**Light vs nutrient supplies**

164 Current Issues of Water Management

Keoladeo National Park (27°10'N, 77°31'E), a World Heritage Site, is situated in eastern Rajasthan. The park is 2 kilometers (km) south-east of Bharatpur and 50 km west of Agra (cf. Figure 1). Figure 1 provides a location map for the park. The Park is spread over 29 square kilometres area. One third of the Park habitat is wetland system with varying types of trees, mounds, dykes and open water with or without submerged and emergent plants. The uplands have grasslands (savannas) of tall species of grass together with scattered trees and shrubs present in varying densities. The area consists of a flat patchwork of marshes in the Gangetic plain, artificially created in the 1850s and maintained ever since by a system of canals, sluices and dykes. Water is fed into the marshes twice a year from inundations of the Gambira and Banganga rivers, which are impounded on arable land by means of an artificial dam called Ajan Bund, located in the south of the park (cf. Fig. 2). It was developed in the late 19th century by creating small dams and bunds in an area of natural depression

 The 29 km (18 mi) reserve, locally known as Ghana, is a mosaic of dry grasslands, woodlands, woodland swamps, and wetlands. These diverse habitats serves as homes to 366 bird species, 379 floral species, 50 species of fish, 13 species of snakes, 5 species of lizards, 7 amphibian species, 7 turtle species, and a variety of other invertebrates. Keoladeo National Park is popularly known as "bird paradise". Over 370 bird species have been recorded in the park. The park's location in the Gangetic Plain makes it an unrivalled breeding site for

**2. Keoladeo National Park, Bharatpur, India: A case study** 

to collect rainwater and by feeding it with an irrigation canal.

Fig. 1. Location map of the Keoladeo National park, a World Heritage site

It is known that the population persistence boundaries in water column depth–turbulence space are set by sinking losses and light limitation [1]. In shallow waters, the most strongly limiting process is nutrient influx to the bottom of the water column (e.g., from sediments). In deep waters, the most strongly limiting process is turbulent upward transport of nutrients to the photic zone. Consequently, the highest total biomasses are attained in turbulent waters at intermediate water column depths and in deep waters at intermediate turbulences. These patterns have been found insensitive to the assumption of *fixed versus flexible algal carbon -to – nutrient stoichiometry*. They arise irrespective of whether the water column is a surface layer above a deep water compartment or has direct contact with sediments. This helps us understand the relevant dynamical processes in the physical systems in natural as well constructed wetlands.

### **Biotic part of Keoladeo National Park**

KNP is a natural wetland which can be categorized as a flood – plain type. The economic value of the park is dependent on tourist activities. The tourists are mainly attracted by Siberian Crane, the migratory bird which adds aesthetic value to KNP. It provides a large habitat for migratory birds; Siberian crane being the flagship species. With reference to migratory birds, the biomass is divided into two categories: "Good" and "Bad". The excess

Wetlands for Water Quality Management – The Science and Technology 167

*b*: measures the severity of the intra-specific competition among individuals of good

For the following parameter set, the nature of these critical points (0,0),(100,0),(20,12.8) turn

<sup>1</sup> *a b c d DD* = 0.2, 0.002, 0.005, 0.5, 20, 0.05, 0.1 = = = = = = =

(0, 0) is a saddle point. (100, 0) is an unstable node. The critical point located at (20, 12.8) is

2 2 2 2

*dG GP G aG bG cGB d dt G D x*

=− − − +

1

Equal diffusivity constants were assumed for good and bad biomasses. The value of the diffusion coefficient for the avian predator (birds) was taken to be 10-6. The vegetation diffusion coefficient is 10-7. Figures show contours of equal densities on a suitably chosen spatial scale.We assume that the horizontal extent of the wetland system is large compared to the depth of the wetland system. Each figure is accompanied with a scale specifically tailored to the species it represents. Dark blue represents small spatial densities, light blue represents slightly higher spatial densities, yellow and red represent higher spatial densities. The results show that "good" biomass has uniform spatial distributions in certain domains.

The "good biomass" acquires stable stationary patterns (Figure 3). The "bad" biomass performs swinging motion and selects a steady state spatial pattern given in Figure 4. Figure 5 presents stationary spatial distribution of the species clubbed under the category "bad" after the model system is allowed to run after a long period of time. Simulations in two spatial dimensions are needed to unravel mysteries of system's spatio-temporal dynamics. The complete system displays three kinds of oscillatory motion which represents three different health conditions of the wetland [6]. The per capita availability of water to "bad biomass" is known to be the control parameter. An earlier study by one of the authors (VR) may be helpful in this regard as vegetation plays a critical role in BOD5 removal and denitrification of the available nitrogen. The crucial factor for constructed wetland design for waste water treatment would be to control the density of the good and bad biomass; the

+ ∂

2 2

∂

∂ ∂

2

1

=− + +

θ φ

The spatial distribution of " bad" biomass contains two distinct humps.

