Estuarine, Coastal and Shelf Science 151 (2014) 256e271
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Modeling ecosystem processes with variable freshwater inflow to the Caloosahatchee River Estuary, southwest Florida. I. Model development Christopher Buzzelli a, *, Peter H. Doering a, Yongshan Wan a, Detong Sun a, David Fugate b a b
Coastal Ecosystems Section, South Florida Water Management District, 3301 Gun Club Rd., West Palm Beach, FL 33406, USA Department of Marine and Ecological Sciences, Florida Gulf Coast University, 10501 FGCU Blvd. South, Fort Myers, FL 33965, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 April 2014 Accepted 17 September 2014 Available online 2 October 2014
Variations in freshwater inflow have ecological consequences for estuaries ranging among eutrophication, flushing and transport, and high and low salinity impacts on biota. Predicting the potential effects of the magnitude and composition of inflow on estuaries over a range of spatial and temporal scales requires reliable mathematical models. The goal of this study was to develop and test a model of ecosystem processes with variable freshwater inflow to the sub-tropical Caloosahatchee River Estuary (CRE) in southwest Florida from 2002 to 2009. The modeling framework combined empirically derived inputs of freshwater and materials from the watershed, daily predictions of salinity, a box model for physical transport, and simulation models of biogeochemical and seagrass dynamics. The CRE was split into 3 segments to estimate advective and dispersive transport of water column constituents. Each segment contained a sub-model to simulate changes in the concentrations of organic nitrogen and phosphorus 3 (ON and OP), ammonium (NHþ 4 ), nitrate-nitrite (NOx ), ortho-phosphate (PO4 ), phytoplankton chlorophyll a (CHL), and sediment microalgae (SM). The seaward segment also had sub-models for seagrasses (Halodule wrightii and Thalassia testudinum). The model provided realistic predictions of ON in the upper estuary during wet conditions since organic nitrogen is associated with freshwater inflow and low salinity. Although simulated CHL concentrations were variable, the model proved to be a reliable predictor in time and space. While predicted NO x concentrations were proportional to freshwater inflow, NHþ 4 was less predictable due to the complexity of internal cycling during times of reduced freshwater inflow. Overall, the model provided a representation of seagrass biomass changes despite the absence of epiphytes, nutrient effects, or sophisticated translocation in the formulation. The model is being used to investigate the relative importance of colored dissolved organic matter (CDOM) vs. CHL in submarine light availability throughout the CRE, assess if reductions in nutrient loads are more feasible by controlling freshwater quantity or N and P concentrations, and explore the role of inflow and flushing on the fates of externally and internally derived dissolved and particulate constituents. © 2014 Elsevier Ltd. All rights reserved.
Keywords: estuary freshwater model nutrients light seagrass
1. Introduction Many of the changes in estuarine physical, biogeochemical and biological attributes are consequences of altered patterns of freshwater inputs (Sklar and Browder, 1998; Alber, 2002). Anthropogenic manipulation of freshwater inflow alters salinity distributions that affect estuarine organisms and the structure of food webs (Livingston et al., 1997; Powell et al., 2003; Tolley et al., 2005; Gillson, 2011; Petes et al., 2012). Nutrient loading is a function of
* Corresponding author. E-mail address:
[email protected] (C. Buzzelli). http://dx.doi.org/10.1016/j.ecss.2014.08.028 0272-7714/© 2014 Elsevier Ltd. All rights reserved.
freshwater discharge and the incoming concentrations of N and P (Boynton et al., 2008; Greening et al., 2011; Taylor et al., 2011). Symptoms of estuarine eutrophication are often ascribed to the increased loading of dissolved nutrients (e.g. nitrogen and phosphorus or N and P) from the adjacent watershed (Cloern, 2001; Kemp et al., 2005). Although decreasing nutrient loads can have demonstrable benefits, it is possible that an estuary may exhibit oligotrophication or other non-linear trajectories (Nixon et al., 2009; Duarte et al., 2009). While nutrient loads are often directly proportional to discharge, anthropogenic withdrawal of freshwater can confound estuary ecology in light of efforts to manage watershed nutrient inputs (Flemer and Champ, 2006).
C. Buzzelli et al. / Estuarine, Coastal and Shelf Science 151 (2014) 256e271
Variability in discharge on different scales directly affects flushing time, the input of colored dissolved organic matter (CDOM), and estuarine biogeochemistry (McPherson and Miller, 1987; Miller and McPherson, 1991; Bowers and Brett, 2008). Flushing time is one of the key modulators of allochthonous and autochthonous materials (Dettmann, 2001; Sheldon and Alber, 2006; Swaney et al., 2011; Buzzelli et al., 2013d). In fact, phytoplankton production and trophic transfer are directly influenced by freshwater inflow and flushing time (Lucas et al., 2009a; Phlips et al., 2012). In many estuaries, watershed-derived CDOM is an important component of submarine light availability for seagrass habitats (Gallegos and Kenworthy, 1996; Livingston et al., 1998; McPherson et al., 2011; Buzzelli et al., 2012; Le et al., 2013). The relationships among inflow, nutrient loading, salinity gradients, flushing time, phytoplankton, light, and seagrass survival are nonlinear and inter-dependent (Buzzelli, 2011). Salinity serves as an indicator of freshwater inflow that can be used to help define desirable conditions for biota (Chamberlain and Doering, 1998; Doering et al., 2002; SFWMD, 2012). It is possible to develop statistical models to predict salinity with changes in freshwater inflow or inlet dynamics (Morey and Dukhovskoy, 2012; Qiu and Wan, 2013). Salinity time series are essential for salinityproperty and box-model methods (Officer, 1980; Kaul and Froelich, 1984; Hagy et al., 2000; Hagy and Murrell, 2007). Box models have great utility since physical processes can be resolved at appropriate scales for application to ecological models (Kremer et al., 2010). Stand-alone ecological models for phytoplankton, sediment microalgae (SM), oysters, and seagrasses help in the evaluation and definition of environmental requirements such as salinity conditions, light availability, or nutrient sensitivity (Pinckney, 1994; Muylaert et al., 2005; Buzzelli et al., 2012, 2013a). Simulation models for water column and benthic habitats are commonly linked to more sophisticated hydrodynamic models (Cerco and Seitzinger, 1997; Cerco, 2000; Cerco and Moore, 2001; Powell et al., 2003; Cerco and Noel, 2007). However, these computerintensive models are often built upon assumptions and formulations that exceed an empirically based understanding of estuarine biogeochemical processes. Simplified modeling platforms that incorporate desirable features of various approaches are needed to help improve resource management. The quantification and evaluation of multiple stressors on estuarine ecology from days to decades requires an integrative framework linking watershed inputs, circulation, water quality, and biota (Sohma et al., 2008; Condie et al., 2012). Focused models can be used to explore potential ecological feedbacks and responses to variations in environmental drivers including altered inflow, temperature, light and nutrients, and grazing (Scavia and Liu, 2009; Lucas et al., 2009). Successful development of these models are useful to determine what can be predicted, identify missing information, and examine potential estuarine ecological responses to proposed watershed actions (Buzzelli et al., 2013c). The goal of this study was to develop and test a model of ecosystem processes with variable freshwater inflow to the subtropical Caloosahatchee River Estuary (CRE) in southwest Florida from 2002 to 2009. The modeling framework combines empirically derived inputs of freshwater and materials from the watershed, daily predictions of salinity, a box model for physical transport, and simulation models of biogeochemical and seagrass dynamics. The objectives were to 1) create a three segment model to estimate net transport between the watershed and oceanic boundaries, 2) develop biogeochemical simulation models for each segment, 3) calibrate and test model results relative to empirical data from the CRE, and (4) quantify sensitivity of model predictions to variations in key parameters.
