A three-dimensional water quality modeling approach for exploring the eutrophication responses to load reduction scenarios in Lake Yilong (China)

Environmental Pollution 177 (2013) 13e21

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A three-dimensional water quality modeling approach for exploring the eutrophication responses to load reduction scenarios in Lake Yilong (China) Lei Zhao a, d, Yuzhao Li b, Rui Zou c, *, Bin He d, Xiang Zhu d, Yong Liu b, *, Junsong Wang d, Yongguan Zhu a a

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871, China Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA 22030, USA d Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming 650034, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 May 2012 Received in revised form 16 January 2013 Accepted 22 January 2013

Lake Yilong in Southwestern China has been under serious eutrophication threat during the past decades; however, the lake water remained clear until sudden sharp increase in Chlorophyll a (Chl a) and turbidity in 2009 without apparent change in external loading levels. To investigate the causes as well as examining the underlying mechanism, a three-dimensional hydrodynamic and water quality model was developed, simulating the flow circulation, pollutant fate and transport, and the interactions between nutrients, phytoplankton and macrophytes. The calibrated and validated model was used to conduct three sets of scenarios for understanding the water quality responses to various load reduction intensities and ecological restoration measures. The results showed that (a) even if the nutrient loads is reduced by as much as 77%, the Chl a concentration decreased only by 50%; and (b) aquatic vegetation has strong interaction with phytoplankton, therefore requiring combined watershed and in-lake management for lake restoration. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Hydrodynamic and water quality model Lake Yilong Water quality Scenario analysis Eutrophication

1. Introduction Eutrophication has been the primary problem facing most surface waters worldwide (Smith, 1983, 2009; Martins et al., 2008; Wu and Xu, 2011). Although the intricate mechanism for eutrophication has not been determined because of the nonlinear response of water quality to nutrient loading, it is certain that nutrient loads reduction is essential for water quality improvement and ecological restoration (Rosenberga and Lars-Ove, 1988; Vieira and Lijklema, 1989; Charpa, 1997; Liu et al., 2008b). Mechanistic models can reflect quantitative response relationships between load reduction and water quality as well as be able to conduct scenario analyses for decision making. Water quality modeling (WQM) has therefore been well developed in the past decades and successfully served as scientific tools supporting decision makings for controlling the exogenous or ingenuous pollutions (Ahmad et al., 2001; Zou et al., 2006, 2009; Liu et al., 2008a; Purandara et al., 2012). The WQM

* Corresponding authors. E-mail addresses: [email protected] (R. Zou), [email protected] (Y. Liu). 0269-7491/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envpol.2013.01.047

with scenario analysis is considered to effectively support strategic decision making (Schoemaker, 1982; Wack, 1985). Compared with other assessment frameworks, WQM-based scenario analysis not only reflects whether water quality could reach the targeted state under certain assumptions, but also represents several ‘futures’ or different points of view simultaneously (Höjer et al., 2008). Employing scenario analysis could also evaluate all aspects of the local decision making processes and it was thus widely used as an effective decision making support tool on water quality restoration (Arnold et al., 1994; Miller et al., 2002). The integration of scenario analysis with the WQM has been used in a variety of studies covering different areas of focus. Some scenarios were analyzed to investigate whether future agricultural land use would cause deterioration of water quality (Rounsevell et al., 2005; Alcamo, 2001; Ewert et al., 2005). For example, Kepner et al. (2004) defined future land-use scenarios to demonstrate how modeling tools could be used to evaluate the spatial impacts of urban growth patterns on surface water hydrology. Similarly, scenario analysis was also employed to investigate other factors impacting water quality. In the water quality management for Lake Plastira, a mathematical eutrophication-dissolved oxygen model was used to

