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Jul 9, 2015 - Development and application of mathematical models to support total maximum daily load for the Taihu Lake's influent rivers, China. Ce Wanga ...
Ecological Engineering 83 (2015) 258–267

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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng

Development and application of mathematical models to support total maximum daily load for the Taihu Lake’s influent rivers, China Ce Wanga , Jun Bia,* , Robert B. Ambrose a b

b

State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China U.S. Environmental Protection Agency (retired), United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 21 January 2015 Received in revised form 13 June 2015 Accepted 28 June 2015 Available online 9 July 2015

The control of nitrogen and phosphorus pollution from the watershed and influent rivers to Taihu Lake became a significant issue of concern to the Chinese government following a large algal bloom in 2007. It is more scientific to make policies on river pollution control based on total maximum daily load (TMDL) development which requires the estimation of allowable maximum pollutant load of the river of interest. “The Twelfth Five-Year Guideline – The overall program on integrated regulation of Taihu Lake Basin” strongly recommends the development of a TMDL analysis for the Zhushan Bay watershed, including three representative influent rivers – the Taigeyunhe, Caoqiaohe and Yincungang Rivers. Dynamic mechanistic models used in developing TMDL predominates over other mathematical approaches mainly because they can simulate the fate and transport of pollutant, link pollution source with water quality response, and evaluates various management scenarios when required data resource are available. In this study, a site-specific empirical model is developed and linked to the Water Quality Analysis Simulation Program (WASP), a general, mechanistic model of water quality. The resulting modeling system considers the essential physical features of the complex Zhushan Bay watershed and quantifies the relationship of in-stream nutrient concentrations and watershed loads. It is used to investigate a set of TMDL load reductions to meet water quality standards. Analysis of the model calibration and validation to long-term observational data shows that the combined model performs satisfactorily for prediction of pollutant fate and evaluation of various modeling scenarios to meet the target TMDL condition. The calculated TMDL reductions can provide a scientific basis for the authority to make water pollution management decisions. ã 2015 Elsevier B.V. All rights reserved.

Keywords: Taihu Lake Influent river Nutrient TMDL WASP

1. Introduction

Taihu Lake is located in the Yangtze River delta between 30 560 – 31 330 N and 119 530 –120 360 E. This is the third largest freshwater lake in China, with a surface area of 2338 Km2 and an average water depth of about 2.0 m (Fig. 1) (Zhai et al., 2010; Zhu et al., 2013). The Taihu Lake basin is a highly developed region, providing 14% of China’s GDP with only 0.4% of the its land area and 2.9% of its population (Li et al., 2010). This river-fed lake is a valuable natural resource, providing flood control, water supply, navigation, fishery, tourism and culture which greatly benefit regional economic and social development (Mao et al., 2012). There are 22 influent rivers to the lake. These influent rivers are generally identified as the major contributor of nutrient enrichment in the lake. They receive point source (PS) pollution from industrial effluent and wastewater treatment plant (WWTP) discharge. They also receive non-point source (NPS) pollution from domestic sewage discharge, livestock drainage, wash off of soil nutrients and fertilizers in agricultural land via surface runoff (Chen et al., 2011; Duan et al., 2009; Tong and Chen, 2002). The total maximum daily load (TMDL) approach to water quality management, proposed by U.S. EPA, has been applied 

Following the occurrence of a harmful algal bloom in Taihu Lake during the summer of 2007, the Chinese government decided to control and manage lake eutrophication using three consecutive five-year plans. Their final objectives are to improve drinkable water quality for local residents and sustain the health of the aquatic ecosystem. “The Twelfth Five-Year Guideline—The overall program on integrated regulation of Taihu Lake Basin” (hereinafter referred to as the “Guideline”), issued by National Development and Reform Commission of China, declares that water quality improvement of influent rivers is an important task because nutrient loads from the watershed could greatly contribute to nutrient concentrations in the lake (National Development and Reform Commission, 2013).

* Corresponding author. Fax: +86 25 89680566. E-mail address: [email protected] (J. Bi). http://dx.doi.org/10.1016/j.ecoleng.2015.06.036 0925-8574/ ã 2015 Elsevier B.V. All rights reserved.

