A modeling approach for a cascade of reservoirs in the Juquiá-Guaçu River (Atlantic Forest, Brazil)

A modeling approach for a cascade of reservoirs in the Juquiá-Guaçu River (Atlantic Forest, Brazil)

Ecological Modelling 356 (2017) 48–58 Contents lists available at ScienceDirect Ecological Modelling journal homepage: www.elsevier.com/locate/ecolm...

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Ecological Modelling 356 (2017) 48–58

Contents lists available at ScienceDirect

Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel

A modeling approach for a cascade of reservoirs in the Juquiá-Guac¸u River (Atlantic Forest, Brazil) Marcela B. Cunha-Santino a,b , Ângela T. Fushita a , Irineu Bianchini Jr. a,b,∗ a

Universidade Federal de São Carlos, Departamento Hidrobiologia, Rodovia Washington Luiz, km 235, CEP: 13565-905, São Carlos, SP, Brazil Universidade Federal de São Carlos, Programa de Pós-Graduac¸ão em Ecologia e Recursos Naturais, Rodovia Washington Luiz km 235, CEP: 13565-905, São Carlos, SP, Brazil b

a r t i c l e

i n f o

Article history: Received 10 October 2016 Received in revised form 10 April 2017 Accepted 12 April 2017 Keywords: Ecosystem services Limnological assessment Math model Eutrophication Reservoir cascade

a b s t r a c t In a cascade of reservoirs, the events that occur in the upstream reservoir can be transferred to the downstream ones. Thus, the water quality of the second and subsequent reservoirs usually changes. Based on a zero-dimensional model, this work describes the mass balances of 23 limnological variables in a system of 6 cascade reservoirs located in a well-preserved hydrographic basin (within the Brazilian Tropical Atlantic Forest). Ecosystem services are also mentioned to stress the importance of this system to improve the water quality of the Juquiá-Guac¸u River. Samples were taken from the reservoirs´ı inputs, in the lacustrine region, and in the Juquiá-Guac¸u River downstream of each reservoir. According to the zero-dimensional model (continuous stirred tank reactor), it can be concluded that: i) the retention intensities of the elements varied within each reservoir itself and among the reservoirs. These differences occur because chemical and biotic processes concerning retentions are different, and the specific physical conditions (e.g. water velocity, flow, stratification) related to the retentions of each element are different; ii) not all reservoirs presented high assimilation coefficients for all variables, the six reservoirs were very efficient in terms of retaining the elements; iii) the system reduced the amounts of 87% of the variables; iv) for variables related to erosion and runoff the cascade of reservoirs was not able to decrease the values; v) the The high percentage of retention of the limnological variables enables us to evaluate the importance of these reservoirs to reduce eutrophication (nitrogen and phosphorus compounds), turbidity, TS, color, coliforms (total and fecal) from the Juquiá-Guac¸u River. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Reservoirs show significant differences in their morphometric properties, retention time, coupling with watershed and hydrodynamics (Straˇskraba, 1996). Owing to longitudinal gradients in basis morphology, flow, velocity, water retention time, suspended solids, as well as nutrients and light availability, reservoirs present a remarkable degree of spatial heterogeneity in metabolic processes (e.g. primary production, cycling of allochthones and autochthonous detritus). Variations in the structure and dynamics of phytoplankton, zooplankton and planktonic ciliate communi-

∗ Corresponding author at: Universidade Federal de São Carlos, Departamento Hidrobiologia, Rodovia Washington Luiz, km 235, CEP: 13565-905, São Carlos , SP, Brazil. E-mail address: [email protected] (I. Bianchini Jr.). http://dx.doi.org/10.1016/j.ecolmodel.2017.04.008 0304-3800/© 2017 Elsevier B.V. All rights reserved.

ties are also expected (Lansac-Tôha et al., 2004; Silva et al., 2005; Nogueira et al., 2008, 2010; Moura et al., 2013). Three regions are usually defined along the longitudinal axis. The first, dominated by the river inflow defines the riverine zone; this region is dominated by a higher flow, a shorter water retention time, as well as higher levels of nutrients, suspended solids and light extinction compared to the downstream portion. In the transition zone, there is an increase in the basin width, decreasing the flow velocity, and increasing water retention time, sedimentation and light penetration. The lacustrine region, near the dam area, typically has a longer water residence time, lower concentration of nutrients and particles and higher water transparency (Kimmel et al., 1990). Apart from the spatial heterogeneity created by establishing different regions in a reservoir, in the reservoir cascades a specific event occurring in the upstream reservoir can be transferred to the downstream. The water quality of the first reservoir is usually similar to the water quality of an isolated reservoir. The water quality of

