An Investigation of the Influences of Bedrock Lithology and Vegetation on Low‐Order Stream Frequency in the Luquillo Mountains, Puerto Rico

Home , Drainage density

Master of Environmental Studies

Lauren Stachowiak 5/1/2012

Readers Dr. Frederick Scatena Dr. Edward Doheny

Acknowledgements

A lot of time and energy and thought go into completing a master’s thesis, and not all of it comes from the graduate student. For this reason, I would like to thank several people in this section for offering their invaluable expertise and advice. Firstly, I would like to acknowledge the high level of dedication and commitment my two advisors, Dr. Frederick Scatena and Dr. Edward Doheny, have given me throughout the completion of my project. I definitely would have been lost without their help. Secondly, I would like to give thanks to Dr. Dana Tomlin, whose vast knowledge of GIS and willingness to help with any matter of questions I threw at him were crucial to the completion of my model and the results I achieved. I would also like to thank Miguel Leon for all the data he readily provided and for his open-door policy for solving small details that inevitably caused me trouble.

Table of Contents

Item Page Number List of Figures, Tables, and Equations 3 Abstract 4 Introduction 5 Study Area 6 Methods 7 Data Layer Reprojection 7 Stream Network Generation 9 Generating Watershed Boundaries 13 Calculating Drainage Density 16 Isolating Streams by Environmental Subclass 19 Results 21 Bedrock Lithology Data 21 Vegetation Data 23 Combined Parameter Data 25 Elevation Data 26 Mean Annual Rainfall (MAR) 27 Discussion 29 Geology vs. Drainage Density 29 Vegetation vs. Drainage Density 31 Environmental Subclass Influences 33 Elevation vs. Drainage Density 33 Drainage Density vs. MAR 34 Conclusions 36 Future Work 37 Cartographic Models 38 Works Cited 39 Appendix 40

List of Figures Item Page Number 1. Flow Direction 10 2. Flow Accumulation 11 3. Threshold Applied Binary Raster 12 4. Vector Stream Network 13 5. Watershed Pour Points 14 6. Initial Watershed Boundaries 15 7. Final Watershed Layer 16 8. Intersection Operation 17 9. Example of Stream Bisection 18 10. Streams by Bedrock Lithology 21 11. Geology vs. Drainage Density 22 12. Streams by Vegetation 23 13. Vegetation vs. Drainage Density 24 14. Veg./Geol. vs. Drainage Density 25 15. Streams by Elevation 26 16. Elevation vs. Drainage Density 26 17. MAR & Drainage Density vs. Elev. 27 18. Drainage Density vs. MAR 28 19. In Situ Model 38 20. Upstream Flow Model 38

List of Tables Item Page Number 1. Calculated Drainage Density 19 2. Area & Stream Length (Vegetation) 24 3. Area & Stream Length (Combined) 25 4. MAR & DD per Subclass 28

List of Equations Item Page Number 1. Raster Calculator Threshold Expression 11 2. Mean Annual Rainfall 27

Abstract

Drainage density of a river system is usually influenced by the extent to which erosional forces weather the landscape, and the extent that local vegetation regimes maintain the landscape. This study investigated the influence of environmental factors on the drainage density of the Luquillo Mountains in NE Puerto Rico. The major parameters used in this study included underlying bedrock lithology and forest type. There are three primary lithologies within the study area, including Quartz Diorite, Volcanoclastics, and a metamorphic contact zone rock called Hornfels. The four forest types used included Tabonuco, Colorado, Palm, and Elfin. The use of custom-made GIS models in this project is extensive and will replace the immediate need for additional field research. Even so, the results of this study would benefit from field verifications. The data for this project included both raster (cell-based) and vector (geometric shapes), and were used in conjunction for a multiple of cell statistical and map algebra operations. Due to the accuracy of GIS-based data available, it was convenient to use these data in the spatial models generated for this project. The environmental models found a strong influence of vegetation upon the landscape, to the point of over-powering the collective influence of geology. In general, the volcanoclastic lithology had higher drainage density than the others, but when drainage density was compared with the combination of geology and forest type groups, differences in drainage density by lithology were not as apparent. In addition, drainage density values were found to decrease with increasing mean annual rainfall. This is likely due to the strong landscape buffering characteristics of the prevalent vegetation, and the frequent but low-intensity daily rain events. Simply looking at only the influences of geology or vegetation on channel formation implies that geology has the stronger effect. However, when analyzing the influences of geology and vegetation together upon channel formation the differences in geology are greatly subdued.

I. Introduction

Drainage networks in tropical ecosystems are heavily influenced by the magnitude and frequency of annual rainfall as well as soil characteristics (Walsh 1996). However, the relationship between hydrologic drainage density and bedrock lithology is not as fully understood. One primary reason for this is the extreme variations in local geology between site locations. A “coverall” of a uniform bedrock characteristic that can be applied as a general parameter for large areas does not exist. More simply explained, climate is relatively unchanging over vast areas and can be applied to large swaths of land, whereas bedrock geology can be highly articulate and can change quickly across short distances. Therefore, a local analysis of bedrock lithology must be completed in order to determine possible influences, if any, are present upon river morphology. This study investigates the influence of bedrock geology, rainfall, and vegetation type on the frequency and length of low order streams in the Luquillo

Mountains of NE Puerto Rico. The working hypothesis is that there will be a difference in the occurrence of headwater streams with different lithologies and forest types. However, these differences will be less apparent within a forest type.

The second half of the project focuses on the influences of climate on drainage density.

The greatest affect climate has on the biophysical environment is the influence on vegetation regimes and eco-regions (Collins and Bras 2010). Conceptually accepted models of landscape evolution, with respect to climatic influences, follows that wetter climates have greater runoff and erosive activity up to the point of creating established and pervasive vegetation, at which point runoff and erosion quickly stall out (Collins and Bras 2010). However, precipitation events exceeding the threshold for soil moisture use and transpiration by plants will have return flows

5 with erosive abilities. Therefore, the peak of erosivity falls upon semi-arid landscapes, troughs through temperate to semi-humid regions, and then peaks again in excessively humid environments (Collins and Bras 2010).

