 otherwise Eq. 3.6
where: Qs = sediment yield (m3/s), w = channel width (m), s = sediment density ratio
(dimensionless, 2.65), g = acceleration due to gravity (9.8m/s2), ρ = specific weight of
water (1000kg/m3) τ = unit boundary shear stress (Pa), τc = critical boundary shear stress
(Pa)
The unit boundary shear stress and critical boundary shear stress can be estimated
according to the following relationships, assuming steady, uniform flow:
gRSρ=τ (depth-slope product) Eq. 3.7
50c*
c gd)1s( ρ−τ=τ Eq. 3.8
where: R = hydraulic radius (m), S = slope (m/m), τ*c = critical dimensionless shear stress
(0.030 for alluvial streams, 0.045 for mid-elevation streams) following a positive
relationship between slope and τ*c discussed in (Mueller et al., 2005), d50 = median grain
size (m)
Data on the width and hydraulic radius at varying discharges were obtained from
the USGS measurements. The following at-a-station hydraulic geometry power
relationships were estimated for each site:
57
b1Qcw = Eq. 3.9
f2QcR = ` Eq. 3.10
where: Q = discharge (m3/s), and c1,c2, b, and f are coefficients and exponents empirically
derived from a logarithm-transformed linear regression.
The estimates for unit boundary shear stress, critical boundary shear stress, and
hydraulic geometry relationships for width and depth were substituted into the Meyer-
Peter and Muller relationship. This yields an equation relating the bedload discharge (Qs,
m3/s) to the flow discharge, median particle size, and slope:
2/350
*c
f22/3
b150s )gd)1s(S)Qc(g(
g)1s(8Qc)S,d,Q(Q ρ−τ−ρρ−
= Eq. 3.11
The relative effectiveness function, Ф, is the product of the probability density
function (LP3 flow duration curve), pdf(Q), and the bedload sediment transport curve, Qs
(Figure 3.4). This relative effectiveness function represents the amount of sediment
transported over time, and the effective discharge is the discharge where this function is a
maximum (i.e. derivative of the function is 0), such that:
Ф = pdf(Q)*Qs Eq. 3.12
0dQdΦ Qeffective = Eq. 3.13
The effective discharge was estimated for only the alluvial and mid-elevation
streams because our initial analysis and other studies (Torizzo and Pitlick 2004, Lenzi et
al. 2006a) indicate these sediment transport equations were not considered appropriate for
the boulder-lined steepland streams. Parameters used for each site in the calculation of
58
Figure 3.4 The effective discharge occurs at the maximum of the relative effectiveness
curve that is generated by multiplying the flow duration and sediment transport functions.
In this illustration, using data from the Río Mameyes near Sabana gage, the effective
discharge is roughly coincident with the presence of woody shrubs and trees.
59
the flow duration curves, bedload sediment transport equation, and the relative
effectiveness function are listed in Table 3.3.
RESULTS
Riparian Vegetation
The average elevation of the first occurrence of the different riparian vegetation
types (mosses, grasses, herbs, shrubs, trees) display a consistent zonation with elevation
along the cross-sectional profile of the channel, as is illustrated for one of the sites in
Figure 3.3. Canopy cover, the abundance of leaf litter, and soil development also increase
from the channel to the adjacent forest. Our surveys and observations in other streams in
the region indicate that this zonation is best developed along channels that have open or
partially open canopies where there is sufficient light for grasses and herbaceous
vegetation to establish and also sufficient shade for the development of cushion mosses.
While local environmental conditions (e.g. light, substrate, hydraulic shielding) can
constrain the establishment of vegetation at any particular location, the average elevation
of the first occurrence of different vegetation forms, litter cover, and soil development is
consistently related to the frequency of flow inundation both within and between sites.
Moreover, mosses, herbs, and grasses start establishing at elevations that are inundated
weekly or monthly, and are slightly above the baseflow water level. Shrubs and trees are
present at higher stages where they are inundated, at least briefly, several times a year.
The average elevation of the first occurrence of the different vegetation types is
also related to sediment transporting flows and the effective discharge, as illustrated by
data from the one of the Rio Mameyes sites (Figure 3.4). Mosses and short grasses
60
dominate at a stage below the threshold of sediment transport but above the most frequent
flows (the peak in the probability density function of discharge). That is, they first occur
at stages that are frequently inundated by sub-effective flows. Herbs first occur at a stage
associated with intermediate discharges that are around the threshold for sediment
mobilization. Woody shrubs and trees establish at a less frequent discharge that is
coincident with the effective discharge of bedload sediment. The greatest variation in the
vegetation types occurs around the threshold for sediment mobilization, where grasses,
herbs, shrubs, and trees commonly occur together.
Bankfull and Effective Discharge
The median exceedance probability of the bankfull (morphologic) discharge at the
four alluvial sites was found to be 0.16% and had a corresponding median recurrence
interval of 50 days (Table 3.5). The median exceedance probability of the calculated
effective discharges was 0.20% and had a corresponding median recurrence interval of 39
days. The flow-frequency of both bankfull and effective discharge for the alluvial sites
were not significantly different (Student’s t-test, P > 0.1). This confirms the assumption
that the bankfull stage is coincident with the channel-forming discharge in this region.
For comparison, the average flow-frequency of the mean annual discharge among all
sites is 23%; far more frequent than both the bankfull and effective discharge.
Multivariate Regression Trees
The data at the alluvial sites was first partitioned into two clusters: tall vegetation
(120cm in height or greater), and short vegetation (stature shorter than 120cm) (Figure
3.5). The cluster defined by short vegetation has a median exceedance probability of
8.5% and is inundated more frequently than the tall vegetation cluster (0.60%). Within
61
both clusters, the data were further split into two divisions based on the same
characteristics: clay sized substrate (including soil), and substrate coarser than clay. Thus,
the data for alluvial sites were effectively divided into four clusters: A1) short vegetation
with coarse substrate (median exceedance probability = 14.2%), A2) short vegetation
with clay/soil (0.96%), A3) tall vegetation with coarse substrate (0.84%), and A4) tall
vegetation with clay/soil (0.30%). Based on a comparison of means from logarithm-
transformed data (using Tukey’s HSD test), clusters A1 and A4 were found to be
significantly different from each other and clusters A2 and A3 (P < 0.01). Clusters A2
and A3 were not significantly different from each other (P > 0.1). Similarly, the
frequency of bankfull discharge, effective discharge, and the cluster defined by tall
vegetation growing on soil or clay (A4) were not significantly different (P > 0.1). This
suggests that in these alluvial streams, the morphologic bankfull stage and the effective
channel forming discharge is coincident with the presence of tall vegetation and soil
development along the banks.
The vegetation and substrate data for the steepland sites separated into clusters
that were based on similar factors as the data for alluvial sites (Figure 3.6). The MRT
analysis first split the data into two clusters based on the presence of soil. Continuous and
discontinuous soil formed one cluster (0.25%), and the absence of soil formed the other
(1.73%). Moreover, the cluster with soil had a lower flow-frequency than the cluster
without soil. These two clusters were both further partitioned by vegetation type:
presence of trees and shrubs, and presence of only herbs, mosses, and grasses. Hence, the
data for steepland sites were divided into the following four clusters: S1) no soil and
herbs/mosses/grasses (2.74%), S2) no soil and trees/shrubs (0.21%), S3) soil and
62
Figure 3.5 Box plots of the flow frequency for surveyed data points at alluvial sites that
have been partitioned into clusters based on vegetation, substrate, and soil characteristics
using a multivariate regression tree technique (see section 4.3). The means of the
logarithm-transformed flow-frequency data in cluster A4 (tall vegetation and soil),
bankfull discharge, and effective discharge (all marked by an asterisk) are not
significantly different.
63
Figure 3.5 cont.
64
Figure 3.6 Box plots of the flow frequency for surveyed data points at mid-elevation and
steepland sites that have been partitioned into clusters based on vegetation, substrate, and
soil characteristics using a multivariate regression tree technique. Based on a comparison
of flow-frequency, the means of the logarithm-transformed data in clusters S2 (soil
absent / shrubs and trees), and S4 (soil present / shrubs and trees) are not significantly
different from the means of logarithm-transformed data in clusters A4 (tall vegetation and
soil), bankfull discharge, and effective discharge (all marked by asterisk) at adjacent
alluvial sites that are shown in Figure 5.
65
Figure 3.6 cont.
66
herbs/mosses/grasses (1.14%), and S4) soil and trees/shrubs (0.13%). Further subdivision
and partitioning in both models did not create additional clusters that were statistically
different.
It is also important to acknowledge that the location of the reach was not chosen
as a split within these models, therefore the clusters are applicable to all sites. The model
coefficient of determination (r2) for the MRT, based on cross-validation, for alluvial sites
is 0.54, indicating that 54% of the variance among all surveyed points was explained by
the division into these clusters. For steepland sites, the model r2 is 0.45. However, at any
given site, the division of data into these clusters accounted for a higher proportion of the
variance (average at-site r2 = 0.63). This indicates that although aggregating data between
sites introduces error that is not necessarily present at a given site, the clusters can be
compared among sites.
To determine the characteristic features of the steepland sites that are analogous to
the bankfull stage in alluvial streams, the flow frequency of each of the steepland clusters
(S1-S4) were systematically tested against the clusters developed for the bankfull flow-
frequency in the alluvial sites (Table 3.4). The comparison of means of the log-
transformed flow frequency between each zone using Tukey’s HSD test indicated that the
steepland zone S2 (no soil, trees/shrubs), zone S4 (soil, trees/shrubs), zone A4 (tall
vegetation and clay/soil, for alluvial sites), bankfull discharge, and effective discharge are
not significantly different (P > 0.1). On the basis of flow-frequency, the zone defined by
soil development and the presence of woody shrubs and trees in steepland sites (zone S4)
was most analogous to the bankfull stage in alluvial streams. The bankfull stage for the
alluvial streams occurs on average 1.3m (±0.09m, 1 S.D.) above the baseflow water level,
67
Tab
le 3
.4 T
he m
edia
n he
ight
abo
ve w
ater
tabl
e, u
nit d
isch
arge
, flo
w fr
eque
ncy,
and
recu
rren
ce in
terv
al o
f eac
h zo
ne d
efin
ed b
y th
e
mul
tivar
iate
regr
essi
on tr
ee a
naly
sis.
Zone
s A
1-A
4 ar
e ba
sed
on s
urve
y da
ta p
oint
s on
the
4 al
luvi
al s
tream
gag
es, w
here
as z
ones
S1-
S4 a
re b
ased
on
data
from
the
4 st
eepl
and
gage
s.
Zone
R
ipar
ian
Feat
ures
# su
rvey
po
ints
#
Gag
es
Hei
ght
abv
Bas
eflo
w
(m)
Med
ian
Uni
t Q
(m3 /s
/km
2 )
Med
ian
Freq
uenc
y (%
tim
e)
Med
ian
Rec
urre
nce
(day
s)
A1
Sho
rt V
eget
atio
n, C
oars
e S
ubst
rate
84
4
0.25
0.
08
14.2
%
8 A
2 S
hort
Veg
etat
ion,
Soi
l/Cla
y 19
4
0.56
0.
64
0.96
%
19
A3
Tall
Veg
etat
ion,
Coa
rse
Sub
stra
te
21
4 0.
71
0.74
0.
84%
21
A
4 Ta
ll V
eget
atio
n, S
oil/C
lay
29
4 0.
99
1.41
0.
30%
39
S
1 N
o S
oil,
Mos
s/H
erbs
79
4*
0.
25
0.41
2.
74%
10
S
2***
N
o S
oil,
Tree
s/S
hrub
s 20
4*
0.
80
2.01
0.
21%
41
S
3 S
oil,
Mos
s/H
erbs
18
4*
0.
32
0.88
1.
14%
14
S
4***
S
oil,
Tree
s/S
hrub
s 39
4*
0.
92
2.51
0.
13%
55
B
ankf
ull
4
4 1.
27
2.29
0.
16%
50
E
ffect
ive
4
4 1.
20
1.61
0.
20%
39
* da
ta fr
om Q
. Gua
ba re
mov
ed fr
om a
naly
sis
***
zone
not
sig
nica
ntly
diff
eren
t fro
m b
ankf
ull i
n al
luvi
al c
hann
els
base
d on
a T
ukey
's H
SD
test
, α =
0.0
5 68
Tab
le 3
.5 T
he d
isch
arge
(Q, m
3 /s),
unit
disc
harg
e (U
nit Q
, m3 /s
/km
2 ), flo
w fr
eque
ncy
(Fre
q., %
tim
e), a
nd re
curr
ence
inte
rval
(Rec
.,
days
) ass
ocia
ted
with
the
bank
full
stag
e (f
or a
lluvi
al si
tes)
, the
eff
ectiv
e di
scha
rge
(for
low
land
and
mid
-ele
vatio
n si
tes)
, and
the
bank
full
anal
og th
at is
def
ined
in th
is m
anus
crip
t.
Ban
kful
l
Effe
ctiv
e
Ban
kful
l Ana
log
Gag
e N
ame
Q
U
nit
Q
Freq
. R
ec.
Q
U
nit
Q
Freq
. R
ec.
Zo
ne
Q
Un
it Q
Fr
eq.
Rec
.
Rio
Esp
iritu
San
to n
r Rio
Gra
nde
61
.5
2.7
0.14
%
42
25
.7
1.1
0.51
%
16
A
4 33
.7
1.5
0.34
%
26
Rio
Faj
ardo
nr F
ajar
do
96
.9
2.5
0.08
%
90
65
.3
1.7
0.16
%
46
A
4 55
.4
1.4
0.21
%
43
Rio
Gra
nde
nr E
l Ver
de
39
.7
2.1
0.18
%
57
63
.2
3.3
0.09
%
98
A
4 30
.1
1.6
0.27
%
48
Rio
Mam
eyes
at M
amey
es
46
.2
1.3
0.31
%
27
54
.0
1.5
0.24
%
33
A
4 38
.0
1.1
0.42
%
30
Rio
Mam
eyes
nr S
aban
a
---
---
---
---
37
.1
2.1
0.12
%
50
S
4 75
.4
4.2
0.03
%
92
Rio
Sab
ana
at S
aban
a
---
---
---
---
6.
9 0.
7 0.
70%
18
S4
23.4
2.
3 0.
09%
48
Que
brad
a S
onad
ora
nr E
l Ver
de
--
- --
- --
- --
-
---
---
---
---
S
4 6.
7
2.6
0.27
%
40
Que
brad
a G
uaba
nr N
agua
bo
--
- --
- --
- --
-
---
---
---
---
S
4 2.
0
6.6
0.03
%
346
Rio
Icac
os n
r Nag
uabo
---
---
---
---
--
- --
- --
- --
-
S4
9.2
2.
8 0.
16%
50
69
has a corresponding unit discharge of 2.3m3/s/km2 (±0.62), a flow frequency of 0.16%
(±0.09), and an average recurrence interval of 50 days (±0.31). The analogous steepland
zone that corresponds to the first occurrence of soil and woody vegetation occurs on
average 0.92m (±0.31) above the baseflow water level, has a corresponding unit
discharge of 2.5 m3/s/km2 (±0.85), a flow frequency of 0.13% (±0.10), and an average
recurrence interval of 55 days (±23).
To determine the consistency of this bankfull analog throughout the stream
network, the bankfull analog was compared across all the sites (Table 3.5). For 8 of the 9
gaged channels, the bankfull analog occurs at a flow frequency ranging from 0.03% and
0.42% (median = 0.24%), with a recurrence interval between 26 and 92 days (median =
46 days). There was no significant relationship between drainage area and flow-
frequency of the bankfull analog (r2 = 0.15, P > 0.1), suggesting that it does not vary
systematically with area throughout the stream network. The only site that is dramatically
different in terms of flow frequency is Quebrada Guaba, with a flow frequency of 0.03%
and a recurrence interval of 346 days. This is also the smallest stream, and suggests that
the technique of flow frequency estimation based on riparian features may not work for
the 1st order channels. However, as evidence by the other 8 reaches, the bankfull analog is
consistent in flow frequency throughout the stream network, albeit with local variation.
DISCUSSION
Riparian Features
The multiple regression trees developed for both the alluvial and steepland sites
generated several statistically significant clusters of points, based on the first occurrence
70
of riparian features, that have different average flow-frequencies. The alluvial sites were
partitioned on the basis of vegetation height (tall vs. short), and substrate size (soil/clay
vs. coarser substrate). The steepland sites were partitioned on the basis of soil
development (present or absent) and vegetation type (moss, herbs, grass, shrubs, trees).
The partitioning of both upland and alluvial sites suggests that the first occurrence of
short-stature vegetation (herbs, mosses, and grasses) occurs on coarse substrates and in
areas that are inundated by flows of moderate frequency and intensity. Moreover, the
zones defined by these features are associated with the mean annual discharge and sub-
effective flows. In contrast, tall, mature, woody vegetation and soil development are
related to less frequent flows of higher-magnitude that approximate the effective
discharge. Furthermore, these features are related to the bankfull and effective flows of
alluvial streams and can be used to identify a bankfull analog in steepland streams.
Because this analog occurs at a relatively constant flow-frequency throughout the stream
network, this analog can be used to determine channel boundaries at ungaged reaches in
the region.
The characteristics of the two zones that are based on riparian features that
estimate bankfull discharges at both the alluvial and steepland sites are remarkably
similar. In the alluvial sites, bankfull stage is associated with the first occurrence of tall
vegetation (shrubs, trees and some grasses) and clay substrate/soil. In the steepland sites,
the bankfull analog is also marked by the first occurrence of woody shrubs/trees and soil
development. However, the MRT analysis indicates that in the alluvial sites, vegetation
height was found to be more important than vegetation type in the cluster divisions.
Conversely, in the steepland sites, vegetation type was more important than vegetation
71
height. This difference is due to the large proportion of different grass species found at
open alluvial sites. Despite being all considered the same vegetation type, the grasses
differ in height (some greater than 120cm) according to their proximity to the channel, so
that vegetation height more strongly reflects flow frequency than does vegetation type. In
contrast, the type of vegetation was more important in forming clusters for steepland sites
because there is less dominance of one vegetation type over the rest at these sites. This
difference in vegetation between alluvial and steepland reaches is driven by the fact that
the alluvial reaches are higher order streams, lower in elevation, have greater incident
light, and are generally surrounded by non-forest land-use (mostly pastures and rural
development areas).
However, it appears that this analog does not apply as well to small 1st order
streams. In these small channel and swales, the riparian features are more influenced by
local factors than by fluvial disturbance. It should also be noted that there is a large
amount of variability within any given zone defined by riparian features. Flow
frequencies within a cluster can span an order or two of magnitude, which represent
drastically different flood magnitudes. The large degree of natural variability is
responsible for this variance. Although the first occurrence of vegetation that was
surveyed along each transect is primarily influenced by the frequency of flooding, the
exact stage where the vegetation grows relative to the stream channel is also influenced
by local factors such as hydraulic shielding by boulders, differences in light, and
substrate stability. These small differences in height translate into a larger difference in
flow-frequency and generate a large degree of natural noise. Fortunately, the repeated
measure of different vegetation types in a reach reduces this variation and provides a
72
reliable estimate of the corresponding flow frequency. This analysis also indicates that it
is valid to identify the high-flow riparian features at gaging stations using the techniques
identified in this paper and then identifying them at non-gaged reaches, much like
bankfull morphology is identified in alluvial reaches. Field identification at non-gaged
sites is reasonably accurate and precise because the first occurrence of woody vegetation
and soil is easily recognizable.
Bankfull and Effective Discharge
The recurrence intervals in this study that are associated with both the bankfull
discharge in alluvial channels and the bankfull analog in steepland channels are between
40 and 90 days. This range is significantly more frequent than commonly reported values
of 1-3 years (Wolman and Miller 1960, Dunne and Leopold 1978, Rosgen 1994,
Knighton 1998). The difference is due both to the methodology used and the flashy
nature of these streams. Recurrence intervals presented here were calculated according to
a partial duration series analysis using 15-minute instantaneous discharge data, rather
than an annual maximum series and/or daily discharge records used in many studies. By
definition, the annual maximum series used in these classic publications is drawn from
one annual peak per year, thus forcing recurrence intervals greater than or equal to 1 year.
While annual maximum series analysis may be pertinent for large temperate basins, it
fails to capture the intra-annual flows that are responsible for structuring the vegetation in
and adjacent to the channels in these flashy and relatively small streams. Although some
of these publications acknowledge that bankfull discharge can be observed “several times
per year” (Rosgen 1994) in many rivers, little guidance is given to assess the recurrence
interval of multiple flows per year. A partial duration series captures the many flow-
73
events over the bankfull threshold found in these flashy streams because it allows for
multiple floods each year. Had an annual maximum series been used in this study,
bankfull stage would be exceeded by the peak flow in every year (recurrence interval of
1.0 year). Furthermore, if daily discharge data (rather than 15-min data) had been used in
this study to calculate recurrence intervals (using partial duration series), the frequent
short-lived peaks would be damped such that only one or two events per year would have
exceeded the bankfull threshold (recurrence interval = 0.5 to 1 year).
Despite differences in recurrence intervals between this study and others, the
flow-duration values of bankfull reported in this study are comparable to other studies.
We report average bankfull flow durations of ~0.27%, or approximately 1 day of
inundation per year. For comparison, the bankfull duration of streams in England and
Wales is reported to be 0.60% (2 days per year) (Nixon 1959), and between 0.4% to 3.0%
(1.5 to 11 days per year) in mountain streams in Colorado and Wyoming (Andrews
1980). Dunne and Leopold (1978) asserted that bankfull flow duration often varies
between 1.3% and 4.5%, with an average of 2.1% (8 days per year). The bankfull stage
here is inundated slightly less total time than the rivers mentioned in these other studies,
although there are more floods of bankfull magnitude per year. This suggests that
bankfull formation among these different rivers is related more to the total amount of
time bankfull stage is exceeded rather than the timing between floods.
The similarity of bankfull on the basis of duration among very different rivers
suggests common organizing principles of these systems. This organizing principle may
be related to the energy expenditure and geomorphic effectiveness of large floods. Costa
and O’Connor (1995) assert that the geomorphic effectiveness and total energy
74
expenditure of a flood is dependent on both the duration and peak stream power per unit
area. Flood peaks in mountain streams are brief (hours in duration) and strong, reflecting
rapid movement of water down steep hillslopes and channels (Swanson et al. 1998).
While these floods are intense, they are short-lived so that several events of bankfull
magnitude each year may be required for channel and riparian zone maintenance. In
contrast, floods in large lowland basins often inundate the floodplain for up to several
days, but may only occur once a year. The floods in the Luquillo Mountains are
characterized as having a short duration, but high peak stream power, and thus
intermediate total energy expenditure. Consequently, they may have a similar
geomorphic effectiveness as floods in other basins that are longer in duration, but have
lower peak unit stream power.
However, these geomorphically effective flows are not necessarily channel-
forming in the steepland boulder and bedrock-lined reaches. The channel-forming
discharge in bedrock mountain rivers may be higher than the effective discharge of
sediment transport due to the high threshold of stream power required for bedrock
incision and movement of large-sized boulders (Costa and O’Connor 1995). There is also
discussion in the literature that the effective discharge is not necessarily a discrete value,
but rather a range of flow-events that are responsible for the greatest amount of
geomorphic work (Goodwin 2004). Here we are only using effective discharge as a guide
to the magnitude of the flow-frequency of the channel-altering events. Some researchers
have even posited that there are two dominant discharge ranges for steep mountain rivers:
a relatively frequent flow responsible for maintaining channel forms, and a more
infrequent high flow responsible for large-scale channel shaping (Lenzi et al. 2006b). On
75
this token, an extremely rare multi-year recurrence flood in these streams may be
responsible for bedrock incision and mass sediment movement, but it is the more frequent
effective discharge that consistently affects riparian vegetation, defines channel
boundaries, and is responsible for channel-maintenance.
The same floods that are geomorphically effective are also responsible for
maintaining riparian vegetation patterns, as evidenced by the relationship between the
effective discharge in alluvial and mid-elevation channels and the boundary of woody
vegetation in this study. The channel-maintaining discharge based on bedload transport is
at the same magnitude as the threshold for woody vegetation, as both are apparently
influenced by similar periodic flows. Since these two concepts are so tightly linked, the
bankfull analog can be used as a constant marker of effective flows. Downstream
hydraulic geometry requires that the bankfull and effective discharge occur at a constant
frequency throughout a basin (Leopold and Maddock 1953). Because the bankfull analog
defined in this study marks a boundary of equal flow frequency and is also consistent
with the supposed channel-maintaining discharge, it can be used as a channel boundary
marker for downstream hydraulic geometry studies to compare channel cross-section
geometry throughout the stream network.
Applicability to other stream systems
The results of this study are directly applicable to streams in the Luquillo region
of Puerto Rico. However, the techniques of quantifying flow frequencies associated with
different types of vegetation and riparian features used here should be applicable in a
range of environments. It is expected that streams with both a similar flashy flow-regime
76
and a humid tropical climate with similar vegetation types will have analogous riparian
features occurring at flow-frequencies consistent with those determined in this study.
Riparian features have also been noted as indicators of ‘bankfull’ in a variety of
temperate montane systems, including the South Island of New Zealand (Wohl and
Wilcox 2005), the Rocky Mountains (Wohl et al. 2004), the Pacific Northwest
(Montgomery and Gran 2001), and the alpine region of Poland (Radecki-Pawlik 2002).
Each of these streams are in different physiographic provinces. Some are humid
environments with rapidly growing vegetation, whereas others, such as parts of the
Rocky Mountains, are semi-arid environments with limited vegetation. It would be
spurious to assume that the features that approximate bankfull found in this study occur at
the same flow-frequency as those in temperate mountain streams. However, it does
indicate that similar features based on soil, substrate, and vegetation do apply to other
systems and that local/regional flow frequency zones can be identified using techniques
outlined here. Nevertheless, similar studies are needed on other gaged streams to
determine the flow frequency associated with such riparian features in different
environments.
CONCLUSIONS
In the study area of the Luquillo Mountains, we used a network of stream gages to
determine a zone of constant flow-frequency, based on riparian features, in steepland
streams where a bankfull stage was absent. The results indicated that in these steepland
streams, the discharge associated with the average first occurrence of soil and woody
vegetation has the same flow-frequency as the bankfull discharge of adjacent alluvial
77
streams. Likewise, the elevations associated with the first occurrence of woody
vegetation in alluvial streams can be similarly used to estimate bankfull. The results also
indicate that bankfull stage, effective discharge, and presence of tall vegetation and a
clayey substrate all occurred at a stage associated with a discharge that is exceeded the
same amount of the time. Thus, throughout the stream network, high-flow riparian
features such as the presence of soil development and perennial vegetation can provide a
common benchmark of flow-frequency. Furthermore, the general approach of surveying
the first occurrence of riparian features and using multivariate statistical analysis linking
these occurrences to 15-minute flow duration can provide an internally consistent
framework for identifying flow frequencies within a region.
ACKNOWLEDGMENTS
The authors thank Ellen Wohl, Tamara Heartsill-Scalley, Jerry Mead, and
Douglas Jerolmack for their strengthening comments on earlier versions of this
manuscript. We also thank the International Institute of Tropical Forestry for logistical
support. Funding for this study was provided by the National Science Foundation
Biocomplexity Grant (NSF #030414)—Rivers, Roads, and People: Complex Interactions
of Overlapping Networks in Watersheds.
78
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87
CHAPTER 4
LONGITUDINAL PATTERNS IN STREAM CHANNEL GEOMORPHOLOGY IN THE TROPICAL MONTANE STREAMS OF THE LUQUILLO MOUNTAINS,
PUERTO RICO*
A.S. Pike, F.N. Scatena, and E.E. Wohl
ABSTRACT
An extensive dataset of 238 surveyed reaches in five adjacent watersheds draining
the Luquillo Mountains in northeastern Puerto Rico was used to examine downstream
changes in channel geometry, grain size, stream power, and shear stress along humid
tropical mountain streams. Surveyed data were used to compare the influences of
lithologic and hydraulic forces in shaping channel morphology. The Luquillo Mountains
are a steep landscape composed of volcaniclastic and igneous rocks that exert a strong
localized lithologic influence on the stream channels. Longitudinal profiles are generally
graded but have convexities and protrusions that reflect the influence of multiple rock
types. Non-fluvial processes, such as landslides along steep hillslopes (>12°), deliver
coarse sediment (>2000 mm) to the channels and may locally determine channel gradient
and geometry. Median grain size is strongly related to drainage area and slope, and
coarsens in the headwaters before fining in the downstream reaches; a pattern associated
with a mid-basin transition between colluvial and fluvial processes. However, the streams
also have strong hydraulic forcing due to high unit discharge. Downstream hydraulic
geometry relationships between discharge, width and velocity (although not depth) are
well developed for all watersheds (exponents are 0.33 for width, 0.12 for depth, and 0.55
* Submitted to Earth Surface Processes and Landforms in March 2008. All data analysis, interpretation of results, and manuscript writing were done by author of this dissertation.
88
for velocity). Stream power displays a mid-basin maximum in all basins, although the
ratio of stream power to coarse grain size (indicative of hydraulic forcing) increases
downstream. Excess dimensionless shear stress at bankfull flow wavers around the
threshold for sediment mobility of moderate-sized grains within a coarser matrix, and
does not vary systematically with bankfull discharge; a feature common in self-forming
“threshold” alluvial channels. The results suggest that although there is apparent bedrock
and lithologic control on local reach-scale channel morphology, strong fluvial forces
acting over time have been sufficient to override boundary resistance and give rise to
systematic basin-scale patterns.
89
INTRODUCTION
Recent advances in understanding the linkages between tectonics and surface
processes have spurred interest in the evolution of mountain and bedrock streams
(Whipple 2004, Bishop 2007). Mountain stream channels have complex morphologies
and a number of studies implicate several different controls on their development,
including: tectonic and structural (VanLaningham et al. 2006), bedrock (Snyder et al.
