Decadal biogeochemical history of the south east Levantine basin: Simulations of the river Nile regimes

Journal of Marine Systems 148 (2015) 112–121

Contents lists available at ScienceDirect

Journal of Marine Systems journal homepage: www.elsevier.com/locate/jmarsys

Decadal biogeochemical history of the south east Levantine basin: Simulations of the river Nile regimes Yair Suari ⁎, Steve Brenner Department of Geography and Environment, Bar-Ilan University, Ramat-Gan, Israel

a r t i c l e

i n f o

Article history: Received 1 October 2014 Received in revised form 30 January 2015 Accepted 4 February 2015 Available online 10 February 2015 Keywords: Nile river Biogeochemical model Levantine basin

a b s t r a c t The south eastern Mediterranean is characterized by antiestuarine circulation which leads to extreme oligotrophic conditions. The Nile river that used to transport fresh water and nutrients into the basin was dammed in 1964 which led to a drastic reduction of fresh water fluxes, and later, changes in Egyptian agriculture and diet led to increased nutrient fluxes. In this paper we present the results of simulations with a biogeochemical model of the south eastern Mediterranean. Four experiments were conducted: (1) present day without riverine inputs; (2) Nile before damming (pre-1964); (3) post-damming 1995 Nile; and (4) fresh water and nutrient discharges of Israeli coastal streams. The present day input simulation (control run) successfully reproduced measured nutrient concentrations, with the exception of simulated chlorophyll concentrations which were slightly higher than observed. The pre-1964 Nile simulation showed a salinity reduction of 2 psu near the Egyptian coast and 0.5 psu along the Israeli coast, as well as elevated chlorophyll a concentrations mostly east of the Nile delta and north to Cyprus. The spring bloom extended from its present peak during February–March to a peak during February– May. The 1995 Nile simulation showed increased chlorophyll a concentrations close to the Egyptian coast. The Israeli coastal stream simulation showed that the effect of the Israeli coastal stream winter flow on chlorophyll converged to control concentrations within about one month, demonstrating the stability and sensitivity of the model to external forcing. The results of this study demonstrate the significance of fresh water fluxes in maintaining marine productivity, which may have large scale effects on the marine ecosystem. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The Mediterranean sea is the largest (ca. 2,500,000 km2) and deepest (average 1500 m, deepest 5250 m) marginal sea on earth. It is connected through the Straits of Gibraltar in the west to the Atlantic Ocean, and in the east to the Marmara and Black Seas at the Dardanelles, and, more recently through the Suez Canal to the Red Sea and Indian Ocean. A shallow ridge at the Straits of Sicily divides it into the western and eastern basins. The eastern Levantine basin, the region of interest in this work, is located at the southeastern corner of the Mediterranean (Fig. 1). The main characteristics of this marine region include: antiestuarine thermohaline circulation, counter-clockwise long-shore current, high summer surface temperature and salinity, and extreme oligotrophic conditions. The precipitation:evaporation ratio decreases on a west-to-east gradient, which intensifies all of those characteristics. The eastern Mediterranean water masses are formed by Atlantic water flowing eastward through the Straits of Gibraltar and the Straits

⁎ Corresponding author at: The School of Marine Sciences and Marine Environment, Ruppin Academic Center, Michmoret 40297, Israel. Tel.: +972 52 8524520. E-mail address: [email protected] (Y. Suari).

http://dx.doi.org/10.1016/j.jmarsys.2015.02.004 0924-7963/© 2015 Elsevier B.V. All rights reserved.

of Sicily, warming and evaporating, and increasing in salinity from 36.15 at Gibraltar to 39 in the eastern Levantine (Maillard and Balopoulos, 2002). The modified Atlantic water, as well as the locally formed saline Levantine surface water, cools and sinks during winter, forming the Levantine intermediate water, which flows westward at depths of 200–500 m. Surface and intermediate water of the Levantine basin are subjected to seasonal changes, with surface water temperature ranging approximately from 16 to 28 °C and salinity approximately from 38.7 to 39.3. During summer, a sharp seasonal thermocline and halocline are formed to a depth of about 100 m. Levantine waters are known to be highly oligotrophic (Azov, 1991; Berman et al., 1986; Gitelson et al., 1995; Krom et al., 1991). In fact surface nutrient concentration is below the detection limit for conventional methods and therefore, our knowledge of surface concentration is sketchy at best. The oligotrophy is caused by an entry of nutrient-poor Atlantic surface water at Gibraltar and further nutrient consumption within the surface eastward flow. This process leads to the formation of a nutrient depleted layer deepening on a west to east gradient (Pujo-Pay et al., 2011). The eastern Mediterranean is characterized by high deep water N:P ratio of ca. 23 and surface P limitation (Kress et al., 2005; Krom et al., 1991, 2005; Thingstad et al., 2005; Zohary and Robarts, 1998). Phytoplankton are N and P co-limited during summer.

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

Fig. 1. East Mediterranean mean SeaWiFS chlorophyll concentration (1997–2010) map. The model domain is marked by a black rectangle at the southeastern corner.