II. Study Area

The Luquillo Mountain Range is located in northeast Puerto Rico just south of the Tropic of Cancer at 18 degrees north latitude and 65 degrees west longitude (Scatena 1989). This area is pervasively humid and has a climate of regularly high precipitation. The region experiences strong trade winds throughout the year, which bring high mean annual precipitation amounts of about 5,000 mm/year at the mountain summits, with about 2,600 mm/year at the base (Scatena

1989). Furthermore, a major weather system of the area is the normal and regular occurrence in late summer of strong tropical storms and hurricanes. As such the environment is classified as very humid with a typical lush vegetation regime consistent with the available amounts of yearly rainfall.

To begin, a brief discussion on the three rock types in the study area is necessary to explain weathering characteristics expected of each lithology. The volcaniclastic bedrock is a clastic sedimentary rock most comparable to sandstone. The origins of this rock type are due to the lithification of volcano sediment that was deposited during the Cretaceous and lower Tertiary geologic past (Scatena 1989). The by-products of weathering for the volcaniclastics are clays. It can be expected to erode more quickly and to a greater extent due to the weak cohesiveness of the grain particles and the low overall resilience of the rock to erosional forces and weathering.

Quartz diorite is the second lithology present in the Luquillo range, and resulted from a andesitic magma like the volcaniclastics (Scatena 1989). Even though the diorite is enriched with quartz, a weathering-resilient mineral and something the volcaniclastics lack, the quartz is

6 surrounded by other grains that are quick to weather. Thus, the quartz diorite rock weathers faster than the volcaniclastics and produces a sandy soil. Lastly, the hornfels lithology is the metamorphic contact zone where the intrusive quartz diorite broke the surface and essentially

“baked” with the volcaniclastics. This is a hard erosion-resistant rock found at upper range elevations in the Luquillo range.

I. Methods: Introduction

The GIS created for this project consisted of a series of nested procedures to generate the appropriate output layers necessary to run the topographic analyses. Three base layers were used to initiate the process, including a 10 meter cell resolution digital elevation model, a polygonal shapefile delineating bedrock lithology boundaries, and a vegetation shapefile consisting of polygons representing specific regions of forest vegetation. The boundaries of the vegetation shapefile are delineated such that urban development has been removed. All vegetation included in this project can be considered virgin and unaffected by the growth of the low-lying urban centers along the coast. In addition, private land which may have been bought and developed has also been removed from the vegetation dataset.

Following sections will detail the individual processes completed to determine the hydrology metrics of the study area. However, prior to starting the GIS analyses, it was necessary to process the data into workable and comparable formats; so these initial steps will be described first. All workflows and tools used in this methods section are outlined in the appendix, including their respective locations within the ArcMap interface.

II. Methods: Data Layer Reprojection

Immediately after acquiring the appropriate data necessary to begin the research project, all three data layers were reprojected into identical coordinate systems. This combining of spatial

7 references was crucial because certain spatial statistic operations, such as slope, require projected distance units, such as feet or meters, rather than angular degrees. In addition, conventional practices in multi-layer analyses in Arc dictate that all layers being used in a GIS should be in the same coordinate system to prevent tedious errors in the future. The geology layer was in a Clark

1866 Polyconic projection with a linear unit of meters. The vegetation layer had a defined coordinate system of a North American Datum (NAD) 1927 State Planar Projection of Puerto

Rico, also calibrated in a linear unit of meters. Lastly, the digital elevation model (DEM) was defined with a NAD 1927 Lambert Conformal Conic projection with a linear unit of meters and an angular unit of degrees.

Considering all three layers were defined with a projected versus geographic coordinate system of the same units, it would have been possible to progress without reprojecting. However, the methods for this paper followed convention and the geology and DEM layers were ultimately defined using the vegetation layer projection. The projection of the vegetation layer was chosen because it offered the most appropriate spatial reference for the study area. State planar projections are highly accurate for the areas of the earth for which they were made, and as such the LCZO lies within the natural boundaries of this projection system.

This reprojection was completed by setting the spatial reference system of the data frame to match that of the vegetation layer. Each successive data layer added was then exported as itself using the coordinate system of the data frame. The DEM process of reprojection was slightly more involved, including a layer extent modification which allowed the DEM to be clipped to the display. This addition significantly reduced the size of the DEM, which allowed for faster data processing in future analyses.

III. Methods: Stream Network Generation

The spatial analyst toolset within ArcToolbox was used extensively with the GIS used for generating the stream network. Specifically, the hydrology modeling toolset was utilized based on conventional flow hydraulic modeling in ArcMap. Those hydrology tools can be found but turning on the Spatial Analyst extension and navigating through the toolbox. The base layer used for these analyses was the DEM, which is a raster data layer representing a landscape of elevation values on a pixel by pixel basis. The steps that follow detail exactly how each tool was used and for what purpose.

To begin, the DEM was subjected to the Fill tool, with the DEM as the input raster. This tool fills in any sinks that may exist in the landscape to prevent the unrealistic entrapment of the virtual water that is being simulated in the data frame. These sinks represent places such as the drop pools of waterfalls, which in nature would fill with water and then overflow over the landscape, but in a GIS act as traps later in the hydrology modeling. For cells that represent these traps, new elevation values are given to smooth out the depression and fill the area in preparation for the next step in the process.

The output raster of the Fill operation was used as the input raster of the Flow Direction tool.

This operation focuses on each cell and finds the steepest downslope cell surrounding it and then assigns that original cell a directional value. The output raster is a landscape of numerical values which represent the eight cardinal directions. Figure (1) below shows the output direction raster.

