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Estuarine, Coastal and Shelf Science 97 (2012) 66e77

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Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Modification of the vertically generalized production model for the turbid watersof Ariake Bay, southwestern Japan

S.C. Tripathy a,b,*, J. Ishizaka b, E. Siswanto c, T. Shibata a, Y. Mino b

aDepartment of Earth and Environmental Sciences, Graduate School of Environmental Studies, Nagoya University, Furo cho, Chikusa-ku, Nagoya 464 8601, JapanbHydrospheric Atmospheric Research Center, Nagoya University, Furo cho, Chikusa-ku, Nagoya 464 8601, Japanc Institute of Geospatial Science and Technology, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor 81310, Malaysia

a r t i c l e i n f o

Article history:Received 29 August 2011Accepted 14 November 2011Available online 23 November 2011

Keywords:primary productionturbid watereuphotic depthinherent optical propertiesAriake Bay

* Corresponding author. Hydrospheric AtmospherUniversity, Furo cho, Chikusa-ku, Nagoya 464 8601, Ja

E-mail address: sarat@hyarc.nagoya-u.ac.jp (S.C. T

0272-7714/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.ecss.2011.11.025

a b s t r a c t

The vertically generalized production model (VGPM), which was designed for open ocean waters(Behrenfeld and Falkowski, 1997a; henceforth BF), was evaluated using in situ measurements of primaryproductivity (PP) in the characteristically turbid coastal waters of Ariake Bay, southwestern Japan, todevelop a regionally modified version of the model. The euphotic depth (Zeu)-integrated PP (IPP)calculated from the VGPM using in situ chlorophyll a (Chl a) and sea surface temperature (SST) wassignificantly overestimated (by factors of 2e3), but 52% of the observed variability was explained. Theweak correlation could have partially resulted from overestimations by the sub-models embedded in theoriginal VGPMmodel for estimation of Zeu (Morel and Berthon, 1989; henceforth MB) and the optimal Chla-normalized PP (pBopt). The sub-model estimates of pBopt and Zeu with in situ pBopt and Zeu showedsignificant improvement, accounting for 84% of the variability and causing less overestimation. Zeu wasthe most important parameter influencing the modeled IPP variation in Ariake Bay. Previous researchsuggested that the Zeu model, which was based on surface Chl a, overestimated in situ Zeu by a factor of 2e3, resulting in weak correlation between the modeled and in situ IPP. The Zeu sub-model was notaccurate in the present study area because it was basically developed for clear (case 1) waters. A betterestimation of Zeu could be obtained from the in situ remote sensing reflectance (Rrs) using a quasi-analytical algorithm (QAA) in this turbid water ecosystem. Among the parameters of PP models, pBoptis conventionally considered the most important. However, in this study pBopt was of secondary impor-tance because the contribution of pBopt to the variation in modeled IPP was less than the contribution ofZeu. The modeled and in situ pBopt were weakly correlated with 50% of the data points that overestimatedthe in situ values. The estimation of Chl awas improved by optimizing the Chl a algorithm with in situ Rrsdata. Incorporating the QAA-based Zeu and the optimized Chl a and constant (median) pBopt value led toimproved performance of the VGPM for the study area. Thus, even though the VGPM is a global openocean model, when coupled with turbid water algorithms for Zeu and Chl a and constant (median) pBopt, itprovided realistic estimates of IPP in the turbid water ecosystem of Ariake Bay.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Coastal seas are regions of high biological productivity that playa significant role in the global carbon cycle. While accounting foronly 8% of the surface area of the global ocean (Sverdrup et al.,1942), they contribute up to 30% of the oceanic primary produc-tion (Walsh et al., 1988; Longhurst et al., 1995). High primaryproductivity (PP) in these ecosystems is triggered by the supply of

ic Research Center, Nagoyapan.ripathy).

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inorganic nutrients from riverine and/or terrestrial inputs, coastalupwelling, and heterotrophic nutrient recycling (Walsh et al., 1991;Holt et al., 2009). Coastal waters often have more complex bio-optical properties compared to the open ocean and are thus clas-sified as case 2 waters. In case 2 waters, suspended sediments andyellow substances vary independently of phytoplankton concen-tration and play a dominant role in determining the optical prop-erties of the water body (IOCCG, 1999). Besides the tidal effect, thestrong link of these regions to terrestrial inputs of nutrients, sus-pended substances (SS), and colored dissolved organic matter(CDOM) creates high spatial and temporal variabilities that makethese ecosystems difficult to predict with much confidence(Harding et al., 2002).

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e77 67

Primary productivity in nutrient-rich coastal waters is a func-tion of three basic variables: phytoplankton biomass, light avail-ability, and biomass-specific carbon fixation rate (Behrenfeld andFalkowski, 1997b). The PP should be predictable in terms of theabove three basic variables, if appropriately expressed. Previousstudies have described spatial and seasonal changes in coastalwater PP (e.g., Malone, 1977; Joint and Pomeroy, 1981), but thefindings from specific locations cannot be easily related to othercoastal systems. Thus, site-specific understanding of the variabilityin the factors influencing PP is vital.

One of the major goals of modern biological oceanography is toacquire a better understanding of PP in various oceanic provinces,with a special emphasis on marine carbon cycling and climatechange on regional to global scales (Smith et al., 1989). Ona regional scale, the PP of the sea is quantified by field measure-ments. On a global scale, this is done using satellite remote sensing,which offers synopticity and large areal coverage (Raateoja et al.,2004). However, the coastal ocean has been ignored to a largeextent in the global carbon budget (Borges et al., 2005). This couldbe due to the complexity of the problem, or the unavoidableinfluence of SS and CDOM in the measurement and modeling ofsatellite-based PP in coastal areas (Sathyendranath et al., 1989;Smyth et al., 2006).

Remotely sensed ocean color cannot provide adequate infor-mation on oceanic PP without the support of models (Babin et al.,1995) and sea truth data. Several empirical, analytical, and bio-optical models are currently used for deriving ocean PP. Thevertically generalized production model (VGPM) formulated byBehrenfeld and Falkowski (1997a; henceforth BF) is one of thesimplest and most commonly used models for estimating PP fromsatellite-based chlorophyll data. The VGPM is a light dependent,vertically integrated model that characterizes environmentalfactors affecting primary production into those that influence therelative vertical distribution of primary production and those thatcontrol the optimal efficiency of the productivity profile(Behrenfeld and Falkowski, 1997b). The advantage of the VGPM isthat it has minimal parameterization of input variables and incor-porates remotely sensed satellite data to derive PP.

