FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICSIN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN1

Barbra C. Utley, George Vellidis, Richard Lowrance, and Matt C. Smith2

ABSTRACT: Sediment oxygen demand (SOD) is believed to be an important process affecting dissolved oxygen(DO) concentrations in blackwater streams of the southeastern coastal plain. Because very few data on SOD areavailable, it is common for modelers to take SOD values from the literature for use with DO models. In thisstudy, SOD was measured in seven blackwater streams of the Suwannee River Basin within the Georgia coastalplain for between August 2004 and April 2005. SOD was measured using four in situ chambers and was foundto vary on average between 0.1 and 2.3 g O2 ⁄ m ⁄ day across the seven study sites throughout the study period.SOD was found to vary significantly between the watersheds within the Suwannee River Basin. However, landuse was not found to be the driving force behind SOD values. Statistical analyses did find significant interactionbetween land use and watersheds suggesting that an intrinsically different factor in each of the watersheds maybe affecting SOD and the low DO concentrations. Further research is needed to identify the factors driving SODdynamics in the blackwater streams of Georgia’s coastal plain. Results from this study will be used by the Geor-gia Department of Natural Resources – Environmental Protection Division as model input data for the develop-ment and evaluation of DO total maximum daily loads in the Georgia coastal plain.

(KEY TERMS: sediment oxygen demand; dissolved oxygen; coastal plain; Georgia; blackwater streams;watersheds; water quality.)

Utley, Barbra C., George Vellidis, Richard Lowrance, and Matt C. Smith, 2008. Factors Affecting SedimentOxygen Demand Dynamics in Blackwater Streams of Georgia’s Coastal Plain. Journal of the American WaterResources Association (JAWRA) 44(3):742-753. DOI: 10.1111/j.1752-1688.2008.00202.x

INTRODUCTION

The tributaries of the main blackwater river sys-tems (Ochlockonee, Satilla, St. Mary’s, and Suwannee)in Georgia’s coastal plain (Figure 1) regularly violateGeorgia Department of Natural Resources – Environ-mental Protection Division (Georgia EPD) dissolved

oxygen (DO) standards. The blackwater rivers andstreams, named for the black color of their deep water,are tinted by organic acids leached from the swampson the tributary floodplains (Meyer, 1990; Dosskeyand Bertsch, 1994; Meyer et al., 1997). These riversystems are also characterized by low topographicgradients and from late spring to late autumn, byhigh temperatures and low flows. They are also

1Paper No. J06010 of the Journal of the American Water Resources Association (JAWRA). Received January 26, 2006; accepted January 4,2008. ª 2008 American Water Resources Association. No claim to original U.S. government works. Discussions are open until December 1,2008.

2Respectively, Doctoral Candidate, Biological Systems Engineering Department, Virginia Polytechnic Institute and State University,Blacksburg, Virginia 24061; Professor, Biological and Agricultural Engineering Department, University of Georgia, Tifton, Georgia 31793-0748; Ecologist, USDA-ARS Southeast Watershed Research Laboratory, Tifton, Georgia 31793; and Research Leader, USDA-ARS AnimalManure and By-Products Laboratory, ANRI, Beltsville, Maryland 20705 (E-Mail ⁄ Vellidis: yiorgos@uga.edu).

JAWRA 742 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Vol. 44, No. 3 AMERICAN WATER RESOURCES ASSOCIATION June 2008

characterized by low DO concentrations and lowconcentrations of suspended sediments (Meyer, 1990).

In many regions of the United States (U.S.), lowDO is a common freshwater impairment. Clearly, lowDO is not a pollutant. However, it is commonly pre-sumed that DO concentrations below the standardare associated with increased biological activityresulting from N (nitrogen) and P (phosphorus)enrichment. This increased biological activity is gen-erally excessive algal growth. When excessive algalblooms decay, DO is depleted due to the biochemicaloxygen demand (BOD) of the decomposition process.BOD exerts an oxygen demand in the water columnand contributes to biotic and abiotic oxygen demandin the sediments (Lee, 2003). As the organic matterdecays, aerobic bacteria deplete the available oxygenwithin the lower water column at a faster rate thanoxygen diffusion from surface waters (Rabalais,2002). Therefore, hypoxic conditions will remain ifoxygen consumption rates are greater than oxygenresupply.

All States, Territories, and Tribes of the U.S. arerequired to regularly assess water bodies within theirjurisdictions and develop total maximum daily loads(TMDLs) for waters not meeting established waterquality standards, including DO, in accordance with

Section 303(d) of the Clean Water Act and the USEnvironmental Protection Agency Water QualityPlanning and Management Regulations (40 CFR Part130). During 2003, 91% of all coastal plain streamsconsidered impaired in Georgia violated DO stan-dards. These streams were placed on the Georgia303(d) list.

However, recent research in Georgia and Louisianaindicates that low DO may be a natural condition forsummer months in coastal plain streams (Boschet al., 2002; Ice and Sugden, 2003; Vellidis et al.,2003). Without a good understanding of the ecologicalprocesses governing DO dynamics in coastal plainstreams, it is not possible to address the cause of lowDO. One of the key ecological processes affecting DOconcentrations is sediment oxygen demand (SOD),also known as benthic oxygen demand.

Sediment Oxygen Demand

Sediment oxygen demand is the rate at which DOis removed from the overlying water column by bio-chemical processes in the stream bed sediments(Hatcher, 1980). The sediments that make up thebenthic zone of the stream originate from naturalstream conditions, nonpoint source runoff, and waste-water effluents (Hatcher, 1980; Matlock et al., 2003).Significant rates of sediment oxygen uptake havebeen observed in rivers and estuaries that do notreceive large amounts of solids from point sources.SOD rates observed under these natural conditionsare due to soluble organic substances in the watercolumn, which are derived from naturally occurringsediments containing aquatic plants and animals aswell as detritus discharged into the water body fromnatural runoff (Truax et al., 1995).