M.B. Cunha-Santino et al. / Ecological Modelling 356 (2017) 48–58

the second reservoir and the subsequent ones usually changes. The extent to which a reservoir modifies the water quality of another reservoir below it depends on whether the higher reservoir is deep, stratified or shallow. The intensity of these changes in the water quality depends on the stream order, trophic levels in the reservoir and the distance between reservoirs (Straˇskraba and Tundisi, 1999). The decrease in water velocity and increase in retention time favor the occurrence of chemical and biological oxidation (e.g. mineralization) and decantation of various elements in the sediments, of which phosphorus is one of the most susceptible (Wetzel, 2001; Cunha-Santino and Bianchini, 2005). In addition, photoreactions promote organic matter bleaching (Brezonik, 1994; Santos et al., 2006), increasing the depth of the photic zone. These physical processes change the chemical and physical characteristics of the water, often contributing (directly or indirectly) to the improvement of water quality (e.g. decrease in eutrophication) and general conditions of aquatic environments (Barbosa et al., 1999). The importance of water quality for hydrologic services includes cultural services, e.g. recreation and support services, e.g. providing water and nutrients to estuaries (Brauman et al., 2007). Concerning eutrophication, the chemical alterations induced by the reservoirs in the rivers can be characterized as a regulating service (i.e. water purification), as phosphorus and other nutrients tend to be trapped in the sediment (Postel and Carpenter, 1977). The reduction in velocity and the increase in water transparency may favor the primary production constituting another regulatory service, which favors a supporting service (Brauman et al., 2007). Taking this into account, this study outlines the effects of a cascade of reservoirs on limnological variables; therefore, it was assumed that: i) all the variable values tend to decrease throughout the reservoir sequence; ii) the variables most affected by this process are those related to chemical and biological oxidations (e.g. chemical and biological oxygen demands and color). Thus, according to a zerodimensional model, this work describe the mass balances of 23 limnological variables in a system of 6 cascade reservoirs located in a well-preserved hydrographic basin (Juquiá-Guac¸u River, Brazil). We also discuss the ecosystem services (sensu Costanza, 2008) so as to emphasize the importance of this system to improve the water quality of the Juquiá-Guac¸u River, which is located in the Atlantic Forest, currently recognized as a biodiversity hotspot in Brazil. This biome retains only 7.5% of its primary vegetation (Myers et al., 2000; Colombo and Joly, 2010), which is a result of centuries of human exploitation. Identifying the ecosystem services of the aquatic system (e.g. water security: quality and quantity; nutrient cycling: maintenance of floodplain fertility; provision of aquatic organisms: food and medicine; recreation; flood control infrastructure; MA, 2005) is a suitable approach to improve the aquatic biodiversity conservation goals and plan the provision of ecosystem goods and services to society.

2. Materials and methods 2.1. Study area description The sequence of reservoirs (Franc¸a, Fumac¸a, Barra, Porto Raso, Alecrim, and Serraria) was constructed to supply hydroelectric power plants. Moreover, these aquatic environments are located within a private nature reserve (Reserva Votorantim). This area covers around 35,000 ha, corresponding to 1.5% of the remaining Atlantic Forest of São Paulo State (Brazil), which has been preserved for more than 50 years. The reservoirs are located in the JuquiáGuac¸u River sub-basin, covering several municipalities (Fig. 1). This sub-basin is part of the Ribeira de Iguape River basin, which due to its large size (28,306 km2 ), covers the states of São Paulo

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and Paraná (Votorantim, 2016). The hydrological regime of the Juquiá-Guac¸u River is substantially controlled by atmospheric precipitation (1600–1700 mm annual average), highlighting the period from December to April, which has the most water (CBA, 1993). The annual mean temperature is 22 ◦ C (Souza, 2002), and according to the rainfall database (SIGRH, 2008), prefix DAEE: E4-116 (23◦ 57 00 S and 47◦ 13 00 W; altitude: 600 m asl; period: 1969–2004), the region has an average monthly rainfall of 144 ± 71 mm. On average, the driest month is July (74.3 mm) and January is the rainiest (265.4 mm). According to Köppen climate classification (1931), the climate of this region is Cfb, hot dry winter (Alencar et al., 1976; Aoki and Saraiva, 1982). The predominant soils are oxisol dystrophic Cambrian (LVAd) clayey and haplic cambissoil Tb dystrophic (CXvd) clayey gravels (Oliveira et al., 1999). The local vegetation is considered transitory between a rain forest and a semi-deciduous forest (Aragaki and Mantovani, 1998).

2.2. Limnological survey A limnological survey and sampling were conducted in January 2008. The samples were taken from the inflow region of the reservoirs in the lacustrine region (nearby the dam), and in the Juquiá-Guac¸u River downstream of each reservoir (Fig. 1). In each sample site (SS; n = 16), the following variables were measured every 10 cm (from the surface to the bottom) with a multi-parameter water quality sonde (YSI, model 6600): electrical conductivity (EC), dissolved oxygen (DO), temperature (air: Ta; water: Tw), turbidity, pH, chlorophyll. Water samples (n = 3) were collected to determine the color, total solids (TS), alkalinity, total coliforms (TColi), fecal coliforms (FColi), biological oxygen demand (BOD5 ), chemical oxygen demand (COD) and organic nitrogen (N-Org) concentrations that were determined according to APHA; AWWA and WEF (1998). Total inorganic nitrogen compounds (TIN=N-NH4 + N-NO2 + N-NO3 ) and phosphorus (total and dissolved) were determined according to colorimetric methods (Koroleff, 1976; Mackereth et al., 1978). Total nitrogen (TN) was computed as the sum of TIN and N-Org. Total particulate phosphorus (TPP) was estimated by the difference between the total phosphorous (TP) and total dissolved phosphorous (TDP) concentrations. Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) concentrations were determined using the combustion/non-dispersive infrared gas analysis method (Shimadzu analyzer, model 5000A). The total amount of carbon (TC) was determined by the sum of DOC and DIC. Moreover, the depth (z) of the sampling site and the Secchi disk transparency depth (zsd ) were measured. In accordance with the maximum length, age and volume of each reservoir, and monthly average flows of the JuquiáGuac¸u River (SIGRH, 2008; prefix DAEE 4E-026; 23◦ 56 32 S and 47◦ 05 54 W; period: 1981–1996) the retention time (RT; Ambrosetti et al., 2003), the hydraulic flushing (HF = 1/RT = dilution rate; Thomann and Müller, 1987), the densimetric Froude number (Fd ; Orlob et al., 1969), and the number of water renovation (WR = age × 365/RT) were calculated. The shore line (L), maximum length (l) and the fetch of the reservoirs were determined by the MapInfo 7.5 software, using satellite image CBERS-2 collected on 12/09/2006, path 155, and row 127. The same satellite image was used to calculate the areas of the sub-basins of the Juquiá-Guac¸u and Bagres rivers (upstream from the nature reserve), and their percentage of vegetation cover. The physical characteristics of the reservoirs (i.e. basis quota, height, shore line, area and volume) were used to evaluate its morphometric parameters (zm = maximum depth; zr = relative depth; za = average depth; b = maximum width; ba = average width; DL = shore line development; DV = volume development). The mixing zone depths (zmix )

