Evaluating the hydrological impact of removing woody biomass for biofuel production through unsaturated zone modeling

Due to climate change concerns, governments and consumers are demanding higher environmental accountability for transportation fuels, particularly as related to carbon emissions. Wood-based energy markets have been proposed as a means to ensure sustainable forests, enhance energy security, promote environmental quality, and realize social benefits. Biomass crops may offset fossil fuels and reduce CO2 contributions to greenhouse gases while improving soil and water quality. Key issues among stakeholders include soil and water quality and loss of biodiversity as collecting small-diameter woody biomass may significantly alter post-timber harvesting landscapes. Little is known about how land use changes impact the entire ecological function of the watershed. The objectives of this study were to explore the potential of differences between land use changes and see if the water balance of the watershed would also change. This will help us understand the environmental impacts of different forms of biomass removal in the production of jet fuel. The start of holistic land management strategies focused on hydrologic implications of the entire food web has begun. Hydrologic measurements were collected from 28 one acre plots subject to different land treatments, analyzed, and compared to a site-specific water balance model UNSAT-H to evaluate if changes in biomass removal influence the subsurface hydrology. Results showed a correlation between compacted soils exhibiting more evaporation when compared to noncompacted sites. A correlation of less drainage to the water table correlates to a higher clay content value; this correlation did not exist, and was therefore not statistically significant. Since the soils had such unique characteristics at each plot, parameterization and extrapolation of the UNSAT-H model for the whole Pacific Northwest is not possible solely using the data of this LTSP site.

EVALUATING THE HYDROLOGICAL IMPACT OF REMOVING WOODY BIOMASS FOR BIOFUEL PRODUCTION THROUGH UNSATURATED ZONE MODELING by Constance Taylor Smith A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Master of Science Department of Civil and Environmental Engineering The University of Utah August 2017 Copyright © Constance Taylor Smith 2017 All Rights Reserved The University of Utah Graduate School STATEMENT OF THESIS APPROVAL Constance Taylor Smith The thesis of has been approved by the following supervisory committee members: Michael Ernest Barber , Chair 03/17/17 Date Approved Jennifer Lee Weidhaas , Member 03/17/17 Date Approved Steven John Burian , Member 03/20/17 Date Approved and by Michael Ernest Barber the Department/College/School of , Chair/Dean of Civil and Environmental Engineering and by David B. Kieda, Dean of The Graduate School. ABSTRACT Due to climate change concerns, governments and consumers are demanding higher environmental accountability for transportation fuels, particularly as related to carbon emissions. Wood-based energy markets have been proposed as a means to ensure sustainable forests, enhance energy security, promote environmental quality, and realize social benefits. Biomass crops may offset fossil fuels and reduce CO2 contributions to greenhouse gases while improving soil and water quality. Key issues among stakeholders include soil and water quality and loss of biodiversity as collecting small-diameter woody biomass may significantly alter post-timber harvesting landscapes. Little is known about how land use changes impact the entire ecological function of the watershed. The objectives of this study were to explore the potential of differences between land use changes and see if the water balance of the watershed would also change. This will help us understand the environmental impacts of different forms of biomass removal in the production of jet fuel. The start of holistic land management strategies focused on hydrologic implications of the entire food web has begun. Hydrologic measurements were collected from 28 one acre plots subject to different land treatments, analyzed, and compared to a site-specific water balance model UNSATH to evaluate if changes in biomass removal influence the subsurface hydrology. Results showed a correlation between compacted soils exhibiting more evaporation when compared to noncompacted sites. A correlation of less drainage to the water table correlates to a higher clay content value; this correlation did not exist, and was therefore not statistically significant. Since the soils had such unique characteristics at each plot, parameterization and extrapolation of the UNSAT-H model for the whole Pacific Northwest is not possible solely using the data of this LTSP site. iv TABLE OF CONTENTS ABSTRACT....................................................................................................................... iii LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES ........................................................................................................... ix ACKNOWLEDGEMENTS ............................................................................................. xiv Chapters INTRODUCTION ...............................................................................................................1 Objectives ....................................................................................................................... 3 Description of the LTSP Sites and Treatment Process ................................................... 6 WATER BALANCE MODEL ..........................................................................................14 Methodology ................................................................................................................. 15 Data Collection ......................................................................................................... 15 Selection of the Model .............................................................................................. 16 Calibration and Validation of Hydraulic Parameters in UNSAT-H ......................... 17 Results ........................................................................................................................... 21 Discussion ..................................................................................................................... 22 OVERALL CONCLUSION AND SUMMARY ...............................................................49 Appendices A: SUMMARY OF UNSAT-H SIMULATION RESULTS FOR TWO YEARS OF MODELING ......................................................................................................................51 B: THE BASIS OF UNSAT-H ..........................................................................................64 C: HYDROLOGIC PARAMETER INPUT INFORMATION FOR UNSAT-H ..............80 D: GRAPHICAL SUMMARIES OF UNSAT-H SIMULATION RESULTS FOR TWO YEARS ............................................................................................................................104 E: GRAPHICAL SUMMARIES OF DATATRAC WATER CONTENT MEASUREMENTS FOR TWO YEARS ........................................................................133 F: SOIL COMPOSITION OF EACH TREATMENT TYPE BY SUBSURFACE DEPTH .............................................................................................................................162 REFERENCES ................................................................................................................170 vi LIST OF TABLES Tables 1.1 Tabular Form of Treatment Removal Technologies................................................... 12 2.1 ROSETTA Simulation Results for Each Plot for Hydraulic Parameters .................... 32 2.2 General Performance Ratings for Recommended Statistics ....................................... 32 2.3 Hydrologic Soil Parameters and Parameter Value Constraints .................................. 33 2.4 Performance Results for Two-Year Simulation of UNSAT-H ................................... 34 2.5 Hydraulic Parameters Entered for Each Soil Profile Depth for Treatment Plots A-B 36 2.6 Results from UNSAT-H Program for Two Years of Simulation for Treatment Plots AC ........................................................................................................................................ 37 2.7 Results of 2-Year Simulation for Water Budget for All LTSP Plots .......................... 39 2.8 Summary of Evaporation and Basal Liquid Flux Results by Treatment Type ........... 40 2.9 Plot A19II Percent Change in VWC ........................................................................... 41 2.10 Plot F08III Percent Change in VWC ........................................................................ 42 2.11 Regression and ANOVA Statistical Results Between All Parameters for (0-15 cm) Soil Profile ........................................................................................................................ 43 2.12 Regression and ANOVA Statistical Results Between All Parameters for (15-30 cm) Soil Profile ........................................................................................................................ 44 2.13 Regression and ANOVA Statistical Results Between All Parameters for (30-100 cm) Soil Profile ........................................................................................................................ 45 2.14 Average Slope Values for Each LTSP Plot .............................................................. 47 A.1 Summary of UNSAT-H Simulation Results for Each Plot for 2-Year Simulation………………………………………………………………………………..52 A.2 VWC Measurements for Spring 2015-2016 for Treatment A, Plot 11 ...................... 54 C.1 Seven Hydraulic Parameters for All Twelve USDA Textural Classes Described by ROSETTA………………………………………………………………………………..81 C.2 ROSETTA Software Results for All Plots for 0-15 cm Soil Depth Profile ................82 C.3 ROSETTA Software Results for All Plots for 15-30 cm Soil Depth Profile ..............84 C.4 ROSETTA Software Results for All Plots for 30-100 cm Soil Depth Profile ............85 C.5 Summary of Soil Information Input in UNSAT-H for Each Soil Profile and Each Plot……………………………………………………………………………………….87 C.6 Seasonal PBIAS Calculated Values for Fall 2014 Season ..........................................92 C.7 Seasonal PBIAS Calculated Values for Spring 2015 Season .....................................94 C.8 Seasonal PBIAS Calculated Values for Winter 2015 Season .....................................96 C.9 Data for Statistical Analysis for Unique Soil Parameters for (0-15 cm) Soil Profile .98 C.10 Data for Statistical Analysis for Unique Soil Parameters (15-30 cm) Soil Profile .100 C.11 Data for Statistical Analysis for Unique Soil Parameters (30-100 cm) Soil Profile ..........................................................................................................................................102 viii LIST OF FIGURES Figures 1.1 Location of LTSP Site in OR GIS Map .......................................................................10 1.2 Location of LTSP Site via Satellite Imagery ...............................................................11 1.3 ArcMap Model of LTSP Study Plots and Location of Soil Moisture Probes..............11 1.4 LTSP Site for Treatment A: (OM0 C0) No Compaction Bole Only ...........................12 1.5 LTSP Site for Treatment G: (OM2 C1) Compaction Total Tree + FF ........................13 1.6 LTSP Site Picture Taken on 7/11/16 ...........................................................................13 2.1 Soil Moisture and Temperature Sensor 5TM Installation Configuration ....................31 2.2 UNSAT-H Simulation for Two Years of Plot A14I - VWC with Precipitation ..........38 2.3 Treatment Plot A19II VWC during Peak Rainfall Event in December 2014 ..............41 2.4 Treatment Plot F08III VWC during Peak Rainfall Event in December 2014 .............42 2.5 LTSP Site Depicting Calculated Slope Values in Percentage .....................................46 2.6 LTSP Site Depicting Calculated Aspect Values in Azimuth Angle ............................48 B.1 Input .inp file for Treatment B091 for 2-Year Simulation ......................................... 78 B.2 Output .out File for Treatment B09I for First Year of Simulation…………………..79 D.1 Treatment Plot A11III Volumetric Water Content with Precipitation for 2-Year Simulation………………………………………………………………………………105 D.2 Treatment Plot A14I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................106 D.3 Treatment Plot A18IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................107 D.4 Treatment Plot A19II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................108 D.5 Treatment Plot B09I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................109 D.6 Treatment Plot B16IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................110 D.7 Treatment Plot B20II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................111 D.8 Treatment Plot B33III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................112 D.9 Treatment Plot C01I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................113 D.10 Treatment Plot C07II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................114 D.11 Treatment Plot C25IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................115 D.12 Treatment Plot C28III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................116 D.13 Treatment Plot D04II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................117 D.14 Treatment Plot D06III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................118 D.15 Treatment Plot D13I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................119 D.16 Treatment Plot D22IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................120 D.17 Treatment Plot E10I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................121 D.18 Treatment Plot E15II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................122 D.19 Treatment Plot E17IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................123 x D.20 Treatment Plot E26III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................124 D.21 Treatment Plot F05I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................125 D.22 Treatment Plot F08III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................126 D.23 Treatment Plot F24IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................127 D.24 Treatment Plot F32II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................128 D.25 Treatment Plot G02I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................129 D.26 Treatment Plot G12III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................130 D.27 Treatment Plot G30II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................131 D.28 Treatment Plot G31IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................132 E.1 DataTrac Plot A11III Volumetric Water Content with Precipitation for 2-Year Simulation………………………………………………………………………………134 E.2 DataTrac Plot A14I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................135 E.3 DataTrac Plot A18IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................136 E.4 DataTrac Plot A19II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................137 E.5 DataTrac Plot B09I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................138 E.6 DataTrac Plot B16IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................139 E.7 DataTrac Plot B20II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................140 xi E.8 DataTrac Plot B33III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................141 E.9 DataTrac Plot C01I Volumetric Water Content with Precipitation for 2-Year Simulation .......................................................................................................................142 E.10 DataTrac Plot C07II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................143 E.11 DataTrac Plot C25IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................144 E.12 DataTrac Plot C28III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................145 E.13 DataTrac Plot D04II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................146 E.14 DataTrac Plot D06III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................147 E.15 DataTrac Plot D13I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................148 E.16 DataTrac Plot D22IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................149 E.17 DataTrac Plot E10I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................150 E.18 DataTrac Plot E15II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................151 E.19 DataTrac Plot E17IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................152 E.20 DataTrac Plot E26III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................153 E.21 DataTrac Plot F05I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................154 E.22 DataTrac Plot F08III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................155 E.23 DataTrac Plot F24IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................156 xii E.24 DataTrac Plot F32II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................157 E.25 DataTrac Plot G02I Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................158 E.26 DataTrac Plot G12III Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................159 E.27 DataTrac Plot G30II Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................160 E.28 DataTrac Plot G31IV Volumetric Water Content with Precipitation for 2-Year Simulation ........................................................................................................................161 F.1 Soil Composition for Treatment Type A, 0-30 cm Depth………………………….163 F.2 Soil Composition for Treatment Type A, 30-100 cm Depth .....................................163 F.3 Soil Composition for Treatment Type B, 0-30 cm Depth .........................................164 F.4 Soil Composition for Treatment Type B, 30-100 cm Depth .....................................164 F.5 Soil Composition for Treatment Type C, 0-30 cm Depth .........................................165 F.6 Soil Composition for Treatment Type C, 30-100 cm Depth .....................................165 F.7 Soil Composition for Treatment Type D, 0-30 cm Depth .........................................166 F.8 Soil Composition for Treatment Type D, 30-100 cm Depth .....................................166 F.9 Soil Composition for Treatment Type E, 0-30 cm Depth .........................................167 F.10 Soil Composition for Treatment Type E, 30-100 cm Depth ...................................167 F.11 Soil Composition for Treatment Type F, 0-30 cm Depth ........................................168 F.12 Soil Composition for Treatment Type