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Fig. 1. The geographic location of Taihu Lake and representative area in Jiangsu Province.

worldwide for various impaired water bodies. This approach scientifically allocates pollutant loadings in a watershed among PS and NPS sources for comprehensive control of pollutants of concern (e.g., nutrients, metals, organic toxicants)(Boyacioglu and Alpaslan, 2008; EPA, 2008; Kang et al., 2006). It also provides a guideline for Best Management Practice (BMP) to achieve TMDL reduction goals within a watershed (Borisova et al., 2008). Mathematical models often play an important role in the TMDL process (Borah et al., 2006; DePinto et al., 2004). In general, dynamic model, load duration curve, general watershed model and export coefficients are commonly used in developing TMDL (EPA, 2007b). Load duration curve method correlates measured flows with water quality criterion to estimate loading capacities at different flow regimes (EPA, 2007a). General watershed model (e.g., GWLF) estimates monthly loads of nutrients with basic watershed processes (Borah et al., 2006). Export coefficients method applies load rates from various land uses to calculate NPS pollution load (Shen et al., 2011). By contrast, dynamic model can be used to describe the fate and transport of water quality constituents in receiving water bodies, quantify the cause-effect relationship between pollutant sources and water quality variations, and determine the dynamics of watershed and water body response to management pollution sources. The models supplement observed data with predicted outcomes during periods of limited observed data availability. They are then used to calculate total pollutant loads as circumstances dictate, evaluate different “what-if” future management scenarios and predict constituent concentrations with combinations of load reductions under combinations of flow regime, land use and temperature conditions. Some dynamic models often used to support TMDL development include watershed models (e.g., LSPC), hydrodynamic models (e.g., EFDC) and water quality models (e.g., WASP) (Shen et al., 2005; Steg, 2007; Wool et al., 2003). TMDL development has received less attention in China, particularly in the area of nutrient management. A number of nutrient TMDL programs have been proposed in some watersheds, but due to the difficulty of acquiring critical datasets from local authorities, these programs yielded limited results. According to the Guideline, a nutrient TMDL program is recommended to control watershed loads from influent rivers in order to decrease the occurrence of lake eutrophication. The TMDL development should consider the impacts of cumulative nutrient loads over a long period. TMDLs are typically expressed as the percent

reduction of long-term (multi-year) average loading rates rather than day to day loading rates (EPA, 2007b; Stow and Borsuk, 2003). The longer the simulation period is during modeling, the more it can account for natural fluctuations in nutrient loadings as a function of wet, dry or average years for rainfall and stream flow. This study focuses on the Zhushan Bay watershed within Jiangsu Province. It includes three influent rivers established as a representative area—the Taigeyunhe, Caoqiaohe and Yincungang. These rivers are prioritized to control water pollution based on severity of pollution. As shown Fig. 1, the watershed covers a complex river network with many crisscross tributaries. In general, the direction of water flow through the influent rivers is from northwest to southeast (Hu et al., 2008). Sometimes backwater and still water occurs due to natural and artificial factors (more details in Section 2.1). Because of the flat terrain and active hydrologic manipulation in this complex landscape, it is not feasible for us to use a standard watershed runoff model to support this TMDL plan. This study uses a long-term observational data set and physical features of watershed to develop a site-specific empirical loading model, which is linked to a mechanistic model—the Water Quality Analysis Simulation Program (WASP). These models are used to interpret water quality variations for the three influent rivers identified as impaired by nutrients. The models are then used to calculate TMDL load reductions under different management scenarios to meet environmental quality standards for surface water draining to Zhushan Bay of Taihu Lake. 2. Material and methods 2.1. River network characteristics The Zhushan Bay watershed lies in a plain with a high density river network composed of three main streams and many small tributaries (Fig. 1). Field investigations and 30-m Digital Elevation Model (DEM) data (Supplementary material, Fig. S1) characterize the study area terrain as flat, with gradual river bottom slopes. Flows in the Taigeyunhe, Caoqiaohe and Yincungang Rivers are controlled by irrigation, pumping and fish pens, as well as natural runoff. In this complex river network, the predominant flow direction of the three rivers is toward Taihu Lake. Occasionally, backwater occurs during wind-driven “seiches” or oscillations in the lake that cause periodic high water along the shoreline. Besides these natural events, some abrupt decreases in flow (see