2003), storm pulses (Gupta 1988), and non-fluvial processes such as landslides/debris
flows (Brummer and Montgomery 2003, Stock and Dietrich 2006) and glaciers (Wohl et
al. 2004). Other studies have demonstrated the characteristic morphology of mountain
streams (Grant et al. 1990, Montgomery and Buffington 1997, Wohl and Merritt 2001),
their hydraulic geometry (Wohl 2004), the complexity of sediment transport (Blizard and
Wohl 1998, Lenzi et al. 2004, Torizzo and Pitlick 2004), and the distribution of sediment
within mountain drainages (McPherson 1971, Grimm et al. 1995, Pizzuto 1995,
Constantine et al. 2003, Golden and Spring 2006). Of all montane streams, those in the
tropics are among the most extreme fluvial environments in the world (Gupta 1988). A
combination of steep slopes, high mean annual rainfall, and intense tropical storms
generate an energetic and powerful flow regime. The high rates of erosion and
dramatically dissected landscapes prevalent in the world’s tropical mountainous regions
attest to the power of these rivers. Yet the channel morphology that is sculpted by fluvial
processes in tropical montane environments is generally unknown. This paper
investigates controls on mountain stream channel morphology in the Luquillo Mountains
of Puerto Rico, a tectonically active landscape with varying bedrock and structural
90
controls that is rapidly eroding due to extremely wet tropical conditions, frequent intense
storms, and a high susceptibility to mass-wasting.
Montane streams in both tropical and temperate environments share some
common characteristics. Steep gradients, tectonic activity, and multiple rock types yield
resistant channel boundaries that are dominated by bedrock and coarse clasts (Grant et al.
1990). Vertical valley walls and confined channel boundaries inhibit floodplain
development and may locally determine channel width (Montgomery and Gran 2001,
Finnegan et al. 2005). Longitudinal profiles are typically segmented by knickpoints and
waterfalls. There is often high boundary roughness, intense turbulence, high entrainment
rates and stochastic bedload movement (Wohl et al. 2004).
However, some tropical mountain streams may have unique features that vary
from their temperate counterparts. The absence of glaciation excludes glacial landforms,
such as u-shaped valleys and coarse moraine deposits, that are prevalent in some
temperate montane basins. Relatively high rates of chemical and physical weathering
rapidly denude tropical landscapes and may affect rates of channel-sediment diminution
and patterns of downstream fining (Brown et al. 1995, White et al. 1998, Rengers and
Wohl 2007). Frequent landslides triggered by heavy rains introduce pulses of coarse
sediment to the channels and strongly link fluvial and colluvial forces (Larsen et al.
1999). Large woody debris that is common in temperate streams is rapidly decomposed
in the tropics, despite high inputs from surrounding mature forests and hurricanes
(Covich and Crowl 1990). Rapid runoff production generates flashy, frequent, short-
duration floods (Schellekens et al. 2004, Niedzialek and Ogden 2005), and periodic high-
91
magnitude floods associated with hurricanes and other tropical disturbances effectively
rework boulder channels (Gupta 1975, Scatena and Larsen 1991).
The relatively few studies that have addressed the underlying controls structuring
the morphology of tropical mountain streams demonstrate the influence of a variety of
factors. Ahmad et al. (1993) and Gupta (1995) concluded that the lithology of many
streams in the Caribbean plays a strong role in locally determining channel morphology,
dictating the course of the river, and governing the distribution of large boulders. These
streams, similar to many mountain streams, commonly have bedrock-lined channels
whereas traditional depositional forms built by sand, gravels, cobbles, and boulders are
only found sporadically. Lewis (1969) demonstrated that local lithologic factors, such as
bed material cohesion and channel constriction, influenced at-station hydraulic geometry
in the Río Manati of north-central Puerto Rico. However, it was also demonstrated that
consistent scaling of downstream hydraulic geometry was developed across multiple
lithologies. In the streams of Jamaica and Puerto Rico, Gupta (1975) emphasized the role
of high discharge relative to drainage area as a key hydraulic control shaping channel
morphology. Similar characteristics were noted in the Río Chagres in Panama, where
hydraulic controls due to notably high unit discharge are apparently sufficient to override
lithologic controls and develop a basin with well-developed downstream hydraulic
geometry (Wohl 2005). This study also utilized extensive basin-scale field
reconnaissance to quantify downstream patterns in tropical mountain stream morphology,
and contended that similar high-quality field surveys in different tropical regions are
essential to further knowledge about these systems.
92
Geomorphic features of rivers that reflect underlying controls include longitudinal
profiles, hydraulic geometry, grain sizes, and the spatial distribution of fluvial energy
expenditure and shear stresses. Many alluvial rivers develop systematic changes in slope,
channel geometry, and grain size from their headwaters to the coast in response to
changes in discharge and sediment yield (Paola and Seal 1995). These changes result in
many well-known basin-scale patterns such as concave-upward longitudinal profiles and
progressive downstream fining, whereby adherence or significant deviations from the
theoretical patterns reflect the relative importance of lithologic and hydraulic controls.
Longitudinal profiles of rivers often reflect the lithologic and tectonic controls on
channel development (Kirby and Whipple 2001). A theoretical profile of a graded stream
has a smoothly concave-upward shape; steep in the headwaters and flat near the mouth
(Hack 1957). A river of this form has achieved an assumed balance between the erosion
from fluvial processes and the resistance from lithologic and tectonic forces. Deviations
from this idealized grade, such as changes in concavity (Seidl et al. 1994) and the
presence of segmentation/knickpoints (Crosby and Whipple 2006, Goldrick and Bishop
2007) can indicate the influence of non-fluvial forces. For example, faults and structural
barriers may confine a river and constrain slope (Whipple 2004), multiple bedrock units
with varying resistance to river incision often create slope breaks, protrusions, and/or
knickpoints (Wohl et al. 1994), and landslides/debris flows can locally constrain the
channel gradient and concavity (Grant et al. 1990, Stock and Dietrich 2003).
Downstream hydraulic geometry (DHG) characterizes systematic downstream
changes in channel geometry as power-law relationships with discharge, and may be used
to quantify the influence of fluvial controls on channel form (Leopold and Maddock
93
1953). DHG has successfully described river patterns worldwide in many physiographic
environments. However, it is intended to describe changes in self-forming alluvial rivers
that readily adjust their geometry in response to changes in discharge and sediment
transport. The ubiquity of DHG in these self-forming rivers has been explained from a
combination of basic hydraulic and sediment transport processes (e.g., Singh 2003,
Parker et al. 2007). However, the complicated hydraulics and sediment transport
processes associated with boulder- and bedrock-armored channels in many mountain
rivers may confound these relationships. Consistent power-law relations in downstream
channel geometry have been observed in some mountain rivers, even though these
streams alter their morphology at longer time scales than most alluvial rivers (Molnar and
Ramirez 2002, Wohl and Wilcox 2005). In fact, mountain rivers with well-developed
DHG tend to have an above-average ratio of total stream power (a measure of hydraulic
driving forces) to coarse grain size (a measure of boundary resistance) (Wohl 2004). In
contrast, mountain rivers that are strongly controlled by geologic rather than hydraulic
controls will often display poorly defined DHG (Wohl et al. 2004).
The distribution of grain sizes throughout the stream network can also yield
insight into the underlying lithologic controls. Grain size in the stream channel is largely
dependent on the underlying bedrock, the input from hillslopes, and the mechanisms of
weathering and transporting clasts. In fluvial systems where the bed material is readily
mobile, there is often a balance between discharge, sediment transport, and slope.
Consequently, grain size often declines with increasing drainage area such that the largest
grains are found in the in headwater channels and smaller grains are found in lower
reaches (Paola and Seal 1995, Rice 1999, Constantine et al. 2003). In steep montane
94
catchments where landslides introduce large pulses of coarse (and potentially immobile)
sediment, this balance is upset, and grain-size patterns are often discontinuous (Grimm et
al. 1995, Pizzuto 1995, Brummer and Montgomery 2003). The downstream pattern in
grain sizes indicates the relative influence of coarse material deposited from hillslope
processes and the ability of the channel to transport sediment.
Many river networks also tend towards an assumed optimal state of energy
expenditure throughout their evolution such that certain indices of energy expenditure are
either constant or linear along the river profile (Molnar and Ramirez 2002). Nonlinearity
in stream power, whereby energy expenditure is concentrated in specific reaches rather
than uniformly dispersed, can indicate underlying geologic control (Graf 1983, Lecce
1997). Similarly, many stream networks have a mid-basin maximum in stream power, the
location of which is dependent on slope, the flow regime, and the structure of the basin
(Knighton 1999). Large gradients in bed stress or energy expenditure also yield gradients
in sediment flux, causing certain parts of the river to erode and others to deposit
sediments in an effort to remove these gradients. In bedrock- and boulder-lined channels
where coarse sediment is not readily mobile, the ability of the channels to adjust their
morphology to remove these gradients in energy expenditure may be hindered.
Lastly, the downstream trend in boundary shear stress at bankfull discharge
provides insight into sediment mobility and the relative stability of channels. The Shields
parameter, a dimensionless bed shear stress that is expressed as a ratio of slope, depth,
and size of the bed material, is a quantitative indicator of flow competence and is strongly
related to alluvial channel form (Dade and Friend 1998, Dade 2000). In many self-
forming alluvial channels, the Shields parameter at bankfull flow does not vary
95
systematically throughout a basin. Assuming a constant critical dimensionless shear
stress, this lack of scaling between the Shields stress and bankfull discharge implies that
many alluvial channels are at the threshold for incipient sediment mobility. However, in
gravel- and boulder-lined mountain channels, both the Shields parameter and critical
dimensionless shear stress often vary widely throughout the basin, depending on flow
resistance (Mueller et al. 2005). If the flow regime in montane channels is sufficient to
mobilize the bulk of the sediment, we would expect the Shields parameter to be
consistently higher the critical dimensionless shear stress; lower if the sediment is too
coarse to transport. Furthermore, if the controls on sediment transport shift from non-
fluvial forces upstream to fluvial forces downstream, we would expect the excess
dimensionless shear stress to increase with bankfull discharge.
In this study, we present results from an extensive field survey of a tropical
mountain stream network. We compare channel profiles and subsequent downstream
changes in cross-sectional geometry, grain size, and channel energetics in five adjacent
watersheds in the Luquillo Mountains of northeastern Puerto Rico. This comprehensive
dataset allows us to test two alternate hypotheses on the potential controls on stream
channel morphology. First, if local lithologic factors such as multiple rock types, resistant
channel boundaries, and coarse sediment delivery from landslides dominate the form of
the river, we would expect the channel profile to be segmented, display poorly developed
hydraulic geometry, have seemingly random pattern of grain sizes, and have insufficient
stream power and boundary shear stress to mobilize available sediment. Conversely, if
the high unit discharge and associated stream power of the energetic tropical flow regime
are sufficient to overcome lithologic resistance and mobilize coarse sediment, then we
96
would expect downstream changes in channel morphology to have systematic trends
similar to those found in many alluvial rivers.
STUDY AREA
The Luquillo Mountains in northeastern Puerto Rico rise steeply from sea-level to
over 1000m in elevation over a distance of 15 to 20 km. They are characterized by steep
slopes, rugged peaks, and highly dissected valleys. The landscape is composed of several
lithologies and a variety of land cover. The streams have their headwaters in the Luquillo
Experimental Forest (LEF), a 113 km2 protected forest reserve under the management of
the United States Forest Service. The study area consists of five adjacent watersheds
draining the LEF: Río Blanco, Río Espiritu Santo, Río Fajardo, Río Mameyes, and Río
Sabana (Figure 4.1). The watersheds are similar physiographically, although they vary in
size, lithology, and land cover. Drainage areas of these watersheds are 72 km2, 92 km2,
67 km2, 44 km2, and 35 km2, respectively. All of the watersheds, except for the Río
Sabana, reach the upper-most ridges of the Luquillo Mountains.
The humid subtropical maritime climate is influenced by both northeasterly trade
winds and local orographic effects that interact to form steep gradients in precipitation.
Mean annual rainfall increases with elevation from approximately 1500 mm per year at
the coast to >4500 mm/yr at elevations above 1000 m (García-Martinó et al. 1996). The
principal weather systems affecting climate are convective storms, easterly waves, cold
fronts, and tropical storms (van der Molen 2002). Rainfall is a near-daily occurrence
(Schellekens et al. 1999), and high-intensity rainfall events and floods can occur in any
given month. Hurricanes and tropical storms are common from August through October,
97
Figure 4.1 Location map of northeastern Puerto Rico. Shown are the 238 surveyed
reaches in 5 adjacent watersheds and the regional topography, geology, and land cover.
98
Figu
re 4
.1 c
ont.
99
and typically bring high daily rainfall in excess of 200 mm/day (Heartsill-Scalley et al.
2007); the maximum recorded daily rainfall is >600 mm/day (Scatena and Larsen 1991).
The streams of the Luquillo Mountains have been classified as “flood dominated”
channels that have a hydrologic setting similar to other montane environments in the
Greater Antilles and regions along active tectonic zones in the humid tropics (Gupta
1988, Ahmad et al. 1993). Floods are intense and peak discharges can be 1000 times
greater than baseflow. The unit discharge at baseflow is approximately 0.02 m3/s/km2,
whereas the highest peak unit discharge ever recorded at a regional stream gage was
19.7 m3/s/km2 (United States Geological Survey, updated 2006). Peak-flow hydrographs
are short-lived and typically have a duration of less than one hour. Stormflow runoff is
quickly flushed through the system such that the streams return to baseflow within 24
hours of large events. Large floods are driven by storm events, as opposed to the seasonal
floods associated with snowmelt in many temperate mountain streams. Consequently,
discharges that are close to the annual peak are often experienced independently several
times in a year (Scatena et al. 2004).
The Luquillo Mountains were formed by early Tertiary volcanism and tectonic
uplift associated with oceanic island-arc subduction. The landscape consists of several
dominant lithologies: volcaniclastics, plutonic intrusions and dikes, contact
metamorphics, and alluvium (Seiders 1971a, Briggs and Anguilar-Cortés 1980). The
volcaniclastic rocks, comprised of marine-deposited volcanic sediments of late
Cretaceous age, form the bulk of the Luquillo Mountains. They include units of
sandstones, siltstone, mudstones, breccias, conglomerates, tuff, and lava, that are
complexly faulted and steeply tilted (>30º). A Tertiary quartz diorite (granodiorite)
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batholith underlays the southern side of the study area. It outcrops in an area of
approximately 24 km2, and is drained largely by the Río Blanco watershed, but also by
small parts of the upper Río Espiritu Santo and Río Mameyes watersheds. It is rapidly
eroding at an estimated denudation rate of 25-50 m/million yr; one of the highest
documented weathering rates of silicate rocks on the Earth’s surface (Brown et al. 1995,
White et al. 1998). A 1-2 km zone of contact metamorphism surrounds the granodiorite.
These contact metamorphosed volcaniclastic rocks (hornfels facies) exhibit greater
hardness than both their unmetamorphosed equivalents and the granodiorite (Seiders
1971b). Because of their relative resistance to erosion, these rocks form steep cliffs and
the tallest peaks in the region. Several vertical dikes traverse the volcaniclastic rocks,
mainly at lower elevations (<150 m). The mountains are fringed by a lowland coastal
plain composed of Quaternary alluvium.
Past climates in the region are thought to be similar to the present, due to both
comparable elevation and location of the mountain range in the subtropical maritime belt
(Graham and Jarzen 1969). Pollen assemblages and plant microfossils of subtropical and
warm-temperate communities found in sedimentary sequences on the island suggest that
the mountains of Puerto Rico had a subtropical climate in the Oligocene, with additional
cooler higher elevation environments not present today. However, the Luquillo
Mountains’ climate is generally considered to have oscillated around a humid subtropical
state over time, without glaciers or dramatic changes (Scatena 1998).
The main stream channels and rivers are relatively old; on the order of tens of
millions of years. The emplacement of the plutonic rocks and supposed uplift of the
Luquillo fault block occurred in the Eocene (Cox et al. 1977, Kesler and Sutter 1979),
101
although significant erosion of the landscape and initial formation of the modern stream
network probably did not occur until the Miocene (Monroe 1980). Stratigraphic records
preserved on the northwestern side of the island suggest that no surface streams delivered
clastic sediment into the sea until this time period. Since then, the stream network has
been continually formed by a humid climate, and the main upland channels that are
confined by steep valleys walls have presumably not migrated significantly.
All five watersheds currently drain protected primary forest in their upper
elevations, mature secondary forest at mid-elevations, and both abandoned (reforesting)
agricultural fields/grazing pastures and scattered urbanized developments along the
coastal plain. Each river flows through a mangrove-lined estuary before reaching the
coast. However, the land cover in the region has been continually changing since the
Spanish colonization of the island in the late 17th century. Many low-elevation areas
(<300 m) of northeastern Puerto Rico were cleared for agriculture between 1830 and
1950. This caused an estimated 50% increase in runoff, and an order of magnitude
increase in sedimentation on the coastal plain (Clark and Wilcock 2000). Subsequent land
clearance on steep valley slopes resulted in widespread erosion and landslides that
delivered a large load of coarse sediments to the river. Large portions in the upper
elevations of the LEF were never deforested during the 19th and 20th centuries due to
government protection, steep slopes, and high rainfall (Scatena 1989). Since 1950,
urbanization and reforestation of former agricultural land in low-lying areas has resulted
in elevated storm runoff and decreased sedimentation, allowing transport of previously
deposited coarse alluvial sediment in coastal plain streams (Clark and Wilcock 2000, Wu
et al. 2007).
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Hillslopes are typically steep, in excess of 30° in many headwater areas, and are
consequently strongly linked to channel processes (Scatena and Lugo 1995). Landslides
are the dominant process that physically weathers the regolith and delivers sediment to
the channel (Simon and Guzman-Rios 1990, Larsen et al. 1999). Other hillslope
weathering processes such as sheetwash, soil creep, and treefall-induced mass movement
are prevalent but less important to the total sediment yield (Larsen 1997). There is an
abundance of clay in the deeply-weathered soil and thick saprolite that is derived from
both volcaniclastic and granodiorite bedrock (Frizano et al. 2002, Schellekens 2004).
However, there is typically little fine sediment that persists in the streams channels
(Simon and Guzman-Rios 1990). Flood-discharges quickly wash fine sediment from the
channel, and the streams are generally clear within a day of a large storm.
Triggered by intense rains, landslides are common at upper elevations,
particularly on areas underlain by granodiorite, and on hillslopes that exceed 12° gradient
(Larsen and Torres-Sánchez 1998). Forested areas underlain by granodiorite rocks
experience twice as many landslides as comparable areas underlain by volcaniclastics
(43 landslides/km2/100yr on granodiorite; 21 landslides/km2/100yr on volcaniclastics)
(Larsen 1997). Landslides are capable of delivering very large boulders to the stream
channels, and the corresponding volume of material transported is substantial
(700 Mg/km2/yr on granodiorite; 480 Mg/km2/yr on volcaniclastics). The large majority
(80-90%) of total sediment delivery to the streams is attributed to landslides, and the
associated pulse of sediment delivery can locally alter the channel morphology. Since
1979, there have been numerous sliding events, and two large landslides have temporarily
103
dammed permanent streams, persisting for a few weeks before being removed by
stormflow (personal observation by F.N. Scatena).
The first-order drainage network consists of a dense, dendritic network of small
ephemeral channels that range from leaf-filled swales to mossy cobble-lined channels
that become active only during large rainfall events (Scatena 1989, Schellekens 2004).
Larger first-order perennial streams have channels dominated by boulders in steeply
sloped reaches, and clay and soil-lined channels in reaches with more gentle slopes.
Second- and third-order streams have steep-gradient reaches, exposed bedrock channels,
matrices of large boulders interspersed with finer sediment, and periodic waterfalls (up to
30 m in height). Many of the upland streams are characterized by cascade and step-pool
morphologies, whereas the lower reaches are plane bed and pool-riffle sequences (sensu
Grant et al. 1990, Montgomery and Buffington 1997, Trainor and Church 2003).
Structural control of channel pattern is apparent in many places as witnessed by
rectangular stream bends at fault intersections, streams following bedrock joints, and
knickpoints at lithologic boundaries. Due to rapid decomposition, these channels lack the
large coarse woody debris dams that influence the morphology of many channels in
humid temperate environments (Covich and Crowl 1990).
Fourth- and fifth-order streams occur only at lower elevations, flowing across the
coastal plain as relatively gentle gradient pool-riffle sequences. Alluvial inset deposits,
high-flow channels, floodplains, and terraces are common features in these lower reaches
(Ahmad et al. 1993, Clark and Wilcock 2000). These larger lowland streams are
relatively straight, are not constricted by bedrock, and have laterally migrating high-flow
104
channels that indicate that the alluvial channels adjust in response to varying discharge
and sediment supply.
METHODS
A total of 238 stream cross-sections (see Appendix) were surveyed in the
summers of 2003-2006; 11 in the Río Blanco, 88 in the Río Espiritu Santo, 31 in the Río
Fajardo, 91 in the Río Mameyes, and 17 in the Río Sabana (Figure 4.1). Cross-section
locations were chosen to capture the entire range of elevation, drainage areas, and
substrate type. The surveyed cross-sections are located on 1st to 5th order streams, have
drainage areas between 0.1 km2 to 79 km2, and are located on average approximately
every 30 m in elevation and 500 m in distance along the channel.
Cross-sections were surveyed at a straight uniform section within a reach.
Relative distance and elevation were measured at evenly spaced intervals along a transect
spanning from vegetated bank to bank, using a Sokkia Total Station Laser Theodolite
(Set 530). The most meaningful comparison of cross-sectional geometry is at the bankfull
stage that also correlates to the effective discharge of sediment and occurs at a constant
flow frequency throughout the basin (Leopold and Maddock 1953, Wolman and Miller
1960). However, the absence of floodplains in the mountainous reaches confounds the
identification of bankfull stage. In place of bankfull in steepland streams, cross-sections
extended to the boundary of the active channel that is marked by the edge of perennial
woody vegetation (shrubs and trees) and incipient soil development. A previous analysis
of flow-frequency at gaged stream reaches indicates that this active channel boundary
coincides with a flood discharge that occurs at the same frequency of both the bankfull
105
and effective discharge in adjacent alluvial channels (Pike and Scatena, in review). Using
these riparian features as markers, active channel width, average depth, and cross-
sectional area were calculated for each cross-section. Channel slope was similarly
measured as the difference in elevation of the water surface over 10 uniformly spaced
points spanning approximately five channel widths upstream of the cross-section.
Active-channel (bankfull) discharge at each cross-section was estimated by a
regional equation based on long-term stream gage data (Pike 2006). At nine stream gages
(with at least 10 years of record) in the study watersheds, the active channel discharge (as
marked by the first occurrence of woody shrubs, trees, and soil) corresponds to a flood
that is exceeded 0.16% of the time, and has an average unit discharge of 2.2 m3/s/km2
(Pike and Scatena, in review). In this region, rainfall and runoff increase with elevation
due to the precipitation gradient; higher elevation basins have more runoff than low
elevation basins of comparable size. Thus, active channel discharge was best estimated as
a function of drainage area multiplied by a linear model relating runoff to average basin
elevation:
)406.0*h0042.0DA(Q avgAC += n = 9, r2 = 0.97, P < 0.001 Eq. 4.1
where: QAC is the active channel discharge in m3/s, DA is drainage area in km2,
and havg is the average upstream elevation in m. Both of these variables are estimated for
each reach using a 10m Digital Elevation Model (DEM) and a GIS-based flow
accumulation algorithm. Longitudinal profiles were also constructed from a 10 m DEM.
Concavity was estimated along the main stem based on the relationship between slope
and drainage area, using points spaced every 10 m drop in elevation:
θkDAS = Eq. 4.2
106
where: θ is the concavity index, k is a steepness coefficient, DA is drainage area
(km2), and S is slope (m/m).
Elevation, drainage area, slope, and active-channel discharge (Equation 4.1) were
also estimated along the main-stem profile at a series of points where the streams
intersect 10m contour lines; that is, at every 10m drop in elevation.
Downstream hydraulic geometry relationships were calculated by least-squares
log-linear regressions between active channel discharge and channel geometry
measurements. Active channel discharge (Q) correlates with active channel width (w),
average flow depth (d), and mean velocity (v), such that: w=c1Qb, d=c2Qf, v=c3Qm
(Leopold and Maddock 1953). By conservation of mass, the product of the coefficients
(c1,c2,c3) and sum of the exponents (b,f,m) must equal 1; c1*c2*c3=1, b+f+m=1. Mean
velocity was calculated as the active channel discharge divided by the channel cross-
sectional area (QAC/A). Since this indirect calculation of velocity is a function of
discharge, the strength of the regression equation was estimated as the correlation
between the logarithms of active channel discharge and cross-sectional area.
Grain size in the active channel was estimated using a modified Wolman Pebble
Count method (Wolman 1954). Approximately 100 clasts were selected randomly by
pacing across the width of the stream. The median diameter of each clast was measured,
and classified into the following seven size categories: bedrock (no size), megaboulder
(>2000 mm), boulder (256-2000 mm), cobble (64-256 mm), gravel (2-64 mm), sand
(.063-1 mm), and fines (silt/clay, 0.001–0.063 mm). From these grain size measurements,
we determined the median grain size (d50), coarse grain size (d84), and the percent of
bedrock exposed within the active channel.
107
Sediment mobility was calculated using the survey data and estimates of shear
stresses. Sediment is considered mobile when the dimensionless boundary shear stress
exceeds the dimensionless critical shear stress. Boundary shear stress, τ (N/m), was
calculated as:
ρgRSτ = Eq. 4.3
where: ρ is water density (1000 kg/m3), g is acceleration due to gravity (9.8 m/s2), R is
hydraulic radius (m), S is slope (m/m).
Dimensionless shear stress, τ*, or Shields stress, required to mobilize the coarse
sediment was estimated as:
( ) 50s gdρρττ*
−= Eq. 4.4
where: ρs is sediment density (2650 kg/m3), d50 is the median grain size (in m).
Dimensionless critical shear stress, τ*c, was estimated from an equation
developed for steep gravel-bed rivers by Mueller et al. (2005), that relates the
dimensionless reference shear stress (assumed to be critical) to slope (S):
021.0S18.2τ*c += Eq. 4.5
Typically, critical dimensionless shear stress is assumed to be constant throughout
the basin. However, the aforementioned equation shows the critical dimensionless shear
stress varies by accounting for excess bed roughness in steep reaches with large grains, as
supported from data from numerous steep gravel-bed rivers.
Total stream power per unit channel length, Ω (W/m), is defined as:
ρgQSΩ = Eq. 4.6
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Stream power was calculated for each reach using the survey data and estimated
active-channel discharge.
RESULTS
Longitudinal Profiles
The longitudinal profiles of each of the five rivers have unique shapes that are
related to their underlying geology. A theoretical graded concave-upward profile is
steepest in the headwaters, and displays a systematic downstream decline in slope. Yet
the profiles here are segmented by a series of convex protrusions and slope breaks that
deviate from a systematic grade. For example, the volcaniclastic headwaters of the Río
Fajardo and the contact metamorphic upper reaches of the Río Mameyes display a
traditionally concave shape (Figure 4.2). Similarly, the alluvial reaches are well-graded
and have few slope breaks. However, local factors also shape the profile. For example,
the steep streams on volcaniclastic rocks have knickpoints that generally correspond to
bedrock faults identified on USGS 1:20,000 geologic maps (Seiders 1971a, Briggs and
Anguilar-Cortez 1980). Also, locally exposed outcrops create small convexities in the
longitudinal profiles. Most striking are the anomalously convex profiles of granodiorite
streams. The headwaters of granodioritic Río Blanco are unusually flat before cascading
steeply down the side of the batholith and leveling out along the alluvial coastal plain
(Figure 4.2). The inflection point where the stream sharply steepens occurs at the edge of
a non-glacial hanging valley. This flat form is also seen in the headwaters of the Río
Espiritu Santo.
109
Figure 4.2 Longitudinal profiles of the main stem of each river highlighting the
relationship between local profile shape and lithology. Drainage areas as well as major
mapped faults (vertical bars) are indicated. Slope-area plots (inset), using points spaced
every 10m in elevation along the main stem, are shown to indicate changes in the
concavity index (θ).
110
Figure 4.2 cont.
111
Where the stream flows across one lithology, the longitudinal profile is locally
graded and has a traditional concave pattern. Where the stream flows over two or more
rock types, there is often a slope break at the contact, especially where the adjoining
rocks have varying resistance to erosion. The boundary between contact metamorphic
rocks and other lithologies, as on the main stem of the Río Mameyes and Río Espiritu
Santo, is accompanied by a pronounced convexity (Figure 4.2). Furthermore, slight
changes in the composition of the volcaniclastic rocks, from a sandstone unit to a
mudstone unit, are often the site of waterfalls and/or steep gradients (personal
observation). Similar notable breaks occur where upland alluvial formations, typically
terrace deposits, merge with volcaniclastics, such as a knickpoint on the Río Fajardo that
occurs at the boundary between a mid-elevation structural bench and the surrounding
volcaniclastics. Also, the lowest elevation waterfall in the region occurs at the transition
where the Río Sabana flows across a locally exposed volcaniclastic formation
approximately 7 km from its headwaters.
The concavity index (θ) of the main stem of each river profile, calculated from
slope-area relationships, is related to both the underlying rock type, and drainage area.
Concavity values for headwater portions of the profiles (DA < 10 km2) are starkly
different between areas underlain by granodiorite and those by volcaniclastics.
Concavities on volcaniclastics (Rio Fajardo, Rio Mameyes, and Rio Blanco) are in the
low to moderate range (θ = 0.15 to 0.42). On granodiorite and other lithologies (Rio
Blanco and Rio Espiritu Santo), concavities values are high (θ = 0.99 to 1.21) along the
gently-sloped reaches and convex (θ = -0.75 to -1.74) where the channel steepens. There
is a break in the slope-area relationship at approximately 10km2 along each profile.
112
Consequently, lowland and alluvial portions of the profiles (DA > 10km2), are all
strongly concave (θ = 1.07 to 4.65).
Hydraulic Geometry
Downstream hydraulic geometry relationships for the active channel width,
hydraulic radius, and mean velocity were calculated using the cross-sectional data and
estimated active channel discharge (Figure 4.3). The coefficient of determination (r2) for
these hydraulic geometry relationships are 0.71 for width, 0.21 for depth, and 0.66 for
velocity (as determined by the relationship with cross-sectional area). Discharge displays
a strong power-law relation with both width and velocity, but not depth. The active
channel systematically widens in the downstream direction, despite potential constriction
from bedrock outcrops and confined valley walls. Yet the streams do not deepen
substantially downstream. Instead, they display strong local variation. Comparably deep
pools and shallow riffles are observed in both headwater and lowland reaches.
