Abstract

Poyang Lake, the largest freshwater lake in China, plays a key role in regulating the hydrology, water quality and ecosystem in the middle reaches of the Yangtze River. Recent industrialization and urbanization in Jiangxi province have led to rapid increase in water consumption and water quality deterioration. In this research, a numerical model is developed to simulate the hydrodynamics and water quality evolutions in Poyang Lake and its surrounding river network. The study links an in-house one-dimensional river network model with a MIKE multi-layered three-dimensional hydro-environmental model. The validated model is used to investigate the impact of a proposed downstream barrage on Poyang Lake's flow and ecosystem. All of the five considered barrage operation schemes demonstrate the capability of increasing the water level and inundation area during the dry period, especially in the lake's downstream region. The barrage schemes help reduce the lake's nutrient concentrations, due to the enhanced dilution at higher water levels. Nonetheless, the barrage leads to DO depletion which could pose a threat to the aquatic wildlife and habitats. This study's findings contribute to a better understanding of the hydro-environmental influence of the proposed barrage and thus the optimisation of its operation to mitigate the adverse influence.

HIGHLIGHTS

  • A hybrid model is developed for the hydro-environmental simulation in Poyang Lake basin and its associated rivers.

  • Investigation of the hydro-environmental impact of the Poyang Lake barrage using the validated hybrid model.

  • The barrage increases the water level and inundated area but reduces nutrient and DO concentrations.

  • Negative influences of the barrage can be limited by optimising the barrage operation.

INTRODUCTION

Located in Jiangxi province in southeastern China, Poyang Lake is the largest freshwater lake of the country. It sits on the southern bank of the middle and lower reaches of Yangtze River, and has an area of 16.2 × 104 km2, accounting for 9% of Yangtze River basin (Zhang et al. 2015b). Poyang Lake connects with the Yangtze River through a channel in the north and also receives water from a complex river system in the south, consisting of five major rivers namely Ganjiang, Fuhe, Xiushui, Xinjiang and Raohe. The lake region serves as a natural hydrological moderator for the Yangtze River, providing huge volume for water retention and pollutant dilution and an irreplaceable habitat for numerous aquatic organisms along the Yangtze River basin. The lake also supports agriculture, tourism and is the key freshwater source for around 12.4 million people in nearby cities of Nanchang, Jingdezhen and Jiujiang (Zhang et al. 2014).

Since the completion of the Three Gorges Dam (TGD) and its associated reservoir in 2003, the flow characteristics of Yangtze River have recorded significant changes which created knock-on effects on its surrounding water bodies (Lai et al. 2014b). More outflows from Poyang Lake to the Yangtze River reduced the water storage capability of Poyang Lake (Guo et al. 2012a). The lake's water levels have witnessed a declining trend in the 21st century. Monitoring stations around Poyang Lake all logged the lowest historic water levels over the last two decades and the average water level drop ranges from 14.0 to 13.3 m over the last 60 years (Guo et al. 2012b). The main reasons behind the water level drop include decreasing rainfall and influx from upstream rivers, leading to a prolonged dry period. As a result, the self-purification ability of the lake reduces and hence the water quality worsens, resulting in the deterioration of ecology. Within the period of 2000–2007, the river length with the level of water quality no better than Class III (poor quality) increased from 170 to 221 km, while the number of polluted river sections increased from 6 to 22 (Gao et al. 2010). The main pollutants in the region include TN, TP, NH3-N and COD (Luo et al. 2014), which can be directly tracked to the five major rivers as a result of increased industrial activities, municipal wastes and agricultural fertilizers and poultry farming (Chen et al. 2016).

The decline in water quantity and quality in Poyang Lake has sparked concerns over freshwater security for the humans and wildlife in the region. The Poyang Lake barrage project was therefore proposed to prevent the lake's water level from further declining because of climate change, urbanization and hydraulic control on the Yangtze River (Hu & Ruan 2011). The proposed project involves a series of sluice gates being installed along the narrowest section of the channel at the northern mouth of the lake (Figure 1). The main goal of the project is to manage the water level in Poyang Lake by closing the sluice gates during dry seasons. In the wet season, the sluice gates are fully opened allowing free exchange of water, energy and biology between the lake and Yangtze River (Lai et al. 2016). In support of the proposed barrage to be built, local government offices and hydrological authorities have designed various operational schemes for the project (Zhang et al. 2011). These schemes aim to manage water resources and mitigate potential flooding hazards in the Yangtze River basin. Nonetheless, these operations could alter the natural processes in the hydrological, hydrodynamic, sediment transport, water quality and ecological aspects and weaken the existing interaction and natural connection between the Yangtze River and Poyang Lake. Sudden changes in the water level and flow velocity of the hydro-environment could affect the pollutant transport due to the disturbance on the flow driven material convection. Large difference in water level between the Yangtze River and Poyang Lake could also hinder the daily movement and migration of aquatic animals in the region. Serious degradation of water quality and outburst of algae may result in eutrophication, disappearance of wetlands and damage to wildlife habitat for endangered birds, fish and mammals. Therefore, it is crucial that quantitative analysis is carried out to evaluate the impacts of these schemes.

Figure 1

Location of potential barrage in Poyang Lake.

Figure 1

Location of potential barrage in Poyang Lake.

