Integrated Modelling of the Multifunctional Ecosystem...

Faculty of Bioscience Engineering

2011-2012

Integrated Modelling of the Multifunctional

Ecosystem of the Drava river

Sacha Gobeyn

Promotor: Prof. Dr. ir. Peter Goethals

Tutor: Javier Ernesto Holguin Gonzalez

Master’s dissertation submitted in partial fulfilment of the requirements for the degree of

Master of Bioscience Engineering

I, SACHA GOBEYN, declare that this is the result of my own work and that no previous

submission for a degree has been made here or elsewhere. Works by others, which served as

sources of information, have been duly acknowledged by references to the authors.

The author and the promoters give the authorisation to consult and to copy parts of this work

for personal use only. Any other use is under the limitation of copyrights laws; specifically it

is obligatory to specify the source when using results from this thesis after having obtained

the written permission.

Ghent, June 2012

Promotor Tutor Author

Prof. dr. ir. P. Goethals Javier Ernesto Holguin Gonzalez Sacha Gobeyn

This research was performed at:

Laboratory for Environmental Toxicology and Aquatic Ecology Department Applied Ecol-

ogy and Environmental Biology Faculty of Bio-engineering Sciences, Ghent University J.

Plateaustraat 22, B-9000 Gent (Belgium) Tel. 0032 (0)9 264 37 65. Fax. 0032 (0)9 264 41 99

ii

Acknowledgements

Sweet Memory

Talking about a sweet memory

It goes round and round in my head

Pretty soon I’ll want the real thing instead

But for now I got this sweet memory

Sunny day, Sunny day

Not a cloud crosses the sky

- Melody Gardot

First in line I would like to thank my parents, mommy and daddy, for the support, the

freedom and chances they gave me.

I want thank my promotor prof. Goethals, for the support and the many ideas. Next I want

to thank Javier, my tutor, for the guidance in Croatia and for putting so much time and effort

in my research. Not only as a tutor, but also as a person, I learned many things from you.

I could not have had a better person to guide me a year long. I can’t say Croatia and don’t

mention my favorite peruvian all time! Jannet, you are really a wonderful person! We had

some really good times in Croatia which I will never forget. Furthermore, I would also like to

thank the people of the Laboratory for Environmental Toxicology and Aquatic Ecology for

the many suggestions and help. One person I would like to thank explicity; Koen Lock for

helping us determine the macro-invertebrates.

My research could not have been completed without the proper help in Croatia. Marijan

Sivric, thank you for receiving us so well and helping us with the research. Tamara, you did

everything for us, you were always available to help us. Furthermore you helped us around

in Varazdin, which was wonderfull. Ivan, thanks for picking us up every morning, so early

(dobro jutro ;)). Thanks to the whole Varkom team, you did so much for us, I don’t know

how to repay you for the help!

iii

I would like to thank all the bio-engineers that I met through the five years. I gained some

good friends at the faculty, some computer geeks, some lab geeks, some wanna-be-pro-cyclers,

... (please, fill your name in one of these categories). Thank you ”land & water” class, we had

some great times and I hope to see you all back in a few years or so. Thanks to all others,

for the drinks, the food, the movies, the sports activities, the jokes, ...

Up next, I want to thank my housemates, you guys have evolved to a new species ”de

blekersdijkers”. You people are one of a kind and I think one by one I started to see u as

family. I think we did some awesome and stupid stuff together, which costed me a lot of sleep.

I had a wonderful 4 years with you people. The late nights, 20 cents, cats, hedge jumping,

food combinations, youtube clips, flour, dirty jokes, ugly glasses, beers, scary movies, whisky,

sports and cultural activities (if u know what i mean), and of course weirdest comments

PERIOD kept me from becoming (in)sane. I will miss you.

So that was it! Joking! I should not forget one of the most important people, my light of fire

(I just heard you burned down the lab? get it?). Thank you for keeping my coffee addiction

alive, thanks for cuddles, thanks for pointing out that Coldplay is (was) not that bad, for

always buying gifts, for booking every flight, actually thank you for arranging everything :).