F, 30-100 cm Depth....................................168 F.13 Soil Composition for Treatment Type G, 0-30 cm Depth .......................................169 F.14 Soil Composition for Treatment Type G, 30-100 cm Depth ...................................169 xiii ACKNOWLEDGEMENTS I would like to express my deepest gratitude and cordial thanks to my advisor Dr. Michael E. Barber for his full support, patience, encouragement, and expert guidance throughout my study and research. He gave me all of the tools I needed to succeed at this project, and will be eternally grateful to have worked with him. I also want to express my appreciation for Dr. Ramesh Goel and Dr. Steve Burian for having served on my committee. Their support, thoughtful questions and comments were valued greatly when I came to them for help. I would like to extend my gratitude to the United States Department of Agriculture's National Institute of Food and Agriculture for funding this project. Additionally, my many thanks to Weyerhauser for allowing research to be conducted on their Long Term Soil Productivity (LTSP) site and climate data available for us to use. I would also like to thank Scott Holub for all of his help with troubleshooting raw data from the LTSP site, answering my heaping amount of questions, and helping me analyze the data. Additionally, I want to send my warm thanks to Michael Fayer, developer of the computer code "UNSAT-H." He always helped me troubleshoot my code for the treatment plots, and would always lend a helping hand if I ever needed help understanding how the program worked. Thanks also to my fellow graduate students, indefinitely to Sulochan Dhungel, and staff at the Department of Civil and Environmental Engineering at the University of Utah for their support. Finally, I would like to thank my mom, my sister, and my friends for their unconditional love and support during the last two years. They gave me the encouragement that I needed when I felt incompetent, and I would not have been able to complete graduate school without their support. xv CHAPTER 1 INTRODUCTION Sustainable production of bioenergy is necessary to meet future world-wide energy demands while helping to offset the global impacts of increased carbon dioxide from traditional fossil fuels (Aransiola et al., 2014; Beringer et al., 2011; Berndes, 2002; Johansson and Azar, 2007). The concept of sustainability, the ability to meet the needs of the present without compromising the ability to meet the needs of the future, continues to gain attention as human population growth creates ever greater pressure on diminishing natural resources (Mann et al., 2000). According to the 2016 Federal Activities Report on the Bioeconomy released on February 2016, the Biomass R&D Technical Advisory Committee has recommended "targeting a potential 30% penetration of biomass carbon into the U.S. transportation market by 2030" (The Biomass Research and Development (R&D) Board, 2016). Scientists have been researching ways to produce bioenergy without adversely impacting food, land, and other environmental resources. The Northwest Advances Renewables Alliance (NARA), a broad alliance of private industry and educational institutions takes a holistic approach to building a supply chain within WA, OR, ID, and MT based on using forest residuals to make aviation biofuel. NARA's objective is to increase efficiency for each supply chain step from forestry operations to conversion processes; creating new bio 2 products; providing economic, environmental, and social sustainability analyses; engaging stakeholder groups; and improving bioenergy literacy for students, educators, professionals, and the general public. Forest residuals from logging operations will be used as feedstock to fulfill the project aims of creating a sustainable industry to produce aviation biofuels and important co-products. The Alliance is funded through a five-year grant in the amount of $40 million provided by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture. Key issues among the stakeholders include soil and water quality, loss of biodiversity, climate, market sustainability, and competition between industries with less expensive conventional biofuels. Many of these issues are currently being studied by different entities such as education, sustainability measurement, feedstock, conversion and outreach as part of the NARA teams. On November 14, 2016, Washington state-based Alaska Airlines made history by flying the first commercial flight from Seattle, WA to Washington, D.C. The plane used a 20% blend of the NARA aviation biofuel, which is chemically indistinguishable from regular jet A fuel. If the airline were able to replace 20% of its entire fuel supply, it would reduce greenhouse gas emissions by about 142,000 t of carbon dioxide. This is equivalent to taking approximately 30,000 passenger vehicles off the road for one year. The University of Utah is responsible for the assessing the hydrologic concerns of the water balance and is part of a larger effort by the sustainability measurement. This is specifically aimed at examining the potential repercussions of incremental woody biomass removal associated with various degrees of residual ground cover (or biomass) removal in the production of bio jetfuel in the Pacific Northwest. 3 Objectives The overarching goal of this study is to investigate the potential hydrologic impacts of residual ground cover (biomass) removal in the production of biojet fuel in the Pacific Northwest. The region of interest for this project is the vadose zone of the subsurface. The vadose zone is the portion of the earth's surface that encompasses the soil and unsaturated sediments that lie above the water table. Test sites have been treated with two different types of compaction and three different types of biomass cover for investigation, totaling seven different types of ground treatment. These sites have equipment measuring volumetric water content and temperature on an hourly basis at respective depths in the vadose zone (10 cm, 20 cm, 30 cm, and 100 cm below ground surface). Specifically, the vadose zone investigation will include: 1) induced hydrologic variations due to deviation of evaporation and infiltration processes, 2) the effect of evaporation and infiltration processes based on the type and extent of biomass removal, and 3) the potential applicability of extending this site-scale study to a large, watershed-scale appropriate for the Pacific Northwest. Biomass crops, like conventional food and fiber crops, affect soil quality by causing changes in: organic matter, the relative flux of nutrients, erosion, and soil compaction resulting from equipment movement during planting, maintenance, and harvest. All of these changes are also affected by the biological activity of microfauna and macrofauna, which regulate nutrient dynamics, structure, and stability of the soil (Mann et al., 2000). The results for woody biomass such as Douglas Fir Trees in the Pacific Northwest could show different results and impacts, making this research necessary. The following three objectives will be used to help achieve this goal: (1) Collect and process data measured at the test plots and develop a predictive water 4 quantity model to evaluate site-scale regional impacts of small-scale biomass removal; (2) To evaluate if there is a correlation between differences in land cover as a function of unique soil characteristics, such as: cation exchange capacity, clay content percentage, soil carbon percentage, slope of the land, microbial population, pH, electric conductivity; (3) To determine the capability of extending site-scale regional impacts of small-scale biomass removal to watershed-scale semiarid regional impacts of large-scale biomass removal. These objectives will help quantify the effects of woody biomass removal on the soil and water balance of the study site, and therefore demonstrate the sustainability of harvesting woody biomass forest residuals as a source of biomass for the NARA bioenergy feedstock. The vadose zone groundwater modeling software will mimic the onsite measured values of volumetric water content over time, and calculate for the infiltration/evaporation water budget values, and will be described in greater detail in Chapter 2. Based on the different treatment removal technologies of two different forms of compaction and three different forms of biomass land cover, there could be potential impacts to water quantity and their timing of infiltration/evaporation, respectively. It has been previously stated that very severe compaction at high moisture contents cause soil deformation, thus decreasing soil water potential (Soane et. al., 1994). Furthermore, it has been studied previously that compaction impairs the conditions of the soil by increased soil bulk density, increased nitrification rates, reduced porosity and water infiltration rates, 5 damaged soil structure and aggregate stability (Mann et al., 2000). In some soils, even small deformations will cause large decreases in the saturated hydraulic conductivity of that particular soil. For the reasons listed, the literature would suggest that the compacted soil sites will promote more evaporation and less infiltration to the subsurface. Secondly, the seven different treatments are initially assumed to have fluctuations in parameters listed above, such as differences in microbial populations and saturated conductivity values to list a few of many variables. Lastly, to extrapolate the results of this investigation to a watershed-scale would aid in contributing to solutions for broadscale problems in largescale biomass removal. To extrapolate the results would be to project, extend, or expand the simulated data into an area not known or experienced so as to arrive to a new knowledge of the unknown area by inferences based on an assumed continuity, correspondence, or other parallelism between it and what is known. This definition encompasses the process of "scaling up," or deriving inferences and rules that can be applied to broad scales on the basis of data collected at smaller scales (Miller et al., 2004). Therefore, the three study objectives described above will be evaluated using the following null hypotheses and test their validity: i) Ho: Increased biomass removal from the LTSP site will have no impact on infiltration rates or the water budget of the subsurface. ii) Ho: There is no distinction between differences in land cover as a function of unique soil characteristics, such as: slope, saturated conductivity, soil temperature, cation exchange capacity, and microbial populations present. iii) Ho: Data from the site-scale regional impacts can be applied to watershed-scale regional impacts of large-scale biomass removal through the Pacific Northwest. 6 Description of the LTSP Sites and Treatment Process Data for the investigations conducted in this thesis were collected from Weyerhaeuser's LTSP site (see Figure 1.1) in the southern Willamette Valley, OR (see Figure 1.2). To reduce redundancy in each chapter, a common description of study sites and biomass treatment options is provided below. As a part of Weyerhaeuser's effort to sustainably manage its more than six million acres of forested timberland in the U.S., it continues to conduct, evaluate, and support research associated with the North American LTSP program. The program was founded in 1989 as a "grass roots" proposal that grew to a national program of the USDA Forest Service, with its main goal to examine the longterm consequences of soil disturbance on fundamental forest productivity (http://www.fs.fed.us/psw/topics/forest_mgmt/ltsp/). This particular LTSP site near Springfield, OR was created to support the NARA project. A total of 28 one-acre plots were selected by Weyerhaeuser to aid in this investigation and round out an existing regional study, to extend into warmer and drier parts of the Douglas-fir ranges, as to contribute more understanding into the broader LTSP network. Treatments were randomly assigned, and laid out in such that any plot could feasibly receive that particular random assignment. The original site selection criteria were for one harvest unit in the vicinity of Cottage Grove/Springfield, OR, on uniform soil with low rock content of an area large enough to contain the study plots with an appropriate buffer between plot to allow equipment accessibility and movement. The selected unit is East of Springfield, OR and South of the Mackenzie River on Weyerhaeuser ownership on the Booth Kelly 400 Rd. (Sec 1 18S 01W) at 44.032 Latitude and -122.76 Longitude. These plots are part of a larger interconnected network for hydrologic analysis. The 7 site is between 1985 and 2190ft elevation on gentle slopes of 2 to 20%. Soil information was collected from Weyerhaeuser's NARA LTSP Plot Summary Analysis. The majority of the information used to characterize the soil class were the percent silt, percent sand, and percent clay values, cation exchange capacity, carbon organic matter amount, pH, and electric conductivity. The soil percentage values were then interpreted to determine the main soil class using the Natural Resources Conservation Service (NRCS) Soil Texture Calculator (http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2 _054167). Additionally, the soil percent-ages were input into the USDA ARS ROSETTA Model (https://www.ars.usda.gov/pacific-west-area/riverside-ca/us-salinity-laboratory/do cs/rosetta-model/) to cross reference the soil class, as well as other fitting parameters, which will be discussed in greater detail in the Calibration and Validation of Hydraulic Parameters in UNSAT-H section. The average annual precipitation at this location is 50.9" (1292.9 mm). Summers tend to be dry with less than one-third that of the wettest winter month, and with less than 1.54" (39.1 mm) of precipitation in a summer month (http://www.intellicast.com/Local/ History.aspx? location=USOR0118). The month with the most precipitation on average is November with 8.44" (214.4 mm) of precipitation. The month with the least precipitation on average is July with 0.64" (16.3 mm) of precipitation. The warmest month, on average, is August with an average temperature of 82°F and the coolest month on average is December, with an average temperature of 46°F. General LTSP "Core" Treatments consists of a factorial combination of compaction (C0, none; C1, moderate) and aboveground OM removal (OM0, bole/trunk only; OM1, whole tree; OM2, whole tree plus forest floor removal). Three levels of organic matter 8 removal and two levels of compaction results to total of 7 different treatment plots. Multiple passes with heavy machinery were used to compact soils. Seven different treatment combinations have been applied to 28 study plots; 4 replicate plots of each treatment. The different treatment types based on two different levels of compaction and three different levels of biomass land cover are categorized as follows, and presented via tabular form in Table 1.1, with their designated treatment type denoted by the letter in the top right corner. Additionally, Figure 1.3 depicts each LTSP study plot location on the ArcMap model, including the location of the soil moisture sensor probes and weather stations. C0 - No Compaction - No ground trafficking on plot. C1 - Compaction - Fixed traffic lanes where plot is leveled with forestry machinery. OM0 - Boles only - Boles/Trunks only, meaning harvest consists of removing saw log top (5" minimum diameter), while all other limbs and tops remain on the site. OM1 - Total Tree - Whole-tree type harvest where approximately 75+% of limb/top material is removed along with the boles/trunks. Remaining material will be dispersed and equal across plots. OM2 - Total Tree + Forest Floor - Whole-tree type harvest where ~90-95% of limb/top material is removed along with the bole/trunk. Forest floor and legacy woody debris also removed. Typical examples of the LTSP plots after woody biomass removal are shown in Figure 1.4 and Figure 1.5. Note the clearly visible compaction tracks caused by harvesting 9 activities in Figure 1.5. The most recent photo taken of the site was taken on July 11, 2016, and shows the tiny saplings planted earlier that summer, and can be seen in Figure 1.6 below. The objectives of this study will be completed by collecting the necessary weather and soil characteristics data via Weyerhaeuser's LTSMP Site in Eugene, OR. The mean absolute error (MAE) was calculated for each plot for each season of the year over the course of two years between its measured and simulated value. UNSAT-H Version 3.0: Unsaturated Soil Water and Heat Flow Model, developed by the Pacific Northwest National Laboratory for the United States Department of Energy, will be used to develop a predictive water balance model that correlates with data collected from the soil moisture probes installed in the field. The kind of information that will then be predicted from the model will be quantified amounts of runoff, infiltration, evaporation, overall water balance, and so forth. That information will be used to determine the applicability to evaluate watershed-scale regional impacts. The overall outcome is a better indication of the amount of woody biomass that can be removed as forest residuals following conventional harvest without a reduction in productive capacity of the site. Productive capacity of the site will be determined using the infiltration/evaporation calculations; a lower evaporation rate coupled with higher infiltration rate dictates a high productive capacity, and therefore the favorable outcome. On the contrary, higher evaporation rates coupled with lower infiltration rates defines a low productive capacity, and therefore the nonfavorable outcome. 10 Figure 1.1 Location of LTSP Site in OR GIS Map 11 Figure 1.2 Location of LTSP Site via Satellite Imagery Figure 1.3 ArcMap Model of LTSP Study Plots and Location of Soil Moisture Probes 12 Table 1.1 Tabular Form of Treatment Removal Technologies Compaction Harvest OM0 - Bole only OM1 - Boles and crowns removed "Total Tree" OM2 - Boles, crowns, forest floor removed C0 C1 No Compaction OM0 C0 - A Boles Removed, No Compaction OM1 C0 - B Boles and Crowns Removed / No Compaction Moderate Compaction OM0 C1 - C Boles Removed, Moderate Compaction OM1 C1 - D/F Boles and Crowns Removed / Moderate Compaction OM2 C1 - E/G Boles, Crowns, Forest Floor Removed / Moderate Compaction Figure 1.4 LTSP Site for Treatment A: (OM0 C0) No Compaction Bole Only 13 Figure 1.5 LTSP Site for Treatment G: (OM2 C1) Compaction Total Tree + FF Figure 1.6 LTSP Site Picture Taken on July 11, 2016 by Weyerhaeuser. Copyright 2016 by Weyerhaeuser. Reprinted with permission. CHAPTER 2 WATER BALANCE MODEL Forest soils serve as underground reservoirs for water. Multiple studies provide evidence that forest-harvesting practices undoubtedly have a significant effect upon surface soil properties, and in semiarid regions where the clearing of land promotes desertification (Bonan, 1999). In rain-dominated portions of the Pacific Northwest, annual water yield may be enhanced by the removal of forest vegetation from small upland watersheds, yet questions still remain regarding the effects of logging operations on infiltration under the variety of climatic, physiographic, and vegetative conditions of this region. A particular study paired watersheds near Fort Bragg located in Northern California, and investigated the impacts of selected harvesting practices of a second-growth Douglas fir and redwood forest on peak infiltration. However, the effect of logging operations upon summer lowflow quantity and timing was not evaluated. This study found there was indeed an alteration of the amount and seasonal distribution of infiltration (Keppeler et al., 1990). The objective of this study is to examine the ecological environment through the measurement of soil moisture at the site, and develop a predictive water quantity and quality models based on each different type of removal (A-G). Based on this objective the following hypothesis will be tested: Ho: Increased biomass removal from the LTSP site will have no impact on infiltration rates or the water budget of the subsurface. 