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above, we choose an empirical approach to calculate PS and NPS loads to the river network. For each river reach, monthly average mass flux at the upstream and the downstream stations are calculated using monthly average constituent concentrations and monthly average flows. This averaging period smooths over small fluctuations, including short term zero and negative river flows. Each water quality variable in the three rivers, namely TN, TP, NH4– N and BOD5, is calculated based on Eq. (1)–(9) below. In the Taigeyunhe River, we combine the observed data from the two stations because they are both below all the PS discharges and very close to each other. We calculate the total load in the river at Station B, then back-calculate the upstream NPS load by subtracting the observed PS load from total load:

Section 3.1) are driven by withdrawals for industrial and agricultural activities in the three rivers. The computational network for the three rivers is designed to correspond to major watershed physical features, as shown in Fig. 2. The confluence of Caoqiaohe River and Taigeyunhe River is located at Segment 3. The mainstream length of the three rivers is approximately 20 Km. For external watershed loading calculations, each river is divided into upstream and downstream regions based on the location of water quality (WQ) stations. 2.2. Observed data collection River flow, water quality monitoring and PS loading data covering the period from 2007 to 2011 were collected to support nutrient TMDL development. While observed flow data were monitored at daily intervals in the Taigeyunhe and Caoqiaohe Rivers, no flow station was established in the Yincungang River. Water quality data measured monthly at WQ Stations A and B (see Fig. 2) include 7 constituents: 5-day biochemical oxygen demand (BOD5), dissolved oxygen (DO), ammonia nitrogen (NH4–N), nitrate nitrogen (NO3–N), total nitrogen (TN), total phosphorous (TP), water temperature (WTEMP) and pH. In the Taigeyunhe River there were 5 permitted PS discharges (4 industries + 1 WWTP). The Caoqiaohe and Yincungang Rivers each have 4 point source discharges (3 industries + 1 WWTP). The point sources are located in segments 10 and 26 in the Taigeyunhe–Caoqiaohe network, and in segment 53 in the Yincungang network. PS loading data were collected at daily intervals by automatic devices installed adjacent to the effluent outlet. Constituents include chemical oxygen demand (COD) (automatic device: SERE 2000, France), NH4–N (automatic device: TresCon A111, Germany), TN (automatic device: TNP-4110, Japan) and TP (automatic device: TNP-4110, Japan). More detailed information about the data set and corresponding data sources is available in the Supplementary material.

Q i  C B  f unit ¼ Ltotal;B

(1)

LNPSðupstreamÞ ¼ Ltotal;B  Lpoint

(2)

In the Caoqiaohe River, WQ station B is located above all the wastewater and industrial discharges. This station would be influenced primarily by upstream NPS loads, whereas downstream WQ station A would include both PS and NPS loads. The downstream NPS loads can be back-calculated by subtracting the upstream NPS and PS loads from the total downstream load: Q i  C B  f unit ¼ LNPSðupstreamÞ

(3)

Q i  C A  f unit ¼ Ltotal;A

(4)

LNPSðdownstreamÞ ¼ Ltotal;A  LNPSðupstreamÞ  Lpoint

(5)

For the Yincungang River, flows are assumed to be the same as the Caoqiaohe River because their physical features are similar. In this river, all PS discharges are above WQ station B. Upstream NPS load is back-calculated by subtracting PS load from total upstream load. Downstream NPS load is back-calculated by subtracting total upstream load from total downstream load:

2.3. Modeling approach 2.3.1. Empirical model The measured flow records from two gauging stations are insufficient to accurately calibrate the daily hydrodynamics of the three rivers. Based on the site-specific complications described

Q i  C B  f unit ¼ Ltotal;B

(6)

Upstream Canal

Taigeyunhe River Flows from spreadsheet

20 19 18 17 16 15

PS Canal

14 13 12 11

Flow Station

10

9

WQ Station B

8 Caoqiaohe River

7

Upstream

WQ Station B

42 41 40 39 38 37 36 35 34 33 32 Yincungang River

Canal

31 30

29 28 27

Downstream

WQ Station A

5 4 3

26

25 24

Canal PS

62 61 60 59 58 57 56 55 54

Upstream

6 PS

2

23 22 21

Flow Station WQ Station A 53 52

51 50 49

WQ Station B

48

47 46

Downstream

Fig. 2. The sketch map of river network of Zhushan Bay watershed.

