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

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An Investigation of the Influences of Bedrock Lithology and Vegetation on Low‐Order Stream Frequency in the Luquillo Mountains, Puerto Rico An Investigation of the Influences of Bedrock Lithology and Vegetation on Low‐Order Stream Frequency in the Luquillo Mountains, Puerto Rico 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. 1 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 2 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 3 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. 4 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.
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