Numerous research efforts have been spent in developing hydrodynamic models for Poyang Lake in the past decade. Lai et al. (2011) built a 2-D numerical simulation of hydrodynamic and pollutant transport for Poyang Lake, although the validation and subsequent analysis of the developed model are limited. Later, Lai et al. (2013) built a new model called the CHAM that dynamically couples a 1-D unsteady flow model and a 2-D hydrodynamic model, which was used to compare the significance of lake inflows and Yangtze River on Poyang Lake level (Lai et al. 2014a) and to investigate the impact of TGD impoundment on Poyang Lake (Lai et al. 2012, 2014b). Li et al. (2014) developed an integrated model that consists of a hydrological model WATLAC and 2-D hydrodynamic model MIKE21 as an attempt to simulate the response and characteristics of the dynamic Poyang Lake catchment system. The same research group later investigated the hysteretic relationship between the lake response and the hydrodynamic change in the Yangtze River (Zhang & Werner 2015). Li et al. (2015) combined a 2-D hydrodynamic model in MIKE with dye tracer simulations to investigate the transport time scale in Poyang Lake for various water level variation periods. Li & Yao (2015) expanded the topic by adapting a similar approach using a hydrodynamic model in conjunction with transport and particle tracking sub-models. Both studies provided an insight on the management of chemical and ecological processes in Poyang Lake and demonstrated early attempts in modelling the transport behaviours in such a large river-lake system. Lai et al. (2016) discussed the possible impacts of the barrage project on the hydrology and hydrodynamics of the lake using a 2-D EFDC model, focusing on the water level, velocity, lake current and water exchange period. Zhang et al. (2015a) developed an inundation model for Poyang Lake with remote sensing data as a means of validation.

Continuous advancement of numerical technology suitable for hydro-environmental simulations under complex topography and bathymetry conditions offers an encouraging outlook. Smoothed Particle Hydrodynamics (SPH) is frequently used in the modelling of turbulent open channel flow over rough boundaries as a mesh-free numerical scheme (Kazemi et al. 2017, 2020). Wu et al. (2019), Yang & Liang (2020) and Yang et al. (2020) adopted the sophisticated random walk methods in environmental analyses. Nonetheless, these mesh-free approaches have yet to be applied to the challenging hydro-environment of Poyang Lake. Studies of the barrage project and evaluation of its impact on hydraulic and environmental issues using these alternative approaches are lacking.

The potential environmental effects of the barrage project in Poyang Lake have rarely been explored via numerical modelling. In this study, we aim to utilise a hybrid multi-dimensional hydrodynamic and water quality model to investigate the impact of the various operation schemes of the proposed lake barrage on the hydro-environment of Poyang Lake. The results can help understand the potential consequences of the barrage and contribute towards the sustainability of Poyang Lake and its neighbouring regions.

MATHEMATICAL METHODS

Hydrodynamic models

The hydrodynamic model is based on the incompressible Reynolds averaged Navier-Stokes equations. They can be simplified into Saint-Venant equations for open channel flows in rivers and lakes, which are also known as the shallow water equations (SWE) (Barré de Saint-Venant 1871). The general Saint-Venant equations adopt the principles of mass conservation and momentum conservation, and the assumptions of Boussinesq and the hydrostatic pressure distribution below the free surface. The governing equations of the three-dimensional (3-D) hydrodynamic model, which comprise of local continuity equation (Equation (1)) and horizontal momentum equations (Equations (2) and (3)), are as follows.
formula
(1)
formula
(2)
formula
(3)
where (x, y, z) represent the Cartesian coordinates and (u, v, w) are the three corresponding velocity components, t is time, g is the gravitational acceleration, η is the free surface elevation, d is the still water depth, S is the discharge magnitude of the point sources, f= 2ωsinφ is the Coriolis parameter (in which ω is the Earth's angular rate of revolution and φ is the geographic latitude), ρ is the water density, ρ0 is the reference density of water, pa is the atmospheric pressure and (us, vs) are the discharge velocities of inflow water, νt is the vertical turbulent viscosity. (Fu, Fv) are the horizontal stress terms relating to the horizontal eddy viscosity. The total water depth, h=η + d, can be obtained from the kinematic boundary condition at the surface once the velocity field is known from the momentum and continuity equations. The surface and bottom boundary condition for u, v and w are:
At z = η:
formula
(4)
At z = d:
formula
(5)
where (τsx, τsy) are the x and y components of the surface wind stress, (τbx, τby) are the x and y components of the bottom friction stresses.
The 3-D governing equations for the transports of heat (T), salinity (s) and scalar quantity of concentration (c) are based on the general advection-diffusion-reaction equations (DHI 2017b) as follows.
formula
(6)
formula
(7)
formula
(8)
where Dv is the vertical turbulent diffusion coefficient, Ĥ is a source term due to atmospheric heat exchange, (Ts, ss, cs) are the temperature, salinity and pollutant concentration of the source inflow, respectively, kp is the first-order decay rate of the scalar quantity, (FT, Fs, Fc) are the horizontal diffusion terms for the temperature, salinity and concentration respectively, which are functions of horizontal diffusion coefficients and eddy viscosity.

The finite volume method is used to discretize the solution domain, involving non-overlapping cell blocks and elements. A layered mesh structure is used, consisting of unstructured mesh in each horizontal layer and a structured connectivity in the vertical direction. The vertical discretization is based on sigma coordinates (DHI 2017a). In the numerical solution, the Roe's scheme, which is an approximate Riemann solver, is used to calculate the convective fluxes at the interface of the cells (Roe 1981). In the Roe's scheme, the dependent variables on both sides of an interface have to be estimated. Second-order spatial accuracy is achieved by employing a linear gradient-reconstruction technique.

The mentioned 3-D equations (DHI 2017a, 2017b) are used as a basis, but can be further simplified into 2-D and 1-D equations. The 2-D hydrodynamic and transport equations can be obtained by integrating these 3-D governing equations over depth. In an open channel flow environment, the governing equations are often simplified into 1-D equations in which velocity components and distribution non-uniformity in a cross-section are assumed to be insignificant. These various sets of governing equations are applied in respective numerical simulations to adapt to certain water environments. The 1-D governing equations of open-channel flows are given as:
formula
(9)
formula
(10)
where A is the wetted cross sectional area of river channel, t is time, Q is the flow discharge, x is the distance of river channel, q is the lateral discharge per unit channel length, g is the gravitational acceleration, Z is the water surface elevation above datum, se is the slope due to local head loss and sf is the friction slope, approximated by sf = n2Q|Q|/A(A/B)4/3 in which n is the Manning coefficient and B is the wetted cross-sectional width, and L is the momentum of lateral discharge: if q ≥ 0, L = q(ubQ/A), where ub is the magnitude of lateral flow velocity along main streamline in river channel, and if q < 0, L=−qQ/A.

