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A water-quality model for the Lagoon of Venice, Italy
Ecological Modelling 184 (2005) 69–81
A water-quality model for the Lagoon of Venice, Italy Giuseppe Bendoricchio, Gabriella De Boni∗ Department of Chemical Processes of Engineering, University of Padua, via Marzolo 9, 35131 Padua, Italy
Abstract A water-quality model for the Lagoon of Venice is proposed. The model is based on the results of an existing, deterministic, hydraulic-dispersive model of the Lagoon to provide the distribution of salinity and residence time in the Lagoon of Venice. This model has been implemented by Magistrato alle Acque di Venezia and Consorzio Venezia Nuova to evaluate the environmental impact of the MOSE Project, that has the aim to defend the city of Venice from extraordinary high tides [CVN, 1997. Allegato allo studio di impatto ambientale del progetto di massima delle opere mobili per la difesa dei centri abitati lagunari dagli allagamenti, vol. 2., CVN, 2002. Studio di nuove configurazioni dei canali di bocca e del relativo adeguamento progettuale delle opere mobili alle bocche di porto]. The water-quality is simulated by statistic analysis on water-quality data, monthly collected in 30 stations. The data-set covers a period of 2 years, and has been collected in the framework of MELa1, the institutional water-quality monitoring program (Magistrato alle Acque di Venezia, Consorzio Venezia Nuova). The Spearman correlation index of salinity and residence time versus the water-quality variables (nitrogen, phosphorus, chlorophyll-a and the trophic index TRIX) has been studied on a yearly average basis and for the spring–summer periods. The spatial distribution of the water-quality variables, based on the yearly average of nutrients, is mostly driven by the dispersive processes and is well correlated to salinity [Bianchi, F., Acri, F., Alberghi, M., Bastianini, M., Boldrin, A., Cavalloni, B., Cioce, F., Comaschi, A., Rabitti, S., Socal, G., Turchetto M.M., 1999. Biologocal variability in the Venice Lagoon. In: Lasserre, P., Marzollo, A. (Eds.), The Venice Lagoon Ecosystem. Input Interaction between Land and Sea, UNESCO, Man and Biosphere Series, vol. 25, pp. 97–125]. The model has been applied to simulate the variation of nutrients and trophic index distribution in the Lagoon as a consequence of an increase of hydraulic dissipation at the Lagoon outlets. The work presented in this paper shows that, coupling a deterministic, distributed-parameters, dynamic, hydraulic-dispersive model to a statistic one that accounts for the correlation between hydraulic related forcing functions (salinity, residence time) and water-quality data is a promising and simple way to evaluate the water-quality of the Lagoon of Venice. Of course, this methodology is applicable because a very large data-set is now available. The usual limitations of the statistical model methodology are present in this application too. E.g., it cannot precisely estimate the values of the water-quality variables, but it can indicate how they react when the system hydrological features change. Besides, the outcomes depend strongly on site characteristics and on the actual ecosystem state. The model has not been validated yet, due to the short data time lag, but the aim of this work is to suggest a simple simulation tool whose reliability is at least the same of that obtained by complex, deterministic, dynamic water-quality models. These
∗
Corresponding author. E-mail addresses: [email protected] (G. Bendoricchio), [email protected] (G. De Boni).
0304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2004.11.013
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models, accounting for several processes and hence including a lot of parameters, require for calibration a much more detailed data-set not yet available. The increase of dissipation is altering nutrient concentrations in the Lagoon of an average +3.2%, while the average variation for TRIX is +0.4%, and for chlorophyll-a is +3.0%. These variations are small enough to confirm a posteriori the validity of the adopted statistical approach. © 2004 Elsevier B.V. All rights reserved. Keywords: Lagoon of Venice; Water quality; Statistical model
1. Introduction The MOSE project has been recently approved by the Italian Government, and provides for the construction of three mobile dams, one for each outlet of the Venice Lagoon, to be closed only when the sea level exceeds a threshold value (CVN, 1997, 2002, MAVCVN, 2002). To achieve a satisfactory effectiveness, and to allow the usual harbour activity, it is necessary to modify the sectional area of the three outlets. In particular, at the Malamocco outlet a navigation chamber will be built, so decreasing the sectional area and the washout, and increasing dissipation. Thus, it is strictly necessary to forecast the alteration in the hydrodynamical features and the possible increase in nutrients, to avoid eutrophic and distrophic events. In the last century, morphological changes and socio-economic development of the Lagoon of Venice and its watershed have generated deep changes in the Lagoon environment and particularly in its water quality. The growth of industrial areas along the Lagoon sides, and the related pollutant discharges, strongly contributed to move away the ecosystem from a natural trophic equilibrium state to an ecotoxical one, in which the ecosystem quality was heavily damaged. The first treatment plants for civil and industrial wastewater, built to reduce pollutant inputs, transformed organic and ammonia nitrogen into nitric nitrogen, thus reducing the toxicity of wastewater. Paradoxically, these efforts supplied large quantities of bioavailable nutrients, resulting in a huge macroalgal (i.e. Ulva) biomass production. The consequent Lagoon eutrophication has generated further alterations to the trophic network. The recent effort to treat point and diffuse pollution sources in the whole Watershed of the Lagoon of Venice resulted in a reduction of nutrient loads, thus in an auto catalytic self purification process. This process
allowed, in short time, the use of all the Lagoon ecosystem resources in order to reconstruct an acceptable trophic state. Presently, consequently to the efforts spent to reduce pollution sources, the trophic state of the Lagoon has improved. The water quality is satisfactory over long periods of the year and for large areas of the Lagoon. The ecosystem has reached a mesotrophic equilibrium state that is typical of transitional lagoon water bodies. This spatial and time variability, due to hydrodynamic, dispersive, physical, chemical and biological processes, suggests that the Lagoon has not yet reached a satisfactory ecosystem steady state. For this reason, a careful evaluation of the effects of further hydrodynamic works, able to alter the present hydrodynamic conditions of the Lagoon and to increase energy dissipation at the Lagoon outlets, is strongly suggested.
2. Materials and methods 2.1. The Venice Lagoon institutional monitoring program In order to reach an adequate knowledge of the ecosystem state, in particular of the Lagoon water quality, as requested by the Ronchi–Costa Decree, the “Magistrato alle Acque di Venezia” and “Consorzio Venezia Nuova” set up an institutional water-quality monitoring program in the Venice Lagoon. The socalled MELa1 Program has been carried out for 3 years from September 2000, and provides a gauging campaign every 4 weeks in 30 points all over the Lagoon, in neap tide conditions. The gauging stations are chosen according to meaningfullness, representativeness, essentiality and historicity criteria, and are clustered in three different morphological groups: 20 shallow waters, 8 channel and 2 sea stations.
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The data considered in this paper concern the first 2 years of the institutional monitoring program (from September 2000 to August 2002). In particular, for each of the 28 inner gauging stations (the two sea stations are excluded) the averages, over the entire period and over the spring–summer season, are considered in this paper. 2.2. The modelling procedure
Fig. 1. The distribution of the gauging stations of the monitoring institutional program in the Lagoon of Venice, MELa1. B = ‘bassofondo’ (shallow waters), C = ‘canale’ (channel), M = ‘mare’ (sea) stations.
Fig. 1 shows the gauging stations distribution, while Table 1 summarizes all the data supplied by MELa1 monitoring program. The knowledge provided by the institutional monitoring program completes the already available datasets produced in previous studies of the Lagoon system, and allows to estimate the evolution of the water quality (Montobbio et al., 2002). The prosecution along the years of this integrated monitoring activity will allow to follow the long term evolution trends and to evaluate (ex post) the effects of hydraulic works and pollution abatement measures.