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Fig. 1. Localization of reservoirs and of the sampling sites (1: Juquiá-Guac¸u River; 2: Bagres River; 3: Franc¸a Reservoir; 4: Juquiá-Guac¸u River downstream the Franc¸a Dam; 5: Pocinho Stream; 6 Fumac¸a Reservoir; 7: Juquiá-Guac¸u River downstream the Fumac¸a Dam; 8: Barra Reservoir; 9: Juquiá-Guac¸u River downstream the Barra Dam; 10: Porto Raso Reservoir; 11: Juquiá-Guac¸u River downstream the Porto Raso Dam; 12: Alecrim Reservoir; 13: Juquiá-Guac¸u River downstream the Alecrim Dam; 14: Travessão River; 15: Serraria Reservoir; 16: Juquiá-Guac¸u River downstream the Serraria Dam).

were identified from the vertical profiles of relative thermal resistance (Wetzel, 2001). 2.3. Mathematical model To describe the mass balance of the substances, it is assumed that the reservoirs are completely mixed systems with a step input (Eqs. (1) and (2); Chapra and Reckhow, 1983). ˛=

Q + k, V

(1)

C=

W (1 − e−˛t ), ˛V

(2)

where: ␣ = assimilation factor, d−1 ( sink); k = first order reaction rate constant, d−1 ; Q ⁄V = hydraulic flushing (HF), d−1 ; Q = upstream flow rate (average); V = volume of reservoir; C = steady state concentration; W = the loading term (e.g. the daily load of substance), kg d−1 ; t = the time required to reach the equilibrium concentration. Thus, in our study there are three basic assumptions: i) the reservoirs can be represented by a zero-dimensional model (i.e., Continuous Stirred Tank Reactor, CSTR); ii) all reservoir are in a steady state (the initial and final values are constant), necessarily generating “step” loading function; (iii) the hydraulic retention time is sufficient for the reservoir to reach equilibrium. CSTRs are useful to describe more complex systems. Such systems can be described by a distributed parameters model using a network of CSTRs (eventually with feedback). Each CSTR is characterized by a

proper volume V and kinetic constant k and outflow Q from the i-esim CSTR to the other and by a load L from the other CSTRs into the i-esim including the external environment (Jørgensen and Bendoricchio, 2001). Another important application of CSTR models concerns a zero-dimensional system with complex transfer processes. According to the general hydraulic theory series of CSTRs, a cascade of n CSTRs characterized by the same parameters can be used to simulate the attenuation of the entering concentration of a substance (Chow, 1964). The proposed model assumes that the reservoir system is in balance and the limnological variable values downstream derive from the initial concentrations except for the quantities of elements that were trapped (or “generated”) in the reservoir (Søndergaard et al., 2003) during the RT period. The alpha coefficients (␣) obtained from the parameterization report that there were losses (e.g. sedimentation, biological absorption, chemical reactions and adsorption) or gains of the elements in the reservoirs compared to the initial concentrations (the inputs of some tributaries, lateral runoff, atmospheric precipitation, internal loading, decomposition, etc.). Thus, the positive coefficients indicate that the element is retained in the reservoir. Positive values for ␣ also suggest that the retention of substance is greater than the adduction. On the other hand, the negative coefficients indicate that the substance is concentrated in the reservoir or, the retention of the substance is smaller than the input. The model also assumed that the downstream concentrations are equal to the upstream reservoir.

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Table 1 Morphometric and hydraulic characteristics of the reservoirs; where: Year = initial year of hydroelectric plant operation; BQ = basis quota; a.s.l = above sea level; MOH = maximum operational height; DA = drainage area; A = maximum operational area; V = maximum operational volume; zm = maximum depth; zr = relative depth; zmix = mixing zone depth; za = average depth; Fd = densimetric Froude number; Q = average flow rate; RT = retention time; HF = average hydraulic flow; WR = mean number of water renewal of reservoir since initial year of hydroelectric plant operation until 2016; b = maximum width (or breadth); ba = average width; L = shore line; DL = shore line development; DV = volume development; l = maximum length. Reservoir

Year

BQ a.s.l. (m)

MOH a.s.l. (m)

DA (km2 )

A (km2 )

V (m3 )

DV

zm (m)

zr (%)

za (m)

zmix (m)

Franc¸a Fumac¸a Barra Porto Raso Alecrim Serraria

1957 1964 1986 1982 1974 1978

603.0 480.0 312.0 273.0 187.5 32.0

640.0 531.0 402.0 306.0 237.5 83.0

951 1073 1450 1499 1632 1730

12.7 5.2 2.0 1.5 1.5 2.1

1.35E + 08 9.00E + 07 5.81E + 07 2.04E + 07 2.93E + 07 3.76E + 07

0.86 1.01 0.96 1.25 1.14 1.04

37 51 90 33 50 51

0.92 1.98 5.62 2.40 3.57 3.10

10.6 17.2 28.8 13.8 19.0 17.7

4.5 4.2 2.9 3.5 2.3 5.6

Q (m3 s−1 )