*dB B <sup>B</sup> eB a GB dt W x dP GP P <sup>P</sup> dt G D x*

=− − +

*b d* θ

⎛ ⎞ ⎛ − +⎞ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ <sup>−</sup>

θ

2

∂

+ ∂

2

φ

(3)

(4)

φ θ

: conversion coefficients for the bird species (resident as well as migratory),

Critical Points for the subsystem (2) are (0, 0), ( )( ) <sup>1</sup> ,0 , , . *a D a bG G D*

**2.2 The biotic part of the wetland system in space and time** 

biomass,

out to be as follows.

an unstable focus.

*t*: time measured in days.

φ

growth of the wild grass species, *paspalum distichum* restricts the growth of bulbs, tubers and roots, on which avifauna feed on.

*Paspalum distichum* is known to deplete oxygen in the natural aquatic systems is the dominant species. The paspalum and its family acts as a bad biomass for the birds and the floating vegetation. The following species of the floating vegetation, *Nymphoides indicum, Nymphoides cristatum, Nymphaea nouchali, Nymphaea stellat,* and other useful species are categorized as "good" biomass [7, 8]. The fishes and the water fowl are the most suffered species. Although visits of the tourists bring revenue to the state, it also creates a disturbance gradient.

### **2.1 Good biomass, bad biomass and birds in the Keoladeo National Park: The biotic system**

Rai [6] modeled the dynamics of the biotic system of the wetland part of KNP by the following set of ordinary differential equations.

$$\begin{aligned} \frac{d\mathbf{G}}{dt} &= a\mathbf{G} - b\mathbf{G}^2 - c\mathbf{G}B - d\frac{\mathbf{G}P}{G+D},\\ \frac{dB}{dt} &= eB - \frac{B^2}{W\_1} - a\_2\mathbf{G}B, \\ \frac{dP}{dt} &= -\theta P + \phi \frac{\mathbf{G}P}{G+D\_1}. \end{aligned} \tag{1}$$

With


This system can be broken into two subsystems. (i) The competition system with "good" and "bad" biomass as component populations. (ii) The prey - predator system with good biomass and the bird population. Oscillatory dynamics are possible in the subsystems, but it is sensitive to initial conditions. The prey–predator subsystem performs oscillatory motion in significant region of the parameter space. The subsystem is essentially a Rosenzweig – MacArthur kind of system [9]. It is known to produce oscillatory dynamics in a significant region of the parameter space.

The good biomass and the resident birds occasionally joined by migratory ones constitute a subsystem. It is given by the following set of ordinary differential equations

$$\begin{aligned} \frac{dG}{dt} &= aG - bG^2 - d \frac{GP}{G+D} \\ \frac{dP}{dt} &= -\theta P + \phi \frac{GP}{G+D\_1} . \end{aligned} \tag{2}$$

With


growth of the wild grass species, *paspalum distichum* restricts the growth of bulbs, tubers and

*Paspalum distichum* is known to deplete oxygen in the natural aquatic systems is the dominant species. The paspalum and its family acts as a bad biomass for the birds and the floating vegetation. The following species of the floating vegetation, *Nymphoides indicum, Nymphoides cristatum, Nymphaea nouchali, Nymphaea stellat,* and other useful species are categorized as "good" biomass [7, 8]. The fishes and the water fowl are the most suffered species. Although visits of the tourists bring revenue to the state, it also creates a

**2.1 Good biomass, bad biomass and birds in the Keoladeo National Park: The biotic** 

Rai [6] modeled the dynamics of the biotic system of the wetland part of KNP by the

2 1

+

This system can be broken into two subsystems. (i) The competition system with "good" and "bad" biomass as component populations. (ii) The prey - predator system with good biomass and the bird population. Oscillatory dynamics are possible in the subsystems, but it is sensitive to initial conditions. The prey–predator subsystem performs oscillatory motion in significant region of the parameter space. The subsystem is essentially a Rosenzweig – MacArthur kind of system [9]. It is known to produce oscillatory dynamics in a significant

The good biomass and the resident birds occasionally joined by migratory ones constitute a

2

=− − <sup>+</sup>

*dG GP aG bG d dt G D dP GP <sup>P</sup> dt G D* θ φ

=− +

1

+

.

,

subsystem. It is given by the following set of ordinary differential equations

1

,

.

,

(1)

(2)

2

*dG GP aG bG cGB d dt G D*

=− − − <sup>+</sup>

2

*dB B eB a GB dt W*

=− −

*dP GP <sup>P</sup> dt G D* θ φ

=− +

roots, on which avifauna feed on.

following set of ordinary differential equations.

*G*: density of the good biomass, g/cm³, *B*: density of the bad biomass, g/cm³,

region of the parameter space.

*D*: a measure of the half-saturation constant, *a*: reproductive growth rate of the good biomass,

*P*: density of resident birds joined by migratory ones.

disturbance gradient.