The institutional monitoring system does not allow to evaluate ex ante the effects of the actions planned in the Lagoon and in its Watershed. Hence, in order to forecast their effectiveness, it is necessary to implement a tool able to simulate system changes due to the planned protection actions. Typically the forecasting tool is a mathematical water-quality and ecosystem model, widely and effectively used in a number of polluted water bodies restoration programs. A water-quality model usually concerns with the trophic network features of the water body, and includes the macrodescriptors as state variables (nutrients, suspended solids, dissolved oxygen, primary producers) and, very roughly, secondary producers (Jørgensen and Bendoricchio, 2001; Chapra, 1997). Some water-quality models, used to simulate ecotoxicity, are able to simulate micropollutants too, but this is not the scope of this paper. Mathematical models of wide and dynamic hydraulic systems, as is the Lagoon, need a hydrodynamic and dispersive module in order to simulate the time and spatial distribution, interalia, of a conservative tracer and the residence time. For the Venice Lagoon some hydrodynamics and dispersive models have been already set up and successfully applied (Chignoli e Rabagliati, 1975;
Table 1 Scheme of the MAV—MELa1 monitoring program data for the Lagoon of Venice Method
Variable measured
Gauging stations
Laboratory chemical analysis (at one or two levels)
All (metals only in 13 of them)
Mobile oceanographic probe (profile all along the column) Radiometer
Salinity, alcalinity, total suspended solids (TSS), phaeopigments, chlorophyll-a, N–NH4 , N–NO2 , N–NO3 , TDN, TN, DON, TDP, P–PO4 , TP, TOC, POC, DOC, metals (As, Cu, Hg, Pb, Zn, Cd, Cr, Ni) Turbidity, chlorophyll-a, conducibility, dissolved O2 , O2 saturation percentage, temperature, pH, Eh Photosynthetically active radiation (PAR)
Secchi disc
Water transparency
All 1 Sea 7 Shallow water 3 Channel All
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Volpi e Sguazzero, 1977; Sguazzero et al., 1978; Umgiesser, 1986; Casulli e Cheng, 1992; De Marchi, 1993; Umgiesser and Bergamasco, 1993; Casulli e Cattani, 1994; Umgiesser et al., 2003; CVN, 1997, 2002). However, how will became clear, such complex and detailed models are not required in the scope of this work. On the contrary, water-quality models application in a system like the Venice Lagoon is much more difficult, not because of the particular processes to be simulated (similar to the processes described in literature for other water bodies), but because of the strong spatial and time variability of the forcing functions driving those processes. The Lagoon is a shallow and dynamic water body, where the interaction between the water column and sediments is very fast and intense, the hydrodynamic gradients are large and affect strongly the nutrient concentration, the energy input (mainly due to wind and solar radiation) deeply alters the transparency of the waters and light availability, and consequently primary production in the whole water column. The large spatial and time variability, the system sensitivity to meteorological and sediments conditions, the number and accuracy of the experimental data needed to calibrate a water-quality deterministic models make very hard to simulate the Lagoon waterquality with such a model. Of course, many water-quality deterministic models too have been set up and applied to the Lagoon of Venice, in particular to evaluate the impact of the MOSE project, but the complexity of such models is so high, that is actually impossible to calibrate the parameters with acceptable uncertainty (Solidoro, 2002; Solidoro et al., 2002; Umgiesser et al., 2003; Melaku Canu et al., 2003a,b, 2004). Instead the aim of this work is to propose a model that is simpler to be implemented and less affected by errors, because it simulates just the order of magnitude of concentrations and the range of their variations. These results can be useful in order to implement a decision support system for the Venice Lagoon management. In this sense the ability of a deterministic waterquality model (its usefulness) could be equal to, may be lower than, the ability of a statistical model, based on the large data-base recently provided by the institutional monitoring program.
Statistic relationships between hydraulic and biologic variables have been already used in estuarine ecosystems to simulate the impact of alteration in hydrodynamic features on secondary production (Livingston et al., 2000). Clearly, the statistic water-quality simulation is valid just for the set and values of forcing functions considered by the recorded and processed data. The statistical model is not capable to simulate scenarios where the system is forced by quite different values of forcing functions. If the daily water-quality simulation is not required and the seasonal time step is accepted, and if a rough spatial description, based on the interpolation of the data collected in 28 gauging stations is accepted too, the statistical model is the simplest available tool to give a first estimation of the water quality. The modelling procedure presented takes advantage of the large experience accumulated with the hydrodynamic and dispersive models, used to simulate the spatial distribution of a conservative tracer and its residence time in the Lagoon of Venice. Nowadays, these models are very reliable, because of the hydraulics monitoring network set up in the Lagoon of Venice, and because of the simplicity and reliability of salinity measures, that are the basis of the simulation of a conservative tracer distribution. In particular, this paper is based on the results of a hydrodynamic and dispersive model, used by institutional commissions to study the impact of the protection structures (CVN, 1997, 2002; MAV 2002). The implementation procedure of the model has been established by the Workgroup of the Committee instituted by the Special Law 798/84, to simulate salinity and residence time distribution. This model couples a general two-dimensions finite-elements module of the whole Lagoon and a detailed curve–grid module of the three outlets. A group of nine synthetic tides, based on statistic analysis of a 44 years data-base, and 18 real tides, selected by a historical data-base, have been used to run the model. Residence time is a point-specific measure, and is defined as the average time that a unit conservative tracer, injected in that point of the Lagoon, needs to flow throughout one of the three outlets. The residence time distribution has been simulated coupling the general model of the Lagoon with a dispersive module, in which the dispersion coefficient is proportional to the mean
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square residual of the flow velocities. In this case the mean tide has been used to run the model: this tide is defined as a synthetic one that, when indefinitely repeated, affects the water quality and morphology of the Lagoon as real tidal sequence does. This module has been developed by Delft Hydraulics in 1983 (CVN, 1997, 2002; MAV 2002). The paper presents the statistical model carried out to study the processes involving the biotic components of the system, and the resulting water quality of the Lagoon when forced by the hydraulic structures under construction.
3. Results and discussion 3.1. The relationship between hydrodynamic and trophic variables in the Lagoon of Venice Salinity and residence time can be used as variables accounting for hydrodynamic dispersion in the water body: analysing the relationship of hydrodynamical versus trophic variables is a way to estimate the influence of the dispersion on trophic variables values. Calculated Spearman correlation coefficients (Legendre and Legendre, 2001) of the yearly and summer–spring average salinity and residence time versus the main trophic variables are shown in Table 2: they refer only to the data of the inner Lagoon gauging stations (20 shallow water and 8 channel stations). The trophic index TRIX (Vollenweider et al., 1998) is considered too (despite its usual application to Table 2 Spearman correlation coefficients of yearly and spring–summer average data in each gauging station DIN
P–PO4
Chl-a
TRIX (log)
−0.88 0.43
−0.80 0.40
−0.50 0.72
−0.45 0.19
Spring–summer Salinity −0.78 Tres 0.37
−0.70 0.31
−0.69 0.59
−0.81 0.51
Year Salinity Tres
DIN: dissolved inorganic nitrogen concentration, as sum of nitrite ion (NO2 − ), nitrate ion (NO3 − ) and ammonium ion (NH4 + ) concentrations P–PO4 = orthophosphate ione concentration (PO4 3− ) Chla = chlorophyll-a concentration, measured with fluorimetric method (probe) TRIX: trophic logarithmic index in bold the meaningful correlations (≥50).
