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Applications of Reservoir Limnology Theory and Steady-State Modeling

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Applications of Reservoir Limnology Theory and Steady-State Modeling University of Arkansas, Fayetteville ScholarWorks@UARK Theses and Dissertations 5-2018 Applications of Reservoir Limnology Theory and Steady-State Modeling to Eutrophication Management in Beaver Lake, Arkansas Matthew Rich University of Arkansas, Fayetteville Follow this and additional works at: http://scholarworks.uark.edu/etd Part of the Biogeochemistry Commons, Fresh Water Studies Commons, and the Hydrology Commons Recommended Citation Rich, Matthew, "Applications of Reservoir Limnology Theory and Steady-State Modeling to Eutrophication Management in Beaver Lake, Arkansas" (2018). Theses and Dissertations. 2775. http://scholarworks.uark.edu/etd/2775 This Thesis is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [email protected], [email protected]. Applications of Reservoir Limnology Theory and Steady-State Modeling to Eutrophication Management in Beaver Lake, Arkansas A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Crop, Soil, and Environmental Sciences by Matthew W. Rich University of Arkansas Bachelor of Science in Environmental, Soil, and Water Science, 2013 May 2018 University of Arkansas This thesis is approved for recommendation to the Graduate Council. _____________________________ J. Thad Scott, Ph.D. Thesis Director _____________________________ _________________________________ Kristofor R. Brye, Ph.D. William R. Green, Ph.D. Committee Member Ex-officio Member _____________________________ Lisa S. Wood, Ph.D. Committee Member ABSTRACT Reservoir limnology theory predicts that phytoplankton biomass (PB) is greatest in riverine-transition zones and least in lacustrine zones leading to an inverse pattern in water clarity. These theoretical patterns were utilized to create a statistical model of chlorophyll-a (Chl- a), an indicator of PB, and Secchi transparency (ST), an indicator of Chl-a, in Beaver Lake, Arkansas, a 12,800-ha reservoir, in order to hindcast historical conditions. Sampling for Chl-a, ST, and photic depth occurred semimonthly at 12 locations along a 78-km transect from the river inflow to the dam during the 2015 growing season. The ratio of Chl-a and ST measured at each site to the Chl-a and ST measured at the dam (FracDAMChl-a and FracDAMST, respectively) were computed for each sampling date, and regression models were developed to predict FracDAMChl- a and FracDAMST as a function of distance from reservoir inflow. The models were used to estimate Chl-a (r2 = 0.83, p = 0.0003) and ST (r² = 0.98, p < 0.0001) at any location in the lake in years where spatially explicit data were not available. United States Geological Survey (USGS) monitoring data collected at the dam, and three additionally overlapping sites, from 2001 to 2015 were used to develop and test the hindcast models. Residuals of the modeled-measured USGS data suggested that variation in hydrology across years created predictable interannual variation in the spatial patterns in Chl-a and ST across the riverine-lacustrine continuum. Whole-lake averages of Chl-a and ST were related to whole-lake total phosphorus (TP) for modeled, measured, and target data sets between 2001-2015 for the purposes of estimating Vollenweider P loads. The most important finding of this study revealed that average of modeled and measured P loading required reductions of 18.9% and 33.3% to meet the newly adopted Beaver Lake Chl-a and ST standards, respectively. ACKNOWLEDGEMENTS First and foremost, I must acknowledge the persistent and dedicated advisement shown to me by J. Thad Scott over the past eight years. Whether as an undergraduate, an hourly employee in his lab, a career mentor while away from the University of Arkansas, or during my time as a graduate student in his lab, Thad has been an excellent example of scientific dedication. Thank you to my committee members, Drs. J. Thad Scott, Kristofor Brye, William R. Green, and Lisa Wood for their support and guidance throughout my time in the Crop, Soil, and Environmental Sciences Graduate Program. I greatly appreciate the statistical insight and guidance shown to me by Dr. J. Thad Scott, with special thanks to Auriel Fournier. Much appreciation is given to Ashley Rodman, Shannon Speir, Toryn Jones, Sarah Hallett, Taylor Adams, Dr. Michelle Evans-White, and Deanna Mantooth for contributing many hours of field and laboratory assistance, and to Brina Smith for analytical assistance. Thanks to Dr. Duane Wolf, my original undergrad advisor, who, before his retirement, stressed to me the following, “Rich, never let schooling get in the way of your education.” I’ll never forget your words. For guidance and mentoring while I debated returning to the UofA to pursue a graduate degree, I must recognize Drs. Brian J. Roberts and John Marton at the Louisiana Universities Marine Consortium. You’re right, it was worth it. I would like to thank the Beaver Watershed Alliance for providing funding for this study to the University of Arkansas between May 1, 2015 and December 31, 2015, and to Beaver Water District for funding support between January 1, 2016 and December 31, 2016. To Beverly and Jerry, thank you for always believing in me and supporting my decision to return to school at an unconventional age. To Gus, without you I’d be lost - thanks for sticking by my side during the countless hours as I wrote and rewrote this thesis. Good boy, Gus! TABLE OF CONTENTS 1. PROPOSAL AND LITERATURE REVIEW ......................................................................... 1 1.1 Introduction ...................................................................................................................... 1 1.2 Hypotheses ....................................................................................................................... 8 1.3 Methods ............................................................................................................................ 9 1.3.1 Study Locations and Sampling Methods ................................................................... 9 1.3.2 Chl-a and TNTP Laboratory Processing Methods ................................................. 10 1.3.3 Data Manipulations and Statistical Analysis .......................................................... 11 1.4 Results ............................................................................................................................ 12 1.5 References ...................................................................................................................... 14 1.6 Tables ............................................................................................................................. 17 1.7 Figure Legends ............................................................................................................... 18 1.8 Figures ............................................................................................................................ 19 2. RECONSTRUCTING SPATIOTEMPORAL PHYTOPLANKTON BIOMASS TRENDS USING RESERVOIR LIMNOLOGY THEORY ......................................................................... 25 2.1 Introduction .................................................................................................................... 25 2.2 Materials and Methods ................................................................................................... 28 2.2.1 Site Description ....................................................................................................... 28 2.2.2 Sampling Locations, Dates, and Methods............................................................... 29 2.2.3 Laboratory Processing Methods ............................................................................. 30 2.2.4 Data Manipulations and Statistical Analysis .......................................................... 30 2.3 Results ............................................................................................................................ 31 2.3.1 Spatial and Temporal Trends in 2015 Phytoplankton Biomass.............................. 31 2.3.2 Hindcasting Models ................................................................................................ 32 2.4 Discussion ...................................................................................................................... 34 2.4.1 Limitations and Strengths of the Models ................................................................ 35 2.5 References ...................................................................................................................... 37 2.6 Tables ............................................................................................................................. 40 2.7 Figure legends ................................................................................................................ 44 2.8 Figures ............................................................................................................................ 46 3. DERIVING PHOSPHOROUS LOAD REDUCTION ESTIMATIONS FOR EUTROPHICATION MANAGEMENT BY COMBINING RESERVOIR LIMNOLOGY THEORY WITH STEADY-STATE RESERVOIR MODELING ............................................... 51 3.1 Introduction .................................................................................................................... 51 3.2 Materials and Methods ..................................................................................................
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