Downstream hydraulic geometry exponents for all basins are 0.33 for width, 0.12
for depth, and 0.55 for velocity. With increasing discharge, width increases at
approximately three times the rate of depth. This implies that the width/depth ratio
similarly increases in the downstream direction, and that the channel form changes from a
triangular ‘v’-shape (low w/d ratio) with in the headwaters to a more rectangular (high
w/d ratio) form near the mouth. Velocity increases at a rapid rate of change in the
downstream direction resulting in a mean cross-sectional velocity 100 times greater in the
lower reaches than in the headwaters during a flood at active-channel discharge.
DHG relationships for individual watersheds show general consistency among
basins (Table 4.1). The width exponents range from 0.24 to 0.37, the depth exponents
113
Figure 4.3 Downstream hydraulic geometry relationships between active channel
discharge, width, depth, and velocity using data from all surveyed reaches.
114
Table 4.1 Downstream hydraulic geometry divided by watershed. Coefficients,
exponents, and coefficient of determination (r2) are between the active channel discharge
and each corresponding channel geometry variable.
width depth velocity Watershed c1 b r2 c2 f r2 c3 m *r2 n Blanco 4.0 0.37 0.65 0.6 0.09 0.05 0.4 0.54 0.54 11 Espiritu Santo 5.9 0.30 0.63 0.6 0.13 0.22 0.3 0.57 0.55 88 Fajardo 4.9 0.35 0.73 0.9 0.02 0.02 0.2 0.62 0.73 31 Mameyes 5.2 0.35 0.80 0.6 0.13 0.33 0.3 0.52 0.77 91 Sabana 7.7 0.24 0.50 0.4 0.17 0.30 0.3 0.58 0.57 17 ALL 5.4 0.33 0.71 0.6 0.12 0.21 0.3 0.56 0.66 238 * r2 for velocity relationships estimated from discharge vs. cross-sectional area
115
from 0.02 to 0.17, and the velocity exponents from 0.52 to 0.62. The differences in
coefficients and exponents are not strongly correlated to basin-scale factors such as
catchment size or geology. For each watershed, the r2 values for width and velocity
relationships are > 0.5, but less than 0.5 for depth relationships. Consequently, DHG is
considered well-developed for all the watersheds.
For comparison, average DHG exponents for many alluvial rivers world wide are
0.5 for width, 0.4 for depth, and 0.1 for velocity (Park 1977). DHG exponents in
mountain streams deviate slightly from the world average by having a lower width
exponent and greater velocity exponent, with average values of 0.36 for width, 0.38 for
depth, and 0.20 for velocity (Wohl 2004). The Luquillo streams have a width exponent
comparable to other mountain streams, but both the lowest known depth exponent and the
highest velocity exponent for a mountain stream.
Grain Size
The grain size of bed material varies widely throughout the watersheds, but is
clearly related to the underlying rock type (Figure 4.4). For example, long stretches of
step-pool sequences composed of boulders up to several meters in diameter are present in
a steep upland tributary of the Río Espiritu Santo underlain by volcaniclastic rock. In
contrast, the headwaters of the Río Blanco are composed mostly of mobile sand that is
weathered from granodiorite interspersed with large boulders. A typical lowland reach is
composed of cobbles and gravels derived both from the surrounding alluvium and
transported from upper reaches. The largest clasts observed in the river channels are slabs
of volcaniclastic rock and granodiorite corestones that reach 15m in diameter. These are
so large and immobile that they are hydraulically indistinguishable from bedrock.
116
Figure 4.4 Upstream views of typical reaches throughout the basins. The grain size
varies with both the lithology and the position along the stream profile. The average
channel width / median grain size, d50, for each reach are: a) 13.2m / 480mm, b) 6.8m /
1mm, c) 11.0m / 60mm, d) 32.3m / 150mm, e) 14.0m / 330mm, f) 25.4 m / 70mm.
117
Figu
re 4
.4 c
ont.
118
Unique distributions of grain sizes are observed on different lithologic types.
Using the pebble-count data from each study reach, all measured grains (excluding
bedrock) were sorted into logarithmically distributed bins (2φ intervals) for each major
rock type (Figure 4.5). Streambed material on volcaniclastic rocks has a high frequency
of cobble and boulder sized-sediment (64-1028 mm), but also contains lesser proportions
of large boulders (>1028 mm) and gravel. Field observations suggest that different
volcaniclastic formations have varying proportions of large boulders that are dependent
on the formation thickness. Contact metamorphic rocks display a distribution with fewer
large boulders and more sand than their unmetamorphosed equivalents. Mafic dikes have
a high proportion of large boulders, which is unique given the lower elevation.
Granodiorite streams have a bimodal grain-size distribution composed primarily of sand
and large boulders. Alluvial streams contain an abundance of cobble and gravel-sized
grains.
Megaboulders (boulders > 2000 mm diameter) are a relatively common feature in
the channels. Presumably, the megaboulders are corestones that are weathered directly
from bedrock along fracture planes, and subsequently deposited into the stream channels
by landslides. The abundance of megaboulders in the channel correlates with the slope of
the adjacent hillsides. Where the adjacent hillslope exceeds a threshold of 12º, there is
potential for landslides and possible deposition of megaboulders into the stream channel
(Larsen and Torres-Sánchez 1998). Conversely, below this hillslope threshold, the
hillside is relatively stable. Among 45 reaches having shallow adjacent hillslopes that do
not exceed 12º, no megaboulders were observed (Figure 4.6). Of the remaining 193
reaches adjacent to steep hillsides exceeding the 12º threshold, about half (89 reaches,
119
Figure 4.5 Grain size distributions for all measured clasts, grouped by lithology.
120
Figure 4.6 The percentage of megaboulders (boulders > 2000m) is associated with
adjacent hillslope; they are present when the hillslope exceed 12º. This is also the slope
threshold for landslides (Larsen and Torrez-Sánchez 1998) – the process that presumably
delivers these large boulders to the channel.
121
46%) have megaboulders present. Thus, megaboulders are only found in about half of
these reaches that are considered landslide-prone areas based on slope and lithology.
However, not all reaches in landslide-prone areas have megaboulders.
Most of the reaches have a relatively stable framework of large boulders that do
not appear to move, as evidenced by a thick moss-covering and occasional tree growth on
the boulders’ surfaces. Yet within this matrix, there is an abundance of smaller loosely-
packed gravel and sands that is transported during floods (personal observation). Average
dimensionless shear stress in these channels varies considerably but generally decreases
as a function of active-channel channel discharge (Figure 4.7a). Critical dimensionless
shear stress, as estimated by equation 4.5, also decreases with increasing active-channel
discharge (Figure 4.7b). This suggests that many of the headwater reaches have
additional flow resistance and form drag due to large boulders, which leads to an
increased threshold to initial sediment motion. Excess dimensionless shear stress (τ/τc)
does not vary systematically with active-channel discharge (Figure 4.7c). The average
dimensionless boundary shear stress at the active channel discharge exceeds the
dimensionless critical shear stress to mobilize the d50 in approximately 45% of the study
reaches (Figure 4.7c). That is, the median sediment size in approximately half of the
surveyed reaches can potentially be mobilized during the active channel flood that occurs
several times per year. The lack of scaling of excess sheer stress with discharge also
suggests that at the active channel discharge, the channels are generally at the sediment
transport threshold—similar to many alluvial channels.
Given the variety of grain sizes and range of rock types, the pattern of grain size
throughout the basin is not immediately apparent. The median diameter (d50) of particle
122
Figure 4.7 Relationship between active channel discharge and dimensionless shear
stresses and critical shear stresses. a) Dimensionless boundary shear stress is negatively
correlated with active channel discharge, rather than constant. b) Dimensionless critical
shear stress (a function of slope) also decreases with active channel discharge c) Excess
shear stress (as the ratio of dimensionless shear stress to critical shear stress) does not
vary systematically downstream with discharge, suggesting that the channels waver
around the threshold for sediment transport. Approximately 45% of the reaches have
presumably mobile substrate (τ* > τ*c) at the active channel discharge.
123
Figure 4.7 cont.
124
size at a reach correlates poorly with drainage area (r2 = .002, P = 0.83), and significantly
but only moderately with either slope (r2 = .20, P < 0.0001) or stream power (r2 = 0.30,
P < 0.0001). However, a log-linear plot between the ratio of d50 to drainage area (d50:DA)
and slope (sensu Hack 1957) yields a stronger and highly significant correlation (r2 = .74,
P < 0.0001) (Figure 4.8). A similar significant relationship was found for the d84 data (r2
= 0.72, P < 0.0001).
55.050
DAd
007.0S ⎟⎠
⎞⎜⎝
⎛= or
8.1
50 007.0SDAd ⎟
⎠⎞
⎜⎝⎛= Eq. 4.7
57.084
DAd
0026.0S ⎟⎠
⎞⎜⎝
⎛= or
75.1
84 0026.0SDAd ⎟
⎠⎞
⎜⎝⎛= Eq. 4.8
These two relationships state that grain size is a function of both drainage area and slope.
At a given drainage area, grain size is proportional to 1.8 power of slope. Conversely, if
slope remains constant, then grain size is directly proportional to drainage area.
Equation 4.7 was used to estimate median grain size along the profile of the river,
using drainage area and slope derived from the 10m DEM (Figure 4.9). The predicted
grain size function is jaggedly shaped due to local variations in slope. Yet the resulting
pattern shows a distinct downstream coarsening from the headwaters to a mid-basin
maximum. This is followed by subsequent downstream fining. Typically, cobble and
small boulder-sized sediment in the headwaters are replaced by very large boulders
(>2000 mm) in the mid to upper elevation steepland channels. Along the main stem, this
maximum typically occurs at approximately 5km from the headwaters, or approximately
25% to 33% of the distance of the main stem. As the slope declines towards the lowland
reaches, the sediment gradually fines as well.
125
Figure 4.8 The highly significant relationship (r2 = 0.74, P < 0.0001) between median
grain size (d50), drainage area, and slope. Data from this study (dark circles) are shown
alongside data representing humid streams in Maryland and Virginia from Hack (1957,
white circles) where this relationship was first published. The solid line is a least-squares
regression used as the basis for estimating grain size for a given drainage area and slope
(Equation 4.7), and dotted lines represent the 95% prediction interval. Outliers from this
study (crosses) were removed from regression on the basis of uncertainty of either
average grain size estimation or slope measurement.
126
The measured grain size data for reaches along the main stem show general
agreement with the predicted grain size (Figure 4.9). The data best display the coarsening
to fining trend in those rivers where surveyed reaches span a range of slopes and drainage
areas (Río Espiritu Santo, and Río Sabana). On other rivers, such as the Río Fajardo, the
measured grain sizes do not vary as widely because the surveyed reaches had comparable
slopes.
The largest discrepancy between the actual grain size and the predicted grain size
occurs in the steepest reaches (Figure 4.9). Here, the grain size function predicts boulders
in excess of 10m. These reaches are generally waterfalls and cascades where the largest
sediment is practically indistinguishable from bedrock, or is bedrock itself, and
consequently cannot be quantified by a measurable diameter. Field observations did
indicate notably larger grains in the mid-basin steep reaches. Data from waterfalls and
cascades on tributaries also show that the largest grains are found in the steepest reaches
with moderate drainage areas.
Stream Power
Total stream power along the main stem displays a peaked pattern in the
downstream direction (Figure 4.9). Given that total stream power is the product of
discharge and slope, stream power is low in the headwaters where slopes are steep but
there is minimal discharge. It is similarly low near the mouth where there is high
discharge but gentle slopes. Thus, stream power peaks at an intermediate distance where
a combination of adequate discharge and steep slopes generate maximum power. In these
streams, the location of this stream power peak typically occurs at mid to upper
elevations and at a downstream distance approximately a quarter to a third of the profile
127
Figure 4.9 Downstream changes in elevation, drainage area, median grain size, stream
power, and the ratio of stream power to coarse grain size along the main stem profile of
each river. 95% prediction intervals are shown in gray for the trends in grain size. Both
median grain size and stream power display a peaked pattern, the location of which
coincides with steep reaches having ample discharge. The ratio of stream power to coarse
grain size increases in the downstream direction, suggesting the increased relative
influence of hydraulic forces over lithologic controls downstream. A threshold of 10,000
W/m/m differentiates alluvial conditions from bedrock controls.
128
Figu
re 4
.9 c
ont.
129
length. The magnitude of the peak varies according to the watershed, with the larger and
steepest watersheds (such as the Río Blanco) having greater maximum stream power.
The ratio of total stream power to the coarse grain size (Ω/d84, W/m/m) shows the
relative influence of hydraulic forces (as stream power) to lithologic resistance (coarse
grain size). While, both stream power and grain size display a similar peaked trend in the
downstream direction, the ratio between the two shows an opposing trend. Along the
main stems, this ratio generally shows a positive trend in the downstream direction
(Figure 4.9), indicating the relative dominance of stream power in the lowland reaches,
and strong resistance by coarse grains in the headwater reaches. Using a threshold of
10,000 W/m/m to differentiate between supposed alluvial conditions and lithologic
controls (Wohl 2004), it is apparent that the transition from strong lithologic control to
more alluvial condition occurs approximately a third to a half of the profile length.
Furthermore, those watersheds having granodiorite substrate in the headwaters (Rio
Blanco and Rio Espiritu Santo) have a Ω/d84 in excess of this threshold, indicating they
have alluvial conditions in their sandy headwater reaches.
DISCUSSION
The results presented above indicate that the streams of the Luquillo Mountains
have an intricate connection between the underlying lithologic and hydraulic controls,
and the resulting profile shape, grain size distribution, channel geometry, and channel
energetics. Local-scale geologic factors such as the rock type, exposed in-channel
outcrops, and bedrock faults are seemingly dominant in determining the shape of the
longitudinal profiles. Different lithologies correspond to local variations in profile slope
130
and concavity. They also weather into unique particle sizes, and are associated with
specific channel geometries. Large immobile boulders are deposited in the channel by
landslides. Yet increasing discharge and hydraulic controls on sediment transport
override these lithologic influences and give rise to basin-scale patterns. This is
evidenced in that channel geometry and grain size are also strongly related to slope and
discharge (Figures 4.3, 4.8, and 4.9). Here we discuss the importance of each of these
basin-scale patterns and the implications for the dynamics of tropical mountain streams.
The longitudinal profile of each of the study rivers, although generally concave-
upward, display fragmented patterns consistent with lithologic control. The rivers have
slope breaks, knickpoints, and profile convexities that correlate with different rock types
and structural features of the underlying bedrock. The relative strength of different
bedrock types during fluvial incision can yield such segmented profiles (Brocard and van
der Beek 2006). Chemical and physical weathering, as well as debris flows, are the
dominant processes of bedrock incision in these rivers, as noted in other mountainous
drainages (Stock and Dietrich 2006). Many of the common processes of river incision
into bedrock, notably plucking, macro-abrasion, wear, and cavitation (Whipple 2004) are
rarely observed.
The concavity of the longitudinal profiles is related to underlying bedrock and
hillslope processes. Whipple (2004) discusses potential controls on channel concavity.
Low concavities (<0.4), such as that are seen in the headwaters of those rivers draining
volcaniclastics, are associated with short, steep drainages importantly influenced by
debris flows. High concavities (0.7-1.0), such as the gentle-gradient sandy-bed headwater
reaches on granodiorite, are associated with fully alluvial conditions. Convex profiles
131
(negative concavity), seen along granodiorite streams, are typically associated with
abrupt knickpoints owing either to pronounced along-stream changes in substrate
properties (VanLaningham 2003) or to spatial or temporal differences in rock uplift rate
(Whipple 2004). Lastly, extreme concavities (>1.0), present along the lowland and
alluvial reaches, are associates with transitions from incisional to depositional conditions.
Thus, along streams draining volcaniclastics, the transition from low concavity to high-
concavity at approximately 10 km2 marks a transition from dominant colluvial and
hillslope processes incisional channels to a depositional alluvial-type channels. Brummer
and Montgomery (2003) noted a similar break in the slope-area relationships at a
drainage area of 10 km2 in some coastal temperate streams, contending that the associated
change in concavity reflected a shift from dominant colluvial processes in headwater
channels to alluvial processes in downstream channels.
Downstream hydraulic geometry is considered well developed in all of these
basins, despite the influence of non-fluvial processes, differences in lithology, and local
structural features. Mountain rivers are considered to have well-developed DHG when
the coefficient of determination (r2) between discharge and at least two of the three
hydraulic variables is 0.5 or greater (Wohl 2004). The high r2 values for width (0.71) and
velocity (0.66) relationships in the Luquillo streams satisfy this criteria, so that the DHG
for the stream network is considered well-developed.
The well-developed hydraulic geometry can be attributed to the strong influence
of fluvial forces over lithologic resistance, as reflected in the ratio of stream power to
grain size. Wohl (2004) found that mountain streams have well developed DHG when the
ratio of total stream power to the coarse grain size, Ω/d84, is greater than 10,000 W/m/m.
132
Above this threshold, the river presumably has enough power to rework the coarse
sediment and adjust channel parameters in response to downstream changes in discharge.
Below this threshold, combinations of low stream power or large grain sizes inhibit the
river from developing strong DHG relationships. The average Ω/d84 ratio of all surveyed
reaches in the streams of the Luquillo Mountains is approximately 14,000 W/m/m, or
slightly above the threshold. Although the average grain size is very large, so is the
average stream power. Thus, the combination of high discharge and steep slopes
generates sufficient stream power to overcome lithologic controls and adjust channel
geometry accordingly over time.
Clark and Wilcock (2000) noted a DHG reversal in the lowland alluvial reaches in
some of these streams. Values of channel width, depth, and velocity either decreased or
were constant in the downstream direction in the lowland reaches. This hydraulic
geometry reversal trend was found on coastal plain alluvial reaches along the main stem,
or approximately the lower 33% of the main stem profile length. The authors attribute
this reversal to historic and modern land-use changes. Apparently, the shift from forest to
agriculture to urbanization over 400 years altered the sediment supply and flow regime.
Net aggradation of sediment during periods of land-clearance and recent net degradation
from heightened runoff due to urbanization have altered the balance that maintains
channel geometry. However, our data confirms that this reversal is strictly confined to the
lowland alluvial reaches. Hydraulic geometry remains relatively well-developed at the
basin-scale that spans four orders of magnitude in discharge.
The poor correlation between depth and discharge across all watersheds suggests
that local factors are a strong determinant of channel form. Bedrock outcrops, scour
133
pools, and the accumulation of large boulders can all locally determine depth. To
compensate for the small increase in depth, these streams increase drastically in velocity
with increasing downstream discharge. We document the largest downstream velocity
exponent reported for a mountain stream. This sizeable downstream increase in velocity
may be a result of the basin physiography and variations in flow resistance. These island
streams generally have shorter and more truncated profiles than streams on continental
land masses. Yet the short coastal plain is still relatively steep so that the flood waters
flow rapidly to the ocean with minimal resistance. Despite having faster average flow, the
downstream reaches are not the most energetic. Rather, upland streams that have lower
average velocity, but greater slope, shear stress, and flow resistance, expend the greatest
amount of energy.
The peaked distribution of grain size along the profiles of these rivers stands in
contrast to a common systematic downstream fining trend in many alluvial rivers
(Pizzuto 1995, Paola and Seal 1995). However, a similar systematic headwater
coarsening pattern has been noted in several mountain basins in western Washington
(Brummer and Montgomery 2003). In both western Washington and the Luquillo
Mountains, grain size and stream power maxima occur at approximately the same
location as the transition from debris-flow and landslide dominated channels to fluvially
dominated channels. This suggests that a tendency for downstream coarsening may be
ubiquitous in headwater reaches of mountain drainages where debris flow processes set
the channel gradient. Apparently, when landslides dominate the transport and routing of
sediment in low-order headwater channels, a coarsening trend occurs. Downstream fining
occurs as fluvial forces override colluvial forces as the driving sediment transport process
134
in high-order alluvial channels. These observations suggest that basin-wide trends in d50
are also in part hydraulically influenced by variations in stream power, as well as by
landslide deposits.
There is a complex interaction between profile slope, grain size, drainage area and
lithology, as noted by Hack (1957, 1960). Data from this study follow a similar
relationship between these three variables (Figure 4.8) as data from temperate piedmont
streams in Maryland and Virginia (Hack 1957). The streams in Luquillo display the same
adjustment between the grain size, drainage area, and slope as more gentle gradient
streams in a very different physiographic region. The same basic relationship holds even
though the Luquillo streams have steeper slopes and consequently greater d50:DA ratios.
Furthermore, rock type does not factor into this relationship, so that reaches on all
lithologies display the same relationship among the three variables.
The casual mechanisms associated this relationship (i.e. whether slope is
influenced by both the size of the sediment and discharge, or whether slope and discharge
determine the grain size) is seemingly time-scale dependent (sensu Schumm and Lichty
1965). Alluvial channels can adjust slope in response to transport capacity and sediment
supply such that slope is a dependent variable related to water and sediment discharge,
and grain size. Yet in the steep headwater bedrock channels where non-fluvial forces
dominate, slope is generally imposed by lithology, and becomes an independent variable
over the timescales of channel geometry adjustment. Channel sediment is shaped by
persistent short-term fluvial and colluvial processes that organize the bed surface upon a
slope set by longer-term erosion processes (Scatena 1995).
135
Despite the abundance of large boulders throughout the basin, much of the
interstitial bed-material among these boulders is readily mobile. The lack of correlation
between excess dimensionless shear stress and discharge (Figure 4.7) suggests that
throughout the stream network, these are “threshold channels” that are capable of
mobilizing moderately-sized sediment during bankfull floods. The constancy of excess
shear stress is a feature commonly associated with alluvial channels that can readily
adjust slope (Dade 2000). However, Luquillo streams are evidently adjusted to be
threshold channels, despite a geologically-imposed slope. Yet on longer time scales, the
profile slope of these upland channels changes over the course of drainage network
evolution. The upland channels adjust slope to the underlying lithology and consequently
influence the type of sediment that is delivered to the channels. The combination of these
processes and scales suggest that the resulting channel morphology is not exclusively
controlled by a single factor.
CONCLUSION
The morphology of the stream channels in the Luquillo Mountains are influenced
by a combination of both local lithologic controls and strong hydraulic forces. Slope and
grain size in many headwater areas are imposed by properties of the underlying lithology
and coarse sediment delivery by landslides. Longitudinal profiles and concavity are
strongly related to lithologic boundaries. At the reach-scale, non-fluvial factors such as
bedrock outcrops, knickpoints, and fault bends locally affect the channel morphology.
Hillslopes are strongly linked to channel dynamics and colluvial processes are dominant
in many headwater areas.
136
Within the framework set upon by local and non-fluvial constraints, there are
many basin-scale patterns that indicate the river functions similar to a fully alluvial river.
The presence of strongly developed hydraulic geometry relationships, grain size patterns
organized to slope and discharge, and high stream power relative to channel resistance
indicate the influence of overruling fluvial forces. Furthermore, excess dimensionless
shear stress at bankfull wavers around the threshold for sediment mobility indicating the
river is able to systematically transport sediment and organize its own morphology. These
basin-scale patterns attest to the ability of the forceful flow regime generated by the
humid tropical climate to sculpt mountainous streams that share some commonalities
with alluvial rivers.
ACKNOWLEDGEMENTS
The authors would like to thank Doug Jerolmack for his strengthening comments
on an earlier version of this manuscript. We also thank the International Institute of
Tropical Forestry for logistical support. Funding for this study was provided by the
National Science Foundation Biocomplexity Grant (NSF #030414)—Rivers, Roads, and
People: Complex Interactions of Overlapping Networks in Watersheds.
137
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CHAPTER 5
MULTISCALE LINKAGES BETWEEN GEOMORPHOLOGY AND AQUATIC HABITAT IN A TROPICAL MONTANE STREAM NETWORK PUERTO RICO*
A.S. Pike, C.L. Hein, F.N. Scatena, T.A. Crowl, J.F. Blanco, and A.P Covich
ABSTRACT
Linkages between stream geomorphology and migratory aquatic fauna at the
network and pool scales were investigated in three tropical montane watersheds draining
the Luquillo Mountains in Northeastern Puerto Rico. A total of 113 pools at 33 sites,
capturing the full range of stream size and geomorphic conditions in the region, were
physically surveyed and sampled for fish, shrimp, crabs, and snails. In addition, similar
data at 49 pools in a 1st order headwaters stream were used to investigate local-scale
variability in geomorphology and decapod abundance. Principal components analysis
(PCA) identified two geomorphic gradients that account for 59% of the variance in
among-pool variation at the landscape scale, a longitudinal gradient and a substrate
* This chapter contains work and analysis (including a combined dataset) developed in collaboration with Catherine Hein of Utah State University. Portions of this chapter will be submitted for publication in two separate, though complementary, journal articles. One of these is lead-authored by the author of this dissertation, and the other is lead-authored by Catherine Hein.
1. A.S. Pike, C.L. Hein, F.N. Scatena, T.A. Crowl, J.F. Blanco, A.P. Covich. Multiscale linkages between geomorphology and aquatic habitat in a tropical montane stream network, Puerto Rico.
2. C Hein, C.L., A.S. Pike, J.F. Blanco, F.N. Scatena, K.R. Sherrill, A.P. Covich, and T.A. Crowl. Influence of road networks on the community structure of diadromous fauna in tropical island streams.
Several analyses presented in this chapter are modified from the second publication (in preparation), on which I am a second author. These include Table 5.1, Figure 5.2, Table 5.4, Figure 5.3, Figure 5.6, and Table 5.5. These tables and figures illustrate concepts on the distribution of the presence/absence of species that are discussed in Hein et al. (in prep). However, the modified analyses presented in this chapter are unique, due to use of different statistical methods and models on the same dataset, and represent the work of the author of this dissertation. Because they intend to illustrate similar concepts and conclusion to those drawn in Hein et al. (in prep), the presentation of these results and figures are referenced appropriately in the text and captions, as Hein et al. (in prep). Further data analysis, interpretation of results, and all manuscript writing were done by the author of this disseration
149
gradient, and correlations with species presence/absence indicate strong habitat
preferences along these gradients. Non-parametric multiplicative regression (NPMR)
models of species presence/absence estimating the probability of occurrence along the
longitudinal profiles confirmed these patterns and indicate abrupt transitions in
community composition at waterfalls. Predatory fish (gobies, mullets, sleepers, eels)
occupied reaches below waterfalls that hinder their upstream migration, whereas decapod
(atyid and palaemonid shrimp, and crabs) were more common in the headwater reaches
above waterfalls, suggesting that longitudinal gradients are more important than pool-
scale geomorphic and hydraulic factors in governing species distributions at the
landscape scale. At the network-scale above waterfalls, where predatory fish are absent,
the main geomorphic gradients determined by PCA do not adequately predict the
abundance of decapods. At the pool-scale, Stepwise Multiple Linear Regression (SMLR)
models indicate that pool size and substrate characteristics influence decapod
abundances. Furthermore, pool-size and pool-spacing were found to vary predictably
with drainage area. However, gradient analysis shows that the geomorphic features
structuring aquatic habitat do not always vary systematically throughout the stream
network, and are rather patchy at all scales. Our results contrast with the River
Continuum Concept, which argues that stream assemblages vary predictably along steam
size gradients. Rather, our findings are more consistent with more recent ecological
theories (Process Domains Concept, Network Dynamics Hypothesis and Hierarchical
Patch Dynamics perspective) that address naturally occurring breaks in the geomorphic
and network continuum and recognize the importance of each stream’s hierarchical
pattern of habitat transitions.
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INTRODUCTION
Understanding how geomorphic processes influence the spatial distribution and
abundance of aquatic fauna throughout a stream network are important aspects of stream
ecology (Gorman and Karr 1978, Angermeier and Karr 1983, Power et al. 1988, Newson
and Newson 2000, Walters et al. 2003). Overlapping habitat preferences of interacting
organisms not only determine community composition, but their interactions can also
structure food webs and affect ecological function (Reagan and Waide 1996).
Information on habitat preferences of species and the geomorphic processes structuring
stream habitat are critical for developing conservation targets to protect riverine and
coastal ecosystems, particularly in ecologically sensitive tropical streams. As the threat to
tropical freshwater ecosystems increases through dam-building and landuse changes
(Holmquist et al. 1997, Pringle and Scatena 1998, Pringle et al. 2000, March et al. 2003,
Anderson-Olivas et al. 2006, Greathouse et al. 2006), knowing how aquatic organisms
respond to geomorphic gradients in relatively pristine river networks provides the
baseline information needed for conservation and restoration efforts.
Several theories have been proposed to describe the linkages between the
geomorphology of streams and the spatial distribution of aquatic organisms. These
emphasize the roles of systematic longitudinal gradients (Vannote et al. 1981), patchiness
and heterogeneity (Pringle et al. 1988, Townsend 1989, Crowl et al. 1997), hydraulics
(Statzner and Higler 1986), geomorphic disturbance (Montgomery 1999), multiscale
habitat formation (Wu and Loucks 1995, Poole 2002), and river network structure (Benda
et al. 2004). Although they apply to many stream systems worldwide, it is not known
whether their predictions of species distributions hold in tropical island streams where
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migratory aquatic fauna interact with short, steep-gradient, frequently-flooded channels
that are punctuated by waterfalls. To test whether some specific predictions of these
theories hold in tropical mountain streams, this study builds on a prior analysis of
geomorphic controls on food web structure (Hein et al., in prep) by including geomorphic
gradients at the drainage network and pool scales in the streams of the Luquillo
Mountains of Puerto Rico, and investigates the consequent response on the distribution
and abundance of fish and decapods.
The River Continuum Concept (RCC) relates longitudinal variations in aquatic
communities to systematic downstream changes in river systems (Vannote et al. 1981),
and is arguably the most influential perspective in stream ecology. The work was
primarily concerned with streams in humid temperate areas and predicts that aquatic
habitat, food sources, and populations shift downstream in response to longitudinal
changes in channel morphology, canopy openness, light, substrate size, and stream flow.