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coastal steady-state ecosystems). Italian Law states that the TRIX is to be used to classify the trophic state of marine coastal areas (D. Lgs. 152/99). TRIX is defined as follows: TRIX =
log 10[(Chl-a|DO%|NP) + 1.5] 1.2
(1)
where Chl-a is the chlorophyll-a concentration in mg/m3 , |DO%| the absolute deviation of oxygen from saturation (i.e. abs[100—%O]), N the dissolved inorganic nitrogen (DIN) concentration in mg/m3 , and P is the Dissolved Inorganic Posphorus (DIP) concentration in mg/m3 . TRIX values range from 1 (oligotrophy) to 10 (hypertrophy) and, for a water body in good trophic state, it should not exceed 5. In Lagoon of Venice TRIX values fall in the range from about 3 to 7.5. Salinity and residence time are slightly correlated (the Spearman coefficient is 0.40), so the relations of both of them with the trophic variables have to be considered. Correlation of nutrients versus salinity is strongly inverse for the yearly average data. This points out the importance of the dispersive process in intertidal shallow water bodies. The fluorimetric measure of chlorophyll-a concentration shows to be directly correlated with the residence time better than (inversely) with the salinity. This means that the phytoplanctonic biomass is higher where the time to grow is longer. TRIX is related to the logarithm of nutrient concentrations (see definition 1). Thus, the correlation with the logarithm of salinity, that is moderately inverse, has to be considered. If only spring–summer data are considered, a small decrease of the inverse correlation coefficient between salinity and nutrient concentrations is obtained. This shows that, during spring and summer, nutrients depend on dispersion but also on primary production. In the same period, TRIX inverse correlation versus residence time and salinity logarithm (−0.81) increases, and chlorophyll-a is more inversely correlated with salinity than directly with residence time. This analysis seems to prove that, during the productive season, dispersive processes are driving all the processes (primary production included).Fig. 2 shows the most meaningful correlations for yearly averaged data. The parameters of the linear models used to fit the data and to predict each macrodescriptor are also shown
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Fig. 2. Yearly average nutrients (above) and trophic variables (down) data plotted vs. hydrodynamic variables, salinity and residence time, and the related linear models.
in the plot, together with the determination coefficient r2 . Nutrient concentration predictors based on salinity clearly are the most effective (r2 > 60); the chlorophylla predictor effectiveness is slightly lower (r2 = 0.56) while the TRIX predictor is the least effective (r2 = 0.27). The low effectiveness of the TRIX predictor is related to the definition of this trophic index (a linear combination of logarithm of trophic variables, see (1)) and to the fact that it has been defined for coastal zone ecosystems. Its ability to describe lagoon systems where the hydrodynamic processes are more important than the trophic ones, and where chlorophyll-a concentration does not fully account for the primary productivity, still has to be proven. The correlation between the two hydrodynamic variables, salinity and residence time, and trophic variables allows one to use the linear regressions as simple linear models to predict the trophic state of the Lagoon, i.e. nutrients and chlorophyll-a concentrations and, with a lower effectiveness, TRIX values.
The high correlation between salinity and nutrients supports the idea that the hydrodynamic is the dominating process in regulating the tracers dispersion in the Lagoon and that nutrients, at least in the dissolved inorganic form, can be as well considered as conservative tracers. Primary production alters their concentrations too, but the algal bloom characteristic time step is lower than the monthly frequency step of the monitoring program data. This incoherence does not allow highlighting, with the available data-base, nutrients changes due to primary production, and may possibly affect the regression, producing wrong correlation functions. Moreover, nutrient sources from the watershed present their own variability, affecting nutrient concentrations in the Lagoon. Both these reasons cause nutrients data scatter from the regression line. Another important aspect deserving attention is that orthophosphate ion concentration scattering is higher where the salinity is lower, i.e. close to the river inputs. The residence time appears to be the best fluorimetric chlorophyll-a measure predictor: this supports the idea that high residence times foster primary produc-
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Fig. 3. Spring–summer average nutrients (above) and trophic variables (down) data plotted vs. the hydrodynamic variable salinity, and the related linear models.
tion, and this occurs where dispersive processes are less relevant than the biological ones. In Fig. 3, the most meaningful regressions of nutrients and trophic variables versus hydrodynamic variables (see Table 2) in the spring–summer period are plotted, in order to capture the effects of the biotic activity that is particularly intense in this period. The regression coefficients of the linear model and the parameters, resulting from the spring–summer data, are also shown in the plot. The seasonal model is slightly less effective than the yearly model as descriptor of the nutrients dispersion in the Lagoon. For chlorophyll-a too the scattering increases, resulting in a slightly lower simulation quality. On the contrary, when biotic processes dominate the hydrodynamic ones, the index TRIX simulation effectiveness becomes higher. Table 3 summarizes the regression coefficients of the models shown above, for both the yearly and the summer–spring data. Salinity appears to be the best predictor of nutrient concentrations for both yearly and summer–spring
period. Satisfactory predictors are residence time for yearly average chlorophyll-a and salinity for summer–spring average TRIX, while TRIX based on yearly average and chlorophyll-a based on the spring–summer average cannot be satisfactory predicted. Salinity variation is very low (≤ ± 1 psu), i.e. about 3–5% of the average values, while the residence time Table 3 Regression coefficients of the most meaningful linear regression between hydrodynamic variables and macrodescriptors (nutrients and trophic variables), for yearly and spring–summer average data DIN
P–PO4
Chl-a
TRIX (log)
0.68 x
0.62 x
x 0.56
0.27 x
Spring–summer Salinity 0.57 Tres x
0.52 x
0.21 x
0.55 x
Year Salinity Tres
The crossed ones indicates not considered regression, because of their low correlation coefficient (see Table 2).
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variation is large (≤ ± 3 days) i.e. about 20% of the average values. 3.2. Hydrodynamic simulation A deterministic hydrodynamic and dispersive model (CVN, 1997, 2000) has been applied by Magistrato alle Acque di Venezia and Consorzio Venezia Nuova to simulate salinity and residence time changes in the Lagoon of Venice due to the increased dissipation at its outlet. This effect is expected to be caused by the hydraulic structures, designed to protect Venice against major high tide events. Fig. 4 shows the changes of salinity and residence time in the Lagoon due to the increased dissipation. Salinity varies much less (in percent) than residence time. The increased dissipation results in a small reduction of salinity in each gauging station. The residence time increases all over the Lagoon. The more confined northern and southern areas show an increase of about 1 day. The area around Venice shows a decrease of residence time, while between the southern and central outlets the increase in residence time is larger than 1
day. Salinity slightly decreases in the area influenced by the Malamocco Outlet, while residence time increases. This is the effect of the increased dissipation at Malamocco (central) Outlet, where the exchange with sea water is considerably reduced. The section of this outlet is designed to be the most dissipative because of the chamber that will be built here. This strong dissipation results in a lower velocity of the flow and a minor penetration of sea water into the Lagoon. As a consequence, the Lido (northern) outlet becames more active. Around the city of Venice, both salinity and residence time decrease. 3.3. Application of the statistical model As seen before, trophic variables can be simulated in a satisfactory way with simple linear models starting from salinity and residence time that are the basic hydrodynamic variables. The estimated values of trophic variables as result of changes in hydrodynamic ones are shown in Fig. 5. In the central area, close to the city of Venice and connected to the Lido Outlet (northern), both nutrients
Fig. 4. Absolute values of the estimated changes in yearly average salinity and residence time due to the increased dissipation at the Lagoon outlet.
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Fig. 5. Estimated percent changes in yearly average nutrients (above) and trophic variables (down) due to hydraulic protection structures.
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Fig. 6. Estimated percent changes in spring–summer average nutrient (above) and trophic variables (down) due to hydraulic protection structures.