15 The interception of precipitation (rain and snow) by vegetation canopies is a major component of the surface water balance. Annual net interception losses in temperate forests were observed to range from 9 to 48% of gross precipitation in coniferous canopies. Additionally, previous studies of old-growth Douglas-fir ecosystems in Oregon found net interception losses to be 14% of gross precipitation for the time period between OctoberApril, and 24% of gross precipitation for the time period between May-September (Link et al., 2004). Plant communities that exhibit low precipitation inputs during the growing season must rely on plant or soil water storage to provide the water necessary for growth processes. This soil water storage capability and uptake from the unsaturated zone govern various aspects of ecosystem functioning, and therefore are crucial for groundwater modeling. With this in mind, vadose zone groundwater modeling processes are technically more difficult due to roughly characterizing the extremely heterogeneous soil medium (Warren et al., 2005). Furthermore, modeling is required to determine and evaluate the water budget (infiltration/evaporation processes) of the site, due to insufficient data from the experimental devices not calculating these values. The groundwater model can then be used to study implications of climatic changes for future projections. Methodology Data Collection Four soil moisture measurement probes were installed at each plot location on the map, respectively, totaling to approximately 112 probes for the whole LTSP site. The 5TM Soil Moisture and Temperature Sensor by Decagon Devices® was the chosen soil moisture and temperature probe selected for installation. Weyerhaeuser conducted the seven 16 different land alterations, at each plot, Weyerhaeuser installed four separate sensors at 10cm, 20-cm, 30-cm, and 100-cm depths, respectively. For the 10-cm, 20-cm, and 30-cm depths, a trench wall was dug and the sensors were installed with a horizontal orientation to the surface. For the 100-cm depth, an auger hole was drilled into the subsurface and the sensors were installed with a vertical orientation to the surface. In three locations, the 100cm depth was too gravely to install a 5TM sensor, therefore not collecting data at the 100cm depth. A schematic of the installation configuration can be found in Figure 2.1. The 5TM sensor was chosen because of its high accuracy by signal filtering; resulting measurements are then minimized in salinity and textural effects, making it accurate in most soils and even soilless media. An installation demo at short and long subsurface depths can be found on the Decagon Devices (https://www.decagon.com/en/support/videos/ech2o-sensor-installation/). website Additionally, there are two weather stations installed at the sites to measure: precipitation, air temperature, wind speed, relative humidity, and solar radiation at every hour. One weather station in the clear cut section, and the other weather station is located in the forested section. The data were collected and processed to get maximum and minimum daily air temperature [°F], daily average dewpoint temperature [°F], average wind speed per day at 2m height [mile/h], relative humidity and average solar radiation [Langleys/day]. Dew point temperature was calculated from relative humidity and the saturation vapor pressure. Selection of the Model An unsaturated flow recharge model has been used to estimate infiltration and evaporation of the groundwater, which will also help to predict water drainage rates for the 17 simulated conditions. Various numerical models such as Unsaturated-Zone Flow (UZF1), MODFLOW-2005, and UNSAT-H (Fayer, 2000) have been widely used to predict recharge estimates. All of these models use van Genuchten (1980) and Brooks-Corey (1964) water retention functions and the Mualem (1978) and Burdine (1953) hydraulic conductivity functions. All of these models simulate atmospheric interactions, plant transpiration, solute transport, heat transfer, and vapor flow using modified forms of Richard's Equation (Fayer, 2000). Additionally, UZF1 and MODFLOW-2005 contained 2-D and 3-D capabilities that were not used for this project. UZF1 and MODFLOW-2005 use kinematic wave approximations and UNSAT-H uses the finite difference method - this method tends to be more stable when solving problems where contrasts in hydraulic properties exist at layer interfaces (Benson et. al., 2007). UNSAT-H is a 1-D vertical or horizontal model which uses the finite difference method to solve for Richard's Equation (Benson et. al., 2007). Additionally, UNSAT-H is the only model which allows precipitation to be input as a daily amount (in), allowing evapotranspiration to occur throughout the day and provide a more accurate result than UZF1 and MODFLOW-2005. A 1-D numerical model was appropriate for the LTSP site, since it this is a region of relatively flat topography with small to negligible runoff. Thus, the UNSAT-H model was considered most appropriate for this study. Calibration and Validation of Hydraulic Parameters in UNSAT-H The UNSAT-H code must have mathematical descriptions of the hydraulic, vapor, and thermal properties of the soil and the air. To solve for the flow equation for liquid water, the code must be supplied with relationships for both water content and hydraulic 18 conductivity as functions of suction head. The soil water retention function and hydraulic conductivity function work together, constituting the set of hydraulic parameters and properties of the soil required by UNSAT-H. In the case of unsaturated flow, it is difficult to predict water content (θ), hydraulic conductivity (k), and suction head (h) due to the multidimensional, nonhomogeneous characteristics of soil (van Genuchten, 1980). There are many soil water retention relationships that have been determined, such as the linked polynomials, the Haverkamp function, the Brooks and Corey function, the van Genuchten function, and several special functions that account for water retention of very dry soils (Fayer, 2000). Therefore, Equation (2.1) illustrates how water content and hydraulic conductivity are functions of suction head according to the van Genuchten function and Mualem hydraulic conductivity model and has the form 𝜃𝜃 = 𝜃𝜃𝑟𝑟 + (𝜃𝜃𝑠𝑠 − 𝜃𝜃𝑟𝑟 )[1 + (𝛼𝛼ℎ)𝑛𝑛 ]−𝑚𝑚 2.1 where 𝛼𝛼, 𝑚𝑚, and 𝑛𝑛 are curve-fitting parameters, and where it is assumed that m = 1 - 1/n (Mualem, 1976), 𝜃𝜃𝑟𝑟 is the residual water content, 𝜃𝜃𝑠𝑠 is the saturated water content. The conductivity function is based on the Mualem conductivity model (Mualem, 1978) and goes as follows in Equation (2.2). 𝐾𝐾𝐿𝐿 = 𝐾𝐾𝑆𝑆 {1−(𝛼𝛼ℎ)𝑛𝑛−2 [1+(𝛼𝛼ℎ)𝑛𝑛 ]−𝑚𝑚 }2 [1+(𝛼𝛼ℎ)𝑛𝑛 ]𝑙𝑙𝑙𝑙 2.2 where 𝐾𝐾𝐿𝐿 is the unsaturated hydraulic conductivity and 𝐾𝐾𝑆𝑆 is the saturated hydraulic conductivity. 19 The van Genuchten model was chosen because of its widely used reputation and accuracy (Carsel et al., 1988). Also, the prediction of the parameters listed in Equation (2.1) and (2.2) is necessary for this study, due to an infiltration analysis of the soils not being conducted. There are pedotransfer functions (PTFs) to predict the values of water retention, and the saturated and unsaturated hydraulic conductivity (Schaap et al., 2001). ROSETTA is a computer program that utilizes five hierarchical PTFs, where it interprets and translates basic soil data into hydraulic properties, and additionally, provides the water retention parameters (𝜃𝜃𝑠𝑠 , 𝜃𝜃𝑟𝑟 , 𝐾𝐾𝐿𝐿 , and 𝐾𝐾𝑠𝑠 ) and curve fitting parameters (𝛼𝛼, 𝑚𝑚, and 𝑛𝑛) according to van Genuchten (1980). The class average values of the seven hydraulic parameters for the twelve USDA textural classes can be seen in Table C.1 in Appendix C. This table effectively represents the first model of the hierarchical sequence. For the 𝜃𝜃𝑟𝑟 , 𝜃𝜃𝑠𝑠 , 𝛼𝛼, 𝑛𝑛, and 𝐾𝐾𝑠𝑠 parameters, the values were generated by computing the average values for each textural class. The values in parentheses give one standard deviation uncertainties of the values. ROSETTA uses a combination of bootstrap and neural networks to calibrate the results and their respective uncertainty values. Additionally, calculating the coefficient of determination (R2) between predicted and measured/fitted hydraulic parameters and root mean square errors (RMSE) between measured and predicted water contents, saturated hydraulic conductivity, and unsaturated hydraulic conductivity. Soil data containing the sand, silt, and clay percentages from the soil analysis conducted by Weyerhaeuser from each plot were input into ROSETTA. These parameters were averaged over 25 soil samples taken from each plot, where percentages were recorded over three depth profiles: 0-15 cm; 15-30 cm; and 30-100 cm below the soil surface. Table 2.1 gives the results from the ROSETTA simulation based on the soil texture percentages 20 for the soil depth profile of 0-15 cm. The results for each soil depth profile can be found in Tables C.3 and C.4 in Appendix C. These values were entered initially, and then used to validate and calibrate the UNSAT-H model. Both statistical and graphical model techniques were reviewed for the calibration of the UNSAT-H simulations. For graphical techniques, curve fitting to simulate the measured data at the LTSP site for each respective plot as closely as possible for the changes in water content over time was used. For statistical model techniques, the mean absolute error (MAE) and percent bias (PBIAS) values were calculated between the observed and simulated values of water content at each given day. The constraining statistic model technique used for this study was PBIAS, and measures the average tendency of the simulated data to be larger or smaller than their observed water content counterparts. The optimal value of PBIAS is 0.0, with low-magnitude values indicating accurate model simulation. Positive values indicate model underestimation bias, and negative values indicate model overestimation bias (Gupta et al., 1999). PBIAS was chosen to calibrate the model because it has the ability to clearly indicate poor model performance (Gupta et al., 1999). The reported performance ratings and corresponding values developed were adapted from the work of Moriasi (2007), and include ranges of values used to establish general performance, which appear in Table 2.2. Model performance was evaluated as "satisfactory" for values PBIAS ≥ ± 25%. For the general calibration, hydraulic conductivity and ranges for saturated and unsaturated volumetric water content values were altered to ensure the results for seasonal and two-year PBIAS statistical values were under 25%. Table 2.3 details the hydrologic soil parameters within UNSAT-H that were altered, along with its potential range of possible values. 21 ROSETTA Derived Values estimated by the computer software were used as the constraining values for θ using θsat - θr for each respective plot. USDA Baseline Values detailed in Table C.1 in Appendix C were used as the constraining values for ks, α, and η for each respective plot, as well. θs and θr were central in the differences among volumetric water content values. Of all of the hydraulic parameters, the saturated hydraulic conductivity was the most influential and sensitive parameter, and was always the variable to adjust first. The second, third, and fourth most influential parameters were θs, α, and η, respectively, and in that order. With all of these parameters having the proper constraints on their values, the model was calibrated by changing these values gradually. Performance was evaluated on the results of the simulation by calculating the MAE and PBIAS values, and can be found in Table 2.4 below. A "satisfactory" rating of ± 15% - ± 25% was preferred, therefore, multiple iterations of parameter changes were made until PBIAS values were under ± 25%. Seasonal PBIAS calculations can be found in Tables C.5-C.7 in Appendix C. The final hydraulic and curve-fitting parameters can be found for Selected Treatment Plots A-B in Table 2.5, and for all of the plots in Table C.5 in Appendix C. Results An example of the results from the UNSAT-H model for each treatment process having the letter designation A through C for two years of simulation can be found in Table 2.6, and for all of the plots in Appendix A (Table A.1). A time-series plot for two years of simulation for Plot A14I and its respective volumetric water content measurements (θ) can be found in Figure 2.2, and for all of the plots in Appendix D (Figures D.1 - D.28) for two 22 two years of simulation. Discussion The primary objective of this analysis is to understand the potential hydrologic impacts from woody biomass removal by modeling infiltration and evaporation-induced hydrologic variations, and to evaluate the best possible treatment type. In the following section, emphasis on specific soil characteristics and how their production can affect soil and water quality will be highlighted. Field studies applied at a plot scale, such as the LTSP site, and field experiments at a number of plots within a field plot was done at a fairly lowresolution level. There is much spatial heterogeneity in common existence, and field studies often overlook a rich abundance of information found at a higher resolution. Therefore, considerable consequences for the reliability of the models and thus for the validity of the concepts from these models exists (Raat et. al., 2002). Based on Equation (2.1), any attempt to solve for the value of one term will be limited by the accuracy of the other terms, and serve as a propagation of error throughout the results (Fayer et. al., 2000). Restating the first hypothesis below: i) Ho: Increased biomass removal from the LTSP site will have no impact on infiltration rates or the water budget of the subsurface. The water table was assumed to have a depth of 150 cm below the subsurface from soil survey results conducted by Weyerhaeuser. The total basal liquid flux (drainage) water amount that penetrates the water table. Once the water has traveled this vertical distance below the subsurface, it is assumed to have infiltrated the water table and thus impact the water budget. Total infiltration will be defined as the total amount of water that penetrates 23 the water table located at 150 cm. Any amount of water that resides above the water table, which water includes the current water moisture in the 0 to 150 cm soil profile is considered the "total infiltration." For the purposes of this study, the total basal liquid flux (drainage) water amount is the only infiltration amount analyzed and of importance. In Table 2.7 is a tabular form of the actual evaporation (cm), total basal liquid flux (drainage) (cm), and the amount of rainfall evaporated (cm) for each plot. To consolidate the results further, the average of each plot in the four replicate treatment options was done, including the standard deviation in each plot. These results can be found in Table 2.8. After the analysis of the amount of evaporation from each Treatment Type A-G, it was observed that there were statistically higher evaporation rates in Treatments C-F when compared to Treatments A-B, ranging from 12%-32% increases. Treatments D-F fall in the C1 compaction category, as well as the OM1 and OM2 harvest categories. It has been stated that very severe compaction at high moisture contents cause soil deformation, thus decreasing soil water potential (Soane et. al., 1994). Furthermore, it has been studied previously that compaction impairs the conditions of the soil by increased soil bulk density, increased nitrification rates, reduced porosity and water infiltration rates, damaged soil structure, and aggregate stability (Mann et al., 2000). In some soils, even small deformations will cause large decreases in the saturated hydraulic conductivity of that particular soil. The LTSP site conveys the potential characteristics that are exhibited when topsoil is compacted: the water potential in the top layer is decreased due to increased evaporation. Furthermore, Treatment B exhibited the least average amount of evaporation compared to all other treatments. Treatment B contains the sites that were noncompacted and the crowns 24 removed. It can be noted that the lack of crowns impacts the amount of evaporation by lessening it. To investigate why the compacted sites had more evaporation, an infiltration analysis was conducted between a noncompacted plot, A19II, with a compacted plot, F08III. These two plots had similar soil types in the subsurface when compared with one another, and therefore chosen for this infiltration analysis. A peak rainfall event was chosen to ensure a significant response in volumetric water content changes. The peak rainfall event occurred on December 20, 2014 for a total of 7.4-cm of rainfall and continued on December 21, 2014 for a total of 5.14-cm of rainfall. The last day observed was December 22, 2014 with a total rainfall amount of 1.54-cm, bringing the total rainfall over the course of the three days to 14.08-cm of precipitation. Hourly volumetric water content (VWC) values were plotted at each port location, 10-cm, 20-cm, 30-cm, and 100-cm, respectively, with precipitation on the secondary axis. Plots A19II and F08III can be found below in Figure 2.3 and 2.4, respectively. For Plot A19II, there is a substantial increase in VWC before the main portion of the storm occurs. Possible sources of error could have risen from measurement error of the 5TM sensor at 100-cm. Additionally, percent change values were calculated from the daily average VWC values for each port location. A positive percentage change in VWC denotes an increase, meaning the soil is gaining water, while a negative percentage change in VWC denotes a decrease, meaning the soil is losing water. These values for Plot A19II and F08II were recorded in a tabular manner, and can be found in Tables 2.9 and 2.10, respectively. There are differences between Treatment Plot A19II and F08III when comparing the percent change in VWC. With Plot A19II being the noncompacted plot, the percent change being positive in moisture content, that soil moisture is higher when compared to 25 plot F08III at every port location. Contrastingly, with plot F08III being the compacted plot, the percent