Figure (1). Flow direction raster with a landscape of cell values ranging from 1-8 representing the eight cardinal directions. Notice that ridge lines and valley floors are particularly visible as areas of immediate direction changes.

This flow direction raster is the input raster for the Flow Accumulation tool, which was used to generate a new landscape of additive incremental flow. In other words, each cell assumes a value based on the flow of virtual water into it from upstream cells. The upstream environment is easily defined because the input raster for the Flow Accumulation operation was initially based upon the DEM. Higher cell values indicate higher numbers of draining cells in the upstream environment. For example, a cell which represents the exact beginning of a stream will have a value of 0, which a cell at the exact mouth of the outlet will have the highest value for the entire stream system. Visual inspection of this layer in Figure (2) below shows a landscape of primitive stream networks.

Figure (2). Flow Accumulation raster. Notice how the drainage patterns of the stream network are beginning to take shape. The gradation of colors from red to blue indicate cell values ranging from low to high, respectively.

However, considering the Flow Direction and Accumulation tools start hydrology modeling at peaks in elevation, the software assumes that the stream network begins at those peaks as well, thereby giving stream networks with streams that do not actually exist. For this reason it was necessary to incorporate a threshold on upstream drainage size into the hydrology model to prevent first order streams from being delineated where no streams should be present. To do this,

Map Algebra was used to run a Raster Calculator analysis on the output raster of the Flow

Accumulation tool. The expression for the calculator is as follows:

Eq (1). [“Name of the Flow Accumulation output raster”>=50]

In words this expression communicates to the Raster Calculator tool to find all cells for which the upstream accumulation comes from 50 or more cells, and assign these cells a value of 1. Any cells for which this expression is not true, assign them a value of 0. The value of 50 was deliberately chosen because that pixel area represents an area on the surface of the earth equal to

0.5 hectares. This choice allows the computer to model streams at a fine enough scale to catch those of appropriate size and order, but prevents the creation of any streams from areas too small

11 to support them on the surface of the Earth. Figure (3) shows the output raster of Raster

Calculator operation.

Figure (3). This is a refined output raster which shows a modeled stream network for the LCZO in northeast Puerto Rico. The applied area threshold is 0.5 hectares thereby allowing the delineation of first order streams in the study area.

The above output raster was used in conjunction with the generated flow direction output raster in the Stream Order operation to create order within the stream network. The method used in the Stream Order tool was the Strahler method, which is the default in ArcMap. The reason the tool requires the flow direction raster is to ensure the model classifies upstream and downstream properly. The output raster has cell values ranging from 1 to 5, representing streams within the network of first through fifth order.

The final step is to convert this last raster output file into a vector shapefile of polylines using the Stream to Feature operation. This last step is necessary because the vegetation and geology base layers are in vector file format. ArcMap cannot readily perform certain operations using two file formats, therefore the final stream network layer is a vector file of polylines delineating streams through the landscape. This polyline vector file will be used with the two polygon files in a series of overlay analyses later in the GIS model to extract the desired drainage

12 density measurements for both geology and vegetation territories. Figure (4) below shows the final modeled stream network with the preserved symbology for stream order.

Figure (4). Final vector polyline file of the stream network in the LCZO. The vector file has been clipped to the park boundary to omit any streams falling outside the study area. The network has been overlayed on top of the original DEM to show contrast and relief.

IV. Generating Watershed Boundaries A point shapefile of pour points was manually created to represent locations where the

water drains to each outlet of each drainage system. These pour points were placed at the

immediate intersections of stream reaches directly outside the forest boundary. The reason

for this placement is twofold: first, this point selection ensures that the entire length of each

stream is included within the watershed boundary, and second, to create a perfectly

contiguous polygonal surface over the study area to provide watershed area throughout the

national forest. The stream network, for which the pour points were based on was that

network created following the previously outline steps. Figure (5) shows the pour points

overlayed on top of the DEM layer with a hillshade function model to show relief.

Figure (5). The pour points that simulate the outlet locations of the yet-to-be modeled watersheds are shown as blue circles. The previously generated stream network is represented by the red polylines. Notice the pour points follow the perimeter of the streams.

Using the flow direction raster generated in the previous section and the newly created

pour point vector shapefile, a raster delineating watershed boundaries was created using the

Hydrology > Watershed operation. The flow direction versus the flow accumulation raster

was used because watersheds were delineated in the upstream direction from the outlet. Each

pixel resonating “upstream” of the outlet location determined the steepest stepwise direction

of ascent. This behavior can be thought of as the reverse of the flow direction operation,

which determines the steepest stepwise direction of descent for each pixel and then assigns

that pixel a cardinal direction. Rather, the watershed operation works by flowing water

virtually uphill and delineating locations where uphill movement becomes downhill. These

locations are topographical features such as peaks or ridgelines, which are natural boundaries

for watersheds on the surface of the Earth. Figure (6) shows the raster output of the Hydro >

Watershed operation.

Figure (6). Pour points and streams are shown in blue circles and red polylines, respectively. Watershed boundaries are shown in varying pastel colors, overlayed on a hillshade elevation model. This is an output raster data layer.

The raster output layer of watershed boundaries was then converted to a vector polygon

shapefile. The watershed layer was created with unique feature numbers to be used for

identification during later analyses. This conversion step was crucial in this project because

the geology and vegetation layers are vector shapefiles, but is not necessary for raster-based

hydrology modeling. Finally, this new vector watershed layer was clipped to the national

forest boundary to remove excess area generated by the watershed operation. The area is the

result of placing the pour points intentionally outside the forest boundary. Removal of this

extraneous area will prevent lower estimates of drainage densities of low order streams from

being calculated in the following processes. Figure (7) shows the final watershed layer that

has been created.

Figure (7). The final watershed layer. Notice how the pour points are now completely outside the watershed boundary. This is because the watershed boundaries have been clipped to the national forest boundary.