In contrast to the general Mediterranean oligotrophy, a significant near-shore increase in chlorophyll a concentrations, resulting from coastal nutrient input and recycling from the sediment, is observed throughout the eastern Levantine basin (Barale et al., 2008; Figueras et al., 2004; Gitelson et al., 1996) as shown in Fig. 1. Chlorophyll measurements show low surface concentration of 0.1 mg m−3 (Fig. 1) and a Deep Chlorophyll Max (DCM) of ~ 0.25 mg m−3 (Maillard and Balopoulos, 2002) at a depth of 80–120 m, which deepens eastward (Christaki et al., 2001). Levantine phytoplankton cell sizes are mostly very low with ca. 60% of the cells smaller than 2 μm (Tanaka et al., 2007; Yacobi et al., 1995). Nutrients generally enter the marine environment through discharge of fresh water (Reeburgh, 1997). Naturally, the influence of terrestrial nutrient inputs is more evident in the coastal zone, especially in oligotrophic seas where nutrients are scarce. Additionally, rivers and other point sources are progressively becoming important sources of anthropogenic nutrient enrichment to the marine environment (Shahidul Islam and Tanaka, 2004). Within the eastern Levantine basin nitrogen influx by atmospheric inputs and fresh water discharge is estimated to be 72% of the total influx (Krom et al., 2010). The Nile river has always been a major source of water and nutrients for Egyptian land and also for the Mediterranean Levantine basin (Nixon, 2003; Sharaf-el-Din, 1977). Until 1964 the annual Nile water discharge into the Mediterranean was 43 × 109 m3 year−1, most of which flooded between August and October. This flood led to a massive phytoplankton bloom tens of kilometers off the Nile outfalls, increasing cell counts by two-fold from 66 × 103 cell L−1 to 2 × 106 cell L−1 (Halim, 1960). When the Aswan high dam was completed in 1965, the water discharge dropped rapidly to almost no discharge today (Rasmussen et al., 2009). The maximum discharge now occurs in January while before the Nile damming, most of the water was discharged during August to October. Prior to the construction of the Aswan dam, the Nile plume extended to the Lebanese coast during flood and reduced the salinity of coastal water by up to 6 psu (Oren and Hornung, 1972). Apart from this local effect, the deep water formation rate in the Levantine basin increased by about 30% following damming (Skliris and Lascaratos, 2004). Damming caused a shift in the Levantine phytoplankton community structure (Kimor and Wood, 1975). El-Sayed et al. (1995) identified a drop in Egyptian fish landings following 1964, and a gradual subsequent increase to about twice the pre-damming quantities. This increase might have been partially related to changes in fishing effort, but it is also attributed to an increase in nutrient discharge to the Mediterranean resulting from Egyptian population growth, a change in Egyptian diet that became more protein-based, adoption of chemical fertilizers in Egyptian agriculture, and the construction of sewage drainage systems in Egypt that helped disperse nutrients to the Mediterranean via the

113

coastal lagoons (Nixon, 2003). Nixon's quantifications of the potential nutrient discharge change are presented in Table 1. These estimates suggest that by 1995 the inorganic phosphorus discharged doubled compared to the pre-1964 fluxes, and the discharge of inorganic nitrogen increased to about 16 fold. The coastal plain of Israel is transected by ten streams and their tributaries (Bar-Or, 2000). These are small rivers, with a typical catchment area of a few hundred square kilometers and lengths of tens of kilometers. The ten Israeli coastal streams have undergone a similar process to the processes affecting the river Nile and other fresh water streams in the Mediterranean (Ludwig et al., 2009). Due to water shortage and increasing population, the need for water increased and with it most water sources are now being used for human consumption. These streams are now being used as drainage for floods and for treated sewage outlet (Bar-Or, 2000). The influx of nutrients into the eastern Mediterranean via streams is second only to the discharge of nutrient by sewage treatment plants and to industrial discharge. Using the method presented by Herut et al. (2000), and data from Herut et al. (2011), we estimate the annual nutrient flux to coastal waters at 300 × 106 mol nitrogen and 50 × 106 mol phosphorus. Computer modeling of the processes that take place in the eastern Mediterranean sea has been the subject of numerous studies, with an emphasis on hydrography (Brenner et al., 2007; Pinardi et al., 2003; Zavatarielli and Mellor, 1995). The effects of the Nile damming on the Mediterranean water masses (Skliris and Lascaratos, 2004) were simulated but never the effect of the damming on the biogeochemistry. Since the damming of the Nile is considered a major driver for changes in the Levantine basin (Rilov and Galil, 2009) and given the small number of oceanographic measurements conducted in this basin before the dam was constructed, computer simulation can help us assess the effects of the historical changes in the river Nile outflow. In this paper we investigate the role of riverine fresh water and nutrient inputs and specifically, the effect of the historic high water and nutrient fluxes compared with the current nutrient only flux on the Levantine biogeochemistry using a version of the biogeochemical flux model (BFM, Vichi et al., 2007a, 2007b) coupled to a three dimensional circulation model specifically calibrated (Suari, 2012) for this ultraoligotrophic region.

2. Methodology To investigate the effects of coastal streams and river discharges in the southeastern Levantine basin we implemented a coupled hydrodynamic-ecosystem model. Table 1 Annual fluxes of nutrients to the Mediterranean via the river Nile as calculated by Nixon (2003). Pre- and post-1964 fluxes were calculated by multiplication of the upstream concentration with water discharge. Human sources include potential N and P in wastewater discharge of the Egyptian urban population and are calculated by multiplication of the typical Egyptian diet with the population. 106 mol year−1 P

N

Si

Pre-1964 Nile Dissolved Sediment Total

100 125–250 225–350

479 Low 479

3930

Post-1964 Nile Dissolved Sediment Total

11 – 11

14 – 14

53 – 53

Human sources 1965 1985 1995

77 216 510

857 2930 7710

3930

114

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

2.1. Hydrodynamic model The hydrodynamic calculations were performed using the 3-D, free surface, primitive equation Princeton Ocean Model (Blumberg and Mellor, 1987; Mellor, 2002). The model uses a terrain following sigma coordinate system in the vertical and a time splitting technique for the external 2-D (barotropic) and internal 3-D (baroclinic) modes. This model has become a near standard in hydrodynamic simulations of the Mediterranean sea (Brenner et al., 2007; Pinardi et al., 2003; Rosentraub and Brenner, 2007; Skliris and Lascaratos, 2004; Zavatarielli and Mellor, 1995), it has been successfully used in coastal regions worldwide, and is very well documented (Blumberg and Mellor, 1987; Mellor, 2002). A detailed description of the model is available in many previous papers and is therefore not repeated here. 2.2. Ecosystem model: biochemical flux model The biochemical flux model (BFM) (Vichi et al., 2006, 2007a,2007b) is a pelagic–benthic coupled biogeochemical model. The BFM has been used for a wide range of applications, including global ocean simulation (Vichi and Masina, 2009) studies of physical–biological interactions (Carniel et al., 2007; Res et al., 2003), and for pre-operational biogeochemical forecasting (Lazzari et al., 2010; Petihakis et al., 2006; Triantafyllou et al., 2006). The basic concept of the BFM (Fig. 2) is that matter in the ocean, specifically the ecological food web, can be expressed as compartments