change in moisture content in the soil is positive and higher when compared to plot A19II at every port location except for the 100-cm port. Furthermore, during the second day of rainfall (December 21, 2014), plot A19II exhibits a decrease in soil moisture solely in port 1 (10-cm) location; noting the negative percent change value equating to losing 1.30% of its moisture, while the other three ports continue to increase and gain soil moisture. In contrast, plot F08III loses moisture content at a confounding rate, with percent change values for port 1 (10-cm) and port 2 (20-cm) being -3.84% and -4.50%, respectively, equating to losing 3.84% and 4.50% of soil moisture at each location during the second day, respectively. Consequently, plot F08III displays the inability to retain soil moisture, most notably in the first 20-cm of the subsurface. This higher loss in moisture for plot F08III can be attributed to evaporation. With plot F08III being the compacted plot, the soil horizon on the top of the subsurface is most affected, and responds by not retaining soil moisture while simultaneously increasing soil moisture loss rates to evaporation. Furthermore, plot A19II has the ability to retain soil moisture better than plot F08III, even during peak rainfall events, therefore not exhibiting as much evaporation. The question still remains to see if cation exchange capacity, pH, clay content, and other hydrological parameters have an influence on these results. To investigate why the compacted sites (C-G) are seeing more total basal liquid flux drainage, a soil study was conducted. This examination sought a potential correlation between clay, sand, and silt content percentage to drainage to the water table, by means of a multivariable linear regression. In the analysis, the independent variables were percent clay, sand, and silt content values, while the dependent variable was total basal liquid flux 26 drainage. Literature states that with the increase in clay content, there is a greater capacity to hold water when compared to sands and silts (Brooks and Corey, 1964; Penman et. al., 2007). A study suggests that on bare soils, raindrop impact breaks soil aggregates at the surface, and additionally a structural seal is formed at the surface. This seal is characterized by greater density, finer pores, and lower saturated hydraulic conductivities than the underlying soil, and it simultaneously decreases the soil infiltration rate (Ben-Hur, et. al., 2004). If there is clay in the soil, it acts as a cement that holds the particles in this aggregate together. Three regression analyses were done for different hydrologic parameters for the three soil profiles conducted by Weyerhaeuser during the 5TM sensor installation and soil analysis: 0-15 cm, 15-30 cm, and 30-100 cm. When the bin depth went across the 30-cm 5TM sensor, the two different values were averaged together to receive the 30-cm designated value. The statistical results for the analysis of each soil profile for total basal liquid flux drainage simulated values to: percent clay content percentage, pH, electrical conductivity (EC), cation exchange capacity (CEC), Soil Carbon content in Megagrams per hectare (SoilC), mean slope, saturated hydraulic conductivity, average temperature, the five major genus of bacteria present (Bacillus, Clostridium, Shewanella, Thermoanerobacter, and Lactobacillus) were calculated. Organic matter is the material in soil that is directly derived from plants and animals, and it supports most important microfauna and microflora in the soil. For this study, soil carbon was used, and is measured in megagrams per hectare (Mg/ha), in other words, the dry mass per area. Through its breakdown and interaction with other soil constituents, it is largely responsible for much of the physical and chemical fertility of a 27 soil (Hazelton et. al., 2007). Furthermore, organic carbon content is perhaps the most widely measured indicator of soil quality or potential productivity, although how changes in soil carbon will affect other soil characteristics in different soils is not always predictable (Mann et al. 2000). Crop systems that result in increases in organic carbon generally yield gradual, positive changes in soil structure, water-holding capacity, and the storage and availability of nutrients, which in turn, lead to increased abundance and diversity of soil biota. Furthermore, Crop residues and their decomposition are the main factors determining the organic matter content of soils. CEC is a measure of the soil's ability to hold positively charged ions. It is a very important soil property influencing soil structure stability, nutrient availability, soil pH, and the soil's reaction to fertilizers and other ameliorants. CEC is an inherent soil characteristic and is difficult to alter significantly. It influences the soil's ability to hold onto essential nutrients and provides a buffer against soil acidification. Soils with a higher clay fraction tend to have a higher CEC. Organic matter has a very high CEC (Hazleton and Murphy, 2007). If a particular soil has a high CEC value, the soil has been characterized to have a larger capacity to hold onto the negatively charged water molecule. The opposite is also true, if a particular soil has a lower CEC value, the soil has a lower capacity to hold water. Soil pH is a measure of the concentration of hydrogen ions in the soil solution. The lower the pH of soil, the greater the acidity. pH should be maintained at above 5.5 in the topsoil and 4.8 in the subsurface. A well-maintained soil pH will maintain the value of the soil resource, maximize crop and pasture choice, and avoid production losses due to low pH (http://www.soilquality.org.au/factsheets/soil-acidity). Chemical reactions are lowest 28 when the solution or soil is close to a neutral pH of 7.0. The pH characterizes the chemical environment of the soil and may be used as a guide to suitability of soils for various pasture and crop species. Soil pH is also an indicator of the chemical processes that occur in the soil, and is a guide to likely deficiencies and/or toxicities (Slattery et al., 1999). Dragun (1998) also provides guidelines for interpreting soil pH values for environmental evaluation. Based on the pH of the site soil profiles for the (0-15 cm), (15-30 cm), and (30100 cm) depths averaging at 5.1, 5.1, and 4.9, respectively, Dragun (1998) designated the subsurface as strongly acidic in all cases. Since these values do not fall under 4.8, the subsurface is still within a tolerable range. The R square values, standard error values, coefficients, t-values, p-values, and ANOVA analyses can be found in Tables 2.11 - 2.13 below for each respective soil layer. Raw data before statistical analysis can be found in Tables C.9 - C.11 in Appendix C. It is evident that there is no statistically significant correlation between a higher clay content exhibiting less drainage values at any soil profile depth, illustrated by having p-values of 0.179, 0.653, and 0.156, respectively. Furthermore, for all of the soil profiles, the multivariable regression analyses show a small R-squared value for fit, and additionally had a large p-value greater than 0.05 for every parameter. Since these p-value statistics were all much greater than 0.05, it cannot be firmly concluded that these sites exhibit more drainage or less drainage according to these unique soil characteristic. The null hypothesis, Ho, is therefore rejected. The next hypothesis that will be analyzed details the potential differences in land cover as a function of unique soil characteristics. ii) Ho: There is no distinction between differences in land cover as a function of unique 29 soil characteristics. Referring to Table C.5 in Appendix C, similar soil depth profiles and treatment types were compared in an ANOVA analysis to one another, so that mixed effects of differences in parameters are considered. Out of all of the ANOVA results indicated in Tables 2.11 - 2.13, the F critical value is less than 2.5, therefore indicating that all means are different in the analysis. Furthermore, the correlations were insignificant and did not even have a calculable p-value, meaning that there is a significant difference between the means of each group, respectively. The null hypothesis in summary is accepted. There was no clear defining distinction between the land cover applications when comparing it to the plot's unique soil characteristics. While it is true that most of the sites contained loams, clay loams, and clays, each plot had a different soil composition at each respective subsurface depth (Table C.5 in Appendix C and Figures F.1 - F.12 in Appendix F). iii) Ho: Data from the site-scale regional impacts can be applied to watershed-scale regional impacts of large-scale biomass removal through the Pacific Northwest. The null hypothesis in summary is rejected. Through research and investigation, site-scale regional impacts cannot be applied to watershed-scale regional impacts of large-scale biomass removal through the Pacific Northwest. At a watershed-scale, regional studies are insufficient in capturing differing soils, slopes, and aspect ratios. As stated previously, since each treatment plot had a different soil composition, 28 different codes were written for all 28 plots with that specific soil composition. This includes differing values in volumetric water capacity, saturated hydraulic conductivity, Van Genuchten parameters, and soil layers. Since the soil is so diverse in the 28-acre plots, the soil diversity would only be greater in a larger area than the LTSP site. Furthermore, 30 coarse generalizations in soil and area would hinder the accuracy of the model, and would in effect, produce questionable results. Differing slopes in topography and elevation would also contribute to different results in scaling-up. The LTSP site slope values were calculated using a Digital Elevation Model (DEM) in ArcMap 10.2.2., and can be seen in Figure 2.5 As it can be observed, there are a few depressions in the soil (indicated by the dark green areas) and a few small hills in the topography (indicated by the dark red areas). The average slope values were calculated at each of the 28 different site plots, and their values with standard deviations are presented in Table 2.14. The variability of the soil surface slope can affect the manner and rate at which water infiltrates. Additionally, topographic variation in slopes of terrain can produce local differences in solar radiation that can equate to tens of degrees of latitude. This phenomenon is seen more in mountains, but also occurs on hillslopes as well. The LTSP site slope values range between 1.38% and 6.62%. Since these mean values are low in value, slope does not influence the amount of infiltration or runoff of the land surface. Lastly, aspect can affect the amount of evaporation occurring on the soil surface. Aspect is another term used to define the azimuth angle of the slope, and is the direction to which the slope is oriented (north = 0°, east = 90°, south = 180°, west = 270°). The azimuth of the Sun is the compass bearing of the Sun on the horizon, and is east in the morning, south in the Northern Hemisphere at solar noon, and in the west after noon (http://www.cgd.ucar.edu/tss/aboutus/staff/bonan/ecoclim/1sted/Chapter08.pdf). The aspect of the LTSP Site was calculated using the DEM discussed previously, and can be seen in Figure 2.6. As it can be observed, a majority of the plots are South or Southwest facing. 31 There is a complex relationship between slope, aspect, latitude, time of year, and solar radiation, but the main generalization concludes that north slopes receive less radiation. Furthermore, south and gentle slopes receive more radiation than steep slopes. A defining characteristic of the LTSP site is that it is majorly south-facing and has gentle slopes. Thus, the aspect was also not an influential parameter in differing results in evaporation rates. Figure 2.1 Soil Moisture and Temperature Sensor 5TM Installation Configuration 32 Table 2.1 ROSETTA Simulation Results for Each Plot for Hydraulic Parameters Code Description θr θs 3 3 [cm /cm ] [cm /cm3] 3 11 14 18 19 9 16 20 33 1 7 25 28 4 6 13 22 10 15 17 26 5 8 24 32 2 12 30 31 A11III A14I A18IV A19II B09I B16IV B20II B33III C01I C07II C25IV C28III D04II D06III D13I D22IV E10I E15II E17IV E26III F05I F08III F24IV F32III G02I G12III G30II G31IV 0.080 0.077 0.086 0.084 0.086 0.074 0.080 0.080 0.077 0.078 0.085 0.076 0.082 0.085 0.081 0.082 0.081 0.075 0.085 0.068 0.078 0.068 0.078 0.082 0.069 0.086 0.075 0.084 0.437 0.431 0.453 0.447 0.454 0.430 0.437 0.437 0.431 0.432 0.450 0.430 0.444 0.448 0.438 0.446 0.439 0.429 0.448 0.412 0.432 0.414 0.437 0.439 0.417 0.451 0.429 0.446 α [1/cm] η 0.010 0.010 0.012 0.011 0.011 0.008 0.011 0.010 0.010 0.010 0.011 0.009 0.011 0.012 0.012 0.009 0.011 0.009 0.012 0.011 0.010 0.010 0.009 0.012 0.009 0.012 0.009 0.012 1.457 1.469 1.403 1.437 1.422 1.511 1.436 1.457 1.469 1.455 1.430 1.490 1.444 1.403 1.422 1.471 1.443 1.482 1.403 1.472 1.455 1.481 1.499 1.409 1.509 1.396 1.482 1.416 Ks Ko [cm/day] [cm/day] Table 2.2 General Performance Ratings for Recommended Statistics Performance Rating Very good Good Satisfactory Unsatisfactory PBIAS (%) PBIAS < ± 10 ± 10 ≤ PBIAS < ± 15 ± 15 ≤ PBIAS < ± 25 PBIAS ≥ ± 25 10.73 10.53 10.21 11.04 11.32 12.69 9.05 10.73 10.53 9.68 11.17 11.92 10.92 9.02 8.34 11.99 10.00 11.32 9.02 8.10 9.68 8.78 12.36 7.78 12.07 9.32 11.32 9.67 2.733 2.740 3.012 2.763 2.804 2.295 2.980 2.733 2.740 2.863 2.782 2.503 2.749 3.108 3.107 2.452 2.854 2.622 3.108 3.276 2.863 3.077 2.311 3.236 2.581 3.117 2.622 2.987 33 Table 2.3 Hydrologic Soil Parameters and Parameter Value Constraints Parameter Range θ θsat - θr ks ks - ko α αmax - αmin η ηmax - ηmin Test & Implementation ROSETTA Derived Values in Table 2.1 & Table C.2 - C.4 in Appendix - C USDA Baseline Values in Table C.1 in Appendix - C USDA Baseline Values in Table C.1 in Appendix - C USDA Baseline Values in Table C.1 in Appendix - C Treat ment A-I A-II A-III A-IV B-I B-II B-III B-IV C-I C-II C-III C-IV D-I D-II D-III D-IV E-I E-II E-III E-IV F-I F-II F-III Plot 14 19 11 18 9 20 33 16 1 7 28 25 13 4 6 22 10 15 26 17 5 32 8 0.02 0.03 0.02 0.04 0.06 0.04 0.03 0.04 0.01 0.03 0.02 0.04 0.02 0.04 0.05 0.04 0.03 0.02 0.01 0.03 0.02 0.03 0.02 MAE Node 1 (10cm) MAE Node 2 (20 cm) 0.03 0.02 0.02 0.02 0.02 0.05 0.03 0.02 0.01 0.02 0.03 0.02 0.01 0.03 0.02 0.03 0.03 0.01 0.01 0.01 0.01 0.01 0.02 MAE Node 3 (30 cm) 0.01 0.05 0.01 0.02 0.05 0.04 0.00 0.01 0.01 0.02 0.04 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.02 MAE Node 4 (100 cm) 0.05 0.05 0.01 0.05 0.03 0.03 0.01 0.06 0.04 0.01 0.06 0.02 0.03 0.01 0.05 0.02 0.01 0.03 NA 0.01 NA 0.02 0.01 Table 2.4 Performance Results for Two-Year Simulation of UNSAT-H Percent Bias Node 1 [%] 4.6% 9.4% -1.5% 12.6% 18.0% -11.5% -6.7% -12.7% -0.4% -2.2% -2.2% 10.9% 4.1% -4.5% -11.2% -8.7% 5.7% 0.3% 0.2% -4.9% 5.6% -4.1% 4.9% Percent Bias Node 2 [%] 9.4% 4.0% -3.0% 0.4% 1.0% 12.7% -7.9% -4.5% -1.2% 5.2% 6.8% 3.2% -2.3% -5.4% 3.3% -6.6% 6.8% -0.2% 0.8% 2.1% 3.4% -0.5% -4.3% Percent Bias Node 3 [%] 2.8% 13.2% 1.0% 6.2% 24.1% 3.4% -0.5% 0.4% 1.0% -5.8% 9.6% 4.2% -1.7% 3.7% 5.6% 2.7% 4.5% 0.9% -2.7% 3.7% 1.3% 4.5% -2.9% Percent Bias Node 4 [%] 19.5% 5.9% -1.9% 12.2% 7.2% -0.3% 0.6% 3.4% -2.3% 0.5% 6.0% 4.6% -1.9% -0.1% -7.7% 0.6% 0.9% -1.5% NA -2.5% NA -3.6% -1.7% 34 24 2 30 12 31 F-IV G-I G-II G-III G-IV 0.04 0.02 0.03 0.04 0.01 Plot Treatment MAE Node 1 (10cm) MAE Node 2 (20 cm) 0.03 0.01 0.01 0.02 0.01 MAE Node 3 (30 cm) 0.01 0.01 0.01 0.02 0.01 MAE Node 4 (100 cm) 0.02 NA 0.04 NA 0.01 Percent Bias Node 1 [%] 0.3% -6.1% -0.7% 5.5% 0.3% Table 2.4 (continued) Performance Results for Two-Year Simulation of UNSAT-H Percent Bias Node 2 [%] 0.9% -1.0% -1.8% 6.1% 2.5% Percent Bias Node 3 [%] 0.1% 2.0% 2.1% 5.1% 3.2% Percent Bias Node 4 [%] -3.1% NA -8.3% NA 1.8% 35 Treatment A-III A-III A-III A-III B-I B-I B-I A-I A-I A-I A-IV A-IV A-IV A-II A-II A-II B-IV B-IV B-IV B-IV Plot 11 11 11 11 9 9 9 14 14 14 18 18 18 19 19 19 16 16 16 16 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-24.9 cm 25-64.9 cm 65-150 cm 0-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm Soil Profile 0.3750 0.400 0.430 0.3800 0.470 0.280 0.430 0.38 0.4 0.3 0.32 0.46 0.45 0.37 0.47 0.38 0.33 0.5 0.35 0.48 θsat 0.210 0.250 0.300 0.3400 0.250 0.160 0.380 0.25 0.34 0.25 0.15 0.37 0.37 0.25 0.35 0.31 0.23 0.42 0.29 0.4 θr 0.165 0.150 0.130 0.040 0.220 0.120 0.050 0.13 0.06 0.05 0.17 0.09 0.08 0.12 0.12 0.07 0.1 0.08 0.06 0.08 θsat - θr Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Loam Clay Loam Clay Clay Soil Class 0.01018 0.01387 0.01731 0.01731 0.019 0.0080 0.0080 0.019 0.019 0.0080 0.019 0.019 0.0080 0.019 0.019 0.0080 0.036 0.019 0.0080 0.0080 α [1/cm] Table 2.5 Hydraulic Parameters Entered for Each Soil Profile Depth for Treatment Plots A-B 1.45686 1.35759 1.26876 1.26876 1.310 1.0900 1.0900 1.310 1.310 1.0900 1.310 1.310 1.0900 1.310 1.310 1.0900 1.56 1.310 1.0900 1.0900 η 2.07 1.60 1.42 1.10 1.90 1.43 1.21 2.70 1.5 2.65 2.0 1.35 0.85 1.7 1.1 0.56 1.07 0.82 0.6 0.6 Ks [cm/hr] 36 Treatment A-I A-II A-III A-IV B-I B-IV B-II B-III C-I C-II C-IV C-III Plot 14 19 11 18 9 16 20 33 1 7 25 28 48.7 59.2 55.7 63.8 58.0 62.1 46.2 45.3 46.8 55.7 64.7 74.7 Initial Water Storage [cm] 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 Potential Evaporation [cm] 69.1 51.2 85.9 76.3 78.2 53.2 58.1 67.9 66.3 75.3 73.1 74.3 Actual Evaporation [cm] 239.2 200.6 251.7 246.2 186.0 251.1 260.5 229.4 189.6 215.2 258.4 247.5 Total Basal Liquid Flux (drainage) [cm] 49.8 58.9 56.2 64.0 57.6 62.0 46.0 44.6 46.4 55.1 64.3 74.7 Total Final Moisture Storage [cm] 0 0 0 12.1 0 0 14.3 0 0.985 0 0.112 0 Total Runoff [cm] Table 2.6 Results from UNSAT-H Program for Two Years of Simulation for Treatment Plots A-C 356.7 356.7 356.7 344.6 356.7 356.7 342.4 356.7 355.8 356.7 356.6 356.7 Total Infiltration [cm] 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 Actual Rainfa ll [cm] 37 38 Figure 2.2 UNSAT-H Simulation for Two Years of Plot A14I - VWC with Precipitation 39 Table 2.7 Results of 2-Year Simulation for Water Budget for All LTSP Plots Plot Treatment 14 A-I 19 A-II 11 A-III 18 A-IV 9 B-I 20 B-II 33 B-III 16 B-IV 1 C-I 7 C-II 25 C-IV 13 D-I 4 D-II 6 D-III 22 D-IV 15 E-II 26 E-III 17 E-IV 5 F-I 32 F-II 8 F-III 24 F-IV 2 G-I 30 G-II 12 G-III 31 G-IV Total for LTSP Site Actual Evaporation [cm] 69.1 51.2 85.9 76.3 78.2 58.1 67.9 53.2 66.3 75.3 73.1 76.4 83.4 68.7 63.3 85.0 80.6 82.5 81.3 84.9 87.3 86.2 84.0 89.4 94.6 48.7 2115.0 Total Basal Liquid Flux (drainage) [cm] 239.2 200.6 251.7 246.2 186.0 260.5 229.4 251.1 189.6 215.2 258.4 258.8 265.1 246.7 265.4 267.2 277.8 261.8 269.3 258.8 264.5 267.1 271.5 260.5 235.2 289.3 6991.2 Total Amount of Rainfall Evaporated [cm] 19% 14% 24% 21% 22% 16% 19% 15% 19% 21% 21% 21% 23% 19% 18% 24% 23% 23% 23% 24% 24% 24% 24% 25% 27% 14% 21% 40 Table 2.8 Summary of Evaporation and Basal Liquid Flux Results by Treatment Type Treatment Average Evaporation [cm] (SD) Evaporation - Percent Increase from Treatment B Average Total Basal Liquid Flux (drainage) [cm] A B C D E F G 70.6 (12.7) 64.4 (9.6) 72.3 (3.5) 72.9 (7.6) 84.4 (3.4) 84.9 (2.3) 79.2 (18.0) 10% NA 12% 13% 31% 32% 23% 234.4 (20.0) 231.8 (28.7) 227.7 (27.1) 259.0 (7.5) 265.8 (7.9) 264.9 (3.9) 264.2 (19.6) Infiltration Average - Percent Amount of Increase Rainfall from Evaporated Treatment [cm] B 20% 1.217 18% NA 20% -1.769 20% 11.734 24% 14.668 24% 14.280 22% 13.978 41 Figure 2.3 Treatment Plot A19II VWC during Peak Rainfall Event in December 2014 Table 2.9 Plot A19II Percent Change in VWC Date Precip (cm) 12/20/2014 12/21/2014 7.40 5.14 Percent Change 5.14 1.54 Percent Change 12/21/2014 12/22/2014 A19II Port 1 (10 cm) 0.368 0.372 1.11% 0.372 0.367 -1.30% Port 2 (20 cm) 0.450 0.458 1.60% 0.458 0.474 3.68% Port 3 (30 cm) 0.445 0.469 5.10% 0.469 0.482 2.87% Port 4 (100 cm) 0.423 0.479 11.8% 0.479 0.481 0.40% 42 Figure 2.4 Treatment Plot F08III VWC during Peak Rainfall Event in December 2014 Table 2.10 Plot F08III Percent Change in VWC Date Precip (cm) 12/20/2014 12/21/2014 7.40 5.14 Percent Change 5.14 1.54 Percent Change 12/21/2014 12/22/2014 F08III Port 1 (10 cm) 0.352 0.353 0.183% 0.353 0.339 -3.84% Port 2 (20 cm) 0.341 0.342 0.309% 0.342 0.327 -4.50% Port 3 (30 cm) 0.355 0.361 1.59% 0.361 0.360 -0.18% Port 4 (100 cm) 0.476 0.478 0.40% 0.478 0.479 0.30% 4.632 3.767 df 13 378 391 7.827 SS 1510892 21092 1531984 600.770 -2.753 -38.594 -65.906 -1.263 0.387 4.954 -9.150 3.318 -5.943 3.980 -8.342 Intercept Clay (%) pH (0-15cm) <2mm EC (0-15cm) <2mm CEC (0-15cm) <2mm SoilC (0-15cm) (Mg/ha) Mean Slope (%) Saturated Conductivity (cm/hr) Average Temperature (°C) Genus of Bacteria: Bacillus (205) Genus of Bacteria: Clostridium (185) Genus of Bacteria: Shewanella (68) Genus of Bacteria: Thermoanerobacter (43) Genus of Bacteria: Lactobacillus (44) ANOVA Analysis Between Groups Within Groups Total Standard Error 371.717 1.946 45.322 198.928 1.106 1.180 3.827 11.677 10.303 3.364 3.796 6.197 1.319 Coefficients 0.624 21.525 28 Parameter Regression Statistics R Square Standard Error Observations 2.078 MS 116222 55.8 0.285 1.616 -1.414 -0.852 -0.331 -1.141 0.328 1.295 -0.784 0.322 -1.767 1.048 -1.346 t Stat 0.057 F 2083 0.780 0.128 0.179 0.409 0.745 0.273 0.748 0.216 0.446 0.752 0.099 0.312 0.200 P-value Table 2.11 Regression and ANOVA Statistical Results Between All Parameters for (0-15 cm) Soil Profile P-value 0 F crit 1.75 43 5.033 3.588 df 13 378 391 6.752 SS 1503164 21188 1524352 272.845 -0.546 39.767 -306.323 -1.519 -0.785 5.254 -6.040 -0.077 -6.950 4.670 -9.486 Intercept Clay (%) pH (15-30 cm) <2mm EC (15-30 cm) <2mm CEC (15-30 cm) <2mm SoilC (15-30 cm) (Mg/ha) Mean Slope (%) Saturated Conductivity (cm/hr) Average Temperature (°C) Genus of Bacteria: Bacillus (205) Genus of Bacteria: Clostridium (185) Genus of Bacteria: Shewanella (68) Genus of Bacteria: Thermoanerobacter (43) Genus of Bacteria: Lactobacillus (44) ANOVA Analysis Between Groups Within Groups Total Standard Error 329.635 1.188 48.115 459.017 1.467 1.143 3.270 9.287 12.107 3.777 3.934 6.501 3.279 Coefficients 0.602 22.136 28 Parameter Regression Statistics R Square Standard Error Observations 1.882 MS 115628 56.1 0.652 0.828 -0.460 0.827 -0.667 -1.035 -0.687 1.606 -0.650 -0.006 -1.840 1.187 -1.459 t Stat 0.081 F 2063 0.525 0.422 0.653 0.422 0.515 0.318 0.503 0.130 0.526 0.995 0.087 0.255 0.167 P-value P-value 0 Table 2.12 Regression and ANOVA Statistical Results Between All Parameters for (15-30 cm) Soil Profile F crit 1.75 44 4.519 3.827 df 13 378 391 4.836 SS 1622774 27538 1650312 335.914 -1.691 17.860 -508.780 -1.242 -0.091 5.704 -25.200 3.400 -4.177 3.767 -6.385 Intercept Clay (%) pH (30-100 cm) <2mm EC (30-100 cm) <2mm CEC (30-100 cm) <2mm SoilC (30-100 cm) (Mg/ha) Mean Slope (%) Saturated Conductivity (cm/hr) Average Temperature (°C) Genus of Bacteria: Bacillus (205) Genus of Bacteria: Clostridium (185) Genus of Bacteria: Shewanella (68) Genus of Bacteria: Thermoanerobacter (43) Genus of Bacteria: Lactobacillus (44) ANOVA Analysis Between Groups Within Groups Total Standard Error 416.735 1.128 76.575 816.421 0.822 0.408 3.584 18.273 9.628 3.287 3.766 6.360 -0.805 Coefficients 0.656 20.6 28 Parameter Regression Statistics R Square Standard Error Observations 1.264 MS 124828 73 -0.178 0.806 -1.499 0.233 -0.623 -1.511 -0.224 1.591 -1.379 0.353 -1.271 1.000 -1.004 t Stat 0.227 F 1713 0.861 0.434 0.156 0.819 0.543 0.153 0.826 0.134 0.190 0.729 0.225 0.334 0.332 P-value P-value 0 Table 2.13 Regression and ANOVA Statistical Results Between All Parameters for (30-100 cm) Soil Profile F crit 1.75 45 46 Figure 2.5 LTSP Site Depicting Calculated Slope Values in Percentage 47 Table 2.14 Average Slope Values for Each LTSP Plot Plot A11III A14I A18IV A19II B09I B16IV B20II B33III C01I C07II C25IV C28III D04II D06III D13I D22IV E10I E15II E17IV E26III F05I F08III F24IV F32II G02I G12III G30II G31IV Mean Slope (%) 2.65 1.96 5.89 4.40 2.92 5.18 2.52 5.47 1.93 5.96 5.93 5.52 1.43 3.45 1.70 5.65 6.62 5.47 5.47 2.47 3.70 3.07 5.77 5.52 3.10 1.38 4.02 5.75 Standard Deviation 1.16 0.85 2.40 1.68 0.68 1.78 0.95 1.62 0.72 3.64 2.10 1.65 0.68 0.89 1.06 1.57 2.07 1.61 1.42 0.87 1.43 1.11 1.44 1.79 0.95 0.66 1.10 1.62 48 Figure 2.6 LTSP Site Depicting Calculated Aspect Values in Azimuth Angle CHAPTER 3 OVERALL CONCLUSION AND SUMMARY Twenty-eight UNSAT-H code files were written and modeled for each of the twenty-eight one-acre sites. It has been witnessed in a previous study of Douglas-Fir tree biomass removal, spatial patterns of forest floor water content could not be related directly to throughfall water patterns (Raat et al., 2002). Soil water evaporation and drainage significantly affect soil water content and moisture redistribution (Yanful et al., 2003). In terms of changes to runoff and sediment production, maximum impact coincides with the period immediately after track construction and harvesting but these effects decrease significantly over the 5-year time frame, well within the interlogging cutting cycle of 3040 years in this region. (Croke et al., 2001). Overall, to conclude that one treatment type is different from another only lies in the compaction application to the soil being noncompacted versus compacted. The evidence stands from the UNSAT-H modeling that for compacted sites, those sites will exhibit more evaporation from the surface. Furthermore, sites with no compaction and crowns removed exhibited the least amount of evaporation. It is recommended that the crowns be removed as well to ensure the least amount of evaporation occurs as to disrupt the water budget of the site the least. A relationship between soil hydrologic parameters and drainage values to the water table could not be determined. NARA is advised to not 50 compact the sites, and to remove the slash and crowns present at the plot to have the least amount of impact on the water budget of the site. Future studies on this project are possible, and include: plant modeling within UNSAT-H on the LTSP site to see if and how regrowth of the Douglas Fir saplings affect the water budget, and additional research into drainage rates to other treatment technologies and procedures other than solely the soil hydrologic parameters. APPENDIX A SUMMARY OF UNSAT-H SIMULATION RESULTS FOR TWO YEARS OF MODELING 14 19 11 18 9 20 33 16 1 7 28 25 13 4 6 22 10 15 26 17 A-I A-II A-III A-IV B-I B-II B-III B-IV C-I C-II C-III C-IV D-I D-II D-III D-IV E-I E-II E-III E-IV Plot Treatment 48.7 59.2 55.7 63.8 58.0 46.2 45.3 62.1 46.8 55.7 74.7 64.7 63.5 60.5 54.6 59.2 59.9 61.3 56.5 61.2 Initial Water Storage [cm] 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 69.1 51.2 85.9 76.3 78.2 58.1 67.9 53.2 66.3 75.3 74.3 73.1 76.4 83.4 68.7 63.3 89.6 85.0 80.6 82.5 Potential Actual Evaporation Evaporation [cm] [cm] 239.2 200.6 251.7 246.2 186.0 260.5 229.4 251.1 189.6 215.2 247.5 258.4 258.8 265.1 246.7 265.4 256.5 267.2 277.8 261.8 49.8 58.9 56.2 64.0 57.6 46.0 44.6 62.0 46.4 55.1 74.7 64.3 62.9 59.9 55.8 58.7 60.0 60.1 54.8 60.3 0 0 0 12.099 0 14.303 0 0 0.985 0 0 0.112 0.0 7.6 12.5 0.0 9.1 0.0 0.0 11.4 356.7 356.7 356.7 344.6 356.7 342.4 356.7 356.7 355.8 356.7 356.7 356.6 356.7 349.1 344.2 356.7 347.6 356.7 356.7 345.3 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 356.7 Total Total Total Total Actual Basal Final Runoff Infiltration Rainfall Liquid Moisture [cm] [cm] [cm] Flux Storage (drainage) [cm] [cm] Table A.1 Summary of UNSAT-H Simulation Results for Each Plot for 2-Year Simulation 52 5 32 8 24 2 30 12 31 F-I F-II F-III F-IV G-I G-II G-III G-IV Plot Treatment 57.7 59.2 58.8 55.6 53.2 49.0 58.8 65.3 Initial Water Storage [cm] Table A.1 Continued 184.6 184.6 184.6 184.6 184.6 184.6 184.6 184.6 81.3 84.9 87.3 86.2 84.0 89.4 94.6 48.7 Potential Actual Evaporation Evaporation [cm] [cm] Total Total Total Total Actual Basal Final Runoff Infiltration Rainfall Liquid Moisture [cm] [cm] [cm] Flux Storage (drainage) [cm] [cm] 269.3 57.2 0.0 356.7 356.7 258.8 58.5 11.1 345.7 356.7 264.5 57.8 5.8 350.9 356.7 267.1 54.7 2.2 354.6 356.7 271.5 52.8 0.0 356.7 356.7 260.5 48.7 6.0 350.7 356.7 235.2 59.0 11.5 345.2 356.7 289.3 64.9 17.0 339.7 356.7 53 54 Table A.2 VWC Measurements for Spring 2015-2016 for Treatment A, Plot 11 Date 3/20/2015 3/21/2015 3/22/2015 3/23/2015 3/24/2015 3/25/2015 3/26/2015 3/27/2015 3/28/2015 3/29/2015 3/30/2015 3/31/2015 4/1/2015 4/2/2015 4/3/2015 4/4/2015 4/5/2015 4/6/2015 4/7/2015 4/8/2015 4/9/2015 4/10/2015 4/11/2015 4/12/2015 4/13/2015 4/14/2015 4/15/2015 4/16/2015 4/17/2015 4/18/2015 4/19/2015 4/20/2015 4/21/2015 4/22/2015 4/23/2015 4/24/2015 4/25/2015 VWC at P1 (10 cm) [m3/m3] 0.325018 0.354047 0.34882 0.354239 0.349954 0.339651 0.33487 0.331718 0.350944 0.336392 0.330867 0.350754 0.346518 0.339645 0.334403 0.343457 0.341713 0.347101 0.343729 0.349987 0.335542 0.333196 0.349132 0.341786 0.34069 0.360756 0.347651 0.33782 0.334002 0.331259 0.32885 0.326453 0.32449 0.322546 0.322182 0.333253 0.354388 VWC at P2 (20 cm) [m3/m3] 0.348797 0.377301 0.371413 0.373672 0.371006 0.359134 0.356218 0.352856 0.372374 0.359835 0.354056 0.369765 0.366394 0.35933 0.354252 0.360311 0.361367 0.369708 0.366604 0.372382 0.357603 0.353489 0.368514 0.362482 0.359842 0.381605 0.366397 0.35661 0.3528 0.350589 0.348756 0.347042 0.345108 0.343176 0.341987 0.34564 0.376958 VWC at P3 (30 cm) [m3/m3] 0.381871 0.417578 0.395945 0.408358 0.401524 0.391713 0.387946 0.385606 0.394707 0.38997 0.386397 0.400255 0.397346 0.391493 0.388369 0.388465 0.388324 0.394696 0.393906 0.411151 0.392008 0.387851 0.397094 0.394281 0.393348 0.421528 0.401241 0.391306 0.387515 0.385214 0.383479 0.381952 0.380806 0.379833 0.378965 0.379637 0.39285 VWC at P4 (100 cm) [m3/m3] 0.372252 0.372887 0.374176 0.374212 0.37561 0.374487 0.373423 0.372981 0.372539 0.372852 0.373 0.372714 0.373799 0.373974 0.373533 0.373055 0.372742 0.372557 0.372557 0.373376 0.37413 0.373662 0.373 0.373046 0.373248 0.374164 0.37631 0.374487 0.373671 0.37323 0.372945 0.372788 0.372391 0.371845 0.371781 0.371159 0.371039 55 Table A.2 Continued Date 4/26/2015 4/27/2015 4/28/2015 4/29/2015 4/30/2015 5/1/2015 5/2/2015 5/3/2015 5/4/2015 5/5/2015 5/6/2015 5/7/2015 5/8/2015 5/9/2015 5/10/2015 5/11/2015 5/12/2015 5/13/2015 5/14/2015 5/15/2015 5/16/2015 5/17/2015 5/18/2015 5/19/2015 5/20/2015 5/21/2015 5/22/2015 5/23/2015 5/24/2015 5/25/2015 5/26/2015 5/27/2015 5/28/2015 5/29/2015 5/30/2015 5/31/2015 6/1/2015 VWC at P1 (10 cm) [m3/m3] 0.348431 0.338805 0.334753 0.337686 0.33399 0.330781 0.327929 0.32508 0.322078 0.319522 0.317467 0.315811 0.314386 0.31314 0.311633 0.338213 0.360304 0.344524 0.341073 0.341011 0.350791 0.338601 0.333172 0.329538 0.326894 0.326982 0.353857 0.35083 0.337628 0.333055 0.330182 0.327467 0.325213 0.32321 0.321327 0.319307 0.340587 VWC at P2 (20 cm) [m3/m3] 0.37066 0.358811 0.354486 0.352794 0.35337 0.351104 0.34904 0.34698 0.345048 0.34302 0.341425 0.339946 0.338925 0.338005 0.337156 0.358328 0.384402 0.365792 0.361941 0.362244 0.372291 0.359447 0.353778 0.350478 0.348135 0.344335 0.374597 0.373603 0.35813 0.35295 0.350498 0.348389 0.346704 0.345286 0.343922 0.342531 0.359869 VWC at P3 (30 cm) [m3/m3] 0.397808 0.389952 0.387014 0.386068 0.384708 0.38355 0.382315 0.381144 0.380048 0.379108 0.378273 0.377479 0.376773 0.376093 0.375528 0.38467 0.417334 0.395885 0.389022 0.389296 0.401163 0.390293 0.386441 0.384226 0.382545 0.381846 0.398208 0.406152 0.389874 0.386111 0.383954 0.382306 0.380869 0.379753 0.378812 0.378002 0.379161 VWC at P4 (100 cm) [m3/m3] 0.370834 0.371308 0.371836 0.371781 0.371892 0.372123 0.372262 0.372299 0.372262 0.372132 0.372049 0.371994 0.372012 0.372003 0.371929 0.370955 0.370657 0.374943 0.374679 0.373937 0.373992 0.374295 0.374194 0.373864 0.373579 0.372981 0.372594 0.373852 0.375264 0.374624 0.374139 0.373992 0.373855 0.373708 0.37357 0.373423 0.372354 56 Table A.2 Continued Date 6/2/2015 6/3/2015 6/4/2015 6/5/2015 6/6/2015 6/7/2015 6/8/2015 6/9/2015 6/10/2015 6/11/2015 6/12/2015 6/13/2015 6/14/2015 6/15/2015 6/16/2015 6/17/2015 6/18/2015 6/19/2015 6/20/2015 6/21/2015 6/22/2015 6/23/2015 6/24/2015 6/25/2015 6/26/2015 6/27/2015 6/28/2015 6/29/2015 6/30/2015 7/1/2015 7/2/2015 7/3/2015 7/4/2015 7/5/2015 7/6/2015 7/7/2015 7/8/2015 VWC at P1 (10 cm) [m3/m3] 0.353206 0.346129 0.336022 0.33115 0.328016 0.324947 0.322404 0.319555 0.316245 0.313255 0.31069 0.308032 0.305813 0.303828 0.301758 0.29971 0.297637 0.295366 0.29324 0.290764 0.288331 0.285944 0.283742 0.281627 0.279936 0.278185 0.275411 0.272929 0.270085 0.266885 0.2641 0.259811 0.255294 0.252195 0.249098 0.24661 0.244234 VWC at P2 (20 cm) [m3/m3] 0.376658 0.368566 0.358052 0.352989 0.350003 0.347737 0.345873 0.343922 0.341675 0.339609 0.337824 0.336081 0.334637 0.333584 0.33243 0.331228 0.330249 0.329266 0.328281 0.327203 0.326166 0.325114 0.324548 0.323846 0.323445 0.323233 0.322461 0.321462 0.320528 0.319534 0.318696 0.317376 0.316017 0.314846 0.313706 0.312584 0.311645 VWC at P3 (30 cm) [m3/m3] 0.388075 0.39154 0.386909 0.384252 0.382377 0.380949 0.379664 0.378614 0.377605 0.376692 0.375874 0.375081 0.374322 0.373644 0.373064 0.372603 0.372123 0.371549 0.37102 0.370471 0.369968 0.369426 0.36894 0.368461 0.367953 0.367558 0.367039 0.366727 0.366197 0.365741 0.36518 0.364522 0.363815 0.363422 0.362836 0.362442 0.361979 VWC at P4 (100 cm) [m3/m3] 0.372178 0.372391 0.373119 0.373589 0.3738 0.373882 0.373864 0.373864 0.373809 0.373754 0.373699 0.373662 0.373653 0.373515 0.373442 0.373414 0.373258 0.373221 0.373138 0.373027 0.372981 0.372945 0.372834 0.372779 0.372714 0.372566 0.372557 0.372557 0.372557 0.372594 0.372622 0.372613 0.372668 0.372566 0.372502 0.372419 0.372493 57 Table A.2 Continued Date 7/9/2015 7/10/2015 7/11/2015 7/12/2015 7/13/2015 7/14/2015 7/15/2015 7/16/2015 7/17/2015 7/18/2015 7/19/2015 7/20/2015 7/21/2015 7/22/2015 7/23/2015 7/24/2015 7/25/2015 7/26/2015 7/27/2015 7/28/2015 7/29/2015 7/30/2015 7/31/2015 8/1/2015 8/2/2015 8/3/2015 8/4/2015 8/5/2015 8/6/2015 8/7/2015 8/8/2015 8/9/2015 8/10/2015 8/11/2015 8/12/2015 8/13/2015 8/14/2015 VWC at P1 (10 cm) [m3/m3] 0.242976 0.242964 0.238932 0.238083 0.237231 0.236044 0.233922 0.231978 0.229864 0.228255 0.226551 0.224572 0.222283 0.219905 0.217605 0.215749 0.213793 0.211936 0.211012 0.210008 0.209436 0.208522 0.206983 0.204893 0.202446 0.201359 0.200507 0.19913 0.197828 0.19681 0.195805 0.195118 0.194061 0.193306 0.192856 0.191697 0.190293 VWC at P2 (20 cm) [m3/m3] 0.311295 0.311119 0.30883 0.307644 0.307054 0.306476 0.305872 0.305042 0.304078 0.303135 0.302442 0.301494 0.300531 0.299481 0.298294 0.297248 0.296138 0.294827 0.293832 0.292845 0.292413 0.292129 0.291509 0.290354 0.288982 0.287818 0.286788 0.285755 0.284541 0.283424 0.282482 0.281729 0.280693 0.279834 0.279062 0.278108 0.276969 VWC at P3 (30 cm) [m3/m3] 0.361796 0.362211 0.361535 0.361129 0.360519 0.360072 0.359507 0.358971 0.358482 0.358002 0.357325 0.356734 0.356083 0.355539 0.354905 0.354358 0.353841 0.353511 0.353102 0.352561 0.35212 0.351366 0.350469 0.349447 0.348655 0.348502 0.348003 0.347268 0.346664 0.34612 0.345616 0.345348 0.344791 0.34443 0.344047 0.343352 0.342979 VWC at P4 (100 cm) [m3/m3] 0.372382 0.372123 0.372336 0.372336 0.372317 0.372188 0.372114 0.372114 0.372114 0.372105 0.372068 0.372012 0.371929 0.371883 0.371883 0.371716 0.37167 0.37167 0.371633 0.371456 0.371447 0.371373 0.371197 0.371197 0.371206 0.371225 0.371225 0.371206 0.371225 0.371141 0.371132 0.371011 0.371011 0.371002 0.370983 0.370844 0.370816 58 Table A.2 Continued Date 8/15/2015 8/16/2015 8/17/2015 8/18/2015 8/19/2015 8/20/2015 8/21/2015 8/22/2015 8/23/2015 8/24/2015 8/25/2015 8/26/2015 8/27/2015 8/28/2015 8/29/2015 8/30/2015 8/31/2015 9/1/2015 9/2/2015 9/3/2015 9/4/2015 9/5/2015 9/6/2015 9/7/2015 9/8/2015 9/9/2015 9/10/2015 9/11/2015 9/12/2015 9/13/2015 9/14/2015 9/15/2015 9/16/2015 9/17/2015 9/18/2015 9/19/2015 9/20/2015 VWC at P1 (10 cm) [m3/m3] 0.190131 0.188705 0.187893 0.187275 0.186656 0.185383 0.183942 0.182645 0.18174 0.180783 0.179807 0.179046 0.178366 0.178067 0.182718 0.188174 0.190858 0.189419 0.224823 0.207856 0.20753 0.233068 0.217632 0.213425 0.211135 0.210024 0.2086 0.207715 0.206875 0.205503 0.203027 0.20008 0.198972 0.320444 0.290431 0.269767 0.26141 VWC at P2 (20 cm) [m3/m3] 0.275853 0.274499 0.273545 0.272601 0.271655 0.270588 0.269345 0.267845 0.266686 0.265388 0.264046 0.263481 0.263131 0.262348 0.263543 0.270229 0.283029 0.280128 0.302321 0.297216 0.293933 0.324732 0.31349 0.307688 0.304101 0.301625 0.299492 0.297734 0.296565 0.295354 0.29356 0.291657 0.289868 0.360999 0.346638 0.337415 0.332438 VWC at P3 (30 cm) [m3/m3] 0.342791 0.341895 0.341205 0.340555 0.339852 0.33902 0.338407 0.337729 0.336901 0.336198 0.335622 0.334916 0.334197 0.333692 0.33619 0.3437 0.346024 0.343612 0.346588 0.345862 0.347931 0.361674 0.356437 0.354308 0.353252 0.352722 0.35202 0.351256 0.350297 0.349447 0.348675 0.348085 0.34786 0.402993 0.378243 0.372602 0.370303 VWC at P4 (100 cm) [m3/m3] 0.370779 0.370779 0.370779 0.370732 0.370611 0.370565 0.370537 0.370555 0.370453 0.370341 0.370332 0.370229 0.370108 0.370108 0.369987 0.369529 0.369707 0.369837 0.369099 0.368733 0.368818 0.368442 0.368761 0.368724 0.368752 0.36863 0.368564 0.368536 0.368536 0.368536 0.368536 0.368536 0.368536 0.367246 0.36813 0.371057 0.372141 59 Table A.2 Continued Date 9/21/2015 9/22/2015 9/23/2015 9/24/2015 9/25/2015 9/26/2015 9/27/2015 9/28/2015 9/29/2015 9/30/2015 10/1/2015 10/2/2015 10/3/2015 10/4/2015 10/5/2015 10/6/2015 10/7/2015 10/8/2015 10/9/2015 10/10/2015 10/11/2015 10/12/2015 10/13/2015 10/14/2015 10/15/2015 10/16/2015 10/17/2015 10/18/2015 10/19/2015 10/20/2015 10/21/2015 10/22/2015 10/23/2015 10/24/2015 10/25/2015 10/26/2015 10/27/2015 VWC at P1 (10 cm) [m3/m3] 0.256887 0.251803 0.246806 0.244375 0.24217 0.242479 0.23811 0.236116 0.234461 0.233177 0.231172 0.229672 0.230184 0.227767 0.226076 0.224229 0.223122 0.222073 0.220267 0.221292 0.224778 0.222283 0.221156 0.220448 0.219739 0.218439 0.219481 0.217362 0.226683 0.26138 0.253045 0.247339 0.242879 0.238946 0.272096 0.29566 0.28122 VWC at P2 (20 cm) [m3/m3] 0.3292 0.326197 0.32331 0.321316 0.319624 0.318217 0.317033 0.315443 0.314121 0.313094 0.311865 0.310818 0.310317 0.309241 0.308208 0.307337 0.306688 0.305955 0.305113 0.304707 0.311213 0.309076 0.307254 0.306014 0.304792 0.303601 0.303207 0.302585 0.325582 0.340763 0.329282 0.324455 0.320311 0.317113 0.343866 0.349302 0.341123 VWC at P3 (30 cm) [m3/m3] 0.368723 0.367425 0.366244 0.365247 0.364417 0.364053 0.362827 0.362211 