Several factors affect SOD rate. Primary focus isoften given to the biological components such asorganic content of the benthic sediment and microbialconcentrations. Three of the most important para-meters affecting SOD, as described in the literature,are temperature near the sediment-water interface,stream depth (Ziadat and Berdanier, 2004), and theoverlying water velocity (Truax et al., 1995). Specifi-cally, SOD increases linearly with velocity at lowvelocities (<10 cm ⁄ s) but becomes independent athigh velocities (Makenthun and Stefan, 1998). Ziadatand Berdanier (2004) found that depth was the mostimportant hydrologic variable effecting SOD in RapidCreek, South Dakota. The base SOD rate changesthroughout the year due to multiple factors including:DO concentration in the water column, seasonalbenthic population changes, mixing rate of the over-lying water, presence of toxic chemicals, and changesin temperature.

FIGURE 1. Map Showing the Location of the Study Sites Withinthe Suwannee River Basin. Watershed 1 is an agricultural studysite while Watersheds 2 and 3 are forested study sites within theAlapaha River 8-digit HUC. Watershed 4 is the agricultural siteand Watershed 5 is the forested site within the Little River HUC.Watersheds 6 and 7 are the forested study sites within the UpperSuwannee River HUC.

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 743 JAWRA

Sediment oxygen demand values can be measuredthrough laboratory or in situ methods, both of whichare expensive but may produce accurate rate mea-surements (Hatcher, 1980). In situ techniques havebeen found to be more accurate in general than labo-ratory respirometers (Whittemore, 1986). In situchambers measure either the drop in DO concentra-tion over time (batch method) or the difference in DOconcentration in the inflow and outflow (continuousmethod) (Lee et al., 2000).

The most common method for measuring SOD isthe utilization of a batch reactor that encloses a givenamount of sediment with a known volume of waterand measures oxygen depletion over time (Truaxet al., 1995). Measuring SOD in the field requirescontrolling or understanding many alternate vari-ables. For example, increasing the sediment surfacearea allows for the integration of microhabitat patchi-ness, while decreasing the height of the chamberallows for greater sensitivity to low metabolic rates(Boynton et al., 1981).

Also, a sufficient water column should be presentover the sediment to allow the re-establishment ofsteady state conditions (Truax et al., 1995). A sche-matic of an in situ chamber deployed in a streamchannel is shown in Figure 2. Once the oxygen deple-tion data are collected over a given retention time,the SOD rate is calculated based on the area of sedi-ment enclosed, the volume of water contained in thechamber, and the rate of oxygen uptake (Whittemore,1986).

In general, field measurements minimize themanipulation of the sediment and more accuratelyreflect ambient conditions than do laboratory methods.

However, field measurements are conducted undermore dynamic ambient conditions that impact theaccuracy of the tests. Also, field measurements do notguarantee undisturbed sediments as it is very difficultnot to disturb sediments while deploying the chamberswhether this is done via diving or wading into thestream. Currently, there is not a universally acceptedmethod for measuring SOD in the field makingcomparisons of data difficult (Chau, 2002).

Research conducted on streams has found thatbenthic organic carbon is inversely proportional tothe mean annual stream temperature, and benthicrespiration is directly proportional to temperature(Sinsabaugh, 1997). Maximum respiration rates, for ablackwater stream in southern Georgia, have beenrecorded during the summer and early autumn, whilethe minimums were measured during winter andearly spring (Meyer et al., 1997). There was no statis-tically significant correlation between measured SODand sediment characteristics in the Williamette Riverin Oregon (Caldwell and Doyle, 1995). Hill et al.(2002) measured benthic microbial respiration of0.40 ± 0.05 g O2 ⁄ m2 ⁄ day in coastal plain streams and0.40 ± 0.06 g O2 ⁄ m2 ⁄ day in piedmont steams. SODvalues measured in stream systems vary greatly(Table 1), and the variability can be enhanced bypoint source pollution. For example, Truax et al.(1995) reported values from 0.1 to 33 g O2 ⁄ m2 ⁄ dayhave been measured downstream from paper mills inthe southeastern U.S. As seen in Table 1, there is alarge range of SOD values measured in the U.S. Thevalues in the literature vary based on location and acombination of physical parameters. However for aspecific type of stream and sediment combination, forexample blackwater streams with sandy beds, veryfew data are available.

Extensive environmental monitoring is difficultand expensive; therefore, mathematical modeling isfrequently used to simulate natural systems andmake regulatory decisions. DO models require manyparameters including temperature, depth, velocity,BOD, chemical oxygen demand, and SOD (Chaudhuryet al., 1997; Vellidis et al., 2006). Models that do not

FIGURE 2. Schematic of a Deployed SOD Chamber.Diffusers on either side of the chamber promote watercirculation through the annular shape of the chamber.

TABLE 1. Reported SOD Values in the United States.

Region SOD (gO2 ⁄ m2 ⁄ d) Reference

Eastern U.S. 0.15 ± 0.04 Truax et al., 1995South Eastern U.S. 0.55 ± 0.22 Truax et al., 1995Oregon 1.3-4.1 Caldwell and Doyle, 1995Arkansas 0.15-1.36 Matlock et al., 2003Missouri 1.2-2.0 Borsuk et al., 2001South Dakota* 3.80-6.98 Ziadat and Berdanier, 2004

Notes: SOD, sediment oxygen demand.*A control chamber was not used in this study to remove water col-umn respiration from the SOD.

UTLEY, VELLIDIS, LOWRANCE, AND SMITH

JAWRA 744 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

adequately predict SOD can seriously misrepresentthe DO dynamics within the stream. For example,Matlock et al. (2003) found SOD to be responsible for50% of the total oxygen depletion within the streamadding weight to the idea that SOD is one of the mostimportant parameters within a DO model. A model-ing study conducted on the Little River ExperimentalWatershed (LREW) in Georgia’s coastal plain (as acompanion to this study) found that estimating SODas the remaining oxygen demand in the model,assuming all other parameters were known and mea-sured correctly, can seriously misrepresent the trueSOD being exerted on they system (Cathey, 2005;Cathey et al., 2005).

Objectives

This study is part of an on going project to deter-mine the natural range of DO concentrations in Geor-gia’s coastal plain streams. Our objective was toobtain a better understanding of SOD dynamics inblackwater streams draining a range of different landuse types from relatively undisturbed forest ⁄ wetlandwatersheds to highly disturbed agricultural sites. Wehypothesized that SOD rates would vary across dif-ferent land uses. For example, we expected that for-ested watersheds would have higher SOD rates thanagricultural watersheds due to higher rates of allo-chthonous organic matter.