Fig. 1. Sampling locations in Ariake Bay, Japan. The circles and solid dots indica

The VGPM has been used by many researchers to estimate thePP from remote sensing measurements in clear ocean environ-ments (Field et al., 1998; Kahru and Mitchell, 2002; McClain et al.,2002; Moore et al., 2002; Gregg et al., 2003; Kahru et al., 2004;Kameda and Ishizaka, 2005; Yamada et al., 2005; Siswanto et al.,2006). However, there have been few applications of the VGPM tocoastal waters (Harding et al., 2002; Ishizaka et al., 2007; Hydeet al., 2008). Despite considerable understanding of the photo-synthetic process and knowledge of the ocean optics that deter-mine ocean color signals, satellite-based PP data often have limitedsuccess reproducing the observed variability of PP data (Siegel et al.,2001; McClain et al., 2002). Comparisons of PP models (Campbellet al., 2002; Carr et al., 2006) have shown that modeled estimatesof PP are generally within a factor of two of the carbon-basedestimates. The systematic biases associated with the algorithmscould possibly be eliminated by re-parameterizing the originalrelationships in accordance with in situ data. Thus, the develop-ment of site-specific models for estimation of the PP is highlydesirable. Here we attempt to develop a modified version of theVGPM for a turbid coastal water body.

Ariake Bay (Fig. 1), located in southwestern Japan, is approxi-mately 90 km long and 20 kmwide, with an average depth of about20 m. The bay is characteristically dynamic (tidal amplitudew5e6 m), turbid, coastal water body, and is recognized asa eutrophic and highly productive ecosystem because of the supplyof nutrients from river discharge (annual mean total flow rate of thesix main rivers ¼ 222 m3 s�1) and anthropogenic sources(Matsuoka, 2004; Ishizaka et al., 2006; Tripathy et al., 2010). Thebay is mostly dominated by diatoms and / or dinoflagellates. AriakeBay is assumed to be a case 2 water body (Sasaki et al., 2008) due tothe considerable presence of SS and CDOM, with SS dominating.The PP in this bay is strongly influenced by the tidally inducedchange in the light environment (Tripathy et al., 2010; Shibata et al.,2010) and inputs of dissolved inorganic nutrients (Kawaguchi et al.,2002; Ishizaka et al., 2006). Measurements of 13C-based PP havebeen routinely performed in Ariake Bay since 2001 by our researchgroup; however the data are limited due to the constraints asso-ciated with the time-consuming incubation method and ship

te stations for primary production and other measurements, respectively.

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e7768

availability. Tripathy et al. (2010) have shown that spatiotemporalvariation of PP could occur in this bay because of the varyingchlorophyll a (Chl a) and underwater light environment caused bythe tidal cycle. Consequently, it is unlikely that the PP measuredintermittently at few fixed stations adequately represents the PPthroughout Ariake Bay. Satellite-modeled production has thepotential to augment the spatial and temporal samplings of PP.However, it is unknown if open ocean production models such asthe VGPM can accurately estimate the PP in this turbid, coastalenvironment.

Earlier studies have shown that inherent optical properties(IOP) such as absorption and backscattering coefficients can bedirectly linked to the constituents in water (Gordon and Morel,1983; Carder et al., 2004; Lee et al., 2007) and hence canprovide information on their concentrations. This in turn canfacilitate understanding of underwater light attenuation. The IOPcombined with downwelling light from the sun and sky deter-mines the water color, which is usually measured by the remotesensing reflectance, Rrs (Lee et al., 2002). The application of an IOP-based approach is expected to explain the light variation in turbidaquatic ecosystems like Ariake Bay more efficiently. Hence, theobjectives of this study are (1) to develop a regionally modifiedversion of the VGPM by substituting some of its originalembedded parameters with the derived parameters obtained fromin situ Rrs measurements, and (2) to verify that the in situ Rrs-baseddaily integrated primary production (IPP) estimates are accurateand comparable with the in situ IPP. To achieve these objectives,we compared the formulation and parameterization suggested byBehrenfeld and Falkowski (1997a) with in situ measured datasetsand adjusted the model parameters. The IPP for Ariake Bay wasthen derived using the modified VGPM and verified against anindependent dataset.

2. Materials and methods

Measurements of physicochemical and biological oceano-graphic variables were conducted over the entire bay on-board theT/V Kakuyo Maru during late spring, summer, and winter in2001e2009 (Fig. 1). The number of data points (n) varied fordifferent parameters, as described in the following sub-sections.

2.1. Chlorophyll a, photosynthetically active radiation, andattenuation coefficient

Water samples at discrete depths were collected using 12Niskin bottles (each 5 L) mounted on a carousel sampler (GeneralOceanic, USA), along with a conductivityetemperatureedepthprobe (CTD, SBE 911þ). Water samples for Chl a measurementwere filtered through Whatman GF/F glass fiber filters (25 mm)under low suction pressure (<0.01 MPa). The filters were dippedinto N, N-dimethylformamide (Suzuki and Ishimaru, 1990), andthe extracted pigments were measured fluorometrically (TurnerDesign, 10-AU) according to the method of Holm-Hansen andRiemann (1978). The entire analysis was performed undersubdued light to prevent photodegradation of the pigments.Underwater photosynthetically active radiation (PAR) profileswere collected by a 4p sensor (QSP-2200, Biospherical Inc.)attached to the CTD frame. Concurrently, the daily PAR (E0, Ein-stein m�2 s�1) at the sea surface was continuously measured bya quantum scalar irradiance sensor (QSL-2100, Biospherical Inc.)mounted on the on-board incubator. The vertical diffuse attenu-ation coefficient of PAR, KPAR (m�1), was obtained by a regressionfit of the vertical underwater light profiles.

2.2. Absorption coefficients

The quantitative filter technique (QFT) of Mitchell (1990), whichis useful for separating the total absorption coefficient intocontributions by the particulate and dissolved fractions, was usedto measure phytoplankton absorption coefficients. Water samples(250 ml) were filtered onto 25 mm (Whatman GF/F) glass-fiberfilters. Subsequently, these filters were used to measure the totalparticulate absorbance between wavelengths of 350e750 nm inintervals of 1 nm, using a dual beam multi-purpose spectropho-tometer (MPS-2400, Shimadzu Inc.). A blank filter soaked withfiltered seawater (FSW) was used as the reference. The absorbanceat 750 nm was subtracted from all wavelengths as a scatteringcorrection. The corrected optical density ODf(l) obtained fromphytoplankton particles on the filters was converted into opticaldensity in suspension, ODs(l), using Cleveland and Weidemann’s(1993) equation:

ODsðlÞ ¼ 0:378 ODf ðlÞ þ 0:523 ODf ðlÞ2 (1)

Here ODs(l) is the optical density of the particles in suspension.The total particulate absorption coefficient was

apðlÞ ¼ 2:303 ODsðlÞ=X (2)

where 2.303 is the conversion factor from log10 to loge and X (m) isthe ratio between the volume of seawater filtered and the filterarea. After the above measurements, the filters were immersed inpure methanol for pigment extraction for at least 24 h. The de-colorized filters were then rinsed with FSW and re-measured(Kishino et al., 1985) for light absorption, in the same manner asabove. The non-phytoplankton particle absorption coefficientanph(l) was calculated using (Eq. (2)). The difference between ap(l)and anph(l) was considered to be the phytoplankton absorptioncoefficient aph(l). The averages of duplicate spectra were used inthis study.

