Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Chao, Xiaobo

Jia, Yafei

Lizotte, Richard

Locke, Martin

English

Hydraulic Engineering Repository

Conference Paper, Published VersionChao, Xiaobo; Jia, Yafei; Locke, Martin; Lizotte, RichardNumerical Modeling of Flow and Water Quality in an OxbowLake in the Mississippi DeltaZur Verfügung gestellt in Kooperation mit/Provided in Cooperation with:Kuratorium für Forschung im Küsteningenieurwesen (KFKI)Verfügbar unter/Available at: https://hdl.handle.net/20.500.11970/108475Vorgeschlagene Zitierweise/Suggested citation:Chao, Xiaobo; Jia, Yafei; Locke, Martin; Lizotte, Richard (2016): Numerical Modeling of Flowand Water Quality in an Oxbow Lake in the Mississippi Delta. In: Yu, Pao-Shan; Lo, Wie-Cheng (Hg.): ICHE 2016. Proceedings of the 12th International Conference on Hydroscience& Engineering, November 6-10, 2016, Tainan, Taiwan. Tainan: NCKU.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.Verwertungsrechte: Alle Rechte vorbehalten12th International Conference on Hydroscience & Engineering Hydro-Science & Engineering for Environmental Resilience November 6-10, 2016, Tainan, Taiwan.  Numerical Modeling of Flow and Water Quality in an Oxbow Lake in the Mississippi Delta Xiaobo Chao1, Yafei Jia1, Martin Locke2, Richard Lizotte2 1. National Center for Computational Hydroscience and Engineering, The University of Mississippi University 2. Water Quality and Ecology Research Unit, National Sedimentation Laboratory, US Department of Agriculture, Agriculture Research Service Oxford, MS, USA    ABSTRACT This paper presents the application of a two-dimensional numerical model for simulating the flow field and water quality distribution in Beasley Lake, an oxbow lake in the Mississippi Delta. In this lake, wind shear is the major driving force for flow hydrodynamics. The flow fields were simulated using CCHE hydrodynamic model, and the CCHE_WQ model was applied to simulate the concentrations of nutrients and chlorophyll in Beasley Lake. The simulated results were generally in good agreement with field measurements. The model was also applied to conduct analyses of the sensitivity of chlorophyll concentration to the suspended sediment concentration in the lake. It was found that the lake primary productivity is greatly limited by suspended sediment concentration.  KEY WORDS:  Numerical model, Wind-induced flow, Water quality, Beasley Lake, Mississippi Delta   INTRODUCTION  The Mississippi Delta is one of the most intensively farmed agricultural areas of the United States. The quality of surface water resources in this area are particularly vulnerable due to excessive sediment, nutrients, and pesticides transported from upland agricultural fields. Beasley Lake watershed (BLW) located in Sunflower County of Mississippi Delta, was selected as one of the Conservation Effect Assessment Project (CEAP) benchmark watersheds to assess environmental benefits derived from implementing USDA conservation programs. In this lake, weekly or biweekly samples of suspended sediment, nutrients, chlorophyll, bacteria, and other selected water quality variables were collected and analyzed by the USDA-ARS National Sedimentation Laboratory (NSL). Field measurements show that the concentrations of nitrate and ammonia in the lakes were very low, while the concentration of phosphorus was relatively high. Suspended sediment concentrations were also relatively high during the winter and spring, exceeded the published levels known to adversely impact fish growth and health (Rebich and Knight, 2001).  Numerical modeling is an effective and efficient approach for studying water quality constituents in surface water bodies. A water quality model, CCHE_WQ, has been developed by National Center for Computation Hydroscience and Engineering, and applied for predicting the distributions of nutrients, phytoplankton, dissolved oxygen, etc., in natural lakes. The model considers processes in the water column, the bed sediment layer, and their exchange processes. In this model, the effects of sediment on the water quality processes were also taken into account (Chao et al. 2010).   In Beasley Lake, wind shear is the major driving force for flow hydrodynamics. The CCHE hydrodynamic model (Jia et al. 2013) was applied to simulate the flow fields in Beasley Lake, and CCHE_WQ model was applied to simulate the concentrations of nutrients and chlorophyll in the lake. The simulated results were compared with field measurements. The model was also applied to conduct analyses of the sensitivity of chlorophyll concentration to the suspended sediment concentration in the lake.    