MATERIALS AND METHODS

Site Selection

Within Georgia, the Suwannee River Basin containsfour 8-digit HUCs (United States Geological SurveyHydrologic Unit Code): the Withlocoochee, Little,Alapaha, and Upper Suwannee (Figure 1). Twentypotential study sites were identified within theseHUCs. Of these twenty, seven sites were selected forthe study based on the procedures described below.Three sites were located within the Alapaha RiverHUC, two within the Little River HUC, and two withinthe Upper Suwannee River HUC (Figure 1). Two ofthe seven study sites were selected to be in watershedswhere 50% or more of the land use is agriculture. Theother five study sites were selected to be in watershedsthat have greater than 50% forested land use.

Land use was determined at the 12-digit HUCscale using ARCView� (ESRI, Redlands, CA) and1998 land use data collected by the Georgia Depart-ment of Natural Resources. Potential watersheds

between 3000 and 7000 ha were delineated using AV-SWAT 2000 extension (Blackland Research Center,Texas A&M University, Temple, TX) for ARCView�

GIS. Watersheds of this size were chosen so thatstreams would be perennial while main river chan-nels would be avoided. Streams of this size are acces-sible throughout the year except directly after astorm event. Twenty potential watersheds were iden-tified from this first selection. The outlet of thesewatersheds was then identified as a potential SODsite for SOD measurements. Each potential site wasvisited and evaluated based on its suitability for SODmeasurements. Suitability was determined by acces-sibility to the stream channel, bed material, anddepth. For example, outcroppings of bedrock or treeroots close to the sediment surface prevent the cham-ber from properly sealing and would thus make thesite unsuitable for SOD measurements. Finally, sevenwatersheds ⁄ measurement sites were selected. Allsampling sites had an established forested riparianbuffer on both sides of the stream.

Water Quality Measurements

Water temperature, pH, turbidity, oxygen reduc-tion potential (ORP), and DO were measured withYSI� (Yellow Springs, OH) model 6820 and 6920water quality sondes. Data were recorded at five-min-ute intervals with a YSI� handheld microcomputer(models: 650, 610-D, and 610-DM) connected to thesondes. The sondes contained the following probes:6562 DO probe, 6561 pH probe, 6565 pH ⁄ ORP probe,6560 conductivity ⁄ temperature probe, and either a6036 (non-wiping) or a 6026 (wiping) turbidity probe.Ambient stream conditions for all parameters listedabove were recorded at the beginning of each test.While the SOD chamber experiments were in use,stream cross-section, depth, and velocity were mea-sured to calculate an average volumetric flow-rateduring the test. Volumetric flow-rate was calculatedusing the rectangular method of integration. Flowvelocity was measured with a Marsh-McBirney�, Inc.portable water flowmeter, model 2000. All equipmentwas calibrated regularly per manufacturer specifica-tions.

Sediment Oxygen Demand Chambers

The SOD chambers used in the study were designedby Murphy and Hicks (1986) and were on loan fromGeorgia EPD. Two to three chambers were used ateach site to measure oxygen depletion in the sedimentmatrix while a fourth chamber was used as a controland measured oxygen depletion in the water column.

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 745 JAWRA

All chambers remained in the stream for three hours.Throughout the study, the SOD chambers weredeployed between 11:00 and 12:00 and were removedfrom the stream between 14:00 and 15:00.

The aluminum chambers had a volume of 65.15 land covered a surface area of 0.27 m2 on the streambottom. They consisted of three pieces – the ring ormain body of the chamber, the cutting edge, and atop or lid (Figures 3 and 4). The cutting edge and lidbolted to the ring. The ring was 18 cm tall and hadan inner radius of 46 cm. It also had 5 cm outward-facing flanges at the top and bottom. The lid and cut-ting edges were bolted to these flanges. The cuttingedge was 5 cm long and had a matching outward-facing flange at the top and a sharpened edge at thebottom. Once bolted to the ring, the cutting edge wasused to facilitate pushing the ring into the benthicsediments. Good installation required the cuttingedge flange to be pushed up against the benthic sedi-ments which meant that the cutting edge was buriedat least 5 cm deep in the sediments (Figures 2 and4). This prevented water from leaking into or out ofthe chambers through the sediments. The lid had a22.5 cm long, 13 cm diameter pipe welded to its bot-tom side (Figure 4). When the lid was bolted to thering, this pipe extended into the bottom sedimentsand thus created an annulus within the chamber.

Water in the chamber was circulated around theannulus by a 12 V DC submersible pump (March,893-04), powered by a submersible, gel-cell, lead acidbattery. The pump continuously withdrew water froman intake port installed in the side of the pipe at thecenter of the annulus (Figures 2 and 4) and injectedthe water back into the chamber via the two diffuserslocated 180� apart within the annulus (Figures 2 and

4). This configuration forced the water within thechamber to circulate around the chamber annulus,thus promoting continuous mixing and also some-what simulating streamflow. We were not able to suc-cessfully measure flow velocity within the chamber atthe time of the study.

Between the pump and the diffusers, the waterpassed through a plastic cup covering the probesattached to the YSI� sonde. Thus, the YSI� sondewas able to continuously monitor DO concentrationin the chamber as well as several other parameters.The YSI� sonde itself was strapped to the top of thechamber lid. The control chamber differed from theSOD chambers because the bottom of the chamberwas sealed off from the sediment, therefore only theoxygen depletion due to the water column (BOD) wasmeasured over the course of each deployment.

Steps for Deploying SOD Chambers

One day prior to each field test, all YSI� sondeswere inspected and calibrated. At the site, the depthof the stream was measured to ensure that it waswithin our operational parameters. Streams had tobe at least 30 cm deep to ensure that the chamberswere completely submerged but no deeper thanapproximately 70 cm to allow us to install the cham-bers and the associated equipment without diving.While checking the depth of the stream, the sedimentwas also checked for bedrock or large quantities oftree roots that could prevent the chamber from seal-ing completely. Bedrock was not a problem at any of

FIGURE 3. Photograph of an SOD Chamber Usedin This Study Showing the Main Componentsof the System Used to Measure SOD Rates.

FIGURE 4. Photograph of the Underside of an SOD ChamberShowing the Two Diffusers, the Lower Flange and Cutting EdgeUsed to Ensure Proper Installation, and the Pipe Welded to theInside of the Lid, Which Converts the Inside of the Chamber Intoan Annulus.