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2. Materials and methods 2.1. Study site The CRE is located in southwest Florida and has been altered by human activities starting in the 1880's when the river was straightened and deepened (Fig. 1; Antonini et al., 2002; Barnes, 2005). Water control structures at Lake Okeechobee (S-77) and Ortona (S-78) were completed in the 1930's with the last installed in 1966 at Olga (S-79; Antonini et al., 2002). The Franklin Lock at S79 represents the head of the CRE that extends ~48 km downstream to the Sanibel Bridge where it empties into the Gulf of Mexico. Early descriptions of the CRE characterize it as barely navigable due to extensive shoals and oyster bars near the estuarine mouth (Sackett, 1888). A navigation channel was dredged with a causeway built across the mouth of San Carlos Bay to Sanibel Island in the 1960's. 2.2. Model overview The CRE was split into 3 segments for development of box and simulation models (Fig. 1). ArcGIS was used to interpolate bathymetric data (NGVD 1988) and to delineate segment boundaries, estimate the area and average elevation of each segment, and calculate the three-dimensional volumes (Buzzelli et al., 2013b). Segment 1 extends approximately 16,098 m from S-79 to where the CRE expands. The segment has an area of 1.5 107 m2, an average sediment elevation (zseg) of 2.3 m, and a volume of 2.1 107 m3. Segment 2 extends approximately 13,405 m downstream from Segment 1 encompassing 3.0 107 m2 of mid-estuary with an average sediment elevation of 2.5 m. The volume of Segment 2 is 7.2 107 m3. Segment 3 extends 18,628 m to the Sanibel Island Bridge and is bounded to the northwest by Pine Island (Fig. 1). The average sediment elevation of Segment 3 is 1.5 m, the area is 2.7 107 m2, and the volume is 5.2 107 m3. The CRE area between S-79 and the Sanibel Bridge is 7.2 107 m2, has a total length of 48,132 m, an average sediment elevation of 2.1 m, and a volume of 1.5 108 m3. The framework includes the box model set-up (Fig. 2) and equations (Table 1), the external boundary inputs and estuarine calibration data (Figs. 3 and 4), an array of forcing functions that drive model processes, a suite of biogeochemical process equations, and ~75 mathematical coefficients (Table 2). The biogeochemical models use an integration interval (dt) of 0.03125 d ¼ 45 min over simulations spanning 2922 d from 2002 to 2009. 2.3. Salinity and transport model The box model approach was used to represent advective and dispersive exchanges between Segments 1e3 (Officer, 1980; Hagy et al., 2000; Hagy and Murrell, 2007). Each segment was assumed to be a homogeneous box of constant volume in the vertical and horizontal dimensions (Fig. 2). The approach conserves volume and mass within and among the three estuarine segments. The box model was driven by daily time series for freshwater inflow at the estuarine head (Q0) and salinity (S) for each segment (S1, S2, S3) and the downstream boundary (S4). Physical transport of a water column constituent (TRSc) was the sum of advection (Xc), lateral inputs from tributaries and ground water (TGc), and dispersion (Yc; Fig. 2; Eqn. (1)). The time series for Q0 (m3 d1) from 2002 to 2009 (2922 days) at S-79 was derived from the hydrologic data-base (DBHydro) at the South Florida Water Management District (Fig. 3A). The loadings of water column constituents at the upstream boundary (Q0c; g d1) were calculated as the product of Q0 and average monthly concentrations (g m3; Fig. 4). A watershed model was used to estimate
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Fig. 1. Map of the Caloosahatchee River Estuary (CRE) in southwest Florida (inset map). The CRE spans from a water control structure (S-79; filled star) to the Sanibel Bridge (open star). The estuary was divided into three segments (yellow, green, blue). Inputs of watershed materials were from monitoring stations (open circles). Concentrations of water column constituents for model calibration were from monitoring stations within the CRE (open squares). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the daily lateral input from tributaries and ground water to each segment (Qtgw1, Qtgw2, Qtgw3; Y. Wan, unpublished data; Fig. 3B). Total inflow for each segment (Q1, Q2, Q3) was calculated as the sum of the advective component and the lateral inflows (Eqn. 2aec). These total segment inflows were used to predict downstream transport of water column constituents across the segment boundaries (X01, X12; X23, X34; Eqn. 3aed). The loadings of water column constituents from the tributaries and ground water (TG1c, TG2c, TG3c) were the product of the lateral inflows to each segment (Qtgw123) and the corresponding average monthly input concentration (Eqn. 4aec). Monthly constituent concentrations in the tributaries and ground water were derived from Lee County, Florida monitoring stations averaged by segment (Fig. 4). Time series for the average daily salinity of each segment (S1, S2, S3) were derived using a predictive statistical model developed for the CRE (Fig. 3CeD; Qiu and Wan, 2013). The salinity time series were used to calculate the dispersive exchanges (E1, E2, E3; Eqn.5aec) and influence the growth of seagrasses. Dispersion represents the bi-directional, non-advective transport of water column constituents across segment boundaries (Y12 and Y21; Y23 and Y32; Y34 and Y43, Eqn. 6aec; Hagy and Murrell, 2007). 2.4. Biogeochemical models Each of the three segments included a water column sub-model to simulate the concentrations of phytoplankton carbon (Cphyto;
g C m3), organic nitrogen and phosphorus (ON and OP; g N or 3 P m3), ammonium (NHþ 4 or NH; g N m ), nitrate-nitrite (NOx or 3 3 3 NO; g N m ), ortho-phosphate (PO4 or PO; g P m ) and sediment microalgae (SM; g C m2). Downstream Segment 3 also had submodels for the seagrasses Halodule wrightii (Hw; g C m2) and Thalassia testudinum (Tt; g C m2). Biogeochemical processes (gross primary production, nutrient uptake, respiration, remineralization, nitrification, denitrification) were modulated by variations in temperature, depth, and submarine light (Table 1). Water temperature (T) and surface light were modeled using trigonometric functions established for 26 north latitude (Eqn. (7); Buzzelli et al., 2012). Irradiance at the water surface (I0) and photoperiod (Pphoto) were necessary to simulate variations in submarine light (Table 1; Eqns. (8)e(9)). Variable water level (h) was used to calculate depth to affect submarine light penetration. Water level was calculated hourly based on the amplitude, period, and phase of the M2 tide determined for Ft. Myers, FL. Depth (hseg) was calculated as the difference between h and zseg (Table 1; Eqns. (10) and (11)). The total attenuation coefficient for submarine light contained contributions from pure water (kw), color, turbidity (NTU), and chlorophyll a (CHL; Eqn. (12); Christian and Sheng, 2003). Attenuation due to color (kcolor) was estimated using a negative exponential relationship with average salinity of the segment (Sseg, Eqn. (13); McPherson and Miller, 1987; Bowers and Brett, 2008; Buzzelli et al., 2012). Time series for the average turbidity of each segment were derived from monitoring data
0 =S 79
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Do wn b ou nd 4
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Y1
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Y4
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Y3
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me S3 nt 3
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me S2 nt 2
Y2
1
TG
2
X
12
2
Se g
me S1 nt 1
TG
1
X
01
Q
Up b ou nd 0
Fig. 2. Schematic diagram for three-segment model of physical transport in the CRE. The numeric designations are for the upstream boundary (0), the three segments (1, 2, 3), and the downstream boundary (4). Average salinity of the segments (S) and lateral inputs of materials from tributaries and ground water (TG), downstream advective transport across segment boundaries (X), and bi-directional dispersion (Y) are shown. See text for details.