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examine three water management scenarios based on alternative minimal operating water levels (Andreadakis et al., 2003; Kepner, 2004). The WQM-based scenario analysis was also used for exploring the effects of climate change. A simple linear regression between air and water temperature is used to generate the scenarios for river water temperature, and the results suggest that all the hypothetical climate change scenarios would cause water quality impairment. It has shown that there is a significant decrease in DO levels due to the impact climate change has on temperature and flow rates (Rehana and Mujumdar, 2009; Park and Lee, 2002). Lake Yilong is one of the nine largest plateau lakes in Yunnan Province, Southwestern China. The lake water quality deteriorated sharply in 2009 and a regime shift occurred from the macrophytedominated state to the phytoplankton-dominated state (Zhang et al., 2010). Algal blooms occurred frequently since 2009 and caused great economic losses. To better understand the cause of the shift as well as facilitating effective decision making on eutrophication control and ecological restoration in Lake Yilong, it is necessary to establish a quantitative cause-and-effect relationship between anthropogenic interference and lake response through mechanistic mathematical modeling. Considering the necessity of resolving spatially variable hydrodynamics and complex water quality and phytoplankton/macrophyte interactions, the Environmental Fluids Dynamics Code (EFDC) was selected as the computational platform for developing the model. EFDC is a widely applied 3-dimensional (3D) hydrodynamic and water quality simulation framework capable of simulating water circulation,

temperature dynamics, and advanced eutrophication processes involving nutrients, phytoplankton, macrophyte, and predation/ grazing processes. It has been applied for simulation and decision support analysis of surface water such as lakes, reservoirs, bays, wetlands and estuaries (Li et al., 2011; Peng et al., 2011; Seo et al., 2010; Shi et al., 2011; Wu and Xu, 2011). In the present study, the EFDC was customized to represent the main water quality and ecological processes in Lake Yilong, enabling the predictive analysis of multiple scenarios so as to provide possible explanations for the water deterioration as well as to establish a quantitative linkage between external nutrient loading and in-lake water quality. To produce more scientific support for water quality improvement management, three EFDC-based scenarios were designed and analyzed in this study, including (a) response of algal blooms to aquatic vegetation existence in the lake, (b) the variation of load reduction requirement corresponding to different water quality standards, and (c) the eutrophication status of the lake corresponding to different water quality standard compliances. 2. Modeling framework 2.1. Study area Lake Yilong is one of the largest plateau lakes in Yunnan Province (Fig. 1), with an average water surface elevation of 1414 m, an average lake depth of 3.9 m, and a maximum depth of 5.7 m. The lake area is 28.4 km2 and the volume can reach 114.9 million m3. There

Fig. 1. Lake Yilong watershed.

L. Zhao et al. / Environmental Pollution 177 (2013) 13e21

are seven main tributaries flowing into the lake. The watershed areas of the seven tributaries account for more than 70% of the entire basin of Lake Yilong. Historical water quality data from 1998 to 2009 and previous studies (i.e. the Lake Yilong Total Maximum Daily Load, YLTMDL) showed that the aquatic system in Lake Yilong experienced significant inter-annual fluctuations, while a clear signal of regime shift is identifiable for 2009. In 2009, the previously flourishing macrophyte communities in the lake had nearly been eliminated and a sharp increase in nutrient and phytoplankton concentration occurred. According to the newly issued 12th FiveYear Plan for Water Pollution Control of Lake Yilong Watershed, over 1 billion Chinese Yuan (approximately US$160 millions) are expected to be invested in the control of lake eutrophication from 2011 to 2015. To provide scientific basis for the decision making on implementing the lake eutrophication control, it is necessary to acquire a quantitative understanding between the watershed nutrients load reduction and the lake water quality goals. Thus an EFDC-based eutrophication modeling system was developed in this study for Lake Yilong. The Shiping County Environmental Monitoring Center is responsible for monthly water quality monitoring. Data were collected from three observation stations. Water samples were collected twice per month (Supporting Materials). 2.2. EFDC model The EFDC is a widely recognized simulation platform and a multi-task, highly integrated modular computational environmental fluid dynamics package. The water quality model has 21 state variables, and simulates the spatial and temporal distribution of water quality parameters including dissolved oxygen (DO), phytoplankton, various components of carbon, nitrogen, phosphorus and silica cycles, and fecal coliform bacteria (Hamrick and Wu, 1997). EFDC also has a sediment diagenesis module which simulates the diagenesis processes of organic matters in the bed and the resulting fluxes of inorganic substances and sediment oxygen demand back to the water column (Park et al., 2005). The hydrodynamic module of the model solves three-dimensional, vertically hydrostatic, free surface and turbulent averaged equations of motion for a variable-density fluid. Dynamically coupled transport equations for turbulent kinetic energy, turbulent length scale, salinity, and temperature are also solved (Hamrick and Wu, 1997). The general governing equations for EFDC are (Park et al., 2005):



v
v mx my HC v v my HuC þ ðmx HvCÞ þ mx my wC þ vx vy vz vt       v my HAx vC v mx HAy vC v Az vC ¼ þ þ mx my mx vx my vy H vz vx vy vz