UTLEY, VELLIDIS, LOWRANCE, AND SMITH

JAWRA 746 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

the study sites; however, tree roots and other largewoody debris were a problem at the study sites in theLittle River and Upper Suwannee HUCs. Next,the chamber rings were placed in the stream, and thebottom of each chamber was checked to make surethe flange was flush with the stream sediment. Itwas important to minimize sediment re-suspension,especially under low flow conditions, while examiningthe stream and deploying the chambers.

The stream’s current was allowed to wash any re-suspended sediments downstream from the chambersbefore the chambers were covered and sealed. Thecontrol chamber was installed upstream from theother chambers to minimize the amount of disturbedsediments in suspension around it. Because it was acontrol, it was important to avoid any sediment depo-sition in the chamber before it was sealed – the con-trol chamber’s purpose is to measure the oxygendemand of the water column immediately above thesediments. Any re-suspended sediment settling intothe bottom of the control chamber will erroneouslyincrease the measured water column oxygen demand.After the batteries were attached and the pumpswere running, the YSI� sondes were attached to thechambers. The YSI� sondes were programmed to runfor three hours and record data every five minutes.

The study sites were visited two to four times dur-ing the study period from July, 2004 to April, 2005.However, we were not able to visit the sites duringAugust and September 2004 and again in March andearly April 2005 due to high water levels. Betweenthe months of October and March all sites were vis-ited monthly, and measurements were attempted atall sites before the next rotation began. Also, depend-ing on the condition of the equipment, either two orthree SOD chambers were deployed each time. Thecontrol chamber, however, was deployed during eachtest.

Calculation of Sediment Oxygen Demand

Sediment oxygen demand is derived from the slopeof the linear section of a measured oxygen depletioncurve (Figure 5). The small non-linear section at thebeginning of the curve is disregarded when complet-ing the linear regression of the data. This region cor-responds to initial re-suspension of the sedimentduring deployment of the chambers and is not anaccurate measurement of the natural rate (Caldwelland Doyle, 1995). On average, this region includedthe first 10-30 minutes of the test. The simple linearregression was completed to get the largest correla-tion coefficient (R2 value) possible, which was nor-mally above 0.9 (Figure 5). SOD was calculated using(Murphy and Hicks, 1986; Truax et al., 1995).

SOD ¼ 1:44V

Aðb1 � b2Þ; ð1Þ

where SOD is the sediment oxygen demand in gO2 ⁄ m2 ⁄ day; b1 is the slope from the oxygen depletioncurve in mg ⁄ l ⁄ minute; b2 is the slope from the oxygendepletion curve of the control chamber in mg ⁄ l ⁄ min-ute; V is the volume of the chamber in l; A is the areaof bottom sediment covered by the chamber in m2,and 1.44 is the a units conversion constant (Caldwelland Doyle, 1995).

Once SOD is calculated, it is temperature correctedto 20�C using a modified van’t Hoff form of the Arreh-nius equation (Equation 2) and an appropriate litera-ture value for the constant h (Hatcher, 1986; Truaxet al., 1995). Values for h are given by Bowie et al.(1985) but the value most commonly used is 1.047.This is the same value used when DO models areapplied in Georgia and was the value selected for thisstudy.

SODr ¼ SOD20hT�20 ð2Þ

Sediment Analysis

A particle size distribution analysis was completedon sediment cores collected from each experimentalsites. Five cm diameter cores were collected from thetop 13 cm of the sediment. Samples were collectedaround each of the SOD chambers after deployment,and mixed before being stored in a cooler. Sampleswere refrigerated after returning to the laboratoryuntil the analysis could be completed. The hydrome-ter procedure for particle-size analysis as describedby Gee and Bauder (1986) was used. Organic matter

FIGURE 5. An Oxygen Depletion Curve Collected Fromthe Alapaha River Agricultural Study Site on 5 October 2004.A trend line was fitted to the linear portion of the depletion

curve and its slope used to calculate SOD rate with Equation (1).

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 747 JAWRA

in each sample was destroyed using hydrogen perox-ide; the percentage organic matter was calculated bythe difference in the dried sample before and afterthe hydrogen peroxide treatment. All large debris(sticks) was removed from the sample before masswas recorded and the procedure begun.

Statistical Analysis

Sediment oxygen demand levels in the AlapahaRiver, Little River, and Upper Suwannee River 8-digit HUCs with forested or agricultural land usewere tested with an analysis of variance (Proc Mixedand Proc GLM; SAS Institute Inc., 1990). All testscompleted in SAS met the requirements of a normaldistribution and equal variances. To compare agricul-tural and forested watersheds, the data were logtransformed so that data from the agricultural water-sheds would meet the assumption of a normal distri-bution. For the Proc GLM tests, land use and thethree 8-digit HUCs were compared. For the ProcMixed test, HUC and land use areas were treated asfixed effects and sample date was treated as a ran-dom effect. Degrees of freedom were adjusted using

the Satterthwaite approximation method. Meanswere separated using Tukey-Kramer mean separationprocedures.

Two separate statistical tests were performed onthe data in SAS. The Proc GLM analysis assumesbalanced experimental design and very little random-ness, which does not properly describe the project’sexperimental design. Some examples of imbalancewithin the design are: the sites were not all visitedthe same number of times, there are only two agricul-tural sites while there are five forested site, environ-mental factors such as water flow and sedimentcomposition changed throughout the study period. Asa result, a mixed model analysis was also run to lookat the interaction between land use and 8-digit HUCand account for the some of the randomness.

RESULTS

Temperature-corrected SOD rates varied on aver-age between 0.1 and 2.3 g O2 ⁄ m2 ⁄ day for the sevenstudy sites. Table 2 shows a typical dataset created

TABLE 2. Results From the Agricultural Site in the Alapaha River 8-Digit HUC.