MODEL DESCRIPTIONS  Numerical modeling is an effective tool to study the flow fields and water quality constituents in surface water bodies. A two-dimensional water quality model was developed based on CCHE2D hydrodynamic model (Jia et al. 2013), and applied to predict the distributions of nutrients, phytoplankton, dissolved oxygen, CBOD, etc., in natural lakes affected by surrounding watershed. Several biochemical processes were selected for modeling: the phytoplankton kinetics, nitrogen cycle, phosphorus cycle, DO balance and pathogen processes. The conceptual framework for the eutrophication kinetics in the water column was mainly based on the WASP6 model (Wool et al. 2001).  Eight state variables were involved in the interacting systems: ammonia nitrogen (NH3), nitrate nitrogen (NO3), phosphate (PO4), phytoplankton (PHYTO), carbonaceous biochemical oxygen demand (CBOD), dissolved oxygen (DO), organic nitrogen (ON), and organic phosphorus (OP). Three major sediment-associated water quality processes were simulated, including the effect of sediment on light penetration, the adsorption-desorption of nutrients by sediment and the release of nutrients from bed sediment (Chao et al. 2010).   Governing Equations  The free surface elevation of the flow is calculated by the continuity equation:  0h uh vht x y∂ ∂ ∂+ + =∂ ∂ ∂                (1)  The momentum equations for the depth-integrated two-dimensional model in the Cartesian coordinate system are: 1 xyxx sx bxCorhhu u uu v g f vt x y x h x y hττ τ τηρ∂ ∂ −∂ ∂ ∂ ∂+ + = − + + + + ∂ ∂ ∂ ∂ ∂ ∂    (2)  1 yx yy sy byCorh hv v vu v g f ut x y y h x y hτ τ τ τηρ∂ ∂ − ∂ ∂ ∂ ∂+ + = − + + + − ∂ ∂ ∂ ∂ ∂ ∂  (3)  where u and v are the depth-integrated velocity components in x and y directions, respectively; t is the time; g is the gravitational acceleration; η is the water surface elevation; ρ is the density of water; h is the local water depth; fCor is the Coriolis parameter; τxx, τxy, τyx and τyy are depth integrated Reynolds stresses; τsx, τsy and τbx, τby are surface and bed shear stresses in x and y directions, respectively.  In the water column, each one of the water quality constituents can be expressed by the following mass transport equation:   ( ) ( ) ( ) ( )i i i i ix y iC uC vC C CD D St x y x x y y∂ ∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂ ∂+ + = + + ∑  (4)  in which Ci is the concentration of the ith water quality constituent; Dx,and Dy are the diffusion coefficients in x, and y directions, respectively; ΣSi is the effective source term, which includes the kinetic transformation rate, external loads and sinks for ith water quality constituent.   The water quality model was decoupled with CCHE2D hydrodynamic model (Jia et al 2013). The CCHE2D model is based on the finite element method. A second-order upwinding scheme was adopted to eliminate oscillations due to advection. The velocity correction method was applied to solve the pressure and enforce mass conservation. The system of algebraic equations was solved using the Strongly Implicit Procedure (SIP). Based on the flow fields computed by CCHE2D, the concentration distribution of water quality constituents can be obtained by solving mass transport equations (4) numerically.  Sediment-Associated Water Quality Processes  The effect of sediment on the growth of phytoplankton Phytoplankton plays a central role in the carbon and nutrient cycles that comprise the model ecosystem. Total chlorophyll is used as a simple measurement of phytoplankton biomass. The effective source term for phytoplankton is determined by the growth rate, death rate, and settling rate of phytoplankton. The growth rate is calculated by                        p mx N I TG P f f f=                                                    (5)  in which Pmx is the maximum growth rate; and fN, fI and fT are the limitations due to nutrient availability, light intensity, and temperature, respectively. The nutrient limitation factor fN is obtained based on Michaelis-Menten equation and Liebig’s Law of the minimum. The temperature limitation factor fT is calculated by the formula proposed by Cerco and Cole (1995). The light limitation factor fI is obtained by integrating the Steele equation over depth and time:  0 02.72 exp expK Hd eIe m mf I If eK H I I−    = − − −             (6)  in which fd is the fractional daylight; H is the water depth; I0 is the daily light intensity at the water surface (ly/day); Im is the saturation light intensity of phytoplankton (ly/day). Ke is the total light attenuation coefficient, and it is determined by the effects of water, chlorophyll and suspended sediment(SS), and can be expressed by (Stefan et al. 1983, Wool et al. 2001):                                               0.670 0.0088 0.054e chl chlK K C C sγ= + + +      (7)  where K0 is the light attenuation by pure water (m-1);  Cchl is the concentration of chlorophyll; s is the concentration of suspended sediment (SS); γ is a coefficient. The parameter γ can be obtained based on field measurements.  