V. Calculating Drainage Density

The stream networks and watershed boundaries were defined to accurately calculate

drainage density of the study area. The two independent variables, bedrock lithology and

vegetation, were each processed with the stream network separately. In other words, spatial

calculations were conducted initially on a stream-geology and then a stream-vegetation basis.

Final analyses included a triple-nested relationship of a stream-geology-vegetation

interaction. The following steps are outlined under the focus of geology, but recognize that

the steps are identical to what was conducted with the vegetation layer. Namely, only the

base grids were swapped. The same stream network was used for both vegetation and

geology.

The first step is to intersect the geology polygon layer with the watershed layer. This will

create a new shapefile of polygons that have data in the attribute table that lists the bedrock

classification as well as the watershed ID for each feature in the layer. The order of layer

priority is not important here because each layer was a polygon shapefile and the attributes of

both layers was desired in the output layer. It can be thought of as adding the name of the

underlying bedrock to the aboveground watershed polygons, or the watershed data to the

underlying bedrock polygons. Either way, both classifications are preserved in the output file

because the desired shapefile type is a polygon. Figure (8) below shows the changes to each

polygon the intersection tool enforces.

Figure (8): A and C are the initial geology and watershed layers, respectively. B shows the intersection layer. Notice the increase in number of polygons. The Hornfel area shows the best representation of what the intersection tool does. D is the attribute table for B. This concept of intersection priority will become more obvious when the stream network is

considered. After the new polygon layer has been created from the intersection, a field was

added to the attribute table that represented what ultimately became the calculated area of

each polygon. The new column was then populated with area values for each feature in

square meters using Field Calculator.

The stream network was then intersected with the new geology-watershed polygon layer.

It is important to note that unlike the previous intersection, the rank of layers in this operation

is important. The stream network must be the target layer because the output file must be

composed of polylines and not polygons. The attributes from all polygons in the geology-

watershed layer were then transferred to each intersecting stream network. The initial stream

layer was broken up on a polygon basis. This division ensures that for instances where

streams cross geology-watershed boundaries, the stream is bisected. In other words, a single

continuous stream crossing a boundary became two separate stream reaches that meet exactly

at the geology-watershed boundary.

Figure (9): The left image is the entire intersected polyline shapefile of streams. The right image shows how continuous stream reaches were broken up based on the polygons. The symbology is set such that purple streams are those flowing through volcaniclastics, red represents hornfels, and green are those through quartz diorite.

The initial output of the intersection operation is a polyline shapefile with data in the attribute

table on the type of bedrock lithology and watershed ID for each stream feature. A field was

added to the attribute table and the final stream lengths were calculated in meters using Field

Calculator.

This final stream layer now has fields representing classifying bedrock and watershed

qualities, the area in square meters of the polygons through which the streams flow, and the

lengths in meters of those streams. A field was added to this attribute table labeled for

drainage density and using Field Calculator, the quotients from the stream length and

polygon area fields were calculated for each feature in the layer. These final values are the

drainage densities of streams through a watershed depending on a bedrock lithology. Table

(1) below gives an example of the combined geology and vegetation layer attribute table.

Table (1). Final drainage density values are in m/ha based on vegetation and geology classifications. The FID and Shape* fields are base data the computer uses to accurately sort information and can be ignored in this instance.

The following section will explain in detail how the above data can be used to find

relationships between stream hydrology and bedrock lithology. As a reminder, the geology

layer is being used in this methods discussion, but all procedures are the same for the

vegetation and combined analyses.

VI. Isolating First-Second Order Drainage Densities Per Bedrock Classifications

The final procedure within the methods of this project separates only desired information from the collective dataset. This focus of this project is first and second order streams only, yet the above dataset includes all streams with first through fifth orders. In addition, drainage density values must be evaluated on a per-rock type basis to gather pertinent statistics such as mean, sum, and range drainage density metrics, as well as standard deviations. Therefore, the following

19 steps outline how the dataset was pruned down to give only those data that were necessary for each statistical calculation.

To explain these procedures, a sample of streams will be chosen as a representative example.

Namely, first order streams within watersheds with volcaniclastic bedrock lithology will be isolated from the dataset. Considering the priority ranking of the intersection was such that streams were the target features, the first attribute to isolate is the stream order. A tool within the attribute table named Select By Attributes allows the user to choose those features within the entire dataset that fit certain criteria. Therefore, all features having a value of 1 in the

“StreamOrder” field were then selected out of the dataset. It is then possible to select from this current selection of features and filter only those with a “GeologyID” of volcaniclastics. It then follows that this final list of features represent drainage density data on only first order streams with underlying volcaniclastic bedrock.

To conclude the methodology descriptions, it is noted that the location and usage of all tools discussed within this section are outlined in detail within the Appendix.

I. Results The following result section will focus on the products of the statistical and

cartographic modeling described in detail in the previous section. For reference to the

tools used and their respective locations within the ArcMap 10 interface, refer to the

Appendix section. Also, 3-D cartographic models constructed in ArcScene are provided

in the Appendix as well. For any cartographic images displayed in this section, it can be

assumed that the symbology for the features such as streams, geographic areas, and

elevation bands will be kept consistent.

II. Bedrock Lithology

The map below displays a basic representation of the study area, shown here in beige,

which has been overlayed with the derived stream network. The streams have been

displayed using symbology based on the respective underlying bedrock. Volcaniclastics

dominate the lower elevations and wrap the study area. Quartz Diorite cluster toward the

middle of the three rock types at higher elevations. Hornfels are found between the other

two bedrocks, located in what has been determined the contact zones of each lithology.

Figure (10). Volcaniclastics are displayed in green, Quartz Diorites in red, and Hornfels in orange.

The first order drainage densities for each rock type were calculated and the graph

below shows how each respective lithology compares with one another. Error bars

were not included because the graph displays total stream lengths (m) per total rock

type area (ha) in the Luquillo Mountains. Volcaniclastics have the highest drainage

density values at 18.97 m/ha, quartz diorite follows with 17.05 m/ha and hornfels at

16.78 m/ha.