45 44 43

WQ Station A

1

C. Wang et al. / Ecological Engineering 83 (2015) 258–267

LNPSðupstreamÞ ¼ Ltotal;B  Lpoint

(7)

Q i  C A  f unit ¼ Ltotal;A

(8)

LNPSðdownstreamÞ ¼ Ltotal;A  Ltotal;B

(9)

Where Qi (i = Tai, Cao, Yin) (m3/s) is the monthly average flow in each river. Ci (i = A,B) (mg/L) represents monthly average concentration of water quality variable in WQ Station A and B. funit is the units conversion factor 86.4 ((sec/day)(kg/g)). Ltotal,i (i = A,B) (Kg/d) is the total watershed load above WQ Station A and B. LNPS(upstream) (Kg/d) and LNPS(downstream) (Kg/d) are NPS load from upstream and downstream sections of the river, respectively. Lpoint (Kg/d) is the load from PS dischargers to the river of interest. 2.3.2. WASP model WASP is a generalized framework for modeling the fate and transport of contaminant in surface waters based on a set of mass balance equations (Ambrose et al., 2009). Its dynamic compartmental-modeling approach allows the user to investigate water quality problems in one, two, and three dimensional systems which may include both water column and underlying sediment layers. WASP can be used to simulate a variety of water quality state variables, including conventional pollutants (e.g., nutrients) and toxic pollutants (e.g., organic chemicals)(Wool et al., 2001). In this study, we apply WASP to simulate the spatiotemporal concentration variations of water quality constituents as a function of monthly upstream flows and watershed loads, which are derived from the empirical model output. The WASP advanced eutrophication module includes 28 water quality variables for possible use. For this study, we selected a subset, including the DO, nutrient cycling and phytoplankton variables and processes illustrated in Fig. 3. For system parameterization, the simulation time ranges from 2007/1/1 to 2011/12/31, with specified default time step—fraction of maximum time step of 0.9, maximum time step of 1.0 day and minimum time step of 0.0001 day. Euler

(2) (7)

NO3-N

DON

(1)

(4) (3)

(8) (8) (4)

Detrital C

(4) (4)

(3)

(9)

DOP

(5)

(7)

(6)

PO4-P (10)

CBOD (11) SOD

(8)

DO

The four steps outlined in Fig. 4 are necessary for developing a nutrient TMDL: data collection, watershed loads calculation, numerical modeling and management scenarios. Step I: Date collection. The observed and geographical data (e.g., stream length, depth and width) needed for models were gathered from different local authorities. Of those data, river flows and water quality data perform a crucial role in model calibration and TMDL analysis. Step II: Watershed loads calculation. With average monthly observed data, PS and NPS loads to the three rivers are manually calculated based on Eq. (1)–(9). For each river, calculated LNPS (upstream) and LNPS(downstream) are divided equally into each water segment depicted in Fig. 2. Step III: Numerical modeling. The assigned watershed loads are imported into WASP. The simulation time ranges from 2007 to 2011. The simulated concentrations of BOD5, DO, NH4–N, TN and TP are compared with corresponding observed concentrations to calibrate water quality kinetics and then perform model validation. Step IV: Management scenarios. Calculated concentration history is compared with in-stream targets to judge whether the waterbody meets the target or not. Violation of water quality standard more than 10% of the simulation time triggers the need to calculate combinations of watershed load reductions required for stream concentrations to meet the target(Wool et al., 2003). We need to rerun the models with the management scenarios to attain TMDL determination. 2.5. TMDL condition According to the Guideline, based on the of year 2007, nutrient TMDL analysis assessed compliance with Environmental Quality Standards (WQS) for surface water of China, which was the adopted numeric nutrient criteria for rivers in representative area. The future goals of water quality improvement of the three influent rivers are expected to achieve numeric criteria concentrations: BOD5  5.6 mg/L, NH4–N  2.0 mg/L, TN  4.0 mg/L and TP  0.15 mg/L (National Development and Reform Commission, 2013).

Detrital N

Phytoplankton

(5)

2.4. TMDL process

3. Results and discussion

(5)

(4)

(8)

algorithm is adopted as the solution technique.The other key environmental parameters, kinetic parameters, and initial and boundary conditions of flow and water quality constituents are described in the following sections.The time-varying concentrations of DO, CBOD, NH4–N, TN and TP from WASP outputs are used for model calibration and validation.