0.361642 0.361061 0.360324 0.35976 0.359692 0.358971 0.358345 0.3576 0.357059 0.356655 0.356123 0.356554 0.357501 0.356843 0.356715 0.356606 0.356142 0.355668 0.356033 0.355014 0.356461 0.362519 0.361564 0.360227 0.359458 0.358511 0.378979 0.377915 0.373293 VWC at P4 (100 cm) [m3/m3] 0.372649 0.372908 0.373037 0.373212 0.373221 0.372898 0.373 0.373 0.372991 0.372862 0.372779 0.372742 0.372474 0.372548 0.372391 0.372336 0.372225 0.372114 0.372068 0.371706 0.37154 0.37167 0.37167 0.37167 0.37166 0.371531 0.371262 0.371243 0.370713 0.370546 0.370611 0.370825 0.370834 0.371002 0.370387 0.370117 0.371131 60 Table A.2 Continued Date 10/28/2015 10/29/2015 10/30/2015 10/31/2015 11/1/2015 11/2/2015 11/3/2015 11/4/2015 11/5/2015 11/6/2015 11/7/2015 11/8/2015 11/9/2015 11/10/2015 11/11/2015 11/12/2015 11/13/2015 11/14/2015 11/15/2015 11/16/2015 11/17/2015 11/18/2015 11/19/2015 11/20/2015 11/21/2015 11/22/2015 11/23/2015 11/24/2015 11/25/2015 11/26/2015 11/27/2015 11/28/2015 11/29/2015 11/30/2015 12/1/2015 12/2/2015 12/3/2015 VWC at P1 (10 cm) [m3/m3] 0.314471 0.316548 0.316653 0.318301 0.336203 0.331598 0.312671 0.301487 0.296209 0.292375 0.304374 0.343311 0.329716 0.314101 0.32572 0.316817 0.334262 0.330489 0.345254 0.343036 0.344478 0.345906 0.360612 0.336812 0.328715 0.324168 0.322697 0.355028 0.342948 0.333901 0.327266 0.322717 0.319171 0.317479 0.319244 0.349188 0.347094 VWC at P2 (20 cm) [m3/m3] 0.365874 0.361215 0.360333 0.362131 0.376148 0.366857 0.350603 0.343879 0.34029 0.338121 0.350161 0.386988 0.366082 0.352817 0.362428 0.365337 0.399893 0.393773 0.40677 0.403177 0.405818 0.408246 0.425074 0.3955 0.391997 0.390314 0.387649 0.421829 0.407782 0.401355 0.394536 0.391055 0.388352 0.386486 0.384957 0.418943 0.414112 VWC at P3 (30 cm) [m3/m3] 0.389429 0.385149 0.382596 0.393188 0.409166 0.392347 0.383658 0.378667 0.376255 0.374395 0.380934 0.425935 0.391195 0.383271 0.387211 0.384517 0.385349 0.382427 0.394497 0.39533 0.400885 0.405664 0.43132 0.395227 0.389673 0.38717 0.385746 0.414809 0.398922 0.39415 0.390306 0.387844 0.385851 0.38476 0.384802 0.415388 0.404338 VWC at P4 (100 cm) [m3/m3] 0.371206 0.372141 0.373708 0.373873 0.489163 0.380244 0.37838 0.376601 0.375729 0.375255 0.374743 0.381452 0.382006 0.377081 0.376001 0.375838 0.375565 0.375537 0.375109 0.376165 0.37651 0.439911 0.456567 0.382583 0.375555 0.374771 0.374121 0.373579 0.375209 0.375419 0.375182 0.374762 0.374423 0.373836 0.373055 0.372372 0.373506 61 Table A.2 Continued Date 12/4/2015 12/5/2015 12/6/2015 12/7/2015 12/8/2015 12/9/2015 12/10/2015 12/11/2015 12/12/2015 12/13/2015 12/14/2015 12/15/2015 12/16/2015 12/17/2015 12/18/2015 12/19/2015 12/20/2015 12/21/2015 12/22/2015 12/23/2015 12/24/2015 12/25/2015 12/26/2015 12/27/2015 12/28/2015 12/29/2015 12/30/2015 12/31/2015 1/1/2016 1/2/2016 1/3/2016 1/4/2016 1/5/2016 1/6/2016 1/7/2016 1/8/2016 1/9/2016 VWC at P1 (10 cm) [m3/m3] 0.35408 0.334261 0.344312 0.360221 0.344532 0.354235 0.354449 0.35311 0.358648 0.351422 0.341571 0.341968 0.348878 0.365997 0.356709 0.350248 0.350261 0.354928 0.362109 0.348021 0.34347 0.339188 0.336527 0.336026 0.354049 0.346245 0.343886 0.343573 0.341861 0.339504 0.348475 0.373938 0.372561 0.360715 0.356456 0.35288 0.355322 VWC at P2 (20 cm) [m3/m3] 0.424523 0.401076 0.408896 0.431938 0.408867 0.424921 0.420155 0.420213 0.431295 0.417959 0.400312 0.400802 0.411702 0.446959 0.425464 0.415638 0.415137 0.423747 0.43855 0.410917 0.402569 0.39746 0.396139 0.396734 0.423515 0.408787 0.405982 0.403682 0.398209 0.393958 0.393868 0.421899 0.420456 0.405202 0.399915 0.397305 0.395622 VWC at P3 (30 cm) [m3/m3] 0.427488 0.395089 0.397371 0.434858 0.402263 0.427463 0.414579 0.417017 0.43936 0.422151 0.401135 0.399148 0.410135 0.471258 0.431654 0.41186 0.418474 0.43066 0.452458 0.412349 0.40478 0.401411 0.398836 0.400206 0.430549 0.410535 0.405947 0.401837 0.397981 0.394931 0.3957 0.420103 0.420635 0.403458 0.399252 0.396694 0.396378 VWC at P4 (100 cm) [m3/m3] 0.379647 0.376699 0.374761 0.423621 0.378689 0.471964 0.377486 0.376682 0.412565 0.464681 0.375518 0.373928 0.373616 0.510868 0.47127 0.376536 0.374944 0.379097 0.407736 0.391169 0.374221 0.373083 0.372225 0.372215 0.373191 0.375045 0.373864 0.372751 0.372465 0.372382 0.372067 0.37166 0.372815 0.373322 0.372686 0.372243 0.371771 62 Table A.2 Continued Date 1/10/2016 1/11/2016 1/12/2016 1/13/2016 1/14/2016 1/15/2016 1/16/2016 1/17/2016 1/18/2016 1/19/2016 1/20/2016 1/21/2016 1/22/2016 1/23/2016 1/24/2016 1/25/2016 1/26/2016 1/27/2016 1/28/2016 1/29/2016 1/30/2016 1/31/2016 2/1/2016 2/2/2016 2/3/2016 2/4/2016 2/5/2016 2/6/2016 2/7/2016 2/8/2016 2/9/2016 2/10/2016 2/11/2016 2/12/2016 2/13/2016 2/14/2016 2/15/2016 VWC at P1 (10 cm) [m3/m3] 0.355062 0.353429 0.367382 0.371907 0.367569 0.371639 0.374402 0.37735 0.36653 0.385283 0.368004 0.358361 0.364469 0.37634 0.375512 0.363803 0.357589 0.356053 0.37096 0.376603 0.374922 0.367443 0.365357 0.367574 0.370691 0.376088 0.36163 0.373953 0.361712 0.356416 0.35293 0.349689 0.34783 0.35307 0.369922 0.384625 0.366394 VWC at P2 (20 cm) [m3/m3] 0.398245 0.395244 0.41112 0.418519 0.410765 0.417745 0.41968 0.425173 0.409787 0.434728 0.411196 0.401172 0.404533 0.419008 0.418523 0.406727 0.399147 0.398056 0.416309 0.421216 0.416939 0.4086 0.404961 0.40907 0.412756 0.422207 0.403851 0.416523 0.403022 0.39917 0.396694 0.394437 0.392089 0.392352 0.411006 0.433824 0.407418 VWC at P3 (30 cm) [m3/m3] 0.395149 0.394907 0.404734 0.422209 0.408651 0.420948 0.419195 0.435149 0.409476 0.445953 0.41084 0.400775 0.400851 0.415459 0.416963 0.405188 0.400102 0.397437 0.421522 0.420154 0.414438 0.407543 0.404338 0.404888 0.409148 0.42635 0.404265 0.416801 0.403451 0.399162 0.396163 0.394153 0.392766 0.392688 0.397689 0.447231 0.405953 VWC at P4 (100 cm) [m3/m3] 0.371447 0.371122 0.370899 0.370806 0.37356 0.374166 0.37402 0.432976 0.397305 0.432623 0.406865 0.373211 0.372178 0.371929 0.373541 0.373791 0.372954 0.372197 0.374459 0.3759 0.375546 0.374331 0.373322 0.372788 0.372769 0.374036 0.374524 0.37391 0.37369 0.373101 0.372806 0.372354 0.371818 0.371512 0.371187 0.454824 0.378804 63 Table A.2 Continued Date 2/16/2016 2/17/2016 2/18/2016 2/19/2016 2/20/2016 2/21/2016 2/22/2016 2/23/2016 2/24/2016 2/25/2016 2/26/2016 2/27/2016 2/28/2016 2/29/2016 3/1/2016 3/2/2016 3/3/2016 3/4/2016 3/5/2016 3/6/2016 3/7/2016 3/8/2016 3/9/2016 3/10/2016 3/11/2016 3/12/2016 3/13/2016 3/14/2016 3/15/2016 3/16/2016 3/17/2016 3/18/2016 VWC at P1 (10 cm) [m3/m3] 0.359639 0.367594 0.380174 0.373816 0.364701 0.366585 0.367138 0.359513 0.355686 0.352841 0.353496 0.377198 0.365991 0.362754 0.365232 0.363657 0.372916 0.361286 0.365886 0.373813 0.369434 0.369814 0.377312 0.379141 0.369053 0.387101 0.385141 0.390191 0.38683 0.378081 0.365718 0.359884 VWC at P2 (20 cm) [m3/m3] 0.401441 0.409733 0.423658 0.414903 0.405599 0.405505 0.407572 0.401544 0.397907 0.395656 0.393288 0.420723 0.406891 0.404124 0.403491 0.404085 0.414196 0.404614 0.404537 0.41781 0.407301 0.409544 0.420492 0.421585 0.409015 0.431729 0.429757 0.439394 0.434347 0.418133 0.404248 0.400606 VWC at P3 (30 cm) [m3/m3] 0.398993 0.410649 0.425206 0.411192 0.403209 0.401402 0.403154 0.398859 0.396146 0.394094 0.39352 0.40989 0.402129 0.400078 0.399205 0.398853 0.402925 0.399668 0.399894 0.416628 0.403121 0.403498 0.413246 0.417101 0.405775 0.43241 0.435138 0.448521 0.438217 0.414728 0.404848 0.399677 VWC at P4 (100 cm) [m3/m3] 0.373992 0.373576 0.379407 0.379811 0.374322 0.373543 0.373073 0.372898 0.372742 0.372548 0.372243 0.371577 0.372058 0.372419 0.372345 0.372262 0.372151 0.372428 0.372372 0.373457 0.37446 0.373919 0.373717 0.374907 0.375337 0.376525 0.38248 0.475282 0.415303 0.376117 0.374615 0.373478 APPENDIX B THE BASIS OF UNSAT-H 65 UNSAT-H uses a finite-difference implementation of a modified form of Richards' equation that describes unsaturated liquid and vapor flow in soil layers, while additionally having the option for water removal through plant roots (i.e., transpiration) (Khire et. al., 1997). A recharge model was made for assessing the water dynamics of the arid site, and in particular, estimating recharge fluxes by simulating soil water infiltration, redistribution, evaporation, plant transpiration, deep drainage, and soil heat flow. Infiltration is the process of water entry into soil. Once water has infiltrated the soil, the soil water balance equation that forms the basis of the UNSAT-H conceptual model is ∆𝑆𝑆𝑤𝑤 = I − E − T − D B.1 where ∆𝑆𝑆𝑤𝑤 is the change in soil water storage during an interval of time. The water storage equates to the average volumetric water content of the soil multiplied by the depth of the soil. This equation simply states that the change in the amount of water stored in the soil profile is equal to the total infiltration, I, minus the amount of water that is lost to evaporation, E, transpiration, T, and drainage, D. The first term in Equation (B.1) is I, the soil infiltratability (i.e., the instantaneous infiltration rate), and is a function of several factors: the time from the onset of precipitation, the initial water content, the hydraulic properties of the surface soil, and the hydraulic properties of layers deeper within the profile. At the start of the infiltration rate, the infiltratability is maximal. In time, this rate will decrease asymptotically to approaching the value of the saturated conductivity of the surface soil. As the wetted depth of the soil increases, the infiltration rate decreases asymptotically and approaches the saturated 66 conductivity of the most impeding layer within the soil profile conditions. The UNSAT-H model simulates infiltration as a two-step process. First, the infiltration rate is set equal to the precipitation rate during each time step. In this case, the infiltration is controlled by the supply of water (i.e., supply-controlled or flux-controlled), and is most typical in arid environments. In the second stage, infiltration is controlled by the soil profile conditions. Many algebraic equations have been developed to estimate infiltration rates during this second stage, however the UNSAT-H conceptual model does not use an infiltration equation. Instead, the infiltration in this second stage is determined directly by calculating the ability of the soil profile to transmit water downward. In this case, if the surface soil saturates, the solution of that time step is repeated using a Dirichlet boundary condition (with the surface node saturated). The resulting flux from the surface into the profile is the infiltration rate. Runoff/overland flow occurs when the precipitation rate exceeds the infiltration rate, in which water accumulates on the soil surface. There has not been observable surface runoff from the site plots, apart from plot 12 producing a "moderate sized puddle" when precipitation is heavy. Weyerhaeuser also stated that minor subsurface flow occurs, and has been attributed with rodent holes and root channels. During the installation of zerotension lysimeters, the holes would fill with water during heavy rain through horizontal flow. Overland flow is not addressed by the UNSAT-H conceptual model due to being a one-dimensional model. Since overland flow is a multidimensional process that a onedimensional model cannot describe, UNSAT-H can be applied only to areas for which local run-on/off processes can be represented by a uniform precipitation rate over the entire area of interest, or to areas in which overland flow is prevented (i.e., lysimeters). 67 Evaporation is modeled in UNSAT-H as an integrated form of Fick's law of diffusion, and simply justifies that the evaporation rate is equal to the deficit in vapor density between the soil surface and the atmosphere divided by the atmospheric boundarylayer resistance (the region of the atmosphere that is directly affected by the shearing forces originating at the surface). In the second process, water flow, a decrease in the supply of water to the surface leads to surface drying. Since a drier surface is indicative of a lower vapor density, the evaporation is reduced due to the vapor density deficit being smaller. However, an increased water supply to the soil surface would have the opposite effect. The third process is controlled by both the atmospheric vapor density and the atmospheric boundary-layer resistance by transporting water vapor from the surface to the atmosphere. Commonly, the soil surface is wetter (higher vapor density) than air. But there are instances, however, such as during the early morning when the temperature approaches the dew point or after rainfall, the increased atmospheric vapor density decreases the surfaceair vapor deficit and, therefore, decreases evaporation. Other causes for the transfer of water vapor from the soil surface to the atmosphere result from decreased wind speed or reduced eddy diffusion from high atmospheric stability. For infiltration events, the upper boundary condition for water flow can be a flux, where it can be specified as an hourly flux that is equivalent to a precipitation rate. If the suction head of the surface node should become less than the minimum suction head, the upper boundary becomes a constant head that is equivalent to the minimum suction head. During this condition, infiltration is calculated as the sum of the change in storage of the surface node and the flux between the surface node and the node below it. In contrast, the infiltration event could be modeled as a Constant head, and this condition continues until 68 the precipitation rate becomes less than the potential infiltration rate, and the suction head of the surface node exceeds the minimum suction head. Transpiration from plants is an optional addition to the model. Transpiration was not modeled due to simulating virtually bare groundcover and no trees present on site. After preliminary investigation revealed a lack of root development, the saplings are too young to drastically alter the water content measurements and subsurface groundwater flow. The final term of Equation (B.1) is drainage, which is the movement of water downward through the bottom of the zone being simulated. The bottom of the vadose zone was set to 150 cm below the surface throughout the entire site. Even though it is known that there are water table depth fluctuations through seasons of the year, the data requirements for physically based models to simulate water table fluctuation are enormous and are difficult and costly to satisfy in many cases (Coulibaly et al., 2001; Warren et al., 2005). Of interest is the drainage water that reaches the water table; this water was considered to have completely infiltrated into the groundwater system as there exists little chance of it being drawn upward again, and it is also known as groundwater recharge. Thus, recharge was defined as drainage below the 150-cm depth bottom boundary. The following two relations are the basis of the modified Richards' equation: the water flux inside the soil is proportional to the water potential gradient, which is the basis of Darcy's Law, and the change in water content at a specific location is due to the convergence/divergence of water fluxes at another location, the basis of continuity. Additionally, the modified equation allows estimation of the initial soil moisture contents from sparse observations of related quantities (Ren, 2005). Since the modified Richards' equation begins with Darcy's Law, the one-dimensional differential form goes as: 69 qL = −K s δH B.2 δz where qL is the flux density of water (cm/hr), Ks is the saturated hydraulic conductivity (cm/hr), H is the hydraulic potential and z is the depth below the soil surface. Darcy's Law can be extended to unsaturated flow by replacing Ks with liquid conductivity, KL, as a function of matric potential, ѱ, resulting in: qL = −K L (φ) δH B.3 δz Equation (B.3) must be combined with the continuity equation to describe transient flow, stating that the change in water content of a volume of soil equates to the difference between flux into and out of the soil (Fayer et al., 2000). For one-dimensional flow, the continuity equation is: δθ δt =− δqL B.4 δz where θ is the volumetric water content (cm3/cm3), and t is time (h). The equation for transient flow in the unsaturated zone for groundwater thus yields: δθ δt =− δ δz −K L (φ) δH δz B.5 70 It should be noted that UNSAT-H has two sign conventions that relate to heads. The first type is gravitational head: a point in the soil where the elevation of the point with respect to (w.r.t.) the soil surface and is negative. Therefore 𝑧𝑧 is replaced with -𝑧𝑧. Matric head is the second type, and is usually denoted with a negative number for unsaturated soil conditions. In UNSAT-H, matric head is replaced with suction head, ℎ, which is the negative of matric head. Therefore, a positive suction head represents matric head and a negative suction head represents a pressure head. The calculation of hydraulic head then changes from H = ψ + Z to the UNSAT-H form: H = −(h + z) Using the chain rule of differentiation to Equation (B.4), where C(h) represents becomes: δθ δt δH δz = δh δz δθ δt can be replaced by C(h) δh δt . Through derivation using the chain rule, Equation (B.4) C(h) where B.6 δh δt = δ δz δh K L (h) δz + 1 − S(z, t) B.7 + 1, through differentiation of Equation (B.5). S(z,t) is a sink term added to determine later uptake by plants as a function of depth and time. The assumptions that led to Equation (B.7) are: the fluid is treated as incompressible, the air phase is continuous and at constant pressure, flow is onedimensional, liquid water flow is isothermal, and the pore-air pressure is at atmospheric 71 pressure (Lam et al., 1987), and vapor flow is negligible. In the case of unsaturated flow, it is difficult to predict water content (θ), hydraulic conductivity (k), and suction head (h) due to the multidimensional, nonhomogeneous characteristics of soil (van Genuchten, 1980). There are many soil water retention relationships that have been determined, such as the linked polynomials, the Haverkamp function, the Brooks and Corey function, the van Genuchten function, and several special functions that account for water retention of very dry soils (Fayer, 2000). The van Genuchten model was chosen because of its widely used reputation and accuracy (Carsel et al., 1988). Therefore, Equation (B.8) illustrates how water content and hydraulic conductivity are functions of suction head according to the van Genuchten function and Mualem hydraulic conductivity model and has the form 𝜃𝜃 = 𝜃𝜃𝑟𝑟 + (𝜃𝜃𝑠𝑠 − 𝜃𝜃𝑟𝑟 )[1 + (𝛼𝛼ℎ)𝑛𝑛 ]−𝑚𝑚 B.8 where 𝛼𝛼, 𝑚𝑚, and 𝑛𝑛 are curve-fitting parameters, and where it is assumed that m = 1 - 1/n (Mualem, 1976), and 𝐾𝐾𝐿𝐿 = 𝐾𝐾𝑆𝑆 {1−(𝛼𝛼ℎ)𝑛𝑛−2 [1+(𝛼𝛼ℎ)𝑛𝑛 ]−𝑚𝑚 }2 [1+(𝛼𝛼ℎ)𝑛𝑛 ]𝑙𝑙𝑙𝑙 B.9 where the conductivity function is based on the Mualem conductivity model (Mualem, 1978). The next important concept to cover is the fundamental equation used to calculate the diffusion of water vapor in soils via Fick's Law of Diffusion, which can be written as 72 𝑞𝑞𝑣𝑣 = − 𝐷𝐷 𝜕𝜕𝜌𝜌𝑣𝑣 𝜌𝜌𝑤𝑤 𝜕𝜕𝜕𝜕 B.10 where 𝑞𝑞𝑣𝑣 is the flux density of water vapor, cm h-1, 𝜌𝜌𝑤𝑤 is the density of liquid water, g cm3 , D is the vapor diffusivity in soil, cm2 h-1, and 𝜌𝜌𝑣𝑣 is the vapor density, g cm-3. When applying Fick's Law to soils, the tortuous diffusion path and