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Table 4 Percent change in yearly average nutrients and trophic variables forecasted in the whole Lagoon, with average positive and negative changes, and in the area linked to Malamocco Outlet
Table 5 Percent change in spring–summer average nutrients and trophic variables forecasted in the whole Lagoon, with average positive and negative changes, and in the area linked to Malamocco Outlet
DIN (%) P–PO4 (%) Chl-a (%) TRIX (%)
DIN (%) P–PO4 (%) Chl-a (%) TRIX (%)
Lagoon 3.1 Average positive 4.0 Average negative −5.0 Malamocco outlet 2.7
3.4 4.4 −5.3 2.9
3.0 8.9 −9.4 13.0
0.4 0.6 −1.3 0.4
increase, chlorophyll-a decreases and TRIX slightly increases: in fact, TRIX depends (on a logarithm scale) not only on chlorophyll-a concentration, but also on nutrients concentrations and on the oxygen saturation percentage. In the most southern stations both nutrients decrease, as well as chlorophyll-a (slightly) and TRIX. Table 4 reports percent changes in the yearly average concentrations of nutrients and chlorophyll-a. For the whole Lagoon, nutrient concentrations do not change in a meaningful way (about 3%). The average variation compensate positive and negative variation and does not give a meaningful picture of the future situation. For this reason the data have been split in increasing (positive) and decreasing (negative) set of values, and the average of this sets are reported. Still, the average variation of positive and negative values are not meaningful (about 4–5%). The change in chlorophyll-a concentration is not meaningful when averaged on the whole Lagoon scale (about 3%), while considering separately positive and negative variations they result to be much higher (about 9%). The area connected to Malamocco outlet has been separately investigated because in this area the most relevant changes are expected. Nutrients do not show a special behaviour, while the increase in chlorophyll-a concentration is stronger in the area linked to Malamocco outlet, indicating the reduction of sea water washout of the Lagoon water. The yearly average variability gives a good description of the general trends, but it does not highlight the more dangerous conditions for the ecosystem that can occur in spring and summer time. In order to study this phenomenon, the average trophic variables in the spring–summer period have been simulated with the same procedures used for yearly data. It is necessary to stress that spring–summer changes have been calculated using yearly average hydrody-
Lagoon 2.6 Average positive 3.4 Average negative −4.5 Malamocco outlet 2.2
2.5 3.3 −4.4 2.2
4.2 12.3 −12.9 17.8
0.8 1.4 −2.1 0.7
namic data, so that the results can be affected by this incoherence and can be less correct than the yearly results, but still meaningful, at least concerning trends.Fig. 6 shows simulated trophic variables changes in the most productive period of the year. In Table 5 reports percent changes in nutrients and chlorophyll-a spring–summer average concentrations, averaged over the whole Lagoon and over its central area, with related average positive and negative changes. Average data changes of nutrients in spring–summer are low as well as in the yearly simulation (about 3% when positive and 4% when negative), the change in primary production indicators is higher (12.3% when positive and 12.9% when negative for chlorophyll-a). As expected, during the productive season (spring–summer) chlorophyll-a changes are higher than for the yearly data-set. As stated before, this result has to be regarded more as a trend than as absolute values themselves, but still more than 10% of chlorophyll-a in the entire Lagoon and about 20% in the Malamocco area require some effective actions to control the tendency to increase the primary production. In the summer–spring period, TRIX index changes are not omogeneusly distributed over the Lagoon: they are positive and large close to the city, negative and large in the southern area. The model shows a stronger data variability and a much lower r2 value in the spring–summer simulation than in the whole year: this results in a lower reliability in spring–summer simulations compared to the yearly ones.
4. Conclusions Models to simulate the trophic state of the Lagoon can be based on deterministic hydrodynamic and dif-
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fusive models, and set up using statistical models to simulate nutrients and primary production indicators (chlorophyll-a and TRIX). This is possible because the effects of dispersive transport phenomena prevailes over the effects of primary production in large areas of the Lagoon (Bianchi et al., 1999). Statistical analysis is effective enough to simulate scenarios, provided that they are not much different from the state of the system represented in the database processed. In the case of the study presented here, the dissipations fall in a range of variation that could be acceptable for the statistic simulation. Yearly average results are more reliable than the seasonal (spring–summer) ones, because of the strong hydrodynamic approximation used for the latter. Results concerning DIN, P–PO4 and chlorophyll-a are easier to be interpreted than TRIX. This last is an index tested for coastal and steadystate water bodies, while the Lagoon is a shallow and dynamic one. Chlorophyll-a does not represent the entire primary production of the Lagoon. TRIX does not seem to be a good descriptor of the trophic state for the Lagoon of Venice. The simulation of the increased dissipation at the Lagoon sea outlets, based on yearly average data for the entire Lagoon, shows a quite low (3%) increase of nutrients and chlorophyll-a. When the only central area, linked to the Malamocco outlet, is considered, chlorophyll-a increases of about 13%, because of the reduced exchanges with sea water. The effect of the increased dissipation at the Lagoon scale is quite low and can be compensated by a further reduction of nutrients loads into the Lagoon. The spring–summer analysis, even if less reliable than the yearly one, shows an expected tendency to increase the primary production (±12%). Acknowledgements We are grateful to Magistrato alle Acque di Venezia and Consorzio Venezia Nuova for their kind collaboration. References Bianchi, F., Acri, F., Alberghi, M., Bastianini, M., Boldrin, A., Cavalloni, B., Cioce, F., Comaschi, A., Rabitti, S., Socal, G., Turchetto,
M.M., 1999. Biologocal variability in the Venice Lagoon. In: Lasserre, P., Marzollo, A. (Eds.), The Venice Lagoon Ecosystem. Input Interaction between Land and Sea, UNESCO, Man and Biosphere Series, 25, pp. 97–125. Casulli, V., Cattani, E., 1994. Stability, accuracy and efficiency of a semi-implicit method for 3D shallow water flow. Comput. Mathemat. Appl. 27, 99–112. Casulli, V., Cheng, R.T., 1992. Semi-implicit finite difference methods for the 3D shallow water flow. Numeric. Meth. Fluids 15, 629–648. Chapra, S.C., 1997. Surface water-quality modelling. McGraw-Hill, p. 844. Chignoli, C., Rabagliati, R., 1975. Un modello della idrodinamica lagunare. In: Venezia e i problemi dell’ambiente (Ed. Il), Mulino, Bologna, pp. 225–297. CVN, 1997. Allegato allo studio di impatto ambientale del progetto di massima delle opere mobili per la difesa dei centri abitati lagunari dagli allagamenti, vol. 2. CVN, 2002. Studio di nuove configurazioni dei canali di bocca e del relativo adeguamento progettuale delle opere mobili alle bocche di porto. De Marchi, C., 1993. A hydrodynamic model of the network of canals of the city of Venice. MPhil Thesis, University of California, Davis CA. Jørgensen, S.E., Bendoricchio, G., 2001. Fundamentals of Ecological Modeling. Elsevier, p. 530. Legendre, P., Legendre, L., 2001. Numerical Ecology. Developments in Enviromental Modelling 20. Elsevier, p. 853. Livingston, R.J., Lewis, F.G., Woodsum, G.C., Niu, X.F., Galperin, B., Huang, W., Christiansen, J.D., Monaco, M.E., Battista, T.A., Klein, C.J., Howell, R.L., Ray, G.L., 2000. Modelling Oyster Population Response to Variation in Freshwater Input. Estuarine Coastal Shelf Sci. 50, 655–672. Magistrato alle Acque di Venezia—Consorzio Venezia Nuova, 2002. Nuovi interventi per la salvaguardia di Venezia—Studio di impatto ambientale delle opere complementari alle tre bocche di porto e della conca di navigazione alla Bocca di Malamocco. Melaku Canu, D., Solidoro, C., Umgiesser, G., 2003a. Modelling the response of the Lagoon of Venice o variations in physical forcings. Ecol. Model. 170, 265–289. Melaku Canu, D., Solidoro, C., Umgiesser, G., 2003b. Erratum to “Modelling the response of the Lagoon of Venice o variations in physical forcings” [Ecol Model. (170) 265–289]. Ecol. Model. 175, 197–216. Melaku Canu, D., Umgiesser, G., Solidoro, C., 2004. Short-term simulation under winter conditions in the lagoon of Venice: a contribution to the environmental impact assessmet of temporary closure of the inlets. Ecol. Model. 138, 215–230. Montobbio, L., Barbanti, A., Bernstein, A.G., Bocci, M., Ferrari, G., Parati, P., Penna, G., Vazzoler, M., 2002. Monitoring of the Venice Lagoon and its drainage basin: working towards an integrated ecological quality assessment of the ecosystem. In: Proceedings of the International Conference on Sustainability of Aquatic Ecosystem, European Commission, Stresa—Italy, 25–28 November 2002. Solidoro, C., 2002. Nuovi interventi per la salvaguardia di Venezia. Attivit`a di monitoraggio ambientale della Laguna di Venezia.