available through DBHydro (http://www.sfwmd.gov/dbhydroplsql/ show_dbkey_info.main_menu). There were specific coefficients for each of the attenuation components (kw, aNTU, aCHL, acolor, bcolor; Table 2). Submarine light (I) was calculated at mid-depth for phytoplankton (Im; Eqn. (14); Robson, 2005), the sediment surface for seagrasses (Iz; Eqn. (15)), and below the seagrass canopy for sediment microalgae (ISM; Eqn. (16); Pinckney, 1994). ISM required the predicted seagrass shoot biomass (Cshoot) and the solar angle and declinations (b and d; Eqns. (17) and (18)). The masses of the water column constituents resulted from the sum of all sources and sinks (Table 1). The mass of each constituent was divided by the segment volume to derive the concentration at each time step. Phytoplankton was a key variable since it receives external inputs of CHL from the watershed, is the primary sink for inorganic nitrogen (N) and phosphorus (P), is the primary source of autochthonous organic N and P, is a central agent in submarine light extinction, and serves as an ecological indicator (Doering et al., 2006; Buzzelli, 2011). In addition to transport, phytoplankton biomass was affected by gross production (Gphyto), respiration (Rphyto), mortality (Mphyto), exudation (Exphyto), sedimentation (Sedphyto), and the resuspension of SM (RSphyto; Eqn. (19)). Gphyto was calculated as a function of the maximum photosynthetic rate (Pm), an exponential increase with T (Tfx), and hyperbolic relationships for light and nutrients dependent upon half-saturation coefficients (Ik, kDIN, kDIP; Eqn. (19a)). Rphyto and Mphyto included constant rate coefficients (kRphyto and
259
kMphyto) modified by Tfx (Eqn. 19bec; Kuo and Park, 1994). Mphyto is assumed to include both grazing and non-grazing death of phytoplankton cells. Exphyto accounted for an estimated 10% of gross production that is lost through exudation (Eqn. (19d); Larsson and Hagstrom, 1979; Bjornsen, 1988). Phytoplankton sinking was a function of a constant rate (ksed ¼ 0.35 m d1; Kuo and Park, 1994; Lucas et al., 2009b) and hseg (Eqn. (19e); Table 2). Conversely, the source of algal biomass from the sediments resulted from the resuspension of a constant fraction of SM (kresus ¼ 0.025) multiplied by Aseg (Eqn. (19f); Table 1). The ON and OP masses in the water column of each segment resulted from transport, production (ONp and OPp), remineralization (ONrem and OPrem), and sinking from the water column to the sediment (ONws and OPws; Eqns. (20) and (21)). The in situ production of ON and OP was calculated as a fraction of the sum of the phytoplankton loss terms (Rphyto, Mphyto, Exphyto, Sedphyto) converted to N and P units using fixed molar C:N (6.625) and C:P (106) ratios, respectively (fONp ¼ fOPp ¼ 0.7; Eqns. (20a) and (21a)). ONrem and OPrem were functions of the ON and OP masses, Tfx, and the remineralization coefficient (kONrem ¼ kOPrem ¼ 0.08 d1; Table 2; Eqns. (20b) and (21b)). The particulate fractions of the ON and OP masses (fPON ¼ fPOP ¼ 0.1) sank at a rate of 0.35 m d1 (kNPsed, Table 2; Eqns. (20d) and (21c)). The masses of NH, NO, and PO in the water column of each segment were influenced by a similar series of source and sink processes (Eqn. (22)e(24), Table 1). ONrem was split in half (fNOrem ¼ 0.5) to account for the production of both NH and NO (NHp and NOp; Eqns. (22a) and (23a)). All of OPrem provided the local source of PO (Eqn. (24a)). Temperature-dependent nitrification (NHnit or NOnit) was a function of the NH mass with a basal rate of 0.01 d1 and provided a sink for NH but a source of internal NO (Eqn. (22b); Table 1; Walsh et al., 2006). Loss of NO through denitrification (NOden) was a function of the NO mass in the segment, the denitrification coefficient (kden ¼ 0.01 d1; Cornwell et al., 1999), and Tfx (Eqn. (23b)). Uptake by phytoplankton represented a significant loss term for water column NH, NO, and PO. Nutrient uptake affects water column inorganic concentrations which influence rates of gross primary production by phytoplankton (Eqn. (19a)). NHup and NOup were calculated using Gphyto converted to N units using the fixed molar CN ratio and scaled by a preference fraction based on NO x (fNOup ¼ 0.45; Table 2) and a feedback function (fbNH and fbNO; Eqns. (22c) and (23c)). POup had a similar feedback function (Eqn. (24b)). The feedback functions for NH, NO x , and PO (Eqns. (22d), (23d) and (24c)) varied from 0 to 1 and were derived via donor control where the mass of the particular constituent within each segment was modulated by limitation (NHlim, NOlim, POlim) and threshold (NHthresh, NOthresh, POthresh) values (Wiegert, 1975; Sin and Wetzel, 2002). The exchanges of NH, NO, and PO between the sediment and water (NHsw, NOsw, POsw) were estimated using empirically derived values averaged by segment and modified by Tfx (Eqns. (22e), (23e) and (24d); Howes et al., 2008; Buzzelli et al., 2013b). The negative values for kNOsw3 and kPOsw3 denoted the removal of water column NO and PO by the sediment boundary in Segment 3. The mass of SM changed with gross production (GSM), phytoplankton sinking (PHYSM), respiration, (RSM), mortality (MSM), and resuspension (RSSM; Table 1). GSM was modeled similarly to phytoplankton with Pmax, Tfx, and biomass (CSM) but did not incorporate nutrient limitation effects (Eqn. (25a)). An assumed 50% of Sedphyto was connected to the SM pool (fphySM ¼ 0.5; Eqn. (25b)). RSM resulted from the product of the respiration coefficient (kRSM ¼ 0.1 d1), Tfx, and CSM (Eqn. (25c)). MSM represented loss to grazing of SM by benthic fauna in the sediment surface layer and was estimated using C2SM and a mortality coefficient (kMSM ¼ 0.5 m2 gC1; Eqn. (25d); Buzzelli, 2008).