The RCC posits these physical variables present a gradational continuum of habitat
conditions that control aquatic community composition from small headwater streams to
large floodplain rivers. Although the RCC has been an effective framework for
understanding stream attributes within large drainage networks, longitudinal relationships
in Puerto Rico streams may be obscured by local factors within parts of networks and
may not apply to smaller drainage basins (Covich et al. 1996, Greathouse and Pringle
2006).
Other researchers have promoted the concept of patch dynamics to characterize
geomorphic patterns and processes in heterogeneous stream environments (Pringle et al.
1988, Townsend 1989). Many biological communities are influence by the division of
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landscapes into patches maintained by either disturbance or spatial transition in the
processes creating and maintaining habitats. This approach has been useful for comparing
conditions and communities within and between patches and at spatial scales ranging
from microhabitats within pools to heterogeneous reaches. However, a key problem in
applying patch dynamics concepts in montane watersheds is the lack of ability to predict
areas in the network that are characterized by different patch-forming processes.
Still, other researchers argue that neither the RCC nor patch dynamics explicitly
address the spatial structure of geomorphic controls on aquatic habitat. Statzner and
Higler (1986) suggested that hydraulic forces regulate communities on a world-wide
scale. From the source to the mouth of a stream, zones of transition in ‘stream hydraulics’
occur, to which the general patterns of stream faunal assemblages can be related.
Montgomery (1999) proposed the Process Domain Hypothesis (PDC), which
hypothesizes that spatial variability in geomorphic processes governs stream habitat and
disturbance regimes that influence ecosystem structure and dynamics. Process domains
are predictable areas of the landscape within which distinct geomorphic processes operate
and thereby impart spatial variability to lotic communities at the landscape scale.
Identification of these processes can provide a mechanistic understanding of the
distribution of habitats and stream biota predicted by the river continuum and patch
dynamics model.
Recent work has attempted to unify both the concept of patches and longitudinal
continuums at coarse spatial scales. The “Hierarchical Patch Dynamics” perspective (Wu
and Loucks 1995, Poole 2002), and “Network Dynamics Hypothesis” (Benda et al. 2004)
progress beyond the RCC’s linear perspective by considering the stream system as a non-
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linear network. These models have been developed in montane landscapes and describe
each river network as a discontinuum comprised of a longitudinal series of alternating
stream segments with different geomorphological structures. Each confluence in the
stream network punctuates the discontinuum because the sudden change in stream
characteristics creates a gap in the expected systematic pattern of downstream transitions.
Furthermore, the hierarchical patch dynamics perspective asserts that differences in
geomorphic processes structuring aquatic habitat at varying scales give rise to patchiness,
so dividing otherwise systematic gradients in aquatic habitat into a series of multiscale
patches. This discontinuum view recognizes general trends in habitat characteristics
along the longitudinal profiles, but understands the importance of each stream’s
hierarchical pattern of habitat transitions.
Although the RCC and these other hypotheses of geomorphic and biological
linkages have been effectively applied to many stream systems worldwide, mountainous
tropical island streams have several unique physical characteristics that may affect
whether they conform to such predictions (Smith et al. 2003). First, tropical islands tend
to have short, steep drainages, often entering the ocean as low or mid-order streams. They
typically display few river forms (sensu Rosgen 1994) as they flow from the mountains
steeply to the coast. Consequently, they do not vary as a continuum from headwaters to
very large rivers, and longitudinal patterns could differ from continental drainages by
being truncated (Greathouse and Pringle 2006). Second, humid tropical streams often
have frequent torrential flows. Intense tropical precipitation generates stochastic, high
power flow regime, with large floods occurring several times per year. Organisms must
be able to tolerate such a disturbance regime and may seek out habitat that provides
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refuge from swift flow. Third, the steep channels display strong heterogeneity of
geomorphic and hydraulic conditions from reach to reach. The channels have complex
downstream hydraulic geometry and grain size relationships (see Chapter 4), and the
mountainous reaches generally lack floodplains that provide critical ecological and
biogeochemical linkages in many rivers. Furthermore, longitudinal profiles are typically
punctuated by waterfalls, which may pose a barrier to upstream migration.
The aquatic organisms in tropical montane streams may be especially sensitive to
geomorphic gradients because most freshwater species are diadromous, requiring
migration between freshwater and salt waters to complete their life cycles (Covich 1988).
Freshwater assemblages of fish and macroinvertebrates on tropical islands are generally
dominated by migratory freshwater shrimps, fish, eels, and snails (Bass 2003, Smith et al.
2003). The freshwater shrimps spend adulthood in the headwaters and release eggs
during high flow to be transported to the saline waters of the estuary (March 1998,
Benstead et al. 2000). As juveniles or post-larvae, they migrate upstream. Most tropical
island fishes are catadromous and spend their adulthood in the rivers but migrate to the
estuary to spawn, after which the juveniles migrate upstream to feed (Covich and
McDowell 1996). Since all these species they must ascend the river network, hydrologic
and geomorphic barriers may be important in determining their distributions.
In this study, we investigate relationships between geomorphology and aquatic
fauna in the streams draining the Luquillo Mountains in northeastern Puerto Rico. This
study complements a companion paper (Hein et al., in prep) that assesses changes in
community composition at the landscape scale, and biological interactions between
species. Expanding upon those results, this study investigates the biological response to
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key geomorphic gradients that structure aquatic habitat at the network and pool scales.
We have four primary objectives. First, we identify the key geomorphic gradients present
at the network and pool scales in three relatively pristine tropical montane stream
networks. Second, we investigate how influential these gradients are to determining the
distribution of species. Third, we use the best landscape-scale predictors of species
presence/absence to map the longitudinal distribution of species. Fourth, at pools where
decapods are present, we develop models to predict their relative abundances based on
local-scale pool geomorphology. We ultimately compare our results with predictions of
existing conceptual models of the longitudinal variations in aquatic habitat.
STUDY AREA
This study was conducted in the streams draining the Luquillo Mountains in
Northeastern Puerto Rico. The Luquillo Mountains rise steeply from sea-level to over
1000m in elevation over a distance of 15 to 20km. The streams have their headwaters in
the Luquillo Experimental Forest (LEF), a 113km2 protected forest reserve under the
management of the United States Forest Service. The study area consists of three adjacent
watersheds draining the LEF: Río Mameyes, Río Espiritu Santo, and Río Fajardo (Figure
5.1). The watersheds are similar physiographically, although they vary in size. Drainage
areas of these watersheds are 44km2, 92km2, and 67km2, respectively.
The climate is maritime subtropical, with mean annual temperatures ranging from
an average of 22ºC in the winter to 30ºC in the summer (Ramirez and Melendez-Colom
2003). Due to the tropical climate, stream temperature displays little longitudinal and
temporal variation. Rainfall is near-daily occurrence, and mean annual rainfall is
156
Figure 5.1 a) Location of 33 sample sites located in three adjacent watersheds draining
the Luquillo Mountains in northeastern Puerto Rico. Sites were chosen to span the
longitudinal gradient from the headwaters to near the coast. The location of the first
major waterfalls, representing a barrier to aquatic migration, are also shown. b) At each
sample site, between 2 and 4 replicate pools were surveyed, allowing comparison of the
variability among pools within a reach, and pools between reaches. c) An additional 49
pools in Quebrada Prieta, a 1st order headwater stream, are used in this study to compare
the variability of pools within a homogenous reach.
157
Figure 5.1 cont.
158
approximately 3500mm/yr at mid-elevations (Garcia-Martino et al. 1996). The climate is
weakly seasonal, and high-intensity rainfall events and floods can occur in any given
month (Schellekens et al. 1999).
Similar to montane streams in the Greater Antilles in general, Luquillo streams
have steep gradients, channels lined with coarse boulder-sized sediment, numerous
bedrock cascades, and abrupt waterfalls (up to 30m in height) (Ahmad et al. 1993). First-
order perennial streams have bouldery channels in steeply sloped reaches, and clay and
soil-lined channels in reaches with more gentle slopes. Second and third-order streams
have steep gradient reaches, exposed bedrock channels, large boulders, and periodic
waterfalls. Many of the upland streams are characterized by cascade and step-pool
morphologies, whereas the lower reaches are plane bed and pool-riffle sequences (sensu
Grant et al. 1990, Montgomery and Buffington 1997, Trainor and Church 2003). Due to
rapid decomposition, these channels lack the large coarse woody debris dams that create
aquatic habitat in many channels in humid temperate environments (Covich and Crowl,
1990). Fourth and fifth-order streams occur only at lower elevations along the coastal
plain as gentle gradient pool-riffle sequences.
There are three dominant lithologies underlying the study region: volcaniclastics,
granodiorite, and coastal plain alluvium (Seiders 1971). The morphology of both the
stream channel and the banks, as well as the composition of the channel bed, are directly
related to the underlying lithology (Ahmad et al. 1993). Streams draining volcaniclastics
are steep and typically have a bed composed of large boulders (up to several meters in
diameter), interspersed with finer cobbles and gravels, as well as sporadic bedrock
outcrops (see Chapter 4). In contrast, streams draining granodiorite are almost entirely
159
composed of sand and large case-hardened boulders that can be several meters in
diameter. Streams flowing across the coastal plain alluvium typically have a
comparatively gentle gradient compared to channels on bedrock, and have channel beds
composed of cobbles, gravels, and fines
All three watersheds currently drain protected primary forest in their headwaters,
mature secondary forest at lower elevations, and both abandoned (reforesting)
agricultural fields/grazing pastures and urbanized developments along the coastal plain.
Each river flows through a mangrove-lined estuary before reaching the coast. Despite
some development along the coastal plain, these watersheds are considered among the
most pristine in Puerto Rico (Santos-Román et al. 2003). Both the Rio Espiritu Santo and
Rio Fajardo have a low-head dam spanning the main stem at low elevations, and the Rio
Mameyes has a non-dam water intake device. However, these obstructions do not pose a
significant barrier to upstream migration (March et al. 2003).
Water quality often plays a strong role in species distributions, but the quality of
water in the study watersheds is relatively pure. Water chemistry data indicate that major
cations and anion concentrations do not exceed water quality standards (McDowell and
1994, Scatena et al. in review). Islandwide, water quality is generally covariate with land
use and discharge (Santos-Román et al. 2003). Forested upland watersheds have
relatively low nutrient concentrations (McDowell and Asbury 1994). In contrast, nutrient-
rich sewage effluent and/or agricultural runoff often released into lower elevation streams
creates poorer water quality conditions near both urbanized and agricultural/pasture areas
(Scatena 2001). Although there is some development at lower elevations, the high
discharge of these rainforest streams significantly dilutes the relatively small amount of
160
effluent delivered to the channels, and water quality does not change appreciably along
the length of the Rio Mameyes (Ortiz-Zayas et al. 2005). Furthermore, the presence of
freshwater snails—a sensitive bioindicator of water quality—at lower and mid elevations
in all of the study streams confirms the strong chemical integrity of these rivers (Blanco
and Scatena 2006). Consequently, it is assumed that any variations in water quality
between study sites is minor and that it should correlate with the landcover of the
upstream catchment.
Stream Community
Similar to other pan-tropical islands, the stream community in the Luquillo
mountains consists predominantly of diadromous fishes including gobies, sleepers, and
eels, atyid and palaemonid shrimp, freshwater crabs, and neritid snails. All of these
species are require direct linkages between fresh and salt water to complete their life
cycles. Diadromous migrations of fishes are thought to be a response to difference in
aquatic productivity between oceanic and stream habitats. Gross et al. (1988) notes that
catadromy is prevalent at tropical latitudes because fresh waters are more productive
relative to the ocean and fish consequently migrate upstream to feed.
One species of river goby (Awaous Tajasica) and two species of sleepers
(Gobiomorus dormitor and Eleotris pisonis) are common at low elevations (Covich and
McDowell 1996). These gobies ascend rivers as juveniles and return to brackish waters to
spawn. Both the river goby A. tajasica and bigmouth sleeper G. dormitor are bottom-
dwelling fish and feed on juvenile shrimp and aquatic insects. The spinycheek sleeper, E.
pisonis, is found in shallow, muddy, and sandy bottom streams, and prefers estuarine
environments and low-elevation tributaries with little to moderate salinity.
161
The mountain mullet (Agonostomus monticola) is an omnivorous fish found at
low to intermediate elevations and less steeply sloped channels. Mountain mullets move
downstream to spawn in brackish waters but spend most of their adult lives in fresh
water. They are well suited for life in turbulent mountainous streams with deep pools and
feed on shrimp, insects, and algae. Another major predator at low to mid-elevations is the
American eel (Anguilla rostrata). Eels primarily feed on shrimp, aquatic insects, and
small fishes, and typically grow to 50-60cm in length. Worldwide, fifteen species of eels
all share the same basic life cycle: they spawn at sea and after long-distance migrations
from the ocean (specifically from the Sargasso Sea in the Atlantic Ocean for American
and European eels), juveniles ascend upstream to specific areas for feeding (Erdman et al.
1984). The sirajo goby, Sicydium plumieri, is common throughout the streams,
particularly at higher elevations (Keith 2003). These fish are herbivorous and actively
graze on periphyton. Juveniles migrate upstream from the seas and grow to 15cm as
adults. They can climb up the edges of steep waterfalls using their specialized mouth
parts and pelvic fin sucking discs to ascend to high elevations (Covich and McDowell
1996). In Hawaii, similar gobies have been observed climbing vertical waterfalls up to
350m high (Schoenfuss and Blob 2003).
Atyid shrimp are the most abundant large consumers in these streams (Crowl et
al. 2000). There are three primary species of Atya: A. lanipes, A. innocuous, and A.
scabra. They are considered both scrapers and filter feeders, feeding on leaf litter, algae
and lichen, and suspended particles in flowing water (Covich and McDowell 1996). The
common A. lanipes and A. innocuous spend most of their time near the substrate in pools,
whereas the rare A. scabra prefer riffles. It is thought that atyid shrimp prefer stream
162
channels with large boulders in which they can penetrate crevices to feed and escape
larger predators (Covich et al. 1996). A fourth species, Xiphocaris elongata (Yellow-
nosed shrimp), is also very common throughout the streams. They are shredders, using
small saw-like pinchers to shred pieces of leaves (Crowl et al. 2006). They are very
active swimmers and spend most of their time in the water column. Juveniles migrate
upstream in masses along the wetted channel margin and they are able to climb sheer
waterfalls (March et al. 2003). Palaemonid shrimp, Macrobrachium spp., are widespread
throughout Caribbean and gulf coasts of the neotropics (Bass 2003, Covich et al. 2006),
and several species of this genus inhabit the Luquillo streams. Macrobrachium carcinus
(bigclaw river shrimp) is one of the largest (Covich and McDowell 1996), and are
generally found in upper portions of rivers where currents are rapid. Macrobrachium
species are omnivorous; usually feeding on detritus and on smaller shrimps. An
additional four species, M. acanthurus, M. crenulatum, M. heterochirus, and M.
faustinum have been reported in the streams (Covich et al. 2006). Females produce larvae
that must reach brackish water to complete their development before returning upstream
to grow. They can spend short amounts of time outside of water provided that the
relatively humidity is high, and can consequently climb waterfalls along channel margins
(Covich et al. 2000).
The amphibious crab, Epilobocera sinuatifrons, completes its life cycle entirely in
fresh water–a unique life cycle for crabs (Covich and McDowell 1996). They are usually
found in gravel beds and leaf packs in streams, although adults can move on land. They
are widespread in greater Antilles between 100m and 300m. Lastly, the neritid snail,
Neritina virginea, is common at low elevations in larger order rivers. They feed on lichen
163
and algae on boulders, and migrate slowly upstream in masses along wetted boulders and
channel margins (Schneider and Lyons 1993, Blanco and Scatena 2005). Damaged shells
indicate they occasionally fall prey to predatory fish. They require hydrologic
connectivity between the sea and the estuary; they are absent in Puerto Rican rivers that
have active sand-bars at the coast blocking access to the ocean (Blanco and Scatena
2006). They are also highly sensitive to water quality changes, and their migratory
passages may be blocked by poorly constructed road crossings (Blanco and Scatena
2007).
METHODS
Field Methods
In order to capture the landscape, reach, and pool-scale influences of
geomorphology on aquatic biota, we utilized two datasets in this study. The first dataset,
developed and sampled in collaboration with Catherine Hein and Dr. Todd Crowl of Utah
State University, consists of complementary geomorphic survey data and biological
sampling at 113 pools located at 33 sites in three watersheds (Figure 5.1). Due to the
breadth of these sites throughout the stream network, this data was used to assess the
influence of landscape and reach scale geomorphology. The second dataset consists
geomorphic and biological surveys at a series of 49 pools in a 1km reach in Quebrada
Prieta, a mid to upper elevation 1st order headwater channel. Relationships between the
geomorphology and the abundance of decapods using this second dataset shows the
influence of local-scale pool geomorphology on decapod abundance, as the reach is
relatively homogenous and does not vary along any landscape-scale gradients. This
164
dataset was collected by Coralys Ortiz Maldonado and María Ocasio, two undergraduate
interns that were supervised by the authors of this paper as part of the Research
Experience for Undergraduates (REU) program, and is described in further detail in
Ortiz-Maldonado (2005).
We physically surveyed the stream channel and sampled for fish, shrimp, crabs,
and snails at 33 sites in three watersheds: Rio Espiritu Santo, Rio Mameyes, and Rio
Fajardo. Sites were selected to capture the range of geomorphic conditions present
throughout the stream network, ranging from upper-elevation headwater streams
(drainage area, 0.1km2) to lowland rivers (58km2). At each site, two to five pools were
sampled for a total of 113 pools. Pools were surveyed and sampled during the summer
months (June-August) of 2004-2006.
A combination of trapping and electrofishing methods were used to sample the
fish, decapods, and snails (Hein et al., in prep). Trapping was done according to the
procedures developed by the Luquillo LTER program (http://luq.lternet.edu). All
individuals were identified to the species level, except juvenile Macrobrachium, which
were excluded from this analysis (Table 5.1).
To describe the geomorphic environment of each sampling reach, we measured
and estimated a total of 57 geomorphic, hydraulic, and topographic variables using a
combination of field measurements and GIS analysis (see Appendix). In the field, cross-
sections were surveyed at each pool at a uniform section. Relative distance and elevation
were measured at evenly spaced intervals along a transect spanning from vegetated bank
to bank. Channel top width, average depth, and cross-sectional area at both baseflow and
active-channel (bankfull) stage were calculated for each cross-section. Pool length, and
165
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166
variation of depth of five points along the thalweg were also measured. Channel slope
was similarly measured as the difference in elevation of the water surface over 10
uniformly spaced points spanning approximately five channel widths throughout the
reach.
Both baseflow and active-channel (bankfull) discharge at each cross-section was
estimated by a regional elevation/drainage area based equation derived from long-term
stream gage data (Pike 2006). Combining discharge estimates with channel
measurements, we estimated several hydraulic variables: stream power, unit stream
power, and the Darcy-Weisbach friction factor (see chapter 4). Grain size in the channel
was estimated using a modified Wolman Pebble Count method (Wolman, 1954).
Approximately 100 clasts were selected randomly by pacing across the width of the
stream. The median diameter of each clast was measured, and classified into the
following seven size categories: megaboulder (>2000mm), boulder (256-2000mm),
cobble (64-256mm), gravel (2-64mm), sand (.063-1mm), and fines (silt/clay, 0.001–
0.063 mm). Bedrock was also included in the count. From these grain size measurements,
we determined the median grain size (d50), coarse grain size (d84), and the percent of
bedrock exposed along the perimeter of the active channel.
Landscape-scale variables were estimated using a 10m digital elevation model
(DEM) and flow accumulation algorithms (Pike 2006). These included the distance from
the headwaters and the coast, drainage area, map slope, and the largest downstream drop
(an indicator strongly correlated with waterfall height). Furthermore, land-cover
characteristics (percent of agriculture/pasture, forest, and urbanization) were estimated
167
for both the upstream catchment and the downstream zone of influence affecting the
migratory corridor.
Pool Length and Spacing
Since pools are the primary habitat for most fish and shrimp in these streams,
quantifying the size and spacing of these pools varies across the landscape is needed to
predict their distribution. Seven stream segments, between 0.5km and 2.5km in length,
were surveyed. These segments spanned a range of stream size, from 1st order headwaters
streams to larger low-elevation main river channels. We walked the distance of each
segment, measuring the length of every pool with a tape measure, as well as measuring
the spacing between pools. The drainage area of the reach was estimated as the average
of the drainage area at the bottom and top ends of the reach.
Statistical Analyses
Principal Components Analysis
We quantified geomorphic differences between pools using principal components
analysis (PCA) (PC-ORD 4 software; MjM Software Design, Glendale Beach, OR,
USA). PCA is a technique used to reduce multidimensional data sets to a fewer number
of variables. It is particularly useful to identify key gradients in large data sets when
many of the measured variables are covariate. PCA is mathematically defined as an
orthogonal linear transformation that transforms the data to a new coordinate system such
that the greatest variance by any projection of the data comes to lie on the first coordinate
(called the first principal component), the second greatest variance on the second
coordinate, and so on. PCA involves the calculation of the eigenvalue decomposition of a
data set to weight a linear combination of variables that form the principal components.
168
This technique relies on least squares methods, and as such, requires that input variables
be relatively normal in their distribution. Prior to analysis, all non-normal input variables
were transformed using a Box-Cox transform (Box and Cox 1964) to achieve
approximate normality. PCA was used here to reduce the 57 geomorphic variables, many
of which are related and covariate, into a reduced number of factors that reflect the
primary gradients describing the geomorphic environment. To assess geomorphic
linkages to aquatic biota, we then related the presence/absence, and relative abundance of
species to these principal components.
Non-parametric Multiplicative Regression (NPMR)
We predicted the probability of species occurrence at unsampled reaches
throughout the stream network using a habitat model based on non-parametric
multiplicative regression (NPMR) (Hyperniche 1.0 software, MjM Software Design,
Glendale Beach, OR, USA). NPMR is a multivariate regression in which the response is
estimated from a multiplicative combination of all predictors, rather than the usual least-
squares multiple linear regression or generalized additive models. The multiplicative
approach captures the natural complexity of species distribution along multiple
environmental gradients. If any of the predictor variables are not conducive to the
presence of the species, then the multiplicative model predicts that species response is
also zero. It simply represents the axiom that organisms must simultaneously meet all
environmental challenges or die (McCune 2006). Furthermore, NPMR estimates response
curves based on one or more humped-shaped functions (Gaussian or sigmoid curves),
which allows the model to capture non-linear responses to environmental gradients and
threshold boundaries. NPMR is an ideal technique for ecological abundance data with
169
zero as a natural lower bound, and has been used effectively to predict abundance of fish
populations (Wedderburn et al. 2007), and the distribution of lichens and forest
populations (McCune 2006).
We developed models for each species using presence/absence data at the site-
scale, rather than pool-scale. This was done for two reasons. First, aggregating pool-scale
presence/absence data to the site scale decreases the likelihood of incorrectly classifying
a species as absent and consequently creates better models. Second, results of the model
were applied to a GIS grid where only landscape-scale factors can be calculated at non-
surveyed sites. The following 13 landscape-scale factors were used: elevation, drainage
area, active channel discharge, distance to headwaters and coast, reach slope, maximum
downstream drop, stream power, maximum downstream stream power, and the
percentage of agriculture, forest, and urbanization upstream and downstream. Each
individual model that was fitted to each species based on landscape scale factors was
applied to a 10m-resolution GIS grid. Values from the grid were extracted at regular
intervals, and plotted as a longitudinal profile.
For each species, a two-parameter model with the best goodness-of-fit was
chosen. Using more than two predictor variables did not add additional goodness-of-fit at
the expense of model complexity (based on a data:predictor ratio minimum and
improvement criterion). With presence/absence data, goodness of fit is determined
through the Bayes factor, log-likelihood ratios (Log-B), a descriptive statistic of “weight
of evidence”. Values of logB reported here are based on internally cross-validatation,
where the point being estimated is excluded from the model to reduce overfitting.
Significance of relationship is determined based on performance over the “naïve model”
170
(McCune 2006), which assumes that the probability of occurrence for all points is the
same at all sites and is the average probability that species.
Stepwise Multiple Linear Regression (SMLR)
The role of reach and pool-scale geomorphic variables on decapod abundance was
also investigated using Stepwise Multiple Linear Regression (SMLR). SMLR is a
technique for choosing the variables to include in a multiple linear regression model.
Forward stepwise regression starts with no model terms, and adds the most statistically
significant term (the one with the highest F statistic or lowest p-value), one step at a time,
until no added variables appreciably increase the residual sum of squares. Although it is
recognized that both the choice of stepwise procedure (forward, backward) and
inclusion/exclusion of starting variables can affect the outcome of the stepwise regression
procedure, we used all the measured geomorphic variables. The 57 Box-Cox transformed
geomorphic variables were used as inputs to predict the logarithm-transformed relative
abundance of each decapod species at pools where they were present. For the Quebrada
Prieta dataset, the 20 geomorphic variables measured at each pool were similarly used as
inputs to predict decapod abundances.
RESULTS
Species Distribution in Geomorphic Space at Network and Pool-Scales
For all 113 pools, the 57 geomorphic variables were reduced to 3 significant
principal component axes that explain a total of 59% of the variance. The first axis
explains 33% of the variance and the second axis explains the remaining 26% of the
variance. A third significant axis explains 9% of the variance, but was not considered in
171
this analysis. Other principal components individually explained less than 6% of the
residual variance, and were also not considered further.
Based on strength of the eigenvalues for each variable, we determined which
geomorphic variables most importantly contribute to each principal component, and
interpreted each axis accordingly (Table 5.2). Principal component 1 is interpreted as a
“longitudinal axis” as it is positively correlated with drainage area (eigenvalue = 0.22),
active channel discharge (0.22), elevation (0.17), distance from the headwaters (0.22) and
the length (0.21), area (0.22), and volume (0.22) of the pool. Principal component 2 is
interpreted as a “hydraulic axis”. It is most strongly correlated with median grain size
(eigenvalue = 0.22), shear stress (0.18), and stream power (0.23), and negatively
correlated with the proportion of fine sediment (-0.16), and proportion of agricultural
land cover in the upstream catchment (-0.19).
Pools plotted along these “longitudinal” and “hydraulic” axes fall into three
general clusters (Figure 5.2). Pools plotting in the upper left hand side of the ordination
have low values of PC1 and higher values of PC2. They represent steep, gradient
headwater pools that have high stream power and large boulders. In contrast, pools
plotting on the right hand side of the ordination (high values of PC1, decreasing values of
PC2) represent mid-to-low elevation pools along the main stem. The cluster of pools
plotting in the middle of the ordination, having intermediate values of PC1 and low
values of PC2, represent lowland tributaries that are of intermediate drainage area, but are
generally low gradient, have low stream power, and contain a higher proportion of fine
sediment.
172
Table 5.2 Eigenvalues for 58 geomorphic variables, indicating the relative influence of
each variable on principal components 1 and 2. Note that axes are better correlated with
landscape-scale variables rather than pool scale factors. (Modified from Hein et al., in
prep).
173
Table 5.2 cont.
Description Unit PC1 PC2 Elevation m -0.17 0.15 Average Basin Elevation m -0.02 0.22 Drainage Area km2 0.22 0.02 Distance from Ocean km -0.11 0.13 Distance from Headwaters km 0.22 0.01 Slope m/m -0.18 0.14 Maximum Downstream Drop m -0.16 0.14 % Agriculture Cover Downstream % 0.09 -0.08 % Agriculture Cover Upstream % 0.06 -0.19 % Forest Cover Downstream % -0.12 0.12 % Forest Cover Upstream % -0.08 0.19 % Urban Cover Downstream % 0.05 -0.07 % Urban Cover Upstream % 0.12 -0.14 Pool Maximum Depth m 0.14 0.11 Coefficient of Variation for Depth unitless 0.01 0.04 Pool Length m 0.21 -0.01 Pool Surface Area m2 0.22 0.03 Pool Volume m3 0.21 0.05 Baseflow Discharge m3/s 0.21 0.09 Baseflow Width m 0.20 0.07 Baseflow Wetter Perimeter m 0.20 0.08 Baseflow Hydraulic Radius m 0.12 0.10 Baseflow Maximum Depth along Cross-section m 0.11 0.12 Baseflow Cross-sectional Area m2 0.18 0.10 Baseflow Velocity m/s 0.14 0.04 Baseflow Width/Depth Ratio unitless 0.12 -0.01 Active Channel Discharge m3/s 0.22 0.07 Active Channel Width m 0.20 0.08 Active Channel Wetter Perimeter m 0.20 0.10 Active Channel Hydraulic Radius m 0.14 0.11 Active Channel Maximum Depth along Cross-section m 0.11 0.13 Active Channel Cross-sectional Area m2 0.19 0.11 Active Channel Velocity m/s 0.18 0.02 Active Channel Width/Depth Ratio unitless 0.14 0.03 d16, Fine grain size mm 0.00 0.22 d50, Median grain size mm -0.01 0.20 d84, Coarse grain size mm -0.05 0.17 Geometric Mean of Grain Size mm -0.02 0.22 % Bedrock % -0.01 0.11 % Megaboulder % -0.03 0.15 % Boulder % -0.07 0.08 % Cobble % 0.04 0.00 % Gravel % 0.04 -0.12 % Sand % 0.04 -0.13 % Fines % 0.01 -0.17 Simpson Diversity Index of Grain Size Categories -0.06 -0.02 Grain Size Sorting Coefficient -0.02 -0.16 Grain Size Skew Coefficient -0.04 0.05 Baseflow Shear Stress Pa -0.14 0.18 Active Channel Shear Stress Pa -0.15 0.18 Critical Shear Stress Pa -0.02 0.20 Baseflow Stream Power W/m 0.06 0.24 Active Channel Stream Power W/m 0.07 0.23 Baseflow Unit Stream Power W/m2 -0.02 0.23 Active Channel Unit Stream Power W/m2 -0.01 0.22 Baseflow Darcy-Weisbach Friction Factor -0.16 0.03 Active Channel Darcy-Weisbach Friction Factor -0.19 0.06
174
Figure 5.2 Plot of pools in geomorphic space, with species presence indicated by circles
(large circles = presence, dots = absence). Principal component 1 is interpreted as a
measure of longitudinal position (distance downstream, drainage area, and pool size),
whereas principal component 2 is strongly influenced by stream power and grain size.