To measure the CDOM absorption coefficients aCDOM(l), sub-samples were first filtered through 47-mm glass fiber filters(Whatman GF/F) to remove larger particles. They were then filteredthrough 47-mm Nuclepore membrane filters (pore size: 0.2 mm) toremove tiny particles. The filtrates were kept in the dark for a fewhours to allow equilibration to room temperature. The CDOMabsorbance was then measured by MPS-2400 between 300 and800 nm wavelength using 10 cm path-length quartz cells. Milli-Qwater was used as the reference. The measured absorbance wasnormalized to zero at 600 nm to remove the temperature-dependent artifacts (Pegau and Zaneveld, 1993) observed at 650and 750 nm. A blank (Milli-Q water) value was subtracted fromeach wavelength in the spectrum.

2.3. Radiometric measurements

Profiles of spectral radiationwere measured in the 350e750 nmspectral range with a high-resolution profiling reflectance radi-ometer (PRR-800/810, Biospherical Instruments). This spectror-adiometer, equipped with a cosine collector, measured the above-surface solar downwelling irradiance Ed(0þ, l), the in-water irra-diance profile Ed(z, l), and the in-water upwelling radiance profileLu(z, l) at 13wavelengths (l¼ 380, 412, 443, 465, 490, 510, 532, 555,565, 589, 625, 665, and 683 nm). At some stations, radiometricmeasurements were also obtained from TriOSscience (RAMSES)hyperspectral radiometers. The remote sensing reflectance Rrs(l)was calculated as

RrsðlÞ ¼ LwðlÞ=Ed�0þ; l

�(3)

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e77 69

where Lw(l) and Ed(0þ, l) are the radiance leaving thewater and thedownwelling incident irradiance above the sea surface, respec-tively. Ed(0þ, l) was obtained from deck measurements instead ofin-water measurements just below the sea surface becausemeasurements from surface layers are strongly perturbed by thefocusing and defocusing of sunlight by surface waves (Zaneveldet al., 2001). Lw(l) was determined as described by Lubac andLoisel (2007).

2.4. 13C-Based primary production

The daily PP was determined by the on-deck simulated in situ13C tracer-based incubation method (n ¼ 16). Water was sampledusing 12 Niskin bottles (5 L)mounted on a carousel sampler. At eachstation, water samples were collected at six discrete depths, cor-responding to 100, 50, 25, 10, 5, and 1% of incident PAR. After theaddition of NaH13CO3 (10 atomic %), the bottles were incubated inan on-deck incubator filled with circulating sea water. The neutraldensity filters simulated the corresponding light intensity in theincubator. The incubation experiments were started just beforedawn and continued for 24 h. After the incubation period, theincubated samples were filtered onto pre-combusted (450 �C, 4 h)Whatman GF/F glass fiber filters in dark conditions and the filterswere stored at �20 �C until further analysis was required.Measurements of the 13C atomic percent (13C to 12C ratio) concen-tration of the particulate organic carbonwere conducted by a stableisotope ratio mass spectrometer (Thermo Finnigan) equipped witha CHNeO analyzer (Thermo Quest, CE Instruments). The carbonfixation rate was estimated according to Hama et al. (1983), and theeuphotic depth (Zeu) IPP was estimated by trapezoidal integration.The optimal Chl a-specific primary production rate (PB) within thewater column was taken to be the measured pBopt.

2.5. Primary production model

The VGPM (BF, 1997a) is expressed as

IPP ¼ 0:66125� PBopt � ½E0=ðE0 þ 4:1Þ� � Zeu � Chl0 � DL (4)

where IPP, pBopt, E0, Zeu, Chl0, and DL are the euphotic zoneintegrated daily PP (mgC m�2 d�1), Chl-a normalized maximum PPin the vertical profile (mgC mgChl a�1 h�1), PAR at the sea surface(E m�2 d�1), depth (m) of the euphotic zone (depth where E0reduces to 1% of its value just beneath the surface) estimated fromsea surface Chl a (Chl0) according to Morel and Berthon (1989), seasurface Chl a (mg m�3), and day length (h) calculated as proposedby Kirk (1994), respectively. The light-dependent function E0/(E0 þ 4.1) describes the relative change in the light saturationfraction of the euphotic zone as a function of E0 (Behrenfeld andFalkowski, 1997b). The variable pBopt is expressed by BF asa seventh-order polynomial function of sea surface temperature(SST):

PBopt ¼ �3:27� 10�8ðSSTÞ7þ3:4132� 10�6ðSSTÞ6

�1:348� 10�4ðSSTÞ5þ2:462� 10�3ðSSTÞ4

� 0:0205ðSSTÞ3þ0:0617ðSSTÞ2þ0:2749ðSSTÞ þ 1:2956

(5)

2.6. Chlorophyll a algorithms

To retrieve Chl a, the NASA Chl a standard algorithm (OC4v4;O’Reilly et al., 2000) for case 1 waters (in which phytoplankton andtheir associated materials play a dominant role in determining the

optical properties of the water body) and the functional form of theChl a algorithm (Tassan, 1994) for case 2 waters were chosen.Although the OC4v4 has not previously been proposed for coastalwaters, it is usually applied without geographical restrictions. Toachieve more accurate Chl a retrieval, the linear formulations in theoriginal Tassan (1994) algorithm was replaced by polynomialformulations. Hereafter, the algorithm is referred to as Tassan-like(Siswanto et al., 2011). The OC4v4 and Tassan-like Chl a algo-rithms had the following functional forms, respectively:

Chla ¼ 10ðc0þc1Rþc2R2þc3R3þc4R4ÞR ¼ Log10ðmaxðRrsð440;490;510ÞÞ=Rrs555Þ

(6)

and

Chla ¼ 10ðc1þc2Log10ðRÞþc3Log210ðRÞÞR ¼ ðRrs443=Rrs555ÞðRrs412=Rrs490Þc0

(7)

Here c0, c1, c2, c3, and c4 are constant coefficients that areregionally optimized for better retrieval of the Chl a in Ariake Baythrough routine reiterative fitting. To evaluate the performance ofthe above Chl a algorithms, a type II reduced major axis (RMA)linear regression was used. Type II linear regression is usuallyapplied when both the dependent and independent variables haveuncertainties (Laws and Archie, 1981).