G. Bendoricchio, G. De Boni / Ecological Modelling 184 (2005) 69–81 Esecutivo del 1◦ stralcio triennale (2000–2003) MELa1. Attivit`a A2: trattamento ed analisi dei dati, elaborazoni in linea ed interpretazioni spazio temporali. Interpretazione della dinamica spazio temporale dei dati mediante un modello di produttivit`a primaria. Magistrato alle Acque di Venezia, Consorzio Venezia Nuova. Solidoro, C., Melaku Canu, D., Umgiesser, G., 2002. VELFEEM (Venice Lagoon Finite Element Ecological Model) developement of the model and first analysis on the influences of physical forcing on the ecological processes. In Scientific research and safeguarding of Venice, Istituto Veneto di Scienze, Lettere ed Arti—CORILA, pp. 557–581. Sguazzero, P., Chignoli, C., Rabagliati, R., Voli, G., 1978. Hydrodynamical modeling of the lagoon of Venice. IBM J. Res. Dev. 22, 472–480. Umgiesser, G., 1986. A model for the Venice Lagoon. MPhil Thesis, University of Hamburg.
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Umgiesser, G., Bergamasco, A., 1993. A staggered grid finite element model of the Venice Lagoon. In: Morgan, K., Ofiate, E., Periaux, J., Zienkiewicz, O.C. (Eds.), Finite Elements in Fluids. Pineridge Press, pp. 659–668. Umgiesser, G., Melaku Canu, D., Solidoro, C., Ambrose, R., 2003. A finite element ecological model: a first application to the Venice Lagoon. Environ. Model. Software 18, 131– 145. Vollenweider, R.A., Giovanardi, F., Montanari, G., Rinaldi, A., 1998. Characterization of the trophic conditions of marine coastal waters with special reference to the NW Adriatic Sea: proposal for a trophic scale, turbidity an generalized water quality index. Environmetric 9, 329–357. Volpi, G., Sguazzero, P., 1977. La propagazione della marea nella Laguna di Venezia: un modello di simulazione e il suo impiego nella regolazione delle bocche di porto. Rivista Italiana di Geofisica e Scienze Affini IV (1–2), 67–74.
A water-quality model for the Lagoon of Venice, Italy Giuseppe Bendoricchio, Gabriella De Boni∗ Department of Chemical Processes of Engineering, University of Padua, via Marzolo 9, 35131 Padua, Italy
Abstract A water-quality model for the Lagoon of Venice is proposed. The model is based on the results of an existing, deterministic, hydraulic-dispersive model of the Lagoon to provide the distribution of salinity and residence time in the Lagoon of Venice. This model has been implemented by Magistrato alle Acque di Venezia and Consorzio Venezia Nuova to evaluate the environmental impact of the MOSE Project, that has the aim to defend the city of Venice from extraordinary high tides [CVN, 1997. Allegato allo studio di impatto ambientale del progetto di massima delle opere mobili per la difesa dei centri abitati lagunari dagli allagamenti, vol. 2., CVN, 2002. Studio di nuove configurazioni dei canali di bocca e del relativo adeguamento progettuale delle opere mobili alle bocche di porto]. The water-quality is simulated by statistic analysis on water-quality data, monthly collected in 30 stations. The data-set covers a period of 2 years, and has been collected in the framework of MELa1, the institutional water-quality monitoring program (Magistrato alle Acque di Venezia, Consorzio Venezia Nuova). The Spearman correlation index of salinity and residence time versus the water-quality variables (nitrogen, phosphorus, chlorophyll-a and the trophic index TRIX) has been studied on a yearly average basis and for the spring–summer periods. The spatial distribution of the water-quality variables, based on the yearly average of nutrients, is mostly driven by the dispersive processes and is well correlated to salinity [Bianchi, F., Acri, F., Alberghi, M., Bastianini, M., Boldrin, A., Cavalloni, B., Cioce, F., Comaschi, A., Rabitti, S., Socal, G., Turchetto M.M., 1999. Biologocal variability in the Venice Lagoon. In: Lasserre, P., Marzollo, A. (Eds.), The Venice Lagoon Ecosystem. Input Interaction between Land and Sea, UNESCO, Man and Biosphere Series, vol. 25, pp. 97–125]. The model has been applied to simulate the variation of nutrients and trophic index distribution in the Lagoon as a consequence of an increase of hydraulic dissipation at the Lagoon outlets. The work presented in this paper shows that, coupling a deterministic, distributed-parameters, dynamic, hydraulic-dispersive model to a statistic one that accounts for the correlation between hydraulic related forcing functions (salinity, residence time) and water-quality data is a promising and simple way to evaluate the water-quality of the Lagoon of Venice. Of course, this methodology is applicable because a very large data-set is now available. The usual limitations of the statistical model methodology are present in this application too. E.g., it cannot precisely estimate the values of the water-quality variables, but it can indicate how they react when the system hydrological features change. Besides, the outcomes depend strongly on site characteristics and on the actual ecosystem state. The model has not been validated yet, due to the short data time lag, but the aim of this work is to suggest a simple simulation tool whose reliability is at least the same of that obtained by complex, deterministic, dynamic water-quality models. These
∗
Corresponding author. E-mail addresses: [email protected] (G. Bendoricchio), [email protected] (G. De Boni).
0304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2004.11.013
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models, accounting for several processes and hence including a lot of parameters, require for calibration a much more detailed data-set not yet available. The increase of dissipation is altering nutrient concentrations in the Lagoon of an average +3.2%, while the average variation for TRIX is +0.4%, and for chlorophyll-a is +3.0%. These variations are small enough to confirm a posteriori the validity of the adopted statistical approach. © 2004 Elsevier B.V. All rights reserved. Keywords: Lagoon of Venice; Water quality; Statistical model
1. Introduction The MOSE project has been recently approved by the Italian Government, and provides for the construction of three mobile dams, one for each outlet of the Venice Lagoon, to be closed only when the sea level exceeds a threshold value (CVN, 1997, 2002, MAVCVN, 2002). To achieve a satisfactory effectiveness, and to allow the usual harbour activity, it is necessary to modify the sectional area of the three outlets. In particular, at the Malamocco outlet a navigation chamber will be built, so decreasing the sectional area and the washout, and increasing dissipation. Thus, it is strictly necessary to forecast the alteration in the hydrodynamical features and the possible increase in nutrients, to avoid eutrophic and distrophic events. In the last century, morphological changes and socio-economic development of the Lagoon of Venice and its watershed have generated deep changes in the Lagoon environment and particularly in its water quality. The growth of industrial areas along the Lagoon sides, and the related pollutant discharges, strongly contributed to move away the ecosystem from a natural trophic equilibrium state to an ecotoxical one, in which the ecosystem quality was heavily damaged. The first treatment plants for civil and industrial wastewater, built to reduce pollutant inputs, transformed organic and ammonia nitrogen into nitric nitrogen, thus reducing the toxicity of wastewater. Paradoxically, these efforts supplied large quantities of bioavailable nutrients, resulting in a huge macroalgal (i.e. Ulva) biomass production. The consequent Lagoon eutrophication has generated further alterations to the trophic network. The recent effort to treat point and diffuse pollution sources in the whole Watershed of the Lagoon of Venice resulted in a reduction of nutrient loads, thus in an auto catalytic self purification process. This process
allowed, in short time, the use of all the Lagoon ecosystem resources in order to reconstruct an acceptable trophic state. Presently, consequently to the efforts spent to reduce pollution sources, the trophic state of the Lagoon has improved. The water quality is satisfactory over long periods of the year and for large areas of the Lagoon. The ecosystem has reached a mesotrophic equilibrium state that is typical of transitional lagoon water bodies. This spatial and time variability, due to hydrodynamic, dispersive, physical, chemical and biological processes, suggests that the Lagoon has not yet reached a satisfactory ecosystem steady state. For this reason, a careful evaluation of the effects of further hydrodynamic works, able to alter the present hydrodynamic conditions of the Lagoon and to increase energy dissipation at the Lagoon outlets, is strongly suggested.