Pools plotted on these principal component axes cluster into 3 groups: steep headwater
channels, lowland channels along the main stem, and lowland tributaries. Species (see
Table 5.1 for species codes) show distinct habitat preferences along these two axes:
predatory fish are present in lowland channels, atyid shrimp and crabs thrive in headwater
streams, and palaemonid shrimp occupy most pools. (Modified from Hein et al., in prep)
175
Figure 5.2 cont.
176
Plots of the presence and absence of fish and decapod species show that they have
distinct preferences along these “longitudinal” and “hydraulic” axes (Figure 5.2). In
general, major predatory fish (mullets and eels), as well as gobies and sleepers, plot on
the right and lower center of the ordination, indicating their preference for both lowland
main channels and lowland tributaries. Although not clearly evident in this figure, the
pools that these fish species occupy are also below barrier waterfalls that exceed 4m in
height. In contrast to most fish species, the sirajo goby, S. plumieri, inhabits pools that
span a broad longitudinal range, but are absent in lowland tributaries. Among atyid
shrimp, Atya lanipes is strongly confined to and present in most headwater pools,
whereas the other two Atya species occupy some lowland pools in addition to headwater
pools. Xiphocaris is present in virtually all headwater pools, but also occupies some
lowland pools as well. Palaemonid shrimp are present at most pools, reflecting their
ubiquity in these streams, although M. faustinum displays a slight affinity for lower
elevation pools. Lastly, crabs are present in some lowland streams, but are more common
in headwater channels. These patterns indicate that predatory fish and most decapods
(especially atyid shrimp) generally occupy contrasting pools across the landscape; fish in
lowland channels and tributaries, and decapods in steep headwater pools.
At the reach-scale, the relative abundance and proportional abundance of decapod
species were also plotted along the PCA axes (Figure 5.3). Among the pools were species
were present, Principal component 1 (“longitudinal” axis) was well correlated with the
relative abundance of crabs (r2 = 0.51), Macrobrachium spp (r2 = 0.27), and Xiphocaris
(r2 = 0.13). Similarly, principal component 1 correlated with the proportional abundance
of M. faustinum (r2 = 0.32). Among the pools were each decapod species was present, no
177
Figure 5.3 Plot of pools in geomorphic space, with decapod relative abundance (catch
per unit effort) and proportional abundance (% of species in pool) indicated by circles
(size of circle is proportional to abundance, dots = absence). Correlations between
abundance, at the pools where each decapod species was present, and the principal
component axes are shown.
178
Figure 5.3 cont.
179
other significant correlations between principal components and decapod abundances
were observed. Among the pools that were above waterfalls, the only significant
relationship between these geomorphic gradients and decapod abundance was a positive
correlation between the Principal Component 1 and the relative abundance (r2 = 0.22) and
proportional abundance of M. faustinum (r2 = 0.30). At the scale of the headwater stream
network, no further significant relationships between principal components and decapod
abundances were observed.
At the pool-scale, PCA of the pools within the reach of the Quebrada Prieta
reduced 20 geomorphic variables to 2 significant principal components (Table 5.3). The
first principal component explains 26% of the variance, whereas the second principal
component explains 15%, for a total of 41%. The first principal component is interpreted
to be a measure of pool size, as it is most strongly influenced by pool volume (eigenvalue
= 0.39), and depth (0.38). The second principal component is interpreted to be a measure
of substrate suitability, as it the most influential factors are the proportion of gravel (0.42)
and leaves (0.42) composing the substrate.
Correlations between the abundance of Atya, Xiphocaris, and Macrobrachium and
principal components indicate that there are weak patterns along these two gradients
(Figure 5.4). Atya relative abundance is weakly correlated with principal component 2 (r2
= 0.22), indicating a slight preference for pools with gravel and leaves. Xiphocaris was
significantly correlated with both principal components 1 and 2 (r2PC1 = 0.18, r2
PC2 =
0.20), reflecting the influence of both pool size and leaf availability for these shredders.
Similarly, both principal components were modestly correlated with the proportional
abundance of Macrobrachium (r2PC1 = 0.18, r2
PC2 = 0.21), but not correlated the
180
Table 5.3 Eigenvalues for 20 geomorphic variables measured for pools in the Quebrada
Prieta, indicating the relative influence of each variable on principal components 1 and 2.
Based on the strength of the eigevectors, principal component 1 is interpreted as a
measure of pool size, and principal component 2 is interpreted as an indicator of substrate
suitability (% gravel and % leaves).
Quebrada Prieta Data Description Unit PC1 PC2 Distance from Headwaters m 0.17 0.15 Compass Direction degrees -0.02 -0.22 Active Channel Width m 0.22 -0.02 Maximum Width m -0.11 -0.13 Maximum Depth m 0.22 -0.01 Pool Area m2 -0.18 -0.14 Pool Volume m3 -0.16 -0.14 Average Depth m 0.09 0.08 Standard Deviation of Depth m 0.06 0.19 Coefficient of Variance of Depth n/a -0.12 -0.12 % Silt % -0.08 -0.19 % Sand % 0.05 0.07 % Gravel % 0.12 0.14 % Cobble % 0.14 -0.11 % Boulder % 0.01 -0.04 % Leaf % 0.21 0.01 % Organic Matter % 0.22 -0.03 % Open Substrate % 0.21 -0.05 # Pool Entrances # 0.21 -0.09 # Pool Exits # 0.20 -0.07
181
Figure 5.4 Plot of Quebrada Prieta pools in geomorphic space, with decapod relative
abundance (catch per unit effort) and proportional abundance (% of species in pool)
indicated by circles (size of circle is proportional to abundance, dots = absence).
Correlations between abundance, at the pools where each decapod species was present,
and the principal component axes are shown.
182
proportional abundance of Atya (r2PC1 = 0.05, r2
PC2 = 0.07) and Xiphocaris (r2PC1 = 0.05,
r2PC2 = 0.04).
Longitudinal Trends of Species
The NPMR habitat models identified two landscape scale variables for each
species that best combined to make a multivariate response curve (Table 5.4). An
example is shown in Figure 5.5a, where the probability of eels, A. rostrata, was predicted
as a function of both elevation, and the maximum downstream drop in elevation in a
reach. Applying this multivariate response curve to unsampled reaches throughout the
streams network shows a strong spatial trend: eels are common in low to mid-elevation
streams but abrupt drop out near the location of the first waterfall (Figure 5.5b). Those
species that had models influenced by the maximum downstream drop (A. monticola, A
rostrata, A. lanipes) also had the best fits (e.g. highest logB), indicating that their spatial
distribution is highly pronounced and influenced by waterfalls. Other species with
relatively high goodness of fit statistics (A. tajasica, G. dormitor, and X. elongata) were
predicted by either slope or stream power. These are both variables that distinguish
headwater streams from lowland reaches. In contrast, species models that were best
predicted by landcover variables generally had lower goodness of fit statistics suggesting
that land use is not a major factor in these relatively pristine watersheds.
The NPMR habitat models demonstrate that most species have a pronounced
spatial distribution along the longitudinal profiles, either occurring in lowland reaches or
in the headwaters (Figure 5.6). For many species, the location in the stream network
where they drop out is typically abrupt. The estimated longitudinal trends show that
predatory fish drop out at the boundary of the first waterfall on the main stem. Few are
183
Table 5.4 Landscape scale variables used to predict species presence/absence using a
NPMR 2-parameter model. Goodness of fit is determined through log-likelihood ratios
(logB), a descriptive statistic of “weight of evidence”, where higher values indicate a
stronger model. (Modified from regression tree models presented in Hein et al., in prep).
NPMR 2-PARAMETER MODELS Response Eval Ave Predictor Predictor Variable logB Size Variable 1 Tolerance Variable 2 ToleranceAgomon 7.5 3.7 discharge 1.09E+01 max_drop 9.10E-01angros 7.9 3.6 elevation 1.65E+02 max_drop 4.55E-01atyinn 1.1 9.4 discharge 1.82E+01 agr_downstream 4.00E+01atylan 8.3 4.2 reach_slope 3.85E-02 max_drop 9.10E-01atysca 1.3 2.7 max_drop 9.10E-01 agr_downstream 1.50E+01awataj 5.3 4.5 dist_to_ocean 2.14E+03 reach_slope 3.85E-02elepis 2.5 13.1 agr._downstream 4.50E+01 forest_upstream 2.02E+01episin 2.7 8.2 discharge 1.82E+01 forest_upstream 8.07E+00gobdor 5.7 5.3 reach_slope 5.14E-02 forest_upstream 4.03E+00maccar 2.9 3.4 reach_slope 3.85E-02 agr_downstream 1.00E+01maccre 2.5 4.9 dist_to_ocean 4.29E+03 stream_power 5.88E+03macfau 3.0 2.8 max_drop 9.10E-01 stream_power 2.94E+03machet 3.3 5.8 dist_to_headwater 4.00E+03 discharge 7.27E+00sicplu 2.5 4.3 reach_slope 6.42E-02 stream_power 2.94E+03xipelo 5.3 4.2 discharge 1.45E+01 stream_power 5.88E+03nervir 4.2 3.0 reach_slope 2.57E-02 urb_downstream 5.00E+00
184
Figure 5.5 a) Non-parametric multiplicative regression response curve for eels (Anguilla
rostrata). The curve shows a primary response along the maximum downstream drop
axis; eels are most likely present where the downstream drop < 4m, and are absent above
this threshold. Additionally, there is a slight secondary species response along the
elevation axis. Similar curves were constructed for each species, described in Table 5.4.
b) Eel distribution in the Rio Mameyes as estimated by the NPMR species response
curve. The response, based on elevation and maximum downstream drop, was applied
throughout the watershed. Streamlines are scaled according to the estimated probability
of occurrence. Observed presence (gray circles) and absence (white circles) at study sites
are also shown. The map shows eels are present along the main stem and at low
elevation, but drop out abruptly near the first waterfall and are consequently absent in the
upper-elevation headwaters. (Modified from regression tree models presented in Hein et
al., in prep)
185
Figu
re 5
.5 c
ont.
186
Figure 5.6 Longitudinal views of estimated probability of occurrence for each species.
The longitudinal profile of the river (above) shows the location of sample site (circles)
along the main stem (solid line) and tributaries (dashed line). For each species, the
probability of occurrence (between 0 and 1) along the main stem is indicated by the solid
line, whereas the probability of occurrence on the tributaries is indicated by the dashes
lines. Observed presences (black circles) and absences (white circles) at sample sites are
also shown. As an example, moving upstream along the longitudinal profile, eels are
expected to be present along the main stem until the first waterfall at approximately
11km, where the probability abruptly decreases to 0. Observed presences confirm this
trend. Additionally, eels are expected to drop out along mid to upper elevation tributaries,
where the dotted lines connected to the main stem rapidly decrease. (Modified from Hein
et al., in prep)
187
Figure 5.6 cont.
188
present in the adjoining tributaries. The herbivorous goby, S. plumieri, is common on the
main stem at low elevation, and at upper elevations, and typically absent on low to mid-
elevation tributaries. The atyid shrimp, A. innocuous, A. lanipes, and X. elongata are
absent along the main stem below waterfalls, but are present at upper elevations and
along tributaries. In general, predatory fish and atyid shrimp have contrasting
longitudinal trends and rarely co-occur. High-elevation sites above large waterfalls are
dominated by decapods whereas lowland streams are dominated by fish.
Predatory shrimp (Macrobrachium spp.) show a different trend. M. carcinus is
present at most sites except the uppermost pools, whereas M. crenulatum and M.
faustinum are most commonly present on mid-elevation tributaries. Crabs and snails
display contrasting longitudinal patterns. Snails, N. virginea are common along the main
stem at low elevations, whereas crabs, E. sinuafrons, are present along the main stem at
mid-to-high elevations and along tributaries.
Internal cross-validation (estimating a value at a site with that point removed)
shows that all multiplicative models showed significant improvement over the naïve
model (Table 5.5). Models predicting the presence of species that displayed pronounced
longitudinal variation (A. monticola, A. rostrata, A. lanipes, X. elongata) had the best
percentage of improvement over the naïve model, and the most statistically significant.
For ubiquitous species (S. plumieri, M. carcinus) that do not display strong longitudinal
distributions, and for rare species (A. scabra, E. pisonis), the model predictions were not
as strong.
189
Table 5.5 Internal validation statistics for NPMR models, where the point being
estimated is excluded from the model. All models are significant (p > 0.05) over the
naïve model where the probability of a species being “present” is constant across sites.
(Modified from regression tree models presented in Hein et al. 2008)
190
Tab
le 5
.5 c
ont.
N
MP
R 2
-PA
RA
ME
TER
MO
DE
L IN
TER
NA
L V
ALI
DA
TIO
N
(ALL
SIT
ES
)
Ove
rall
Ove
rall
Naï
ve
Est
. E
st.
Impr
oved
N
ot Im
prov
ed
Impr
ovem
ent
N
ame
xR2
Pre
sent
A
bsen
t p
p>na
ïve
p<na
ïve
P
A
Om
iss
Com
mis
%
O
ddsR
at
logB
av
eB
Chi
Sq
p
agom
on
0.71
17
15
0.
53
16
16
15
14
2 1
90.6
9.
7 7.
5 1.
72
34.5
0.
0000
angr
os
0.74
19
13
0.
59
22
10
19
10
0 3
90.6
9.
7 7.
9 1.
76
36.3
0.
0000
atyl
an
0.97
12
18
0.
40
12
18
12
18
0 0
100.
0 n/
a 8.
3 1.
89
38.1
0.
0000
atyi
nn
0.18
13
20
0.
39
19
14
10
11
3 9
63.6
1.
8 1.
1 1.
08
5.3
0.02
17
at
ysca
0.
15
6 24
0.
20
17
13
5 12
1
12
56.7
1.
3 1.
3 1.
11
6.0
0.01
43
aw
ataj
0.
61
12
20
0.38
15
17
11
16
1
4 84
.4
5.4
5.3
1.47
24
.5
0.00
00
el
epis
0.
35
6 27
0.
18
9 24
5
23
1 4
84.8
5.
6 2.
6 1.
20
11.7
0.
0006
epis
in
0.36
18
13
0.
58
21
10
17
9 1
4 83
.9
5.2
2.7
1.22
12
.6
0.00
04
go
bdor
0.
50
13
18
0.42
15
16
11
14
2
4 80
.6
4.2
5.7
1.53
26
.2
0.00
00
m
acca
r 0.
21
24
6 0.
80
22
8 20
4
4 2
80.0
4.
0 2.
9 1.
25
13.3
0.
0003
mac
cre
0.26
14
19
0.
42
16
17
9 12
5
7 63
.6
1.8
2.5
1.19
11
.5
0.00
07
m
acfa
u 0.
20
24
7 0.
77
19
12
18
6 6
1 77
.4
3.4
3.1
1.25
14
.0
0.00
02
m
ache
t 0.
33
13
20
0.39
22
11
13
11
0
9 72
.7
2.7
3.3
1.26
15
.0
0.00
01
si
cplu
0.
28
22
11
0.67
16
17
14
9
8 2
69.7
2.
3 2.
5 1.
19
11.6
0.
0007
xipe
lo
0.69
25
8
0.76
22
11
22
8
3 0
90.9
10
.0
5.4
1.45
24
.6
0.00
00
ne
rvir
0.54
9
21
0.30
13
17
9
17
0 4
86.7
6.
5 4.
2 1.
38
19.5
0.
0000
NO
TES
: S
ampl
e un
its w
ith e
mpt
y ne
ighb
orho
ods
wer
e ex
clud
ed fr
om th
ese
calc
ulat
ions
. xR
2 =
cros
s-va
lidat
ed p
seud
o-R
-squ
ared
. -9
9.99
99=
mis
sing
if <
2 n
onem
pty
neig
hbor
hood
s.
Ove
rall
pres
ent =
num
ber o
f "pr
esen
t" ca
ses
(non
-zer
o or
exc
eedi
ng b
inar
y cu
toff
valu
e).
Ove
rall
abse
nt =
num
ber o
f "ab
sent
" cas
es (z
ero
or le
ss th
an o
r equ
al to
bin
ary
cuto
ff va
lue.
) N
aive
p =
nai
ve e
stim
ate
of th
e pr
obab
ility
of o
ccur
renc
e =
prop
ortio
n pr
esen
t. E
st p
>nai
v =
num
ber o
f cas
es p
redi
cted
mor
e lik
ely
than
ave
rage
(nai
ve p
) to
be "p
rese
nt"
Est
p<n
aiv
= nu
mbe
r of c
ases
pre
dict
ed le
ss li
kely
than
ave
rage
(nai
ve p
) to
be "p
rese
nt"
Impr
oved
p =
num
ber o
f "pr
esen
t" ca
ses
with
est
imat
ed p
roba
bilit
y >
naiv
e p.
Im
prov
ed a
= n
umbe
r of "
abse
nt" c
ases
with
est
imat
ed p
roba
bilit
y <
naiv
e p.
N
ot im
prov
ed o
mis
s =
Err
or o
f om
issi
on: s
peci
es p
rese
nt b
ut e
st.p
<=
naiv
e p
Not
impr
oved
com
mis
= E
rror
of c
omm
issi
on: s
peci
es a
bsen
t but
est
.p >
= na
ive
p Im
prov
emen
t % =
Per
cent
of c
ases
with
est
imat
es im
prov
ed o
ver n
aive
mod
el.Im
prov
emen
t odd
s ra
tio =
Per
cent
of c
ases
with
est
imat
es im
prov
ed o
ver n
aive
m
odel
.Odd
s of
impr
ovem
ent =
pro
porti
on o
f im
prov
ed/(1
- pr
opor
tion
impr
oved
) 9
999.
9=in
f. lo
gB =
log1
0(B
ayes
fact
or)
0
to 0
.5 --
not
wor
th m
ore
than
a b
are
men
tion
0
.5 to
1 --
sub
stan
tial
1
to 2
--
stro
ng
> 2
--
dec
isiv
e av
eB =
ave
rage
con
tribu
tion
of e
ach
case
to th
e B
ayes
fact
or =
10^
(logB
/N)
Chi
Sq
= 2*
ln(B
ayes
fact
or) =
Dev
ianc
e co
mpa
ring
mod
el to
nai
ve m
odel
p
= pr
obab
ility
of t
ype
I err
or fr
om c
hi-s
quar
e di
strib
utio
n, d
.f.=1
191
Influence of Reach and Pool-Scale Geomorphology
SMLR identified a combination of reach and pool-scale variables that best predict
decapod species abundances at the pools where they are present (Table 5.6). Adjusted
correlation coefficients (r2adj) for 3-term models ranged from 0.30 to 0.66. Reach-scale
variables such as pool length, distance to the ocean, and the maximum downstream drop
were the first significant terms for all species except Atya lanipes. These variables are
negatively correlated with abundance, reflecting the trend that abundances for these
species are generally greater in the headwater reaches than in the relatively few larger
streams they inhabit. Other reach-scale factors such as the proportion of forest upstream
and downstream, and stream power were similarly important. Pool-scale variables,
including the grain size sorting coefficient, and the proportion of cobbles and boulders
were significant factors for Atya and Macrobrachium species—shrimp that are known to
dwell in a complex matrix of well-sorted coarse substrate on the channel bottom (Covich
and McDowell 1996). For Xiphocaris—shrimp that spend much of their time
swimming—abundance was negatively correlated with the stream power of the pool.
SMLR similarly identified strictly the pool-scale variables that best predict Atya,
Xiphocaris, and Macrobrachium abundance using the Quebrada Prieta data (Table 5.7).
SMLR models had adjusted correlation coefficients between 0.51-0.53. Atya and
Xiphocaris abundance were most strongly correlated with pool area. The distance from
the headwaters negatively correlated with Atya abundance, but was positively correlated
with Macrobrachium abundance. Furthermore, the number of pool entrances was
negatively correlated with both Atya and Xiphocaris abundances.
192
Table 5.6 Stepwise Multiple Linear Regression model output and goodness of fit
showing the relative influence of geomorphic variables on predicting decapod abundance.
Note the combination of both reach-scale variables (stream power, forest cover) and
pool-scale variables (substrate).
STEPWISE MULTIPLE LINEAR REGRESSION (REACH-SCALE) Response Species Predictor Variable Sign r2
adj n atylan Grain Sorting Coefficient + 0.17 49 % Forest Downstream + 0.25 Width (baseflow) - 0.30 episin Pool Length - 0.50 30 Wetted Perimeter (active channel) - 0.57 % Forest Upstream + 0.66 macCCH Distance to ocean - 0.27 60 Maximum Downstream Drop + 0.37 % Cobble + 0.41 macfau Distance to ocean + 0.34 52 % Forest Downstream - 0.49 % Boulder + 0.55 xipelo Maximum Downstream Drop + 0.51 68 Unit Stream Power - 0.55 Pool Volume - 0.58
193
Table 5.7 Stepwise Multiple Linear Regression model output and goodness of fit
showing the relative influence of geomorphic variables on predicting decapod abundance
in the Quebrada Prieta. Note the influence of pool size and substrate characteristics.
STEPWISE MULTIPLE LINEAR REGRESSION (POOL-SCALE) Response Species Predictor Variable Sign r2
adj n atylan Pool Area + 0.14 49 Distance from Headwaters - 0.49 Pool Entances - 0.53 mac Distance from Headwaters + 0.30 22 Max Width + 0.40 % Organic Matter + 0.53 xipelo Pool Area + 0.39 49 % Gravel + 0.47 Pool Entrances - 0.51
194
Pool Size and Spacing
The length of 101 pools at seven stream segments spanning a range of stream
sizes were measured and plotted against drainage area (Figure 5.7a). Pool length
increases as a power function of drainage area (r2 = 0.71, Figure 5.7b), from an average
of 2.5m in length at 0.15km2 drainage area, to 50m at 25km2. The spacing between pools
similarly increase as a power function of drainage area (Figure 5.7c). However, this
relationship is less robust (r2 = 0.21) due a large degree of variance within each segment.
Moreover, the rate of increase between pool length and drainage area (exponent = 0.54) is
greater the rate of increase between pool spacing and drainage area (0.27). Consequently,
the ratio of average pool spacing to average pool length decreases with drainage area
(Figure 5.7d). In headwater reaches, pools are spaced approximately 4 times of their
length apart. In the lowermost reaches, pools are spaced closer together, approximately
one pool length apart. Furthermore, the percentage of total segment length that is
classified as a pool increases with drainage area (Figure 5.7e). Pools compose between
15-20% of the length of headwater reaches, whereas they cover up to 35-50% in lowland
reaches along the main stem having greater drainage area.
DISCUSSION
Landscape Scale Patterns
Principal components analysis of the geomorphology of all pools illustrates the
dominance of one primary geomorphic gradient in the Luquillo streams: longitudinal
position within the stream network. Along this gradient, several key habitat features
similarly vary, including pool length, channel size, and the location relative to waterfalls.
195
Figure 5.7 a) Map showing the location of 7 stream segments where pool length and
spacing were measured b) pool length increases as a power function with discharge c)
pool spacing similarly increases as a power function with discharge, but at half the rate d)
consequently, the ratio of pool spacing to pool length decreases with drainage area, and e)
the % of pool in reach increases with drainage area.
196
Figure 5.7 cont.
197
The biological response to this geomorphically imposed gradient is evident in the
longitudinal patterns of species presence/absence. Predatory fish show a preference for
lowland main channels and tributaries, whereas atyid shrimp and crabs showing an
affinity for headwater pools.
The linkages between geomorphology and species preference that are evident
through principal components analysis are similar to those noted by Hein et al. (in prep)
using a different ordination technique. By quantifying the differences among pools on the
basis of their community composition (rather than by geomorphology), using a non-
metric multidimensional scaling ordination technique, this metric of community
composition difference similarly correlated strongly with longitudinal position. The
results from the ordination in Hein et al. (in prep) clearly demonstrate that there are three
general communities: one consisting of mullets and eels, another consisting of gobies,
and the last consisting of atyid shrimp. The location of waterfalls were found to be more
important in determining the distribution of these communities than either local pool-
scale geomorphic or local hydraulic variables. Furthermore, regression tree analysis
presented in Hein et al. (in prep) demonstrates that the presence and absence of many
species can be predicted by a simple split of whether or not a site is above a waterfall.
Access to upper reaches is limited for some migratory species as waterfalls act as a
barrier for most fish. All predatory fishes are limited to areas of the stream network
below 4m vertical drops, whereas shrimps, river crabs, and the sirajo goby are common at
sites above waterfalls.
The NPMR analysis presented here confirms these patterns. Since the distribution
of species is best determined by landscape scale variables, NPMR is an effective way to
198
map the species throughout these stream networks. The longitudinal probability curves
for each species (Figure 5.5.5) further reinforce the pattern of fish-dominated
assemblages in the lowlands shifting to decapod dominance in the headwaters. They also
show that transitions between these two communities are typically abrupt. Furthermore,
the species maps illustrate that fish species richness increases with catchment areas and
stream size, whereas decapod species richness apparently decreases. In actuality, decapod
species richness is relatively constant throughout the network; all species of shrimp must
be present below waterfalls to migrate upstream, but typically only in their juvenile stage.
The absence of adult decapods in lower reaches has been explained as a response to fish
predation (Covich and McDowell 1996, Greathouse and Pringle 2006). The headwaters
provide a critical refuge from fish predators, and contain an abundant supply of leaf-litter
and particulate organic matter to sustain large population shrimps throughout adulthood.
Thus, no new species are added to the headwaters (they only shift from juveniles to
adults), although species do drop out in the upstream direction.
Such biological zonation is common in rivers with abrupt ecological transition or
barriers to fish movement (Rahel and Hubert 1991). The concept of waterfalls as fish
barriers, and consequently as boundaries that fragment aquatic communities, has been
noted in the literature for decades (Stuart 1962, add others). Waterfall-induced (8-12m
high) longitudinal zonation was noted in an inland tropical rainforest river in southern
Mexico, with continual addition of species downstream and little species deletion
(Rodiles-Hernandez et al. 1999). Waterfalls greater than 3m high in rivers on the South
Island of New Zealand effectively divided the fish community between native galaxiids
and non-native trout (Townsend and Crowl 1991). On the steep volcanic Comoros islands
199
off the southeastern coast of Africa, waterfalls of 15m height divided the fish population
such that only adult river gobies inhabit the fast-flowing waters above waterfalls, whereas
catadromous eels are restricted below the waterfalls (Balon and Bruton 1994). Gilliam et
al. (1993) found a similar pattern in steep drainages on the Caribbean island of Trinidad
where species drop out proceeding upstream, and the community becomes truncated at
barrier waterfalls. On the other hand, in some similar tropical streams that lack waterfalls,
such as those along Caribbean coast of Central America, longitudinal position is also an
important factor in structuring fish-assemblages, but the lack of waterfalls prevent abrupt
transitions in aquatic communities (Winemiller and Leslie 1992, Esselman et al. 2006).
Although species distributions are clearly related to the longitudinal changes in
the stream network, the abrupt shifts in communities owning to waterfalls stand in
contrast to the systematic continuum predicted by the RCC. However, some predictions
of the RCC on the distribution of food resources and the consequent biotic response have
been found to hold in the Luquillo streams to a mixed degree (Ortiz-Zayas et al. 2005,
Greathouse and Pringle 2006). These include shifts in stream metabolism and functional
feeding groups, which are tightly linked to instream productivity, light availability, and
turbulence, and consequently have implications for ecosystem functioning. Stream
metabolism, as reflected by the ratio of primary productivity to community respiration
(P/R), is thought to shift from heterotrophic (P/R < 1) in the headwaters to autotrophic
(P/R >1) further downstream, following the trend in riparian shading, algal production,
detrital inputs, and upstream organic matter transport. Ortiz-Zayas et al. (2005) found that
Luquillo streams displayed a contrasting pattern: all reaches were strongly heterotrophic
until the coastal plain reaches just above the estuary. This was attributed to high rates of
200
respiration, suppressed periphyton production due to low light, and large inputs of detrital
carbon from the surrounding mature forest. Intensive herbivory by decapods prevents
biomass accumulation expected at intermediate stream orders (March et al. 2002).
The RCC further predicts that in response to the available food resources,
functional feeding groups will shift predictably downstream from a dominance of
shredders, scrapers, and filterers in the headwaters, to grazers, collectors, and predators
further downstream. Greathouse and Pringle (2006) found that predictions held for
scrapers, shredders, and predators in the Luquillo streams, while collector-filterers
showed a trend opposite to RCC predictions. This collector-filterer trend may be a result
of the prevalence of snails at lower elevations, or may be explained by fish predation
affecting distributions of filter-feeding shrimp. However, the general theme is consistent;
longitudinal distributions of functional feeding generally groups follow longitudinal
patterns in basal resources, but are interrupted by abrupt barriers.
Reach and Pool-Scale Patterns
Some have viewed such abrupt interruptions simply as adjustments to the original
RCC (Bruns et al. 1984, Minshall et al. 1985), whereas others have argued that they serve
as the basis for a new view of a river as a “discontinuum” (Perry and Schaeffer 1987,
Townsend 1989, Rice et al. 2001, Poole 2002). In essence, river discontinuum
perspectives highlight the nonuniform or patchy distribution of habitats and therefore
emphasize habitat heterogeneity, expressed at the scale of meters to kilometers.
Despite the dominance of longitudinal position in structuring aquatic-
communities, local-scale factors do influence species distribution and abundances at the
reach, pool, and microhabitat scales. PCA identified an additional geomorphic gradient of
201
grain size and stream power among the pools that is in part independent of longitudinal
position. Based on presence/absence, some fish and decapod species evidently prefer
pools that have high stream power and large boulders or pools having low stream power
and a higher proportion of fine sediment. However, this geomorphic gradient was not
significant in determining the abundance of decapod species. Thus, at the scale of the
headwater stream network above waterfalls, there are no systematic geomorphic
gradients that determine decapod abundance (see Fig. 5.3). In fact, decapod abundance
varies strongly between adjacent pools, and a particular species may be abundant in one
but absent in the other. This patchiness in decapod abundance may be the result of natural
variation in micro-habitat, or indicative that other factors beyond the hydrologic and
geomorphic variables measured in this study determine their abundance.