RT (d)

HF (d−1 )

WR ref 2016

Fetch (km)

b (km)

ba (km)

L (km)

DL

l (km)

Fd

27.3 30.7 40.9 42.2 45.9 48.5

57.2 34.0 16.4 5.6 7.4 9.0

0.017 0.029 0.061 0.179 0.135 0.112

376 559 666 2219 2077 1547

2.17 2.70 2.90 3.35 3.46 4.33

1.14 1.50 0.97 0.51 1.10 2.01

0.77 0.48 0.27 0.19 0.19 0.29

167.9 48.2 22.0 22.6 34.2 28.9

13.29 5.95 4.38 5.23 7.76 5.59

16.39 10.83 7.56 7.91 7.93 7.31

0.10 0.07 0.06 0.38 0.21 0.17

Franc¸a Fumac¸a Barra Porto Raso Alecrim Serraria

The model parameterization consisted of obtaining the alfa coefficient for each selected variable (i.e. EC, DO, chlorophyll, turbidity, TS, color, BOD5 , COD, alkalinity, TC, DIC, TP, TDP, TPP, N-NO3 , NNO2 , N-NH4 , N-Org, TIN, TN, TColi, FColi) in each reservoir. For this purpose, the following were used in Eq. (2): i) the average values of each input variable (W on a daily basis); ii) the average flow adduction of each reservoir (daily basis); iii) the retention time (=t); iv) the volume. By substituting the value of alpha (iterative method), the values of the calculated variables (C parameter; Eq. (2)) were compared with the average values determined in the field. The selected alpha value was the one that generated the smallest difference between the calculated and experimental values. 2.4. Statistical analysis Statistical analyses were performed in R language (R Core Team, 2015) using the vegan package (Oksanen et al., 2015). Differences in the reservoir conditions were evaluated using non-metric multidimensional scaling (NMDS), by pairwise Euclidian distances. To enhance the determinacy of scaling and orientation of NMDSaxes, the ordination results were post-processed following the default-options of the ‘metaMDS’ function and the quality of this transformation is shown by the “stress” (Oksanen et al., 2015). The limnological variables were fitted afterwards on an ordination using the function ‘envfit’, which calculates determination coefficients (r2 ) and the significance value based on Monte-Carlo permutations (999). All dimensions are considered simultaneous with NMDS and “envfit”. The original ordination’s configuration is left intact and the variables overlaid with “envfit” are independently modeled effects and not part of a globally modeled combination of effects such as those used by CCA (Debinski et al., 2006). 3. Results The oldest reservoir (Franc¸a) presented the highest values of the area, volume, medium width, length, perimeter and DL compared to the others. In the sequence of years, the newest reservoirs tended to present, almost continuously, decreases in the area, zmix , volume, the average width, perimeter, and was less dendritic (Table 1). Over the years, the newest reservoirs also tended to present incremental increases in: i) the maximum depth,; ii) the relative depth; iii) the average depth (Table 1). The deepest reservoir is Barra and the maximum depths of the others range from 33 m (Porto Raso) to

51 m (Fumac¸a and Serraria). The DV values vary near unity, regardless of the age of the reservoir, from 0.86 (Franc¸a) to 1.25 (Porto Raso). The maximum reservoir widths range from 0.97 (Barra) to 2.01 km (Serraria). On average, the Franc¸a Reservoir has a greater width (0.77 km); the lowest average widths (0.19 km) refer to the Alecrim and Porto Raso reservoirs (Table 1). The Serraria Reservoir has the largest fetch value (4.33 km), followed by the Alecrim (3.46 km) and Porto Raso reservoirs (3.35 km); the Franc¸a Reservoir comprises the lowest value of fetch (2.17 km). The zmix was between 2.3 (Alecrim) and 5.6 m (Serraria); this variable was not correlated to the fetch or any other morphometric parameter. Compared to the maximum and mean depths, the mixing depths were lower. Zmix corresponding to 3–12% of Zm , and 10–42% of Za . There was an increase in the Fd values (0.1–0.38) from the oldest reservoir to the Porto Raso Reservoir (39 years old). A sharp decrease in the value of Fd (0.06) was determined in the most recent reservoir (Barra Reservoir). The Porto Raso Reservoir was the only environment that presented an Fd value (0.38) marginally greater than 0.32 (thermal stratification criterion Fd < 1⁄; Orlob et al., 1969) (Table 1). The RT values tended to decrease from the oldest reservoir (Franc¸a, RT = 57.2 days) to the other (from 5.6 to 34 days). The Porto Raso Reservoir was the one with the highest number of renewals (2219 times); the oldest reservoir (Franc¸a) presented the lowest number (Table 1). Two sites of the Franc¸a Reservoir (SS 1 and 2) were clearly separated along the first NMDS-axis, and SS 14 (Serraria Reservoir) was separated along the second NMDS-axis. In the Franc¸a Reservoir inlets, the Juquiá-Guac¸u River (SS 1) drains an area of 24,270 ha, which has 32% of deforested area, whilst the Bragres River (SS 2) drains 26,810 ha of area, which comprises 23% deforestation. In the SS 14, concerning an input of the Serraria Reservoir, the Travessão River drains an area of 1239 ha, which has 19% of deforested area. Except for these three points, the limnological variable values did not present a high variation among the reservoirs (Tables 2 and 3). The NMDS ordination has a stress value = 0.0486 (Fig. 2; Tables 2 and 3); and commonly the values of the variables had a tendency to decrease from the upstream tributaries to the lacustrine region, and downstream of the dams (i.e. chlorophyll, color, turbidity, TS, TC, DOC, COD, TP, TDP, TPP, N-NO2 , TColi, FColi); a clear correlation of the NMDS-axis was evident (r2 = 0.485–0.973; p-value = 0.001–0.011) and a negative influence on the NMDS-axis 1. In this case, the highest decreases were usually recorded in the Franc¸a Reservoir. In addition, three other categories of variation were observed: in the first, the values tended to vary around the