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Table 1 Equations for three segment model of the Caloosahatchee River Estuary. See Fig. 2 for diagrammatic representation and Figs. 3e5 for time series of freshwater inflows (Q0, Qtgw1, Qtgw2, Qtgw3), boundary concentrations (C0, Ctgw1, Ctgw2, Ctgw3), and segment salinity (S1, S2, S3, S4). Description
Equations
(1) Summary equation for physical transport (TRSc; g d1) of constituent (C; g m3) in segments (1,2,3) (2abc) Total inflow (Q; m3 d1) to segments (1,2,3) (3abcd) Downstream advective transport (X; g d1) of constituent (C; g m3) between segments (1,2,3) including boundary (4) (4abc) Lateral transport (TG; g d1) of constituent from tributaries and ground water (TGC; g m3) (5abc) Non-advective exchanges (E; m3 d1) between segments (1,2,3) (6abc) Dispersive transport (Y123; g d1) of constituent (C; g m3) between segments (7) Temperature (T; C)
TRSC ¼ advection þ lateral ± nonadvection ¼ XC þ TGC ± YC
(8) Photoperiod (Pphoto; hrs) (9) Irradiance at water surface (I0; mmole m2 s1) (10) M2 Water Level (h; m) (11) Water Depth of segment (hseg; m) (12) Light extinction coefficient (kt; m1) (13) Light extinction color (kcolor; m1) (14) Light at mid-depth (Im; mmole m2 s1) (15) Light at bottom (Iz; mmole m2 s1) (16) Light at bottom with seagrass (ISM; mmole m2 s1) (17) Solar angle (sinb; l ¼ latitude in radians; t ¼ model hour in radians) (18) Solar declination (d; 4 ¼ day of year in radians) (19) Phytoplankton mass change (Cphyto; gC d1) (19a) Gross primary production (G)
Q1 ¼ Q0 þ Qtgw1; Q2 ¼ Q1 þ Qtgw2; Q3 ¼ Q2 þ Qtgw3 X01 ¼ Q0*C0; X12 ¼ Q1*C1; X23 ¼ Q2*C2; X34 ¼ Q3*C3
TG1c ¼ Qtgw1* Ctgw1; TG2c ¼ Qtgw2* Ctgw2; TG3c ¼ Qtgw3* Ctgw3 *S1 E1 ¼ ðSQ20S 1Þ
0 S1 Q0 S2 Þ E2 ¼ ðE1 S1 þQ ðS2 S3 Þ
0 S2 Q0 S3 E3 ¼ E2 S2 þQ ðS3 S4 Þ
Y12 ¼ E1C1; Y21 ¼ E1C2; Y23 ¼ E2C2; Y32 ¼ E2C3; Y34 ¼ E3C3; Y43 ¼ E3C4 T ¼ 25 5*cos
2*p*day32 365
Pphoto ¼ 12 2*cos 2*p*day 365 " I0 ¼ MAX
Iamp *cos
!!
2*p*ðhour12Þ 2*Pphoto
#
; 0:0
h ¼ MSL þ AM2*cos 2*p* hour1:43 TM2 hseg ¼ h zseg kt ¼ kw þ ½kcolor þ ½aNTU *NTU þ ½aCHL *CHL kcolor ¼ acolor *eðbcolor *sseg Þ " # Im ¼ I0
I0 *eðkt *hseg Þ kt *hseg
Iz ¼ I0 *eðkt *hseg Þ
aSH *Cshoot sin b
ISM ¼ Iz *e d*p cos l*cos d*p *cos t sin b ¼ sin l*sin 180 180 d ¼ 0:39637 22:9133*cosð4Þ þ 4:02543*sinð4Þ 0:3872*cosð2*4Þ þ 0:052*sinð2*4Þ Dphyto ¼ Gphyto Rphyto Mphyto Exphyto Sedphyto þ RSSM ±TRSphyto Im c c Gphyto ¼ Pm * ðIk þI *ðeKT ðTTopt Þ Þ* ðk DIN * ðk DIP *Cphyto þDIN Þ þDIP Þ mÞ DIN
(19b) Respiration (R)
Rphyto ¼ ½kRphyto *eKT ðTTopt Þ *Cphyto
(19c) Mortality (M)
Mphyto ¼ ½kMphyto *eKT ðTTopt Þ *Cphyto
(19d) Exudation (Ex)
Exphyto ¼ ½GPPphyto *kexu Sedphyto ¼ Cphyto * hksed
(19e) Sedimentation (Sed)
c
DIP
seg
(19f) Resuspension (RS) (19g) Transport (TRS) (20) Organic nitrogen mass change (ON; gN d1) (20a) Production (P) (20b) Reminerlization (Rem) (20c) Temperature effect (Tfx) (20d) Sedimentation (Sed)
RSSM ¼ ½Aseg *CSM *kresus TRSphyto ¼ ½Xphyto þ TGphyto ±Yphyto DON ¼ ONp ONrem ONws ±TRSON ONp ¼ ½fONp *CN*ðRphyto þ Mphyto þ Exphyto þ Sedphyto Þ ONrem ¼ ½ON*kONrem *Tfx Tfx ¼ eKT ðTTopt Þ sed ONsed ¼ CON *fPON * kNP h seg
(20e) Transport (TRS) (21) Organic phosphorus mass change (OP; gP d1) (21a) Production (P) (21b) Reminerlization (Rem) (21c) Sedimentation (Sed)
TRSON ¼ ½XON þ TGON ±YON DOP ¼ OPp OPrem OPws ±TRSOP OPp ¼ ½fopp *CP*ðRphyto þ Mphyto þ Exphyto þ Sedphyto Þ OPrem ¼ ½OP*kOPrem *Tfx sed OPsed ¼ COP *fPOP * kNP h seg
(21d) Transport (TRS) 1 (22) Ammonium mass change (NHþ 4 ; gN d ) (22a) Production (22b) Nitrification (22c) Uptake (22d) Donor-Controlled Feedback