However, SLMR models identified reach and pool-scale variables that are
important in determining abundances of decapods in the pools where they are present.
The variables selected were consistent with the known microhabitat preferences of
decapods. For example, filter feeding Atya prefer a cobble substrate to attach to as they
feed. Palaemonid shrimp prefer a complex substrate of boulders with crevices to dwell,
and their abundances were consequently strongly determined by grain size factors. In
contrast, Xiphocaris often swim in the water column, and abundances were found to be
negatively correlated with high stream power that may create a turbulent swimming
environment. Further, most of these decapod species were more abundant in reaches that
had a high proportion of forested land cover in their downstream migratory corridor.
At the pool-scale within the Quebrada Prieta, the geomorphic environment
consists of gradients of pool size and substrate suitability. Although decapod abundances
202
vary modestly along these gradients, pool characteristics are often patchy throughout the
reach. The large degree of variability in decapod abundance between pools in a reach
reflects patchiness of the geomorphic environment. Furthermore, within the Quebrada
Prieta, it has been shown that Atya and Xiphocaris abundance relationships can be
influenced by biotic interactions such as avoiding predatory Macrobrachium, and locally
adjusting their abundance in pools to an optimal density (Covich et al. 1996, Papella,
thesis). Furthermore, microhabitat characteristics within a pool may be responsible for
pool-scale abundances. For example, within a pool, shrimp are known to have distinct
depth and velocity preferences (Scatena and Johnson 2001). With the addition of large
boulders that alter flow and create a complex microhabitat, shrimp abundances are
expected to be patchy even within pools.
Such heterogeneity also arises because of the perception of scale, in which fluvial
landforms are hierarchically organized from valley segments to stream bed particles
(Frissell et al. 1986). The importance of landscape scale factors over reach and pool-scale
variables in determining species distribution in the Luquillo streams seemingly stands in
contrast to predictions of patch dynamics. Yet the idea of patchy and multiscale habitat
formation and its related heterogeneity is often related to longitudinal position. The
Process Domains Concept (Montgomery 1999) contends that fundamental differences in
multiscale landscape processes dictate differences in the community structure of aquatic
fauna. In the Luquillo streams, the patches that act as local habitat (e.g. geomorphic
features such as deep pools, boulder crevices, fine substrate) in the as well as hydraulic
variables (velocity, turbulence, etc) do vary somewhat predictably along the longitudinal
gradient (see Chapter 4). For example, large boulders that structure complex
203
microhabitats in headwater pools are deposited by landslides that are most common along
steep hillslopes. Furthermore, grain size patterns are related to slope and drainage area.
Other habitat features such as the size and frequency of pools, and average flow velocity
also increase downstream with drainage area and mean annual discharge. Thus, as
predicted by the PDC, the geomorphic processes structuring patchy habitat vary also
along the longitudinal gradient and mirror changes in aquatic communities.
CONCLUSION
In conclusion, the longitudinal patterns of aquatic assemblages observed in the
Luquillo streams are best explained by hypotheses that incorporate the natural
discontinuous patterns present in stream networks and consider that different factors
operate at different scales. Hierarchy theory asserts that different geomorphic processes
acting at the landscape, reach, and pool scales give rise to both local patchiness and
basin-scale patterns. No hypothesis specifically addresses discontinuities in community
structure created by waterfalls, but both the Network Dynamics Hypothesis and Process
Domains Concept generally capture the relationships between multiscale geomorphic
processes and aquatic communities. The natural breaks in the river continuum imposed
by geomorphic processes and network structure in these tropical island streams are
apparently most influential in determining the distribution of fish and macroinvertebrates.
However, at the pool and reach scales, decapod abundances are highly variable, and
reflect a complex interaction of geomorphic patchniness and biotic interactions.
204
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CHAPTER 6
CONCLUSIONS AND FUTURE RESEARCH
SUMMARY AND CONCLUSIONS
This purpose of this dissertation was to quantify the longitudinal patterns in
stream channel morphology in a tropical mountain stream network and assess the relative
influences of contrasting hydraulic and lithologic forces that sculpt channel morphology,
as well as to address how the hydrologic and geomorphologic gradients present in the
stream network influence aquatic habitat.
Several studies were presented here to address these research aims. The first
(Chapter 2) developed and expanded GIS-based relationships to estimate hydrologic
parameters throughout that watersheds. These included estimates of mean annual rainfall,
runoff, and discharge based on elevation and flow accumulation models, as well as an
expanded map of the stream network. The spatial framework that was discussed in this
chapter was used a template to develop hydrologic, geomorphic, and ecological patterns
in subsequent chapters.
Chapter 3 developed and used a technique to calibrate high-flow riparian features
to long-term gage records for the purpose of determining an active-channel boundary
marker. The general approach of surveying the first occurrence of riparian features and
using multivariate statistical analysis to link these occurrences to 15-minute flow duration
provided an internally consistent framework for identifying flow frequencies within the
region. In the Luquillo streams, it was found that the incipient presence of soil and woody
shrubs and trees can be used as an indicator of hydrogeomorphic site conditions to
216
identify active-channel boundaries that occur at a constant flow frequency throughout the
stream network. Furthermore, it was found that the total duration of time that bankfull
stage is exceeded in the Luquillo streams is similar to that in many temperate streams, but
that floods of bankfull magnitude occur much more frequently in these streams. This
study concluded that flows with similar frequencies influence the establishment of
riparian vegetation, soil development, and substrate characteristics along tropical stream
channel margins in similar ways to those of temperate and alluvial rivers. Lastly, the
method developed in this study was essential to identifying a common marker of flow
frequency as a basis to compare channel geometry in subsequent chapter.
The next study (Chapter 4) highlighted the central geomorphologic question of
the dissertation. The key conclusion drawn from this study is that although there are
apparent non-fluvial and lithologic controls on local channel morphology, strong fluvial
forces are sufficient to override boundary resistance and give rise to systematic basin-
scale patterns. Lithologic influence is evident in non-uniformly graded longitudinal
profiles and prevalence of large (presumably immobile) boulders that are delivered from
hillslope weathering. Since the time scale of slope adjustment is vastly greater than the
adjustment of channel geometry, lithology imposes a slope upon which hydraulic forces
sculpt the channel on shorter time scales. Yet the dominance of hydraulic influences are
evident through well developed downstream hydraulic geometry, and the apparent ability
of the channels to mobilize coarse sediment throughout the watershed. In an almost
paradoxical sense, the stream network displays many lithologic features similar to other
mountain streams, but the intense hydrologic regime gives rise to a threshold channel that
shares some similarities with fully alluvial rivers.
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The last study (Chapter 5) addressed the ecological implications of the patterns in
geomorphology. The conclusions drawn from this study highlight the importance of the
scale at which ecological processes are observed. At the basin-scale, waterfalls hinder the
upstream migration of some species, and consequently segment the stream community.
Yet above these waterfalls where decapods are present, local-scale geomorphology
influence decapod abundance. However, at these local scales, the geomorphic
environment is patchy, as the variation in pool characteristics varies as strongly from pool
to pool, as from reach to reach. This natural variability in the geomorphic environment
present at these scales gives rise to a similar patchiness in the abundance of biota.
FUTURE RESEARCH
This dissertation provides baseline information about the stream environment to
pursue further avenues of geomorphology research. First, the technique of quantifying the
flow frequency associated with high-flow riparian features using long-term stream gage
records should be applied in a variety of stream that have different flow regimes. Using
this technique in other environments, is there a similar relationship between riparian
vegetation and flow frequency? Does this technique provide consistent results in streams
throughout the island of Puerto Rico? Within other mountainous streams in the humid
tropics? It is expected that stream with both a similar flashy flow regime and humid
tropical climate with similar vegetation types will have analogous riparian features
occurring at flow-frequencies consistent with those determined in this study.
Another further extension of this technique would be to investigate linkages
between the frequency of flooding and the incipient presence of different vegetation
218
species. In this dissertation, vegetation is grouped into broad categories (mosses and
lichens, herbs, grasses, shrubs, and trees), yet there may be differences in the flooding
tolerance of individual species that comprise these categories. Furthermore, more in-
depth studies of in-channel mosses, herbs, and shrubs may provide additional information
about the frequency of sub-effective flows.
The geomorphology discussion in this research poses further questions about the
long-term adjustment of these channels, and implications for landscape evolution. One
issue that necessitates future research is to address site-scale linkages between channel
hydrology and sediment transport. The concept of sediment transport is critical to the
understanding of channel morphology, yet is difficult to precisely quantify in streams
with large boulders. There are few established analytical tools to estimates rates of
sediment transport, and also a lack of data in steep mountain streams to calibrate such
tools. For example, although the sediment transport equations used in this research
suggest that the median and coarse grains in many headwater reaches are mobile, are the
largest boulders mobile? To answer such a question would require developing new
techniques to assess sediment transport in streams with large boulders, and also modify
existing techniques that have been developed on gravel-bed streams to collect empirical
data on the movement of these boulders.
Several questions about the evolution of the stream network remain. Most
notably, how old are these channels? And have the steepland reaches migrated over
geologic time? As mentioned, streams in Puerto Rico have assumed to flow since the
uplift of the Island during the Cretaceous period, and sedimentary deposits across the
island suggest that clastic sediment was delivered to the ocean by streams during the
219
Miocene. However, it is not known whether the streams draining the mountain regions
have had relatively similar paths through geological time, or whether these steepland
streams have adjusted their course through ridge migration. Although it may not be
possible to answer these questions directly, they highlight the importance of further
understanding the history of this landscape. Deep insight into landscape evolution may be
gathered from the use geochronological techniques to evaluate the thermal and
mechanical history of the granodiorite rocks, and to constrain uplift rates over time.
Another pressing question above the evolution of this landscape is whether
waterfalls migrate as slope adjusts through long-term degradation? Field observations
indicate that the location of waterfalls are influenced by a variety of lithologic and
tectonic factors. Some waterfalls occur at lithologic boundaries and changes in
volcaniclastic units, others occur along faults and at locally exposed bedrock outcrops,
and others, particularly those flowing across granodiorite, exploit natural joints in the
rock. Although waterfalls generally occur at higher elevations (>300m), several
anomalous waterfalls are present at lower elevations, suggesting that waterfalls do not
necessarily correlate with elevated peneplain surfaces. In some rivers, waterfalls migrate
headwater over geologic time, whereas others are more permanent and act as a local
base-level for the upstream drainage basin. A detailed survey of the waterfalls through
the Luquillo Mountains, coupled with models of bedrock incision, could illuminate the
evolution of these features. If waterfalls are migrating headward, it would imply that the
long-term tendency of these streams is to minimize gradients in energy expenditure. In
contrast, if the waterfalls tend to be more permanent features, this may imply that the
220
underlying lithology will always exert strong control on the slope of these stream
channels.
Lastly, further linkages between geomorphology and aquatic habitat should be
investigated at the microhabitat scale. The nature of the surveying methods in this study
did not allow for such fine-scale habitat assessment. However, the hydraulics and
distribution of sediment within an individual pool may be just as complex as their
distribution throughout the basin. Do the fish and shrimp living seek out optimal
hydraulic and geomorphologic features within a pool that may influence their abundance?
And if so, how do these vary within the pool? And how does the flow regime of frequent
short-duration floods alter the geomorphology of specific pools? An investigation into
these questions at the microhabitat scale could provide further insight into the complex
interactions between geomorphology, hydraulics, and aquatic habitat.
Ultimately, this research provides baseline information on physical and biological
processes in relatively unaltered tropical streams and can be used to inform such further
studies that document human interactions with stream networks.
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APPENDIX DESCRIPTION OF VARIABLES
Site Information
Watershed – The watershed where the cross-section was located.
Biocomplexity ID – The site identification name for sites included in the Biocomplexity
study. Blank indicates a cross-section that was not included in the Biocomplexity
study.
Cross-Section ID – The identification label assigned to each cross-section. Non-
Biocomplexity sites are labeled by a letter corresponding to the watershed and a
number corresponding to the order in which the cross-section was measured. For
sites included in the Biocomplexity study, the label is prefaced by the letter (U or
D) indicated whether the cross-section was located upstream or downstream of
the road-river crossing, followed by a number indicating the sequential ordering
of the cross-section from the road-river crossing. Those that are labeled “Bridge”
indicate a cross-section taken at a pool directly at the road-river crossing.
PR Datum N – Northing coordinate (m) of the cross-section according to the following
coordinate system used for the Puerto Rico DEM:
NAD_1927_Lambert_Conformal_Conic
Projection: Lambert_Conformal_Conic
False_Easting: 152400.304801
False_Northing: 0.000000
Central_Meridian: -66.433333
Standard_Parallel_1: 18.033333
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Standard_Parallel_2: 18.433333
Latitude_Of_Origin: 17.833333
GCS_North_American_1927
PR Datum E – Easting coordinate (m) of the cross-section according the coordinate
system described above.
Main Stem – Cross-sections along a tributary or the main stem of the river (0 = tributary,
1 = main stem)
Rock Type – Rock type underlying the channel at each cross-section (VC =
Volcaniclastic, GD = Granodiorite, AL = Alluvium, DK = Mafic Dike)
Formation – Formation of the rock type underlying the channel at each cross-section
(Kt =Tabonuco formation, Kf = Frailes formation, Kh = Hato Puerco Formation,
Tqd = Granodiorite, Qa = Quaternary alluvium, Tkmi = Mafic Dike)
h – Elevation of the cross section (m a.s.l.)
havg – Average elevation of the contributing basin upstream of the cross-section (m a.s.l.)
DA – Drainage area of the contributing basin upstream of the cross-section (km2)
Qmean – Mean annual discharge at the cross-section, based on equation 2.3 using drainage
area and average upstream elevation (m3/s)
Distdown – Distance along the stream from the cross-section to coast (m)
Distup – Distance along the stream from the cross-section to the headwaters (m)
S – Stream gradient (m/m)
Hillslopemax – Maximum adjacent hillslope gradient within a 50m radius of the cross-
section (°)
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Baseflow Channel Geometry
Q bf – Discharge at baseflow, Q50 (m3/s), based on a modified equation 2.3
w bf – Baseflow channel width (m)
R bf – Baseflow hydraulic radius (m)
dmax bf – Maximum depth along the cross-section at baseflow (m)
P bf – Wetted perimeter at baseflow (m)
A bf – Cross-sectional area at baseflow (m2)
v bf – Average velocity through the cross-section at baseflow (m/s)
w/d bf – Width to depth ratio of the cross-section at baseflow (m/m)
τ bf = Boundary shear stress at baseflow, based on equation 4.3
Ω bf = Stream power at baseflow, based on equation 4.6
Active Channel Geometry
Q ac – Active-channel discharge, Q99.84 (m3/s), based on equation 4.1
w ac – Active-channel width (m)
R ac – Active-channel hydraulic radius (m)
dmax ac – Maximum depth along the cross-section of the active-channel discharge (m)
P ac – Wetted perimeter of the active-channel (m)
A ac – Cross-sectional area at the active-channel discharge (m2)
v ac – Average velocity through the cross-section at the active-channel discharge (m/s)
w/d ac – Width to depth ratio of the cross active-channel (m/m)
τ ac = Boundary shear stress at the active-channel discharge, based on equation 4.3
Ω ac = Stream power at the active-channel discharge, based on equation 4.6
224
Grain Size and Pebble Count Data
d16 – Fine grain-size fraction, coarser than 16% of the sample (mm)
d50 – Median grain size, coarser than 50% of the sample (mm)
d84 – Coarse grain-size fraction, coarser than 84% of the sample (mm)
dmax – Maximum grain size (mm)
Simpson’s Index – Simpson’s diversity index characterizing diversity in grain size,
based on categorical pebble count data.
Sorting Index – Grain size sorting index, calculated as 1684 dd
Bedrock – Percentage of the pebble count that included bedrock
Megaboulder – Percentage of the pebble count that included megaboulder-sized clasts
(>2000 mm)
Boulder – Percentage of the pebble count that included boulder-sized clasts (256-2000
mm)
Cobble – Percentage of the pebble count that included cobble-sized clasts (64-256 mm)
Gravel – Percentage of the pebble count that included gravel-sized sediment (2-64 mm)
Sand – Percentage of the pebble count that included sand-sized sediment (0.063-1 mm)
Fines – Percentage of the pebble count that included fine sediment (0.001-0.063 mm)
225
Additional Biocomplexity Pool Information
Drop – Maximum downstream drop in elevation according to a 10m DEM (m)
Pdepmax – Maximum pool depth at baseflow (m)
Pdep cv – Coefficient of variation of depth at baseflow based on six measurements
spanning the length of the pool (dimensionless)
Plength – Length of the pool (m)
P area – Surface area of the pool at baseflow (m2)
P volume – Volume of the pool at baseflow based on the product of length and surface
area (m3)
agr d – Proportion of agricultural (deforested non-urban) land cover in the
downstream corridor
agr u – Proportion of agricultural (deforested non-urban) land cover in the upstream
basin
fst d– Proportion of forested land cover in the downstream corridor
fst u – Proportion of forestedl land cover in the upstream basin
urb d – Proportion of urban land cover in the downstream corridor
urb u – Proportion of urban land cover in the upstream basin
226
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDPR
Dat
um N
PR D
atum
EM
ain
Stem
Roc
k Ty
peFo
rmh
h avg
DA
Qm
ean
dist
dow
ndi
stup
SH
illsl
ope m
ax
Mam
eyes
M1
59,7
15
222,
885
1AL
Qa
733
234
.5
6.19
2848
1463
90.
002
0.
0M
amey
esM
259
,085
222,
525
1AL
Qa
1036
730
.6
6.02
3812
1367
50.
002
7.
4M
amey
esM
358
,725
222,
615
1AL
Qa
1041
124
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5.38
4189
1329
80.
008
17
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amey
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455
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223,
965
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Kt70
474
20.4
5.
08
8269
9217
0.01
6
28.3
Mam
eyes
M5
55,3
95
223,
965
1VC
Kt72
483
19.8
5.
03
8756
8731
0.01
6
34.0
Mam
eyes
M6
55,1
25
224,
325
1VC
Kt80
503
17.8
4.
69
9394
8093
0.01
5
46.1
Mam
eyes
M7
58,1
85
222,
795
1AL
Qa
2041
824
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5.37
4933
1255
40.
008
0.
2M
amey
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857
,915
223,
065
1AL
Qa
2042
224
.0
5.36
5376
1211
10.
008
27
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amey
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956
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224,
145
1VC
Kt57
463
21.1
5.
14
7576
9911
0.01
7
29.8
Mam
eyes
M10
56,3
85
224,
145
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Kt58
188
0.5
0.
05
7594
1752
0.08
4
14.0
Mam
eyes
M11
56,7
45
224,
055
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Kt50
448
22.2
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24
7101
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60.
019
11
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1257
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433
23.2
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015
21
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1353
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221,
625
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Kt49
676
80.
6
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1488
517
670.
313
31
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amey
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1453
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221,
445
0VC
Kt60
579
60.
5
0.20
1525
313
990.
299
41
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amey
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1553
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175
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Kt66
082
60.
4
0.16
1555
610
950.
148
27
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amey
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1653
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221,
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Kt46
071
31.
2
0.43
1467
219
790.
129
33
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1755
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223,
515
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Kt15
828
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9387
942
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8
28.3
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eyes
M18
55,3
05
223,
245
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Kt18
846
60.
8
0.20
9624
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8
36.2
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M19
55,3
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705
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Kt31
138
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1020
241
90.
200
28
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2055
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352
60.
2
0.06
1033
212
280.
238
42
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2154
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222,
795
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Kt36
058
10.
3
0.09
1046
915
580.
222
27
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amey
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2257
,285
223,
605
1VC
Kt30
429
23.5
5.
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6204
1128
30.
012
23
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amey
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2354
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224,
595
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Kf10
333
20.
9
0.16
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025
010.
213
26
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2454
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224,
415
1VC
Kt11
353
615
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4.41
1055
769
300.
034
51
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amey
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2554
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224,
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1VC
Kf10
052
216
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4.59
1025
072
370.
030
28
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2654
,315
224,
415
1VC
Kt12
053
715
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4.41
1074
567
420.
044
39
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amey
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2752
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221,
985
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Kt48
760
90.
2
0.05
1447
394
90.
250
30
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amey
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2852
,335
221,
805
0VC
Kt47
575
02.
4
0.93
1481
926
680.
316
40
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amey
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2952
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222,
615
0VC
Kf33
564
71.
6
0.54
1376
628
850.
168
28
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amey
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3052
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222,
705
0VC
Kf32
067
84.
7
1.64
1365
438
330.
132
43
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amey
esM
3153
,235
223,
515
1VC
Kf21
659
011
.8
3.62
1246
950
180.
095
37
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amey
esM
3252
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223,
335
1VC
Kf25
055
36.
4
1.85
1297
844
870.
061
30
.0M
amey
esM
3352
,875
223,
335
0VC
Kf25
065
25.
1
1.72
1298
045
070.
066
23
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amey
esM
3451
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222,
435
1G
DTq
d44
064
02.
1
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1479
826
670.
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13
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3551
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222,
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1G
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d43
063
63.
1
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1461
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470.
047
30
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3651
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222,
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d44
063
90.
9
0.29
1476
020
430.
118
14
.2
App
endi
x. S
ite
Info
rmat
ion
227
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDPR
Dat
um N
PR D
atum
EM
ain
Stem
Roc
k Ty
peFo
rmh
h avg
DA
Qm
ean
dist
dow
ndi
stup
SH
illsl
ope m
ax
Mam
eyes
M37
50,9
85
222,
075
1VC
Kf53
171
51.
1
0.39
1579
016
750.
179
42
.0M
amey
esM
3851
,165
222,
345
0VC
Kf51
661
40.
1
0.05
1558
267
90.
220
29
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amey
esM
3951
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222,
255
1G
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d45
566
71.
7
0.57
1504
924
150.
090
30
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amey
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4053
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225,
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Kf26
734
70.
1
0.01
1152
542
50.
238
30
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amey
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4153
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224,
955
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Kf27
035
40.
1
0.01
1155
049
80.
198
30
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amey
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4253
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Kf27
046
50.
3
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1162
612
550.
164
24
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amey
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4360
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223,
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Qa
332
834
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6.20
2059
1542
80.
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pirit
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nto
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54,4
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310.
208
28
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nto
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54,0
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578
10.
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1622
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630.
071
20
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nto
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54,0
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218,
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081
40.
8
0.32
1669
820
940.
144
25
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nto
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53,7
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115
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194
23
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nto
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54,7
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439
670.
132
28
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pirit
u Sa
nto
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58,3
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1746
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10.
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11
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nto
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54,2
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740.
192
30
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pirit
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nto
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220,
275
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267
42.
3
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790.
225
30
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pirit
u Sa
nto
ES10
55,4
85
220,
365
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Kh32
068
22.
3
0.79
1405
927
900.
183
23
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pirit
u Sa
nto
ES11
56,7
45
214,
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Qa
4050
819
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5.20
1193
311
352
0.01
2
20.4
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ritu
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1256
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mi
5051
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1352
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9
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1860
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550.
247
27
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pirit
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nto
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57,1
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4.46
1131
310
317
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ritu
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1556
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6855
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4.42
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796
240.
039
19
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pirit
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nto
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56,2
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598
13.8
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26
1273
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950.
016
7.
8Es
pirit
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nto
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53,8
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640.
115
32
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pirit
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nto
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54,4
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217,
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951
10.
4
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1551
210
560.
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27
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pirit
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nto
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54,7
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215
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346
70.
5
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1487
316
950.
145
16
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pirit
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nto
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53,3
25
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075
45.
9
2.28
1641
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210.
040
16
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pirit
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nto
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52,9
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Kh54
576
65.
3
2.06
1694
046
910.
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23
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pirit
u Sa
nto
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54,4
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395
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Kh35
073
32.
7
1.00
1538
034
310.
104
28
.6Es
pirit
u Sa
nto
ES23
61,2
45
217,
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1AL
Qa
137
964
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13.0
1
4905
1838
00.
001
0.
0Es
pirit
u Sa
nto
ES24
63,8
55
219,
195
1AL
Qa
026
795
.0
14.0
3
233
2305
20.
001
0.
7Es
pirit
u Sa
nto
ES25
51,7
95
218,
655
1VC
Kh72
080
22.
9
1.21
1952
321
080.
029
21
.2Es
pirit
u Sa
nto
ES26
51,7
05
218,
745
1G
DTq
d72
179
72.
5
1.03
1961
620
150.
031
18
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pirit
u Sa
nto
ES27
51,7
05
218,
745
1G
DTq
d73
077
61.
3
0.52
1972
819
030.
037
10
.4Es
pirit
u Sa
nto
ES28
54,5
85
217,
215
0VC
Kh29
472
22.
7
1.02
1508
937
220.
111
30
.9Es
pirit
u Sa
nto
ES29
54,2
25
216,
675
1VC
Kh33
972
96.
6
2.49
1536
562
660.
227
39
.8Es
pirit
u Sa
nto
ES30
57,2
85
216,
765
1VC
Kh40
539
15.9
4.
46
1101
610
614
0.01
7
26.4
Espi
ritu
Sant
oES
3158
,095
217,
305
1AL
Qa
2346
522
.1
5.42
9429
1220
20.
009
28
.3
228
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDPR
Dat
um N
PR D
atum
EM
ain
Stem
Roc
k Ty
peFo
rmh
h avg
DA
Qm
ean
dist
dow
ndi
stup
SH
illsl
ope m
ax
Espi
ritu
Sant
oES
3258
,815
217,
935
1AL
Qa
1042
932
.0
7.26
8006
1362
50.
011
22
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pirit
u Sa
nto
ES33
55,7
55
216,
585
1VC
Kh10
462
812
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4.12
1343
781
930.
033
22
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pirit
u Sa
nto
ES34
55,1
25
216,
765
1VC
Kh15
367
211
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3.90
1432
673
050.
200
35
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pirit
u Sa
nto
ES35
59,8
05
217,
575
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Qa
441
233
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7.30
6841
1479
00.
001
19
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pirit
u Sa
nto
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59,8
95
217,
575
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641
233
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20.
001
16
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pirit
u Sa
nto
ES37
52,2
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575
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079
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2
1.69
1799
036
400.
168
41
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pirit
u Sa
nto
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52,5
15
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378
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6
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341
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21
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Espi
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23
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232
Wat
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plex
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Sect
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IDPR
Dat
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PR D
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Stem
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Mam
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Mam
eyes
Puen
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54,9
45
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508
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26.9
Mam
eyes
Puen
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Mam
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Mam
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Wat
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ocom
plex
ity I
DCr
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Sect
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dm
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eyes
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0.72
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Mam
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eyes
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Mam
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4
Mam
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Mam
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Mam
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Mam
eyes
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0.25
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Mam
eyes
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Mam
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App
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Ch
ann
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234
Wat
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ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
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bf
dm
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bf
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f v
bf
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Mam
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6
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121
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Mam
eyes
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11
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400.
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Mam
eyes
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Mam
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2
23
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10
16
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72
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27
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24
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Es
pirit
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58
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19
7.3
Es
pirit
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1
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82
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Es
pirit
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0.68
---
---
---
---
---
---
---
---
---
Espi
ritu
Sant
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130.
104.
7
0.
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37
4.
9
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4
0.3
59.9
190
245.
0
Espi
ritu
Sant
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140.
5911
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56
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Es
pirit
u Sa
nto
ES15
0.59
5.2
0.15
0.53
5.6
0.8
0.
7
35
.1
56
223.
4
Espi
ritu
Sant
oES
160.
5817
.7
0.
27
0.
53
17
.9
4.
8
0.1
66.7
4189
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Es
pirit
u Sa
nto
ES17
0.35
7.2
0.29
0.83
8.2
2.3
0.
1
25
.1
32
438
9.2
Es
pirit
u Sa
nto
ES18
0.01
4.2
0.27
0.53
4.6
1.2
0.
0
15
.8
20
310
.1
Es
pirit
u Sa
nto
ES19
0.02
3.0
0.11
0.21
3.1
0.3
0.
0
27
.7
15
423
.3
Es
pirit
u Sa
nto
ES20
0.32
15.6
0.23
0.42
16.1
3.7
0.
1
67
.8
91
126.
4
Espi
ritu
Sant
oES
210.
2911
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0.
34
0.
65
11
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3.
8
0.1
32.9
100
85.9
Espi
ritu
Sant
oES
220.
148.
2
0.
54
0.
99
9.
2
5.
0
0.0
15.1
557
143.
3
Espi
ritu
Sant
oES
231.
5736
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1.
45
2.
49
36
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53
.1
0.
0
24
.9
17
18.1
Espi
ritu
Sant
oES
241.
4850
.0
2.
37
3.
40
50
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11
9.6
0.
0
21
.1
27
17.0
Espi
ritu
Sant
oES
250.
173.
6
0.
10
0.
17
3.
6
0.
4
0.5
35.9
2949
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Es
pirit
u Sa
nto
ES26
0.15
2.1
0.14
0.20
2.1
0.3
0.
5
14
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43
44.6
Espi
ritu
Sant
oES
270.
071.
1
0.
16
0.
27
1.
2
0.
2
0.4
6.5
5926
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Es
pirit
u Sa
nto
ES28
0.14
7.7
0.80
1.69
9.3
7.4
0.
0
9.
6
87
215
5.0
Es
pirit
u Sa
nto
ES29
0.35
14.5
0.55
2.38
18.6
10.3
0.0
26.2
1233
776.
4
Espi
ritu
Sant
oES
300.
5915
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0.
47
1.
13
16
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7.
6
0.1
32.1
7796
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Es
pirit
u Sa
nto
ES31
0.69
17.7
0.54
1.08
18.5
10.0
0.1
32.6
4760
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235
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
w b
f R
bf
dm
ax b
f P
bf
A b
f v
bf
w/d
bf
τ b
f Ω
bf
Espi
ritu
Sant
oES
320.
9114
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0.
41
0.
88
14
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6.
2
0.1
34.9
4496
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Es
pirit
u Sa
nto
ES33
0.56
13.0
0.32
0.70
13.4
4.3
0.
1
40
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10
518
4.3
Es
pirit
u Sa
nto
ES34
0.54
11.7
0.29
0.73
12.0
3.5
0.
2
40
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56
41,
056.
9
Espi
ritu
Sant
oES
350.