(6)

(3) NH4-N

261

(12)

(6) Detrital P (8)

Atmosphere

Fig. 3. Water quality variables and mechanistic processes in WASP: (1) Nitrification (2) Denitrification (3) Growth (4) Respiration (5) Death (6) Dissolution (7) Mineralization (8) Settling (9) Photosynthesis (10) Carbonaceous deoxygenation (11) Sediment oxygen demand (12) Reaeration.

3.1. River flow distribution As shown in Fig. 5, the average flow in Taigeyunhe River is almost twice as much as Caoqiaohe River. From 2007 to 2011, the daily mean flow of Taigeyunhe and Caoqiaohe Rivers equaled or exceeded 19.9 and 8.21 m3/s for 50% of the time. Following large, local rain events, very large increases in flow can occur from one day to the next. After the high flow peaks, flow recedes more slowly. Negative (upstream) and zero flows occurred during 1.8% of the five year study period. These were probably caused by anthropogenic activities that impeded natural variation of flow in river system. As discussed in the sections above, using monthly average flows bypassed the small scale hydrodynamics, which could not be resolved for this system using the available data. Monthly average flows were imported to WASP, and simulations were run using the descriptive “Net Flow” option. WASP routes these flows through the main stream and tributary flow

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Observed data

GIS Layers

PS loading

Districts and boundary

River flow

DEM Gird

Meteorology

Landuse cover

Water quality

Soil type Reach file Monitoring stations

Date Collection

Average monthly flow data Average monthly concentration data Average monthly PS loading data NPS calculations for each river Assign NPS loads to river reach

Watershed loads calculation

WASP Model Import monthly flows to WASP network Import NPS results to WASP network Water quality calibration and validation Rerun

Water quality outputs

TMDL Reductions

Numerical Modeling

TMDL Conditions

No Water Quality Standard

TMDL Determination

Current Loads

Yes Management Scenarios

Fig. 4. Schematic diagram of TMDL process.

paths to calculate net transport across all water segments. For each river, the main flow function routed upstream flows into the network (e.g., upstream “boundary” to Segment 42), and traced the flows all the way down the network and out the downstream end (e.g., Segment 21 to downstream “boundary”). The lateral inflow functions covered other river reaches with the flow path from “boundary” to the specified segment, e.g., from “boundary” to “Segment 40”. 3.2. Model calibration and validation After the monthly flows were set, the manually-calculated watershed loads were evenly apportioned to each segment in the appropriate sections of the rivers. For example, LNPS(upstream) and was divided equally into segment 28 through 42, and LNPS (downstream) was divided equally into segment 21 through 27. Key environmental forcing functions were expressed as daily time functions and input into WASP, including water temperature ( C), solar radiation (Langleys/d), fraction daylight (hours daylight/24), wind speed (m/s) and air temperature ( C). All available in-stream monitoring data, namely BOD5, NH4–N, TN and TP, were used for model calibration and validation. The phytoplankton concentration in rivers associated with nutrient cycling and DO process was specified as 0.015 mg-Chla/L (Meng et al., 2010). It was worth of note that the empirical factor of 1.5 was used to convert BOD5 to CBOD (carbonaceous BOD) as WASP internally considered CBOD as the indicator of equivalent oxygen demand for the carbonaceous

material (Bowie et al., 1985). For comparison, the WQS for BOD5 was converted to CBOD (8.4 mg/L). The Caoqiaohe River was targeted for model calibration. The goal was to accurately characterize spatiotemporal variation and seasonal cycling of the water quality constituents at the monitoring stations. Using the calibrated kinetic parameter values, the simulation results for the Taigeyunhe and Yincungang Rivers could be considered as model validation. The calibration results of CBOD, NH4–N, TN and TP at Caoqiaohe River, depicted in Fig. 6, show a reasonable quantitative and qualitative match between the simulated concentrations and the direct measurements. We tuned the model to minimize mean absolute error (MAE). This was emphasized more than the coefficient of determination (R2), which could be misleading for predictions that match the pattern of the data rather than the actual values. Across the water quality variables, MAE ranged from 0.052 to 1.976 with an average of 0.837. The validation results of water quality variables in Taigeyunhe and Yincungang Rivers are shown in Fig. S12–S26, found in the Supplementary material. Based on performance statistics, the model generated satisfactory simulations relative to the monitoring data, particularly for NH4–N, TN and TP. This indicates that the model has the capability to predict water quality constituents for the five-year period. The seasonal cycling pattern for NH4–N and TN is obvious in both observed and modeled concentrations. The seasonal pattern for TP, however, is not captured. This indicates that the effect of external load on TP concentration greatly exceeds the kinetic processes associated with internal phosphorous