And thank you for being here.

iv

List of abbreviations

BOD Biological oxygen demand

CART Classification and regression trees

CCI Correctly classified instances

COD Chemical oxygen demand

CSO Combined sewer overload

CSTRs Cascade of continuous stirred tank reactors

DO Dissolved oxygen

EQR Ecological quality ratio

EWFD European water framework directive

HPP Hydro-electric power plant

MMIF Multimetric macroinvertebrate index of Flanders

NO3 Nitrate

PO4 Phosphate

PCA Principal component analysis

r Correlation coefficient

RT Regression tree models

R2 Coefficient of determination

RMSE Root mean square error

RWQM no1 River water quality model number 1

SP Sampling point

TN Total nitrogen

TP Total phosphorus

TSS Total suspended solids

WW Wastewater

WWTP Wastewater treatment plant

Abstract

The Drava river is a cross country river which flows for 750 km from the Ital-

ian Alps in South Tirol to the Donau delta at the Croatian-Serbian border. The

Drava river ecosystem with a catchment area of 40490 km2 is, within its category,

one of the most preserved river ecosystems in Europe. This study focusses on

the section of the Drava river ecosystem which is located to the north of the city

Varazdin, a city in the north-east of Croatia. This is a heavily modified river,

which has been impounded and canalized in order to be able to produce electric-

ity through hydro-electric power plants (HPP). Since the construction of the HPP

and the dams, this river has functioned as a multifunctional ecosystem provid-

ing different ecosystem services such as recreation (e.g. fishing), tourism (river

viewing), gravel extraction, biodiversity and fresh water provision for agricultural

purposes and hydro-electricity production. The need for electricity is causing a

tense competition between the quantities of water used for electricity production

and ecosystem preservation. A wastewater treatment plant (WWTP) is located

near the river, which treats the incoming wastewater from the city Varazdin and

releases the treated wastewater in the river. The past decade, the industrial and

economical development in the city has increased the pressure on the WWTP,

which might affect the water quality of the river. For this reason, the main objec-

tive of this research is to contribute to the integrated water quality management

of the Drava river in Croatia by developing a mathematical model to investigate

the water quality and the ecological functioning of this river. In this thesis a

framework for integrated ecological modelling was developed in order to identify

and quantify the major impacts. This modelling tool combines different key ele-

ments of the river system such as the physical-chemical water quality status, the

hydraulics and the hydro-morphology in order to get an insight in the ecological

functioning and the biological water quality of the Drava river. Mathematical

models such as water quality and data driven models were developed, used and

combined to process different information of the river and the ecosystem.

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Contents

1 Introduction 1

2 Literature review 3

2.1 Ecological responses in function of controlling environmental variables in river

ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Modelling water movement and pollutant transport:

water quality models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 Modelling water movement: flow routing . . . . . . . . . . . . . . . . . 6

2.2.2 Modelling pollutant transport: pollutant routing . . . . . . . . . . . . 10

2.2.3 Properties and limitations of the use of CSTR in series approach . . . 12

2.2.4 A short history lesson in water quality modelling . . . . . . . . . . . . 12

2.3 Ecological modelling in an integrated ecological modelling framework to model

biological water quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.1 Ecological models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.2 Integrated ecological models . . . . . . . . . . . . . . . . . . . . . . . . 18

3 Methodology 19

3.1 Introduction and study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Data and information collection to develop the model . . . . . . . . . . . . . 21

3.3 Data exploration and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4 Integrated ecological model building procedure . . . . . . . . . . . . . . . . . 24

3.4.1 Definition of the problem and goal . . . . . . . . . . . . . . . . . . . . 24

3.4.2 Framework definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.3 Model structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4.4 Calibration & validation . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Results 37

4.1 Data exploration and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2 Integrated ecological model building . . . . . . . . . . . . . . . . . . . . . . . 43

4.2.1 Hydraulic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2.2 Water quality model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2.3 Ecological model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

vi

Contents vii

5 Discussion 55

5.1 Model development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.1.1 Data collection and analysis . . . . . . . . . . . . . . . . . . . . . . . . 55

5.1.2 Model calibration and validation . . . . . . . . . . . . . . . . . . . . . 56

5.1.3 Integrated ecological model . . . . . . . . . . . . . . . . . . . . . . . . 58

5.2 Implications for study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6 Conclusions and future perspectives 63

References 65

A Data processing 74

B Model development 85

B.1 Hydraulic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

B.2 Water quality model: mass balance model . . . . . . . . . . . . . . . . . . . . 94

B.3 Water quality model: calibration . . . . . . . . . . . . . . . . . . . . . . . . . 100

B.4 Water quality model: validation . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Chapter 1

Introduction

“Water has become a highly precious resource.

There are some places where a barrel of water costs more than a barrel of oil.”

Lloyd Axworthy

Foreign Minister of Canada

(1999 - News Conference)

River ecosystems are one of the key ecosystems in the natural functioning of the planet.