Date Measurement

Deployment Average* DO

SODc**(g O2 ⁄ m2 ⁄ day) Q (m3 ⁄ s)Temp (�C) DO (mg ⁄ l) Cond (mS ⁄ cm) Turb (NTU)

Initial Final

(mg ⁄ l)

5Oct04 Initial conditions - - - - - - -Control 21.2 6.3 0.1 5.6 5.7 5.0 0.4Chamber 1 21.3 5.5 0.2 20.7 5.2 4.4 0.6Chamber 2 21.3 6.2 0.1 51.6 4.2 4.1 0.5Chamber 3 21.3 6.7 0.1 **** 5.8 3.8 1.4Average SOD - - - - - - 0.8

8Dec04 Initial conditions 17.3 7.5 0.1 **** 6.6 - 0.1Control 17.6 6.3 0.1 **** 6.1 6.0 0.1Chamber 1 17.6 5.7 1.0 94.4 10.1 3.9 2.8Chamber 2 17.5 6.2 0.1 142.5 7.2 6.1 0.8Average SOD - - - - - - 1.8

4Feb05 Initial conditions 6.8 8.0 0.1 2.7 11.0 - 0.3Control 7.4 6.5 0.1 11.0 10.8 10.5 0.1Chamber 1 7.5 6.3 0.1 16.1 12.0 13.5 1.8Chamber 2 7.8 5.6 0.3 118.9 10.8 8.5 4.5Average SOD - - - - - - 3.1

15Apr05 Initial conditions 14.7 8.1 0.1 **** 9.1 - - 0.2Control 15.4 6.5 0.1 **** 8.7 8.3 0.2Chamber 1 15.9 5.7 1.0 0.1 7.2 8.0 1.4Chamber 2 15.4 6.3 0.1 15.8 )5.1 )4.9 ***Average SOD - - - - - - 1.4

Notes: SOD, sediment oxygen demand; HUC, Hydrologic Unit Code; DO, dissolved oxygen.*Ambient stream values were taken upon arrival at the study site and do not represent averages.

**Temperature corrected SOD = SODc.***Appears where SOD could not be calculated due to equipment errors.

****Appears where turbidity could not be measured due to equipment errors.

UTLEY, VELLIDIS, LOWRANCE, AND SMITH

JAWRA 748 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

for each of the sites visited during the study. At thisparticular site, the chambers were deployed fourtimes. Reported temperature, pH, conductivity, andturbidity are the averages of data recorded in thechamber at 5-minute intervals during each deploy-ment. The table also reports the initial and final DOmeasured in the chamber during deployment. All ofthe above parameters were measured with the YSI�

sondes. SODc reported in Table 2 is the temperaturecorrected rate (corrected to 20�C) calculated fromindividual chamber measurements.

The minimum recorded SODc across the 8-digitHUCs ranged from )0.9 to 1.6 g O2 ⁄ m2 ⁄ day, the max-imum recorded SODc ranged from 0.3 to 3.9 gO2 ⁄ m2 ⁄ day, and the average ranged from 0.1 to 2.3 gO2 ⁄ m2 ⁄ day (Table 3). The average SODc for each siteand the average SODc for each land use are shown in

Figure 6 along with error bars. Within the resultslisted in Table 3 are negative SOD rates measured atthe two Alapaha River forested sites. The negativevalues are due to a greater rate of oxygen depletionin the control chamber than in the SOD chambers.Theoretically, a negative SOD value is possible andrepresents the movement of oxygen from the sedi-ments into the water column. If the chambers are notproperly sealed at the cutting edge flange, this mayoccur. However, we took great care to ensure that aproper seal existed so this explanation is unlikely.Another possible explanation is that instrument(sonde) drift in combination with small changes inDO concentrations within the chamber over thethree-hour deployment period resulted in zero or neg-ative values. We have occasionally observed drift withthis instrumentation during previous studies.

Statistical analyses found no significant differ-ences between SOD rates at forested and agricul-tural sites over the entire Suwannee River Basin(p = 0.214 for the Proc GLM and p = 0.5391 for themixed test) – a surprising outcome, which con-tradicted our hypothesis that forested watershedsshould have higher SOD rates. Using Proc GLMand comparing HUCs without regard to land usetypes, the Alapaha River SOD rates were found tobe significantly lower than the Upper Suwannee(p = 0.0216) (Figure 7). When just the forestedwatersheds were compared, the Alapaha River for-ested watersheds’ SOD rates were significantlylower than both the Little River (p = 0.0451) andUpper Suwannee forested watershed rates(p = 0.0014) (Figure 8). The interaction analysisusing Proc Mixed Analysis found that there was asignificant interaction between HUC and land use(p = 0.0203). However, there appeared to be somestatistical strength in the interaction between landuse and HUC. In other words, there is somethingintrinsically different in each of the HUCs that

TABLE 3. Mean Temperature Corrected SOD Rates (to 20�C)Measured at Each Site and the Number of Visits Per Site.

Site n

Mean SODc (g O2 ⁄ m2 ⁄ day)

Min* Max Overall Mean

Alapaha River AG 4 0.8 3.1 1.7Alapaha River FR1 4 )0.9 1.6 0.6Alapaha River FR2 2 )0.2 0.3 0.1Little River AG 4 0.6 1.4 1Little River FR 3 0.9 2.5 1.8Up. Suwannee FR1 4 1.6 3.9 2.3Up. Suwannee FR2 3 1.4 2.7 2

Notes: SOD, sediment oxygen demand.The Min in this table represents the minimum mean SOD ratemeasured at each site. Similarly, Max represents the maximummean SOD rate measured at each site.

*During each site visit, an SOD rate is calculated for each of thechambers deployed. A mean SOD rate is subsequently calculatedfor each site visit.

FIGURE 6. Average SOD Values and Error Bars for Each StudySite and Land Use. ARag is the Alapaha River agriculturalwatershed. LRag is the Little River agricultural watershed. ARfr1

and ARfr2 are the Alapaha River forested watersheds. LRfr is theLittle River forested watershed. USfr1 and USfr2 are the UpperSuwannee forested watersheds. AGavg and FORavg are means ofall the individual SOD rates measured for the respective land uses.

FIGURE 7. Statistical Comparison of HUC SOD Rates in WhichAlapaha SOD Rates Were Significantly Lower Than Upper Suwan-nee SOD Rates. Letters indicate statistically significant differences.

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 749 JAWRA

could be affecting SOD and the low concentrationof DO within the blackwater streams. This was bestrepresented within the statistical models as aninteraction term between land use and HUC.