The hydrodynamic governing equations are discretized using the Preissmann method and the Gauss-Jordan elimination method is used to solve the resulting algebraic equations. The mass transport governing equation is discretized using the implicit upwind scheme to maintain consistency between the hydrodynamic and mass transport equations. The tridiagonal matrix algorithm (TDMA) is used to solve the 1-D solute transport equations.

Water quality model

Water quality is dependent on many physico-chemical processes in water bodies. The 1-D water-quality model is mainly based on the Water Quality Analysis Simulation Program (WASP5), which is a generalized framework that models contaminant behaviours and transport in surface waters (Di Toro et al. 1971). In WASP5, nutrient enrichment, eutrophication and DO depletion processes are simulated to capture the transport and interaction among nutrients, phytoplankton, carbonaceous material and DO in water bodies. The model simulates eight state variables from the transport reactions that typically occur in water bodies. In the current nutrient model, the main water-quality indicators include ammonia (NH3), nitrate (NO3), phosphate (PO4), phytoplankton (PHYT), carbonaceous biochemical oxygen demand (CBOD), dissolved oxygen (DO), organic nitrogen (ON) and organic phosphorus (OP). These water quality indicators are selected due to their close interrelationship in the nutrient cycle and hence the need to be considered collectively in a water quality model. These indicators are also closely related to the eutrophication of the lake water, which is a main type of water pollution in China. Data related to these water quality indicators was available from regular monitoring at gauging stations across the Poyang Lake, which provides the measurements for the calibration of our model. The discretization used in the hydrodynamic simulation is retained for the water quality simulation. Four interacting systems are categorized; the phytoplankton kinetics, the phosphorus cycle, the nitrogen cycle and the DO balance. The 1-D solute transport equation in open channel flow is given by:
formula
(11)
where c is the concentration of a water quality state variable, Dx is the longitudinal dispersion coefficient, Sc is the external bacterial decay term that equals to cKdA, with Kd being the decay factor, and Sk is the kinetic source term due to chemical reaction. The general mass balance equation is then solved for each state variable (Ambrose et al. 1993).

In multi-layered 3-D water quality models, the simulation is performed using the ECO Lab module as part of the software package MIKE21/3 Flow Model FM. The ECO Lab module is a numerical unit designed specifically for ecological and water quality modelling. It is used as a platform for customizing models that describe water quality, eutrophication, heavy metals and ecology in aquatic ecosystems using process-oriented formulations. The biochemical processes involved in this research include the dissolved oxygen process, the nitrogen cycle and the phosphorus cycle.

MODEL SETUP AND APPLICATION

1-D numerical models

In the 1-D model, the study area covers the majority of the middle and lower reach of Yangtze River from Yichang to Datong, together with Dongting Lake and Poyang Lake, as shown by the grey region in Figure 2. The river networks are simplified into 125 sub-channels, 2,831 cross-sections and 91 junctions. Regions with small water flows and low interaction with the main lakes are excluded from the computation. Topographic survey was conducted around 2008 in the Yangtze main channel, Dongting Lake, Poyang Lake and a series of cross sections in river branches. The surveyed results provide key information required for the cross-sectional approximation. All the major rivers and sub-lakes in the Poyang Lake region are included in the model and a close-up of the cross-section layout around the Poyang Lake is shown in Figure 3. The sampling points at a cross-section are spaced at 20–50 m intervals to ensure sufficient sampling resolution in both narrow trenches and open waters in the broad lake areas. The hydrodynamic simulation was performed for the period 2000–2008, while the major water-quality analysis was done for the period between 2004 and 2008. The computational time step was set at 300 seconds. A roughness coefficient was assigned to each point in a cross-section, ranging from 0.02 to 0.04 s/m1/3. In order to account for the operation of the barrage, additional calculation points were placed at the barrage position. Further details of the 1-D hydrodynamic modelling in the middle reach of the Yangtze River can be found in Huang et al. (2016). Recently, the 1-D model domain has been extended from Zhutuo, which is a key control hydrological station for the TGP, to the Yangtze Delta near Shanghai.

Figure 2

1-D model simulation boundary.

Figure 2

1-D model simulation boundary.

Figure 3

1-D model discretization.

Figure 3

1-D model discretization.

Multi-layered 3-D numerical models

Multi-layered 3-D model computation is conducted in the main Poyang Lake, extending from the river mouths of the five main branches in the south to the barrage in the north (Figure 4). Given the enormous fluctuation of the water-surface area, between 50 km2 in the dry season and 5,000 km2 in the wet season, high grid resolution (50–60 m) is required. However, it is computationally unaffordable to adopt uniform grid resolution. The triangular mesh is used to reduce calculations in the regions with gradual variations whilst providing an accurate representation of the narrow watercourses where the flow and pollutant distribution vary rapidly. Irregular inner and outer parts of the lake have been discretized into triangular elements of variable grid sizes. The grid size varies from 60 to 150 m in main channels, and from 600 to 800 m in the homogeneous lake central regions. The final mesh consists of 56,658 vertices, 109,494 elements and 15,556 edges (Figure 5). The 2-D mesh layers are positioned at three depth levels of Poyang Lake to give a 3-D representation of the flow. In total, the simulation consists of 56,658 × 3 nodes with a calculation time step of 300 seconds. This grid system supports an efficient and accurate representation of the topographic connectivity, flow exchange and mass transport between various sub-regions of Poyang Lake. The input files of the multi-layered 3-D model are based on the outputs of the 1-D river network simulation, together with the field-measured data provided by the local water authorities.

Figure 4

Multi-layered 3-D model simulation boundary.

Figure 4

Multi-layered 3-D model simulation boundary.

Figure 5

Multi-layered 3-D model discretization.

Figure 5

Multi-layered 3-D model discretization.