Many organisms depend a great deal on these ecosystems and the services they provide.

The past few decades, the water quality of rivers has been deteriorated, due to pollution

by discharge of waste and contaminants from cities, industry and agriculture. Furthermore,

the natural meandering and natural form of many rivers has been modified by canalization

and impoundment. The river ecosystem holds many potential key services which can benefit

humans. As illustrated by the quote, water has become a highly precious resource. The

challenge for river managers, researchers, decision makers and all people connected to water

is to ensure that the future generations are not looking at an empty barrel.

The problems with water use will intensify if the proper actions are not taken by the resource

managers. Different tools can be used by water managers, stakeholders and researchers in or-

der to provide deep insight in the functioning of river ecosystems. The used tool should provide

an integrated vision on the formulated problem of the multifunctional systems. Integrated

ecological models are tools which provide an accurate insight in the biological functioning

of the river system by integrating different aspects of the river functioning in one structure.

They could be able to asses the impact of a wastewater treatment plant, water regulation and

damming projects on the biological functioning of the system. This biological functioning is

the key ecosystem service provided by the river because this functioning together with the

biodiversity supports the overall health of the communities living in and around the system.

1

Chapter 1. Introduction 2

The Drava river in Croatia is an example of a multifunctional river ecosystem which has

been heavily modified in order to exploit resources and services. This river is located in

upper north-east part of Croatia, next to the city Varazdin. The system plays an important

role in the lives of the 200.000 inhabitants of Varazdin and surroundings because the river

provides different services for the people. The provision of hydro-electricity might be the most

important one, where the course of the river has been significantly modified in order to divert

large quantities of water to three hydro-electric power plants (HPP); Varazdin, Cakovec and

Dubrava HPP. In 2010, these three HPPs provided 10% of all hydro-electricity production

in Croatia. Besides providing energy, this river provides other key ecosystem services such

as flood control, fresh water for recreation, agricultural and fishing activities. However, the

water quality of this river has been affected during the last decade by its misuse as receiving

aquatic ecosystems of treated or untreated discharges of wastes from agricultural, urban and

industrial activities. Furthermore, the pressure on the system keeps rising, since industrial

activities in Varazdin and Croatia are growing.

This problem deserves attention, since the Drava river ecosystem has been identified as one

of the, if not “the”, most valuable ecosystems in the central balkan region. The goal of this

research is to develop different modelling tools, link them and apply them on this complex

system. The major impacts and elements of the system are identified and translated into a

framework for integrated ecological modelling. These models try to integrate all water quality

driving variables (physical-chemical, hydraulic, hydro-morphological and biological variables)

in one structure in order to quantify the major impacts. Furthermore, they could be used to

test different possible water resource management scenario’s. The research will focus on the

model development and the implications of this practice on the Drava river.

The general objective of this research is to contribute to the integrated water quality man-

agement of the Drava river in Croatia. The specific objects are:

1. Develop a possible framework for integrated ecological modelling by making use of

mathematical models such as water quality and data driven models.

2. Illustrate the integrated ecological framework by providing a modelling example.

3. Identify the problems in data collection and processing for these models.

4. Formulate the specific implications for the Drava river in Croatia.

Chapter 2

Literature review

2.1 Ecological responses in function of controlling environ-

mental variables in river ecosystems

In the past, river management actions and research were mainly focused on physical-chemical

water quality status as driver for ecological responses in river systems (Vaughan et al.,

2009). River pollution, caused by an excess of nitrates, phosphates, organic matter and

other physical-chemical parameters, can cause an excessive disturbance of the functioning

of the ecological system. Hynes (1974) presented one of the best examples related to the

response of ecological systems in function of physical-chemical composition of the river wa-

ter (Figure 2.1). The concentration of different components and the distribution of diverse

organisms like bacteria, fungi, macro-invertebrates are represented in the length profile of

the river. The diagrammatic presentation illustrates the impact of a discharge of pollutants

(e.g. wastewater) on the river system. Physical-chemical river pollution is defined as the

change in physical-chemical parameters of the river due to pollution. Up until 2000, this

train of thought was considered as the core of river water quality assessment, research and

management.

Two categories of physical-chemical river pollution can be distinguished. The first category

is called point source pollution, which is a form of pollution concentrated at one point in

the space. This pollution causes deterioration of the water quality stream downwards of the

pollution point. For example, wastewater is disposed by an industrial facility at a specific

location in the river. This wastewater (WW) can be treated in a wastewater treatment

plant (WWTP) and discharged in the river (controlled discharge) or it can be untreated

and disposed in the river (uncontrolled discharge). Both can attribute substantially to the

deterioration of the physical-chemical water quality downstream of the outlet point.