9024
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1.
60
2.
82
25
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40
.1
0.
0
15
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18
10.4
Espi
ritu
Sant
oES
360.
9016
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0.
66
1.
33
17
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11
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0.
1
25
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8
10.4
Espi
ritu
Sant
oES
370.
248.
3
0.
58
1.
11
8.
7
5.
0
0.0
14.3
956
395.
7
Espi
ritu
Sant
oES
380.
267.
0
0.
47
0.
91
7.
3
3.
4
0.1
15.0
343
191.
7
Blan
coB1
0.16
5.3
0.21
0.65
5.8
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0.
1
25
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18
13.6
Blan
coB2
0.15
5.6
0.16
0.23
5.7
0.9
0.
2
35
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13
13.1
Blan
coB3
0.13
3.0
0.30
0.51
3.2
1.0
0.
1
9.
8
26
11.5
Blan
coB4
0.09
4.1
0.18
0.31
4.2
0.8
0.
1
22
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15
8.1
Blan
coB5
0.20
5.2
0.12
0.27
5.5
0.7
0.
3
43
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13
021
3.9
Bl
anco
B60.
011.
6
0.
10
0.
17
1.
7
0.
2
0.0
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251.
9
Bl
anco
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217.
2
0.
08
0.
25
7.
3
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6
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5112
8.5
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anco
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208.
9
0.
15
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39
9.
0
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4
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186
250.
1
Blan
coB9
0.08
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0.
3
32
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90
76.0
Blan
coB1
01.
1125
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0.
32
0.
71
26
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8.
3
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80.8
3512
2.6
Bl
anco
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0.99
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1256
1,17
8.5
Sa
bana
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1716
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0.
14
0.
32
16
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2.
3
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114.
8
4249
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Sa
bana
S20.
173.
3
0.
20
0.
43
3.
4
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7
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8367
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Sa
bana
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0811
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0.
22
0.
76
11
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2.
6
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191
70.2
Saba
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0.09
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0.
1
68
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10
662
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Sa
bana
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1618
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0.
27
0.
86
19
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5.
3
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68.9
143
87.5
Saba
naS6
0.07
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0.88
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0
30
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23
448
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Sa
bana
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2
0.
32
0.
50
3.
4
1.
1
0.1
10.0
302
67.4
Saba
naS8
0.02
5.3
0.10
0.22
5.3
0.5
0.
0
52
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17
3.4
Saba
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0.01
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0.33
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0
14
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50
3.7
Saba
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00.
208.
1
0.
75
1.
58
9.
4
7.
1
0.0
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9425
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Sa
bana
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0.20
8.9
0.41
0.69
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1
21
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45
22.0
Saba
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001.
2
0.
02
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02
1.
2
0.
0
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81.5
160.
7
Sa
bana
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0.21
4.6
0.48
0.99
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1
9.
6
83
36.1
Saba
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40.
2113
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26
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13
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5
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193
157.
9
Saba
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50.
2115
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20
0.
55
16
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3.
2
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78.9
2223
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Sa
bana
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1
20
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56
12.3
Saba
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70.
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4
0.
16
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20
3.
5
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5
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263.
1
Fa
jard
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4 Br
idge
1U0.
6412
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1.
36
3.
13
14
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19
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0.
0
9.
5
25
11.6
Faja
rdo
194
Brid
geBr
idge
0.63
9.6
0.21
0.39
9.8
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0.
3
45
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4
11.4
236
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
w b
f R
bf
dm
ax b
f P
bf
A b
f v
bf
w/d
bf
τ b
f Ω
bf
Faja
rdo
194
Brid
ge1D
0.63
14.5
0.54
0.90
14.8
7.9
0.
1
27
.1
10
11.4
Faja
rdo
977
Brid
ge2U
0.62
8.6
0.68
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0.
1
12
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48
43.7
Faja
rdo
977
Brid
ge1U
0.62
12.6
0.49
0.89
12.8
6.3
0.
1
25
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35
43.6
Faja
rdo
977
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geBr
idge
0.62
20.9
0.14
0.31
21.0
2.9
0.
2
14
9.5
10
43.7
Faja
rdo
977
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ge1D
0.62
23.4
0.14
0.36
23.5
3.3
0.
2
16
8.0
10
43.7
Faja
rdo
Char
co F
rio1D
0.38
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0.57
0.89
11.8
6.7
0.
1
19
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94
62.4
Faja
rdo
Char
co F
rioBr
idge
0.38
10.5
0.21
0.34
10.8
2.2
0.
2
50
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25
45.6
Faja
rdo
Char
co F
rio1U
0.38
12.2
0.72
1.20
12.6
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0.
0
16
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87
45.6
Faja
rdo
Char
co F
rio2U
0.38
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1
22
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64
45.5
Faja
rdo
Char
co F
rio3U
0.38
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1.59
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1
7.
9
92
45.5
Faja
rdo
Char
co F
rio4U
0.38
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2.75
19.7
19.7
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120
45.5
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rdo
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co F
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0.38
11.3
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0
14
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15
874
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Fa
jard
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Frio
7U0.
3810
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5
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jard
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3611
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13
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8
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7
Faja
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215.
4
Faja
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0.37
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300
121.
7
Faja
rdo
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ar2U
0.03
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0.
0
9.
7
80
9.4
Faja
rdo
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ar1U
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0
14
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4
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jard
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0
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4
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jard
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ar2D
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0
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9.4
Faja
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l Trib
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8
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all T
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4
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8
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3.8
Faja
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l Trib
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8
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jard
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2
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3
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3.8
Faja
rdo
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ajar
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0
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jard
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pper
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5
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27.6
Faja
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Fa
jard
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7
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6
Mam
eyes
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0.58
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22.4
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407.
6
Mam
eyes
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lito
Trai
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0.60
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0.88
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0.
1
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13
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Es
pirit
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nto
Back
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7
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Es
pirit
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ool
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0.
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7
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Es
pirit
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10
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1
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Es
pirit
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nto
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ool
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0
0.
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0.
72
8.
3
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2
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Es
pirit
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nd B
risas
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6810
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0.
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0.
74
10
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1
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57.
9
237
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
w b
f R
bf
dm
ax b
f P
bf
A b
f v
bf
w/d
bf
τ b
f Ω
bf
Espi
ritu
Sant
oBe
hind
Bris
a s1U
0.68
7.5
0.41
0.74
7.8
3.2
0.
2
18
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5
7.9
Espi
ritu
Sant
oBe
hind
Bris
asBR
IDG
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6814
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0.
66
1.
32
15
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10
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0.
1
22
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8
7.9
Mam
eyes
Bisl
ey 3
2U0.
014.
1
0.
30
0.
53
4.
5
1.
3
0.0
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507
17.6
Mam
eyes
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ey 3
1U0.
014.
2
0.
32
0.
53
4.
6
1.
5
0.0
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554
17.7
Mam
eyes
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1D0.
015.
4
0.
31
0.
77
6.
5
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0
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522
18.5
Mam
eyes
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012.
0
0.
30
0.
58
2.
5
0.
8
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516
18.6
Espi
ritu
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avila
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105.
6
0.
41
0.
75
6.
4
2.
6
0.0
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4812
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Es
pirit
u Sa
nto
Dav
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0.10
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1
36
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21
12.2
Espi
ritu
Sant
oD
avila
1U0.
107.
0
0.
19
0.
26
7.
1
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3
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2212
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Es
pirit
u Sa
nto
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0.10
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0.37
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1
26
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24
12.2
Espi
ritu
Sant
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avila
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107.
3
0.
18
0.
27
7.
4
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3
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41.2
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amey
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st P
eak
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1
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3.
4
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8
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194
12.9
Mam
eyes
East
Pea
k1U
0.02
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0.34
0.69
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0
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5
43
320
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M
amey
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st P
eak
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5
0.
16
0.
30
1.
7
0.
3
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217
22.5
Mam
eyes
East
Pea
k2D
0.02
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0.56
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0
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7
77
722
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Es
pirit
u Sa
nto
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erde
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3
0.
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7
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6
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6312
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Es
pirit
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El V
erde
2U0.
5911
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0.
68
1.
53
13
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8.
9
0.1
17.0
143
123.
9
Espi
ritu
Sant
oEl
Ver
de1U
0.59
19.1
0.32
0.72
19.6
6.4
0.
1
58
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68
123.
9
Espi
ritu
Sant
oEl
Ver
de1D
0.59
24.3
0.38
0.81
24.0
9.0
0.
1
64
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79
123.
9
Espi
ritu
Sant
oEl
Ver
de2D
0.59
14.7
0.64
1.31
11.1
7.2
0.
1
22
.9
13
512
3.9
Es
pirit
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nto
ES R
t. 3
Pai
ntba
llBR
IDG
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9025
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0.
52
0.
99
25
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13
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0.
1
48
.1
6
10.5
Espi
ritu
Sant
oES
Wat
erfa
ll3U
0.35
6.3
0.50
0.90
7.6
3.8
0.
1
12
.6
14
481,
000.
9
Espi
ritu
Sant
oES
Wat
erfa
ll2U
0.35
6.1
0.90
1.61
7.1
6.4
0.
1
6.
8
25
941,
001.
2
Espi
ritu
Sant
oES
Wat
erfa
ll1U
0.35
14.1
1.03
2.00
16.4
16.9
0.0
13.7
4581
1,54
8.7
Es
pirit
u Sa
nto
ES W
ater
fall
1D0.
3514
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0.
57
2.
35
17
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9.
8
0.0
25.4
1272
776.
4
Espi
ritu
Sant
oJi
men
ez2U
0.69
13.0
0.37
0.79
13.3
4.9
0.
1
35
.5
39
74.5
Espi
ritu
Sant
oJi
men
ez1U
0.69
22.9
0.25
0.50
21.3
5.3
0.
1
92
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27
74.5
Espi
ritu
Sant
oJi
men
ez1D
0.69
30.9
0.53
1.18
30.1
15.9
0.0
58.4
5774
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Es
pirit
u Sa
nto
Jim
enez
Wat
erfa
l2U
0.11
5.8
0.23
0.53
6.1
1.4
0.
1
25
.4
25
012
1.2
Es
pirit
u Sa
nto
Jim
enez
Wat
erfa
l1U
0.11
4.7
0.49
0.79
5.1
2.5
0.
0
9.
7
43
498
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Es
pirit
u Sa
nto
Jim
enez
Wat
erfa
l1D
0.11
3.3
0.15
0.45
3.7
0.6
0.
2
21
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37
727
2.2
Es
pirit
u Sa
nto
Jim
enez
Wat
erfa
l2D
0.11
10.4
0.82
2.60
12.9
10.6
0.0
12.7
2175
296.
4
Mam
eyes
Juan
Die
go3U
0.03
9.0
0.38
0.69
9.2
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0.
0
23
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13
6611
4.1
M
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11
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7
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2150
114.
8
Mam
eyes
Juan
Die
go1U
0.06
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0.
0
18
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63
098
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M
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BRID
GE
0.06
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60
498
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M
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25
0.
43
4.
4
1.
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441
98.9
238
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
w b
f R
bf
dm
ax b
f P
bf
A b
f v
bf
w/d
bf
τ b
f Ω
bf
Mam
eye s
La C
oca
Trai
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0.03
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0.37
0.64
6.7
2.5
0.
0
16
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51
639
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M
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Coc
a Tr
ail
1U0.
033.
5
0.
33
0.
61
4.
1
1.
3
0.0
10.5
479
40.9
Mam
eyes
La C
oca
Trai
l1D
0.03
5.0
0.36
0.83
6.2
2.3
0.
0
13
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76
960
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M
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Coc
a Tr
ail
2D0.
034.
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0.
24
0.
38
4.
8
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370
44.1
Mam
eyes
La L
ingu
ente
2U0.
016.
3
0.
88
1.
46
8.
3
7.
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1265
16.9
Mam
eyes
La L
ingu
ente
1U0.
013.
9
0.
30
0.
57
4.
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2
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424
17.2
Mam
eyes
La L
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1D0.
012.
2
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31
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2.
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9
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451
17.6
Mam
eyes
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2D0.
013.
9
0.
22
0.
48
4.
1
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9
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316
17.8
Mam
eyes
La M
aqui
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0.03
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241
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M
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Mam
eyes
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4
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159
40.9
Mam
eyes
La M
aqui
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141
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199
44.2
Mam
eyes
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Falls
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1
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67
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4
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7
Mam
eyes
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Falls
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1310
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11
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2
Mam
eyes
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Falls
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4
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44
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9
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7
Mam
eyes
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new
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6714
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7
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9
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Mam
eyes
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Mam
eyes
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6711
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Veg
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0.73
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31
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40
50.1
Espi
ritu
Sant
oLa
s Tr
es T
(1)
2U0.
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8
0.
13
0.
26
4.
2
0.
5
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524.
9
Es
pirit
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nto
Las
Tres
T (
1)1U
0.01
4.6
0.20
0.36
4.7
1.0
0.
0
22
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84
4.9
Espi
ritu
Sant
oLa
s Tr
es T
(1)
1D0.
018.
7
0.
66
1.
22
9.
4
6.
2
0.0
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276
4.9
Espi
ritu
Sant
oLa
s Tr
es T
(1)
2D0.
012.
9
0.
15
0.
25
3.
2
0.
5
0.0
19.1
635.
0
Es
pirit
u Sa
nto
Las
Tres
T (
2)2U
0.01
3.2
0.35
0.64
3.7
1.3
0.
0
9.
1
22
47.
2
Es
pirit
u Sa
nto
Las
Tres
T (
2)1U
0.01
3.8
0.14
0.25
3.8
0.5
0.
0
26
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90
7.3
Espi
ritu
Sant
oLa
s Tr
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(2)
1D0.
017.
0
0.
41
1.
17
7.
7
3.
2
0.0
17.0
263
7.3
Espi
ritu
Sant
oLa
s Tr
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(2)
2D0.
012.
9
0.
31
0.
64
3.
3
1.
0
0.0
9.3
199
7.3
Mam
eyes
Mam
eyes
Rt.
31U
0.71
14.9
0.83
1.61
15.4
12.7
0.1
18.0
1916
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M
amey
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amey
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t. 3
1D0.
7116
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1.
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1.
78
16
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18
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0.
0
14
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26
16.8
Espi
ritu
Sant
oPr
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3U0.
022.
5
0.
27
1.
08
3.
9
1.
1
0.0
9.0
436
24.1
Espi
ritu
Sant
oPr
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2U0.
021.
8
0.
14
0.
21
2.
1
0.
3
0.1
13.1
219
26.2
Espi
ritu
Sant
oPr
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1U0.
022.
1
0.
26
0.
46
2.
6
0.
7
0.0
8.0
419
26.2
Espi
ritu
Sant
oPr
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1D0.
027.
1
0.
67
1.
08
8.
5
5.
7
0.0
10.6
1074
26.8
Espi
ritu
Sant
oPr
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2D0.
022.
0
0.
25
0.
43
2.
3
0.
6
0.0
7.9
406
26.8
239
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
bf
w b
f R
bf
dm
ax b
f P
bf
A b
f v
bf
w/d
bf
τ b
f Ω
bf
Mam
eye s
Puen
te R
oto
2U0.
6110
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0.
65
1.
35
10
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7.
1
0.1
15.6
9992
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M
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ente
Rot
o1U
/BRID
GE
0.61
19.3
0.62
1.12
19.6
12.1
0.1
31.2
9492
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M
amey
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ente
Rot
o1D
0.61
6.9
0.62
1.08
7.6
4.7
0.
1
11
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95
92.9
Mam
eyes
Puen
te R
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2D0.
6116
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0.
64
1.
25
16
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10
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0.
1
25
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98
93.0
Mam
eyes
Q. A
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7
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M
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9
36
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Mam
eyes
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non
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9
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0
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4
39
0.1
Espi
ritu
Sant
oSo
nado
ra3U
0.14
6.7
0.68
1.35
8.6
5.8
0.
0
9.
9
19
0039
7.7
Es
pirit
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nto
Sona
dora
2U0.
1414
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79
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16
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Es
pirit
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147.
9
0.
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5
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155.
0
Espi
ritu
Sant
oSo
nado
ra1D
0.15
6.5
0.32
0.77
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0.
1
20
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44
721
3.9
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pirit
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nto
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dora
2D0.
157.
9
0.
27
0.
58
8.
2
2.
2
0.1
29.5
377
214.
1
Espi
ritu
Sant
oTo
ronj
a2U
0.00
1.6
0.28
0.49
2.2
0.6
0.
0
5.
7
36
42.
4
Es
pirit
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nto
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nja
1U0.
001.
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0.
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0.
11
1.
7
0.
1
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762.
5
Es
pirit
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nto
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nja
1D0.
003.
2
0.
34
0.
60
3.
6
1.
2
0.0
9.4
438
2.6
Espi
ritu
Sant
oTo
ronj
a2D
0.00
4.2
0.15
0.27
4.4
0.6
0.
0
28
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19
02.
7
240
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDQ
ac
w a
c R
ac
dm
ax a
c P
ac
A a
c v
ac
w/d
ac
τ a
c Ω
ac
Mam
eye s
M1
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96
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24
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2.
6
11
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23
1,
459
M
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259
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23.2
2.35
3.73
25.8
60.6
1.0
6.2
55
1,39
9
Mam
eyes
M3
52.5
929
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0.
96
1.
34
30
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29
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1.
8
22
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73
3,
984
M
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448
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32.3
0.69
1.70
33.0
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111
7,88
2
Mam
eyes
M5
48.2
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93
1.
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27
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9
14
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5
7,
539
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644
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20.6
1.12
1.75
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24.0
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Mam
eyes
M7
52.3
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25
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Mam
eyes
M9
49.5
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497
Mam
eyes
M11
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9
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130
7,59
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Mam
eyes
M13
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M
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Mam
eyes
M15
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Mam
eyes
M17
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M
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181.
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6
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Mam
eyes
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Mam
eyes
M21
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5.
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219
1,
821
M
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2251
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0.95
23.3
13.5
3.9
23.6
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5,89
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Mam
eyes
M23
1.64
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5
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M
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M
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2543
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12,7
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M
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Espi
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Espi
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246
Wat
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plex
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Sect
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2.5
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155
6,80
8
Mam
eyes
Q. A
non
2U0.
773.
3
0.
48
0.
99
4.
3
2.
0
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52
84
Mam
eyes
Q. A
non
1U/B
RID
GE
0.78
4.4
0.46
0.79
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3
9.
4
50
85
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amey
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1
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11
1
86
M
amey
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3
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63
87
Es
pirit
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nto
Sona
dora
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4110
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1.
01
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31
13
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13
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7
10
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834
26
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Espi
ritu
Sant
oSo
nado
ra2U
9.42
18.0
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2.33
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26.7
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3,69
6
26,3
92
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pirit
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nto
Sona
dora
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4313
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98
2.
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16
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6
13
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072
10
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Espi
ritu
Sant
oSo
nado
ra1D
10.1
414
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0.
58
1.
50
15
.9
9.
2
1.1
25.6
817
14,3
17
Es
pirit
u Sa
nto
Sona
dora
2D10
.16
10.3
0.75
1.21
10.9
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2
13
.8
1,
054
14
,336
Espi
ritu
Sant
oTo
ronj
a2U
0.17
5.0
0.36
0.91
5.6
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0.
1
13
.9
46
0
21
5
Es
pirit
u Sa
nto
Toro
nja
1U0.
173.
5
0.
35
0.
56
3.
9
1.
4
0.1
9.9
457
224
Espi
ritu
Sant
oTo
ronj
a1D
0.18
4.5
0.63
1.16
5.4
3.4
0.
1
7.
2
81
2
23
1
Es
pirit
u Sa
nto
Toro
nja
2D0.
187.
0
0.
41
0.
69
7.
3
3.
0
0.1
17.2
524
238
247
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDd1
6d5
0d8
4dm
axSi
mps
on's
In
dex
Sort
ing
Inde
xBe
droc
kM
egab
ould
erBo
ulde
rCo
bble
Gra
vel
Sand
Fine
s
Mam
eye s
M1
144
192
---
4.26
13.9
1.2
0.0
12.0
27.5
28.7
24.0
6.6
Mam
eyes
M2
2187
457
500
3.01
4.7
0.0
0.0
26.7
20.7
46.5
3.4
2.6
Mam
eyes
M3
2010
851
570
03.
335.
00.
00.
031
.922
.138
.06.
71.
2M
amey
esM
427
160
614
1800
3.20
4.8
6.0
0.0
41.8
16.3
32.6
2.7
0.5
Mam
eyes
M5
2932
812
0827
004.
586.
425
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520
.34.
00.
0M
amey
esM
615
4624
680
03.
444.
124
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013
.110
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40.
0M
amey
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718
110
451
500
3.70
5.0
0.0
0.0
26.3
29.9
32.3
6.0
5.4
Mam
eyes
M8
2311
254
360
03.
054.
90.
00.
035
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20.
0M
amey
esM
911
9849
810
004.
416.
733
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020
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53.
0M
amey
esM
1014
4638
545
03.
215.
215
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018
.28.
048
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09.
1M
amey
esM
1183
188
601
1000
3.63
2.7
24.2
0.0
32.3
32.3
8.9
0.0
2.4
Mam
eyes
M12
1988
173
500
3.04
3.0
8.4
0.0
4.2
42.0
37.0
8.4
0.0
Mam
eyes
M13
2510
523
380
02.
223.
063
.61.
53.
815
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.60.
01.
5M
amey
esM
1469
169
568
1500
3.72
2.9
26.2
0.0
27.9
32.8
12.3
0.0
0.8
Mam
eyes
M15
2712
456
070
02.
824.
51.
80.
036
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.10.
00.
0M
amey
esM
1652
430
1170
1500
2.68
4.7
54.4
6.7
24.4
4.4
10.0
0.0
0.0
Mam
eyes
M17
4437
591
313
003.
564.
618
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143
.59.
720
.20.
00.
0M
amey
esM
1833
306
1185
1700
4.20
6.0
9.1
14.9
35.5
12.4
25.6
0.0
2.5
Mam
eyes
M19
2292
604
650
3.23
5.3
21.3
0.0
34.0
3.2
38.3
0.0
3.2
Mam
eyes
M20
3518
262
195
03.
434.
212
.40.
040
.020
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00.
0M
amey
esM
2136
370
853
900
3.51
4.9
31.8
5.6
37.4
4.7
19.6
0.9
0.0
Mam
eyes
M22
2116
161
112
003.
195.
40.
00.
044
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.126
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90.
0M
amey
esM
2375
502
1582
2400
3.12
4.6
0.0
24.8
46.8
10.1
17.4
0.0
0.9
Mam
eyes
M24
2310
056
222
003.
014.
94.
91.
233
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50.
0M
amey
esM
2521
114
578
1400
3.93
5.3
25.0
0.0
29.5
9.8
30.3
5.3
0.0
Mam
eyes
M26
---
---
---
---
---
---
---
---
---
---
---
---
---
Mam
eyes
M27
8734
971
011
002.
632.
99.
10.
056
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00.
0M
amey
esM
2825
231
658
2.12
5.2
65.4
0.0
17.9
3.8
10.3
2.6
0.0
Mam
eyes
M29
4236
877
420
002.
564.
34.
14.
157
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00.
0M
amey
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3093
294
719
1200
2.83
2.8
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50.4
27.9
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0.0
0.0
Mam
eyes
M31
9134
070
514
002.
522.
855
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027
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41.
70.
9M
amey
esM
3228
146
648
1200
4.37
4.8
23.3
3.8
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17.3
26.3
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0.0
Mam
eyes
M33
3417
059
412
003.
594.
27.
90.
038
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50.
0M
amey
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3424
9746
013
003.
624.
413
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023
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40.
7M
amey
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3520
7328
816
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743.
853
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08.
112
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40.
0M
amey
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3646
345
707
1300
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58.9
12.9
22.6
0.0
0.0
App
endi
x. G
rain
Siz
e an
d P
ebbl
e C
oun
t D
ata
248
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDd1
6d5
0d8
4dm
axSi
mps
on's
In
dex
Sort
ing
Inde
xBe
droc
kM
egab
ould
erBo
ulde
rCo
bble
Gra
vel
Sand
Fine
s
Mam
eye s
M37
2389
199
800
3.10
2.9
46.0
0.0
6.2
21.2
24.8
1.8
0.0
Mam
eyes
M38
2917
463
010
002.
714.
73.
90.
045
.312
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.30.
00.
0M
amey
esM
3921
158
573
700
3.84
5.2
8.7
0.0
35.6
30.8
13.5
11.5
0.0
Mam
eyes
M40
189
254
635
3.52
22.4
0.0
0.0
19.4
40.8
21.4
18.4
0.0
Mam
eyes
M41
2512
738
117
783.
863.
90.
04.
825
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0M
amey
esM
4238
229
785
1626
3.05
4.5
0.0
7.2
38.1
39.2
15.5
0.0
0.0
Mam
eyes
M43
1610
222
991
43.
103.
80.
00.
021
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80.
0Es
pirit
u Sa
nto
ES1
---
---
---
---
---
---
---
---
---
---
---
---
---
Espi
ritu
Sant
oES
364
203
610
1397
2.65
3.1
0.0
0.0
47.5
34.7
17.8
0.0
0.0
Espi
ritu
Sant
oES
412
740
612
4525
402.
743.
10.
016
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pirit
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7630
583
513
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513.
30.
00.
054
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pirit
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nto
ES6
7619
353
312
192.
652.
63.
05.
950
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90.
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pirit
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nto
ES7
2511
435
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73.
463.
70.
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033
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70.
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pirit
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ES8
7623
548
316
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652.
50.
02.
952
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pirit
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ES9
5173
718
2918
293.
686.
012
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019
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00.
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pirit
u Sa
nto
ES10
203
673
1829
1829
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48.9
14.9
3.2
0.0
0.0
Espi
ritu
Sant
oES
1111
178
551
1829
4.19
7.0
8.6
3.2
32.3
30.1
16.1
9.7
0.0
Espi
ritu
Sant
oES
1230
127
351
610
1.44
3.4
82.8
0.0
3.2
8.6
5.4
0.0
0.0
Espi
ritu
Sant
oES
1351
381
1278
1981
3.47
5.0
4.1
15.6
43.4
13.1
23.8
0.0
0.0
Espi
ritu
Sant
oES
1462
279
610
1575
2.95
3.1
30.3
0.0
46.5
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16.2
0.0
0.0
Espi
ritu
Sant
oES
1520
478
713
9527
432.
902.
60.
035
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23.
20.
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pirit
u Sa
nto
ES16
8030
573
713
212.
163.
00.
00.
062
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80.
0Es
pirit
u Sa
nto
ES17
112
213
9722
614.
7237
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021
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pirit
u Sa
nto
ES18
812
755
291
43.
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20.
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97.
3Es
pirit
u Sa
nto
ES19
8943
210
0615
242.
623.
40.
014
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pirit
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6539
483
815
242.
373.
62.
70.
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50.
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pirit
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5117
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53.
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pirit
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7748
314
8525
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404.
410
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00.
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pirit
u Sa
nto
ES23
---
---
---
---
---
---
---
---
---
---
---
---
---
Espi
ritu
Sant
oES
24--
---
---
---
---
---
---
---
---
---
---
---
---
-Es
pirit
u Sa
nto
ES25
112
729
661
03.
1317
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00.
024
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928
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pirit
u Sa
nto
ES26
11
11
1.00
1.0
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0.0
0.0
0.0
0.0
100.
00.
0Es
pirit
u Sa
nto
ES27
11
11
1.00
1.0
0.0
0.0
0.0
0.0
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100.
00.
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pirit
u Sa
nto
ES28
5171
121
3430
483.
446.
50.
037
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00.
0Es
pirit
u Sa
nto
ES29
254
1295
1880
3251
3.10
2.7
48.5
18.4
21.4
7.8
3.9
0.0
0.0
Espi
ritu
Sant
oES
3014
647
012
1924
382.
792.
95.
814
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62.
90.
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pirit
u Sa
nto
ES31
1512
761
015
244.
006.
340
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014
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00.
0
249
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDd1
6d5
0d8
4dm
axSi
mps
on's
In
dex
Sort
ing
Inde
xBe
droc
kM
egab
ould
erBo
ulde
rCo
bble
Gra
vel
Sand
Fine
s
Espi
ritu
Sant
oES
3238
152
381
813
4.00
3.2
33.0
0.0
24.0
24.0
16.0
2.0
1.0
Espi
ritu
Sant
oES
3344
146
506
1549
3.01
3.4
0.0
0.0
31.6
28.4
38.9
1.1
0.0
Espi
ritu
Sant
oES
3415
240
612
7027
433.
792.
920
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50.
00.
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pirit
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nto
ES35
---
---
---
---
---
---
---
---
---
---
---
---
---
Espi
ritu
Sant
oES
36--
---
---
---
---
---
---
---
---
---
---
---
---
-Es
pirit
u Sa
nto
ES37
4020
381
324
383.
474.
50.
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838
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90.
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pirit
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nto
ES38
2519
186
414
223.
275.
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60.
044
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60.
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anco
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11
726
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94.4
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Blan
coB2
11
189
1.65
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11
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110
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B11
101
330
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4978
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2816
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412
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320
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135
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114
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033
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(1)
2U1
7633
063
54.
1619
.70.
00.
029
.021
.029
.05.
016
.0Es
pirit
u Sa
nto
Las
Tres
T (
1)1U
038
305
610
5.41
78.1
12.4
0.0
18.1
21.9
23.8
7.6
16.2
Espi
ritu
Sant
oLa
s Tr
es T
(1)
1D13
7668
616
763.
307.
30.
03.
933
.010
.741
.77.
82.
9Es
pirit
u Sa
nto
Las
Tres
T (
1)2D
038
178
635
4.48
59.6
0.0
0.0
11.9
25.7
27.7
11.9
22.8
Espi
ritu
Sant
oLa
s Tr
es T
(2)
2U1
1933
094
04.
2318
.20.
00.
020
.211
.836
.116
.815
.1Es
pirit
u Sa
nto
Las
Tres
T (
2)1U
119
176
737
3.85
13.3
15.0
0.0
12.5
9.2
44.2
10.0
9.2
Espi
ritu
Sant
oLa
s Tr
es T
(2)
1D1
191
356
1067
3.35
18.9
0.0
0.0
47.3
15.2
15.2
7.1
15.2
Espi
ritu
Sant
oLa
s Tr
es T
(2)
2D1
5441
915
494.