of chemical substances grouped according to characteristics such as the Redfield (1934) ratio, and that chemical processes can be expressed as fluxes between those compartments. The BFM simulates the biogeochemical system using four phytoplankton groups, four zooplankton groups, one group of bacteria, four inorganic nutrients, two types of dissolved organic matter, and dissolved oxygen. Benthic return of those components to the water column is linear to their concentration in the sediment. The simulated rates of ecological fluxes are tuned using model constants (Appendix 1). Phytoplankton primary production is limited by the available light and chlorophyll concentration in the cells, so that the model can simulate varying chlorophyll:carbon ratios. Dissolved nutrient uptake by phytoplankton and bacteria is calculated with a nutrient requirement derived from internal nutrient quotas. Any uptake resulting in nutrient ratios significantly different from an optimal ratio is excreted to the dissolved pool. This method decouples carbon fixing from nutrient uptake and enables simulation of the production of dissolved organic carbon pools, and phytoplankton–bacteria competition for nutrients. Dissolved organic carbon is an important energy source for the bacterial community in this region (Thingstad et al., 2005). 2.2.1. Mathematical structure of BFM The ecological model only calculates biological fluxes for a single grid point, thus making it a zero dimensional model. The ecological state variable values at grid points are then diffused and advected by the physical model. The total derivative for biogeochemical variables

Fig. 2. Scheme of the biogeochemical state variables and pelagic interactions of the BFM. Living Functional Groups (LFG) are indicated with bold-line square boxes, non-living Chemical Functional families (CFF) in the yellow box, and inorganic CFFs with rounded boxes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) The figure has been adopted with permission from Vichi et al. (2007a).

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

115

can be represented by Eq. (1) in which ΔC represents the change in a substance concentration in seawater, Δt represents the model's time step, phys represents the advection and diffusion, and bio represents the biological processes.

of 0.0 and temperature equal to the temperature of a representative near-coast grid point. Fresh water was then mixed to 5 m depth at each internal time step.

!! !! ΔCi ¼ −∇ F phys −∇ F bio Δt

3.2. BFM

ð1Þ

Further details of the model can be found in the references mentioned above. 3. Model forcing, data, and experiments 3.1. Princeton Ocean Model The extent of the Nilotic cell and therefore the model domain were defined based on Fong and Geyer (2002). Then, the physical model's grid, bathymetry, initial conditions, open lateral boundary conditions, and climatological values for temperature and salinity were extracted from the third year of perpetual year forcing results of the Aegean and the Levantine basin models (Korres and Lascaratos, 2003). These were used to calibrate and evaluate the nested regional models within the framework of the Mediterranean forecasting system project (Brenner, 2003; Pinardi and Coppini, 2010; Pinardi et al., 2003). For the experiments described here the grid had a horizontal resolution of 1/20° × 1/20° (~5 km) and 30 vertical sigma layers, with the topmost layer having a thickness of 0.0025 times the bottom depth. Below σ = 0.1, the layers are equally spaced, with a layer thickness of 0.04 times bottom depth. The minimal bottom depth represented by the model is 50 m. The bathymetry was bilinearly interpolated from the ETOPO5 elevation data, which had a resolution of 5′ (National Geophysical Data Center, 2006). The model was forced using surface heat fluxes and a flux correction relaxation to surface salinity and temperature climatological values. Surface forcing, including total heat and fresh water flux, shortwave heat flux, wind stress, evaporation, and precipitation, was bilinearly interpolated to model grid and model time step from the European Center for Medium Range Weather Forecasting monthly mean climatology (Gibson et al., 1997). Surface temperature and salinity were relaxed to the Aegean and Levantine values with a relaxation time of 5 days for present day control simulation and 90 days for the experimental simulations. Results were used to specify the open lateral boundary conditions of temperature, salinity, baroclinic, and barotropic velocity. At outflow points values of the tracers were advected from the first interior grid point. Further details of the methodology are provided in Brenner (2003). Riverine fresh water fluxes were incorporated as elevated free surface height at the two grid points closest to the river mouth. The elevated free surface was inserted at each external time step with salinity

In general, in situ measurements of biogeochemical variables are scarcer than are measurements of physical variables, and are insufficient for creating suitable time varying initial and boundary conditions. For most biogeochemical variables, there are no climatological gridded fields similar to those used to initialize and force physical models. Therefore, the model's initial and boundary conditions were formulated by using the best available data taken from the literature, constructed to vertical profiles as shown in Table 4. It should be noted that the simulations did not include atmospheric nutrient inputs. The model was calibrated by running it as a one dimensional model (vertical column) and adjusting model parameters to reproduce measured concentrations (Suari, 2012). The calibrated model exhibited a spin-up process of about two years after which observed profiles were reproduced. Repeating this procedure is not feasible when running a three-dimensional coupled model due to the extremely high computational demand (Khatiwala, 2008). Open boundary conditions are commonly formulated to resemble initial conditions, and can thus be expected to cause similar spin-up processes close to the open boundaries. This could alter simulation results, especially when simulating relatively long open boundaries, as was the case in the current model domain. Therefore, the data for specifying the boundary conditions were extracted from the tenth year of the calibrated one-dimensional model results as described by Suari (2012). This procedure is expected to bypass most of those biases. The coastal sources of nutrients that were evaluated in the model included the Nile river, the eight larger Israeli coastal streams (Lachish, Soreq, Yarkon, Alexander, Hadera, Taninim, Qishon, Na'aman), and three marine outfalls (Herzliya, Shafdan, Agan) discharging treated effluents and activated sludge. The calculation of the Nile river nutrient discharges is based on data adopted from Nixon (2003), as presented in Table 1. As mentioned above, Egyptian effluents are discharged to coastal lagoons, and since the quantification of nutrients' removal by the lagoons is sketchy we employed the Nixon (2003) approach of using the potential nutrient fluxes. Nutrient fluxes of Israeli coastal stream were calculated using the Herut et al. (2000) calculation method and the mean 2006–2011 concentrations and water discharges of Herut et al. (2011). The addition of riverine fluxes was done in two stages, assuming constant concentration. At each internal time step, the annual flux (Table 2) was multiplied by a monthly fraction and then interpolated to the model time steps. Monthly fractions that were calculated by Vörösmarty et al. (1998) and the Israeli hydrological yearbooks (Weinberg, 2007) are summarized in Table 3.