3

Chapter 2. Literature review 4

The second category of physical-chemical river pollution is called diffuse or non-point source

pollution. Diffuse pollution includes different sources such as, runoff of fertilizer and pesticides

from agricultural soils and rural residential development. The problem of non-point pollution

is complex to solve compared to point pollution source, because the effects of diffuse pollution

both in time and space are difficult to asses (Chuco, 2004).

Figure 2.1: Example of the effects of an organic effluent on the ecological status of the downstream

river system. A and B represent the changes in physical-chemical parameters, C the

change in number of micro-organisms and D the changes in the number of macro-

invertebrates (Hynes, 1974).

River ecosystems across the world are subjected to these two sources of pollution which lead

to two main problems: contamination by hazardous organic compounds and eutrophication

(nutrient enrichment). Eutrophication is a natural phenomenon which is enhanced by an-

thropogenic activities.


Runoff from agricultural activities generates an increase of phosphorus and nitrogen (also

called nutrients) in river systems. Wastewater discharge of industries and municipal com-

munities can also increase nutrient concentrations in water bodies. Nutrient enrichment in

combination with light, can cause excessive bloom of algae. This excessive growth of al-

gae can cause large fluctuations in the concentration of dissolved oxygen and can induce an

in-equilibrium in the carbon balance. A decrease in the water quality represented by these

physical-chemical parameters will likely lead to loss in diversity of aquatic organisms and a

disturbance in the ecosystem functioning (Laws, 2000). This is just one of the examples of

the impacts of changing water quality. Most processes in rivers are highly linked to each other

and the change of one parameter can lead to in-balance of many other quality parameters.

This domino-effect can lead to an irreversible deteriorated state of the river water.

Concerning ecological responses to changes in environmental variables, during the last 10

years the emphasis shifted from physical-chemical parameters to habitat quality parameters

(Gabriels et al., 2007; Everaert et al., 2010; Bockelmann et al., 2004). There is a gradu-

ally growing awareness that habitat variables, linked to the hydro-morphologic structure of

the river play an import role in the ecological functioning of rivers and other (regulated)

waterbody systems Timm et al. (2011). This growing awareness of the importance of hydro-

morphology and habitat quality is mainly driven by the European Water Framework Directive

legislation (EWFD, 2000/60/EC), which aims for a “good ecological status” of all water bod-

ies in all European member states by 2015 (European Commission, 2000).

The term “hydro-morphology” is relativity new and has a wide spectrum of definition. Some

definitions are available in the literature, but none of them are used widespread, which makes

the definition a subject for debate. The EWFD defines hydro-morphology as “the hydrological

and geomorphological elements and processes of waterbody systems.”. Orr et al. (2008) and

Newson & Large (2006) define hydro-morphology as the physical habitat formed by the alter-

ing flow regime (hydrology and hydraulics) and the physical structure of the river boundary

(fluvial geomorphology) (Vogel, 2011). Sipek et al. (2010) do not define hydro-morphology,

but do imply its meaning as an overlap of the disciplines of hydrology, (geo)morphology and

ecology. This is an interesting point of view to approach the discussion of interdisciplinary.

Newson et al. (2012) and Vaughan et al. (2009) point out the lack of interdisciplinary and the

integration of the disciplines ecology, (geo)morphology and hydrology. Kilsby et al. (2006)

makes a great attempt to map the interdisciplinary approach by integrating the structural

(hydrology), compositional (geomorphology) and functional (ecology) component which re-

sults in tree specific fields: hydro-morphology, hydroecology and biogeomorphology.


Improving monitoring and assessment of the habitat variables linked to the hydro-morphology

must to evolve in river science and management, even though results are lingering (Newson

et al., 2012). Examples of linking habitat variables to ecological responses can be found in

the discipline of eco-hydraulics, where mostly macro-invertebrate occurrence and community

distribution (the “eco-”) is linked to hydraulic variables (the “hydraulics”), like flow velocity,

water height, etc... The composition of the macro-invertebrate community is often linked

to parameters associated with stream hydraulics (Newson et al., 2012; Kemp et al., 2000;

Statzner & Higler, 1986). Earlier, Ward & Stanford (1979) identified temperature, flow

and substrate conditions as the major controlling factors for macro-invertebrate species in

unpolluted river systems. Statzner et al. (1988) implies that more complex hydraulic variables

should be used, on top of the simple variables such as water depth and velocity. Statzner

& Higler (1986) suggests that measurements of current velocity, depth, substrate roughness,

surface slope and hydraulic radius should be used in future hydraulic studies applied to

benthic invertebrates. Furthermore, efforts are done to establish an index which assesses

the hydro-morphological quality in function of the several macro-invertebrate species (Kaeiro

et al., 2011; Extence et al., 1999).