SOD vs. Organic Matter and Percentage Sand

The results from the particle size analysis areshown in Figure 9. A statistical analysis was run to

see if there was a significant correlation between per-centage sand, percentage organic matter, and temper-ature corrected SOD values. Even though trendsindicated that, as expected, SOD increased withincreasing organic matter and decreased withincreasing percentage sand content, no statistical sig-nificance was found for SOD rate and percentagesand content (p = 0.2115) or SOD rate and percent-age organic matter (p = 0.0623).

Although there was no significant correlationfound between percentage sand content and SODrate or organic matter and SOD rate, the resultswere marginally non-significant at the p = 0.05 level.It is possible that with a larger sample size the rela-tionship might be stronger. A graphical comparisonof sand content to SOD (Figure 10) shows that thetwo data points from the Upper Suwannee River didnot follow the same trend as the remaining data.When the two points from the Upper SuwanneeRiver are removed, there appears to be an inverserelationship between SOD and percentage sand.Furthermore, the correlation coefficient for organicmatter to SOD was 73.04; therefore, with a largersample size, this trend may have been significant.However, other studies have found no statisticallysignificant correlations between SOD rates and sedi-ment characteristics (Seiki et al., 1994; Caldwell andDoyle, 1995).

FIGURE 8. Statistical Comparison of Forested Watershed SODRates Across 8-Digit HUCs. Alapaha River forested watershed SODrates were significantly lower than either Little River or UpperSuwannee forested watershed SOD rates. Letters indicate statisti-cally significant differences.

FIGURE 9. Results From the Particle Size Analysis for Each Study Site. ARag is the Alapaha River agriculturalwatershed. LRag is the Little River agricultural watershed. ARfr1 and ARfr2 are the Alapaha River forested water-sheds. LRfr is the Little River forested watershed. USfr1 and USfr2 are the Upper Suwannee forested watersheds.

UTLEY, VELLIDIS, LOWRANCE, AND SMITH

JAWRA 750 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

DISCUSSION

Results from this study showed some unexpectedtrends. For example, it was expected that on average,agricultural watersheds would have lower SOD val-ues than forested watersheds due to lower rates ofallochthonous organic matter. However, we foundthat in the Alapaha River watershed, the agriculturalsites had higher SOD rates than the forested sites.This may be a consequence of higher levels of nutri-ents, erosion from farming operations, and legacyeffects after decades of anthropogenic interference. Incontrast, forested sites produced higher rates in theLittle River watershed. The Upper Suwannee, awatershed where the majority of the land is denselyforested, had the highest average SOD values.

Organic matter was found to be less than 2% ofbenthic sediments for all experimental sites and neg-ligible at many sites. However, it is possible that aseries of hurricanes that passed through our studyarea during the autumn of 2004 may have flushedmuch of the benthic organic matter from the tributar-ies we were studying. In this study, coarse organicdebris was removed from the sample before the per-oxide treatment of the sediment sample. Therefore,debris such as small sticks or leaves was not includedin the mass and percentage organic matter calcula-tions and may have contributed to low calculated val-ues. This coarse debris may have been contributingto SOD through the mineralization process; howeverit is more likely that the organic debris was of signifi-cance to SOD by supplying a food source to microbesin the sediment. Their respiration would be the larg-est addition to the cumulative SOD.

The highest SOD rates and organic matterconcentrations were measured near the Okefenokee

Swamp – an area dominated by dense forests, fre-quently flooded land, and swamps. These featurescould be the driving forces for higher SOD rates inthe Upper Suwannee River HUC and provide furtherevidence that streams in forested watersheds withfewer anthropogenic impacts may in fact have higherSOD rates.

Comparisons to Values From Literature

Literature values for SOD vary greatly betweentypes of water systems, for example marine, estua-rine, and freshwater systems. SOD also varies spa-tially, for example Eastern U.S. Rivers vary between0.11 and 0.19 g O2 ⁄ m2 ⁄ day while Southeastern U.S.rivers range between 0.33 and 0.77 g O2 ⁄ m2 ⁄ day(Truax et al., 1995). SOD values have also beenreported by sediment type. For instance, sandy bot-toms range between 0.2 and 1.0 g O2 ⁄ m2 ⁄ day whilemineral soils range between 0.05 and 0.1 gO2 ⁄ m2 ⁄ day (Bowie et al., 1985). The values from thisstudy averaged between 0.3 and 2.3 g O2 ⁄ m2 ⁄ day.Considering that the bottom sediments from all of thesites in the study were found to contain greater than70% sand, the average SOD rates from four of ourstudy sites are considerably above the reported rangeof SOD rates for sandy bottoms. Five of the sites ana-lyzed in this study also exceed the range listed forSoutheastern U.S. Rivers of 0.33 and 0.77 gO2 ⁄ m2 ⁄ day (Truax et al., 1995). The average SODvalue per land use exceeds the range listed for South-eastern U.S. Rivers.

Uncertainty in Values

Sediment oxygen demand values recorded through-out the study only represent brief snap shots of theconditions within a stream reach. However, at thepresent time these values are the only SOD measure-ments available for Georgia’s coastal plain. Usingthese values and the relationships found between theHUCs will allow the State to begin looking at theimportance of SOD within blackwater streams. Nev-ertheless, it will be beneficial to continue measuringSOD within the coastal plain and to increase thenumber of study sites within the Suwannee RiverBasin to increase the accuracy of extrapolating fromstudy site to stream reach and on to HUC. Although,the values recorded only ranged from 0.1 to 2.3 gO2 ⁄ m2 ⁄ day and are well below SOD values consideredhigh, these values do exceed ranges currently listedfor sandy sediments and streams ⁄ rivers within thesoutheastern U.S. It is not clear if the measuredrates could be responsible for the depletion of DO

FIGURE 10. Average SOD vs. Sand Content. The circledUpper Suwannee River data points have the highest SODvalues and the highest organic matter content. A trend ispresent if the circled points are removed from the dataset.

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 751 JAWRA

during summer. This is particularly compellingbecause even though the SOD rates we report havebeen temperature-corrected, the data were not col-lected during the summer months.