Table 2 Mean annual fluxes of the Nile river, the eight Israeli rivers, and the three Israeli marine outfalls. Water discharge values are given in 103 m3 year−1; nutrient fluxes are given in 106 mol year−1. Experiment 2 2 2 2 3 3 3 3 4 4 4 4

Nile Water P N Si Water P N Si Water P N Si

Lachish

Soreq

Yarkon

Alexander

Hadera

Taninim

Qishon

Na'aman

Herzliya

Shafdan

Agan

2.3 0.5 6.8

7.1 0.6 10.1

52.1 4.7 52.1

12.4 0.7 9.4

12.8 1.1 15

19.7 0 5.1

18.3 1.2 18.6

2.3 0.3 2.7

6840 1.7 21.7

5760 48.3 235.1

645 0.9 28.1

42.9 × 106 300 500 3930 4.2 × 106 510 7710

116

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

Table 3 Monthly fraction of annual riverine discharge in three-dimensional experiments. Values are given in percentages. Experiment

Outlet

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2 3 4 4 4 4 4 4 4 4 4 4 4

Nile Nile Lachish Soreq Yarkon Alexander Hadera Taninim Qishon Na'aman Herzliya Shafdan Agan

4 6 18 18 18 18 18 18 18 18 8 8 8

3 7 18 18 18 18 18 18 18 18 8 8 8

3 7 14 14 14 14 14 14 14 14 8 8 8

2 7 8 8 8 8 8 8 8 8 8 8 8

3 8 4 4 4 4 4 4 4 4 8 8 8

3 11 3 3 3 3 3 3 3 3 8 8 8

5 11 3 3 3 3 3 3 3 3 8 8 8

20 11 3 3 3 3 3 3 3 3 8 8 8

26 9 4 4 4 4 4 4 4 4 8 8 8

17 8 5 5 5 5 5 5 5 5 8 8 8

8 7 6 6 6 6 6 6 6 6 8 8 8

5 6 12 12 12 12 12 12 12 12 8 8 8

3.3. Experiments Four experiments were conducted: (1) simulation of present day without riverine inputs served as the control run; (2) simulation of fresh water and nutrient inputs to the Nile prior to the 1964 damming; (3) simulation of fresh water and nutrient inputs to the Nile in 1995; and (4) simulation of fresh water and nutrient discharge of Israeli coastal streams. 4. Results and discussion The control run successfully reproduced measured temperature and currents, as can be seen in Fig. 3. The temperature ranged from 30 °C

during summer to 16 °C during winter. Coastal regions showed higher temperatures through most of the year, especially in the southeast corner of the domain. Summer temperatures ranged between 25 °C at the northern parts to 30 °C at the south, with greater variability than seen in temperatures during the rest of the year. Winter temperature was about 16 ± 2 °C throughout the domain. The effect of the Nile plume on surface salinity was evaluated by subtracting simulated surface salinity prior to 1964 from the control run surface salinity, resulting in the ΔS field presented in Fig. 4. The Nilotic inputs significantly reduced surface salinity to a distance of at least 30 km from the Nile delta throughout the year. The plume not only extends mostly eastward along the shore, but also turns northward to the open sea. During winter the plume is observed mostly near the Egyptian coast, but in early summer, when

Fig. 3. Seasonal surface temperature and current. Maps present the fifth year of the perpetual year model of the southeastern Levantine water with no terrestrial fresh water flux (control run). Values represent averages for the ten days following the presented date. Temperature is shown in the top color bar. Arrows at the bottom right corner represent velocity of 0.5 m s−1 arrow. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

117

Fig. 4. Map of surface salinity difference before and after Nile damming (ΔS = Scontrol − S1964) for fifth perpetual year at February 20th (top) and August 20th (bottom). Nile outfalls are marked by dark dots.

fresh water fluxes begin to rise, the plume extends further north. Fresh water is carried northward along the Israeli coast in a series of eddies from early summer to late fall, reducing the surface salinity of the southern Israeli coast by about 1 psu for that period, and sometimes reducing surface salinity by about 0.5 psu northward to Haifa bay. This salinity reduction was confirmed by Oren and Hornung (1972). Significant amounts of this fresh water are diverted westward by intrusions of coastal water into the open sea through mesoscale eddies and filaments, as displayed in Fig. 4. Such intrusions have been previously observed and studied (Efrati et al., 2013). The simulated salinity difference does not fully comply with historic data (El-Sayed et al., 1995; Halim, 1960; Oren and Hornung, 1972), because it describes the Nile plume as reducing the Israeli coastal salinity by more than 1 psu from early summer to late fall. The difference between simulation and observations can be attributed mainly to the fact that prior to 1964 the Nile discharged large amounts of fresh water for short periods of time, whereas our method of discharge used a smoother time averaged flux, as calculated from the river discharge database (Vörösmarty et al., 1998). A similar method was used to evaluate fresh water discharges of Israeli coastal

streams, documenting no salinity difference larger than 0.2. Thus, the effect of streams on the simulated circulation was most likely negligible. Winter surface NO 3 concentrations (Fig. 5) ranged between 0.5 and 1.3 mmol m− 3, with increasing concentration at the 60–250 m depth range up to 5 mmol m− 3. Summer surface concentrations were the lowest, ranging between 0.3 and 0.4 mmol m− 3. Throughout the year, elevated NO3 concentrations were observed along the Israeli coast and the Nile delta. The modeled concentrations generally reflected typical measured concentrations, although surface concentrations were somewhat higher than measured values (Pujo-Pay et al., 2011; Yacobi et al., 1995). Winter (Nov–May) surface PO4 concentrations (Fig. 5) ranged between 0.04 and 0.06 mmol m−3 and increased to a concentration of 0.25 mmol m−3 at a depth of 300 m. Summer (May–Oct) concentrations ranged between 0.01 and 0.03 mmol m−3. Laboratory analysis of such concentrations requires nanomolar techniques and non-frozen sample analysis so environmental measurements are scarce but the modeled surface concentrations are similar to concentrations reported. The deep water concentrations are also close to climatological values of 0.25 at 400 m depth (Krom et al., 1991; Yacobi et al., 1995; Zohary and

118

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

Fig. 5. Control experiment summer and winter mean surface concentrations of NO3 (mmol m−3), PO4 (mmol m−3), and chlorophyll a (mg m−3).