2.2 Modelling water movement and pollutant transport:

water quality models

Water quality models are simulation tools which try to describe the physical, chemical and

biological processes in water ecosystems by means of mathematical equations. The models

offer a framework for integration of diverse physical, chemical and biological information.

The modelling practice aims to provide insight in the river natural processes and serve as a

backbone (background) for decision making in water management Chapra (1997). Following

text will briefly explain some basic concepts of water quality modelling, followed by some

examples of water quality models.

2.2.1 Modelling water movement: flow routing

The first step in water quality modelling is the description of the water movement, also

referred as flow routing. Modelling of water movement or flow routing, in its broad sense

can be considered as the analysis of tracing water flow through a hydrologic system, given a

certain input to the system. Routing methods, which translate the routing in mathematical

equations are divided in two system routing techniques: lumped and distributed. Lumped

system routing is also called hydrologic routing, while distributed system routing is referred

as hydraulic routing. (Chow, 1981).


Complex hydraulic routing

In general, the hydraulic routing method describes the routing of water through a channel

bed by solving the “de Saint-Venant” equations (St. Venant) (Barre de Saint-Venant, 1871).

The St. Venant equations are a set of two equations based on the mass and momentum

conservation principle.

The continuity or mass balance equation:

∂Q

∂x+∂Across∂t

= q (2.1)

The dynamic or momentum balance equation :

Figure 2.2: Simplification of momentum equation as described by Chow et al. (1988).

with Q = flow rate (m3/s); Across = cross-sectional area of the river (m2); h = height of the

water with the bottom of the river as reference level; g = gravitational acceleration constant

(m/s2); q = lateral inflow per unit of length of the river (m2/s); S0 river slope (-); Sf friction

slope (-); x = longitudinal distance of the river (m).

Given the mass balance and the momentum balance equation, the following assumptions and

simplifications are made:

• Wind shear is omitted.

• Eddy losses are omitted.

• The term√

1 − S20 is approximately equal to 1 since the second power of S0 is a small

number.

• Sudden narrowing or widening of the river is not considered.

• β, the Boussinesq-coefficient is considered to be equal to 1.


The full St. Venant equations are rarely solved in water quality modelling practices because

the solution of the equations tends to be complex and require a lot of computational calcula-

tion time. That is why Chow (1981) suggested simplification to the equations. The kinematic

approach only considers friction and gravity forces, resp. Sf and S0 and drops the pressure

and acceleration terms, suggesting that the energy line of the water is parallel to the river

slope. In this case the flow is steady and uniform. When pressure forces become important

but inertial forces remain unimportant, a diffusion wave model can be applied. Both the

kinematic and dynamic wave solution are only able to model stream downward propagation

of a flood wave and can therefore not be used to model stream upwards propagation of waves

in case of backwater effects and mild slopes (S0 < 0.0001 m/m). The dynamic wave solution is

able to describe the propagation of dynamic waves in the downstream and upstream direction

of the river and can therefore be used for modelling of water movement in case of mild slopes

and backwater effects. The acceleration terms in the momentum equation rarely play a role

in water quality issues and the typical time scale are amplified by the conversion processes.

Because of these reasons, diffuse and kinematic approaches are mostly applied in river water

quality modelling practices (Rauch et al., 1998).

Hydrologic routing

Conceptual hydraulic routing is based on the continuity equation and an empirical or ana-

lytical relationship between the storage of water in the system (or reservoir) and the outflow.

Nash (1955) assumed that the response of the catchment on an instantaneous rainfall event

can be represented by a series of linear reservoirs. A linear reservoir is a reservoir whose stor-

age S (m3) is linearly related to the output Q (m3/s) by a storage constant k (1/s) (Chow,

1981). For every reservoir equation 2.2 is valid:

dS

dt= I(t) −Q(t) (2.2)

withdS

dt= change in storage capacity of the reservoir during time step dt (m3/s); I(t) =

inflow reservoir (m3/s) on time t; Q(t) = outflow reservoir (m3/s) on time t.