Model validation

Field measurements at 32 gauge stations distributed across the Poyang Lake region were used to verify the numerical model. The data included information on water levels and flow rates of the lake from 1991 to 2008. The model was first calibrated using data from 1991 to 2005 and then validated with field data in 2005–2008. The model performance is assessed in terms of the root mean square error (RMSE) and the Nash–Sutcliffe coefficient of efficiency (NSE) (Nash & Sutcliffe 1970). The RMSE is selected for directly quantifying the prediction error of the model, which has the same units as the variable of interest, whereas the NSE is chosen as a dimensionless goodness-of-fit indicator to represent the degree to which the model simulations match the observations. These quantitative assessments provide an evaluation of the model's predictive abilities (Legates & McCabe 1999).
formula
(12)
formula
(13)
in which N is the number of observed points, is the model-predicted value, is the observed value and is the mean value. A zero RMSE value indicates a perfect fit. The smaller the RMSE value, the closer the correlation between the simulated values and measured data. The coefficient of efficiency takes values of −∞ < NSE < 1 (Ritter & Muñoz-Carpena 2013), with the value of 1.0 representing a perfect fit. In general, a NSE value of 0.40 or above as satisfactory. The NSE values higher than 0.60 and 0.75 are considered good fit and very good fit, respectively (Moriasi et al. 2007).

In hydraulic model validation, the simulated surface elevations are compared with the field-measured data at several gauge stations in the main Poyang Lake region. The resulting RMSEs are within the range of 0.330–0.631 m and NSEs are in the range of 0.933–0.977 for 1-D hydrodynamic model (Figure 6), while for the multi-layered hydrodynamic model they are 0.518–0.972 m and 0.683–0.976 respectively (Figure 7). The figures illustrate good matches of two sets of data about the hydrodynamics of the lake. In general, the 1-D and multi-layered hydrodynamic models are able to consistently produce the correct trend and magnitude of the water level fluctuation across the Poyang Lake over a long period of time.

Figure 6

Comparisons between 1-D hydrodynamic predictions and measurements at Hukou, Kangshan and Wucheng.

Figure 6

Comparisons between 1-D hydrodynamic predictions and measurements at Hukou, Kangshan and Wucheng.

Figure 7

Comparisons between multi-layered 3-D hydrodynamic predictions and measurements at Hukou, Kangshan and Wucheng.

Figure 7

Comparisons between multi-layered 3-D hydrodynamic predictions and measurements at Hukou, Kangshan and Wucheng.

Validations of the 1-D and multi-layered 3-D water quality models were performed in similar procedures. Water quality indices; ammoniacal nitrogen (NH3-N), total phosphorus (TP), dissolved oxygen (DO) and water temperature (TEMP) were computed and compared with the measured data.

Figure 8 illustrates the comparison between 1-D computational results and the field-measured data at several gauge stations. The simulated NH3-N concentrations and the TP concentrations are in the same order of magnitude as the field-measured data, but they generally do not match the fluctuating trends well, with the predicted local maximums and minimums often missing the measurements. The resulting RMSEs and NSEs range from 0.184 to 0.372 mg/L and from −0.569 to 0.151, respectively. These represent a large discrepancy and a pure prediction performance. The comparisons between simulated predictions and field-measured data are generally good in the case of DO and TEMP, with the simulated results satisfactorily matching the trends of the field measurements. Across all of the gauge stations with valid field measurement data, validations of DO and TEMP are categorically more satisfactory than those of NH3-N and TP in the 1-D water quality model validation. The DO validation records RMSEs and NSEs to be 1.253–1.862 mg/L and −0.706–0.499, respectively. The TEMP validation records RMSEs and NSEs to be 2.115–3.238 °C and 0.851–0.932, respectively.

Figure 8

Comparisons between measured water quality parameters with 1-D water quality model predictions.

Figure 8

Comparisons between measured water quality parameters with 1-D water quality model predictions.

The multi-layered 3-D water quality modelling results are compared with the field-measured data in 2008 in the validation. The multi-layered 3-D model enables the study to focus on the regions of broader water surface near the central part of the lake. Figure 9 presents the typical validation results in the multi-layered 3-D simulations.

Figure 9

Comparisons between measured water quality parameters and multi-layered 3-D water quality model predictions.

Figure 9

Comparisons between measured water quality parameters and multi-layered 3-D water quality model predictions.

As opposed to the 1-D validation results, the multi-layered 3-D simulated NH3-N and TP concentrations have demonstrated satisfactory correlations with the field measurements. The NH3-N prediction achieves the RMSE and NSE values of 0.322 mg/L and 0.928, respectively. The TP prediction achieves the RMSE and NSE values of 0.007 mg/L and 0.795, respectively. Temperature and algal growth are among the major factors that affect the DO variation. The rise in temperature during the summer triggers DO depletion, as it encourages biological activities and oxygen uptake for algal growth during the wet season, resulting in the decline of DO concentrations. The simulation demonstrates the correct trend of DO variation, with DO prediction achieving the RMSE and NSE values of 1.174 mg/L and 0.213 respectively. The predicted temperature records a very good agreement with the field-measured data with RMSE and NSE values of 0.693 °C and 0.993, respectively. Overall, the model is able to generally reproduce the field-measured results of all the water quality indicators.

Deviation of the computed results from the observed measurements can be caused by many reasons. Pollution associated with certain industrial, social and agricultural activities are difficult to monitor and quantify, leading to the misplacement or negligence of pollutant sources and incorrect discharge loadings. Furthermore, vigorous sand dredging activities in the region constantly change the bathymetry of the lake, which further contribute to the modelling difficulty. In addition, the exclusion of sediment modelling in this model is also a factor that might contribute to the unsatisfactory agreement in validation.