2. Materials and methods 2.1. The Venice Lagoon institutional monitoring program In order to reach an adequate knowledge of the ecosystem state, in particular of the Lagoon water quality, as requested by the Ronchi–Costa Decree, the “Magistrato alle Acque di Venezia” and “Consorzio Venezia Nuova” set up an institutional water-quality monitoring program in the Venice Lagoon. The socalled MELa1 Program has been carried out for 3 years from September 2000, and provides a gauging campaign every 4 weeks in 30 points all over the Lagoon, in neap tide conditions. The gauging stations are chosen according to meaningfullness, representativeness, essentiality and historicity criteria, and are clustered in three different morphological groups: 20 shallow waters, 8 channel and 2 sea stations.
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The data considered in this paper concern the first 2 years of the institutional monitoring program (from September 2000 to August 2002). In particular, for each of the 28 inner gauging stations (the two sea stations are excluded) the averages, over the entire period and over the spring–summer season, are considered in this paper. 2.2. The modelling procedure
Fig. 1. The distribution of the gauging stations of the monitoring institutional program in the Lagoon of Venice, MELa1. B = ‘bassofondo’ (shallow waters), C = ‘canale’ (channel), M = ‘mare’ (sea) stations.
Fig. 1 shows the gauging stations distribution, while Table 1 summarizes all the data supplied by MELa1 monitoring program. The knowledge provided by the institutional monitoring program completes the already available datasets produced in previous studies of the Lagoon system, and allows to estimate the evolution of the water quality (Montobbio et al., 2002). The prosecution along the years of this integrated monitoring activity will allow to follow the long term evolution trends and to evaluate (ex post) the effects of hydraulic works and pollution abatement measures.
The institutional monitoring system does not allow to evaluate ex ante the effects of the actions planned in the Lagoon and in its Watershed. Hence, in order to forecast their effectiveness, it is necessary to implement a tool able to simulate system changes due to the planned protection actions. Typically the forecasting tool is a mathematical water-quality and ecosystem model, widely and effectively used in a number of polluted water bodies restoration programs. A water-quality model usually concerns with the trophic network features of the water body, and includes the macrodescriptors as state variables (nutrients, suspended solids, dissolved oxygen, primary producers) and, very roughly, secondary producers (Jørgensen and Bendoricchio, 2001; Chapra, 1997). Some water-quality models, used to simulate ecotoxicity, are able to simulate micropollutants too, but this is not the scope of this paper. Mathematical models of wide and dynamic hydraulic systems, as is the Lagoon, need a hydrodynamic and dispersive module in order to simulate the time and spatial distribution, interalia, of a conservative tracer and the residence time. For the Venice Lagoon some hydrodynamics and dispersive models have been already set up and successfully applied (Chignoli e Rabagliati, 1975;
Table 1 Scheme of the MAV—MELa1 monitoring program data for the Lagoon of Venice Method
Variable measured
Gauging stations
Laboratory chemical analysis (at one or two levels)
All (metals only in 13 of them)
Mobile oceanographic probe (profile all along the column) Radiometer
Salinity, alcalinity, total suspended solids (TSS), phaeopigments, chlorophyll-a, N–NH4 , N–NO2 , N–NO3 , TDN, TN, DON, TDP, P–PO4 , TP, TOC, POC, DOC, metals (As, Cu, Hg, Pb, Zn, Cd, Cr, Ni) Turbidity, chlorophyll-a, conducibility, dissolved O2 , O2 saturation percentage, temperature, pH, Eh Photosynthetically active radiation (PAR)
Secchi disc
Water transparency
All 1 Sea 7 Shallow water 3 Channel All
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Volpi e Sguazzero, 1977; Sguazzero et al., 1978; Umgiesser, 1986; Casulli e Cheng, 1992; De Marchi, 1993; Umgiesser and Bergamasco, 1993; Casulli e Cattani, 1994; Umgiesser et al., 2003; CVN, 1997, 2002). However, how will became clear, such complex and detailed models are not required in the scope of this work. On the contrary, water-quality models application in a system like the Venice Lagoon is much more difficult, not because of the particular processes to be simulated (similar to the processes described in literature for other water bodies), but because of the strong spatial and time variability of the forcing functions driving those processes. The Lagoon is a shallow and dynamic water body, where the interaction between the water column and sediments is very fast and intense, the hydrodynamic gradients are large and affect strongly the nutrient concentration, the energy input (mainly due to wind and solar radiation) deeply alters the transparency of the waters and light availability, and consequently primary production in the whole water column. The large spatial and time variability, the system sensitivity to meteorological and sediments conditions, the number and accuracy of the experimental data needed to calibrate a water-quality deterministic models make very hard to simulate the Lagoon waterquality with such a model. Of course, many water-quality deterministic models too have been set up and applied to the Lagoon of Venice, in particular to evaluate the impact of the MOSE project, but the complexity of such models is so high, that is actually impossible to calibrate the parameters with acceptable uncertainty (Solidoro, 2002; Solidoro et al., 2002; Umgiesser et al., 2003; Melaku Canu et al., 2003a,b, 2004). Instead the aim of this work is to propose a model that is simpler to be implemented and less affected by errors, because it simulates just the order of magnitude of concentrations and the range of their variations. These results can be useful in order to implement a decision support system for the Venice Lagoon management. In this sense the ability of a deterministic waterquality model (its usefulness) could be equal to, may be lower than, the ability of a statistical model, based on the large data-base recently provided by the institutional monitoring program.
Statistic relationships between hydraulic and biologic variables have been already used in estuarine ecosystems to simulate the impact of alteration in hydrodynamic features on secondary production (Livingston et al., 2000). Clearly, the statistic water-quality simulation is valid just for the set and values of forcing functions considered by the recorded and processed data. The statistical model is not capable to simulate scenarios where the system is forced by quite different values of forcing functions. If the daily water-quality simulation is not required and the seasonal time step is accepted, and if a rough spatial description, based on the interpolation of the data collected in 28 gauging stations is accepted too, the statistical model is the simplest available tool to give a first estimation of the water quality. The modelling procedure presented takes advantage of the large experience accumulated with the hydrodynamic and dispersive models, used to simulate the spatial distribution of a conservative tracer and its residence time in the Lagoon of Venice. Nowadays, these models are very reliable, because of the hydraulics monitoring network set up in the Lagoon of Venice, and because of the simplicity and reliability of salinity measures, that are the basis of the simulation of a conservative tracer distribution. In particular, this paper is based on the results of a hydrodynamic and dispersive model, used by institutional commissions to study the impact of the protection structures (CVN, 1997, 2002; MAV 2002). The implementation procedure of the model has been established by the Workgroup of the Committee instituted by the Special Law 798/84, to simulate salinity and residence time distribution. This model couples a general two-dimensions finite-elements module of the whole Lagoon and a detailed curve–grid module of the three outlets. A group of nine synthetic tides, based on statistic analysis of a 44 years data-base, and 18 real tides, selected by a historical data-base, have been used to run the model. Residence time is a point-specific measure, and is defined as the average time that a unit conservative tracer, injected in that point of the Lagoon, needs to flow throughout one of the three outlets. The residence time distribution has been simulated coupling the general model of the Lagoon with a dispersive module, in which the dispersion coefficient is proportional to the mean
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square residual of the flow velocities. In this case the mean tide has been used to run the model: this tide is defined as a synthetic one that, when indefinitely repeated, affects the water quality and morphology of the Lagoon as real tidal sequence does. This module has been developed by Delft Hydraulics in 1983 (CVN, 1997, 2002; MAV 2002). The paper presents the statistical model carried out to study the processes involving the biotic components of the system, and the resulting water quality of the Lagoon when forced by the hydraulic structures under construction.