TRSOP ¼ ½XOP þ TGOP ±YOP DNH ¼ NHp NHnit NHsw NHup ±TRSNH NHp ¼ ½ON*kONrem *Tfx *ð1 fNOrem Þ NHnit ¼ ½NH*knit *Tfx Gphyto NHup ¼ *ð1 fNOup Þ*ðfbNH Þ CN ðNHNHlim Þ fbNH ¼ max min ðNH NH Þ ; 1:0 ; 0:0 thresh
(22e) Sediment-water (22f) Transport 1 (23) Nitrate-nitrite mass change (NO x ; gN d ) (23a) Production (23b) Denitrification (23c) Uptake (23d) Donor-Controlled Feedback (23e) Sediment-water
lim
NHsw ¼ ½kNHsw *Tfx TRSNH ¼ ½XNH þ TGNH ±YNH DNO ¼ NOp þ NOnit NOden NOsw NOup ±TRSNO NOp ¼ ½ON*kONrem *Tfx *fNOrem NOden ¼ ½NO*kden *Tfx Gphyto NOup ¼ *ðfNOup ÞðfbNO Þ CN ðNONOlim Þ min ðNO ; 1:0 ; 0:0 fbNO ¼ max thresh NOlim Þ NOsw ¼ ½kNOsw *Tfx
c
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Table 1 (continued ) Description
Equations
(23f) Transport 1 (24) Phosphate mass change (PO3 4 ; gP d ) (24a) Production (24b) Uptake
TRSNO ¼ ½XNO þ TGNO ±YNO DPO ¼ POp POsw POup ±TRSPO POp ¼ ½OP*kOP rem *Tfx G POup ¼ phyto CP *fbPO lim Þ min ðPOðPOPO fbPO ¼ max ; 1:0 ; 0:0 thresh POlim Þ
(24c) Donor-Controlled Feedback (24d) Sediment-water (24e) Transport (25) Sediment microalgae (SM; gC m2) (25a) Gross primary production (G) (25b) Phytoplankton input (PHYSM) (25c) Respiration (R) (25d) Mortality (M) (25e) Resuspension (RS) (26) Halodule wrightii (Hw; gC m2) (26a) Gross primary production (GHw)
POsw ¼ ½kPOsw *Tfx TRSPO ¼ ½XPO þ TGPO ±YPO DSM ¼ GSM þ PHYSM RSM MSM RSSM ISM *ðeKT ðTTopt Þ Þ *CSM GSM ¼ Pmax * ðIk þI SM Þ Sedphyto PHYSM ¼ *fphySM Aseg RSM ¼ ½kRSM *eKT ðTTopt Þ *CSM 2 MSM ¼ kMSM *CSM RSSM ¼ ½Aseg *CSM *kresus DHw ¼ GHw RHw ðMHw þ MSMw Þ TRHw CHw GHw ¼ PmHw * ðI IzþIz Þ * 1 CHwmax * S
seg þbSHw
RHw ¼ ½kRHw *eKT ðTTopt Þ *CHw kMHw *eKT ðTTopt Þ þ MHw ¼ S
*kSHw *CHw
kHw
(26b) Respiration (R) (26c) Mortality (M) (26d) Translocation (TR) (27) Thalassia testudinum (Tt; gC m2) (27a) Gross primary production (GHw)
aSHw
seg þbSHw
TRHw ¼ ½ðGHw RHw Þ*fTRHw DTt ¼ GTt RTt ðMTt þ MSTt Þ TRTt 2 0 B 6 6 B Iz CTt * 1 CTtmax *B GTt ¼ 6PmTt * ðIz þI kTt Þ 4 @
aSHw
1
(27c) Mortality (M)
RTt ¼ ½kRTt *eKT ðTTopt Þ *CTt 20 MTt
1
0
6B B 6B B ¼ 6BkMTt *eKT ðTTopt Þ Þ þ B 4@ @
Tt
3
C 7 7 aSTt C C*kSTt 7*CTt ðSseg cSTt Þ A 5 bS
1þe
(27d) Translocation (TR)
3
C 7 7 aSTt C C*eKT ðTTopt Þ 7*CTt ðSseg cSTt Þ A 5 bS
1þe
(27b) Respiration (R)
*eKT ðTTopt Þ *CHw
Tt
TRTt ¼ ½ðGTt RTt Þ*fTRTt
Fig. 3. (A) Time series of average daily inflow (Q0; m3 d1) from S-79 from 2002 to 2009. (B) Time series of average daily Q0 (shaded) and lateral inflow from tributaries and ground water (QTGW; m3 d1) for segments 1e3. (C) Time series of average daily Q0 (shaded) and salinity for segments 1 and 2 (S1 and S2). (D) Time series of average daily Q0 (shaded) and salinity (S) for segment 3 and the downstream boundary (S3 and S4).
C. Buzzelli et al. / Estuarine, Coastal and Shelf Science 151 (2014) 256e271
NH4+ (mg L-1)
262 0.30 0.25 0.20 0.15 0.10 0.05 0.00
02 NOx- (mg L-1)
0.6 0.5 0.4 0.3 0.2 0.1 0.0
PO4-3 (mg L-1)
02 0.30 0.25 0.20 0.15 0.10 0.05 0.00
02 ON (mg L-1)
1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
02 OP (mg L-1)
0.25 0.20
(A) Q0 NH4+
03
04
05
06
07
08
09
10
05
06
07
08
09
10
(B) Q0 NOx-
03
04
(C) Q0 PO4-3
Q0 03
04
05
06
07
08
09
10
(D) Q0 ON
03
04
Qtgw1 Qtgw2 Qtgw3
05
06
07
08
09
10
05
06
07
08
09
10
05
06
07
08
09
10
(E) Q0 OP
0.15 0.10 0.05 0.00
02 CHL (ug L-1)
40 30
03
04
(F) Q0 Chl
20 10 0
02
03
04
3 Fig. 4. Time series of watershed concentrations of (A) NHþ 4 , (B) NOx , (C) PO4 , (D) ON, (F) OP, and (G) CHL used as inputs to the CRE model from 2002 to 2009. Each constituent contributes loading at the estuarine head (Q0) and combined tributary and ground-water inflows for segments 1e3 (Qtgw1, Qtgw2, Qtgw3).