15

determining the cause of the sharp increase in algal bloom intensity in 2009, as well as to determine the effective management levels that would result in compliance of water quality standards. To simulate the dynamic factors causing the drastic change in the aquatic ecosystems of Lake Yilong, it is essential to adopt a model that could reflect complex interactions between nutrients and phytoplankton, aquatic plants, DO and sediment, etc. It thereby requires a construct with maximum complexity in EFDC. Specifically, the EFDC model for Lake Yilong should include all relevant water quality drivers, such as carbon, nitrogen, phosphorus, algae and DO to fully characterize the process of eutrophication. Furthermore, the interactions between the vascular aquatic plants and phytoplankton nutrients were also analyzed. The sediment diagenesis model can be used to predict changes in sediment nutrient flux historically or under certain scenarios, such as watershed management and restoration. It can also characterize the response of sediment oxygen (SOD) to watershed nutrient load changes. A model with the ability to predict sedimentewater interactions can overcome the key limitations of non-predictive water quality models in analyzing the long term response in water quality to watershed and in-lake management, hence supporting more reliable watershed management decision-makings. The following text describes the processes of developing the Lake Yilong model, including grid generation, configuration of initial and boundary conditions, model calibration and scenario analysis. (1) Grid generation A curvilinear grid was developed to discretize Lake Yilong. The lake was represented by using a total of 241 horizontal grids where the smallest grid was approximately 0.05 km2 and the largest was approximately 0.18 km2. The average depth at the shallowest grid was about 1.5 m while the average depth of the deepest grid was about 5.3 m at the water surface elevation of 1414 m above sea level. Although Lake Yilong is shallow, with no significant thermal stratification, phytoplankton and aquatic vegetation are still influenced by vertical distribution in light; therefore, it is desirable to resolve variability in vertical light intensity and nutrient using a three-dimensional spatial resolution. In this model, the grid is vertically discretized into 5 layers, and a total of 1205 computational cells were generated from top to bottom to represent Lake Yilong in its entirety (Fig. 2). (2) Initial conditions

(1)

þ mx my HSc

Initial conditions represent the starting point for the model simulation. In this study, the period simulated was from the summer of 2008 to the summer of 2009. The initial temperature was set as

where C is the concentration of a water quality state variable; u, v, w are velocity components in the curvilinear, sigma x-, y-, and z-directions, respectively; Ax, Ay, Az are the turbulent diffusivities in the x-, y-, and z-directions, respectively; Sc is the internal and external sources and sinks per unit volume; H is the water column depth; mx, my are the horizontal curvilinear coordinate scale factors. Water temperatures are needed for computation of the water quality state variables, and they are provided by the internally coupled hydrodynamic model (Park et al., 2005). 2.3. Configuration for Lake Yilong EFDC model The first necessary step in establishing long-term water quality restoration efforts in Lake Yilong would be to develop a quantitative assessment via a sophisticated water quality model to aid in

Fig. 2. Computational grids of hydrodynamic water quality simulation in Lake Yilong.

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20.0  C based on a value observed in early July. The three velocity vectors were initialized at 0.0 m/s following standard convention in hydrodynamic modeling. The hydrodynamic model was then run with a spinning-up period of 1 month to represent the initial condition for the coupled hydrodynamics-water quality model. Initial water quality conditions were relatively complex compared to hydrodynamic model because the former requires a relatively long period of time to eliminate the impact of initial conditions during the run process of model. To get a more accurate water quality model initialization, the observational data from August 22, 2008 was spatially interpolated to each grid cell to form a bestavailable estimate of the initial condition (Supporting Materials).

Fig. 3. Comparison of simulated and observed elevations of Lake Yilong.