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Fig. 5. Daily measured flows, local precipitation and flow duration curves of Taigeyunhe and Caoqiaohe Rivers from 2007 to 2011.

cycling. The DO simulation is important in the development of nutrient TMDLs because it is the primary response variable to determine the load reductions. As shown in Fig. 7, DO concentration exhibited a distinct seasonal cycling characterized by a winter maximum (January through March) and summer minimum (July through September). This pattern is driven primarily by the effect of water temperature on DO saturation, and secondarily on kinetic processes such as carbonaceous deoxygenation. In the Taigeyunhe River, DO concentrations during the five year period were underestimated when compared to monitoring data. This might may be due to a higher reaeration rate, as the river flow is larger than that in Caoqiaohe River, as well as inaccuracy caused by manual observations. In the Yincungang River, in general, both BOD5 and DO observations at upstream WQ Station B were higher than those at downstream WQ Station A. The combination of high BOD and DO levels at short distance downstream of the discharge could be due to insufficient time for deoxygenation to occur. Fig. 2 shows that PS locations are in the vicinity of WQ Station B. Phytoplankton is another key variable during modeling, however, the modeled phytoplankton concentrations were not calibrated and validated since time-varying monitoring data were unavailable.The average concentrations of phytoplankton was approximately 10.55 and 10.01 mg/L, respectively at downstream end S1 and S43 for the entire five year period. Phytoplankton DO production and phytoplankton growth rate had the similar seasonal and diurnal variations (see Fig. S27, Supplementary material). Phytoplankton DO production in the summer was high during the day, but water temperature was also high which caused DO saturation to be reduced. Also, many DO depletion rates would be high, including phytoplankton respiration, CBOD decay, nitrification, and sediment oxygen demand. This probably triggered the variation trend of DO in the influent rivers was opposite to that of phytoplankton DO production.

3.3. Sensitivity analysis The goal for sensitivity analysis is to understand which model parameters and forcing functions influence the important output variables the most. The mid-month model output is selected to calculate parameter sensitivity as shown in Eq. (10) (Park and Clough, 2009). We take an arithmetic average of all sensitivity results across all months for the entire five year period. Dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorous (DIP) and DO are considered as output variables of interest. S¼

jY Pos  Y Baseline j þ jY Neg  Y Baseline j  100% Y Baseline

(10)

where YPos and YNeg are model outputs for given a +50% and 50% change in the input parameter respectively; YBaseline is model output with no change in the input parameter from the final calibration. The results of sensitivity analysis are illustrated in Fig. 8 (tabular form is listed in Supplementary material). The major influential factors affecting DIN concentration are the nitrogen mineralization rate constant, the phytoplankton growth rate constant, and the phytoplankton nitrogen to carbon ratio. The DIP concentration was most sensitive to the phosphorus mineralization rate constant, the phytoplankton growth rate constant, and the phytoplankton phosphorus to carbon ratio. The DO concentration is highly influenced by changes in the reaeration rate constant, which cause more than a 50% change in the output variable. Errors or uncertainties in this parameter will significantly affect overall model uncertainty. Future field investigations should include sitespecific determination of the reaeration rate. The results of the model sensitivity analysis provide a reasonable guide to model improvements.

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Fig. 6. Calibration results of CBOD, NH4–N, TN and TP in Caoqiaohe River.

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265

Fig. 7. Validation results of DO concentration in Taigeyunhe and Yincungang Rivers during 2007–2011.