Changes in the above-ground carbon biomasses of Hw and Tt (CHw and CTt) resulted from gross production (GHW and GTt), respiration (RHw and RTt), mortality through leaf senescence (MHw and MTt), mortality due to salinity fluctuations (MSHw and MSTt), and translocation to below-ground components (TRHw and TRTt; Eqns. (26) and (27)). GHW and GTt each included terms for the maximum rate of photosynthesis (PmHw and PmTt), light limitation (IkHw and IkTt), a biomass maximum feedback function (CHwmax and CTtmax), salinity-response functions, Tfx, and the respective shoot biomasses (Eqns. (26a) and (27a); Herzka and Dunton, 1997; Burd and Dunton, 2001; Eldridge et al., 2004). The salinity-response functions for both Hw and Tt in the CRE were derived experimentally (Doering and Chamberlain, 2000; Doering et al., 2002; Buzzelli et al., 2012). Daily values for the specific rate of Hw blade turnover, and, the length of Tt blades over a range of experimental salinities (3.5e35) were scaled from 0 to 1 and fit to hyperbolic and sigmoidal curves, respectively (aSHw bSHw; aSTt, bSTt, cSTt; Table 2). RHw, RTt, MHw, and MTt were formulated similarly as respiration for phytoplankton (Eqns. (26b) and (27b); Eqns. (26c) and (27c)). The calculation of MSHw and MSTt included
the salinity-response function and a coefficient for salinity-based mortality (kSHw ¼ kSTt ¼ 0.001 d1; Table 2; Eqns. (26c) and (27c)). Constant fractions of net production (G-R) were translocated out of the shoots to the root-rhizomes (fTRHw ¼ 0.3; fTRTt ¼ 0.2; Table 2; Eqns. (26d) and (27d)). 2.5. Calibration and sensitivity testing Calibration involved iterative adjustments of coefficients to improve the correspondence between model predictions and 3 observed concentrations of CHL, NHþ 4 , NOx , PO4 , ON, and OP among the three segments. Data were available for a series of stations within the CRE through DBHydro of the SFWMD (CES01-09). Monthly average concentrations for each variable were aggregated by segment from the field data available from 2002 to 2009. The averages and standard deviations for each variable and segment were calculated for the dry (NovembereApril) and wet (MayeOctober) seasons for both the model predictions and the field data. The agreement between the seasonal-segmented averages of the model and field concentrations were compared using simple
C. Buzzelli et al. / Estuarine, Coastal and Shelf Science 151 (2014) 256e271
263
Table 2 List of model parameters. Parameter
Value
Unit
Description
Source
Iamp MSL TM2 AM2 kw aNTU aCHL aSH acolor bcolor Pm Ik Topt KtB kDIN kDIP kRphyto kMphyto ksed kexu CN CP CCHL kresus fONp fPON kONrem kOPrem fOPp fPOP kNPsed fNOrem kden fNOup knit kRSM kMSM fphySM NHlim NHthresh NOlim NOthresh POlim POthresh kNHsw1 kNHsw2 kNHsw3 kNOsw1 kNOsw2 kNOsw3 kPOsw1 kPOsw2 kPOsw3 PmHw IkHw kRHw kMHw kSHw aSHw bSHw CmaxHw fTRHw PmTt IkTt kRTt kMTt kSTt aSTt bSTt cSTt CmaxTt fTRTt Aseagrass
1000 0.0 12.42 0.111 0.15 0.062 0.058 0.002 2.89 0.096 2.5 100 25 0.069 0.05 0.005 0.15 0.15 0.35 0.1 6.625 106 50 0.025 0.7 0.1 0.08 0.08 0.7 0.1 0.35 0.5 0.01 0.45 0.01 0.1 0.5 0.5 200,000 400,000 200,000 400,000 500,000 1,500,000 277,572 273,507 113,524 277,583 67,273 15,273 74,299 12,270 33,803 0.2 319 0.01 0.005 0.001 1.257 7.256 10 0.3 0.1 150 0.005 0.0025 0.001 1.01 4.55 16.8 600 0.2 3.36Eþ07
mmole m2 d1
Amplitude of surface irradiance Mean sea level Period of M2 tide Amplitude of M2 tide Attenuation due to water Attenuation factor for turbidity Attenuation factor for chlorophyll a Attenuation due to canopy biomass Constant for salinity-color relationship Constant for salinity-color relationship Algal max photosynthetic rate Algal half-saturation for light Optimum temperature for rate processes Rate constant for temperature effect Algal half-saturation for nitrogen Algal half-saturation for phosphorus Algal basal respiration rate Algal basal mortality rate Algal sedimentation rate Fraction of gross production to exudation Redfield C:N molar ratio Redfield C:P molar ratio Carbon:chlorophyll a ratio Fraction of sediment microalgae resuspended Fraction of phytoplankton loss to ON production Fraction of ON that is particulate ON remineralization constant OP remineralization constant Fraction of phytoplankton loss to OP production Fraction of OP that is particulate Particulate sedimentation rate Fraction of ON remineralization to NO-x Basal rate of denitrification Fraction of phytoplankton N uptake - NO-x Basal rate of nitrification Sediment microalgae basal respiration rate Sediment microalgae basal mortality rate Fraction of phytoplankton sinking to SM pool Donor-controlled limitation value for NH mass Donor-controlled threshold value for NH mass Donor-controlled limitation value for NO mass Donor-controlled threshold value for NO mass Donor-controlled limitation value for PO mass Donor-controlled threshold value for PO mass NH exchange sediment to water 1 NH exchange sediment to water 2 NH exchange sediment to water 3 NO exchange sediment to water 1 NO exchange sediment to water 2 NO exchange sediment to water 3 PO exchange sediment to water 1 PO exchange sediment to water 2 PO exchange sediment to water 3 Halodule wrightii max photosynthetic rate Halodule wrightii light constant Halodule wrightii shoot respiration rate Halodule wrightii shoot mortality rate Halodule wrightii shoot mortality rate-salinity Halodule wrightii shoot salinity constant a Halodule wrightii shoot salinity constant b Halodule wrightii shoot max biomass Halodule wrightii fraction translocated to RR Thalassia testudinum max photosynthetic rate Thalassia testudinum light constant Thalassia testudinum shoot respiration rate Thalassia testudinum shoot mortality rate Thalassia testudinum shoot mortality rate-salinity Thalassia testudinum shoot salinity constant a Thalassia testudinum shoot salinity constant b Thalassia testudinum shoot salinity constant c Thalassia testudinum shoot max biomass Thalassia testudinum fraction translocated to RR Area of SAV in segment 3
Local data *** NOAA Ft. Myers NOAA Ft. Myers Calculated from Gallegos 2001 McPherson and Miller 1987 McPherson and Miller 1987 Buzzelli et al., 1999 McPherson and Miller 1987 McPherson and Miller 1987 Muylaert et al., 2005 Grangere et al., 2009 Buzzelli et al., 1999 Buzzelli et al., 1999 James et al., 2005 James et al., 2005 Lucas et al., 2009 Kuo and Park 1994 Lucas et al., 2009 Larsson and Hagstrom 1979 Walsh et al., 2006 Walsh et al., 2006 Walsh et al., 2006 Buzzelli et al., 1999 Calibration Calibration Calibration Calibration Calibration Calibration Kuo and Park 1994 Calibration Howes et al., 2008 Calibration Walsh et al., 2006 Buzzelli et al., 1999 Buzzelli et al., 1999 Calibration Calibration-CRE data Calibration-CRE data Calibration-CRE data Calibration-CRE data Calibration-CRE data Calibration-CRE data Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Buzzelli et al., 2013a Burd and Dunton 2001 Burd and Dunton 2001 Burd and Dunton 2001 Burd and Dunton 2001 Calibration Calibration - Doering et al., 2002 Calibration - Doering et al., 2002 Calibration - CRE data Calibration Eldridge et al., 2004 Herzka and Dunton 1997 Eldridge et al., 2004 Eldridge et al., 2004 Calibration Calibration e Doering et al., 2002 Calibration e Doering et al., 2002 Calibration e Doering et al., 2002 Calibration e CRE data Calibration CRE data
m hours m m1 m1 NTU1 m3 mg1 m1 m2 gC1 m1 Unitless d1 mmole m2 d1 C 1 C g N m3 gP m3 d1 d1 m d1 Unitless fraction mole C moleN1 mole C moleP1 gC gChla1 Unitless fraction Unitless fraction Unitless fraction d1 d1 Unitless fraction Unitless fraction m d1 Unitless fraction d1 Unitless fraction d1 d1 m2 gC1 Unitless fraction gN gN gN gN gP gP gN d1 gN d1 gN d1 gN d1 gN d1 gN d1 gP d1 gP d1 gP d1 d1 mmole m2 d1 d1 d1 d1 Unitless Unitless g C m2 Unitless fraction d1 mmole m2 d1 d1 d1 d1 Unitless Unitless Unitless gC m2 Unitless fraction m2
C. Buzzelli et al. / Estuarine, Coastal and Shelf Science 151 (2014) 256e271
coefficients (kRphyto, kden, kONrem, kDIN) were selected because of their importance in water column biogeochemical cycling. In separate simulations each of these coefficients was varied by 0% (base), 10%, 20%, 50%, þ10%, þ20%, and þ50%. The effects of the altered coefficients on concentrations of CHL, dissolved inorganic nitrogen (DIN ¼ NHþ 4 þ NOx ), and the total light extinction coefficient (kt) throughout the entire CRE were quantified using the average percent difference between the base and specific test simulation (%diff ¼ (test base)/base*100). Segment-specific model concentrations of CHL and DIN were multiplied by the respective segment volumes (V1, V2, V3), summed, and divided by the total volume of the CRE to derive volume-weighted concentrations for the entire CRE (CHLCRE and DINCRE). Daily values for kt were averaged across all three segments (ktCRE). There were a total of 84 simulations to test for model sensitivity (4 coefficients 7 values 3 response variables).