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Table 2 Chemical variables of reservoirs. Sampling site (SS) label 1–16 respect the longitudinal axis from cascade of reservoirs from upstream to downstream, listed in Fig. 1. The names of the reservoirs are above the sampling point regarding the lacustrine region (upstream of the dam). Franc¸a: SS1 to 3; Fumac¸a: SS4 to 6; Barra: SS 7–8; Porto Raso: SS9 and 10; Alecrim: SS 11 and 12; Serraria: SS 13–15; SS16: downstream of the Serraria Reservoir. Variable/SS

DO (% sat) DO (mg L−1 ) pH TS (g L−1 ) COD (mg L−1 ) Alkalinity (mg L−1 ) TC (mg L−1 ) DIC (mg L−1 ) DOC (mg L−1 ) TP (␮g L−1 ) TDP (␮g L−1 ) TPP (␮g L−1 ) N-NO3 (␮g L−1 ) N-NO2 (␮g L−1 ) N-NH4 (␮g L−1 ) N-Org (␮g L−1 ) TIN (␮g L−1 ) TN (␮g L−1 ) Dissolved N/P TN/TP

Franc¸a

Fumac¸a

Barra

P. Raso

Alecrim

Serraria

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

109.1 9.54 7.07 1.05 9 6.3 7.8 1.7 6.2 61.6 27.5 34.1 109.9 8.2 55.0 745.0 173.0 918.0 25.5 14.9

100.1 9.03 6.83 0.93 17 5.9 10.5 2.2 8.4 52.9 31.9 21.0 92.3 5.5 37.5 729.2 135.3 864.4 20.5 16.3

83.7 7.03 6.86 0.63 6 6.8 4.8 1.8 3.1 13.0 9.4 3.6 17.3 2.2 17.5 549.2 37.0 586.2 45.3 45.0

104.8 9.20 6.97 0.50 8 7.0 7.2 2.6 4.6 20.3 13.8 6.5 110.7 3.8 12.5 637.5 127.0 764.5 42.0 37.7

115.5 9.33 7.29 0.03 2 6.8 5.1 1.8 3.3 13.0 10.1 2.9 23.9 2.8 42.5 574.2 69.3 643.4 47.0 49.3

100.7 8.46 6.92 0.03 5 6.8 5.1 1.7 3.4 10.1 8.0 2.2 32.0 2.5 15.0 935.0 49.5 984.5 89.4 97.1

116.5 10.00 6.74 1.02 7 5.9 6.8 1.9 4.9 18.1 11.6 6.5 184.9 2.8 20.0 713.3 207.8 921.1 61.6 50.8

104.8 8.89 6.63 0.65 2 6.1 5.4 1.8 3.6 10.1 7.3 2.9 151.0 2.5 37.5 762.5 191.0 953.5 100.9 94.0

87.0 7.53 7.08 0.77 7 6.0 5.7 2.1 3.6 17.4 6.5 10.9 213.1 2.2 12.5 520.8 227.8 748.6 91.6 43.0

82.5 7.03 6.86 0.03 7 6.6 5.2 1.9 3.3 15.9 4.4 11.6 177.8 2.7 22.5 577.5 203.0 780.5 140.8 49.0

73.3 8.68 6.74 0.86 10 5.6 5.6 2.0 3.5 15.9 5.8 10.1 243.3 2.7 30.0 520.0 276.0 796.0 111.1 49.9

96.0 8.22 6.53 0.75 4 5.6 5.0 1.7 3.3 13.0 5.1 8.0 243.2 2.8 30.0 570.0 276.0 846.0 134.1 64.9

109.6 9.38 6.74 0.04 5 6.6 5.3 1.9 3.4 13.0 5.8 7.3 257.7 2.8 20.0 513.3 280.5 793.8 111.1 60.9

147.9 11.97 8.83 0.04 5 9.0 5.8 1.9 3.8 28.3 17.4 10.9 8.8 2.7 40.0 760.0 51.5 811.5 33.9 28.7

103.7 8.67 6.71 0.03 4 7.6 5.5 1.9 3.6 13.77 5.8 8.0 69.7 2.8 17.5 449.2 90.0 539.2 70.4 39.2

99.1 8.39 6.97 0.04 4 7.3 5.5 2.2 3.2 18.12 5.8 12.3 20.4 2.5 15.0 485.0 37.9 522.9 65.8 28.9

Table 3 Physical and biological variables of reservoirs. Sampling site (SS) label 1–16 respect the longitudinal axis from cascade of reservoirs from upstream to downstream, listed in Fig. 1. The names of the reservoirs are above the sampling point regarding the lacustrine region (upstream of the dam). Franc¸a: SS1–3; Fumac¸a: SS4–6; Barra: SS 7–8; Porto Raso: SS9 and 10; Alecrim: SS 11 and 12; Serraria: SS 13 to 15; SS16: downstream of the Serraria Reservoir. Variable/SS

Ta (◦ C) Tw (◦ C) EC (mS cm−1 ) Chlorophyll (␮g L−1 ) Turbidity (UNT) Color (mg Pt L−1 ) BOD5 (mg L−1 ) TColi (CFU 100 ml−1 ) FColi (CFU 100 ml−1 ) z (m) zsd (m)