reduced cross-sectional area available for flow requires adjustments. The three-phase nature of soils requests the need for both adjustments, and are included in the new diffusivity term to be written as 𝐷𝐷 = 𝛼𝛼(𝜃𝜃𝑠𝑠 − 𝜃𝜃)𝐷𝐷𝑎𝑎 B.11 where α is the tortuosity factor, 𝐷𝐷𝑎𝑎 is the diffusivity of water vapor in air, cm2 s-1, and the quantity (𝜃𝜃𝑠𝑠 − 𝜃𝜃) represents the air-filled porosity. Generally, α is treated as a constant, and the most common formulation is to set α = 0.66 (Penman 2007). Fick's Law can also be written to explicitly include gradients for suction head and temperature by using the chain rule of differentiation, thus making Equation (B.10) become 𝑞𝑞𝑣𝑣 = − 𝐷𝐷 𝜕𝜕𝜌𝜌𝑣𝑣 𝜕𝜕ℎ 𝜌𝜌𝑤𝑤 𝜕𝜕ℎ 𝜕𝜕𝑧𝑧 − 𝐷𝐷 𝜕𝜕𝜌𝜌𝑣𝑣 𝜕𝜕𝜕𝜕 𝜌𝜌𝑤𝑤 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 B.12 where T is the temperature, K. Consequently, the vapor density at a specific point in the soil can then be related to the saturated vapor density, 𝜌𝜌𝑣𝑣𝑣𝑣 , and relative humidity, HR, by 𝜌𝜌𝑣𝑣 = 𝜌𝜌𝑣𝑣𝑠𝑠 𝐻𝐻𝑅𝑅 B.13 73 Therefore, Equation (B.12) can be rewritten by substituting Equation (B.13) as the relative humidity and temperature function into the vapor density, using the product rule for differentiation and setting 𝜕𝜕𝐻𝐻𝑅𝑅 ⁄𝜕𝜕𝜕𝜕 = 0 for relative humidity conditions greater than 0.6 because the resulting temperature effect on HR is so small: 𝑞𝑞𝑣𝑣 = − 𝐷𝐷 𝜌𝜌𝑤𝑤 𝜌𝜌𝑣𝑣𝑣𝑣 𝜕𝜕𝐻𝐻𝑅𝑅 𝜕𝜕𝜕𝜕 − 𝐷𝐷 𝜌𝜌𝑤𝑤 𝐻𝐻𝑅𝑅 𝜕𝜕𝜌𝜌𝑣𝑣𝑣𝑣 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 B.14 Equation (B.14) unambiguously contains the effect of soil temperature on vapor diffusion. The first term characterizes isothermal vapor diffusion while the second term characterizes thermal vapor diffusion. Therefore, the relative humidity can be determined using the soil suction head (Campbell, 1985) 𝐻𝐻𝑅𝑅 = exp − ℎ𝑀𝑀𝑀𝑀 𝑅𝑅𝑅𝑅 B.15 where M is the molecular weight of water, g mol-1, g is the gravitational constant, cm s-1, and R is the universal gas constant, erg mol-1 K-1. Although Equation (B.14) underpredicts water vapor flow, Philip and de Vries (1957) proposed that vapor is effectively transported through the liquid phase by condensation and evaporation processes operating within individual pores. By adding an enhancement factor, η, to the thermal vapor diffusion term these two processes will be accounted for. This leads to Equation (B.16) 𝑞𝑞𝑣𝑣 = 𝐷𝐷 𝜌𝜌𝑤𝑤 𝜌𝜌𝑣𝑣𝑣𝑣 𝑀𝑀𝑀𝑀 𝑅𝑅𝑅𝑅 𝐻𝐻𝑅𝑅 𝜕𝜕ℎ 𝜕𝜕𝜕𝜕 − 𝐷𝐷 𝜌𝜌𝑤𝑤 𝜂𝜂𝐻𝐻𝑅𝑅 𝜕𝜕𝜌𝜌𝑣𝑣𝑣𝑣 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕 B.16 74 This law accounts for the effect of temperature gradients and enhanced vapor diffusion in soil. To determine the surface and lower boundary of the soil column, boundary conditions are specified. For infiltration events, the upper boundary condition for water flow was set to a flux, which was set equal to the hourly flux equivalent to the precipitation rate. If the suction head of the surface node becomes less than the minimum suction head, the upper boundary becomes the value of the minimum suction head. Thus, the infiltration is then calculated as the sum of the change in storage of the surface node and the flux between the surface node and the node below it. This condition continues until the precipitation rate becomes less than the potential infiltration rate, and the suction head of the surface node therefore exceeds the minimum suction head, thus reverting it back to a flux boundary. Similarly, for evaporation events, the surface boundary condition was set to a flux. This condition required input through daily weather data, consisting of the daily maximum and minimum air temperatures, daily average dewpoint temperature, total daily solar radiation, and average daily wind speed. The maximum and minimum air temperatures were used to calculate the sinusoidal variation in air temperature, Ta, throughout that day, using 2𝜋𝜋 𝑇𝑇𝑎𝑎 = 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 + 𝑇𝑇𝑎𝑎𝑚𝑚𝑝𝑝 cos 24 (𝑡𝑡𝑑𝑑 − 15) B.17 where 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 is the average of the maximum and minimum air temperatures, K; 𝑇𝑇𝑎𝑎𝑎𝑎𝑎𝑎 is the amplitude of the air temperature, K; and 𝑡𝑡𝑑𝑑 is the time of day, hour. Equation (B.17) assumes the minimum daily temperature occurring at 0300 h and the maximum daily 75 temperature occurring at 1500 h. To ensure that no discontinuity at midnight occurs, before 0300 h, the maximum air temperature from the previous day is used in Equation (B.17), and the minimum air temperature from the next day is used after 1500 h in Equation (B.17). The dewpoint temperature, (and therefore, the atmospheric vapor density), wind speed and cloud cover are assumed to remain constant during the day. Since heat flow in the vadose zone is not being modeled, the boundary condition for evaportranspiration was a dependent variable of the PET rate. The UNSAT-H program can calculate the daily value using the form of the Penman equation reported by Doorenbos and Pruitt (1977), in which the units originally used were retained in the UNSAT-H model 𝑃𝑃𝑃𝑃𝑃𝑃 = 𝑠𝑠𝑅𝑅𝑛𝑛𝑛𝑛 𝑠𝑠+𝛾𝛾 + 𝛾𝛾 𝑠𝑠+𝛾𝛾 0.27 1 + 𝑈𝑈 (𝑒𝑒𝑎𝑎 − 𝑒𝑒𝑑𝑑 ) 100 B.18 where s is the slope of the saturation vapor pressure-temperature curve, mb K-1; Rni is the isothermal net radiation, mm d-1; 𝛾𝛾 is the psychrometric constant, mb K-1; U is the 24-h wind run, km d-1; 𝑒𝑒𝑎𝑎 is the saturation vapor pressure at the mean air temperature, mb; and 𝑒𝑒𝑑𝑑 is the actual vapor pressure, mb. This calculated PET value is distributed through the day according to the hourly factors that were generated with a sine wave function. For the hours between 0600 and 1800, 88% of the daily PET is applied sinusoidally. During the remaining time, hourly PET rates are 1% of the daily value. The second boundary specified is the lower boundary. At a depth of 150 cm in the model, a unit gradient option was chosen due to corresponding to gravity-induced drainage and being most appropriate for applying soil profiles that extend below the root zone and drainage is not inhibited. 76 The mass balance error is calculated by first calculating the soil-water storage at the end of a time step using 𝑗𝑗 𝑗𝑗 𝑆𝑆𝑤𝑤 = 𝜃𝜃1 𝑧𝑧2 −𝑧𝑧1 2 𝑗𝑗 + 𝜃𝜃𝑛𝑛 𝑧𝑧𝑛𝑛 −𝑧𝑧𝑛𝑛−1 2 𝑗𝑗 𝑧𝑧𝑖𝑖+1 −𝑧𝑧𝑖𝑖−1 + ∑𝑛𝑛−1 𝑖𝑖=2 𝜃𝜃𝑖𝑖 2 B.19 Then, the mass balance error (Ew) for the time step can be obtained using 𝑗𝑗 𝑗𝑗−1 𝐸𝐸𝑤𝑤 = 𝐼𝐼 𝑗𝑗 − 𝐸𝐸 𝑗𝑗 − 𝑇𝑇𝑗𝑗 − 𝐷𝐷 𝑗𝑗 − 𝑆𝑆𝑤𝑤 − 𝑆𝑆𝑤𝑤 𝑗𝑗 B.20 𝑗𝑗−1 where the terms 𝐼𝐼 𝑗𝑗 , 𝐸𝐸 𝑗𝑗 , 𝑇𝑇𝑗𝑗 , 𝐷𝐷 𝑗𝑗 , and 𝑆𝑆𝑤𝑤 − 𝑆𝑆𝑤𝑤 refer to the amounts of infiltration, evaporation, transpiration, drainage, and change in storage, respectively, having occurred during that particular time step. The UNSAT-H model consists of three programs: DATAINH, UNSATH, and DATAOUT. The purpose of DATAINH is to process the input data in a way that the UNSATH program can recognize, therefore reducing the likelihood that UNSATH will fail to run from input errors. It is an interactive program that reads data from a *.inp file, checks for errors, and performs certain calculations. An example of the input file for Plot B09I is illustrated by Figure B.1. Then, DATAINH writes the data in binary form to a file with the same name as the input file, but with the extension bin. This *.bin file created by DATAINH serves as the input file for UNSATH. The steps start with data input and end with the final summary output of the simulation to file *.res. Simulation data that is output to the *.res file include initial conditions, water content, water flow, temperatures, water balance terms, and at the 77 end of the file, the simulation end results. After the file has been made, DATAOUT can read the *.res file and create an output file in the *.out file format, but with a .out extension. An example of the .out file for Plot B09I is illustrated by Figure B.2. 78 Figure B.1 Input .inp file for Treatment B091 for 2-Year Simulation 79 Figure B.2 Output .out File for Treatment B09I for First Year of Simulation APPENDIX C HYDROLOGIC PARAMETER INPUT INFORMATION FOR UNSAT-H N 84 140 242 201 308 11 87 476 6 28 172 330 Texture Class Clay Clay Loam Loam Loamy Sand Sand Sandy Clay Sandy Clay Loam Sandy Loam Silt Silt Clay Silty Clay Loam Silt Loam 0.07 0.09 0.05 0.11 0.04 0.06 0.12 0.05 0.05 0.06 0.08 0.10 (0.07) 0.439 (0.08) 0.482 (0.04) 0.489 (0.12) 0.481 (0.05) 0.387 (0.08) 0.384 (0.11) 0.385 (0.03) 0.375 (0.04) 0.390 (0.07) 0.399 (0.08) 0.442 (0.09) (0.09) (0.08) (0.08) (0.09) (0.06) (0.05) (0.06) (0.07) (0.10) (0.08) (0.08) θs [cm3/cm3] (0.11) 0.459 θr [cm3/cm3] -2.3 -2.08 -2.18 -1.79 -1.57 -1.68 -1.48 -1.45 -1.46 -1.95 -1.80 (0.57) (0.59) (0.30) (0.64) (0.56) (0.71) (0.57) (0.25) (0.47) (0.73) (0.69) log(α) [log(1/cm)] -1.83 (0.68) 0.221 0.182 0.225 0.121 0.161 0.124 0.082 0.502 0.242 0.168 0.151 (0.14) 1.261 (0.74) (0.13) 1.046 (0.76) (0.13) 1.641 (0.27) (0.10) 0.983 (0.57) (0.11) 1.583 (0.66) (0.12) 1.120 (0.85) (0.06) 1.055 (0.89) (0.18) 2.808 (0.59) (0.16) 2.022 (0.64) (0.13) 1.081 (0.92) (0.12) 0.913 (1.09) log(n) Ks [log(10)] [log(cm/day)] 0.098 (0.07) 1.169 (0.92) Table C.1 Seven Hydraulic Parameters for All Twelve USDA Textural Classes Described by ROSETTA 81 Description C01I G02I D04II F05I D06III C07II F08III B09I E10I A11III G12III D13I A14I E15II B16IV E17IV A18IV A19II B20II D22IV F24IV C25IV E26III Code 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 24 25 26 0.077 0.069 0.082 0.078 0.085 0.078 0.068 0.086 0.081 0.080 0.086 0.081 0.077 0.075 0.074 0.085 0.086 0.084 0.080 0.082 0.078 0.085 0.068 θr [cm3/cm3] 0.431 0.417 0.444 0.432 0.448 0.432 0.414 0.454 0.439 0.437 0.451 0.438 0.431 0.429 0.430 0.448 0.453 0.447 0.437 0.446 0.437 0.450 0.412 θs [cm3/cm3] 0.010 0.009 0.011 0.010 0.012 0.010 0.010 0.011 0.011 0.010 0.012 0.012 0.010 0.009 0.008 0.012 0.012 0.011 0.011 0.009 0.009 0.011 0.011 α [1/cm] 1.469 1.509 1.444 1.455 1.403 1.455 1.481 1.422 1.443 1.457 1.396 1.422 1.469 1.482 1.511 1.403 1.403 1.437 1.436 1.471 1.499 1.430 1.472 η 10.53 12.07 10.92 9.68 9.02 9.68 8.78 11.32 10.00 10.73 9.32 8.34 10.53 11.32 12.69 9.02 10.21 11.04 9.05 11.99 12.36 11.17 8.10 Ks [cm/day] Table C.2 ROSETTA Software Results for All Plots for 0-15 cm Soil Depth Profile CLAY LOAM LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM LOAM TEXTURE 82 Description C28III G30II G31IV F32III B33III Code 28 30 31 32 33 Table C.2 Continued θr [cm3/cm3] 0.076 0.075 0.084 0.082 0.080 θs [cm3/cm3] 0.430 0.429 0.446 0.439 0.437 α [1/cm] 0.009 0.009 0.012 0.012 0.010 1.490 1.482 1.416 1.409 1.457 η Ks [cm/day] 11.92 11.32 9.67 7.78 10.73 CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM TEXTURE 83 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 24 25 Code C01I G02I D04II F05I D06III C07II F08III B09I E10I A11III G12III D13I A14I E15II B16IV E17IV A18IV A19II B20II D22IV F24IV C25IV 0.072 0.076 0.086 0.086 0.085 0.086 0.078 0.092 0.089 0.088 0.091 0.084 0.083 0.086 0.084 0.086 0.091 0.091 0.090 0.086 0.091 0.090 Description θr [cm3/cm3] 0.425 0.432 0.449 0.451 0.450 0.449 0.429 0.468 0.460 0.453 0.466 0.444 0.440 0.452 0.448 0.453 0.466 0.466 0.463 0.453 0.466 0.463 θs [cm3/cm3] 0.007 0.009 0.013 0.012 0.012 0.013 0.013 0.014 0.013 0.014 0.012 0.012 0.013 0.011 0.011 0.012 0.012 0.012 0.012 0.012 0.012 0.012 α [1/cm] 1.538 1.498 1.390 1.396 1.409 1.390 1.418 1.338 1.376 1.358 1.393 1.409 1.395 1.416 1.423 1.403 1.393 1.393 1.400 1.403 1.393 1.400 η 13.52 12.29 8.49 9.32 9.93 8.49 6.67 10.84 10.42 8.23 12.20 8.76 7.36 10.74 10.53 10.21 12.20 12.20 11.91 10.21 12.20 11.91 Ks [cm/day] Table C.3 ROSETTA Software Results for All Plots for 15-30 cm Soil Depth Profile LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM TEXTURE 84 E26III C28III G30II G31IV F32III B33III 0.082 0.087 0.087 0.090 0.090 0.069 Description θr [cm3/cm3] θs [cm3/cm3] 0.440 0.457 0.458 0.464 0.465 0.417 α [1/cm] 0.011 0.010 0.010 0.012 0.011 0.009 1.429 1.434 1.454 1.387 1.406 1.509 η Ks [cm/day] 9.23 11.93 12.15 11.80 12.19 12.07 CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM CLAY LOAM LOAM TEXTURE Description C01I G02I D04II F05I D06III C07II F08III B09I E10I A11III G12III Code 1 2 4 5 6 7 8 9 10 11 12 0.090 0.089 0.098 0.095 0.091 0.097 0.077 0.098 0.097 0.096 0.094 3 0.461 0.453 0.489 0.477 0.463 0.485 0.426 0.488 0.484 0.479 0.472 θr θs 3 3 [cm /cm ] [cm /cm3] 0.013 0.015 0.018 0.016 0.015 0.017 0.012 0.018 0.018 0.017 0.017 α [1/cm] 1.363 1.328 1.250 1.296 1.328 1.261 1.423 1.257 1.249 1.269 1.286 η 10.00 7.87 20.67 13.08 9.55 18.76 6.56 19.99 19.34 16.16 12.62 Ks [cm/day] CLAY CLAY CLAY CLAY CLAY CLAY CLAY LOAM CLAY CLAY CLAY CLAY TEXTURE Table C.4 ROSETTA Software Results for All Plots for 30-100 cm Soil Depth Profile 26 28 30 31 32 33 Code Table C.3 Continued 85 Description D13I A14I E15II B16IV E17IV A18IV A19II B20II D22IV F24IV C25IV E26III C28III G30II G31IV F32III B33III Code 13 14 15 16 17 18 19 20 22 24 25 26 28 30 31 32 33 Table C.4 Continued 0.095 0.090 0.092 0.095 0.092 0.098 0.099 0.096 0.094 0.097 0.098 0.084 0.097 0.095 0.098 0.096 0.093 0.478 0.459 0.465 0.475 0.466 0.490 0.494 0.483 0.473 0.487 0.488 0.442 0.486 0.477 0.490 0.480 0.469 θr θs 3 3 [cm /cm ] [cm /cm3] 3 0.017 0.016 0.016 0.017 0.014 0.017 0.017 0.016 0.014 0.015 0.015 0.014 0.015 0.016 0.016 0.017 0.017 α [1/cm] 1.277 1.317 1.306 1.282 1.333 1.261 1.269 1.286 1.337 1.318 1.308 1.370 1.304 1.296 1.281 1.282 1.291 η 14.97 8.87 10.13 13.74 10.11 20.39 21.43 15.51 12.44 16.61 17.09 6.85 16.33 13.08 18.97 15.16 11.62 Ks [cm/day] CLAY CLAY CLAY CLAY CLAY CLAY CLAY CLAY CLAY CLAY CLAY CLAY LOAM CLAY CLAY CLAY CLAY CLAY TEXTURE 86 14 14 14 19 19 19 19 11 11 11 11 18 18 18 9 9 9 20 20 20 20 33 33 Plot Treat ment A-I A-I A-I A-II A-II A-II A-II A-III A-III A-III A-III A-IV A-IV A-IV B-I B-I B-I B-II B-II B-II B-II B-III B-III 0-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm Soil Profile 0.38 0.40 0.30 0.37 0.47 0.46 0.38 0.38 0.40 0.43 0.38 0.32 0.46 0.45 0.47 0.28 0.43 0.45 0.45 0.30 0.30 0.39 0.47 θsat 0.25 0.34 0.25 0.24 0.35 0.30 0.31 0.21 0.25 0.30 0.34 0.15 0.37 0.37 0.25 0.16 0.38 0.25 0.35 0.23 0.25 0.25 0.31 θr θsat θr 0.13 0.06 0.05 0.13 0.12 0.16 0.07 0.17 0.15 0.13 0.04 0.17 0.09 0.08 0.22 0.12 0.05 0.20 0.10 0.07 0.05 0.14 0.16 Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Clay Clay Loam Clay Loam Clay Clay Clay Loam Loam Soil Class α [1/cm] 0.019 0.019 0.008 0.036 0.019 0.008 0.008 0.010 0.014 0.017 0.017 0.019 0.019 0.008 0.019 0.008 0.008 0.036 0.020 0.019 0.008 0.019 0.036 1.310 1.310 1.090 1.560 1.310 1.090 1.090 1.457 1.358 1.269 1.269 1.310 1.310 1.090 1.310 1.090 1.090 1.560 1.350 1.310 1.090 1.310 1.560 N Table C.5 Summary of Soil Information Input in UNSAT-H for Each Soil Profile and Each Plot Ks [cm/hr] 2.72 1.50 2.65 2.50 1.80 1.50 0.90 2.07 1.60 1.42 1.10 2.00 1.35 0.85 1.97 1.43 1.21 2.33 1.90 1.50 0.74 2.50 2.35 87 Treat ment B-III B-III B-IV B-IV B-IV B-IV C-I C-I C-I C-I C-II C-II C-II C-II C-III C-III C-III C-IV C-IV C-IV C-IV D-I D-I Plot 33 33 16 16 16 16 1 1 1 1 7 7 7 7 28 28 28 25 25 25 25 13 13 Table C.5 Continued 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-24.9 cm 25-64.9 cm Soil Profile 0.35 0.27 0.33 0.48 0.36 0.48 0.37 0.41 0.38 0.28 0.40 0.42 0.35 0.39 0.42 0.48 0.55 0.47 0.47 0.43 0.44 0.43 0.40 θsat 0.30 0.24 0.15 0.40 0.30 0.37 0.20 0.31 0.33 0.20 0.25 0.33 0.25 0.36 0.30 0.37 0.45 0.30 0.35 0.35 0.40 0.30 0.30 θr 0.05 0.03 0.18 0.08 0.06 0.12 0.17 0.10 0.05 0.08 0.15 0.09 0.10 0.03 0.12 0.11 0.10 0.17 0.12 0.08 0.04 0.13 0.10 θsat θr Clay Clay Loam Clay Loam Clay Loam Clay Clay Loam Loam Clay Clay Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Loam Soil Class 0.008 0.019 0.040 0.036 0.025 0.008 0.019 0.036 0.008 0.008 0.019 0.019 0.019 0.008 0.019 0.019 0.008 0.019 0.019 0.019 0.008 0.019 0.019 α [1/cm] 1.090 1.310 1.600 1.560 1.250 1.090 1.310 1.560 1.090 1.090 1.310 1.310 1.310 1.090 1.310 1.310 1.090 1.310 1.310 1.310 1.090 1.310 1.310 N 1.81 1.30 2.50 1.90 1.55 1.20 1.18 0.91 1.24 1.54 3.13 2.73 2.23 1.47 2.00 1.80 1.10 1.50 0.91 1.30 1.00 2.50 1.50 Ks [cm/hr] 88 Treat ment D-I D-II D-II D-II D-II D-III D-III D-III D-IV D-IV D-IV D-IV E-I E-I E-I E-II E-II E-II E-II E-III E-III E-III E-III Plot 13 4 4 4 4 6 6 6 22 22 22 22 10 10 10 15 15 15 15 26 26 26 26 Table C.5 Continued 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm Soil Profile 0.45 0.48 0.45 0.40 0.42 0.46 0.42 0.37 0.38 0.45 0.44 0.39 0.40 0.43 0.42 0.40 0.40 0.43 0.42 0.40 0.35 0.40 0.40 θsat 0.39 0.15 0.25 0.30 0.40 0.20 0.25 0.35 0.20 0.35 0.40 0.36 0.20 0.30 0.38 0.20 0.30 0.35 0.40 0.25 0.30 0.30 0.30 θr 0.06 0.33 0.20 0.10 0.02 0.26 0.17 0.02 0.18 0.10 0.04 0.03 0.20 0.13 0.04 0.20 0.10 0.08 0.02 0.15 0.05 0.10 0.10 θsat θr Clay Clay Loam Clay Loam Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Loam Clay Loam Soil Class 0.008 0.019 0.019 0.008 0.014 0.036 0.019 0.008 0.025 0.036 0.010 0.008 0.012 0.017 0.023 0.009 0.011 0.016 0.016 0.011 0.011 0.014 0.014 α [1/cm] 1.090 1.310 1.310 1.090 1.333 1.560 1.310 1.090 1.350 1.560 1.150 1.090 1.446 1.381 1.295 1.482 1.416 1.306 1.306 1.472 1.429 1.370 1.370 N 1.00 1.80 1.90 1.40 0.80 3.00 2.50 0.60 1.65 2.50 1.85 1.00 3.00 1.50 0.80 3.50 3.00 2.50 1.50 2.00 1.80 1.60 1.60 Ks [cm/hr] 89 17 17 17 17 5 5 5 32 32 32 32 8 8 8 24 24 24 24 2 2 2 2 30 Plot Treat ment E-IV E-IV E-IV E-IV F-I F-I F-I F-II F-II F-II F-II F-III F-III F-III F-IV F-IV F-IV F-IV G-I G-I G-I G-I G-II Table C.5 Continued 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm Soil Profile 0.40 0.40 0.43 0.42 0.45 0.40 0.40 0.35 0.40 0.43 0.41 0.40 0.35 0.45 0.45 0.35 0.38 0.38 0.40 0.43 0.35 0.40 0.47 θsat 0.20 0.30 0.35 0.40 0.35 0.30 0.30 0.15 0.30 0.35 0.36 0.20 0.20 0.42 0.25 0.25 0.25 0.25 0.25 0.40 0.20 0.20 0.15 θr θsat θr 0.20 0.10 0.08 0.02 0.10 0.10 0.10 0.20 0.10 0.08 0.05 0.20 0.15 0.03 0.20 0.10 0.13 0.13 0.15 0.03 0.15 0.20 0.32 Clay Loam Clay Loam Clay Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Loam Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Clay Loam Soil Class α [1/cm] 0.012 0.012 0.014 0.014 0.010 0.012 0.016 0.012 0.011 0.017 0.017 0.010 0.013 0.012 0.009 0.015 0.015 0.015 0.015 0.009 0.015 0.015 0.019 1.403 1.403 1.333 1.333 1.455 1.396 1.296 1.409 1.406 1.282 1.282 1.481 1.418 1.423 1.499 1.450 1.318 1.318 1.328 1.498 1.328 1.328 1.310 N Ks [cm/hr] 3.00 2.50 1.50 0.80 1.50 1.00 1.00 2.50 2.00 1.50 0.80 2.00 1.50 0.80 1.55 1.45 1.35 0.95 2.00 1.50 1.50 1.00 2.50 90 Treat ment G-II G-II G-III G-III G-III G-III G-IV G-IV G-IV G-IV Plot 30 30 12 12 12 12 31 31 31 31 Table C.5 Continued 15-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm 0-14.9 cm 15-24.9 cm 25-64.9 cm 65-150 cm Soil Profile 0.40 0.30 0.50 0.42 0.40 0.40 0.47 0.45 0.43 0.46 θsat 0.25 0.25 0.20 0.30 0.25 0.20 0.35 0.37 0.37 0.42 θr 0.15 0.05 0.30 0.12 0.15 0.20 0.12 0.08 0.06 0.04 θsat θr Clay Loam Clay Clay Loam Clay Loam Clay Clay Loam Clay Loam Clay Clay Soil Class 0.010 0.016 0.012 0.012 0.017 0.008 0.036 0.036 0.019 0.016 α [1/cm] 1.454 1.296 1.396 1.393 1.286 1.090 1.560 1.560 1.310 1.281 N 1.50 0.80 2.50 2.50 2.00 0.80 2.00 1.50 1.00 0.70 Ks [cm/hr] 91 Treatment A-I A-II A-III A-IV B-I B-II B-III B-IV C-I C-II C-III C-IV D-I D-II D-III D-IV E-I E-II E-III E-IV F-I F-II F-III Plot 14 19 11 18 9 20 33 16 1 7 28 25 13 4 6 22 10 15 26 17 5 32 8 MAE Node 1 (10cm) 0.0227 0.0289 0.0148 0.0382 0.0684 0.0348 0.0169 0.0495 0.0162 0.0224 0.0131 0.0512 0.0225 0.0221 0.0442 0.0262 0.0344 0.0183 0.0136 0.0127 0.0275 0.0181 0.0241 MAE Node 2 (20 cm) 0.0420 0.0210 0.0128 0.0121 0.0260 0.0496 0.0285 0.0200 0.0074 