Processes of adsorption-desorption of nutrients by SS  Adsorption and desorption are important processes between dissolved nutrients and SS in the water column. In water quality processes, the reaction rates for adsorption-desorption are much faster than that for the biological kinetics, an equilibrium assumption can be made (Wool et al. 2001). Many experimental results show the Langmuir equilibrium isotherm is a better representation of the relations between the dis-solved and particulate nutrient concentrations (Bubba et al. 2003, Chao et al 2010).    Release of Dissolved Nutrients from Bed Sediment  Bed release is an important source of inorganic and organic nutrients in the water column. In many models, the release rate of nutrients from bed sediment is determined based on the concentration gradient across the water-sediment interface. In fact, the bed release rate is also affected by pH, temperature and dissolved oxygen concentration (Romero 2003, Chao et al. 2010).    MODEL APPLICATION TO BEASLEY LAKE  Study Site  The Mississippi River Alluvial Plain is one of the most intensively farmed agricultural areas of the United States. The quality of surface water resources in this area are particularly vulnerable due to excessive sediment, nutrients, and pesticides transported from upland agricultural fields. Beasley Lake watershed (BLW) (Fig.1) located in Sunflower County, Mississippi, was originally one of the three watersheds studied in the Mississippi Delta Management Systems Evaluation Area project (MDMSEA), which sought to develop and assess alternative innovative farming systems for improved water quality and ecology in the Mississippi Delta (Locke, 2004). In 2004, BLW was selected as one of the Conservation Effect Assessment Project (CEAP) benchmark watersheds to assess environmental benefits derived from implementing USDA conservation programs.   In this study, CCHE hydrodynamic and water quality models were applied to simulate the flow fields and water quality distributions in Beasley Lake. The BLW has a total drainage area of approximately 12th International Conference on Hydroscience & Engineering Hydro-Science & Engineering for Environmental Resilience November 6-10, 2016, Tainan, Taiwan.  9.15 km2, and the receiving oxbow lake (0.25 to 0.30 km2) was formed from a cutoff meander of the Big Sunflower River. The average water depth of the lake is about 2m. The watershed topography is very flat, with a maximum 5 m difference in elevation from the top of the watershed boundary to the lake surface. Therefore, the watershed drainage is largely dependent on man-made ditches and a large riparian area. The watershed is a relatively closed system except during extreme wet periods (usually in spring or fall) when the lake overflows into the Sunflower River. The Sunflower River levee defines the northern part of the watershed boundary, and a large forested area (1.25 km2) is located on the eastern side of the lake.  Weekly or biweekly samples of suspended sediment, nutrients, chlorophyll, DO and other selected water quality variables were collected at Stations B1, B2 and B3. The measured water quality data at those stations were used for model calibration and validation. Three of the major inflows of the lake were located at the Stations BC3, BC5 and SRP3 and monitored for water quality and quantity by the ARS National Sedimentation Laboratory (NSL). The inlet boundaries of water quality constituents were calculated based on those field measurements.                Fig. 1 Study Site: Beasley Lake in the Mississippi Delta  Measured Results The measured suspended sediment concentration, light intensity, water temperature, etc., were used as inputs data for the water quality model. Fig. 2 shows the measured daily light intensity near the lake.  Simulation Results Flow simulation  In Beasley Lake, wind shear is the major driving force for flow fields. CCHE2D hydrodynamic model was applied to simulate the wind-driven flow. Fig. 3 shows the flow patterns of the lake induced by wind. In this shallow closed oxbow lake, the wind-driven circulation will normally promote the complete mixing processes. 01002003004005006007008009001/1 3/1 4/30 6/29 8/28 10/27 12/26Light intensity (ly)Date of 2012 Fig. 2 Measured daily light intensity    Fig. 3 Wind-driven circulation in Beasley Lake  Water quality simulation  CCHE_WQ model was applied to simulate the water quality distributions in the lake. Fig. 4 shows the simulated concentration of chlorophyll of the lake. The simulated results were generally in good agreement with field measurements provided by NSL. In the summer, the chlorophyll concentration is relatively higher due to the higher temperature; However, the chlorophyll concentration is relatively lower in the winter due to the lower temperature as well as higher SS concentration.   