Franc¸a

Fumac¸a

Barra

P. Raso

Alecrim

Serraria

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

24.4 22.0 0.031 3.03 47.78 275 0.96 69000 4000 8.25 0.34

22.4 20.4 0.026 3.11 13.38 155 0.57 52000 1880 1.51 0.73

24.1 23.9 0.032 0.65 3.97 17 0.97 800 0 12.76 2.97

19.4 21.7 0.032 0.59 8.98 120 0.34 19600 80 4.18 0.94

22.3 26.2 0.03 0.45 3.81 27 0.22 5600 100 3.1 1.7

23.7 24.0 0.031 0.56 3.39 27 0.79 10400 20 15.34 1.8

21.2 23.0 0.031 0.61 5.53 70 0.57 10600 180 1.76 1.64

25.4 23.6 0.031 0.80 2.51 30 0.61 9800 40 15.43 2.73

19.3 22.5 0.033 0.65 4.32 65 1.59 6000 200 4.01 1.3

19.6 23.4 0.032 1.20 3.49 44 1.94 7330 240 15 1.6

20.2 23.0 0.031 0.68 5.12 65 0.67 2600 160 2.51 1.12

20.5 23.1 0.032 1.03 7.86 50 0.59 4600 20 16 1.36

23.0 23.1 0.031 0.34 7.63 66 0.86 1000 100 5.2 0.93

23.4 26.1 0.03 1.26 8.48 43 2.05 330 20 2.77 1.12

21.9 24.4 0.032 0.81 7.1 32 1.86 330 0 14.64 1.33

23.6 23.7 0.033 0.20 7.82 61 1.04 1660 80 0.83 0.5

average, without presenting a high variation (i.e. Ta, Tw, EC, DO, DO sat, pH, DIC) and a positive influence on the NMDS-axis 2, except for DIC. The other category comprises the soft increase of values along the sequence of reservoirs (alkalinity, BOD5 ) having a positive influence on the NMDS ordination. In the last group, the values present a high variation and without a definite trend; this category includes most of the nitrogen compounds, having low or no correlation with NMDS ordination (r2 = 0.058–0.3787; p-value = 0.027–0.711). At not one sampling site, was the depth of the disappearance of the Secchi disk equal to the total depth (Table 3). The zsd value decreased in the reservoir sequence as it changed from 2.97 (SS 3) to 1.33 m (SS 15). Not one variable showed an increase in all the reservoirs, leading to in negatives values for alpha for all the environments (Fig. 2). The indicators that presented a significant negative trend (i.e. 4 events of an increase in values) throughout the sequence of reservoirs were: alkalinity, chlorophyll, EC and TIN. BOD5 and TN presented 3 events of increase. Color, TC, TP and TDP only presented decreases throughout the reservoir sequence. Turbidity, TS, DOC, N-NO3 , and FColi presented only one event of increase. DO, COD, DIC, TPP, NNO2 , N-NH4 , and TColi presented 2 events of increase (i.e. negative alpha). According to the alpha values (≈ intensity), the predominance of the processes of reducing values compared to the events of increase can be observed. The positive and negative alpha values

were interbedded between the variables and the reservoirs (Fig. 2). Taking into account the alpha parameter (Fig. 3) results from the sum of HF and k (Eq. (1)), it was observed that in several cases the reaction ratios assume negative values. In this context, the Franc¸a Reservoir presented the highest and largest positive values of k (13), followed by the Serraria (6), Alecrim (3), Barra (2) and Fumac¸a and Porto Raso (1) reservoirs. The dissolved N/P ratio tended to increase from the Franc¸a reservoir to the Porto Raso Reservoir. Afterwards, the values decreased. In turn, the TN/TP ratio increased from the Franc¸a reservoir to the Barra Reservoir and afterwards, the values decreased, showing in this case, the predominance of trapped phosphate compounds compared to nitrogen (Table 2). Except for the Porto Raso Reservoir (␣ = 0.0001 d−1 ), EC showed negative or null alpha values. On the other hand, color, TC, TP, and TDP showed decreases in all the reservoirs (i.e. positive values for alpha). Depending on the variable, the positive and negative coefficients vary along the reservoir cascade. However, the positive coefficients tended to prevail (68% of cases), and the retention time did not present a clear relation with any coefficient (positive or negative); Fig. 2. In the Porto Raso Reservoir, N-NH4 concentration generated the highest negative value for the alpha parameter (␣ = −0.25 d−1 ), and FColi was the variable that showed the highest positive alpha value in the Alecrim Reservoir (␣ = 0.33 d−1 ). The variables that gener-

M.B. Cunha-Santino et al. / Ecological Modelling 356 (2017) 48–58

53

Fig. 2. Variations of alpha parameter in relation to the limnological variable and reservoir; where HF = hydraulic flushing.

ated positive alpha values (i.e. prevalence of retention processes) with a tendency to increase along the reservoirs´ı position were: DO, TS, BOD5 , COD, TP, TDP, and N-Org. The EC variations and alkalinity generated negative alpha values with a tendency to be more negative throughout the sequence of the reservoirs. The variations of alpha coefficients linked to chlorophyll, turbidity, color, TC, DIC, DOC, TPP, N-NO3 , N-NO2 , N-NH4 , TIN, TN, TColi, FColi did not show any clear trend compared to the position of the reservoirs (Fig. 2).

Concerning the retention events (Fig. 2), the variables presented the following distribution: i) those that have been retained in all the reservoirs (color, TC, TP, TDP); ii) those that were maintained in five (turbidity, TS, DOC, N-NO3 , FColi); iii) which have been maintained in four reservoirs (DO, COD, DIC, TPP, N-NO2 , N-NH4 , TColi) and iv) those which were retained in three (BOD5 , N-Org, TN) or less reservoirs (EC, chlorophyll, alkalinity, TIN).