2.7. Analysis approach

To construct a modified version of the VGPM for Ariake Bay,the following steps were adopted: (1) performance of the originalVGPM was verified by comparing VGPM-based IPP (with in situChl a and SST as inputs) with in situ IPP, (2) the individualcontribution of the model embedded parameters (pBopt and Zeu) forVGPM-based IPP variation were assessed, (3) a quasi-analyticalalgorithm (QAA) method was used to derive Zeu by decompos-ing the in situ Rrs, (4) a better retrieval of Chl a was obtained byoptimizing the turbid water Chl a algorithm (Tassan, 1994) withthe in situ Rrs data, (5) a constant (median) pBopt was chosen for thedataset, (6) the sub-models of VGPM were replaced by the cor-responding parameters determined in this study, and (7) the IPPestimated by the new VGPM was verified using an independent insitu IPP dataset.

3. Results and discussion

3.1. Evaluation of the VGPM with 13C-based production

As the first step in model evaluation, the daily IPP estimated bythe VGPM from (Eq. (4)) was used with parameterization of pBoptfollowing (Eq. (5)) and the form of Zeu from Morel and Berthon(1989; henceforth MB). These estimated values were verified withthe in situ IPP (n ¼ 16) when in situ measurements (Chl a, PAR, SST,and Zeu) were used for the model inputs. The in situ IPP varied from484 to 6646 mgC m�2 d�1. The VGPM-based production, using insitu inputs, significantly overestimated (�1.2 e 2.5) the in situ IPPbut accounted for approximately 50% [r2 ¼ 0.52, root mean-squareerror (RMSE)¼ 3191, p< 0.001] of the observed variability (Fig. 2a).The weak correlation could be partly due to the fact that the pBopt(mgC mgChl a�1 d�1) and Zeu (m) proposed by BF and MB showedweak correlation (Fig. 3a, b) with in situ pBopt (r

2 ¼ 0.23, RMSE¼ 6.8,p < 0.05) and Zeu (r2 ¼ 0.24, RMSE ¼ 11, p < 0.05) data. The Zeuvalues in particular significantly overestimated (�1.2e 2) the in situvalues (Fig. 3b).

Substituting the BF and MB suggested pBopt and Zeu values withthe corresponding in situ pBopt and Zeu for the model components

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e7770

significantly improved estimates of the VGPM-based productionwere obtained, explaining 84% (r2 ¼ 0.84, RMSE ¼ 676, p < 0.0001)of variation in the in situ IPP (Fig. 2b). Thus, one or both of thesubstituted parameters appeared to strongly influence the corre-lation between the VGPM-based and in situ IPP. Consequently, theindividual contributions of pBopt and Zeu to the correlation betweenVGPM-based and in situ IPP were investigated. The results showedthat the BF pBopt was weakly correlated (r2 ¼ 0.23) with the in situpBopt values, with half of the data points being overestimated(Fig. 3a). Substitution of the in situ pBopt with the BF pBopt values in theVGPM formulation resulted in similar r2 but higher RMSE values(r2 ¼ 0.86, RMSE ¼ 1206, p < 0.0001), indicating a contribution ofpBopt to the correlation of IPP (Fig. 2c). Similarly, theMB Zeu estimateswere weakly correlated with the in situ Zeu (r2 ¼ 0.24) but signifi-cantly greater than (�2 e 3) the in situ Zeu, resulting in a drasticdecrease in the correlation (r2 ¼ 0.31, RMSE ¼ 2360, p < 0.025)between in situ and VGPM-based production (Fig. 2d).

The original VGPM-based IPP showed moderately weak corre-lation and overestimated the in situ IPP. To explain this over-estimation, the model was verified in three steps. First, the VGPMaccording to (Eq. (4)) was validated using in situ Zeu and pBopt asinputs. Significantly improved correlation (r2 ¼ 0.84; Fig. 2b) andlower RMSE indicated that the VGPM assumption is applicable forIPP estimations in Ariake Bay, at least with the input of in situ Zeuand pBopt. Second, the BF pBopt model (Eq. (5)) and in situ Zeu wereused as input for the IPP calculation. The BF pBopt model over-estimated 50% of the data points (Fig. 3a). Thus, the overestimationof the VGPM-based IPP can be attributed in part to the over-estimation of pBopt. Third, the MB Zeu model and in situ pBopt wereused for the IPP calculation. The derived Zeu was greatly over-estimated and showed low correlation with the in situ value(Fig. 3b). This could be ascribed to the moderately weak relation-ship (r2¼ 0.44) between Chl0 and Zeu in the study area. Hence, using

0

7500

15000

0 4000 8000

0 4000 80000

10000

20000

a

c

VG

PM

-bas

ed I

PP

(mgC

m-2

d-1)

In situ IPP

r2 = 0.52

Slope = 1.74

RMSE = 3191

r2 = 0.86

Slope = 1.61

RMSE = 1206

Fig. 2. Scatter plots of in situ and VGPM-based daily primary productivity obtained by usingin situ pBopt and modeled Zeu. The solid and dotted line represents the measured slope and

the derived Zeu (from Chl0) for the IPP calculation resulted ina significantly lower correlation and high RMSE between theVGPM-based and in situ IPP; indicating that the difference in Zeuhad a stronger impact on the correlation of IPP from VGPM andobservations.

The MB Zeu model, based on Chl0, did not hold well in Ariake Bayas it was developed for case 1 waters, where the underwater lightattenuation and thus Zeu are primarily functions of the absorptionby Chl a and water, unlike in Ariake Bay. Hence we used in situ Rrs-based measurements to derive Zeu and Chl a to develop a modifiedversion of the VGPM. Furthermore, due to the limited dataset andabsence of any consistent predictor for pBopt, the median (constant)value of the in situ pBopt dataset was used to calculate IPP. Incorpo-rating the QAA-based Zeu, optimized algorithm Chl a, and medianpBopt, themodifiedmodel, VGPMAB, produced very close estimates ofIPP (Fig. 7). A few data points were overestimated, especially forlow in situ IPP levels. This could be ascribed to the combined effectof using median pBopt and overestimating the QAA-based Zeu in highturbidity regions.

Notably, the VGPM, although a global open ocean model, per-formed reasonably in this turbid coastal water ecosystem. This wasconsistent with the findings reported by Harding et al. (2002) andHyde et al. (2008) in Chesapeake Bay and Massachusetts Bay in theUnited States, respectively. Furthermore, it is surprising that thelight-dependent function (E0/E0 þ 4.1) in the VGPM, whichdescribes the light saturation depth for PP in the water column,worked fairly well in this turbid bay. This was different from theresults reported by Ishizaka et al. (2007) in Sagami Bay, where theymodified the light-dependent function for a better estimation ofthe IPP.