Equation 2.2 represents the mass balance principle. The idea is to express a given unit

hydrograph of river by routing water through a cascade of n reservoirs. The river system

can be considered as a cascade of linear reservoirs. The reservoir itself is a “black box” and

the transport of water is represented by an empirically or analytically determined function.

Equation 2.2 is translated in different symbolics (equation 2.3). Figure 2.3 illustrates how a

cascade of linear reservoirs works.

dV

dt= Qin −Qout (2.3)

with: dVdt = change in volume in the tank during time step dt; Qin = inflow tank (m3/s);

Qout = outflow tank (m3/s).

Figure 2.3: Illustration of the concept of linear reservoir in series. The left side illustrates the se-

quence of the unit reservoirs, while the right side illustrates the behavior of the flow in

function of the time in one unit (United States. Army. Corps of Engineers, 1997).

Additional terms can be added to the right side of equation 2.3 in respect with the sign:

inflow - positive sign & outflow - negative sign. For instance evaporation processes can be

considered by adding a Qe term (negative sign), inflow by side rivers by adding Qr, inflow

through discharge of wastewater Qw, ... (Chuco, 2004)

The relation between the outflow and storage are generally expressed in stage-discharge re-

lationships. An analytically way to express this relationship is by applying the Manning

equation:

Qout =1

nAR

2/3h S

1/2f (2.4)


with: Qout = outflow tank (m3/s); n = manning roughness (-); A = cross area (m2); Rh =

hydraulic radius (m2); Sf = friction slope.

Another way to express the relation is to set up an empirical relationship:

Qout = αhβ (2.5)

with α and β two parameters which are determined by calibration of time series of flow and

water height. The concept of representing the river as a cascade of linear reservoir has been

applied by several authors (Benedetti et al., 2007; Deksissa et al., 2004; Kannel et al., 2007)

in water quality modelling and is linked to the concept of continued stirred tank reactors,

which will be explained in the next part of the text.

2.2.2 Modelling pollutant transport: pollutant routing

Pollutant routing deals with the transport of soluble substances in a river. Two types of

deterministic models will be highlighted: the advection-dispersion model and the conceptual

model.

Complex pollutant transport: advection-dispersion model

The advection-dispersion model is based upon the principle of conservation of mass of solutes

and Fick’s diffusion law:

∂C

∂t= [

∂

∂x(Dx

∂C

∂x) +

∂

∂y(Dy

∂C

∂y) +

∂

∂z(Dz

∂C

∂z)] (2.6)

−[∂

∂x(vxC) +

∂

∂y(vyC) +

∂

∂z(vzC)] −R

with C= concentration of pollutant (g/m3); t = time (s); x, y, z = distances in x, y and z

directions (m); ux,y,z = average velocity in the x, y and z direction (m/s); Dx,y,z = Dispersion

coefficients in the x, y and z direction (m2/s); R = reaction transformation rate (g/(m3s).

Equation 2.6 represents the routing of a pollutant in a river in three dimensions. The advection

(second term), diffusion (first term) and reactions (third) term represent the three governing

processes in river systems. Analogues to the St. Venant equations, the equation is rarely

applied in its full form (Rauch et al., 1998).


Conceptual pollutant routing

In general, conceptual pollutant routing is based on the assumption that a natural water body

can be represented by a cascade of continuous stirred tank reactors (CSTRS) Chapra (1997):

“A completely mixed system, or continuously stirred tank reactor (CSTR), is among the

simplest systems that can be used to model a natural water body”

The contents in a considered river stretch (reservoir) are assumed to be sufficiently well mixed

and uniformly distributed. Furthermore, it assumes immediate mixing of the incoming with

the present pollutants. The concept of a cascade of CSTRS has been successfully applied in

river water quality modelling (Chuco, 2004). The concept is illustrated in Figure 2.4.

Figure 2.4: A cascade of CSTRS applied for river water quality models Chuco (2004).

The mass balance for each component, including transformations, in the river stretch during

a time period dt is given by:

dm

dt=

d(CV )

dt=

∑in=1

QinCin −∑out=1

QoutC + −rV (2.7)

with m = total mass of the pollutant (g); concentration of the pollutant (g/m3); t = time

(s); V = volume of the system (m3); CinQin = incoming load (g/s); Qout = outflow (m3/s);

rV = Reaction transformation rate (g/(m3s)).