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EC Alkalinity

120

100

100

TColi FColi

80

Percentage

Percentage

80

60

60

40

40

20 20

0

0 0

57

91

107,5

113

120,5

129,5

0

57

91

Time (day)

107,5

113

120,5

129,5

Time (daY) 120

TC DIC DOC

100 100

TP TDP TPP

80

Percentage

Percentage

80 60

40

60

40 20 20 0 0

57

91

107,5

113

120,5

129,5

Time (day)

0

57

91

107,5

113

120,5

129,5

Time (day)

400

350

BOD5 COD

300

Percentage

250

200

150

100

50

0 0

57

91

107,5

113

120,5

129,5

Time (day) Fig. 3. Simulations of the variations in electrical conductivity, alkalinity, TColi, FColi, TP, TDP, TPP, TC, DIC, DOC, BOD5 and COD along the sequence of reservoirs. Wherein: zero-day refers to the input (tributaries of the Franc¸a Reservoir) and 120.5 days refers to the effluent flow of Serraria Reservoir. All input values were set at 100%.

The retention time of reservoirs did not present a clear relation with the quantity of negative or positive coefficients. For the Franc¸a Reservoir (RT: 52.2 days) and the Serraria Reservoir (RT: 9 days)

20 positive coefficients were computed, followed by the Barra Reservoir (RT: 16.4 days) which had 16, the Porto Raso Reservoir (RT: 52.2 days) which had 14, the Fumac¸a Reservoir (RT: 34 days)

M.B. Cunha-Santino et al. / Ecological Modelling 356 (2017) 48–58

which had 13 and the Alecrim Reservoir (RT: 7.4 days) which had 11 positive coefficients. On average, the Alecrim Reservoir presented the highest retention potential (average of the positive coefficients = 0.088 d−1 ), followed by the subsequent reservoirs: Serraria (0.085 d−1 ), Franc¸a (0.060 d−1 ), Porto Raso (0.039 d−1 ), Barra (0.034 d−1 ), and Fumac¸a (0.011 d−1 ). The comparison of the number of occurrences of alpha > 0 with morphometric parameters did not reveal any association. The number of occurrences of k > zero had direct relations with: area (r2 : 0.72), DL (r2 : 0.79) and L (r2 : 0.82). The relationship between the positive alpha and k positive was only marginal (r2 : 0.54) According to the average retention potential (i.e. average of the positive alphas), the yield of trapped material was calculated for each reservoir for its retention time period. In this context, the Franc¸a Reservoir presented the highest average yield (72%), followed by the Alecrim and Serraria reservoirs (30%), the Barra Reservoir (23%), the Fumac¸a and Porto Raso reservoirs (16%). When considering the cascade of reservoirs as a single system (RT = 129.6 d), it can be observed that the retention process prevailed for almost all variables; only DIC, alkalinity, BOD5 , and EC were not lower downstream (i.e. there was a prevalence of negative coefficients). The retention pattern of the reservoir cascade was similar to that observed in the Franc¸a Reservoir. In this context, comparing the coefficients of the Franc¸a Reservoir (x) and the reservoir system (y), by linear regression, the r2 = 0.64 and slope = 0.94 were obtained. Figs. 3 and 4 show the simulation of the limnological variables according to the measured alfa parameters and the RT of each reservoir. Differences in the compound loss patterns were observed, as well as specific contributions of the Franc¸a Reservoir for the loss processes in these reservoirs. The amounts related to the last day of the simulation (129.5 days) were compared with those obtained experimentally. In this case, a great similarity of results can be observed (Fig. 5). The comparison of the simulated values with those obtained from the field generated r2 = 0.92.

4. Discussion The analyses of the limnological data prove that the reservoirs are located in a watershed which is relatively well preserved. The aquatic environments involved (rivers and reservoirs) are usually slightly acidic, with high dissolved oxygen concentrations, and often above the saturation values. The concentrations of COD and BOD5 are usually low. The variables related to eutrophication show that the reservoirs can be classified as meso-eutrophic (sensu Vollenweider, 1968; Green et al., 2015), although the main input rivers, located outside the reserve, have high concentrations of phosphorus and nitrogen compounds. These areas, upstream from the Franc¸a Reservoir, are associated with agriculture practices (SIFESP, 2016). The water temperature, Fd values, and morphometric characteristics (e.g. zmix , zm , za , zr , fetch, ba , DL ) showed that the reservoirs tended to be maintained stratified (Orlob et al., 1969), and considering the geographical region, are the warm monomictic type (Hutchinson and Löffler, 1956). This type of vertical water movement tends to trap in the summer, spring and fall, part of the elements in the hypolimnion, which will eventually be released depending on the depth of the reservoir outlet (Handerson-Sellers, 1984; Straˇskraba and Tundisi, 1999). This differentiated pattern of water exchange suggests that the values of the parameter alpha may be under dimensioned since the hydraulic residence times refer to the reservoirs as a whole, and the movement can take place preferably at the epilimnion, or hypolimnion. Thus, the alpha values represent the prevailing trend of the variables.