The contribution of Zeu to the variability in the modeled IPP wasthemaximum among all the embedded parameters of VGPM in thisarea. Behrenfeld and Falkowski (1997a) reported that the errors in

0

7500

15000

0 4000 8000

0

4000

8000

0 4000 8000

b

d

(mgC m-2 d-1)

r2 = 0.84

Slope = 0.84

RMSE = 676

r2 = 0.31

Slope = 0.83

RMSE = 2360

(a) modeled pBopt and Zeu, (b) in situ pBopt and Zeu, (c) modeled pBopt and in situ Zeu and (d)the 1:1 line, respectively.

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e77 71

the IPP resulting from the estimation of Zeu were of secondaryimportance compared to the errors associated with the Chl a andpBopt estimation, which was inconsistent with our results. Afterincorporating the QAA-based Zeu, the model performance wassignificantly improved, even though QAA-Zeu explained only 58% ofthe observed variability in the measured Zeu. Thus, if more accuratemodels for the estimation of Zeu can be developed, the accuracy ofmodeled IPP in turbid water ecosystems like Ariake Bay is likely tobe further enhanced.

The daily IPP estimates fromVGPMAB overestimated (�1.2e 1.3)the independent IPP datasets obtained by a fast repetition-ratefluorometer (FRRF) (Fig. 7b). This could be due to two reasons:(1) the sampling locations were in close proximity to the ChikugoRiver mouth where turbidity was high and (2) the use of a constant(median) pBopt. Results showed that the QAA method used in thisstudy overestimated Zeu measurements in high turbidity water.

0

20

40

0 20 40

0

40

80

120

0 40 80 120

BF

sugg

este

d P

Bop

t (m

gC m

gChl

a-1

d-1

)

In situ PBopt (mgC mgChl a-1 d-1)

r2 = 0.23

RMSE = 6.8

p < 0.05

r2 = 0.24

RMSE = 11

p < 0.05

MB

sug

gest

ed Z

eu (

m)

In situ Zeu (m)

a

b

Fig. 3. Scatter plots of (a) BF suggested and in situ pBopt and (b) MB suggested and in situZeu (n ¼ 16). The solid and dotted line represents the measured slope and 1:1 line,respectively.

3.2. Resolving Zeu from inherent optical properties

The Zeu parameter is a measure of water clarity. It is a qualityindex for an ecosystem and has strong implications for primaryproduction in the upper water column (Platt and Sathyendranath,1988; Behrenfeld and Falkowski, 1997b). In case 2 waters, errorsin the Chl0-based calculation of Zeu were found to be considerablylarger (Kirk, 1994). Because the light attenuation coefficients ofocean waters are generally determined by other components inaddition to phytoplankton particles (Sathyendranath et al., 1989), itis imperative to take all factors into account when calculatingvertical light attenuation. The underwater light attenuation incoastal environments (case 2), like Ariake Bay, is enhanced due toadditional in-water optical constituents such as SS and CDOM. Theinherent optical properties (IOP) such as absorption (a) and back-scattering (bb) of water can be directly linked to the waterconstituents (Gordon and Morel, 1983; Lee et al., 2007). Becausedetermination of Zeu by implementation of a Chl a-based modelfailed in Ariake Bay, we attempted to understand the possiblerelationship between IOP and vertical light attenuation there.

A multiple regression analysis (r2 ¼ 0.81, p < 0.0001, n ¼ 134)between the inverse of the in situ Zeu (1/Zeu) and the measuredsurface aph, anph, and aCDOM resulted in the expression

1=Zeu ¼ 0:913anph þ 0:261aph þ 0:070aCDOM þ 0:036 (8)

The constant term (0.036) in the above equation was consistentwith the experimental value (0.027) for pure water absorption, aw(Smith and Baker, 1978). The highest contribution rate for the Zeuvariation was from anph (23e89%), followed by aph (2e26%) andaCDOM (1e10%). Thus, absorption by non-phytoplankton particlesanph (p < 0.001) was the dominant factor for underwater lightattenuation in Ariake Bay. This was consistent with the results re-ported by Shibata et al. (2010). A strong linear relationship(r2 ¼ 0.80, p < 0.0001, n ¼ 134) between the in situ 1/Zeu and anph(Fig. 4a) further showed that anph alone could explain 80% of thevariability in the measured 1/Zeu. The Zeu calculated from themultiple regression relationship were well correlated (r2 ¼ 0.71,RMSE ¼ 1.79, p < 0.001) with the in situ Zeu (Fig. 4b). However, theslope deviated from the 1:1 line as the euphotic depth increased.

To estimate the primary production based on satellite data, eachinput variable must either be a constant, directly measured fromsatellites, or parameterized from other satellite measurements(Hyde et al., 2008). To obtain the total absorption(aph þ anph þ aCDOM) from the Rrs, we adopted the quasi-analyticalalgorithm (QAA) developed by Lee et al. (2002). This enabledderivation of the total absorption (a) and backscattering coeffi-cients (bb) of oceanic waters by inversion of the spectral Rrs(l). Both

a and bb at 490 nm, a(490) and bb(490), were derived for eachstation. Subsequently the Zeu was estimated according to Lee et al.(2007).

The available in situ Rrs data (n ¼ 80) during the years2001e2008 were used as input for the QAA to obtain a(490) andbb(490), which subsequently were used for estimation of Zeu. TheQAA total absorption and backscattering-based Zeu (henceforthQAA-based Zeu) showed significantly improved correlation(r2 ¼ 0.58, RMSE ¼ 2.23, p < 0.001) with the in situ Zeu (Fig. 4c)compared to the Chl0-based Zeu (Fig. 3b, r2 ¼ 0.24, RMSE ¼ 6.8,p < 0.05), as described earlier. Thus, the IOP-based approach toobtain Zeu was better than the biomass-based approach for AriakeBay. However, the QAA-based Zeu overestimated (underestimated)the in situ values at smaller (larger) euphotic depths.

An attempt was also made to derive Zeu from the decomposedRrs, according to Lee et al. (2002). The QAA has two steps. First, itcalculates the total absorption, a at a reference wavelength l0 andthen propagates the calculation to other wavelengths. Second, thetotal absorption a is spectrally decomposed into the contributionsof phytoplankton pigments and CDOM. The correlation betweendecomposed Rrs-based Zeu and in situ Zeu (figure not shown) wasweaker (r2 ¼ 0.53, RMSE ¼ 3.47, p < 0.001) than the relationshipbetween QAA-based Zeu and in situ Zeu. We therefore followed the

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e7772

total absorption and backscattering based method (Lee et al., 2007)for derivation of Zeu in Ariake Bay.