3. Results and discussion 3.1. The relationship between hydrodynamic and trophic variables in the Lagoon of Venice Salinity and residence time can be used as variables accounting for hydrodynamic dispersion in the water body: analysing the relationship of hydrodynamical versus trophic variables is a way to estimate the influence of the dispersion on trophic variables values. Calculated Spearman correlation coefficients (Legendre and Legendre, 2001) of the yearly and summer–spring average salinity and residence time versus the main trophic variables are shown in Table 2: they refer only to the data of the inner Lagoon gauging stations (20 shallow water and 8 channel stations). The trophic index TRIX (Vollenweider et al., 1998) is considered too (despite its usual application to Table 2 Spearman correlation coefficients of yearly and spring–summer average data in each gauging station DIN
P–PO4
Chl-a
TRIX (log)
−0.88 0.43
−0.80 0.40
−0.50 0.72
−0.45 0.19
Spring–summer Salinity −0.78 Tres 0.37
−0.70 0.31
−0.69 0.59
−0.81 0.51
Year Salinity Tres
DIN: dissolved inorganic nitrogen concentration, as sum of nitrite ion (NO2 − ), nitrate ion (NO3 − ) and ammonium ion (NH4 + ) concentrations P–PO4 = orthophosphate ione concentration (PO4 3− ) Chla = chlorophyll-a concentration, measured with fluorimetric method (probe) TRIX: trophic logarithmic index in bold the meaningful correlations (≥50).
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coastal steady-state ecosystems). Italian Law states that the TRIX is to be used to classify the trophic state of marine coastal areas (D. Lgs. 152/99). TRIX is defined as follows: TRIX =
log 10[(Chl-a|DO%|NP) + 1.5] 1.2
(1)
where Chl-a is the chlorophyll-a concentration in mg/m3 , |DO%| the absolute deviation of oxygen from saturation (i.e. abs[100—%O]), N the dissolved inorganic nitrogen (DIN) concentration in mg/m3 , and P is the Dissolved Inorganic Posphorus (DIP) concentration in mg/m3 . TRIX values range from 1 (oligotrophy) to 10 (hypertrophy) and, for a water body in good trophic state, it should not exceed 5. In Lagoon of Venice TRIX values fall in the range from about 3 to 7.5. Salinity and residence time are slightly correlated (the Spearman coefficient is 0.40), so the relations of both of them with the trophic variables have to be considered. Correlation of nutrients versus salinity is strongly inverse for the yearly average data. This points out the importance of the dispersive process in intertidal shallow water bodies. The fluorimetric measure of chlorophyll-a concentration shows to be directly correlated with the residence time better than (inversely) with the salinity. This means that the phytoplanctonic biomass is higher where the time to grow is longer. TRIX is related to the logarithm of nutrient concentrations (see definition 1). Thus, the correlation with the logarithm of salinity, that is moderately inverse, has to be considered. If only spring–summer data are considered, a small decrease of the inverse correlation coefficient between salinity and nutrient concentrations is obtained. This shows that, during spring and summer, nutrients depend on dispersion but also on primary production. In the same period, TRIX inverse correlation versus residence time and salinity logarithm (−0.81) increases, and chlorophyll-a is more inversely correlated with salinity than directly with residence time. This analysis seems to prove that, during the productive season, dispersive processes are driving all the processes (primary production included).Fig. 2 shows the most meaningful correlations for yearly averaged data. The parameters of the linear models used to fit the data and to predict each macrodescriptor are also shown
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Fig. 2. Yearly average nutrients (above) and trophic variables (down) data plotted vs. hydrodynamic variables, salinity and residence time, and the related linear models.
in the plot, together with the determination coefficient r2 . Nutrient concentration predictors based on salinity clearly are the most effective (r2 > 60); the chlorophylla predictor effectiveness is slightly lower (r2 = 0.56) while the TRIX predictor is the least effective (r2 = 0.27). The low effectiveness of the TRIX predictor is related to the definition of this trophic index (a linear combination of logarithm of trophic variables, see (1)) and to the fact that it has been defined for coastal zone ecosystems. Its ability to describe lagoon systems where the hydrodynamic processes are more important than the trophic ones, and where chlorophyll-a concentration does not fully account for the primary productivity, still has to be proven. The correlation between the two hydrodynamic variables, salinity and residence time, and trophic variables allows one to use the linear regressions as simple linear models to predict the trophic state of the Lagoon, i.e. nutrients and chlorophyll-a concentrations and, with a lower effectiveness, TRIX values.
The high correlation between salinity and nutrients supports the idea that the hydrodynamic is the dominating process in regulating the tracers dispersion in the Lagoon and that nutrients, at least in the dissolved inorganic form, can be as well considered as conservative tracers. Primary production alters their concentrations too, but the algal bloom characteristic time step is lower than the monthly frequency step of the monitoring program data. This incoherence does not allow highlighting, with the available data-base, nutrients changes due to primary production, and may possibly affect the regression, producing wrong correlation functions. Moreover, nutrient sources from the watershed present their own variability, affecting nutrient concentrations in the Lagoon. Both these reasons cause nutrients data scatter from the regression line. Another important aspect deserving attention is that orthophosphate ion concentration scattering is higher where the salinity is lower, i.e. close to the river inputs. The residence time appears to be the best fluorimetric chlorophyll-a measure predictor: this supports the idea that high residence times foster primary produc-
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Fig. 3. Spring–summer average nutrients (above) and trophic variables (down) data plotted vs. the hydrodynamic variable salinity, and the related linear models.
tion, and this occurs where dispersive processes are less relevant than the biological ones. In Fig. 3, the most meaningful regressions of nutrients and trophic variables versus hydrodynamic variables (see Table 2) in the spring–summer period are plotted, in order to capture the effects of the biotic activity that is particularly intense in this period. The regression coefficients of the linear model and the parameters, resulting from the spring–summer data, are also shown in the plot. The seasonal model is slightly less effective than the yearly model as descriptor of the nutrients dispersion in the Lagoon. For chlorophyll-a too the scattering increases, resulting in a slightly lower simulation quality. On the contrary, when biotic processes dominate the hydrodynamic ones, the index TRIX simulation effectiveness becomes higher. Table 3 summarizes the regression coefficients of the models shown above, for both the yearly and the summer–spring data. Salinity appears to be the best predictor of nutrient concentrations for both yearly and summer–spring
period. Satisfactory predictors are residence time for yearly average chlorophyll-a and salinity for summer–spring average TRIX, while TRIX based on yearly average and chlorophyll-a based on the spring–summer average cannot be satisfactory predicted. Salinity variation is very low (≤ ± 1 psu), i.e. about 3–5% of the average values, while the residence time Table 3 Regression coefficients of the most meaningful linear regression between hydrodynamic variables and macrodescriptors (nutrients and trophic variables), for yearly and spring–summer average data DIN
P–PO4
Chl-a
TRIX (log)
0.68 x
0.62 x
x 0.56
0.27 x
Spring–summer Salinity 0.57 Tres x
0.52 x
0.21 x
0.55 x
Year Salinity Tres
The crossed ones indicates not considered regression, because of their low correlation coefficient (see Table 2).