**system** 

With

With

Critical Points for the subsystem (2) are (0, 0), ( )( ) <sup>1</sup> ,0 , , . *a D a bG G D b d* θ φ θ ⎛ ⎞ ⎛ − +⎞ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ <sup>−</sup>

For the following parameter set, the nature of these critical points (0,0),(100,0),(20,12.8) turn out to be as follows.

$$a = 0.2, b = 0.002, c = 0.005, d = 0.5, D = D\_1 = 20, \rho = 0.05, \phi = 0.1\tag{3}$$

(0, 0) is a saddle point. (100, 0) is an unstable node. The critical point located at (20, 12.8) is an unstable focus.

#### **2.2 The biotic part of the wetland system in space and time**

$$\begin{aligned} \frac{d\mathbf{G}}{dt} &= a\mathbf{G} - b\mathbf{G}^2 - c\mathbf{G}B - d\frac{\mathbf{G}P}{\mathbf{G}+D} + \frac{\hat{\sigma}^2 \mathbf{G}}{\hat{\sigma}\mathbf{x}^2} \\ \frac{d\mathbf{B}}{dt} &= eB - \frac{B^2}{\mathcal{V}\mathbf{l}\_1} - a\_2 \mathbf{G}B + \frac{\hat{\sigma}^2 B}{\hat{\sigma}\mathbf{x}^2} \\ \frac{dP}{dt} &= -\theta P + \phi \frac{\mathbf{G}P}{\mathbf{G}+D\_1} + \frac{\hat{\sigma}^2 P}{\hat{\sigma}\mathbf{x}^2} \end{aligned} \tag{4}$$

Equal diffusivity constants were assumed for good and bad biomasses. The value of the diffusion coefficient for the avian predator (birds) was taken to be 10-6. The vegetation diffusion coefficient is 10-7. Figures show contours of equal densities on a suitably chosen spatial scale.We assume that the horizontal extent of the wetland system is large compared to the depth of the wetland system. Each figure is accompanied with a scale specifically tailored to the species it represents. Dark blue represents small spatial densities, light blue represents slightly higher spatial densities, yellow and red represent higher spatial densities. The results show that "good" biomass has uniform spatial distributions in certain domains. The spatial distribution of " bad" biomass contains two distinct humps.

The "good biomass" acquires stable stationary patterns (Figure 3). The "bad" biomass performs swinging motion and selects a steady state spatial pattern given in Figure 4. Figure 5 presents stationary spatial distribution of the species clubbed under the category "bad" after the model system is allowed to run after a long period of time. Simulations in two spatial dimensions are needed to unravel mysteries of system's spatio-temporal dynamics.

The complete system displays three kinds of oscillatory motion which represents three different health conditions of the wetland [6]. The per capita availability of water to "bad biomass" is known to be the control parameter. An earlier study by one of the authors (VR) may be helpful in this regard as vegetation plays a critical role in BOD5 removal and denitrification of the available nitrogen. The crucial factor for constructed wetland design for waste water treatment would be to control the density of the good and bad biomass; the

Wetlands for Water Quality Management – The Science and Technology 169

vegetation component of the system. The chemical fertilizer upstream deteriorates the water quality (WQ) of the wetland. Construction of an artificial wetland in the vicinity of the

Fig. 5. The spatial distribution of the "bad" biomass after the system is run for a long period

These man-made wetlands are used to treat aquaculture and municipal water, to regulate the water quality of shrimp ponds and manage pollution from pond effluents. The wetland treated effluents satisfy standards for aquaculture farms. Since the technology to use the constructed wetlands to treat waste water of high BOD5 is limited, these are generally used to polish secondary effluents. Other applications of constructed wetlands are (a) to treat acid mine drainage, (b) to treat storm water, and (c) the enhancement of existing wetlands.

The suggestion to use wetland technology for waste water treatment is attractive for both ecological and economic reasons. Constructed wetlands are efficient in removing pathogens [2]. It performs better than conventional waste water treatment methods although the lack of knowledge of principles of pathogen removal in plants hampers optimum performance. Interactions between soil matrix, micro-organisms and plants and higher retention time of the waste water in these biologically complex systems make phyto-remediation more effective than conventional systems. Phyto-remediation involves complex interactions between plant roots and micro-organisms in the rhizo-sphere. The efficient functioning of

• Microbial degradation of organic substrates (i.e. BTEX, petroleum–derived hydrocarbons,

Wetland systems efficiently treat water polluted by heavy metals, chromium and

**3. Constructed wetlands for water pollution management** 

wetland systems is hampered due to following factors:

The metal removal in these systems involves following mechanisms:

• Filtration and sedimentation of suspended particles,

• High redox potentials,

HET, phenols).

magnesium.

• Acidity of effluents, i. e., low pH and

natural one would restore the water quality standards.

of time

Fig. 3. Spatial distribution of the "good" biomass (G)

Fig. 4. The distribution of the "bad" biomass (B) - see section 2, para 2

Fig. 3. Spatial distribution of the "good" biomass (G)

Fig. 4. The distribution of the "bad" biomass (B) - see section 2, para 2

vegetation component of the system. The chemical fertilizer upstream deteriorates the water quality (WQ) of the wetland. Construction of an artificial wetland in the vicinity of the natural one would restore the water quality standards.

Fig. 5. The spatial distribution of the "bad" biomass after the system is run for a long period of time