The results revealed that the absorption by non-phytoplanktonparticles anph could only account for 80% of the observed variability(Fig. 4a) in 1/Zeu. Furthermore, multiple regression analysis (Eq. (8))showed that among the above three components, anph contributedthe most to the Zeu variation. Our result is consistent with findingsfor Ariake Bay by other researchers (Ooshima and Abe, 2005;Shibata et al., 2010), who observed that non-phytoplankton parti-cles were the primary cause of the underwater light attenuation.The dominance of the non-phytoplankton particle absorptionobserved in this study was comparable to that found in other turbidcoastal areas. In studies in Florida, the contribution rates ofabsorption by suspended substances in Florida Bay (Phlips et al.,1995) and the Indian River Lagoon (Christian and Sheng, 2003)were reported to be 54e92% and 59e78%, respectively.

The Zeu derived using the multiple regression relationship (Eq.(8)) showed strong correlation (r2 ¼ 0.71) with the in situ Zeu(Fig. 4b), signifying that the derivation of Zeu from absorptionmeasurements was more accurate compared to the Chl a-basedapproach in Ariake Bay. Because of the lack of in situ data, (Eq. (8))did not incorporate the contribution of backscattering coefficientsfor Zeu variation.

Furthermore, to derive Zeu from remote sensing measurements,the in situ Rrs data were used to derive the a and bb coefficients at490 nm by employing the QAA (Lee et al., 2002). The Zeu valueswere calculated from the a and bb coefficients (Lee et al., 2007). TheQAA-based Zeu estimates were significantly better (Fig. 4c) than theZeu measured from surface Chl a. However, the overestimates atshallower Zeu, where the influence of turbidity was large, implied

0

10

20

30

40

0 10

0

0.1

0.2

0.3

0.4

0 0.1 0.2 0.3 0.4

a

In situ anph (m-1)

In s

itu

1/Z

eu

r2 = 0.80

p < 0.001

n = 134

In sit

QA

A-b

ased

Zeu

(m)

r2 = 0.58

RMSE = 2.2

p < 0.001

Fig. 4. Scatter plots of (a) in situ 1/Zeu and anph, (b) calculated Zeu from multiple regression rerepresents the measured slope and 1:1 line, respectively.

that the QAA applicability was weaker in the high turbidity zone ofthe study area. The role of backscattering was important indescribing the underwater light attenuation, especially in turbidwater. Backscattering coefficients derived from the QAA showedmoderately strong linear correspondence (r2¼ 0.49) with the in situZeu (figure not shown), indicating that information on light scat-tering is essential to examine further details of the influence of non-phytoplankton particles on Zeu variation in Ariake Bay.

3.3. Variation in pBopt

pBopt is potentially the most important variable in PP modeling,yet it is still inadequately described and its predictability needsfurther refinement (Behrenfeld and Falkowski, 1997a; Siswantoet al., 2006; Hyde et al., 2008). Remote sensing based estimatesof pBopt are possible by defining predictive relationships betweenpBopt and one or more environmental variables (e.g., temperature,light, Chl a) that can be obtained from remote sensing measure-ments (Behrenfeld et al., 2002). We tried to link the in situ pBopt withother environmental variables that could influence the variabilityin pBopt and could be remotely sensed. The in situ pBopt variedbetween 28.54 and 101.76 (58.0 � 18.77 mgC mgChl a�1 d�1).A third-order polynomial relationship between pBopt and SSTresulted in a low coefficient of determination (r2 ¼ 0.26). Moreover,the in situ pBopt did not show any distinct relationship with SST, PAR,and/or Chl a. Hence a pBopt model using the above environmentalvariables was not practicable in Ariake Bay. Furthermore, becauseof the small in situ dataset (n ¼ 16), fitting any kind of relationshipfunction between pBopt and other environmental variables was notfeasible in this study.

20 30 40

0

10

20

30

40

0 10 20 30 40

b

r2 = 0.71

RMSE = 1.79

p < 0.001

In situ Zeu (m)

Cal

cula

ted

Zeu

(m

)

u Zeu (m)

c

3

lationship and measured Zeu, (c) QAA-based Zeu and in situ Zeu. The solid and dotted line

Fig. 5. Relationship of in situ and modeled pBopt with in situ SST. The symbols representSST-dependent models proposed by different studies.

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e77 73

Earlier attempts to construct SST-dependent pBopt models(Behrenfeld and Falkowski, 1997a; Gong and Liu, 2003; Kamedaand Ishizaka, 2005; Siswanto et al., 2006) have resulted invarying shapes for the pBopt function. The present results showedthat the derivation of pBopt by implementation of the above modelformulations did not work effectively (Fig. 5) in Ariake Bay.Temperature alone could not explain the pBopt variation. This couldbe because the variations in pBopt were affected by the cumulativeeffects of light history, day length (photoinhibition), nutrientconcentrations, total biomass, phytoplankton size structure, andtaxonomic composition, which are not correlatedwith temperature(Cote and Platt, 1983; Behrenfeld and Falkowski, 1997a). Further-more, Chl a-specific absorption (a*ph) also showed a weak

0.1

1

10

100

1000

0.1 1 10 100 1000

0.1

1

10

100

1000

0.1 1 10 100 1000

OC

4v4

orig

inal

Chl

a (

mg

m-3

)

In situ Chl a (mg m-3)

In situ Chl a (mg m-3)

Tas

san

orig

inal

Chl

a (

mg

m-3

)

a

c

r2 = 0.75

RMSE = 0.47

p < 0.0001

r2 = 0.73

RMSE = 0.31

p < 0.0001

Fig. 6. Scatter plots of in situ Chl a versus (a) original OC4v4 coefficients-based Chl a, (b) opoptimized Tassan coefficients-based Chl a. The solid and dotted line represents the measur

relationship (r2 ¼ 0.12) with pBopt. Absence of a distinct predictiverelationship between pBopt and any of the environmental variablesin Ariake Bay and a small in situ dataset led us to use the in situmedian pBopt rate (62.12 mgC mgChl a�1 d�1) as a constant forcalculating the IPP. Constant pBopt rates have also been applied forthe derivation of PP in Antarctic coastal waters (Dierssen et al.,2000) and Massachusetts Bay (Hyde et al., 2008), when pBoptcould not be reliably predicted using any environmental variables.

pBopt was found to be of secondary importance in this studybecause of its relatively small contribution (compared to Zeu) to theobserved variability in modeled IPP. The importance of pBopt incalculation of VGPM-based IPP was investigated using the in situPBopt (Fig. 2b) and BF PBopt (Fig. 2c). The results of this study showedthat the BF PBopt model could not be effectively used to estimate theIPP in Ariake Bay. The temperature-dependent PBopt model sug-gested by BF has shown varying results in different study areas (e.g.,Muller-Karger et al., 2004; Kameda and Ishizaka, 2005; Siswantoet al., 2006). Hence it was essential to develop a better predictiveformulation for PBopt in Ariake Bay. Our results showed that eventhough the RMSE using the BF PBopt was larger, a similar coefficientof determination (r2) was observed when the in situ PBopt and BFPBopt were applied to the IPP calculation. Moreover, a median(constant) pBopt could be used for the IPP calculation by the VGPM inthis study.