correlation (r; James et al., 2005). Calibration was assumed to be complete when correlation coefficients among all water constituents in all three segments were maximized. There were limited data available to calibrate submarine light dynamics, biogeochemical rate processes, and benthic biomass estimates. Water column gross production was measured at four locations along the CRE in February, April, May, June, and July of 2009 (Phlips and Mathews, 2009). The assessment of sedimentwater exchanges of dissolved N and P from February 2008 included the determination of denitrification and sediment microalgal biomass (Howes et al., 2008; Buzzelli et al., 2013b). Data from these studies were aggregated and averaged across the entire CRE for qualitative comparison to model results. Total light extinction in the water column of the CRE was monitored at multiple stations sporadically from 2005 to 2009 (CES01-CES09; Fig. 1). The average monthly kt predicted by the model was analyzed graphically relative to the field values for each segment for this period of record. Seagrass biomass was monitored approximately monthly at four sites in the lower CRE from 2004 to 2007 (Mazzotti et al., 2007). Data on Hw and Tt shoot biomasses (gdw m2) were converted to C units (g C m2) to derive a time series for each species that were compared to model predictions (Duarte, 1990). The model framework included 23 state variables, ~75 coefficients, and hundreds of physical and biogeochemical processes. Based on previous experience with estuarine water quality models, preliminary simulations, and many calibration tests, four
2
04
05
06
07
08
09
1.5
kntu
15
0 02
10
1.0 0.5 0.0 03
04
05
06
07
08
09
10
35
3.0
(E) Segment 2
30
model data
5
2.5
25
4
salinity
-1
kcolor
5
(B) Segment 2
ktotal (m )
20
10
model data
03
2.0
salinity kchl
3 2
2.0
20
1.5
15
1.0
10
1
0.5
5 03
04
05
06
07
08
09
0 02
10
0.0 03
04
05
06
07
08
09
10 3.0
35
6
(C) Segment 3
salinity kchl
30
model data
salinity
3 2
(F) Segment 3
2.5
kcolor kntu
25
4
2.0
20
1.5
15
1.0
10
1
5
0 02
0 02
03
04
05
06
07
08
09
10
m-1
salinity
-1
ktotal (m )
3
6
-1
2.5
25
4
1
ktotal (m )
3.0
(D) Segment 1
30
5
5
Freshwater inflow to the CRE reflected the dry-wet seasonality of south Florida (Buzzelli et al., 2013c, 2013d). Releases of freshwater through S-79 in both dry and wet seasons followed the operational schedule to maintain pool elevations in the C-43 canal and Lake Okeechobee (SFWMD, 2010; Buzzelli et al., 2014a). Both the sub-tropical seasonality and water management practices that
m-1
(A) Segment 1
0 02
3.1. Freshwater inflow, salinity, and light
35
6
0 02
3. Results
m-1
264
0.5 0.0 03
04
05
06
07
08
09
10
Fig. 5. Time series of light extinction coefficient (kt m1) from 2002 to 2009. Comparison between data (open circles) and model (lines) for Segment 1 (A), Segment 2 (B), and Segment 3 (C). Time series of average segment salinity (S; gray shade) and the components of kt. Included are light extinction due to chlorophyll a (kchl; green), color (kcolor; yellow), and turbidity (kntu; black dash) and S for Segment 1 (D), Segment 2 (E), and Segment 3 (F). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
C. Buzzelli et al. / Estuarine, Coastal and Shelf Science 151 (2014) 256e271 1.5
0.15
50
(D) Segment 1 OP 40
0.9
0.09
30
0.6
0.06
20
0.3
0.03
10
04
05
06
07
08
09
10
0.00 02
03
04
05
07
08
09
0 02
10
0.15
40
0.9
0.09
30
0.6
0.06
20
0.3
0.03
10
04
05
06
07
08
09
10
0.00 02
03
04
05
06
07
08
09
0 02
10
0.15
1.5
(C) Segment 3 ON 0.9
0.09
15
0.6
0.06
10
0.3
0.03
5
05
06
07
08
09
10
0.00 02
07
08
09
10
04
05
06
07
08
09
10
09
10
(I) Segment 3 CHL 20
04
03
(F) Segment 3 OP 0.12
03
06
25
1.2
0.0 02
05
(H) Segment 2 CHL
0.12
03
04
(E) Segment 2 OP
1.2
0.0 02
03
50
(B) Segment 2 ON
g m-3
06
mg m-3
03
mg m-3
0.12
1.5
g m-3
(G) Segment 1 CHL
1.2
03
04
05
06
07
08
09
0 02
10
mg m-3
g m-3
(A) Segment 1 ON
0.0 02
265
03
04
05
06
07
08
Fig. 6. Time series of monthly concentrations in the water column from CRE monitoring data (points) and the simulation model (line). (AeC) Organic nitrogen (ON; g m3) in Segments 1e3. (DeF) Organic phosphorus (OP; g m3). (GeI) Chlorophyll a (CHL; mg m3).
0.25
0.7
(A) Segment 1 NH4+
0.30
(D) Segment 1 NOx-
0.6
0.20
g m-3
0.5 0.15
0.4
0.10
0.3
0.20 0.15 0.10
0.2 0.05 0.00 02
0.05
0.1
03
04
05
06
07
08
09
10
0.0 02
03
04
05
06
07
08
09
10
(B) Segment 2 NH4+
0.20
(E) Segment 2 NOx-
0.6
0.4
0.10
0.3
03
04
05
06
07
08
09
10
(H) Segment 2 PO4-3
0.25
0.5 0.15
0.00 02 0.30
0.7
0.25
g m-3
(G) Segment 1 PO4-3
0.25
0.20 0.15 0.10
0.2 0.05 0.00 02
0.05
0.1
03
04
05
06
07
08
09
10
0.25
0.0 02
03
04
05
06
07
08
09
10
0.35
(C) Segment 2 NH4+
0.20
0.00 02
03
04
05
06
07
08
09
10
0.15
(F) Segment 3 NOx-
0.30
(I) Segment 3 PO4-3
0.12
g m-3
0.25 0.15
0.20
0.09
0.10
0.15
0.06
0.10 0.05 0.00 02
0.03
0.05
03
04
05
06
07
08
09
10
0.00 02
03
04
05
06
07
08
09
10
0.00 02
03
04
05
06
07
Fig. 7. Time series of monthly concentrations in the water column from CRE monitoring data (points) and the simulation model (line). (AeC) Ammonium 3 3 3 1e3. (DeF) Nitrate þ nitrite (NO x ; g m ). (GeI) Ortho-phosphate (PO4 ; g m ).