55

There are four possibilities for storage and release of elements according to alpha parameters. The first, the alpha values >HF and k is positive; in this case, part of the element is retained in the reservoir and another part, diluted, and exported. This scenario was well represented in the Franc¸a Reservoir. It is the most predictable result, and overall, it was well demonstrated by color, turbidity, phosphorous and coliforms variations throughout the reservoirs. For these variables, the reservoir cascade represents an efficient system, in order to hold elements to downstream. For this case, an effective ecosystem service can be attributed to the cascade of reservoirs in order to reduce TS, eutrophication and biological contamination (i.e. coliforms). The relationship between the number of occurrences k > zero and DL , L, and area suggests that retentions linked with chemical reactions or biological processes are, in these reservoirs, connected to the primary production (owing to the light incidence area; Straˇskraba, 1980) and with events occurring in the littoral regions, and subsystems with a prevalence of higher RT. In the second case, alpha = HF (and thus, k = 0), the element will only be diluted (i.e. stored in the water) and exported downstream. This situation also promotes the reduction of variable values downstream. This situation can be illustrated by the variations of N-NO3 concentrations in the Franc¸a Reservoir and N-NH4 in the Fumac¸a Reservoir. In this case, some ecosystem service of the system can still be recognized, such as water purification owing to the reduction of nitrogen compound concentration. When alpha < HF, but positive (and necessary k < 0), the inputs (external or internal) are significant, and the outflow process only occasionally prevents the occurrence of some desirable values. This situation can be seen by almost all the alpha values in the Fumac¸a Reservoir. In this case, there is a decrease in the concentrations, but much smaller than it could potentially be, considering the volume of the reservoir and outflow magnitude. In this situation, the maintenance of a condition that could be worse could be characterized as an ecosystem service. In the final case, alpha is negative (and necessary k < 0); in this case the loading takes precedence over the outflow, internal reactions and settling in the hypolimnion. In this situation, the inputs (external or internal) are significant, and the export process just reduces higher values. This scenario is observed by several occurrences in Fig. 2 (e.g. Fumac¸a, Porto Raso and Alecrim reservoirs). The ecosystem services, such as, water purification, decrease in eutrophication, recreation, fish supply and other freshwater tends to be minimized. However, considering our results, it is important to note that the retention and release capacity of different elements changes in the same reservoir, which can be effective for one element but not for another. Based on the costs concerning polluted water treatment, these attenuations can be easily used to evaluate these ecosystem services. These results can also help to manage the preservation of the main attributes of the Juquiá-Guac¸u watershed (Henderson-Sellers, 1993), as well as the mitigation strategies to prevent the undesirable thresholds from being reached (Stillman et al., 2016). There are relatively few events of increased variable values considering the reservoirs. The nitrogen compounds (N-NO3 , N-NH4 , N-Org, TIN, and TN) are the group most affected by this procedure. The increase values of those variables are probably linked to phytoplankton fixation (Howarth et al., 1988), and contribute to the occurrence of negative alpha, considering that alpha is, by definition (Eq. (1)), a parameter linked to the sink. According to the results obtained from this study, the longitudinal changes of EC, alkalinity, DIC, and BOD5 also exemplify the condition with negative alpha values. These variables always tended to increase throughout the cascade of reservoirs, as they denote growths of values continuously, and these events suggest the predominance of runoff and weathering of the drainage basin (Likens and Borman, 1975). As well as the variations of alpha parameters, there are some other interactions that occur in the

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Turbidity TS Color

100

N-NO3 N-NO2 N-NH4

80

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60

60

40

40

20

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0 0

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107,5

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129,5

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N-org TIN TN

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Chlorophyll

100

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100 80

60

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0 0

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Time (day)

100

DO

Percentage

80

60

40

20

0 0

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107,5

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120,5

129,5

Time (day) Fig. 4. Simulations of the variations in turbidity, TS, color, N-NO3 , N-NO2 , N-NH4 , N-Org, TIN, TN, chlorophyll and DO along the sequence of reservoirs. Wherein: zero-day refers to the input (tributaries of the Franc¸a Reservoir) and 120.5 days refers to the effluent flow of Serraria Reservoir. All input values were set at 100%.

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FColi TColi TN TIN N-org N-NH4 N-NO2 N-NO3 TPP TDP TP DOC DIC TC Alkalinity COD BOD5 Color TS Turbidity Chlorophyll DO EC

Experimental values

-100

-50

57

Simulated values

0

50

100 -100

-50

Sink (%)

0

50

100

Sink (%)

Fig. 5. The percentage values of sinks (experimental and simulated values) considering the set of the six reservoirs.

whole cascade of reservoirs, such as change in the spectral quality of water (Fukushima et al., 2016). In this case, the zsd variation derived from 2 processes: the decrease in TS concentration and the increase in chlorophyll.

5. Conclusion According to the adopted experimental procedures and the selected model it was concluded that: the retention intensities of the elements alter within the reservoir itself and among the reservoir cascades. These differences are a consequence of the various reasons for loss (i.e. DO availability, temperature, light, oxi-reduction potential, biota), and the specific physical conditions (i.e. water velocity, flow, stratification, water outlet height, hypolimnion and epilimnion depths), as well as the fact that the retentions of each element are distinctive. For this reason, although not all the reservoirs presented high assimilation coefficients for all the variables, the six reservoirs were very efficient in retaining the elements. Different from the initial assumption, the most affected limnological variables was the one related to the sedimentation (physical and chemical processes) instead of biological oxidation. The system reduced the values of 87% of the variables; only variables related to erosion and runoff of the cascade of reservoirs were not able to decrease the values. This high percentage of affected variables enabled us to evaluate the role of these reservoirs for the mitigation of eutrophication (nitrogen and phosphorus compounds), turbidity, TS, color, coliforms (total and fecal) of the Juquiá-Guac¸u River.

Acknowledgments The authors are grateful to Companhia Brasileira de Alumínio (CBA-Votorantim) and CNEC Engenharia for providing the data of the reservoirs (year, BQ, MOH, DA, A, V), for subsided the field sampling, and for the concession of limnological data. We also thank to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq grant number 305263/2014-5).

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