(3) Boundary conditions The horizontal and surface boundary conditions in the model represent external driving forces to the lake dynamics. The lateral boundary conditions consisted of tributary inflow rates and associated temperature and water quality constituent concentrations. The surface boundary conditions were described by temporally variable meteorological conditions. In the Lake Yilong model, tributary boundary conditions were configured based on the monitoring data collected during the 2008e2009 study period. For those tributaries with available inflow rate and water quality concentrations, boundary conditions were set directly based on observed data, while for those ungaged inflows, the flow rates were derived based on a flow balance analysis using the hydrodynamic model. In addition to tributaries, atmospheric deposition was another major source of nutrient loading. The concentrations of total phosphorous (TP) and total nitrogen (TN) in the study area from dry deposition were 0.00019 g/m2/day and 0.0069 g/m2/day, respectively, whereas concentrations of TP and TN from wet deposition were 0.039 mg/L and 0.95 mg/L, respectively. These loadings were configured in the Lake Yilong model using the corresponding input data slots in EFDC model. Atmospheric boundary conditions include hourly data of atmospheric pressure, air temperature, relative humidity, precipitation, evaporation, solar radiation, cloud cover, wind speed and wind direction. In the modeling process, hourly data were obtained from the Shiping County Weather Station (SPWS) and were converted into the compatible format of EFDC to represent the atmospheric boundary conditions. (4) Simulation and calibration Model calibration is an essential process in water quality model development (Lung, 2001). Calibration of hydrodynamic and water quality model in Lake Yilong was implemented in a phased manner: the hydrodynamic model was developed and calibrated first before running the water quality model for fast computing. After the hydrodynamic model was calibrated, the water column nutrientphytoplankton-macrophyte simulation modules as well as the sediment diagenesis module were activated to begin the water quality calibration process. 3. Results and discussions 3.1. Hydrodynamic simulation and calibration The model simulation was implemented with a time step of 270 s, which was determined to be small enough to guarantee both stability and accuracy through multiple sensitivity analysis. The hydrodynamic model for Lake Yilong was calibrated through comparing the simulated water surface elevation and water temperature against observed data (Fig. 3). As shown, the simulation

results were in good agreement with the observed daily water level, indicating an overall balance in water mass related processes such as inflows, outflows, direct precipitation, and evaporations. After the flow balance was verified, the hydrodynamic model was further evaluated using observed water temperature data. Model simulated temperature was output for every 6 h and compared against observed data at three stations across the lake (Fig. 4). The comparison shows that the model accurately reproduced the observed spatial and temporal variability in water temperature in the lake, suggesting that the model has reached reasonable representation of the hydrodynamic and thermal dynamics processes in the lake, hence forming a foundation for further calibrating the water quality processes in the lake. 3.2. Water quality simulation and calibration The water quality model calibration was conducted through fine-tuning the model parameters to reproduce observed water quality at the three monitoring stations. The constituents used for calibration include Chl a, DO, NHþ 4 , TN and TP (Fig. 5). The water quality model calibration involved an iterative process where key parameter values were adjusted and simulated water quality concentrations evaluated against observed data. This process was repeated many times until the simulated values could reproduce the observed temporal and spatial patterns. In the eutrophication model, the key parameters related to phytoplankton, nitrogen, phosphorus, and carbon would need to be calibrated. In addition to the dynamic process included in the eutrophication model described in modeling framework, a key process in the Lake Yilong model was to simulate the impact of fish predation on aquatic vegetation. In the early spring of 2009, local authorities had been stocking a large number of herbivorous fish (grass carp). Limited by the observation data and the complexity of the process, it was not feasible to simulate grass carp stocking, growth, predation and excretion kinetics unless the process was simplified in the EFDC model; therefore, an additional predation parameter was introduced into the EFDC mathematical equation to parameterize the impact of grass carp. The predation pressure on the macrophyte from the grass carp was parameterized as a predation coefficient linearly increasing from the beginning to the end of the stocking period, which then maintains a constant value after the stocking period is completed. The model performance was evaluated based on data from all the monitoring stations, while only the results for the West Lake Station are plotted in Fig. 5. As shown, the model reproduced both the observed trends and general magnitude of water quality in the lake. For example, according to the model’s predictions, the Chl a concentration in the lake would be much higher in 2009 than that in 2008, which matches the observed data very well. In the meantime, the model also predicted a significant increase in TN and TP concentrations from 2008 to 2009, which is also consistent

L. Zhao et al. / Environmental Pollution 177 (2013) 13e21

17

30.0

COD1(mg/L)

R2 =0.7308 20.0 Simulated value 10.0

Chl a (ug/L)

0.0 08/8/22

300 250 200 150 100 50 0 08/8/22

Observed Value

08/11/30

09/3/10 Date

09/6/18

09/9/26

R2 =0.6547 Observed value Simulated value 08/11/30

09/3/10 Date

09/6/18

09/9/26

08/11/30

09/3/10 Date

09/6/18

09/9/26

08/11/30

09/3/10 Date

09/6/18

09/9/26

DO(mg/L)

20.0 15.0

R2 =0.1313

10.0 5.0 0.0 08/8/22

NH4(mg/L)

1.0 0.8 0.6 0.4 0.2 0.0 08/8/22

8.0

TN(mg/L)

6.0 4.0 2.0 0.0 08/8/22 Fig. 4. Comparison of simulated and observed temperatures.