Geology vs. 1st Order DD 19.5 19 18.5 18 17.5 m/ha 17 16.5 16 15.5 Volcaniclastic Granitic Diorite Hornfels

Figure (11). Graph showing drainage density of first order streams by rock type. DD was calculated by dividing total stream length (m) by the total area of each bedrock type (ha)

First order streams were used to show comparisons of drainage density values

between rock types because the occurrence of first order streams crossing geologic

boundaries was low. Therefore, more accurate calculations of stream length could be

achieved, thereby showing more robust differences between rock types. The appendix

includes a graph of drainage density by bedrock type where all streams per boundary

were included.

III. Vegetation The figure below depicts the study area overlayed with the stream network

symbolized based on forest type. There are only 17 stream segments, all of them first

order, which flow through Elfin forest. As is shown, the majority of streams flow through

Tabonuco forest type which is found at lower elevations. All stream orders, specifically

first through fifth, are represented within this forest type. There are a total of 1105

streams within Tabonuco forest. Colorado forests appear throughout the mid- to high-

elevations and contain first through fourth order streams. A total of 614 streams flow

through Colorado forest. Lastly, as shown below, Palm forests can be found from low to

high-elevations and interspersed between the different forest regimes. A total of 332 first

through fourth-order streams flow through this specific forest type.

Figure (12). Tabonuco forests are displayed in red, Elfin in dark blue, Colorado in yellow, and Palm in green.

Below is a graph showing drainage densities of streams by forest type. In the same analysis as the bedrock types, only first order streams are shown here. Total stream lengths (m) were collected per forest type with total area (ha) to find drainage density values.

Vegetation vs. Ist Order DD 20 18 16 14 12 10 m/ha 8 6 4 2 0 Tabonuco Colorado Palm Elfin

Figure (13). Graph showing first order drainage density by forest type. DD was calculated with the following equation: total stream length (m) / total boundary area (ha).

The Elfin forest had the lowest drainage density compared to the other three forest types with a final value of 6.07 m/ha. Colorado forest had a total drainage density of streams at 17.49 m/ha. Palm and Tabonuco forest types were similar in drainage densities, with values of 18.21 m/ha and 19.01 m/ha, respectively.

The table below shows that Tabonuco and Palm forests have high DDs because of differing counter-weights of area and stream lengths, respectively.

Vegetation Area (ha) Stream Length (m) DD (m/ha) Tabonuco 5711.07 108545.5 19.0 Colorado 3463.36 60567.69 17.5 Palm 1818.84 33129.78 18.2 Elfin 370.99 2251.55 6.1 Table (2). The reason Palm forests have such high DD even with ~4000 less hectares is because the forests contain a comparatively high total stream length value

IV. Combined Parameters As described in the methods, the third analysis was the combination of the two primary

environmental parameters. As the graph below shows the combination with the highest

drainage density of all twelve possibilities was Tabonuco-Hornfels. The combination with

lowest overall drainage density was Elfin-Volcanoclastics. Overall, the Elfin forest type has

the most variation among the three different underlying bedrocks.

1st Order Drainage Density 25

m/ha 10

0 Colorado Tabonuco Palm Elfin

Figure (14). Each forest type has a corresponding bedrock lithology. Volcanoclastics are blue, Quartz Diorites are red, and Hornfels are green.

Variations in total geometric area calculations per forest type have greatly influenced the first order stream densities.

LENGTH VC GD Horn Colorado 23171 21436 15959 Tabonuco 73524 8863 18121 Palm 14974 7484 10670 Elfin 323 32 1895

AREA VC GD Horn Colorado 1227.76 1302.43 926.4 Tabonuco 3898.81 478.02 871.33 Palm 760.12 410.52 607.8 Elfin 55.77 2.37 301.93 Table (3). Shows the total area (ha) and stream lengths (m) for first order streams. Notice the low area value for Elfin-GD and the high stream length for Tabonuco-Horn.

V. Elevation The extracted elevation bands were found to have varying first order stream densities

with increasing meters above sea level. The following graphics include: a map showing first

order streams symbolized based on respective elevation bands, a graph of drainage densities

by elevation band, and a graph showing derived mean annual rainfall calculations.

Figure (15). Increasing elevation is represented in a color scheme of light to dark blue. 400-500m streams are dark green, 500-600m are light green, 600-700m are orange, and 700-800m are red.

1st Order Drainage Density 18.5 18 17.5 17 m/ha 16.5 16 15.5 400‐500 500‐600 600‐700 700‐800

Figure (16). Total first order stream lengths (m) and total band areas (ha) were used to calculate the drainage density values shown above.

4300 18.5

4200 18 4100

4000 17.5

3900 17 3800

3700 16.5 Mean Annual Rainfall Drainage Density (m/ha) 3600 16 3500

3400 15.5 400‐500 500‐600 600‐700 700‐800 Elevaon Band (masl)

Figure (17). This shows increasing mean annual rainfall calculations extracted from Garcia-Martino et al (1996) derived equations. Notice that increasing mean annual rainfall does not follow with increasing drainage densities upslope.

VI. Mean Annual Rainfall The formula to derive mean annual rainfall was taken from Garcia-Martino et al. (1996)

and uses average elevation as a condition for precipitation. The formula is shown below

Eq. (2) MAR = [2300 + (3.8 * - (0.0016 * 2)]

The graph on the following page was created using spot points taken from Walsh (1996) and

extracted MAR values for each subclass of environmental parameter. The drainage density

values here have been calculated using lengths of all order streams, instead of only first

order. This was done to stay consistent with Walsh (1996), in which all stream orders were

used in the network to calculate mean annual rainfall and charted on a scatter plot. The

following table breaks down the drainage density and MAR values for each subclass of

environmental data.