08
(NHþ 4;
09
10 3
gm
) in Segments
0.28 0.08 0.90 0.38 0.79 0.03 0.07 0.42 0.45 0.02 0.65 0.37 0.88 0.98 0.65 0.94 0.51 0.69 0.63 0.75 0.76 0.07 0.08 0.51 0.02 0.02 0.01 0.02 ± ± ± ± 0.06 0.05 0.04 0.05 0.24 0.18 0.11 0.19 ± ± ± ± 0.57 0.49 0.40 0.48 0.01 0.01 0.01 0.02 ± ± ± ± 0.07 0.05 0.03 0.05 0.05 0.05 0.02 0.06 ± ± ± ± 0.15 0.15 0.07 0.12 0.01 0.01 0.01 0.02 ± ± ± ± 0.05 0.03 0.02 0.03 4.0 3.8 2.6 4.1 ± ± ± ± 14.2 11.6 8.5 11.4 0.01 0.02 0.01 0.02 ± ± ± ± 0.06 0.06 0.03 0.05 0.06 0.12 0.10 0.03 ± ± ± ± 1.20 0.98 0.52 0.90 0.04 0.03 0.01 0.05 ± ± ± ± 0.12 0.10 0.03 0.09 0.06 0.07 0.03 0.09 ± ± ± ± 0.20 0.16 0.04 0.13 0.02 0.01 0.01 0.02 ± ± ± ± 3.9 5.2 1.1 4.6 ± ± ± ±
0.06 0.05 0.03 0.05
0.21 0.02 0.02 0.44 0.14 0.34 0.43 0.51 0.67 0.76 0.02 0.75 0.91 0.84 0.54 0.68 0.003 0.26 0.05 0.04 0.04 0.03 0.03 0.03 0.43 0.31 0.25 0.33 6.8 5.1 3.6 5.2 0.05 0.05 0.04 0.05 0.99 0.81 0.59 0.79 4.5 2.5 1.5 3.4 ± ± ± ±
Dry Seg 1 7.4 2 7.1 3 3.9 CRE 6.2 Wet Seg 1 9.4 2 8.8 3 3.2 CRE 7.1
CHL
0.05 0.04 0.04 0.04
± ± ± ±
0.02 0.02 0.01 0.02
0.28 0.11 0.07 0.15
± ± ± ±
0.07 0.09 0.03 0.11
0.07 0.05 0.04 0.06
± ± ± ±
0.02 0.01 0.02 0.02
ON
± ± ± ±
0.01 0.10 0.07 0.19
OP
± ± ± ±
0.01 0.02 0.02 0.01
CHL
± ± ± ±
2.3 1.5 0.7 2.1
0.14 0.13 0.09 0.12
± ± ± ±
0.04 0.04 0.03 0.04
0.29 0.29 0.15 0.25
± ± ± ±
0.06 0.06 0.05 0.09
0.07 0.05 0.03 0.05
± ± ± ±
0.01 0.01 0.01 0.02
ON
± ± ± ±
0.13 0.08 0.06 0.12
OP
± ± ± ±
0.02 0.01 0.01 0.01
0.61 0.50 0.09 0.63
ON PO3 4 NO-x NHþ 4 CHL
r
PO3 4 NO x NHþ 4 Model
PO3 4 NO x
The agreement between field observations and model predictions varied by constituent (CHL, ON, OP, NH, NO, PO), segment (1, 2, 3), year (2002e2009), season (dry vs. wet), and month (Figs. 6 and 7). The model provided better predictions of ON concentrations in Segments 1 and 2 during the 2002e2006 wet period compared to drier conditions from 2007 to 2009 (Fig. 6AeB). Agreement was reversed for ON in Segment 3 as model concentrations better approximated monthly average values during the last three years of simulation (Fig. 6C). The model generally underestimated average ON concentrations within the CRE (0.33 and 0.48 g m3 vs. 0.79 and 0.90 g m3) with correlation coefficients between the data and model of 0.51 (dry) and 0.42 (wet; Table 3). Average Model OP was similar in magnitude to field concentrations in both the dry and wet seasons (0.03 and 0.05 g m3 vs. 0.05 and 0.05 g m3). The model overestimated OP concentrations in Segment 2 from 2002 to 2007 (Table 3; Fig. 6E). CHL was the most variable of the constituents in the calibration data (Table 3). The variance in average monthly CHL observed in the CRE increased with the onset of drier conditions in 2007. CHL concentrations from the model were within range of observations in Segments 1 and 2 from 2002 to 2006 (Fig. 6GeH). The correlations between the CHL model and data were 0.63 and 0.51 in the dry and wet seasons, respectively (Table 3). While the model best approximated data observations in Segment 1 (rdry ¼ 0.61 and rwet ¼ 0.76), the model over-estimated CHL in the wet season across all three segments (14.2 vs. 9.4 mg m3, 11.6 vs. 8.8, 8.5 vs. 3.2 mg m3). Average monthly concentrations of NH in the model and field observations varied greatly from 0.01 to 0.24 g m3 in Segments 1 and 2 (Fig. 7AeB). NH concentrations were reduced and less variable in Segment 3 (Fig. 7C). In situ NH concentrations were slightly greater in wet (0.05 g m3) vs. dry (0.04 g m3) seasons (Table 3). The model overestimated NH concentrations for all three segments in the dry season (0.14 vs. 0.05 g m3, 0.13 vs. 0.04 g m3, 0.09 vs. 0.04 g m3) with comparatively low correlation coefficients of 0.003, 0.26, and 0.05 (Table 3). The reduced NH concentrations
NHþ 4
3.2. Model calibration
Data
support regional flood protection are influenced by climatic variations in rainfall (SFWMD, 2012). Climatic variability accounted for comparatively wet conditions from 2002 to 2006 followed by drier conditions and drought across south Florida from 2007 to 2009. Q0 ranged from 0.0 to 5.4 107 m3 d1 during 2002e2009 (Fig. 3). Inflow was generally greater from 2002 to 2006 before the onset of drought. Lateral inflows from tributaries and ground water (Qtgw123) followed patterns of Q0 with values approximately an order of magnitude less than discharge at S-79 (Fig. 3B). The largest segment (Segment 2) had the greatest amount of lateral freshwater inflow. S ranged from 0 to 20 in Segment 1, 0 to 30 in Segment 2,