Robarts, 1998). Throughout the year the highest surface concentrations were found along the Israeli coast. Winter chlorophyll a surface concentrations (Fig. 5) were the highest and ranged between 0.15 and 0.2 mg m−3, with significantly higher concentrations in the eastern part of the domain, although not near the Israeli coast, where surface concentrations were somewhat lower. In regions with lower surface concentrations a DCM was present at a depth of 50 m, with concentrations ranging between 0.25 and 0.3 mg m−3. Modeled winter surface concentrations were somewhat higher than were measured concentrations, mainly due to a sharp increase in concentrations during the spring bloom that peaks in March (Fig. 6). DCM concentrations and depth were roughly the same as measured data (Maillard and Balopoulos, 2002). Summer surface concentrations ranged between 0.1 and 0.15 mg m−3, with elevated concentrations along the Israeli coast. The DCM was situated at a depth of 80–100 m, with concentrations ranging between 0.15 and 0.2 mg m−3. Summer concentrations were very similar to measured data, although the depth of the DCM was about 20 m shallower than the measured depth. The elevated chlorophyll surface concentration at the eastern part of the domain in winter is

probably the result of elevated diffusion at the southern current system that enters the domain from the north at 33–34°E. The control run generally reproduced the eastern Levantine biogeochemical features. Considering the long open boundaries characterizing the domain the results highly depended on the form of open boundaries' formulation. Our method of using open boundary values interpolated in space and time from a calibrated one-dimensional version of the model, and this enabled the simulation of such a domain. The model reproduced winter elevated surface chlorophyll a and nutrient concentrations. However, chlorophyll a concentrations were not correlated completely with regional nutrient concentrations, especially during winter and fall when light intensity led to high chlorophyll a at the southeast corner of the domain. Surface regional trends partial magnification of the chlorophyll and nutrient concentrations near the Israeli coast. This magnification can be explained not only by diffusion of nutrient-rich deep water, but also by the coastal upwelling observed near the Nile delta and near the Israeli coast. The resolution of the simulations presented here is insufficient for determining the relative impact of these upwelling

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

119

Fig. 6. Seasonal cycle of mean top 10 m chlorophyll a concentrations in several regions for the three biogeochemical experiments. The map at the top left figure illustrates the extent of the sub-regions, the top right plot shows the control run results, and the three lower plots show Δchlorophyll a for the other experiments, as calculated by subtracting climatological chlorophyll a concentration from modeled concentration.

processes, but since elevated chlorophyll a concentrations are observed in remote sensing studies (Barale et al., 2008; Figueras et al., 2004; Gitelson et al., 1996), and since elevated coastal nutrient concentrations are usually related to anthropogenic terrestrial fluxes, a closer look into the magnitude of coastal upwelling in this system should be performed in future studies. The seasonal cycle of the domain, as well as sub-domain means of surface chlorophyll a in the control run (Fig. 6), shows that the simulated concentrations are generally uniform throughout the domain, with

an exception of lower concentrations at the Nile delta, especially during summer. The Israeli coast and eastern domain show somewhat higher concentrations. A comparison of the pre-1964 data and the control run shows that the added nutrients at the Nile delta caused higher chlorophyll a concentrations mostly along the Israeli coast, and to a lesser extent at the eastern part of the domain, but had a moderate effect at the Nile delta itself. This effect is most likely caused by the advection of fresher Nile water with a higher nutrient content to the north east along the Israeli

120

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

coast rather than westward toward the Nile delta region. The pre-1964 model also showed lengthening of the spring bloom. Whereas the control run demonstrated maximum chlorophyll a concentrations in March, the pre-1964 model extended the spring bloom to June, with the annual peak in May. The 1995 experiment (Fig. 6) shows that the added nutrients had a more moderate impact on the domain scale chlorophyll a concentration than the pre-damming model, although nutrient fluxes were higher. The 1995 experiment also affected the temporal distribution of chlorophyll a concentrations by reducing the fall–spring elevated concentration period. Thus, this period extended from October to May in the control run but only from October to March in the 1995 experiment. The high concentration of nutrients in 1995 had a most pronounced effect on the elevation of the Nile delta chlorophyll a concentrations, especially during summer. This reconciles the evidence indicating an oligotrification of the eastern Mediterranean (Stergiou et al., 1997) and the fact that more nutrients are now entering the basin. Results of the Israeli coastal stream experiment (Fig. 6) show a moderate effect on the domain's chlorophyll a concentrations, never exceeding 0.07 mg m−3. The effect was mostly apparent in the Israeli coastal waters during peak winter fluxes and is reduced because of the fact that most of the flux flows during winter when ambient nutrients are relatively abundant. The fact that surface concentrations converged to control run values by June, about one month after the winter fluxes are substantially reduced, shows that the model is both stable and sensitive to external forcing. Although the model does not reproduce the exact measured values it seems capable of simulating trends in the Levantine biogeochemical environment. A comparison of the control simulation with the pre- and postdamming models highlights the pulsating nature of the Nile spatial effect. Thus, temporal trends of pre-damming fresh water, chlorophyll a, and nutrient concentrations do not form constant gradients with respect to the distance from the Nile outfall. Instead, these trends are accounted for by the fresh water eddies that the Nile discharge plume creates and which propagate with the coastal current along the Israeli coast. Fresh water flux during the pre-damming period was much larger, and most of the Nile nutrients flowed during summer, when the ambient nutrient concentrations in the control run were low. The nutrient enriched fresh water eddies of the predamming experiment dispersed and caused elevated chlorophyll concentrations in the entire basin, further than the low water fluxes and higher nutrient concentrations, of the 1995 experiment. A similar picture, with elevated chlorophyll a concentrations in the Israeli coastal region and less so in the Nile delta, would be produced if the nutrients were rapidly consumed at the Nile delta, transferred to higher trophic levels, and later recycled along the Israeli coast. But since trophic level parametrization is identical for all model regions, such recycled nutrients would also be rapidly consumed. Chlorophyll a surface concentrations represent the balance between chlorophyll a production by phytoplankton and phytoplankton grazing by zooplankton (Table 5). In the present simulations, this process can be seen in the form of temporal and regional chlorophyll a concentration variations. The temporal trend is demonstrated in the post-damming experiment, in which the spring bloom led to elevated chlorophyll a concentrations at the Nile delta, and to a subsequent zooplankton concentration elevation. Grazing of the phytoplankton biomass formed by the bloom led to a chlorophyll a drop to the lowest concentrations, as seen in all three simulations. This might suggest that today's planktonic community (as simulated in the post-damming experiment) is unstable and more prone to temporal variations. The regional effect of grazing can be seen in the form of lowered chlorophyll a concentrations in the Nile delta during summer, likely resulting from elevated zooplankton grazing that reduces chlorophyll. A further detailed study of the zooplankton–phytoplankton interaction is required to increase our understanding of the system's sensitivity to varying nutrient loads, mostly caused by anthropogenic activity.