RESULTS AND DISCUSSION

Proposed barrage scenarios

The proposal to construct the Poyang Lake barrage was first put forward in 2008. Its aim is to prevent Poyang Lake's water levels from continuously declining, triggered mainly by the deployment of the TGD. The proposal suggests to build a series of sluice gates along the narrowest section of the channel of 2.8 km wide, in the north of the Poyang Lake. Different barrage schemes are proposed by various local authorities interested in the Poyang Lake's environment (Table 1). Scheme S1 is proposed after a series of hydrological studies conducted by the Jiangxi provincial government. Scheme S2, S3 and S4 are proposals put forward by the Yangtze River Water Conservancy Committee. Scheme S5 is designed by the China Institute of Water Resources and Hydropower Research (IWHR). Scheme S0 is referred to the no barrage condition, corresponding to the natural state. These proposals detail the barrage operations and water level controls in terms of the five periods of the Poyang Lake control over a year: (1) inflow period, (2) storage period, (3) recharging period, (4) supply period, and (5) outflow period. The numerical model developed in this study is applied to examine the impact of different barrage schemes on the Poyang Lake's hydro-environment. The operational patterns of the schemes in Table 1 are translated into the variations of water levels at the location of the barrage over a period from 2005 to 2008. These water levels are specified in the model as the boundary condition at the barrage in the multi-layered 3-D simulation. Controlling the water levels at the barrage essentially changes the flow pattern in the whole lake and its interaction with the Yangtze River, and hence the hydrodynamics and solute transport of the entire lake.

Table 1

Proposed barrage control schemes

SchemeS1S2S3S4S5
Inflow Period (Flood release) 
Period (day/month) 01/05–31/08 01/04–31/08 01/04–31/08 01/04–31/08 01/04–31/08 
Water Level (m) >15.5 >15.5 >15.5 >15.5 <15.5 
Storage Period (Maintain water levels) 
Period (day/month) 01/09–30/09 01/09–30/09 01/09–30/09 01/09–30/09 01/09–30/09 
Minimum Flow (m3/s) 2,100 – – – – 
Water Level (m) 15.5 14.5–15.5 14.5–15.5 14.5–15.5 14.5–15.5 
Recharging Period (Supply to downstream) 
Period (day/month) 01/10–31/10 01/10–31/10 01/10–31/10 01/10–31/10 01/10–31/10 
Water Level (m) > 14 11.5–14.5 12–14.5 11.5–14.5 12–12.5 
Supply Period (Maintain water levels) 
Period (day/month) 01/11–29/02 01/11–29/02 01/11–29/02 01/11–29/02 01/11–29/02 
Minimum Flow (m3/s) 925 – – – Qout = Qin 
Water Level (m) > 15.5 10–11 12 10 12.5 
Outflow Period (Flood release) 
Period (day/month) 01/03–30/04 01/03–31/03 01/03–31/03 01/03–31/03 01/03–31/03 
Minimum Flow (m3/s) – – – – 10,000 
Water Level (m) 12 14 12 10 11 
SchemeS1S2S3S4S5
Inflow Period (Flood release) 
Period (day/month) 01/05–31/08 01/04–31/08 01/04–31/08 01/04–31/08 01/04–31/08 
Water Level (m) >15.5 >15.5 >15.5 >15.5 <15.5 
Storage Period (Maintain water levels) 
Period (day/month) 01/09–30/09 01/09–30/09 01/09–30/09 01/09–30/09 01/09–30/09 
Minimum Flow (m3/s) 2,100 – – – – 
Water Level (m) 15.5 14.5–15.5 14.5–15.5 14.5–15.5 14.5–15.5 
Recharging Period (Supply to downstream) 
Period (day/month) 01/10–31/10 01/10–31/10 01/10–31/10 01/10–31/10 01/10–31/10 
Water Level (m) > 14 11.5–14.5 12–14.5 11.5–14.5 12–12.5 
Supply Period (Maintain water levels) 
Period (day/month) 01/11–29/02 01/11–29/02 01/11–29/02 01/11–29/02 01/11–29/02 
Minimum Flow (m3/s) 925 – – – Qout = Qin 
Water Level (m) > 15.5 10–11 12 10 12.5 
Outflow Period (Flood release) 
Period (day/month) 01/03–30/04 01/03–31/03 01/03–31/03 01/03–31/03 01/03–31/03 
Minimum Flow (m3/s) – – – – 10,000 
Water Level (m) 12 14 12 10 11 

Barrage impact on the hydrodynamic behaviour of Poyang Lake

Simulations with each scheme's barrage control produced hydrodynamic results on the year 2007. Three locations, including Xingzi (northern), Wucheng (northwestern) and Kangshan (southern), are chosen to represent the changes in water levels at different parts of Poyang Lake. The surface elevations at these locations under the influence of the schemes are illustrated in Figure 10. The divergence of water levels under different schemes only occurs during the winter months (January to April, October to December), when the barrage is in operation. The convergence of all schemes into the same water levels is observed throughout the wet seasons from May to September. All the lines overlap during the period from May to September, which verifies the intention of the barrage to have no control on the lake's flow during the wet summer season.

Figure 10

Scheme comparison of water levels at Xingzi, Wucheng and Kangshan.

Figure 10

Scheme comparison of water levels at Xingzi, Wucheng and Kangshan.

Among the five schemes, S4 exerts the least degree of barrage control and thus observes the lowest water levels at the barrage during the dry period, i.e. closest to the natural water levels. The water levels in S2 and S3 follow a similar trend and gradually increase. Both S1 and S5 result in the highest water levels in all three locations, demonstrating that the hydrodynamics of Poyang Lake reacts to the barrage control according to the degree of intervention by the barrage.

The magnitude of the water level differences between the schemes, however, depends on locations. The difference between water levels generated by different schemes in the winter season at Kangshan is smaller than those in Wucheng and Xingzi. Kangshan is at a southern location further away from the barrage and with a higher elevation than the other two monitoring points, so the barrage's influence decreases with the distance. In contrast, locations in the northern and eastern regions will be influenced by the barrage in a greater degree, as at Xingzi and Wucheng.