2.2.3 Properties and limitations of the use of CSTR in series approach

This section is a short summary of the text presented by Benedetti & Sforzi (1999) and

Reda (1996). The properties of hydraulic modelling with the CSTR scheme is summarized

as followed:

1. Water flows from an upstream reservoir to a downstream reservoir.

2. The mass balance in a tank is only affected by the outflow of the upstream tank.

3. The water surface in every tank is assumed to be constantly horizontal. The change

of water level at the downstream boundary defines a new horizontal water line in the

tank.

4. The outflow is defined by a discharge-rate curve relationship.

The first two properties only assure the downstream propagation of a wave. The most im-

portant limitation of the CSTR in series approach is the lack of upstream propagation of

waves in rivers with a subcritical regime, also called backwater effects. Backwater effects

are effects where the longitudinal water profile (water depth) of the river is affected to a

certain upstream distance. This effect occurs in open channels in a subcritical regime when

a singularity is present at a given cross section. This singularity can be a dam, a submerged

sharp-crest weir or any other structural obstacle or uplift in the river. Furthermore, back-

water effects may also occur in deltaic reaches at a confluence with a big tributary. Lateral

inflow can affect subcritical flow upstream from the discharge point. Also, the downstream

propagation within one single tank is not possible because the water surface in every tank is

assumed to be horizontal. Consequently it is not possible to simulate the slope of the water

in one tank.

2.2.4 A short history lesson in water quality modelling

The oxygen sag curve presented by Streeter & Phelps (1925) was the first water quality

model ever presented in literature. The model combines the principles of oxygen demand and

reaeration in order to simulate the effect of pollution through time and space on the dissolved

oxygen in the river. Figure 2.5 shows an illustration of a typical dissolved oxygen sag curve.

In 1960, extended versions of the Streeter-Phelps were introduced.


Figure 2.5: Illustration of a dissolved oxygen sag curve in function of the time (Spellman, 1996)

Water quality modelling evolved from the 2 state variable model (Streeter & Phelps, 1925)

to models with more than 10 state variables which included modelling of photosynthesis,

respiration and nutrient cycling. In 1970, Masch et al. (1970) introduced the river water

quality model QUAL1 which was later expanded to QUAL2E (Brown & Barnwell, 2003) and

QUAL2K (Chapra & Pelletier, 2003). The QUAL2K model is a one dimensional model which

simulates the steady state hydraulics (non-uniform, steady flow), the diurnal heat budget and

the diurnal water quality kinetics.

Reichert et al. (2001) developed a river water quality model which describes oxygen, carbon,

nitrogen and phosphorus cycling in the water column and sediment layer of the river. The

idea was to integrate a sewer, WWTP and river quality model in one model structure. This

model, the river water quality model no. 1 (RWQM no1) had to be compatible with the

existing activated sludge models (ASM) presented by Henze et al. (2000) in order to support

the development of an integrated sewer - treatment - river model. The EWFD imposed a good

ecological quality for all the rivers in Europe by 2015 which caused the shift from emission

to immission (= actual concentration of pollutants in the river) based decisions. Benedetti

et al. (2007) and Deksissa et al. (2004) indicate that the RWQM no1 is a useful tool for this

integrated approach in data scarce situations and in urban catchments modelling. Somlyody

et al. (1998) give an overview of the main differences between ASM (and thus RWQM no1) and

the QUAL2E model. MIKE11 (DHI Water & Environment, 2003) and AQUATOX (Clough,

2009) are two other examples of water quality models which are available.


2.3 Ecological modelling in an integrated ecological modelling

framework to model biological water quality

The ecologic status of river water mainly depends on the physical-chemical conditions, the

hydrologic or hydraulic regime and geomorphologic characteristics of the river. The immis-

sion concentration (physical-chemical conditions or chemistry) of the river water, the hydro-

morphology, the ecology (ecological water quality) and its interaction are the starting points

for integrated ecological models to predict ecological water quality (Figure 2.6).

Figure 2.6: Interaction of the different disciplines: Ecology (ecological water quality), chemistry

(physical-chemical water quality) and hydro-morphology (Holguin, 2009).

Generally, two approaches can be distinguished in ecological modelling. The first approach is

mechanistic, which is based on physical, chemical and biological laws. Mechanistic models are

hard to use in aquatic ecology since the involved biological processes are complex to represent

in mathematical equations. The following text deals with data driven models, based on

soft computing techniques (Goethals, 2005) such as regression techniques, classification and

regression trees, fuzzy logic and bayesian belief networks (BNN) for predicting ecological

responses (e.g. macro-invertebrates community composition) in rivers based on environmental

(e.g. physical-chemical, geomorphologic and hydraulic) state variables. The response variable

which was considered in this research and is presented in this document is the ecological quality

ratio (EQR). The EQR is used in biological assessment of waterbodies. The EQR value of one

represents type-specific excellent reference conditions and values close to zero bad ecological

status (European Commission, 2000).