ID MAR Drainage Density (m/ha) (mm/yr) Volcanoclastic 4139 27.68 Quartz Diorite 4772 25.47 Hornfels 4916 21.72 Tabonuco 3835 35.19 Colorado 5036 28.46 Palm 5008 26.34 Elfin 5719 6.07 400-500 4005 28.7 500-600 4377 26.63 600-700 4768 32.22 700-800 5135 26.23 WALSH 1 2000 45 WALSH 2 3000 65 WALSH 3 4000 72.5 WALSH 4 5000 75 WALSH 5 6000 77.5 Table (4). Drainage density and MAR values for each environmental subclass. Walsh (pts 1-5) points were manually taken from the Walsh (1996) scatter plot.

All‐Order DD (m/ha) vs. MAR (mm/yr) 90

Walsh 5 80 Walsh 4 Walsh 3

70 Walsh 2

50 Walsh 1

40 Tab 600‐700 Vol COL 30 QD

Drainage Density (m/ha) 400‐500 700‐800 500‐600 20 P Hor 10 Elﬁn

0 1000 2000 3000 4000 5000 6000 7000 MAR (mm/yr)

Figure (18). Data points taken from this analysis compared with those from Walsh (1996). Overall drainage density of all order values of this study were lower compared with Walsh.

I. Discussion

This section will discuss possible explanations for the results outlined in the previous

section. As can be seen in the data from the previous section, several interesting relationships

and calculations were discovered from the GIS analyses. More specifically, there are

noticeable but not always large differences in drainage densities among geology, vegetation,

and combinations of both. In addition, relationships among subclasses based on average

elevations and mean annual rainfall were unsuspected. It will be these points of interest that

will be further analyzed in the following discussion. Namely, the questions to be addressed

include:

A. Why do areas with volcaniclastic geology have such high drainage densities?

B. Why are Elfin forests so different in terms of drainage density than the other three vegetation subclasses?

C. What subclass has more influence over channel formation, geology or vegetation?

D. Why does drainage density for elevation bands follow a low-high-low pattern with increasing elevation?

E. Why does total drainage density of all streams (versus 1st order only) decrease with increasing rainfall?

Data will only be shown again in this section under specific requirements as a means to make quick reference to certain topics. For a more detailed review of data tables, charts, maps, and graphs please refer back to the results section

A. Why do areas with volcaniclastic geology have such high drainage density?

As can been seen in figure (11) from the results section, there are strong differences in total drainage density among volcanoclastics, quartz diorites, and hornfels. Stated more plainly, volcanoclastics exhibit high total drainage density values of first order streams. On the opposite

29 side, quartz diorite and hornfels have similar but low drainage density values compared with volcanoclastics.

The high drainage densities present in volcanoclastic distributions is likely the result of slope and the soil characteristics from the weathered parent material. Volcanoclastics weather to highly clay-rich soils, which naturally have low soil permeability and low water transmissivity

(Scatena1989). Similar drainage density studies conducted by Walsh in the humid sub-tropics determined that within two sites, Grenada and St. Lucia in the islands of Dominica, of comparable mean annual rainfall, higher drainage densities were to be found in those areas of volcanic lithology resulting in low permeable soils (Walsh 1996).

The influences of grain size also extend to hillslope and hydrologic processes, by which grain size decreases with increasing drainage area (Pike, Scatena and Wohl 2010). This can be thought of as the effective fining sequence of the downslope environment. Considering the geographic distributions of volcanoclastic bedrock exist at lower elevations, this could possible create an additive effect to the already clay-rich (small grain size) soil compositions. However, it should be noted that lower elevations and the hillslope environment are separate.

The hornfels and quartz diorites have comparatively low drainage densities than do the volcanoclastics. The quartz diorite distributions are present along higher elevation bands, and as such they receive more mean annual rainfall per year. However, quartz diorite weathers into quartz-rich sandy soils, which are characterized by high permeability and soil water transmissivity (Scatena 1989). Precipitation infiltrates deeper in the water column down through the soil profile. These infiltration regimes are especially true of high-precipitation storm events

(a common occurrence within the study area), whereby the water table is perched with greater total soil saturation levels at shallower depths. This propensity for sources of water to seep and

30 leach down through the soil profile rather than across the aboveground environment through overland flow dictates low total stream lengths per basin area. In addition, Harden and Scruggs

(2003) found that infiltration rates were also greatly influenced by the downslope subsurface environment and slope rather than simply the soil profile. Therefore, it is appropriate to attribute the low drainage density values of this bedrock lithology to both the soil characteristics arising from weathering as well as the slope of the landscape.

Lastly, as shown in figure (11) of the previous results section, hornfels have the lowest drainage density values at just less than 17 m/ha. Hornfels can be accurately classified as extremely tough, weather-resistant contact zone lithologies, which can result from igneous intrusions and melting of existing bedrock (Scatena 1989). For this study area, hornfels are the melted contact zone between the volcanoclastics and quartz diorite and they present with extremely steep slopes. While this particular bedrock lithology exists within the study area most conducive to moisture and precipitation, the total drainage density values are low. It is likely that these low density values can be attributed to the steep slope processes rather than of the hydrologic characteristics of the soil.

B. Why are Elfin forests so different in terms of drainage density than the other three vegetation subclasses?

The relationships taken from the data regarding vegetation and drainage densities can fall into two separate categories. The first category is biomass sequestration within dominant plant species, and the second is simply the mathematics of how drainage density is calculated. To address the first, different species of trees within the separate forest types sequester and build biomass differently. Some forests are dominated by plants which have high leaf area indices, some with sturdy or robust stems, and yet others with extensive root systems. The tabonuco forests have well-developed canopy systems and dominant species contribute large biomass to

31 the leaves for a higher leaf area index (LAI) than other plant species (Scatena 1989). Tabonuco forests have high LAI, and species comprising the Bisley watershed, which is both tabonuco and

Colorado, have intermediate LAI. Considering tropical plant species must compete for valuable sunlight in areas of thinly mantled soil compared with humid areas in higher latitudes, more biomass is sequestered to leaves than roots. As such, those forests with medium-high to high biomass and LAIs may have less resistance to erosive hillslope processes and higher susceptibility to channel formation.