The rise in water levels due to barrage control will lead to the increase of water volumes and the expansion of water surface areas in Poyang Lake during the dry seasons. Under natural conditions, 2007 recorded a water surface area of 1,831 km2 and the water volume in the lake of 66.9 × 108 m3. Results generated from the numerical model under different schemes are summarized in Table 2 below.

Table 2

Water surface area and volume of Poyang Lake under Scheme 0–5

SchemeS0S1S2S3S4S5
Water Surface Area (km21,831 2,224 1,995 2,148 1,914 2,251 
Water Volume (×108 m366.9 80.7 71.3 76.3 69.0 81.4 
SchemeS0S1S2S3S4S5
Water Surface Area (km21,831 2,224 1,995 2,148 1,914 2,251 
Water Volume (×108 m366.9 80.7 71.3 76.3 69.0 81.4 

As expected, S5 gives the largest increase in both the water surface area and volume among all the schemes. S4 has the smallest impact on the surface areas and volume. In general, the influence of barrage on the water volume is relatively small with the biggest impact coming from S5 at 14.5 × 108 m3, which is still less than 20% of the lake capacity. The increase in volume in S4 is only at 2.1 × 108 m3, which is less than 3% of the lake capacity. Because the Poyang Lake is broad but shallow, the barrage has led to the significant increase in the surface areas through the rise of water levels.

The increase in water levels under the barrage control can bring subsequent environment concerns. Reduction in water level fluctuation in the lake will diminish the flow exchange between the Yangtze River and Poyang Lake, hence decreases the water flow rate and velocities. Lower water turnover rate could result in longer retention time and may cause eutrophication. Moreover, rise in water level will inundate lake regions that potentially include wetland areas and wildlife habitats, in which temporary dry condition and sun radiation are essential. Further investigation is needed on the impact of the barrage scheme on these aspects.

The effect of the barrage schemes on the Poyang Lake water environment was also analysed. Detailed simulation temporally and spatially across the Poyang Lake are shown in the form of subplots for the water quality indicators DO, NH3-N and PO4-P. Given the shallow nature of Poyang Lake with water depth generally below 10.0 m, temperature stratification is expected to be very weak even after the construction of the barrage. Temperature is therefore excluded from the detailed discussions due to its small variation. In the submaps, the colour spectrum ranges from purple (low concentration) to red (high concentration) with white region representing dry out region where water level is low and the flow is negligible. Most of the white areas belong to the shallow sand shoals at relatively high elevations.

Barrage impact on DO concentration of Poyang Lake

In Figures 11 and 12, the DO concentrations across the whole of Poyang Lake domain are present. Figure 11 represents DO concentrations in its natural state in 2007. Figure 12 shows the DO distribution under the influence of S1. In its natural state, the DO concentrations in the early months (Figure 11(a) and 11(b)) show great variation along the main channel ranging between 4.0 and 11.0 mg/L. As the water levels rise in the flooding season, the DO concentrations begin to decrease from 5.0 to 8.0 mg/L in April (Figure 11(c)) to below 3.0 mg/L in July (Figure 11(e)). Although there are influxes of high DO concentrations from some of the boundaries in the west side of the lake, the overall DO concentration in the domain remains below 3.0 mg/L (Figure 11(f)). The increase in temperature, reduction in flow rate and growth of algae and microorganisms are potential causes of such concentration decline during the wet season. As the dry season returns, water levels drop and the water flow in streams increases and facilitates oxygen recharge, so the narrow line of higher DO concentration is seen again in Figure 11(h).

Figure 11

Scheme 0's DO spatial distribution in 2007.

Figure 11

Scheme 0's DO spatial distribution in 2007.

Figure 12

Scheme 1's DO spatial distribution in 2007.

Figure 12

Scheme 1's DO spatial distribution in 2007.

High water levels induced by the barrage scheme S1 have created a vastly different spatial distribution of DO concentration in the early months. During the dry season, rise in water levels reduces the current-induced aeration in the lake and both Figure 12(a) and 12(b) show a majority of DO concentrations at around 3.0 mg/L with a burst of high DO concentrations in the northwestern part of the lake due to high-DO inflows at the boundaries. As the flooding season approaches, continuous increase on water levels, combined with the rise in temperature, leads to a reduction in the DO concentrations. Throughout the summer period (Figure 12(d)–12(f)), the majority of the lake's DO concentrations are below 3.0 mg/L, which are below the DO standard of 5.0 mg/L for aquatic wildlife. As water levels decline in the winter months, little variation can be seen in DO concentrations (Figure 12(g)–12(h)). High DO influxes from the northwestern boundaries alter the general low DO concentrations in the lake.

The rise in water levels hinders the re-aeration process in the lake and the overall reduction in DO concentrations is evident from the results. The elevated water levels reducing lake-river exchange and water velocities. Sufficient DO concentrations are vital for the survival and growth of aquatic wildlife and water vegetation. Large increase in water levels in the region under the barrage control could damage and endanger the living habitats of many wildlife species in the water environment.

Barrage impact on NH3-N concentration of Poyang Lake

In Figures 13 and 14, the spatial distributions of the NH3-N concentrations in Poyang Lake are shown. The natural state of the distribution with no barrage influence is shown in Figure 13. During the early dry period in the lake, large quantities of NH3-N enter the lake from boundaries with a concentration ranging from 0.025 to 1.00 mg/L, resulting in distinct pathways of higher NH3-N concentrations (Figure 13(a) and 13(b)). The vigorous fluctuations of NH3-N concentration under different schemes during the first few months of the year could be due to the influence of nearby inflows with rapidly changing NH3-N concentrations. This could happen when the inflows have multiple upstream pollutant sources such as industrial plants and agricultural farms which discharge chemicals and fertilizers to water bodies. As a result, the NH3-N concentrations fluctuate greatly. When the flood season begins, increase in water levels dilutes the NH3-N concentration (Figure 13(c) and 13(d)). Blue regions in the figures with NH3-N concentrations of 0.050–0.075 mg/L start to fade and disappear into the purple region that has concentrations below 0.010 mg/L. During the wet season, the NH3-N concentration in the domain is thus diluted, with the concentration below 0.010 mg/L (Figure 13(d) and 13(f)). Inflows with higher NH3-N concentration from the west boundaries of the lake are quickly neutralized and do not affect the overall concentration of the domain. In winter, drop in water levels results in the significant rise of NH3-N concentration in narrow and deep streams along the lake's main channel, as shown in Figure 13(h).