Physiological adjustments to changes in light condition affectChl : accessory pigment ratios and alter the optical absorption crosssections a* (Kirk, 1994). Shibata et al. (2010) showed the photo-adaptive response of phytoplankton to varying light conditions inAriake Bay. Attempts to develop absorption-based pBopt models(Marra et al., 2007) were also unsuccessful, as pBopt and a*ph wereweakly related (r2 ¼ 0.14) in the study area. Much of the

0.1

1

10

100

1000

0.1 1 10 100 1000

0.1

1

10

100

1000

0.1 1 10 100 1000

In situ Chl a (mg m-3)

In situ Chl a (mg m-3)

Tas

san

opti

miz

ed C

hl a

(m

g m

-3)

d

b

r2 = 0.78

RMSE = 0.23

p < 0.0001

r2 = 0.74

RMSE = 0.25

p < 0.0001

OC

4v4

opti

miz

ed C

hl a

(m

g m

-3)

timized OC4v4 coefficients-based Chl a, (c) original Tassan coefficients-based Chl a, (d)ed slope and 1:1 line, respectively.

0

1000

2000

3000

0

4000

8000

0 1000 2000 3000

0 4000 8000

In situ IPP (mgC m-2 d-1)

VG

PM

AB-b

ased

IP

P (m

gC m

-2 d

-1)

r2 = 0.85

RMSE = 715

p < 0.0001

In situ IPP (mgC m-2 d-1)

VG

PM

AB-b

ased

IP

P (m

gC m

-2 d

-1)

r2 = 0.68

RMSE = 187

p < 0.005

a

b

Fig. 7. Comparison of modified VGPM (VGPMAB)-based IPP with (a) 13C-based in situIPP (n ¼ 14 (Rrs data for 2 PP stations were not available)), (b) FRRF single instanta-neous cast-based IPP (n ¼ 9). The solid and dotted line represents the measured slopeand 1:1 line, respectively.

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e7774

unexplained variance in pBopt is due to physiological adjustments bythe phytoplankton to variable growth conditions, which cannot beadequately accounted for by a single-factor (e.g., SST) PBopt model(Behrenfeld and Falkowski, 1997a). Hence, improved VGPMperformances require transformation of the single, statistical PBoptfunction into multiple, mechanistic models (e.g., Balch and Byrne,1994; Kameda and Ishizaka, 2005).

3.4. Optimization of Chl a algorithms using the in situ Rrs

The Chl a was derived from the in situ Rrs using the originalOC4v4 and Tassan-like algorithms. Iterative fittings on the Chla algorithms were performed (as described in Section 2.6) tooptimize, with respect to the in situ Chl a and Rrs data, the coeffi-cients for better retrieval of Chl a in Ariake Bay. Statistical param-eters along with the original and optimized coefficients are shownin Table 1. Henceforth, the optimized versions of the OC4v4 and

Table 1Results of statistical analysis showing the performances of original and locally optimizedlinear regression fit between in situ and algorithm derived bio-optical variables in logel

Chl a algorithms Constant coefficients Cn, n ¼ 0, 1, 2, 3, 4 R

OC4v4 0.366, �3.067, 1.930, 0.649, �1.532 0OC4v4AB 0.188, �2.836, 23. 136, 37.750, �21.437 0TS �0.012, 0.342,�2.511, �0.277 0TSAB �0.278, �0.047, �2.416, 1.480 0

The original coefficients from Tassan’s algorithm (see Tassan, 1994) are not mentionedcoefficients modified by Siswanto et al. (2011) for Yellow and East China Seas were used

Tassan-like algorithm for Ariake Bay are termed OC4v4AB and TSAB,respectively.

The in situ Chl a used to construct Chl a algorithms ranged from1.10 to 83.56 mg m�3 (17.10 � 18.27 mg m�3). Data collected duringsummer, spring, and winter seasons represented 46, 13, and 41%of the total sample, respectively. The mean Chl a concen-trations during spring (4.16 � 2.87 mg m�3) and winter(5.44� 3.87mgm�3) weremore or less similar, with slightly highervalues during winter. During summer themean Chl a concentration(31.20� 18.55 mgm�3) was the highest, which could be ascribed tothe occurrence of “algal bloom” during this season in the studyarea.

The Chl a derived from the original OC4v4 and Tassan-likealgorithms showed strong significant correlation with the in situChl a (Fig. 6a, c). The coefficients of determination for both algo-rithms were similar (r2 ¼ 0.73 and 0.75), with higher RMSE values(0.47) for Tassan-like algorithms. The OC4v4 underestimated athigh Chl a concentrations (corresponding to the occurrence of algalblooms during summer in the bay), whereas the Tassan-like algo-rithm overestimated, especially at stations with comparatively lowChl a. High Rrs555 corresponded to comparatively low Chla stations, indicating the presence of high suspended sediments,which in turn resulted in high Rrs and thus overestimation of Chl a.The optimized version of the Tassan-like (TSAB) Chl a algorithmwasbetter than the OC4v4 (OC4v4AB) algorithm (Fig. 6b, d) and depic-ted improved r2 and lower RMSE values (Table 1), indicating thatTSAB would facilitate improved retrieval of Chl a in Ariake Bay.

Production values determined using satellite models alsodepend on the accuracy of satellite retrievals of Chl a, which arelimited in waters containing SS and/or CDOM (Smyth et al., 2006).The predominant source of error originated from extraction of theChl a data from remotely sensed measurements of water-leavingradiance. In this study, Chl a was derived from the optimizedTassan-like algorithm, designed for coastal waters, which showedstrong correlation with in situ Chl a. For Chl a algorithm optimi-zation, in situ Rrs data were used, and this could be the cause of thestrong correlation.