5. Conclusions Our work shows the effect that riverine nutrient inputs on the southeastern Mediterranean biogeochemistry. Specifically, the high water volume of the pre-damming Nile plume extended significantly further than did the low water volume of the post-damming plume. The larger plume caused elevated nutrient concentration in the open Mediterranean waters, as opposed to the current situation where Nile nutrients are being consumed by primary consumers in coastal waters, although the combined anthropogenic and natural nutrient fluxes in the postdamming experiment were about two-fold for PO4 and 16-fold for NO3. The region of elevated chlorophyll concentrations also extended further in the pre-damming experiment than it did in the postdamming experiment. The Israeli coastal stream simulation showed that the model was stable and more sensitive to external forcing than to internal noise. Since reduction of fresh water fluxes and eutrophication of fresh water bodies are expected to become a global phenomenon (Hanjra and Qureshi, 2010), the effects on the marine environment should be investigated more thoroughly. Computer models such as the one presented here are probably the only tool that will enable simulation and understanding of each of these effects separately. Acknowledgments This research was conducted in Bar-Ilan University, the depatment of Geography an environment and supported by the European Commission through the Sixth Framework Program European Coastal Sea Operational Observing and Forecasting System (ECOOP) Contract Number 36355. Appendix 1. Model parameters

Table 4 BFM state variables, symbols, and elemental components. State variable

Symbol

Constituent

Diatoms (20–200 m) Nanophytoplankton (2–20 m) Picophytoplankton (0.2–2 m) Large phyto (20–200 m) Pelagic bacteria Heterotrophic nanoflagellates (2–20 m) Microzooplankton (20–200 m) Mesozooplankton (omnivorous) Mesozooplankton (carnivorous) DOM labile DOM carbohydrates POC Nitrate Ammonium Phosphate Silicate Reduction equivalents Oxygen Benthic variables Dissolved organic matter in Oxic layer Dissolved organic matter in anoxic layer Particulate organic detritus in sediment

P1 P2 P3 P4 B Z1 Z2 Z3 Z4 R1 R2 R3 N1 N3 N4 N5 Nr O2

C, N, P, Si, Chl C, N, P, Chl C, N, P, Chl C, N, P, Chl C, N, P C, N, P C, N, P C, N, P C, N, P C C, N, P C, N, P, Si N N P Si S O

Q1 Q11 Q6

C, N, P C, N, P C, N, P, Si

Table 5 Values of the BFM zooplankton feeding matrix, as calibrated for the southeastern Levantine.