A study conducted within the Bisley watershed in the northeast section of the Luquillo

Mountains found that small LAIs were more readily found in tree species dominated by forests located in ravines and forest openings. More specifically, these were areas where canopy regeneration had not occurred or in ecotones between two different forest types (Scatena 1989).

These geographic locations coincide with the distributions of the palm and elfin forest types, which can be found nestled within deep river valleys (palm) and in areas of forest openings such as those found at higher elevations and peaks (elfin). It is possible that greater root mass and extensive root systems of plant species within these two forest types can account for greater resistance to erosion and channel formation. This could in turn slow lateral soil water movement and cause greater downward infiltration rates of water, thereby lowering total above ground stream extent.

Lastly, drainage density comparisons among vegetation types can be attributed to total geographic area of each forest type in relation to the others. For instance, tabonuco forests account for 51% of the Luquillo Experimental Forest; therefore, it is possible that greater swaths of this particular forest type simply allow more opportunity for stream channel development. In addition, palm forest types have naturally low area totals, but are found clustered tightly within

32 stream valleys, which guarantees high total stream length values. Considering the equation for drainage density is [total stream length / total geographic area] any forest type which is classified by low area and high stream length will have naturally high density values, without true influence from the forest itself.

C. What subclass has more influence, geology or vegetation?

Basically, all of the possible scenarios describing the impacts of vegetation and geology on channel formation come to one over-powering conclusion; vegetation has a profound stabilizing effect on streams in this entire study area. Between the three subclasses of geology there are significant differences in drainage densities from volcanoclastics to hornfels to quartz diorites.

The same cannot be said for the four vegetation types. There is not a large difference among areas when vegetation is considered alone, even for the lower total areas like palm and elfin.

Simply looking at only the influences of geology or vegetation on channel formation implies that geology has the stronger effect. However, when analyzing the influences of geology and vegetation together upon channel formation the differences in geology are greatly subdued. Thus it can be inferred that vegetation over-rides geology in terms of drainage density as a metric for channel formation.

D. Why does drainage density for elevation bands follow a low-high-low pattern with increasing elevation?

No strong relationship appears to exist between elevation and first and second-order drainage density; however, there are two possible underlying variables which might be responsible for the observed pattern. The first revolves around coinciding locations of weathering-resistant bedrock, and the other deals with the known extent of first order streams at high elevations.

To address the first possible explanation to the up-down pattern, a recap of bedrock lithology is necessary. As a reminder, hornfels are the weather-resistant, contact-zone metamorphic rock that exists between the volcanoclastics and quartz diorite lithologies. They are also prevalent within the 500-600 masl elevation band, which shows dramatic decreases in total drainage density. It is possible this drop from the 400-500 masl elevation band is attributable to overlapping geographic distributions of a hornfel bedrock lithology.

The explanation for the second perceived drop in drainage density at the 700-800 masl elevation band could be the result of the GIS model created for the methods section. Recall that a critical contributing area threshold was applied to the flow accumulation raster, which resulted in a binary grid of values demarking the derived stream network. The tighter the threshold that is applied in the model, the finer the articulation of the resulting channel network. The threshold applied for this project was a 0.5 hectare contributing area threshold, which provided accurate levels of detail in each watershed. However, it is possible the threshold was too broad for flow accumulation cells at higher elevations or near peaks, thereby preventing these headwater streams from being included in the network. If this has happened, total stream length at the 700-

800 masl elevation band would be less for the same derived area calculations, thereby giving lower drainage density values.

E. Why does total drainage density of all streams (versus 1st order only) decrease with increasing rainfall?

There are several possible reasons for why drainage density values went down with increasing mean annual rainfall calculations. Firstly, areas that experience higher MAR values, like Elfin and Colorado forests, have light but consistent rainfall. These areas are found primarily in cloud forests and at elevations where light condensation and precipitation occur. For instance, rainfall can, and likely does, occur every day but not at high intensity. Walsh (1996) found that

34 intensity and frequency of high intensity storm events have a higher influence on drainage densities in the tropics than total rainfall measurements alone. Therefore, mean annual rainfall of an area cannot simply be used as a proxy for drainage density measurements.

Following this line of thought, Harden and Scruggs (2003) also found that rainfall measurements alone did not adequately reflect calculated drainage densities for the Luquillo study area. Rather, it is likely that many combinations of environmental parameters work cohesively to influence the drainage density of a catchment than does one parameter by itself.

This study looked at only geology, vegetation, and elevation (to derive mean annual rainfall data). It is quite possible there are other variables influencing the drainage density of the study area not included here.

I. Conclusions

This research project was created to better understand the intricate relationships and influences different environmental parameters have upon channel formation and drainage density. Given two separate parameters, a GIS was created to specifically analyze and calculate precisely how drainage density values varied among environmental subclasses. Hydrologic modeling provided the stream network, derived from the 10 m DEM, to accurately measure the distributions of streams within geologic and vegetative boundaries. Ultimately, total stream lengths and parameter distribution areas were used to provide the drainage densities for each of the seven environmental subclasses (three for geology and four within vegetation).

Inherent weaknesses in cartographic modeling can cause skewed results or inconsistencies among comparative studies. For example, drainage density values for all-order streams gathered from this study were compared with values in Walsh (1996) with a significant gap in values. The

Walsh (1996) study values were much higher than the drainage density values calculated for this project. This is likely the result of varying scales of mapping used as the basis for watershed area and stream length calculations. In addition, the critical contributing area threshold used in this study was much lower than thresholds applied in the past. An upstream pixel accumulation of 50 cells (0.5 hectares) or greater was used in this study, while past research projects have used thresholds of 600 pixels (6 hectares) and upwards. It is possible the threshold applied was too small and ephemeral streams were included in the stream network that should not have been.