4. Discussion The observed data showed that the water quality in 2009 was significantly worse than that in the past few years. The EFDC model reproduced the trends accurately. Before 2008, the Chl a concentration never exceeded 150 mg/L; however, in the summer of 2009, its concentration reached a maximum of 300 mg/L. Meanwhile, the biomass of macrophyte in 2009 declined sharply and is much lower than that in 2008. The data showed that Chl a concentration sharply increase in 2009 from the much lower level in 2008, accompanied by significant increase in TP and TN concentration. Based on watershed survey in 2009, there was no sudden increase in nutrient loading from the

08/11/30

09/3/10 Date

09/6/18

09/9/26

09/3/10 Date

09/6/18

09/9/26

0.3

TP(mg/L)

with observational data. In addition to the trends in nutrient concentrations and algal blooms, the simulated DO values also reproduced the observed data for seasonal and short-term changes very well.

R2 =0.7955

0.2

R2 =0.0619

0.1 0.0 08/8/22

08/11/30

Fig. 5. Comparison of water quality variables at West Lake Station.

external sources; therefore, the sharp increase in water column nutrient concentration cannot be attributed to external loading changes. It means that although the TN and Chl a data demonstrate a statistically significant correlation between algal bloom outbreaks and increases in nitrogen concentrations in 2009, the statistical analysis could not explain the origin of the nitrogen that caused the algal blooms. While our model results revealed that the sharp

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increase in TN and TP concentration in the lake was not from external sources, but from internal sources of nutrients which were stored in the macrophyte biomass. The previous study (i.e. YLTMD) has also proved that (a) the macrophyte was consumed by herbivorous fishes and nutrients were then released to the water column to form high concentrations; and (b) the diminish of macrophyte as well as the resultant high nutrient concentration form positive force to stimulate blue-green algae blooms in the lake. 5. Scenario analysis on eutrophication responses to load reduction After the model was developed and calibrated, it was used to analyze a series of water quality compliance scenarios to provide quantitative information regarding the magnitude of watershed load reduction. The above analysis showed that aquatic ecosystem structure plays an important role in maintaining water quality. Therefore, three basic scenarios were designed to quantify (a) the importance of aquatic vegetation to algal bloom control; (b) the load reduction requirement under different water quality targets; and (c) the relationship between nutrients load reduction and eutrophication condition as represented by algal blooms. 5.1. Scenario analysis I: impact of aquatic vegetation on algal bloom control To analyze the impact of aquatic vegetation on algal blooms, two scenario simulations were conducted. The first simulation is named the baseline loading without macrophyte (BLWM) scenario, which represents the baseline condition after the 2009 regime shift where macrophyte vegetation was destroyed. To represent the long term impact, the model was driven with the same boundary conditions as in the 2008e2009 simulation but continue for 20 years to reach steady-state in water quality. The other simulation is named baseline loading plus macrophyte (BLPM) scenario, which represents the condition where macrophyte vegetation is fully recovered to the condition before the regime shift, while all the dynamic processes, boundary conditions, and simulation period are the same as in the BLWM simulation. With this design, any difference in algal bloom intensity between the two simulations would be attributed to existence of the macrophyte vegetation. Fig. 6 shows the simulated Chl a concentration at the West Lake Station for the 20th year of both the scenarios. The results clearly show that even with the same watershed loadings and meteorological conditions, the algal bloom intensity in Lake Yilong can be

Chl a concentration ( ug/l)

400

300

200

Table 1 Water quality concentration goals in Lake Yilong.