9420
.50.
02.
022
.419
.426
.515
.314
.3M
amey
esM
amey
es R
t. 3
1U1
152
267
483
3.21
16.3
0.0
0.0
19.1
46.4
17.3
17.3
0.0
Mam
eyes
Mam
eyes
Rt.
31D
138
140
635
3.10
11.8
0.0
0.0
8.0
18.0
44.0
30.0
0.0
Espi
ritu
Sant
oPr
ieta
3U25
857
218
1730
482.
282.
70.
024
.560
.85.
97.
80.
01.
0Es
pirit
u Sa
nto
Prie
ta2U
5145
799
214
733.
324.
40.
017
.845
.820
.613
.12.
80.
0Es
pirit
u Sa
nto
Prie
ta1U
7630
563
591
42.
772.
90.
00.
050
.030
.412
.72.
04.
9Es
pirit
u Sa
nto
Prie
ta1D
024
858
411
683.
4810
8.1
0.9
2.7
40.7
18.6
8.0
0.9
28.3
Espi
ritu
Sant
oPr
ieta
2D0
3217
640
63.
9659
.30.
00.
05.
931
.419
.611
.831
.4
253
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDd1
6d5
0d8
4dm
axSi
mps
on's
In
dex
Sort
ing
Inde
xBe
droc
kM
egab
ould
erBo
ulde
rCo
bble
Gra
vel
Sand
Fine
s
Mam
eye s
Puen
te R
oto
2U25
6415
281
32.
602.
411
.10.
07.
128
.353
.50.
00.
0M
amey
esPu
ente
Rot
o1U
/BRID
GE
5115
953
710
163.
623.
39.
90.
032
.427
.928
.80.
90.
0M
amey
esPu
ente
Rot
o1D
3217
853
015
243.
854.
139
.63.
618
.918
.018
.01.
80.
0M
amey
esPu
ente
Rot
o2D
1764
323
787
3.06
4.4
50.5
0.0
9.0
18.0
17.1
5.4
0.0
Mam
eyes
Q. A
non
2U0
1556
192
2.36
74.6
0.0
0.0
0.0
5.0
50.5
4.0
40.6
Mam
eyes
Q. A
non
1U/B
RID
GE
025
5916
01.
7876
.90.
00.
00.
02.
071
.03.
024
.0M
amey
esQ
. Ano
n1D
00
135
261
2.21
116.
10.
00.
00.
031
.310
.10.
058
.6M
amey
esQ
. Ano
n2D
014
6521
42.
7480
.40.
00.
00.
08.
745
.27.
738
.5Es
pirit
u Sa
nto
Sona
dora
3U76
305
1981
3505
4.10
5.1
5.2
20.9
33.9
23.5
16.5
0.0
0.0
Espi
ritu
Sant
oSo
nado
ra2U
7433
098
618
543.
393.
60.
015
.040
.030
.015
.00.
00.
0Es
pirit
u Sa
nto
Sona
dora
1U51
711
2134
3048
3.44
6.5
0.0
37.2
31.9
11.7
19.1
0.0
0.0
Espi
ritu
Sant
oSo
nado
ra1D
152
483
1321
2438
3.18
2.9
0.0
25.2
41.1
28.0
5.6
0.0
0.0
Espi
ritu
Sant
oSo
nado
ra2D
7625
482
320
574.
443.
313
.313
.324
.831
.417
.10.
00.
0Es
pirit
u Sa
nto
Toro
nja
2U1
7643
794
05.
4320
.922
.90.
022
.915
.219
.010
.59.
5Es
pirit
u Sa
nto
Toro
nja
1U1
9552
894
03.
6423
.00.
00.
039
.613
.226
.417
.03.
8Es
pirit
u Sa
nto
Toro
nja
1D1
3833
011
683.
6318
.20.
00.
022
.38.
742
.710
.715
.5Es
pirit
u Sa
nto
Toro
nja
2D0
1335
616
764.
9784
.30.
00.
018
.019
.821
.621
.618
.9
254
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDD
rop
Pdep
max
Pdep
cv
Plen
gth
Pare
aPv
olum
eag
r d
agr
ufs
t d
fst
uur
b d
urb
u
Faja
rdo
194
Brid
ge1U
0.5
4.25
0.
27
60.0
77
2
1,
190
1.
00
0.
43
-
0.
49
-
0.
08
Fa
jard
o19
4 Br
idge
Brid
ge0.
5
--
---
---
---
---
-1.
00
0.
43
-
0.
49
-
0.
08
Fa
jard
o19
4 Br
idge
1D0.
5
2.
12
0.20
19
5.0
2,82
8
1,54
1
1.00
0.42
-
0.47
-
0.11
Faja
rdo
977
Brid
ge2U
1.0
2.02
0.
46
33.0
28
4
20
5
0.96
0.40
0.03
0.56
0.01
0.04
Faja
rdo
977
Brid
ge1U
1.0
1.20
0.
39
44.0
55
6
27
8
0.96
0.40
0.03
0.56
0.01
0.04
Faja
rdo
977
Brid
geBr
idge
1.0
---
---
56.0
1,
172
16
5
0.96
0.40
0.03
0.56
0.01
0.04
Faja
rdo
977
Brid
ge1D
1.0
0.40
0.
16
85.0
1,
989
27
8
0.96
0.40
0.03
0.56
0.01
0.04
Faja
rdo
Char
co F
rio1D
2.0
0.91
0.
30
30.0
33
6
20
2
0.97
0.04
0.02
0.96
0.01
0.00
Faja
rdo
Char
co F
rioBr
idge
2.0
---
---
---
---
---
0.97
0.04
0.02
0.96
0.01
0.00
Faja
rdo
Char
co F
rio1U
2.0
2.30
0.
46
41.0
50
0
37
4
0.97
0.04
0.02
0.96
0.01
0.00
Faja
rdo
Char
co F
rio2U
2.0
1.92
0.
24
53.0
63
6
35
0
0.97
0.04
0.02
0.96
0.01
0.00
Faja
rdo
Char
co F
rio3U
2.0
1.87
0.
27
23.0
14
0
13
2
0.97
0.04
0.02
0.96
0.01
0.00
Faja
rdo
Char
co F
rio4U
2.0
3.05
0.
36
61.0
1,
092
1,
200
0.
97
0.
04
0.
02
0.
96
0.
01
0.
00
Fa
jard
oCh
arco
Frio
5U2.
0
2.
13
0.41
47
.0
531
445
0.
97
0.
04
0.
02
0.
96
0.
01
0.
00
Fa
jard
oCh
arco
Frio
7U2.
0
1.
66
0.52
48
.0
480
216
0.
97
0.
04
0.
02
0.
96
0.
01
0.
00
Fa
jard
oLa
Tin
aja
3U4.
0
3.
60
0.39
43
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513
297
0.
91
-
0.
08
1.
00
0.
01
0.
00
Fa
jard
oLa
Tin
aja
2U4.
0
3.
60
0.48
22
.0
198
488
0.
93
-
0.
07
1.
00
0.
01
0.
00
Fa
jard
oLa
Tin
aja
1U2.
5
3.
30
0.50
39
.0
460
456
0.
93
-
0.
06
1.
00
0.
01
0.
00
Fa
jard
oRiv
er B
ar2U
2.0
0.64
0.
40
13.5
37
13
0.
95
0.
15
0.
03
0.
84
0.
02
0.
01
Fa
jard
oRiv
er B
ar1U
2.0
1.42
0.
63
10.5
91
59
0.
95
0.
15
0.
03
0.
84
0.
02
0.
01
Fa
jard
oRiv
er B
ar1D
2.0
1.20
0.
46
13.5
14
2
72
0.
96
0.
15
0.
03
0.
84
0.
02
0.
01
Fa
jard
oRiv
er B
ar2D
2.0
0.56
0.
13
6.0
37
8
0.
96
0.
15
0.
03
0.
84
0.
01
0.
01
Fa
jard
oSm
all T
ribut
ary
2U3.
5
0.
58
0.28
3.
0
9
3
0.
94
-
0.
05
1.
00
0.
01
-
Fa
jard
oSm
all T
ribut
ary
1U3.
5
0.
51
0.19
3.
5
11
3
0.94
-
0.05
1.00
0.01
-
Faja
rdo
Smal
l Trib
utar
y1D
3.5
0.72
0.
34
5.0
19
5
0.
94
-
0.
05
1.
00
0.
01
-
Fa
jard
oSm
all T
ribut
ary
2D3.
5
1.
15
0.34
4.
0
19
9
0.94
-
0.05
1.00
0.01
-
Faja
rdo
Upp
er F
ajar
doTr
ib2U
6.0
0.81
0.
49
5.5
9
6
0.79
-
0.20
1.00
0.01
-
Faja
rdo
Upp
er F
ajar
doTr
ib1U
6.0
0.83
0.
43
5.0
10
8
0.
79
-
0.
20
1.
00
0.
01
-
Fa
jard
oU
pper
Faj
ardo
3U6.
0
1.
37
0.32
39
.0
273
196
0.
81
-
0.
19
1.
00
0.
01
-
Fa
jard
oU
pper
Faj
ardo
2U6.
0
2.
06
0.57
18
.0
175
184
0.
82
-
0.
18
1.
00
0.
01
-
Fa
jard
oU
pper
Faj
ardo
1U6.
0
2.
21
0.36
36
.0
364
411
0.
83
-
0.
17
1.
00
0.
01
-
M
amey
esAn
gelit
o Tr
ail
1U3.
0
2.
17
0.21
31
.0
496
251
0.
13
-
0.
79
1.
00
0.
09
0.
00
M
amey
esAn
gelit
o Tr
ail
1D3.
0
3.
45
0.30
54
.0
1,08
0
1,86
8
0.13
-
0.79
1.00
0.09
0.00
Mam
eyes
Ange
lito
Trai
l2D
3.0
---
---
---
---
---
0.13
-
0.79
1.00
0.09
0.00
Espi
ritu
Sant
oBa
ckw
ay S
choo
l2U
0.5
1.59
0.
57
43.5
55
2
20
4
0.96
0.19
-
0.65
0.04
0.16
Espi
ritu
Sant
oBa
ckw
ay S
choo
l1U
0.5
1.59
0.
34
78.0
91
3
36
7
0.96
0.20
-
0.64
0.04
0.16
Espi
ritu
Sant
oBa
ckw
ay S
choo
l1D
0.5
1.45
0.
51
35.0
35
0
17
7
0.96
0.20
-
0.64
0.04
0.16
Espi
ritu
Sant
oBa
ckw
ay S
choo
l2D
0.5
1.02
0.
40
61.5
48
9
19
9
0.96
0.20
-
0.64
0.04
0.16
Espi
ritu
Sant
oBe
hind
Bris
as2U
0.1
1.27
0.
33
27.3
29
2
13
9
0.88
0.25
-
0.57
0.12
0.17
App
endi
x. A
ddit
ion
al B
ioco
mpl
exit
y P
ool I
nfo
rmat
ion
255
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDD
rop
Pdep
max
Pdep
cv
Plen
gth
Pare
aPv
olum
eag
r d
agr
ufs
t d
fst
uur
b d
urb
u
Espi
ritu
Sant
oBe
hind
Bris
a s1U
0.1
0.81
0.
37
12.0
90
38
0.
87
0.
25
-
0.
57
0.
13
0.
17
Es
pirit
u Sa
nto
Behi
nd B
risas
BRID
GE
0.1
1.76
0.
47
42.5
63
1
44
1
0.87
0.25
-
0.57
0.14
0.17
Mam
eyes
Bisl
ey 3
2U3.
0
0.
60
0.46
3.
2
13
4
0.11
-
0.81
1.00
0.08
-
Mam
eyes
Bisl
ey 3
1U3.
0
0.
68
0.28
3.
2
13
5
0.11
-
0.81
1.00
0.08
-
Mam
eyes
Bisl
ey 3
1D3.
0
0.
75
0.49
3.
8
21
8
0.11
-
0.81
1.00
0.08
-
Mam
eyes
Bisl
ey 3
2D3.
0
0.
62
0.20
4.
5
9
3
0.
11
-
0.
81
1.
00
0.
08
-
Es
pirit
u Sa
nto
Dav
ila3U
2.0
0.83
--
-22
.3
125
58
0.84
0.18
-
0.73
0.16
0.09
Espi
ritu
Sant
oD
avila
2U2.
0
0.
44
0.33
9.
0
58
10
0.
84
0.
18
-
0.
73
0.
16
0.
09
Es
pirit
u Sa
nto
Dav
ila1U
2.0
0.59
0.
28
30.7
21
5
41
0.
86
0.
18
-
0.
73
0.
14
0.
09
Es
pirit
u Sa
nto
Dav
ila1D
2.0
0.47
0.
22
12.0
65
14
0.
87
0.
18
-
0.
73
0.
14
0.
09
Es
pirit
u Sa
nto
Dav
ila2D
2.0
0.39
0.
31
11.3
82
15
0.
88
0.
18
-
0.
73
0.
12
0.
09
M
amey
esEa
st P
eak
2U7.
0
0.
52
0.32
6.
3
20
19
0.
08
-
0.
87
1.
00
0.
05
-
M
amey
esEa
st P
eak
1U7.
0
0.
68
0.29
12
.5
36
25
0.08
-
0.87
1.00
0.05
-
Mam
eyes
East
Pea
k1D
7.0
0.55
0.
26
7.3
11
10
0.08
-
0.87
1.00
0.05
-
Mam
eyes
East
Pea
k2D
7.0
1.27
0.
27
3.0
13
17
0.08
-
0.87
1.00
0.05
-
Espi
ritu
Sant
oEl
Ver
de3U
2.0
1.03
--
-13
.8
128
36
0.81
0.04
-
0.95
0.19
0.01
Espi
ritu
Sant
oEl
Ver
de2U
2.0
1.79
0.
35
32.5
37
7
28
9
0.81
0.04
-
0.95
0.19
0.01
Espi
ritu
Sant
oEl
Ver
de1U
2.0
0.96
0.
39
20.6
39
3
13
1
0.82
0.04
-
0.95
0.18
0.02
Espi
ritu
Sant
oEl
Ver
de1D
2.0
1.95
0.
51
38.0
92
3
34
3
0.83
0.04
-
0.95
0.17
0.02
Espi
ritu
Sant
oEl
Ver
de2D
2.0
1.80
0.
19
24.0
35
4
17
2
0.83
0.04
-
0.94
0.17
0.02
Espi
ritu
Sant
oES
Rt.
3 P
aint
ball
BRID
GE
0.1
2.50
0.
37
200.
0
5,
000
2,
632
-
0.
15
-
0.
77
1.
00
0.
08
Es
pirit
u Sa
nto
ES W
ater
fall
3U9.
0
1.
25
0.41
8.
6
54
33
0.
46
-
0.
40
1.
00
0.
13
-
Es
pirit
u Sa
nto
ES W
ater
fall
2U9.
0
2.
62
0.18
12
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73
77
0.47
-
0.40
1.00
0.13
-
Espi
ritu
Sant
oES
Wat
erfa
ll1U
9.0
2.76
0.
41
27.0
38
1
45
5
0.47
-
0.40
1.00
0.13
-
Espi
ritu
Sant
oES
Wat
erfa
ll1D
9.0
3.10
0.
60
15.5
22
5
15
2
0.48
-
0.39
1.00
0.14
-
Espi
ritu
Sant
oJi
men
ez2U
0.5
1.19
0.
28
30.5
39
7
14
9
0.93
0.12
-
0.82
0.07
0.06
Espi
ritu
Sant
oJi
men
ez1U
0.5
1.00
0.
37
94.0
2,
155
49
5
0.93
0.12
-
0.82
0.07
0.06
Espi
ritu
Sant
oJi
men
ez1D
0.5
2.20
0.
51
55.0
1,
699
87
7
0.93
0.12
-
0.82
0.07
0.06
Espi
ritu
Sant
oJi
men
ez W
ater
fall
2U5.
0
1.
55
0.47
16
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94
22
0.44
-
0.52
1.00
0.05
0.00
Espi
ritu
Sant
oJi
men
ez W
ater
fall
1U5.
0
1.
00
0.69
17
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80
42
0.44
-
0.51
1.00
0.05
0.00
Espi
ritu
Sant
oJi
men
ez W
ater
fall
1D5.
0
0.
80
0.71
8.
0
26
5
0.45
-
0.51
1.00
0.05
0.00
Espi
ritu
Sant
oJi
men
ez W
ater
fall
2D5.
0
2.
60
0.69
8.
2
85
87
0.
46
-
0.
49
1.
00
0.
05
0.
00
M
amey
esJu
an D
iego
3U5.
0
1.
01
0.66
9.
2
83
32
0.
08
-
0.
86
1.
00
0.
06
-
M
amey
esJu
an D
iego
2U5.
0
1.
01
0.27
8.
9
98
60
0.
08
-
0.
86
1.
00
0.
06
-
M
amey
esJu
an D
iego
1U5.
0
0.
75
0.55
3.
9
25
10
0.
09
-
0.
86
1.
00
0.
06
-
M
amey
esJu
an D
iego
BRID
GE
5.0
0.73
0.
44
3.5
15
5
0.
09
-
0.
86
1.
00
0.
06
-
M
amey
esJu
an D
iego
1D5.
0
0.
77
0.44
5.
2
21
6
0.09
-
0.86
1.00
0.06
-
Mam
eyes
La C
oca
Trai
l2U
5.0
0.81
0.
38
8.5
51
21
0.10
-
0.83
1.00
0.07
-
Mam
eyes
La C
oca
Trai
l1U
5.0
0.68
0.
42
4.3
15
6
0.
10
-
0.
83
1.
00
0.
07
-
M
amey
esLa
Coc
a Tr
ail
1D5.
0
0.
87
0.36
4.
5
23
10
0.
10
-
0.
83
1.
00
0.
07
-
256
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDD
rop
Pdep
max
Pdep
cv
Plen
gth
Pare
aPv
olum
eag
r d
agr
ufs
t d
fst
uur
b d
urb
u
Mam
eye s
La C
oca
Trai
l2D
5.0
0.49
0.
23
3.0
14
3
0.
10
-
0.
83
1.
00
0.
07
-
M
amey
esLa
Lin
guen
te2U
5.0
1.48
0.
40
6.5
41
47
0.13
-
0.79
1.00
0.09
-
Mam
eyes
La L
ingu
ente
1U5.
0
0.
60
0.11
4.
2
16
5
0.13
-
0.79
1.00
0.09
-
Mam
eyes
La L
ingu
ente
1D5.
0
0.
56
0.25
4.
0
9
4
0.
13
-
0.
78
1.
00
0.
09
-
M
amey
esLa
Lin
guen
te2D
5.0
0.50
0.
33
5.2
20
5
0.
13
-
0.
78
1.
00
0.
09
-
M
amey
esLa
Maq
uina
2U4.
0
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12
0.56
7.
0
15
13
0.
14
-
0.
76
1.
00
0.
10
-
M
amey
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Maq
uina
1U4.
0
0.
35
0.77
5.
6
13
2
0.14
-
0.76
1.00
0.10
-
Mam
eyes
La M
aqui
naBR
IDG
E4.
0
0.
14
---
2.5
10
1
0.
14
-
0.
76
1.
00
0.
10
-
M
amey
esLa
Maq
uina
1D4.
0
0.
52
0.94
3.
8
8
1
0.
14
-
0.
76
1.
00
0.
10
-
M
amey
esLa
Maq
uina
2D4.
0
0.
46
0.36
1.
9
5
1
0.
14
-
0.
76
1.
00
0.
10
-
M
amey
esLa
Min
a Fa
lls2U
7.0
1.02
0.
58
6.3
51
19
0.08
-
0.86
1.00
0.06
0.01
Mam
eyes
La M
ina
Falls
1U7.
0
2.
97
0.75
9.
5
96
71
0.
08
-
0.
86
1.
00
0.
06
0.
01
M
amey
esLa
Min
a Fa
lls1D
7.0
0.83
0.
47
8.7
47
23
0.08
-
0.86
1.00
0.06
0.01
Mam
eyes
La V
ega
2U (
new
)1.
0
1.
02
0.16
66
.0
931
574
0.
32
0.
05
0.
46
0.
94
0.
22
0.
01
M
amey
esLa
Veg
a2U
(ol
d)1.
0
0.
64
---
34.5
59
3
20
5
0.32
0.05
0.46
0.94
0.22
0.01
Mam
eyes
La V
ega
1U1.
0
1.
06
---
47.5
79
3
40
1
0.32
0.05
0.46
0.94
0.22
0.01
Mam
eyes
La V
ega
RO
AD1.
0
1.
87
0.46
35
.0
392
428
0.
35
0.
06
0.
41
0.
93
0.
24
0.
01
M
amey
esLa
Veg
a1D
1.0
0.83
--
-30
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351
146
0.
35
0.
06
0.
41
0.
93
0.
24
0.
01
M
amey
esLa
Veg
a2D
1.0
0.83
0.
32
115.
0
1,
944
1,
038
0.
39
0.
06
0.
35
0.
93
0.
26
0.
01
Es
pirit
u Sa
nto
Las
Tres
T (
1)2U
2.0
0.36
0.
51
4.5
17
2
0.
57
0.
29
0.
20
0.
63
0.
23
0.
08
Es
pirit
u Sa
nto
Las
Tres
T (
1)1U
2.0
0.68
0.
34
15.0
69
14
0.
57
0.
29
0.
20
0.
63
0.
23
0.
08
Es
pirit
u Sa
nto
Las
Tres
T (
1)1D
2.0
1.36
0.
30
13.0
11
3
81
0.
58
0.
28
0.
20
0.
63
0.
22
0.
09
Es
pirit
u Sa
nto
Las
Tres
T (
1)2D
2.0
0.33
0.
31
5.2
15
3
0.
58
0.
28
0.
20
0.
63
0.
22
0.
10
Es
pirit
u Sa
nto
Las
Tres
T (
2)2U
4.0
0.63
0.
23
8.4
27
11
0.52
0.37
0.21
0.58
0.27
0.05
Espi
ritu
Sant
oLa
s Tr
es T
(2)
1U4.
0
0.
29
0.19
7.
1
27
4
0.53
0.36
0.21
0.58
0.26
0.07
Espi
ritu
Sant
oLa
s Tr
es T
(2)
1D4.
0
0.
78
0.67
9.
0
63
28
0.
53
0.
36
0.
21
0.
58
0.
26
0.
07
Es
pirit
u Sa
nto
Las
Tres
T (
2)2D
4.0
0.59
0.
33
5.3
15
6
0.
53
0.
34
0.
21
0.
59
0.
26
0.
06
M
amey
esM
amey
es R
t. 3
1U0.
4
1.
65
0.26
65
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965
826
0.
15
0.
15
-
0.
79
0.
85
0.
07
M
amey
esM
amey
es R
t. 3
1D0.
4
1.
78
0.42
52
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832
944
0.
02
0.
15
-
0.
78
0.
98
0.
07
Es
pirit
u Sa
nto
Prie
ta3U
7.0
0.60
--
-1.
4
3
1
0.
50
-
0.
36
1.
00
0.
14
-
Es
pirit
u Sa
nto
Prie
ta2U
7.0
0.48
0.
46
4.0
7
1
0.50
-
0.36
1.00
0.14
-
Espi
ritu
Sant
oPr
ieta
1U7.
0
0.
57
0.16
2.
4
5
2
0.
50
-
0.
36
1.
00
0.
14
-
Es
pirit
u Sa
nto
Prie
ta1D
7.0
1.14
0.
28
6.8
48
39
0.50
-
0.36
1.00
0.14
-
Espi
ritu
Sant
oPr
ieta
2D7.
0
0.
87
0.45
8.
4
17
5
0.51
-
0.35
1.00
0.14
-
Mam
eyes
Puen
te R
oto
2U2.
5
2.
03
0.25
55
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556
388
0.
14
-
0.
77
1.
00
0.
09
0.
00
M
amey
esPu
ente
Rot
o1U
/BRID
GE
2.5
1.78
0.
35
63.0
1,
216
76
4
0.14
-
0.77
1.00
0.10
0.00
Mam
eyes
Puen
te R
oto
1D2.
5
1.
61
0.26
76
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524
360
0.
14
-
0.
76
1.
00
0.
10
0.
00
M
amey
esPu
ente
Rot
o2D
2.5
1.75
--
-65
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1,05
3
700
0.
14
-
0.
76
1.
00
0.
10
0.
00
M
amey
esQ
. Ano
n2U
1.3
0.64
0.
51
17.5
39
12
0.
72
0.
77
-
0.
19
0.
28
0.
04
M
amey
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. Ano
n1U
/BRID
GE
1.3
1.26
0.
77
26.0
75
29
0.
73
0.
76
-
0.
19
0.
27
0.
05
257
Wat
ersh
edBi
ocom
plex
ity I
DCr
oss-
Sect
ion
IDD
rop
Pdep
max
Pdep
cv
Plen
gth
Pare
aPv
olum
eag
r d
agr
ufs
t d
fst
uur
b d
urb
u
Mam
eye s
Q. A
non
1D1.
3
2.
07
0.37
17
.0
100
89
0.73
0.75
-
0.20
0.27
0.05
Mam
eyes
Q. A
non
2D1.
3
0.
88
0.36
12
.0
32
15
0.73
0.75
-
0.20
0.27
0.05
Espi
ritu
Sant
oSo
nado
ra3U
7.0
1.71
--
-12
.8
86
74
0.49
-
0.37
1.00
0.14
-
Espi
ritu
Sant
oSo
nado
ra2U
7.0
2.29
0.
39
9.5
136
120
0.
49
-
0.
37
1.
00
0.
14
-
Es
pirit
u Sa
nto
Sona
dora
1U7.
0
2.
00
0.32
12
.0
95
91
0.50
-
0.36
1.00
0.14
-
Espi
ritu
Sant
oSo
nado
ra1D
7.0
1.13
0.
30
7.2
47
16
0.50
-
0.36
1.00
0.14
-
Espi
ritu
Sant
oSo
nado
ra2D
7.0
1.15
0.
45
8.5
67
19
0.50
-
0.36
1.00
0.14
-
Espi
ritu
Sant
oTo
ronj
a2U
7.0
0.47
0.
27
2.0
3
1
0.51
-
0.35
1.00
0.15
-
Espi
ritu
Sant
oTo
ronj
a1U
7.0
0.24
0.
17
3.1
5
0
0.51
-
0.34
1.00
0.15
-
Espi
ritu
Sant
oTo
ronj
a1D
7.0
0.71
0.
13
6.2
20
8
0.
51
-
0.
34
1.
00
0.
15
-
Es
pirit
u Sa
nto
Toro
nja
2D7.
0
0.
43
0.29
5.
8
24
4
0.52
-
0.34
1.00
0.15
-
258
INDEX active-channel, 5-7, 19, 32, 35, 50, 105-107, 109, 113-115, 122-123, 165, 167, 170, 172,
174, 181, 193, 216, 217
alluvium, 40-41, 100-101, 116, 159-160
Atya, 177, 180, 183, 192, 202-203
bankfull, 1-2, 5-7, 31-37, 41, 45, 50, 61-63, 65, 67, 70-71, 73-78, 89, 95-96, 105-106,
136-137, 166-167, 217
Caribbean, 39, 92, 163, 200
crabs, 149-150, 161, 163, 165, 175, 177, 189, 198
Digital Elevation Model (DEM), 6, 15, 17-21, 23, 106, 125, 167
downstream hydraulic geometry (DHG), 1, 2, 7, 33, 37, 57-58, 76, 88, 90, 92-94, 96,
107, 113-116, 132-133, 137, 155, 217
eels, 150, 155, 161-162, 177, 183, 185, 187, 198, 200
effective discharge, 31, 36-37, 50, 56, 58-63, 65, 67, 71, 73, 75-76, 78, 105, 106
fish, 3, 7, 8, 149-150, 152, 155, 161-162, 164-165, 168, 170, 175, 177, 183, 189, 198-201
flow-frequency, 6-7 31-33, 35-37, 50, 51, 55-56, 61-63, 65, 67, 70-73, 75-78, 86, 105,
217-218
Geographic Information Systems (GIS), 4-6, 16, 19, 21, 27, 106, 165, 170, 216
grain size, 1, 7, 10, 34, 57, 88-89, 93-96, 107-108, 116-117, 119-120, 122, 125-128, 130-
132, 134-137, 155, 167, 172, 174-175, 192, 194, 202, 204, 219
granodiorite, 40, 100-101, 103, 109, 112, 116, 119, 130-131, 159, 220
landscape evolution, 1, 7, 91, 96, 136, 219
259
landslides, 2, 7, 11, 40, 88, 90-91, 93, 95-96, 102-103, 119, 121-122, 131, 134-136, 204
longitudinal profiles, 1, 88, 91, 93, 106, 109-110, 112, 131, 136, 150, 154-155, 170,
183, 187, 217
Macrobrachium, 163, 165, 177, 180, 189, 192, 203
megaboulders, 107, 119, 121-122, 167, 174
metamorphic, 100, 109, 112, 119
pools, 7-8, 42, 104, 113, 116, 134, 149-153, 156-157, 159, 162, 164-165, 167, 170-175,
177-178, 180-182, 189, 192-196, 198, 201, 204
riffles, 104, 113, 159, 162
riparian vegetation, 31-32, 35-37, 42, 53, 60, 76, 217
River Continuum Concept (RCC), 150, 152-154, 200-201
sediment transport, 1-2, 31, 34, 36, 56-61, 75, 90, 94, 96, 122-123, 131, 134, 219
snails, 149, 155, 161, 163, 165, 189, 201
shear stress, 7, 57-60, 93, 95-96, 108, 122-123, 134, 136-137, 172, 174
stream power, 7, 10, 75, 88-89, 94-96, 108-109, 125, 127-130, 132-135, 137, 167, 170,
172, 174-175, 183, 192-193, 202
United States Geological Survey (USGS), 16, 22-24, 39, 41, 50, 51, 53, 57, 109
volcaniclastic, 40, 88, 100-101, 103, 109, 112, 116, 119, 131-132, 159, 220
water quality, 160-161, 164
Xiphocaris, 163, 177, 180, 183, 192, 202-203
260
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