However, due to problems associated with atmosphericcorrection, especially in turbid coastal waters, the performance ofthe optimized algorithm may be low when satellite Rrs is used asinput. Gordon and Clark (1980) predicted that the satellite-derivedChl awould be very inaccurate at concentrations � 10 mg m�3 dueto poor atmospheric correction. High Chl a (�10 mg m�3) and SS inAriake Bay were also reported, which means that better interpre-tations of coastal pigments and SS are highly desirable to improvethe Chl a algorithm for case 2 water in areas like Ariake Bay.

Marra et al. (2007) reported that the absorption properties aremore important predictors of daily productivity in some environ-ments than the quantity of chemically extracted Chl a. Furthermore,they showed that absorption is a more appropriate quantity thanChl a for use in normalizing productivity. Phytoplankton absorptioncan be segregated from Rrs-based total absorption (Lee et al., 2002).In this study, the precision of decomposing phytoplanktonabsorption from Rrs was reasonably accurate (53%). The

algorithms for retrieving Chl a. Slope (S) and intercept (I) are derived from type IIog transformed scales.

2 S I RMSE N

.73 0.510 0.350 0.31 169

.74 0.861 0.135 0.25 169

.75 0.596 0.791 0.47 169

.78 0.883 0.114 0.23 169

here as they are different depending on the range of Chl a concentration. Constantfor Tassan’s algorithm here.

Table 2List of symbols and abbreviations used.

Symbols Description Units

CDOM Colored dissolved organic matterChl a Chlorophyll a concentration mg m�3

Chl0 Sea surface Chl a mg m�3

DL Daylength hE0 Irradiance at sea surface Einstein m�2 d�1

EZ Irradiance at Z Einstein m�2 d�1

Ed Spectral downwelling irradiance mW cm�2 nm�1

IOP Inherent optical propertiesIPP Euphotic depth-integrated

primary productionmgC m�2 d�1

KPAR Vertical diffuse attenuationcoefficient of irradiance

m�1

Lw Water-leaving radiance mW cm�2 nm�1 sr�1

PAR Photosynthetically active radiation mEinstein m�2 s�1

PB Chl a-normalized primary production mgC mgChl a�1 d�1

pBopt Maximum PB in the water column mgC mgChl a�1 d�1

RMSE Root mean-square errorRrs Remote sensing reflectance, Rrs ¼ Lw/Ed sr�1

SS Suspended substancesSST Sea surface temperature

�C

VGPM Vertically generalized production modelZeu Euphotic depth ma Total absorption coefficient,

a ¼ aph þ anph þ aw

m�1

a*ph Chl a-specific absorption coefficient m�2 mg Chl a�1

aCDOM Absorption by CDOM m�1

ag Combined value of anph and aCDOM m�1

anph Absorption by non-phytoplankton particles m�1

aph Absorption by phytoplankton m�1

aw Pure water absorption m�1

bb Backscattering coefficient m�1

z Discrete depth ml Wavelength nm

S.C. Tripathy et al. / Estuarine, Coastal and Shelf Science 97 (2012) 66e77 75

phytoplankton absorption obtained from Rrs data could be used asa substitute for Chl a in IPP estimation. If a close relationshipbetween phytoplankton absorption and PP can be established inthe study area, it will help to overcome the related issues associatedwith Chl a estimation in turbid waters.

3.5. Modified VGPM for Ariake Bay

The VGPMmodel (Eq. (5)) was regionally modified (VGPMAB) byoptimizing Zeu and using a constant value for pBopt. The model ofMorel and Berthon (1989) for estimation of Zeu was replaced withthe QAA-based Zeu, and the BF SST-dependent pBopt model wasreplaced by the median of in situ pBopt. Furthermore, the optimizedTassan’s algorithm-based Chl a was used as a substitute for in situChl a. The IPP obtained by VGPMAB had a lower RMSE(715 mgC m�2 d�1, p < 0.0001) and was strongly correlated(r2¼ 0.85) with in situ IPP (Fig. 7a). Thus, close estimates of daily IPPcould be obtained using the QAA-based Zeu and improved estimatesof Chl a as input to the original VGPM formulation.

3.6. Comparison of the IPP from modified VGPM and instantaneousFRRF cast

Tripathy et al. (2010) showed that PP estimates derived fromsingle instantaneous measurements by a FRRF were stronglycorrelated with 13C-based PP estimates, which is still consideredthe standard technique for PP quantification worldwide. Given thisbackground, an attempt was made to validate the IPP estimatesfrom the new model (VGPMAB) with an independent IPP dataset(n ¼ 9) obtained by single instantaneous FRRF measurements(Fig. 7b). Results showed that the modeled IPP was overestimated(�1.1 e1.3) but reasonably correlated with (r2 ¼ 0.68, RMSE ¼ 187)the FRRF-IPP.

4. Summary and conclusions

A modified version of the VGPM for Ariake Bay could be effec-tively developed by deriving Zeu from the inherent optical proper-ties of the water, by optimizing the satellite Chl a algorithm withthe in situ Rrs and Chl a data and by using a constant (median) pBopt.The performance of the modified VGPM was almost equal to theoriginal VGPM performance with in situ inputs. The approach usedin this study could be useful for developing PP models of turbidcoastal waters elsewhere, with local adjustments.

Some conclusions inferred from the results of this study are asfollows:

1. Although the VGPM is a global open ocean model, whencoupled with a turbid water algorithm for Zeu and Chl a it canprovide a realistic estimate of IPP from Rrs data in the studiedturbid water ecosystem.

2. The low precision of the original VGPM was mainly due tooverestimation by the sub-models for pBopt and Zeu, embeddedin the original VGPM.

3. Zeu was the dominant factor influencing the modeled IPP.Hence accurate modeling of Zeu is probably one of the mainroutes for better estimation of the IPP in Ariake Bay.

4. Because of the limited dataset, distinct relationships betweenpBopt and other environmental variables such as SST and Chla could not be established for Ariake Bay.

5. The MB Zeu model based on the surface Chl a did not performwell in this turbid water body. The estimation of Zeu could beimproved by using inherent optical properties (total absorptionand backscattering) from in situ Rrs in the QAA.

6. The Chl a estimation was improved by using an optimizedsatellite algorithm suitable for turbid environments.

7. A moderately strong correlation and low RSME between thedaily IPP from the modified VGPM and the FRRF singleinstantaneous cast indicated that the new model couldreasonably be verified using in situ IPP in this turbid waterbody.

Acknowledgments

We are thankful to the captain, officers, and other crewmembers of T/V Kakuyo Maru of Nagasaki University for theirremarkable assistance during the on-board sampling andmeasurements. The first author is thankful to the Ministry ofEducation, Culture, Sports, Science and Technology (MEXT), Japan,for providing support during the study period. This study wassupported by the Grant-in-Aid for Scientific Research 20310012.Thanks are also due to four anonymous reviewers who helped us toimprove this manuscript.

Appendix

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