Comparison of Field Values to Modeled Values for theCoastal Plain

Cathey (2005) and Cathey et al. (2005) modeledlow DO levels within Georgia’s coastal plain, specifi-cally within the LREW – a 334 km2 watershed con-tained within the Little River HUC studied in thisproject. The Cathey (2005) study used experimentaldata to calibrate and validate the Georgia DOSagmodel – a steady state, one-dimensional, advection-dispersion, mass-transport, deterministic modelwhich is used by Georgia EPD for DO TMDL develop-ment. Most parameters within the model were deter-mined from the data collected within the LREW overthe last 20 years. However, SOD and reaeration ⁄ tur-bulence data were not available. Standard equationswere used to represent the processes of reaerationand turbulence. Therefore, SOD was left as the equil-ibrating factor for the model and was modified to cali-brate the model. This required a SOD value ofaround 6 g O2 ⁄ m2 ⁄ day. During sensitivity analysis,SOD was found to be the most sensitive parameterwithin the model. However, SOD data from the studyreported here indicate that SOD values may be muchlower than 6 g O2 ⁄ m2 ⁄ day. This creates an interest-ing problem for calibrating the model, as it requiresan additional sink for DO or redistribution of theexcess uptake.

CONCLUSIONS AND RECOMMENDATIONS

Dissolved oxygen rates in coastal plain streamshave been documented to be well below the GeorgiaDO standards during the summer months. We mea-sured SOD from the fall of 2004 through the springof 2005; however, measurements were not taken dur-ing the warmest time of year from late July throughlate August. Nevertheless, because the measuredSOD rates are higher than those reported in the liter-ature for similar types of streams, it is possible thatSOD plays a key role in depleting DO concentrationduring periods of high temperatures and low flow inthe blackwater streams of the coastal plain.

Sediment oxygen demand rates should continue tobe measured to build up a database of year roundinformation to be applied to TMDL development andother regulatory actions. When and if funds are avail-

able, it would be beneficial to study SOD on the mainriver channels. However, a dive-certified team wouldbe required to deploy the chambers most of the time,especially during high flow periods. As the need forquality SOD data continues to grow, we believe ameasurement technique that can be left in situ forlong periods of time (months to year round) withoutconstant maintenance should be developed. No mat-ter how much care was taken when deploying thechambers at the study sites, the sediment was greatlydisturbed. Developing a long term, in situ measure-ment system could open an important window intounderstanding the dynamics of SOD and how itshould be modeled.

Selecting SOD Values for Models

At the beginning of this study, we hypothesizedthat associating land use to measured SOD ratescould help modelers choose more accurate SOD val-ues. Although there was no significant differencebetween agricultural and forested watersheds withour current sample size, there were significant differ-ences between HUCs. The finding that environmentalparameters are watershed-specific has been reportedby other studies in the coastal plain of Georgia(Gregory et al., 1995; Carey et al., 2005).

Sediment oxygen demand values for forested water-sheds can be more accurately assigned for DO modelsby looking at the smaller watershed (Little River,Alapaha, Upper Suwannee, and Withlacoochee) ratherthan the entire river basin (Suwannee). The high sandcontent at the Alapaha forested sites and the relativelyhigh organic content at the Upper Suwannee forestedsites are easily measured factors that can help model-ers pick the most accurate SOD value for their model.Also, comparing measured SOD values to those cur-rently available in the literature for sandy bottomstreams or streams in the Southeastern U.S. showsthe importance of measuring SOD in the region underquestion instead of choosing a value from literaturebased on sediment type or region alone.

ACKNOWLEDGMENTS

Funding for this project was provided by a grant from theUSDA-CSREES Integrated Research, Education, and ExtensionCompetitive Grants Program – National Integrated Water QualityProgram (Award No. 2004-5113002224), by Hatch and State fundsallocated to the Georgia Agricultural Experiment Stations, and byUSDA-ARS CRIS project funds. We would also like to acknowledgeMr. Herman Henry, Mr. Andy Knowlton, Ms. Wynn Page, and Dr.Bob Hubbard. Without their assistance, the experimental portion ofthis work would not have been possible. Finally, we wish to thankthe Georgia Department of Natural Resources – Environmental

UTLEY, VELLIDIS, LOWRANCE, AND SMITH

JAWRA 752 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Protection division for loaning us their SOD chambers for the dura-tion of this study. Products and trade names are provided for infor-mation only and do not imply endorsement by the University ofGeorgia or USDA-ARS.

LITERATURE CITED

Borsuk, M.E., D. Higdon, C.A. Stow, and K.H. Reckhow, 2001. ABayesian Hierarchical Model to Predict Benthic OxygenDemand From Organic Matter Loading in Estuaries andCoastal Zones. Ecological Modeling 143:165-181.

Bosch, D., R. Lowrance, G. Vellids, J. Sheridan, and R. Williams,2002. Dissolved Oxygen and Stream Flow Rates: Implicationsfor TMDLs. In: Proceedings of the Total Maximum Daily Load(TMDL) Environmental Regulations Conference, A. Saleh (Edi-tor). ASAE, St. Joseph, Michigan, p. 92.

Bowie, G.L., W.B. Mills, D.B. Porcella, C.L. Campbell, J.R. Pagen-kopf, G.L. Rupp, K.M. Johnson, P.W.H. Chan, and S.A. Gherini,1985. Rates, Constants & Kinetic Formulations in SurfaceWater Quality Modeling (Second Edition). EPA Ofc. Of Researchand Development, Athens, Georgia.

Boynton, W.R., W.M. Kemp, C.G. Osborne, K.R. Kaumeyer, andM.C. Jenkins, 1981. Influence of Water Circulation Rate on inSitu Measurements of Benthic Community Respiration. MarineBiology 65:185-190.

Caldwell, J.M. and M.C. Doyle, 1995. Sediment Oxygen Demand inthe Lower Willamette River, Oregon 1994.USGS: Water-Resources Investigations Report 95-4196, Portland, Oregon.

Carey, R., G. Vellidis, R. Lowrance, and C. Pringle, 2005. NutrientEnrichment and Stream Periphyton Growth in the SouthernCoastal Plain of Georgia. ASAE Technical Paper No. 05-2197.ASAE, St. Joseph, Michigan, 14 pp.

Cathey, A.M., 2005. The Calibration, Validation, and SensitivityAnalysis of DOSag, an In-Stream Dissolved Oxygen Model. M.S.Thesis, University of Georgia, Athens, Georgia, 161 pp.