Class III (mg/L) Class IV (mg/L) Class V (mg/L) Allowed exceedance (%)

TN

TP

CODMn

NH4eN

1.0 1.5 2.0 50%

0.05 0.10 0.20 50%

6 10 15 50%

1.0 1.5 2.0 50%

significantly lower when macrophyte vegetation is restored. In other words, the management methods for controlling algal bloom in Lake Yilong might include options of watershed management and in-lake restoration. The results of this scenario indicate that the inlake ecological restoration alone can result in significant depression of algal bloom without any watershed management measures. The result also reveals the risk of un-informed decision making in a lake watershed, and highlights the importance of scientifically sound planning for any actions in lake watersheds. 5.2. Scenario II: load reduction requirement for water quality standards compliance The results of Scenario I indicate that under existing watershed loading condition, the water quality of Lake Yilong would not be able to attain the targeted water quality standards even if the macrophyte vegetation were restored. Therefore, it is necessary to determine the magnitude of watershed load reduction required to achieve compliance of water quality standards for the lake. Table 1 lists three different levels of water quality targets for Lake Yilong. The three targets are based on the Class III, IV, and V standards of the China Surface Water Environmental Quality Standards (GB38382002). In GB3838-2002, Class III is corresponding to beneficial use of drinking water sources, Class IV for industrial water supply and Class V for agricultural irrigation purpose. The reason of setting three levels of targets instead of one was to determine the achievability and feasibility of each targets based on their respective load reduction requirement. To evaluate the compliances of water quality targets, two aspects were considered: (a) the water quality standards should to be achieved on an annual average basis; and (b) the instantaneous concentration is not allowed to exceed the water quality target concentrations for more than 50% of the time. Therefore, the final compliance was established based on the more stringent of the two aspects. The scenario analysis was conducted via an iterative process where the baseline model in Scenario I, i.e., BLWM, was used as the basis, but the watershed loadings being scaled down to a prespecified ratio. The model was then run for 20 years and the results of the 20th year were used to evaluate the compliance from the aforementioned two aspects. After multiple trials, load reduction ratios required for Class III, IV, and V targets were determined to be 77%, 55%, and 35%, respectively (Table 2 and Fig. 7). These Table 2 Load reduction rates to meet certain water quality goals.

100

Monitoring sites

TN (mg/L)

TP (mg/L)

COD (mg/L)

Load reduction ratio

Lake West

1.00 1.50 2.00 0.79 1.22 1.62 0.73 1.18 1.63

0.042 0.067 0.101 0.034 0.055 0.082 0.034 0.056 0.084

4.56 7.08 9.28 3.58 5.58 7.49 3.23 5.23 7.21

35% 55% 77% 35% 55% 77% 35% 55% 77%

0 A

S

O

N

D

J

F

M

A

M

J

J

A

Months Fig. 6. Simulated Chl a concentration (mg/L) on scenario I at West Lake Station. The top blue line represents the BLWM scenario and the bottom red one for BLPM scenario. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Lake Mid

Lake East

L. Zhao et al. / Environmental Pollution 177 (2013) 13e21

19

16 Ave TN

Med TN

TN Standard

Ave TP

TP Standards

Ave COD

Med COD

COD Standard

14

Concentration (µg/L)

12

Med TP

10 8 6 4 2 0 Class III Scenario

Class IV Scenario West Lake Station

Class V Scenario

Concentration ( µg/L)

(a) West lake station 16

Ave TN

Med TN

TN Standard

14

Ave TP

Med TP

TP Standard

Ave COD

Med COD

COD Standard

12 10 8 6 4 2 0 Class III Scenario

Class IV Scenario

Class V Scenario

(b) Middle lake station

Concentration (µg/L)

16 Ave TN

Med TN

Standard N

14

Ave TP

Med TP

Standard P

12

Ave COD

Med COD

Standard COD

10 8 6 4 2 0 Class III Scenario

Class IV Scenario

Class V Scenario

(c) East lake station Fig. 7. Water quality conditions after load reduction at three monitoring stations.

results suggest that the current watershed loading far exceed the allowable levels for attaining the pre-set targets, preventing Lake Yilong from meeting the desired beneficial uses. This helps explain a puzzling question the government often confronts: why the previous watershed management efforts didn’t produce expected

compliance of water quality targets? As the modeling results show, significant further load reduction in addition to what have previously accomplished is required to allow the compliance, therefore, it is not reasonable to expect that the compliance of the water quality targets can be reached in near future. On the contrary, a