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variation is large (≤ ± 3 days) i.e. about 20% of the average values. 3.2. Hydrodynamic simulation A deterministic hydrodynamic and dispersive model (CVN, 1997, 2000) has been applied by Magistrato alle Acque di Venezia and Consorzio Venezia Nuova to simulate salinity and residence time changes in the Lagoon of Venice due to the increased dissipation at its outlet. This effect is expected to be caused by the hydraulic structures, designed to protect Venice against major high tide events. Fig. 4 shows the changes of salinity and residence time in the Lagoon due to the increased dissipation. Salinity varies much less (in percent) than residence time. The increased dissipation results in a small reduction of salinity in each gauging station. The residence time increases all over the Lagoon. The more confined northern and southern areas show an increase of about 1 day. The area around Venice shows a decrease of residence time, while between the southern and central outlets the increase in residence time is larger than 1
day. Salinity slightly decreases in the area influenced by the Malamocco Outlet, while residence time increases. This is the effect of the increased dissipation at Malamocco (central) Outlet, where the exchange with sea water is considerably reduced. The section of this outlet is designed to be the most dissipative because of the chamber that will be built here. This strong dissipation results in a lower velocity of the flow and a minor penetration of sea water into the Lagoon. As a consequence, the Lido (northern) outlet becames more active. Around the city of Venice, both salinity and residence time decrease. 3.3. Application of the statistical model As seen before, trophic variables can be simulated in a satisfactory way with simple linear models starting from salinity and residence time that are the basic hydrodynamic variables. The estimated values of trophic variables as result of changes in hydrodynamic ones are shown in Fig. 5. In the central area, close to the city of Venice and connected to the Lido Outlet (northern), both nutrients
Fig. 4. Absolute values of the estimated changes in yearly average salinity and residence time due to the increased dissipation at the Lagoon outlet.
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Fig. 5. Estimated percent changes in yearly average nutrients (above) and trophic variables (down) due to hydraulic protection structures.
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Fig. 6. Estimated percent changes in spring–summer average nutrient (above) and trophic variables (down) due to hydraulic protection structures.
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Table 4 Percent change in yearly average nutrients and trophic variables forecasted in the whole Lagoon, with average positive and negative changes, and in the area linked to Malamocco Outlet
Table 5 Percent change in spring–summer average nutrients and trophic variables forecasted in the whole Lagoon, with average positive and negative changes, and in the area linked to Malamocco Outlet
DIN (%) P–PO4 (%) Chl-a (%) TRIX (%)
DIN (%) P–PO4 (%) Chl-a (%) TRIX (%)
Lagoon 3.1 Average positive 4.0 Average negative −5.0 Malamocco outlet 2.7
3.4 4.4 −5.3 2.9
3.0 8.9 −9.4 13.0
0.4 0.6 −1.3 0.4
increase, chlorophyll-a decreases and TRIX slightly increases: in fact, TRIX depends (on a logarithm scale) not only on chlorophyll-a concentration, but also on nutrients concentrations and on the oxygen saturation percentage. In the most southern stations both nutrients decrease, as well as chlorophyll-a (slightly) and TRIX. Table 4 reports percent changes in the yearly average concentrations of nutrients and chlorophyll-a. For the whole Lagoon, nutrient concentrations do not change in a meaningful way (about 3%). The average variation compensate positive and negative variation and does not give a meaningful picture of the future situation. For this reason the data have been split in increasing (positive) and decreasing (negative) set of values, and the average of this sets are reported. Still, the average variation of positive and negative values are not meaningful (about 4–5%). The change in chlorophyll-a concentration is not meaningful when averaged on the whole Lagoon scale (about 3%), while considering separately positive and negative variations they result to be much higher (about 9%). The area connected to Malamocco outlet has been separately investigated because in this area the most relevant changes are expected. Nutrients do not show a special behaviour, while the increase in chlorophyll-a concentration is stronger in the area linked to Malamocco outlet, indicating the reduction of sea water washout of the Lagoon water. The yearly average variability gives a good description of the general trends, but it does not highlight the more dangerous conditions for the ecosystem that can occur in spring and summer time. In order to study this phenomenon, the average trophic variables in the spring–summer period have been simulated with the same procedures used for yearly data. It is necessary to stress that spring–summer changes have been calculated using yearly average hydrody-
Lagoon 2.6 Average positive 3.4 Average negative −4.5 Malamocco outlet 2.2
2.5 3.3 −4.4 2.2
4.2 12.3 −12.9 17.8
0.8 1.4 −2.1 0.7
namic data, so that the results can be affected by this incoherence and can be less correct than the yearly results, but still meaningful, at least concerning trends.Fig. 6 shows simulated trophic variables changes in the most productive period of the year. In Table 5 reports percent changes in nutrients and chlorophyll-a spring–summer average concentrations, averaged over the whole Lagoon and over its central area, with related average positive and negative changes. Average data changes of nutrients in spring–summer are low as well as in the yearly simulation (about 3% when positive and 4% when negative), the change in primary production indicators is higher (12.3% when positive and 12.9% when negative for chlorophyll-a). As expected, during the productive season (spring–summer) chlorophyll-a changes are higher than for the yearly data-set. As stated before, this result has to be regarded more as a trend than as absolute values themselves, but still more than 10% of chlorophyll-a in the entire Lagoon and about 20% in the Malamocco area require some effective actions to control the tendency to increase the primary production. In the summer–spring period, TRIX index changes are not omogeneusly distributed over the Lagoon: they are positive and large close to the city, negative and large in the southern area. The model shows a stronger data variability and a much lower r2 value in the spring–summer simulation than in the whole year: this results in a lower reliability in spring–summer simulations compared to the yearly ones.
4. Conclusions Models to simulate the trophic state of the Lagoon can be based on deterministic hydrodynamic and dif-
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fusive models, and set up using statistical models to simulate nutrients and primary production indicators (chlorophyll-a and TRIX). This is possible because the effects of dispersive transport phenomena prevailes over the effects of primary production in large areas of the Lagoon (Bianchi et al., 1999). Statistical analysis is effective enough to simulate scenarios, provided that they are not much different from the state of the system represented in the database processed. In the case of the study presented here, the dissipations fall in a range of variation that could be acceptable for the statistic simulation. Yearly average results are more reliable than the seasonal (spring–summer) ones, because of the strong hydrodynamic approximation used for the latter. Results concerning DIN, P–PO4 and chlorophyll-a are easier to be interpreted than TRIX. This last is an index tested for coastal and steadystate water bodies, while the Lagoon is a shallow and dynamic one. Chlorophyll-a does not represent the entire primary production of the Lagoon. TRIX does not seem to be a good descriptor of the trophic state for the Lagoon of Venice. The simulation of the increased dissipation at the Lagoon sea outlets, based on yearly average data for the entire Lagoon, shows a quite low (3%) increase of nutrients and chlorophyll-a. When the only central area, linked to the Malamocco outlet, is considered, chlorophyll-a increases of about 13%, because of the reduced exchanges with sea water. The effect of the increased dissipation at the Lagoon scale is quite low and can be compensated by a further reduction of nutrients loads into the Lagoon. The spring–summer analysis, even if less reliable than the yearly one, shows an expected tendency to increase the primary production (±12%). Acknowledgements We are grateful to Magistrato alle Acque di Venezia and Consorzio Venezia Nuova for their kind collaboration. References Bianchi, F., Acri, F., Alberghi, M., Bastianini, M., Boldrin, A., Cavalloni, B., Cioce, F., Comaschi, A., Rabitti, S., Socal, G., Turchetto,
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