Cathey, A.M., G. Vellidis, M.C. Smith, R. Lowrance, and R. Burke,2005. The Calibration Validation and Sensitivity Analysis ofGeorgia DoSag: An In-Stream Dissolved Oxygen Model.Watershed Management to Meet Water Quality Standards andEmerging TMDL (Total Maximum Daily Load), Proceedings ofthe Third Conference, 5-9 March 2005, Atlanta, Georgia. ASAEPublication Number 701P0105.

Chau, K.W., 2002. Field Measurements of SOD and SedimentNutrient Fluxes in a Land-Locked Embayment in Hong Kong.Advances in Environmental Research 6:135-142.

Chaudhury, R.R., J.A.H. Sobrinho, R.M. Wright, and M. Sreenivas,1997. Dissolved Oxygen Modeling of the Blackstone River(Northeastern United States). Water Resources 32(8):2400-2412.

Dosskey, M.G. and P.M. Bertsch, 1994. Forest Sources and Path-ways for Organic-Matter Transport to a Blackwater Stream – AHydrologic Approach. Biogeochemistry 24(1):1-19.

Gee, G.W. and J.W. Bauder, 1986. Particle Size Analysis. In: Meth-ods of Soil Analysis Part I: Physical and Mineralgical Methods,A.L. Page (Editor). American Society of Agronomy Inc., Madi-son, Wisconsin, pp. 383-409.

Gregory, B., C. Pringle, G. Vellidis, and R. Lowrance, 1995. Effectsof Riparian Zone Management on Water Quality and Inverte-brate Community Structure in the Tifton Upland Region of theGeorgia Coastal Plain. North American Benthological Society,Keystone, Colorado.

Hatcher, K., 1980. Sediment Component of Oxygen Demand inStreams. Institute of Natural Resources, University of Georgia,Athens, Georgia.

Hatcher, K., 1986. Introduction to Part 3: Sediment OxygenDemand Measurement. In: Sediment Oxygen Demand: Pro-

cesses, Modeling, and Measurement, Kathryn Hatcher (Editor).Institute of Natural Resources, University of Georgia, Athens,Georgia, pp. 301-305.

Hill, B.H., A.T. Herlihy, and P.R. Kaufmann, 2002. Benthic Micro-bial Respiration in Appalachian Mountain, Piedmont andCoastal Plains Streams of the Eastern USA. Freshwater Biology47:185-194.

Ice, G. and B. Sugden, 2003. Summer Dissolved Oxygen Concentra-tions in Forested Streams of Northern Louisiana. SouthernJournal of Applied Forestry 27(2):92.

Lee, G.F., 2003. Evaluating Nitrogen and Phosphorus Control inNutrient TMDLs. Stormwater: The Journal for Surface WaterQuality Professionals 13:10-24. http://www.forester.net/sw_0201_evaluating.html, accessed February 23, 2003.

Lee, K., T.M. Isenhart, R.C. Shultz, and S.K. Mickelson, 2000. Mul-tispecies Riparian Buffers Trap Sediment and Nutrients DuringRainfall Simulations. Journal of Environmental Quality29:1200-1205.

Makenthun, A.A. and H.G. Stefan, 1998. Effect of Flow Velocity onSediment Oxygen Demand: Experiments. Journal of Environ-mental Engineering 124(3):222-230.

Matlock, M.D., K.R. Kasprazak, and G.S. Osborn, 2003. SedimentOxygen Demand in the Arroyo Colorado River. Journal of theAmerican Water Resources Association 39(2):267-275.

Meyer, J.L., 1990. A Blackwater Perspective on Riverine Ecosys-tems. BioScience 40(9):643-651.

Meyer, J.L., A.C. Benke, R.T. Edwards, and J.B. Wallace, 1997.Organic Matter Dynamics in the Ogeechee River, a BlackwaterRiver in Georgia, USA. Journal of the North American Bentho-logical Society 16:82-87.

Murphy, P. and D. Hicks, 1986. In-Situ Method for MeasuringSediment Oxygen Demand. In: Sediment Oxygen Demand:Processes, Modeling, and Measurement, Kathryn. Hatcher(Editor). Institute of Natural Resources, University of Georgia,Athens, Georgia, pp. 307-323.

Rabalais, N., 2002. Nitrogen in Aquatic Ecosystems. Ambio31(2):102-111.

SAS Institute Inc., 1990. SAS ⁄ STAT User’s Guide, Version 6. SASInstitute, Cary, North Carolina.

Seiki, T., H. Izawa, E. Date, and H. Sunahara, 1994. SedimentOxygen Demand in Hiroshima Bay. Water Resources 28(2):385-393.

Sinsabaugh, R.L., 1997. Large-Scale Trends for Stream BenthicRespiration. Journal of the North American Benthological Soci-ety 16:119-122.

Truax, D.D., A. Shindala, and H. Sartain, 1995. Comparison ofTwo Sediment Oxygen Demand Measurement Techniques. Jour-nal of Environmental Engineering 121(9):619-624.

Vellidis, G., P. Barnes, D.D. Bosch, and A.M. Cathey, 2006. Mathe-matical Simulation Tools for Developing Dissolved OxygenTMDLs. Transactions of the ASABE 49(4):100-1022.

Vellidis, G., R. Lowrance, M.C. Smith, S. Crow, and A. Milton,2003. Establishing the Natural Range of Dissolved OxygenLevels in Streams of the Southern Coastal Plain of Georgia.In: /i> Proceedings of the Total Maximum Daily Load (TMDL)Environmental Regulations Conference, A. Saleh (Editor).ASAE, St. Joseph, Michigan, pp. 414-419.

Whittemore, R.C. 1986. Recent Studies on the Comparison ofin-Situ and Laboratory Sediment Oxygen Demand Techniques.Tech. Bull. No. 489. Nat. Council of the Paper Industry for Airand Stream Improvement, Inc., New York, New York.

Ziadat, A.H. and B.W. Berdanier, 2004. Stream Depth SignificanceDuring in-Situ Sediment Oxygen Demand Measurements inShallow Streams. Journal of the American Water ResourcesAssociation 40(3):631-638.

FACTORS AFFECTING SEDIMENT OXYGEN DEMAND DYNAMICS IN BLACKWATER STREAMS OF GEORGIA’S COASTAL PLAIN

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 753 JAWRA