00.020.040.060.080.10.121/1 3/1 4/30 6/29 8/28 10/27 12/26Concentration of chlorophyll (mg/l)Date of 2012modelmeasurementsFig. 4 Concentration of chlorophyll in the lake       Beasley Lake Beasley Lake Watershed B1 B2 B3 BC3 BC5 SRP3 12th International Conference on Hydroscience & Engineering Hydro-Science & Engineering for Environmental Resilience November 6-10, 2016, Tainan, Taiwan.  SENSITIVITY STUDY Field data show that major water quality problems in the Mississippi Delta lakes were caused by excessive sediment loads carried by runoff from surrounding cultivated lands. To show the sensitivity of chlorophyll concentration to SS, a series of hypothetical lake SS concentrations were input to the model, while the flow conditions and nutrient loadings at inlet boundaries were kept the same. The current conditions of SS, nutrients, and chlorophyll were set as base conditions (100% SS by 100% chlorophyll). It was assumed the SS levels were varied from 10% to 500% of the base condition, and the model was applied to simulate the responses of chlorophyll concentrations in the lake. The simulation results (Fig. 5) show when lake SS is reduced by 50%, the simulated mean chlorophyll concentration may increase about 40%. When lake SS is doubled, the chlorophyll concentration may reduce to 37% of the base condition.    0%25%50%75%100%125%150%175%200%0% 50%100%150%200%250%300%350%400%450%500%Percentage of current chlorophyll concentration Percentage of current SS concetration  Fig. 5 Sensitivity of chlorophyll concentration to SS concentration  CONCLUSIONS  CCHE2D hydrodynamic model and water quality model have been successfully applied to simulate the flow fields and water quality distributions in Beasley Lake. Three major sediment-associated water quality processes were considered in the water quality model, including the effect of SS on the light attenuation coefficient, the processes of adsorption-desorption of nutrient by sediment, and the release of nutrient from bed sediment. The trends and magnitudes of chlorophyll concentrations obtained from the numerical model generally agreed with field observations. The model was also applied to conduct the sensitivity study of chlorophyll concentration to the suspended sediment concentration. The concentration of chlorophyll is inversely related to the sediment concentration. ACKNOWLEDGEMENTS  This work is a result of research sponsored by the USDA Agriculture Research Service under Specific Research Agreement No. 58-6408-1-609.  REFERENCES  Bubba, M.D., Arias, C.A. and Brix, H. ( 2003). Phosphorus adsorption maximum of sands for use as media in subsurface flow constructed reed beds as measured by the Langmuir isotherm. Water Research, 37: 3390–3400.  Cerco, C. F. and Cole, T. (1995), User’s Guide to the CE-QUAL-ICM: Three-dimensional Eutrophication Model, Technical Report EL-95-1 5, U.S. Army Corps of Engineers, Vicksburg, MS. Chao, X., Jia, Y., Shields, F. D. Jr., Wang, S.S.Y. and Charles M. Cooper (2010), Three-Dimensional Numerical Modeling of Water Quality and Sediment-Associated Processes with Application to a Mississippi Delta Lake, Journal of Environmental Management, 91(7), 1456-1466. Jia, Y., Chao, X., Zhang, Y., Zhu, T. (2013), Technical Manual of CCHE2D, Version4.1, NCCHE-TR-02-2013. Locke, M.A. (2004).  Mississippi Delta Management Systems Evaluation Area: Overview of water quality issues on a water-shed scale. In Water quality assessments in the Mississippi Delta: Regional solutions, national scope. M.T. Nett, M.A. Locke, and D.A. Pennington, eds., ACS Symposium Ser. 877, pp. 1-15. Rebich, R.A and Knight, S. S. (2001). The Mississippi Delta Management Systems Evaluation Area Project, 1995-99. Missis-sippi Agriculture and Forestry Experiment Station, Information Bulletin 377, Division of Agriculture, Forestry and Veteri-nary Medicine, Mississippi State University. Romero, J.R. et al (2003). Computational aquatic ecosystem dynamics model: CAEDYM v2, Science Manual, University of Western Australia. Stefan, H.G. et al. (1983). Model of light penetration in a turbid lake, Water Resources Research, Vol. 19, No. 1, 109-120. Wool, T.M.,et al. (2001). Water Quality Analysis Simulation Program (WASP) version 6 User’s Manual, USEPA, Atlanta, GA.          12th International Conference on Hydroscience & Engineering Hydro-Science & Engineering for Environmental Resilience November 6-10, 2016, Tainan, Taiwan.  

Numerical Modeling of Flow and Water Quality in an Oxbow Lake in the Mississippi Delta

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Numerical Modeling of Flow and Water Quality in an Oxbow Lake in the Mississippi Delta

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