Figure 13

Scheme 0's NH3-N spatial distribution in 2007. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2021.184.

Figure 13

Scheme 0's NH3-N spatial distribution in 2007. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2021.184.

Figure 14

Scheme 1's NH3-N spatial distribution in 2007.

Figure 14

Scheme 1's NH3-N spatial distribution in 2007.

Under the influence of the barrage, the NH3-N concentrations in S1 are reduced in the dry season due to the increase in water levels in this period. As shown in Figure 14, the very high NH3-N concentrations along the narrow water causeways have disappeared. Boundary inflows with high NH3-N concentrations can still be seen in the east and northwest regions (Figure 14(b)), however these influxes are soon diluted once they enter the lake area. With high water levels maintained throughout the year, the dilution effect is apparent throughout the flooding period (Figure 14(d) and 14(f)), prior the winter months when the concentration slightly rises again. The increased water level and water volume in Poyang Lake thus enhance the dilution of NH3-N and hence result in a reduction in the NH3-N concentration.

Barrage impact on PO4-P concentration of Poyang Lake

The spatial distributions of the PO4-P concentration in Poyang Lake are shown in Figures 15 and 16. In Figure 15, no barrage is included and the PO4-P distribution shown is at its natural state in the Poyang Lake environment. In the early months of the year, the PO4-P concentration is high along the main channels, as the low water levels restrict the spread of the input pollutants out of the channels. Concentrations in the range of 0.015–0.075 mg/L can be observed (Figure 15(a) and 15(b)). When the water levels rise in April, the PO4-P concentration spreads out to a larger region as indicated by the increase in blue color in Figure 15(c). The influx of PO4-P from boundaries remains high in June at 0.060–0.065 mg/L as seen in Figure 15(d). As the flooding season arrives, the increase in water volume dilutes the PO4-P concentration. Although inflows with high PO4-P concentration can still be observed in Figure 15(e) and 15(f), the overall concentration in the majority of the lake is below 0.005 mg/L. In the later months of the year, drop in water levels in the lake triggers the increase of PO4-P concentration along the lake's main channel, as seen in Figure 15(h).

Figure 15

Scheme 0's PO4-P spatial distribution. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2021.184.

Figure 15

Scheme 0's PO4-P spatial distribution. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2021.184.

Figure 16

Scheme 1's PO4-P spatial distribution.

Figure 16

Scheme 1's PO4-P spatial distribution.

The PO4-P distributions under the influence of S1 are shown in Figure 16. The increase in water levels has alleviated the PO4-P concentration in the middle of the lake domain during the dry season (Figure 16(b)). As the water levels rise further due to flooding, the PO4-P distribution becomes similar to that in S0, including the outburst of PO4-P concentration in June (Figure 16(d)), which is due to the inflow of a few highly concentrated pollutant streams from Raohe and Xinjiang in the east. The dilution effect on PO4-P concentration is observed throughout the flooding period until October, by which time the winter season begins with lower water levels and higher PO4-P concentrations (Figure 16(f)). The spatial distributions of PO4-P concentration have shown that the implementation of barrage has successfully reduced PO4-P concentration.

CONCLUSIONS

The aim of this study is to develop a numerical model that can simulate the hydro-environmental evolution in the Poyang Lake, which has complicated geometry and experiences large water level fluctuations. The model has been used to assess the hydraulic and water-quality changes as a result of the different operations of a proposed barrage project, which is designed to control the lake water level and water exchange between the Lake and Yangtze River. The combined 1-D and multi-layered 3-D hydro-environmental models have been successfully calibrated and validated.

In terms of hydrodynamic impact of the barrage, the schemes can effectively raise water levels in the lake in winter months, although the water level rise becomes insignificant in the southern part of the lake, which has a large distance from the barrage. A drawback of the water level increase is the inundation of wetlands and wildlife habitats, as well as the reduction of water exchange inside the lake and thus the local accumulation of contaminants in the long term. In terms of the impact on water quality, all five operation schemes are able to reduce the nutrient concentrations in Poyang Lake during the dry season because of the enhanced dilution effect associated with the increased water level and water volume. The concentrations of both NH3-N and PO4-P are below the present values with no barrage in place. The different degrees of reduction depend on the designed water level increments in different schemes. The greater the increase in water levels, the lower the nutrient concentrations. This is a welcoming change, as the barrage can mitigate the pollution issues in the Poyang Lake during dry seasons. However, the rise in water levels also triggers a reduction in DO concentration due to a lower level of water exchange and the reduced re-aeration rate as a result of the reduced flow speed. The oxygen depletion may be hazardous to aquatic wildlife and ecosystem. Schemes 1 and 5 considered in the paper give the largest increase in water levels and thus the greatest impact on the lake hydro-environment.

Apart from the positive and negative impacts of the barrage on the Poyang Lake environment, further investigations will be carried out concerning the barrage influence on downstream regions along the Yangtze River. It should also be noted that our present study does not consider sediment transport, which can be justified as the sediment concentration is low in the lake and the simulation time is relatively short. For long-term predictions, the influence of the sediment transport and other broad climate, hydrological and environmental changes shall also be considered. The barrage should incorporate a flexible design so that its operation can be easily altered to adapt to the new situation in the future.

ACKNOWLEDGEMENTS

We thank the support by the National Key Research and Development Program of China (2016YFC0402605). G. Huang appreciates the financial support by the National Natural Science Foundation of China (52079130, 51509137), and the Model Development and Application of Water Environment in the Yangtze River Basin (2019-LHYJ-0102) H. Ho thanks the PhD scholarship provided by the Croucher Foundation. D. Liang thanks the financial support by the Royal Academy of Engineering (ISS1516\8\34).