Predator

Prey P1 Z1 Z2 Z3 Z4

P2 0 1 0.4 0

P3 0 0.7 0.6 0.2

P4 0 0 0.2 1

B 0 0.8 0.4 0.2

Z1 0 0 0 0.7

Z2 1 0 0 0

Z3 1 1 0 0

Z4 0 1 0.8 0

Y. Suari, S. Brenner / Journal of Marine Systems 148 (2015) 112–121

References Azov, Y., 1991. Eastern Mediterranean—a marine desert. Mar. Pollut. Bull. 23, 225–232. Barale, V., Jaquet, J., Ndiaye, M., 2008. Algal blooming patterns and anomalies in the Mediterranean Sea as derived from the SeaWiFS data set (1998–2003). Remote Sens. Environ. 112, 3300–3313. Bar-Or, Y., 2000. Restoration of the rivers in Israel's coastal plains. Environ. Chall. 123, 311–321. Berman, T., Azov, Y., Schneller, A., Walline, P., Townsend, D.W., 1986. Extent, transparency and phytoplankton distribution of the neritic waters overlying the Israeli coastal shelf. Oceanol. Acta 9, 439–447. Blumberg, A.F., Mellor, G.L., 1987. In: Heaps, N.S. (Ed.), A Description of a Threedimensional Coastal Ocean Circulation Model. American Geophysical Union, Washington, DC, pp. 1–16. Brenner, S., 2003. High-resolution nested model simulations of the climatological circulation in the southeastern Mediterranean Sea. Ann. Geophys. 21, 267–280. Brenner, S., Gertman, I., Murashkovsky, A., 2007. Preoperational ocean forecasting in the southeastern Mediterranean Sea: implementation and evaluation of the models and selection of the atmospheric forcing. J. Mar. Syst. 65, 268–287. Carniel, S., Vichi, M., Sclavo, M., 2007. Sensitivity of a coupled physical–biological model to turbulence: high-frequency simulations in a northern Adriatic station. Chem. Ecol. 23, 157–175. Christaki, U., Giannakourou, A., Van Wambeke, F., Gregori, G., 2001. Nanoflagellate predation on auto- and heterotrophic picoplankton in the oligotrophic Mediterranean Sea. J. Plankton Res. 23, 1297–1310. Efrati, S., Lehahn, Y., Rahav, E., Kress, N., Herut, B., Gertman, I., Goldman, R., Ozer, T., Lazar, M., Heifetz, E., 2013. Intrusion of coastal waters into the pelagic eastern Mediterranean: in situ and satellite-based characterization Earth System. Biogeosciences 10, 3349–3357. El-Sayed, M.K., Morcos, S.A., Halim, Y., Rizkalla, S., 1995. The Impact of the River Nile and the Suez Canal on the Living Marine Resources of the Egyptian Mediterranean Waters (1958–1986). Food and Agriculture Organization of the United Nations, Rome. Figueras, D., Karnieli, A., Brenner, A., Kaufman, Y.J., 2004. Masking turbid water in the southeastern Mediterranean Sea utilizing the SeaWiFS 510 nm spectral band. Int. J. Remote Sens. 25, 4051–4059. Fong, D.A., Geyer, W.R., 2002. The alongshore transport of freshwater in a surface-trapped river plume. J. Phys. Oceanogr. 32, 957–972. Gibson, J.K., Kallberg, P., Uppala, S., Hernandez, A., Nomura, A., Serrano, S., 1997. ECMWF reanalysis project. Rep. Ser. 1, 71. Gitelson, A.A., Yacobi, Y.Z., Karnieli, A., Kress, N., 1995. Remote estimation of chlorophyll concentration in polluted marine waters in Haifa Bay, Southeastern Mediterranean. Toxics and Water Monitoring, p. 44. Gitelson, A.A., Karnieli, A., Goldman, N., Yacobi, Y.Z., Mayo, M., 1996. Chlorophyll estimation in the Southeastern Mediterranean using CzCs images: adaptation of an algorithm and its validation. J. Mar. Syst. 9, 283–290. Halim, Y., 1960. Observations on the Nile bloom of phytoplankton in the Mediterranean. ICES J. Mar. Sci. 26, 57–67. Hanjra, M.a., Qureshi, M.E., 2010. Global water crisis and future food security in an era of climate change. Food Policy 35, 365–377. Herut, B., Kress, N., Hornung, H., 2000. Nutrients pollution at the lower reaches of Mediterranean coastal rivers in Israel. Water Sci. Technol. 42, 147–152. Herut, B., Shefer, E., Gordon, N., Galil, B., Tibor, G., Tom, M., Rilov, G., Silverman, J., 2011. The quality of Mediterranean Israel Coastal Waters During 2010. Khatiwala, S., 2008. Fast spin up of ocean biogeochemical models using matrix-free Newton–Krylov. Ocean Model 23, 121–129. Kimor, B., Wood, E.J.F., 1975. A plankton study in the eastern Mediterranean Sea. Mar. Biol. 29, 321–333. Korres, G., Lascaratos, A., 2003. A one-way nested eddy resolving model of the Aegean and Levantine basins: implementation and climatological runs. Ann. Geophys. 21, 205–220. Kress, N., Thingstad, T.F., Pitta, P., Psarra, S., Tanaka, T., Zohary, T., Groom, S., Herut, B., Mantoura, R.F.C., Polychronaki, T., Rassoulzadegan, F., Spyres, G., 2005. Effect of P and N addition to oligotrophic Eastern Mediterranean waters influenced by nearshore waters: a microcosm experiment. Deep-Sea Res. II 52, 3054–3073. Krom, M.D., Kress, N., Brenner, S., 1991. Phosphorus limitation of primary productivity in the Eastern Mediterranean Sea. Limnol. Oceanogr. 36, 424–432. Krom, M.D., Woodward, E.M.S., Herut, B., Kress, N., Carbo, P., Mantoura, R.F.C., Spyres, G., Thingstad, T.F., Wassmann, P., Reiser, C.W., Kitidis, V., Law, C.S., Zodiatis, G., 2005. Nutrient cycling in the south east Levantine basin of the eastern Mediterranean: results from a phosphorus starved system. Deep-Sea Res. II 52, 2879–2896. Krom, M.D., Emeis, K.-C., Van Cappellen, P., 2010. Why is the Eastern Mediterranean phosphorus limited? Prog. Oceanogr. 85, 236–244. Lazzari, P., Teruzzi, A., Salon, S., Campagna, S., Calonaci, C., Colella, S., Tonani, M., Crise, A., 2010. Pre-operational short-term forecasts for Mediterranean Sea biogeochemistry. Ocean Sci. 6, 25–39. Ludwig, W., Dumont, E., Meybeck, M., Heussner, S., 2009. River discharges of water and nutrients to the Mediterranean and Black Sea: major drivers for ecosystem changes during past and future decades? Prog. Oceanogr. 80, 199–217. Maillard, C., Balopoulos, E., 2002. In: Brown, M. (Ed.), Recent Advances in Oceanographic Data Management of the Mediterranean and Black Seas: The MEDAR/MEDATLAS 2002 Database. VLIZ Special Publication, Brussels, Belgium, pp. 10–12. Mellor, G.L., 2002. Users Guide for a Three Dimensional, Primitive Equation, Numerical Ocean Model.