0.0309 0.0296 0.0162 0.0082 0.0119 0.0251 0.0197 0.0313 0.0095 0.0074 0.0125 0.0173 0.0126 0.0114 MAE Node 3 (30 cm) 0.0139 0.0517 0.0173 0.0274 0.0502 0.0393 0.0063 0.0131 0.0069 0.0145 0.0429 0.0180 0.0100 0.0107 0.0294 0.0121 0.0251 0.0115 0.0188 0.0179 0.0102 0.0219 0.0142 MAE Percent Node 4 Bias Node (100 cm) 1 [%] 0.0450 6.8% 0.0593 -8.7% 0.0107 2.5% 0.0436 15.6% 0.0289 19.7% 0.0378 -9.1% 0.0082 -4.6% 0.0454 17.4% 0.0425 2.7% 0.0052 4.9% 0.0462 -2.9% 0.0223 13.6% 0.0365 5.7% 0.0081 -0.1% 0.0710 -11.8% 0.0208 -7.3% 0.0170 10.8% 0.0478 3.4% NA 3.3% 0.0107 -2.2% NA -2.3% 0.0291 3.4% 0.0183 7.1% Table C.6 Seasonal PBIAS Calculated Values for Fall 2014 Season Percent Bias Node 2 [%] 14.0% 4.9% 1.6% 1.0% 5.8% 13.2% -6.9% -4.3% 0.2% 8.5% 6.4% 3.1% -0.2% -0.8% 5.5% -4.7% 8.5% 1.4% -0.2% 3.5% 7.0% 3.1% 0.8% Percent Bias Node 3 [%] 3.7% 12.8% 3.0% 6.6% 22.4% 0.0% -1.5% 2.5% 0.7% -4.3% 10.5% 3.9% -0.2% 1.9% 5.8% 2.7% 6.6% 2.2% -4.9% 4.6% 4.7% 5.6% 0.1% Percent Bias Node 4 [%] 17.4% -1.5% -2.7% 10.8% 7.2% -5.5% -1.2% -3.6% -8.9% -0.5% -0.5% 1.5% -5.3% -1.9% -14.1% -1.2% 0.8% -9.2% NA -2.3% 1.5% -4.9% -2.6% 92 Treatment F-IV G-I G-II G-III G-IV Plot 24 2 30 12 31 Table C.6 Continued MAE Node 1 (10cm) 0.0106 0.0195 0.0127 0.0434 0.0143 MAE Node 2 (20 cm) 0.0046 0.0040 0.0072 0.0293 0.0203 MAE Node 3 (30 cm) 0.0050 0.0288 0.0173 0.0327 0.0137 MAE Percent Node 4 Bias Node (100 cm) 1 [%] 0.0274 -0.6% NA -5.2% 0.0555 0.3% 0.0614 7.9% 0.0136 2.4% Percent Bias Node 2 [%] -0.8% -5.2% -1.2% 7.4% 5.2% Percent Bias Node 3 [%] -1.2% -0.9% 2.8% 6.3% 2.9% Percent Bias Node 4 [%] -4.9% 7.9% -13.4% -10.0% 1.2% 93 Treatment A-I A-II A-III A-IV B-I B-II B-III B-IV C-I C-II C-III C-IV D-I D-II D-III D-IV E-I E-II E-III E-IV F-I F-II F-III Plot 14 19 11 18 9 20 33 16 1 7 28 25 13 4 6 22 10 15 26 17 5 32 8 MAE Node 1 (10cm) 0.0106 0.0481 0.0263 0.0249 0.0461 0.0537 0.0367 0.0436 0.0148 0.0449 0.0204 0.0214 0.0114 0.0625 0.0624 0.0546 0.0251 0.0225 0.0168 0.0396 0.0171 0.0407 0.0146 MAE Node 2 (20 cm) 0.0252 0.0106 0.0147 0.0091 0.0161 0.0422 0.0415 0.0323 0.0128 0.0216 0.0240 0.0069 0.0172 0.0252 0.0191 0.0354 0.0167 0.0110 0.0037 0.0087 0.0092 0.0159 0.0121 MAE Node 3 (30 cm) 0.0100 0.0422 0.0079 0.0236 0.0482 0.0337 0.0040 0.0102 0.0034 0.0246 0.0373 0.0148 0.0089 0.0044 0.0231 0.0070 0.0171 0.0052 0.0234 0.0081 0.0058 0.0115 0.0143 MAE Node 4 (100 cm) 0.0469 0.0405 0.0036 0.0586 0.0304 0.0144 0.0034 0.0507 0.0172 0.0016 0.0484 0.0236 0.0247 0.0147 0.0422 0.0109 0.0123 0.0201 NA 0.0147 NA 0.0112 0.0146 Table C.7 Seasonal PBIAS Calculated Values for Spring 2015 Season Percent Bias Node 1 [%] 0.7% -14.1% -7.7% 10.4% 13.3% -13.9% -10.0% -16.5% -3.7% -11.6% -5.2% 4.9% 0.4% -14.9% -16.6% -15.6% 0.4% -4.3% -4.3% -11.3% 4.4% -13.3% 0.0% Percent Bias Node 2 [%] 8.4% 2.4% -3.0% -1.7% 0.8% 11.2% -10.2% -6.9% -3.3% 5.9% 5.5% 0.2% -4.2% -6.4% 5.3% -8.4% 4.5% -2.6% -0.6% -0.5% 2.4% -4.2% -2.2% Percent Bias Node 3 [%] 2.7% 10.3% 0.5% 5.8% 21.6% 2.5% -1.1% -2.2% 0.7% -7.3% 9.2% 3.7% -1.7% -0.6% 5.8% 1.6% 4.0% 0.7% -6.1% 2.0% -0.5% 2.9% -4.1% Percent Bias Node 4 [%] 18.8% 12.0% 0.9% 15.1% 7.7% 4.4% -0.7% 0.3% 3.7% 0.1% 2.6% 5.3% -1.8% -0.4% -6.3% 2.1% 3.1% -3.7% NA -3.4% NA -0.1% -1.4% 94 Treatment F-IV G-I G-II G-III G-IV Plot 24 2 30 12 31 Table C.7 Continued MAE Node 1 (10cm) 0.0305 0.0331 0.0282 0.0348 0.0150 MAE Node 2 (20 cm) 0.0089 0.0087 0.0116 0.0206 0.0075 MAE Node 3 (30 cm) 0.0095 0.0091 0.0109 0.0227 0.0107 MAE Node 4 (100 cm) 0.0048 NA 0.0141 NA 0.0112 Percent Bias Node 1 [%] -7.4% -8.8% -7.4% 0.7% -2.9% Percent Bias Node 2 [%] -2.6% -2.0% -3.2% 5.6% 0.6% Percent Bias Node 3 [%] -2.8% -1.3% 2.2% 5.9% 2.6% Percent Bias Node 4 [%] -0.3% NA -2.9% NA 2.4% 95 Treatment A-I A-II A-III A-IV B-I B-II B-III B-IV C-I C-II C-III C-IV D-I D-II D-III D-IV E-I E-II E-III E-IV F-I F-II F-III Plot 14 19 11 18 9 20 33 16 1 7 28 25 13 4 6 22 10 15 26 17 5 32 8 MAE Node 1 (10cm) 0.0051 0.0297 0.0276 0.0526 0.0701 0.0359 0.0163 0.0113 0.0106 0.0548 0.0113 0.0574 0.0234 0.0103 0.0150 0.0401 0.0433 0.0137 0.0088 0.0147 0.0096 0.0061 0.0153 MAE Node 2 (20 cm) 0.0266 0.0128 0.0445 0.0130 0.0109 0.0334 0.0358 0.0177 0.0129 0.0116 0.0167 0.0184 0.0074 0.0089 0.0199 0.0294 0.0440 0.0046 0.0089 0.0083 0.0128 0.0073 0.0554 MAE Node 3 (30 cm) 0.0123 0.0355 0.0081 0.0267 0.0370 0.0405 0.0072 0.0262 0.0075 0.0222 0.0445 0.0082 0.0052 0.0074 0.0221 0.0050 0.0301 NA 0.0206 0.0119 0.0045 0.0113 0.0092 MAE Node 4 (100 cm) 0.0489 0.0642 0.0063 0.0356 0.0247 0.0391 0.0072 0.0343 0.0560 0.0044 0.0437 0.0225 0.0517 NA 0.0817 0.0226 0.0171 0.0535 NA 0.0159 NA 0.0426 0.0232 Table C.8 Seasonal PBIAS Calculated Values for Winter 2015 Season Percent Bias Node 1 [%] 0.5% -8.8% -7.5% 23.4% 20.1% -9.3% -4.4% 3.0% -2.3% -13.3% -2.8% 15.5% 6.3% -1.9% -4.2% -11.2% 13.3% 3.7% -2.2% -4.0% 2.2% -0.3% 4.7% Percent Bias Node 2 [%] 8.4% 2.9% -10.9% -2.8% 1.7% 8.5% -8.5% -3.8% -3.3% -2.7% 3.6% 4.3% -1.7% 1.3% 5.3% -6.9% 12.5% 0.4% -2.4% 2.3% 3.6% -1.8% -15.3% Percent Bias Node 3 [%] 3.3% 8.2% -1.5% 6.5% 15.4% -6.1% -2.0% -7.0% -1.9% -6.4% 10.8% 1.8% 0.3% 1.7% 2.9% 0.6% 8.2% NA -5.3% 3.0% 0.2% 2.8% -2.2% Percent Bias Node 4 [%] 19.6% -5.8% -1.5% 8.6% 6.1% -6.4% -2.3% -6.0% -12.9% -0.7% -1.2% 3.9% -9.4% NA -16.9% -2.7% 2.0% -11.0% NA -3.7% NA -9.6% -4.1% 96 Treatment F-IV G-I G-II G-III G-IV Plot 24 2 30 12 31 Table C.8 Continued MAE Node 1 (10cm) 0.0156 0.0167 0.0188 0.0594 0.0094 MAE Node 2 (20 cm) 0.0104 0.0075 0.0086 0.3088 0.0096 MAE Node 3 (30 cm) 0.0088 0.0068 0.0170 0.0356 0.0108 MAE Node 4 (100 cm) 0.0456 NA 0.0671 NA 0.0127 Percent Bias Node 1 [%] 3.9% -4.5% 5.2% 15.6% 0.0% Percent Bias Node 2 [%] -3.0% -1.7% -2.1% 7.6% 2.2% Percent Bias Node 3 [%] -2.5% -0.6% 4.4% 4.2% 1.9% Percent Bias Node 4 [%] -10.9% NA -17.3% NA -0.9% 97 Clay (%) 30.0 28.0 36.0 33.0 35.0 26.0 31.0 30.0 28.0 29.0 34.0 27.0 32.0 35.0 32.0 31.0 31.0 27.0 35.0 23.0 Plot A11III A14I A18IV A19II B09I B16IV B20II B33III C01I C07II C25IV C28III D04II D06III D13I D22IV E10I E15II E17IV E26III 5.2 5.2 5.1 5.1 5.1 5.3 5.1 5.1 5.4 5.3 5.1 5.2 5.2 5.1 5.3 5.4 5.1 5.1 5.0 5.4 pH (015cm) <2mm EC (015c m) <2m m 0.16 0.27 0.16 0.19 0.19 0.16 0.17 0.20 0.14 0.16 0.13 0.18 0.18 0.18 0.20 0.17 0.15 0.24 0.15 0.22 35.7 36.5 34.1 38.1 37.1 39.0 31.7 37.0 36.1 27.9 34.0 39.4 27.7 44.8 34.4 36.9 37.5 33.9 39.9 33.4 CEC (015cm) <2mm 62.0 67.2 58.0 62.3 58.9 59.3 64.2 56.7 55.1 60.5 49.0 60.2 63.6 58.3 69.5 48.6 64.8 52.8 50.1 61.7 SoilC (015cm) Mg/ha 2.654 1.965 5.887 4.400 2.924 5.183 2.523 5.468 1.930 5.963 5.927 5.522 1.429 3.446 1.701 5.647 6.620 5.474 5.468 2.474 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 2.07 11.6 27 26 11 2.72 12.0 27 26 11 2.00 12.6 27 26 11 2.50 12.8 27 26 11 1.97 12.4 25 31 15 2.50 13.7 25 31 15 2.33 13.3 25 31 15 2.50 12.8 25 31 15 1.18 12.3 33 21 6 3.13 12.4 33 21 6 1.50 12.4 33 21 6 2.00 12.5 33 21 6 1.80 12.3 25 23 10 3.00 13.0 25 23 10 2.50 13.7 25 23 10 1.65 13.3 25 23 10 3.00 13.5 21 14 4 3.50 14.2 21 14 4 3.00 14.5 21 14 4 2.00 14.4 21 14 4 Table C.9 Data for Statistical Analysis for Unique Soil Parameters for (0-15 cm) Soil Profile 251.67 239.24 246.19 200.57 186.02 251.11 260.54 229.44 189.58 215.24 258.38 247.55 265.07 246.73 258.76 265.36 256.54 267.25 261.76 277.81 Total Basal Liquid Flux [cm] 98 Clay (%) 29.0 23.0 28.0 33.0 23.0 36.0 27.0 34.0 Plot F05I F08III F24IV F32III G02I G12III G30II G31IV 5.4 5.4 5.1 5.3 5.2 5.1 5.4 5.0 pH (015cm) <2mm Table C.9 Continued EC (015c m) <2m m 0.17 0.21 0.18 0.19 0.24 0.14 0.26 0.18 31.8 37.9 31.9 40.5 40.8 40.8 35.4 30.2 CEC (015cm) <2mm 64.3 61.0 56.2 63.3 63.0 70.2 60.4 57.3 SoilC (015cm) Mg/ha 3.703 3.069 5.766 5.518 3.103 1.380 4.021 5.752 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 1.50 13.3 20 17 4 2.00 13.5 20 17 4 1.55 13.6 20 17 4 2.50 13.0 20 17 4 2.00 14.2 24 30 11 2.50 14.6 24 30 11 2.50 13.6 24 30 11 2.00 14.2 24 30 11 269.33 264.47 267.12 258.84 271.54 235.23 260.53 289.33 Total Basal Liquid Flux [cm] 99 Clay (%) 39.0 34.0 39.0 39.0 43.0 34.0 38.0 23.0 24.0 36.0 38.0 35.0 36.0 35.0 34.0 36.0 39.0 35.0 36.0 32.0 Plot A11III A14I A18IV A19II B09I B16IV B20II B33III C01I C07II C25IV C28III D04II D06III D13I D22IV E10I E15II E17IV E26III 5.1 5.2 5.0 4.9 5.0 5.1 5.2 5.0 5.2 5.2 4.9 5.0 5.0 5.1 5.2 5.1 5.0 5.0 4.9 5.4 pH (015cm) <2mm 0.08 0.11 0.08 0.08 0.11 0.09 0.07 0.10 0.08 0.09 0.08 0.08 0.09 0.09 0.08 0.07 0.11 0.11 0.08 0.09 EC (015cm) <2mm 28.9 33.6 33.0 37.8 35.9 40.4 30.2 39.0 36.0 30.0 35.8 41.3 29.0 44.5 34.3 37.7 35.6 35.5 41.8 32.2 CEC (015cm) <2mm 51.1 56.2 42.4 55.9 54.8 48.8 56.1 48.5 57.8 61.9 48.5 50.5 57.5 49.3 60.4 50.0 58.6 53.7 44.5 57.4 SoilC (015cm) Mg/ha 2.654 1.965 5.887 4.400 2.924 5.183 2.523 5.468 1.930 5.963 5.927 5.522 1.429 3.446 1.701 5.647 6.620 5.474 5.468 2.474 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 1.60 11.6 27 26 11 2.72 12.0 27 26 11 1.35 12.6 27 26 11 1.80 12.8 27 26 11 1.97 12.4 25 31 15 1.90 13.7 25 31 15 1.90 13.3 25 31 15 2.35 12.8 25 31 15 0.91 12.3 33 21 6 2.73 12.4 33 21 6 0.91 12.4 33 21 6 1.80 12.5 33 21 6 1.90 12.3 25 23 10 2.50 13.0 25 23 10 2.50 13.7 25 23 10 2.50 13.3 25 23 10 1.50 13.5 21 14 4 3.00 14.2 21 14 4 2.50 14.5 21 14 4 1.80 14.4 21 14 4 Table C.10 Data for Statistical Analysis for Unique Soil Parameters for (15-30 cm) Soil Profile 251.7 239.2 246.2 200.6 186.0 251.1 260.5 229.4 189.6 215.2 258.4 247.5 265.1 246.7 258.8 265.4 256.5 267.2 261.8 277.8 Total Basal Liquid Flux [cm] 100 Clay (%) 36.0 30.0 39.0 38.0 27.0 39.0 34.0 39.0 Plot F05I F08III F24IV F32II G02I G12III G30II G31IV 5.2 5.0 5.1 4.9 5.1 4.9 5.2 5.0 pH (015cm) <2mm Table C.10 Continued 0.09 0.09 0.08 0.11 0.09 0.07 0.11 0.09 EC (015cm) <2mm 32.2 39.8 33.9 39.6 38.5 35.6 33.9 31.7 CEC (015cm) <2mm 61.0 53.9 51.2 52.9 60.6 43.9 53.3 55.0 SoilC (015cm) Mg/ha 3.703 3.069 5.766 5.518 3.103 1.380 4.021 5.752 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 1.00 13.3 20 17 4 1.50 13.5 20 17 4 2.00 13.6 20 17 4 2.00 13.0 20 17 4 1.50 14.2 24 30 11 2.50 14.6 24 30 11 1.50 13.6 24 30 11 1.50 14.2 24 30 11 269.3 264.5 267.1 258.8 271.5 235.2 260.5 289.3 Total Basal Liquid Flux [cm] 101 Clay (%) 51.0 43.0 54.0 54.0 54.0 49.0 50.0 47.0 40.0 53.0 49.0 49.0 55.0 43.0 50.0 44.0 54.0 45.0 43.0 36.0 Plot A11III A14I A18IV A19II B09I B16IV B20II B33III C01I C07II C25IV C28III D04II D06III D13I D22IV E10I E15II E17IV E26III 4.8 5.0 4.8 4.9 4.9 4.9 5.0 4.8 4.9 5.0 4.9 4.8 5.0 4.8 4.9 4.8 4.8 5.0 4.8 5.0 pH (015cm) <2mm 0.02 0.03 0.04 0.03 0.03 0.02 0.03 0.04 0.04 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 EC (015cm) <2mm 47.3 34.6 44.9 55.6 51.6 46.1 36.8 47.3 37.5 30.9 41.7 48.1 27.6 56.1 41.1 40.2 41.0 38.2 50.4 40.0 CEC (015cm) <2mm 94.1 133.6 84.7 119.9 105.7 78.9 126.3 86.5 115.6 97.1 96.7 91.2 95.0 93.4 116.3 78.6 110.1 111.7 91.4 94.5 SoilC (015cm) Mg/ha 2.654 1.965 5.887 4.400 2.924 5.183 2.523 5.468 1.930 5.963 5.927 5.522 1.429 3.446 1.701 5.647 6.620 5.474 5.468 2.474 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 1.25 11.6 27 26 11 2.00 12.0 27 26 11 1.30 12.6 27 26 11 1.20 12.8 27 26 11 1.30 12.4 25 31 15 1.35 13.7 25 31 15 1.15 13.3 25 31 15 1.50 12.8 25 31 15 1.35 12.3 33 21 6 1.85 12.4 33 21 6 1.15 12.4 33 21 6 1.45 12.5 33 21 6 1.10 12.3 25 23 10 1.55 13.0 25 23 10 1.25 13.7 25 23 10 1.40 13.3 25 23 10 1.20 13.5 21 14 4 2.00 14.2 21 14 4 1.30 14.5 21 14 4 1.60 14.4 21 14 4 Table C.11 Data for Statistical Analysis for Unique Soil Parameters for (30-100 cm) Soil Profile 251.7 239.2 246.2 200.6 186.0 251.1 260.5 229.4 189.6 215.2 258.4 247.5 265.1 246.7 258.8 265.4 256.5 267.2 261.8 277.8 Total Basal Liquid Flux [cm] 102 Clay (%) 48.0 29.0 48.0 50.0 41.0 48.0 48.0 52.0 Plot F05I F08III F24IV F32II G02I G12III G30II G31IV 5.0 4.9 4.9 4.8 4.9 4.7 4.7 4.9 pH (015cm) <2mm Table C.11 Continued 0.03 0.03 0.03 0.03 0.03 0.02 0.03 0.03 EC (015cm) <2mm 36.2 58.7 39.3 50.3 52.0 52.5 53.4 36.1 CEC (015cm) <2mm 117.0 87.9 95.6 114.9 107.6 91.6 105.1 100.8 SoilC (015cm) Mg/ha 3.703 3.069 5.766 5.518 3.103 1.380 4.021 5.752 Mean Slope (%) Genus Genus Genus Saturat Avera of of of ed ge Bacteri Bacteria Bacteria Conduc Tempe a (Clostrid (Shewan tivity rature (Bacillu ium ella (cm/hr) (°C) s - 205) 185) 68) 1.00 13.3 20 17 4 1.20 13.5 20 17 4 1.20 13.6 20 17 4 1.20 13.0 20 17 4 1.40 14.2 24 30 11 1.40 14.6 24 30 11 1.20 13.6 24 30 11 0.85 14.2 24 30 11 269.3 264.5 267.1 258.8 271.5 235.2 260.5 289.3 Total Basal Liquid Flux [cm] 103 APPENDIX D GRAPHICAL SUMMARIES OF UNSAT-H SIMULATION RESULTS FOR TWO YEARS 105 Figure D.1 Treatment Plot A11III Volumetric Water Content with Precipitation for 2Year Simulation 106 Figure D.2 Treatment Plot A14I Volumetric Water Content with Precipitation for 2-Year Simulation 107 Figure D.3 Treatment Plot A18IV Volumetric Water Content with Precipitation for 2Year Simulation 108 Figure D.4 Treatment Plot A19II Volumetric Water Content with Precipitation for 2-Year Simulation 109 Figure D.5 Treatment Plot B09I Volumetric Water Content with Precipitation for 2-Year Simulation 110 Figure D.6 Treatment Plot B16IV Volumetric Water Content with Precipitation for 2Year Simulation 111 Figure D.7 Treatment Plot B20II Volumetric Water Content with Precipitation for 2-Year Simulation 112 Figure D.8 Treatment Plot B33III Volumetric Water Content with Precipitation for 2Year Simulation 113 Figure D.9 Treatment Plot C01I Volumetric Water Content with Precipitation for 2-Year Simulation 114 Figure D.10 Treatment Plot C07II Volumetric Water Content with Precipitation for 2Year Simulation 115 Figure D.11 Treatment Plot C25IV Volumetric Water Content with Precipitation for 2Year Simulation 116 Figure D.12 Treatment Plot C28III Volumetric Water Content with Precipitation for 2Year Simulation 117 Figure D.13 Treatment Plot D04II Volumetric Water Content with Precipitation for 2Year Simulation 118 Figure D.14 Treatment Plot D06III Volumetric Water Content with Precipitation for 2Year Simulation 119 Figure D.15 Treatment Plot D13I Volumetric Water Content with Precipitation for 2Year Simulation 120 Figure D.16 Treatment Plot D22IV Volumetric Water Content with Precipitation for 2Year Simulation 121 Figure D.17 Treatment Plot E10I Volumetric Water Content with Precipitation for 2-Year Simulation 122 Figure D.18 Treatment Plot E15II Volumetric Water Content with Precipitation for 2Year Simulation 123 Figure D.19 Treatment Plot E17IV Volumetric Water Content with Precipitation for 2Year Simulation 124 Figure D.20 Treatment Plot E26III Volumetric Water Content with Precipitation for 2Year Simulation 125 Figure D.21 Treatment Plot F05I Volumetric Water Content with Precipitation for 2-Year Simulation 126 Figure D.22 Treatment Plot F08III Volumetric Water Content with Precipitation for 2Year Simulation 127 Figure D.23 Treatment Plot F24IV Volumetric Water Content with Precipitation for 2Year Simulation 128 Figure D.24 Treatment Plot F32II Volumetric Water Content with Precipitation for 2Year Simulation 129 Figure D.25 Treatment Plot G02I Volumetric Water Content with Precipitation for 2Year Simulation 130 Figure D.26 Treatment Plot G12III Volumetric Water Content with Precipitation for 2Year Simulation 131 Figure D.27 Treatment Plot G30II Volumetric Water Content with Precipitation for 2Year Simulation 132 Figure D.28 Treatment Plot G31IV Volumetric Water Content with Precipitation for 2Year Simulation APPENDIX E GRAPHICAL SUMMARIES OF DATATRAC WATER CONTENT MEASUREMENTS FOR TWO YEARS 134 Figure E.1 DataTrac Plot A11III Volumetric Water Content with Precipitation for 2-Year Simulation 135 Figure E.2 DataTrac Plot A14I Volumetric Water Content with Precipitation for 2-Year Simulation 136 Figure E.3 DataTrac Plot A18IV Volumetric Water Content with Precipitation for 2-Year Simulation 137 Figure E.4 DataTrac Plot A19II Volumetric Water Content with Precipitation for 2-Year Simulation 138 Figure E.5 DataTrac Plot B09I Volumetric Water Content with Precipitation for 2-Year Simulation 139 Figure E.6 DataTrac Plot B16IV Volumetric Water Content with Precipitation for 2-Year Simulation 140 Figure E.7 DataTrac Plot B20II Volumetric Water Content with Precipitation for 2-Year Simulation 141 Figure E.8 DataTrac Plot B33III Volumetric Water Content with Precipitation for 2-Year Simulation 142 Figure E.9 DataTrac Plot C01I Volumetric Water Content with Precipitation for 2-Year Simulation 143 Figure E.10 DataTrac Plot C07II Volumetric Water Content with Precipitation for 2-Year Simulation 144 Figure E.11 DataTrac Plot C25IV Volumetric Water Content with Precipitation for 2Year Simulation 145 Figure E.12 DataTrac Plot C28III Volumetric Water Content with Precipitation for 2Year Simulation 146 Figure E.13 DataTrac Plot D04II Volumetric Water Content with Precipitation for 2-Year Simulation 147 Figure E.14 DataTrac Plot D06III Volumetric Water Content with Precipitation for 2Year Simulation 148 Figure E.15 DataTrac Plot D13I Volumetric Water Content with Precipitation for 2-Year Simulation 149 Figure E.16 DataTrac Plot D22IV Volumetric Water Content with Precipitation for 2Year Simulation 150 Figure E.17 DataTrac Plot E10I Volumetric Water Content with Precipitation for 2-Year Simulation 151 Figure E.18 DataTrac Plot E15II Volumetric Water Content with Precipitation for 2-Year Simulation 152 Figure E.19 DataTrac Plot E17IV Volumetric Water Content with Precipitation for 2Year Simulation 153 Figure E.20 DataTrac Plot E26III Volumetric Water Content with Precipitation for 2Year Simulation 154 Figure E.21 DataTrac Plot F05I Volumetric Water Content with Precipitation for 2-Year Simulation 155 Figure E.22 DataTrac Plot F08III Volumetric Water Content with Precipitation for 2Year Simulation 156 Figure E.23 DataTrac Plot F24IV Volumetric Water Content with Precipitation for 2Year Simulation 157 Figure E.24 DataTrac Plot F32II Volumetric Water Content with Precipitation for 2-Year Simulation 158 Figure E.25 DataTrac Plot G02I Volumetric Water Content with Precipitation for 2-Year Simulation 159 Figure E.26 DataTrac Plot G12III Volumetric Water Content with Precipitation for 2Year Simulation 160 Figure E.27 DataTrac Plot G30II Volumetric Water Content with Precipitation for 2-Year Simulation 161 Figure E.28 DataTrac Plot G31IV Volumetric Water Content with Precipitation for 2Year Simulation APPENDIX F SOIL COMPOSITION OF EACH TREATMENT TYPE BY SUBSURFACE DEPTH 163 Figure F.1 Soil Composition for Treatment Type A, 0-30 cm Depth Figure F.2 Soil Composition for Treatment Type A, 30-100 cm Depth 164 Figure F.3 Soil Composition for Treatment Type B, 0-30 cm Depth Figure F.4 Soil Composition for Treatment Type B, 30-100 cm Depth 165 Figure F.5 Soil Composition for Treatment Type C, 0-30 cm Depth Figure F.6 Soil Composition for Treatment Type C, 30-100 cm Depth 166 Figure F.7 Soil Composition for Treatment Type D, 0-30 cm Depth Figure F.8 Soil Composition for Treatment Type D, 30-100 cm Depth 167 Figure F.9 Soil Composition for Treatment Type E, 0-30 cm Depth Figure F.10 Soil Composition for Treatment Type E, 30-100 cm Depth 168 Figure F.11 Soil Composition for Treatment Type F, 0-30 cm Depth Figure F.12 Soil Composition for Treatment Type F, 30-100 cm Depth 169 Figure F.13 Soil Composition for Treatment Type G, 0-30 cm Depth Figure F.14 Soil Composition for Treatment Type G, 30-100 cm Depth REFERENCES Alavalapati, J.; Lal, P.; 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