Ultimately, it is quite clear that vegetation has the highest and most profound influence on channel formation within the study area. The data clearly show the over-riding affect vegetation has upon drainage density values when combined with geology. The different forest types collectively act as a natural buffer to erosion and weathering forces to dampen the influences of

36 geology alone. Other research projects also found that slope and subsurface characteristics in the downslope environment also influence drainages compared to just soil characteristics, which are ultimately determined from bedrock lithology.

II. Future Work

This project has shed light on potential avenues of work and research projects that could be tackled in the future. The main thing that can be done in the future to ensure problems with comparing results among projects is to standardized methods and mapping procedures. For instance, acceptable standards should be set whenever calculating scale-dependent metrics is required. In addition, using a set workflow for hydrologic modeling within the study area could greatly help to alleviate any inconsistencies in deriving vector data from raster data, as well as lessen the degree of human error in any calculations. Also, gathering and using higher resolution

DEMs or other terrain models would be ideal. Such sources of data could include LiDAR data, which is becoming more prevalent and desired for scientific research due to the possibility for much higher cell resolution compared to conventional DEMs.

Lastly, more research and data collection could be conducted on the hillslope environment to better classify hillslope processes. No knowledge currently exists on the inter-relationships of the hillslope environment and perennial channels. This information would go a long way to define the possible influences of hillslope processes upon the stream channel. In addition, this could help elucidate the weaknesses in defining the critical contributing area thresholds already discussed.

III. Usable Site-Specific Model for Future Projects

Two environmental flow hydrology models were built for this project. The first model, shown in figure (19) below, provides in situ environmental data for each stream reach based on the types of data used as inputs for model execution. For instance if geology and vegetation are used as environmental parameters in the model, the output of model execution will be a vector stream network with each reach classified with both geology and vegetation information.

Figure (19). This is an in situ flow hydrology model that provides environmental data on selectable stream reaches within a derived stream network

The second model uses the environmental parameters as inputs to provide upstream data for each stream reach in the model output. This model is slightly more involved in terms of steps and map algebra operations used. The user can specify outputs such as maximum (to mean

“dominant” in mathematical terms) upstream vegetation. Figure (20) below shows this model.

Figure (20). Flow hydrology model which provides upstream environmental data for each stream reach in the derived vector network.

Works Cited Collins, D. B.G., and R. L. Bras. "Climatic and ecological control sof equilibrium drainage density, relief, and channel concavity in dry lands." Water Resources Research 46 (2010).

Garcia‐Martino, Andres R, Glenn S Warner, Frederick N Scatena, and Daniel L Civco. "Rainfall, Runoff, and Elevation Relationships in the Luquillo Mountains of Puerto Rico." Caribbean Journal of Science, 1996: 24‐41.

Harden, Carol P, and P Delmas Scruggs. "Infiltration on Mountain Slopes: a Comparison of Three Environments." Geomorphology, 2003: 5‐24.

Pike, Andrew S, Frederick N Scatena, and Ellen E Wohl. "Lithological and fluvial controls on the geomorphology of tropical montane stream channels in Puerto Rico." Earth Surface Processes and Landforms, 2010: 1402‐1417.

Scatena, Frederick N. An introduction to the physiography and history of the Bisley Experimental Watersheds in the Luquillo Mountains of Puerto Rico. General Techincal Rport, United States Department of Agriculture, New Orleans: USDA Forest Service, 1989.

Walsh, R. P.D. "Drainage density and network evolution in the humid tropics: Evidence from the Seychelles and the Windward Islands." Zeitschrift Fur Geomorphologie Supplementband (Gebrueder Borntraeger), no. 103 (1996): 1‐23.

Appendix

I. Additional Data and Graphs

Geology vs. ALL Stream DD (m/ha) 40

0 Volcaniclastic Quartz Diorite Hornfels

Vegetation vs. ALL Stream DD (m/ha) 40

0 Tabonuco Colorado Palm Elfin

Elevation vs. ALL Stream DD (m/ha) 40 30 20 10 0 400-500 500-600 600-700 700-800 (masl)

II. ArcMap 10 Tools and Workflows

The Flow Hydrology toolset is found within the ArcToolbox in the Spatial Analyst sub-heading.

Specifically, these tools were used while constructing the flow hydrology analyses.

The second set of tools can be found in the Analysis Tools sub- heading of the ArcToolbox.

Intersection, Clip , and Spatial

Join were the main tools used to analyze spatial data for the vector analyses.

Flow Hydrology Toolset Workflow

Conceptually, this is how the computer processes the flow hydrology branch of the model. From bottom to top the workflow is as follows: Fill > Direction > Accumulation > CCA Threshold (Raster Calculator) > Stream Order

Same ArcScene graphic as above, but taken from different perspective lengthwise across the study area. Also, the symbology was changed to afford better distinction between layers.

The Field Calculator and Calculate Geometry functions show here were used in the attribute table for each dataset to analyze drainage values.

III. ArcScene Graphics of Data and Study Area

This is a 3D cartographic model of the derived stream network displaying reaches based on the underlying bedrock lithology (Green is Volcanoclastic, Yellow is Hornfels, and Red is Quartz Diorite). The Bisley watershed includes the elbow-bend reach in the foreground.

This is the same area as the graphic shown above, except now the stream network displays the forest types through which it flows. Red is Tabonuco, Yellow is Colorado, Green is Palm, and Blue is Elfin.

Horizon level oblique angle of the study area. Viewpoint is from south, looking due north.

Bird’s eye view of the study area. Viewpoint is from west, looking due east.

Bird’s eye view of the study area. Viewpoint is from northeast, looking southwest.