121

National Geophysical DataCenter, 2006. 2-Minute Gridded Global Relief Data (ETOPO2v2). Nixon, S.W., 2003. Replacing the Nile: are anthropogenic nutrients providing the fertility once brought to the Mediterranean by a great river? AMBIO A J. Hum. Environ. 32, 30–39. Oren, O.H., Hornung, H., 1972. Temperatures and salinities off the Israel Mediterranean coast. Bull. Isr. Fish. Res. Stn. 59, 17–31. Petihakis, G., Triantafyllou, G., Korres, G., Pollani, A., Hoteit, I., et al., 2006. Eastern Mediterranean biogeochemical flux model: simulations of the pelagic ecosystem. Ocean Sci. Discuss. 3, 1349–1398. Pinardi, N., Coppini, G., 2010. Preface “Operational oceanography in the Mediterranean Sea: the second stage of development.”. Ocean Sci. 6, 263–267. Pinardi, N., Allen, I., Demirov, E., De Mey, P., Korres, G., Lascaratos, A., Le Traon, P.Y., Maillard, C., Manzella, G., Tziavos, C., et al., 2003. The Mediterranean ocean forecasting system: first phase of implementation (1998–2001). Ann. Geophys. 21, 3–20. Pujo-Pay, M., Conan, P., Oriol, L., Cornet-Barthaux, V., Falco, C., Ghiglione, J.-F., Goyet, C., Moutin, T., Prieur, L., 2011. Integrated survey of elemental stoichiometry (C, N, P) from the western to eastern Mediterranean Sea. Biogeosciences 8, 883–899. Rasmussen, E.K., Petersen, O.S., Thompson, J.R., Flower, R.J., Ahmed, M.H., 2009. Hydrodynamic–ecological model analyses of the water quality of Lake Manzala (Nile Delta, Northern Egypt). Hydrobiologia 622, 195–220. Redfield, A.C., 1934. On the Proportions of Organic Derivatives in Sea Water and Their Relation to the Composition of the Plankton. Liverpool University Press, pp. 176–192. Reeburgh, W.S., 1997. Figures summarizing the global cycles of biogeochemically important elements. Bull. Ecol. Soc. Am. 78, 260–267. Res, C., Vichi, M., May, W., Navarra, A., 2003. Response of a complex ecosystem model of the northern Adriatic Sea to a regional climate change scenario 24, pp. 141–158. Rilov, G., Galil, B., 2009. Marine bioinvasions in the Mediterranean sea — history, distribution and ecology. In: Occhipinti-Ambrogi, A., Savini, D. (Eds.), Biological Invasions in Marine Ecosystems. Springer Berlin, Heidelberg, pp. 549–575. Rosentraub, Z., Brenner, S., 2007. Circulation over the Southeastern continental shelf and slope of the Mediterranean Sea: direct current measurements, winds, and numerical model simulations. J. Geophys. Res. — Ocean 112, C1101. Shahidul Islam, M., Tanaka, M., 2004. Impacts of pollution on coastal and marine ecosystems including coastal and marine fisheries and approach for management: a review and synthesis. Mar. Pollut. Bull. 48, 624–649. Sharaf-el-Din, S.H., 1977. Effect of the Aswan high dam on the Nile Flood and on the Estuarine and coastal circulation pattern along the Mediterranean Egyptian coast. Limnol. Oceanogr. 22, 194–207. Skliris, N., Lascaratos, A., 2004. Impacts of the Nile River damming on the thermohaline circulation and water mass characteristics of the Mediterranean Sea. J. Mar. Syst. 52, 121–143. Stergiou, K.I., Christou, E., Georgopoulos, D., Zentos, A., Souvermezoglou, C., 1997. The Hellenic Seas: physics, chemistry, biology and fisheries. Oceanogr. Mar. Biol. 35, 436–485. Suari, Y., 2012. Modeling the Impact of Riverine Nutrients Input on the South Eastern Levantine Planktonic Ecology. Bar Ilan University, Ramat Gan, Israel. Tanaka, T., Zohary, T., Krom, M.D., Law, C.S., Pitta, P., Psarra, S., Rassoulzadegan, F., Thingstad, T.F., Tselepides, A., Malcolm, E., Woosward, S., Flaten, G.A.F., Skjoldal, E.F., Zodiatis, G., 2007. Microbial community structure and function in the Levantine Basin of the Eastern Mediterranean. Deep-Sea Res. I 54, 1721–1743. Thingstad, T.F., Krom, M.D., Mantoura, R.F.C., Flaten, G.A.F., Groom, S., Herut, B., Kress, N., Law, C.S., Pasternask, A., Rassoulzadegan, F., Psarra, S., Pitta, P., Tanaka, T., Tselepides, A., Wassmann, P., Zodiatis, G., Reiser, C.W., Woodward, E.M.S., Zohary, T., 2005. Nature of phosphorus limitation in the ultraoligotrophic Eastern Mediterranean. Science 309, 1068–1070 (80-.). Triantafyllou, G., Korres, G., Hoteit, I., Petihakis, G., Banks, A.C., 2006. Assimilation of ocean color data into a biochemical flux model of the Eastern Mediterranean Sea. Ocean Sci. Discuss. 3, 1569–1608. Vichi, M., Masina, S., 2009. Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000. Biogeosciences 6, 2333–2353. Vichi, M., Baretta, J.W., Ruadij, P., Zavatarelli, M., Pinardi, N., Polimene, L., Butenschon, M., 2006. Biogeochemical Flux Model (BFM). Vichi, M., Masina, S., Navarra, A., 2007a. A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: numerical simulations. J. Mar. Syst. 64, 110–134. Vichi, M., Masina, S., Navarra, A., 2007b. A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: theory. J. Mar. Syst. 64, 89–109. Vörösmarty, C.J., Fekete, B., Tucker, B.A., 1998. River Discharge Database, Version 1.1 (RivDIS v1. 0 Supplement). Inst. Study Earth, Ocean. Space, Univ, New Hampshire, Durham, NH. Weinberg, G., 2007. Israeli Hydrological Yearbook. Yacobi, Y.Z., Zohary, T., Kress, N., Hecht, A., Robarts, R.D., Waiser, M., Wood, A.M., Li, W.K.W., 1995. Chlorophyll distribution throughout the southeastern Mediterranean in relation to the physical structure of the water mass. J. Mar. Syst. 6, 179–190. Zavatarielli, M., Mellor, G.L., 1995. A numerical study of the Mediterranean Sea Circulation. J. Phys. Oceanogr. 25, 1384–1414. Zohary, T., Robarts, R.D., 1998. Experimental study of microbial P limitation in the Eastern Mediterranean. Limnol. Oceanogr. 43, 387–395.