3.4. TMDL reductions and determinations The final goal of this modeling study is to determine the maximum annual load of CBOD, NH4–N, TN and TP that Taihu

Fig. 8. Sensitivity analysis of DIN, DIP and DO (mg/L) to a 50% change in candidate kinetic parameters (knitr: Nitrification Rate Constant at 20  C; Knit: Half Saturation Constant for Nitrification Oxygen Limit; kdnit: Denitrification Rate Constant at 20  C; KNO3: Half Saturation Constant for Denitrification Oxygen Limit; kdiss: Detritus Dissolution Rate; kmin,N: Dissolved Organic Nitrogen Mineralization Rate Constant at 20  C; kmin,P: Dissolved Organic Phosphorus Mineralization Rate Constant at 20  C; Kmpc: Phytoplankton Half-Saturation for Mineralization Rate; kdeox: CBOD Decay Rate Constant at 20  C; KCBOD: CBOD Half Saturation Oxygen Limit; ka: Global Reaeration Rate Constant at 20  C; DCRB: Detritus to carbon ratio; NCRB: Nitrogen to carbon ratio; PCRB: Phosphorus to carbon ratio; CChla: Carbon to chlorophyll ratio; kGmax: Phytoplankton Maximum Growth Rate Constant at 20  C; k20R: Phytoplankton Respiration Rate Constant at 20  C; km: Phytoplankton Death Rate Constant (Non-Zoo Predation); KMN: Phytoplankton Half-Saturation Constant for N Uptake; KMP: Phytoplankton Half-Saturation Constant for P Uptake.).

Lake’s influent rivers could assimilate without exceeding WQS for surface waters of China. Using the calibrated and validated models, a set of modeling scenarios was developed to predict in-stream concentration variations of four water quality indicators in the three rivers during all simulated years. Input watershed loads are then reduced until water quality standards are met. Operationally, we considered that standards are met when the simulated concentrations in the downstream river segments exceed numeric criteria less than 10% of the time. The watershed loads were reduced by the same scale factor throughout the river network. Results from the series of TMDL scenarios are presented in Table 1. These modeling results suggest that TP abatement should be the focal point of a regional pollution reduction plan for the whole river network in the Zhushan Bay watershed. WASP results indicate a slight phosphorus limitation of phytoplankton community growth, with a P limitation factor ranging from 0.92 to 0.98. The N limitation factor remained at 1.00 throughout the five years, indicating no nitrogen limitation. To control phytoplankton growth, reducing phosphorus further would be more efficient than reducing nitrogen. Nevertheless, nitrogen loading reductions are indispensable for long-term eutrophication control in Taihu Lake, in which seasonally-shifting pattern of N (summer and fall) and P limitations (spring and winter) are present (Paerl et al., 2011). Future management practices should target approximately 35%, 50% (55% for NH4–N, 50% for NO3–N and DON) and 70% of reductions for CBOD, TN and TP, respectively, for the Taigeyunhe– Caoqiaohe Rivers. For the Yincungang River, 25%, 50% and 70% reductions of CBOD, TN and TP, respectively, are needed. When these TMDL conditions are achieved, the concentrations of CBOD, NH4–N, TN and TP at the outlets of the river network should average 6.44, 1.17, 2.86 and 0.11 mg/L, respectively, over a multiyear period of time. Table 2 lists the annual average CBOD, NH4–N, TN and TP loads to the three rivers for the period of record 2007 through 2011, along with the corresponding TMDL determinations. The five year

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Table 1 TMDL reduction scenarios for the whole river network. NH4–N

CBOD Taigeyunhe–Caoqiaohe Scenarios Mean (mg/L) Baseline 9.688 8.724 10% 20% 7.762 6.8 30% 35% 6.319 5.838 40%

Rivers at segment-1 Exceedances Scenarios (%) 72.06 Baseline 49.97 30% 27.61 40% 13.4 50% 9.08 55% 4.98 60%

Yincungang River at segment-43 Scenarios Mean Exceedances (mg/L) (%) Baseline 8.706 49.54 –5% 8.276 39.69 7.846 31.22 10% 20% 6.987 16.24 25% 6.558 11.37 30% 6.128 7.33