DATA AVAILABILITY STATEMENT

Data cannot be made publicly available; readers should contact the corresponding author for details.

REFERENCES

Ambrose
R.
Wool
T.
Martin
J.
1993
The Water Quality Analysis Simulation Program WASP5 Model Documentation and User Manuals
.
Technical report, US Environmental Protection Agency
,
Athens
,
Georgia
.
Barré de Saint-Venant
A.
1871
Théorie du mouvement nonpermanent des eaux, avec application aux crues des rivières et à l'introduction des marées dans leur lit
.
Comptes Rendus de l'Académie des Sciences
73
,
147
154
.
Chen
B.
Wang
P.
Zhang
H.
2016
The review of nitrogen and phosphorus pollution in Poyang Lake water
.
Journal of Jiangxi Normal University (Natural Science)
40
(
4
),
437
441
.
DHI
2017a
MIKE 21 & MIKE 3 Flow Model FM Hydrodynamic and Transport Module Scientific Documentation
.
Technical report, Danish Hydraulic Institute
,
Hørsholm
,
Denmark
.
DHI
2017b
MIKE 21 & MIKE 3 Flow Model FM: MIKE ECO Lab Module Short Description
.
Technical report, Danish Hydraulic Institute
,
Hørsholm
,
Denmark
.
Di Toro
D. M.
O'Connor
D. J.
Thomann
R. V.
1971
A dynamic model of the Phytoplankton population in the Sacramento – San Joaquin Delta
. In:
Nonequilibrium Systems in Natural Water Chemistry, Chapter 5
(
Hem
J. D.
, ed.).
American Chemical Society
,
Washington
, pp.
131
180
.
Gao
G.
Ruan
R.
Ouyang
Q.
2010
Water quality status and changing trend in Poyang Lake
.
Journal of Nanchang Institute of Technology
29
(
4
),
50
53
.
Guo
Y.
Lou
F.
Lou
P.
Yang
X.
2012b
Studies of Poyang Lake Water Environment Dynamics and its Mechanisms
.
Technical report, Jiangxi Province Poyang Lake Water Bureau; Hohai University
.
Hu
C.
Ruan
B.
2011
Study on key technologies of Poyang Lake Water Control Project
.
Journal of China Institute of Water Resources and Hydropower Research
9
(
4
),
243
248
.
Huang
G.
Zhou
J.
Lin
B.
Xu
X.
Zhang
S.
2016
Modelling flow in the middle and lower Yangtze River, China
.
Water Management
170
(
6
),
1
12
.
Kazemi
E.
Nichols
A.
Tait
S.
Shao
S.
2017
SPH modelling of depth-limited turbulent open channel flows over rough boundaries
.
International Journal of Numerical Methods in Fluids
83
(
1
),
3
27
.
Lai
X.
Jiang
J.
Huang
Q.
Xu
L.
2011
Two-dimensional numerical simulation of hydrodynamic and pollutant transport for Lake Poyang
.
Journal of Lake Science
23
(
6
),
893
902
.
Lai
X.
Jiang
J.
Huang
Q.
2012
Water storage effects of Three Gorges project on water regime of Poyang lake
.
Journal of Hydroelectric Engineering
31
(
6
),
132
148
.
Lai
X.
Huang
Q.
Zhang
Y.
Jiang
J.
2014a
Impact of lake inflow and the Yangtze River flow alterations on water levels in Poyang Lake, China
.
Lake and Reservoir Management
30
(
4
),
321
330
.
Lai
X.
Liang
Q.
Jiang
J.
Huang
Q.
2014b
Impoundment effects of the three-gorges-dam on flow regimes in two China's largest freshwater lakes
.
Water Resources Management
28
(
14
),
5111
5124
.
Li
Y.
Zhang
Q.
Yao
J.
Werner
A.
Li
X.
2014
Hydrodynamic and hydrological modeling of the Poyang Lake catchment system in China
.
Journal of Hydrologic Engineering
19
(
3
),
607
616
.
Luo
Y.
Wu
H.
Li
Y.
Wan
Z.
Zhang
Q.
Jia
J.
Xiao
N.
&
Jiangxi Environmental Monitoring Center
2014
The study on the pollutant of flux of lake inflow in Poyang Lake Basin
.
Jiangxi Science
32
(
5
),
587
605
.
Moriasi
D.
Arnold
J.
Van Liew
M.
Bingner
R.
Harmel
R.
Veith
T.
2007
Model evaluation guidelines for systematic quantification of accuracy in watershed simulations
.
Transactions of the ASABE
50
(
3
),
885
900
.
Roe
P.
1981
Approximate Riemann solvers, parameter vectors, and difference schemes
.
Journal of Computational Physics
43
(
2
),
357
372
.
Yang
F.
Liang
D.
Wu
X.
Xiao
Y.
2020
On the application of the depth-averaged random walk method to solute transport simulations
.
Journal of Hydroinformatics
22
(
1
),
33
45
.
Zhang
S.
Jiang
Y.
Liu
X.
Wang
H.
2011
Study on dispatching scheme of Water Control Project in Poyang Lake and its influence on water resources and flood control
.
Journal of China Institute of Water Resources and Hydropower Research
9
(
4
),
257
261
.
Zhang
Q.
Ye
X.
Werner
A. D.
Li
Y.
Yao
J.
Li
X.
Xu
C.
2014
An investigation of enhanced recessions in Poyang Lake: comparison of Yangtze River and local catchment impacts
.
Journal of Hydrology
517
,
425
434
.
Zhang
P.
Lu
J.
Feng
L.
Chen
X.
Zhang
L.
Xiao
X.
Liu
H.
2015a
Hydrodynamic and inundation modeling of China's largest freshwater lake aided by remote sensing data
.
Remote Sensing
7
(
4
),
4858
4879
.
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