L. Zhao et al. / Environmental Pollution 177 (2013) 13e21

long-term watershed management as well as in-lake restoration planning is needed to be developed based on the modeling analysis for continuously improving the water quality and ecological condition in the lake. 5.3. Scenario III: eutrophication response To further investigate the impact of watershed load reduction on Chl a concentration in Lake Yilong, the simulated Chl a concentration of the 20th year of the BLPM are compared against that of the reduction scenario with 77% reduction of watershed loading on the top of the in-lake restoration corresponding to the BLPM scenario. Fig. 8 plots the results for the West Lake Station, showing that with the 77% reduction in watershed loading, the phytoplankton in the lake is significantly depressed due to lower nutrient concentration in the water column resulted from watershed management. However, it is also apparent that even when the lake water attain Class III water quality target along with a full restoration of the macrophyte vegetation, the 77% reduction of watershed loads only led to approximately 50% of Chl a concentration reduction. Since the extreme reduction of 77% in watershed loading would still result in peak Chl a concentration of approximately 80 mg/L that is highly liable to algal bloom, it is apparent that the eutrophication problem is not controlled even with the compliance of the most stringent water quality target (Class III) for Lake Yilong. In scenario I, we presented the results representing the impact of ecological restoration under existing loading level. To further investigate the effects of ecological restoration under much reduced loading condition, another analysis was conducted through comparing the Chl a concentration under 77% reduction and ecological restoration (77R-ER) and that of 77% reduction alone (77R). The averages, median and maximum value of Chl a concentration at the three monitoring stations were compared in Fig. 9. It is shown that the Chl a concentrations for 77R are much higher than those of 77R-ER, indicating that the benefit of in-lake ecological restoration is significant not only under current high loading condition, but also under the future condition where watershed load is highly reduced. Therefore, it is recommended that for Lake Yilong, both watershed management and in-lake restoration need to be implemented to control the eutrophication impairment. Given the data and funding limitation, this study did not assess economic efficiency, social development and specific ecological restoration technologies. Future research is expected to be focus on these areas to provide more effective decision support to the eutrophication control of Lake Yilong.

200 77% reduction BLPM scenario

Chl a concentration (µg/L)

150

100

180

Average Chla

160

Median Chla

140

Maximum Chla

120 100 80 60 40 20 0 BLWM

77R-ER

77R

West Lake Station

BLWM

77R-ER

77R

MIddle Lake Station

BLWM

77R-ER

77R

East Lake Station

Fig. 9. Comparison of average, median and maximum Chl a concentrations (mg/L) in Lake Yilong under BLWM, 77R and 77R-ER scenarios.

6. Conclusions (1)This study developed a 3-D hydrodynamic and water quality model for Lake Yilong, China. The model accurately reproduced the observed water surface elevation, water temperature, and nutrient and algal conditions, indicating a reasonable numerical representation of the actual hydrodynamics and eutrophication dynamics in the lake. (2)The model results showed that the existence of aquatic vegetation had significant impacts on algal blooms in Lake Yilong. Even though the watershed load remained under the same conditions, algal bloom intensity in the lake can be significantly depressed under the vegetation restoration condition than under the condition where aquatic vegetation diminished. Therefore, both watershed load reduction and inlake restoration are necessary for effective eutrophication control in Lake Yilong. (3)The study analyzed the watershed load reduction requirement for achieving compliance of various water quality targets. The results indicated that to achieve the water quality targets corresponding to Class III, IV, and V of the national water quality standard, the watershed load reduction ratios would need to be 77%, 55% and 35%, respectively. (4)The study also suggested that the adaptation of national water quality standards for nutrients as lake water quality targets might not be consistent with the eutrophication control targets. The model results indicate that even if the most stringent nutrient target was met with 77% reduction of watershed loading, the peak Chl a concentration in the lake can still reach 80 mg/L, a concentration that is highly liable to algal bloom. Therefore, future studies are required to investigate more reasonable water quality targets to support future eutrophication control efforts for Lake Yilong.

Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 41101180 and 41222002) and the “Major Science and Technology Program for Water Pollution Control and Treatment” (2013ZX07102-006).

50

0 A/08 S/08

200

Concentration (ug/L)

20

O/08 N/08 D/08

J/09

F/09 M/09 A/09 M/09 J/09

J/09

A/09

months Fig. 8. Simulated Chl a concentration (mg/L) on scenarios of baseline (BLPM) and ultimate (77%) reduction. The top blue line represents the BLPM scenario and the bottom red one for 77% reduction scenario. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envpol.2013.01.047.

L. Zhao et al. / Environmental Pollution 177 (2013) 13e21

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