Scenarios Baseline 20% 30% 40% 50% 60%

TN

TP

Mean (mg/L) 2.469 1.751 1.511 1.272 1.153 1.034

Exceedances (%) 56.97 34.06 26.63 14.65 11.37 6.01

Scenarios

Mean (mg/L) 2.186 1.781 1.579 1.379 1.179 0.979

Exceedances (%) 39.58 30.73 22.91 16.51 9.51 6.23

Scenarios

Baseline 20% 30% 40% 50% 55%

Baseline 20% 30% 40% 50% 55%

Mean (mg/L) 5.601 4.508 3.961 3.414 2.867 2.593

Exceedances (%) 76.16 60.14 51.45 31.11 12.52 4.16

Mean (mg/L) 5.582 4.493 3.949 3.403 2.858 2.585

Exceedances (%) 71.57 48.88 40.08 25.31 12.08 7.98

Scenarios Baseline 40% 50% 60% 70% 75%

Scenarios Baseline 50% 60% 65% 70% 75%

Mean (mg/L) 0.332 0.206 0.174 0.143 0.111 0.095

Exceedances (%) 100 74.3 52.32 27.88 12.79 7.71

Mean (mg/L) 0.337 0.184 0.15 0.134 0.117 0.1

Exceedances (%) 99.62 59.05 34.39 24.17 13.78 5.03

Note: The management scenarios highlighted in bold are adopted for TMDL reduction.

average load provides a baseline for TMDL load reductions. It is evident that the Taigeyunhe River is subjected to the highest pollutant loadings, though it also has the highest flows. The TMDL determinations for each of the rivers was obtained by adjusting existing loads using the preferable management reduction scenario listed in bold in Table 1. Although the DO criteria limit is implicit in the Guideline, we also checked DO concentration in the river network. Fig. 9 shows the probability distribution for DO concentration at Segments 1 and 43 under current and TMDL condition scenarios. Under existing loading conditions, DO in Segment 1 meets or exceeds its

Table 2 Existing loads and TMDL determinations of CBOD, NH4–N, TN and TP for Taigeyunhe, Caoqiao and Yincungang rivers. Taigeyunhe River

CBOD [ton/ yr]

NH4–N [ton/ yr]

TN [ton/ yr]

TP [ton/yr]

2007 2008 2009 2010 2011 Average TMDL determination

6519.65 7069.07 6898.6 7196.99 7842.49 7105.36 4618.48

2066.83 1704.53 1318.03 1519.93 1310.63 1583.99 712.79

3495.43 3028.86 2714.82 3407.28 3972.94 3323.87 1661.93

222.09 185.1 171.77 214.03 167.21 192.04 57.61

Caoqiaohe River

CBOD [ton/ yr]

NH4–N [ton/ yr]

TN [ton/ yr]

TP [ton/yr]

2007 2008 2009 2010 2011 Average TMDL determination

1893.25 2228.93 2779.02 3323.12 3197.12 2684.29 1744.79

914.02 668.6 367.35 500.28 526.44 595.34 267.9

2524.15 2691.05 2388.98 3399.24 3319.51 2864.58 1432.29

81.92 83.63 65.75 73.37 65.93 74.12 22.24

Yincungang River

CBOD [ton/ yr]

NH4–N [ton/ yr]

TN [ton/ yr]

TP [ton/yr]

2007 2008 2009 2010 2011 Average TMDL determination

1928.9 2094.46 2610.41 3536.32 3253.5 2684.72 2013.54

638.25 389.96 392.04 495.62 434.56 470.09 235.04

2214.2 2165.91 2095.48 3030.71 2796.89 2460.64 1230.32

69.61 66.62 60.7 84.29 60.41 68.33 20.5

Fig. 9. DO concentration probability under current and TMDL conditions at downstream end segments in the river network.

standard 85% of the time. When the TMDL is achieved and the Class IV WQS for surface water of China are met, DO concentrations at Segment 1 should meet or exceed its standard 94% of the time. The downstream end of the Yincungang River meets the DO criteria limit more than 90% of the time even under existing baseline conditions. 4. Conclusion We developed a nutrient TMDL for the Zhushan Bay watershed within the Taihu Lake Basin using mathematical models applied with complex watershed physical characteristics and a limited number of monitoring stations. Based on the satisfactory model performance with existing data, this application provides scientific insights on the water quality responses to watershed and point source discharges responding to seasonally-varying environment conditions. To meet the specified TMDL condition in this complex river network, approximately 25–35%, 50–55%, 50–55% and 70– 75% reductions in watershed loadings for CBOD, NH4–N, TN and TP, respectively, should be achieved by future management practices. For the future revisions and evaluation of TMDL plan effectiveness, it is essential to continue to monitor the waterbody and update the TMDL calculation at regular intervals, such as five years.

C. Wang et al. / Ecological Engineering 83 (2015) 258–267

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