2.3.1 Ecological models

This section gives a short overview of the available methods to model ecological water quality

and ecological responses. The author refers to Ahmadi-Nedushan et al. (2006) for an ex-

tended review of the application of these methods. The second part of this text will focus

on some examples of ecological models which are integrated with other type of models (e.g.

water quality models, eco-hydraulic models). These examples serve as indication of current

integrated ecological modelling approaches in (river) aquatic modelling.

Decision trees: classification and regression trees (CART)

The application of classification and regression trees (CART) in ecological modelling is rela-

tively new (O’Brien, 2007). The use of these techniques to predict occurrence, abundance or

biological indices related with macro-invertebrates has gained interest the past years (Ambelu

et al., 2010; Boets et al., 2010; Hoang et al., 2010; Kampichler et al., 2010; Everaert et al.,

2010, 2011). CART, also called decision trees, predict the value of a response variable based

on the value of a set of continuous (regression trees) or discrete (classification trees) predictor

variables. The modelling process follows a recursive method; for every step the most infor-

mative variable is selected as root for a sub-tree. Subsequently, the data set is split up in two

sub data sets. This procedure is continued until a stop criterion is reached.

CART has some unique advantages compared with multivariate statistics. CART is a non-

parametric technique which does not require the specification of a functional form, it is only

based on simple - lower than or greater than - rules. The tree models deal better with non-

linearity and interaction between explanatory variables than other further discussed models

like the ones based on classical or modern regression techniques. Another advantage is the

extreme robustness of these models with respect to outliers (O’Brien, 2007). Besides these

more technical advantages, CART has also some advantage in the field of application in (river

water) management. They provide a very visual and - easy to understand - tool for decision

makers and water managers. Furthermore, classification trees are in particular useful to

develop ecological models in a very short time, and these models are transparent and easy to

interpret (Hoang et al., 2010).

The application of CART has shown to be useful in modelling complex data sets (Breiman

et al., 1984), but as indicated by Goethals (2005), no guidelines exist to support the selection

of learning settings, which makes this method less attractive. Vayssieres et al. (2000) considers

two main problems in constructing an effective decision tree; finding good splits and knowing

when to stop splitting the data set in nodes in order to avoid over-fitting of the data. Besides

the problem of properly pruning, the recursive partitioning method has some disadvantages.

The orthogonal partitioning (perpendicular to the axes) of the data set in the multivariate

space is not always optimal, since it is possible that the optimal split is not defined by solely


one variable (one axes). Another disadvantage is the dichotomous structure of the tree, where

later splits are based on fewer cases than the initial split. Small data sets can therefore become

difficult to model with CART (Vayssieres et al., 2000).

Classical regression techniques

Regression methods (analysis) are a denominator for several modelling and analyzing tech-

niques which focus on the relationship between a dependent variable (univariate) and one or

more independent variables (multivariate). In ecology, these models can be used to describe

the relationship between certain species or ecological responses in function of different driv-

ing predictor variables, e.g. water velocity, water temperature, substrate. One of the oldest

and best known regression technique is (multiple) linear regressions; the technique relates a

response variable to one or more independent predictor variables through a linear relation:

Y = β0 + β1x1 + β2x2 + ...+ βmxm + ε (2.8)

with Y = response variable; xi = predictor variable i; βi = regression coefficient i; ε =

error (unexplained variance and measurement error). However, linear regression is limited by

following assumptions:

1. The variance of the errors of the response variable is assumed to be constant (ho-

moscedasticity); they are identically and independently distributed.

2. The errors are assumed to follow a normal Gaussian distribution.

3. The response variable is assumed to respond in a linear relation to predictor variables.

These assumption are mostly not satisfied in modelling ecological responses in function of

environmental variables.

Modern regression techniques

In the case of ecological data sets, it is preferred to use modern regression methods like gen-

eralized linear models (GLMs) and generalized additive models (GAMs) (Ahmadi-Nedushan

et al., 2006) because these techniques can deal with the limitations of classical regression

techniques. GLMs (Nelder & Wedderburn, 1972) are a modern regression tool which are able

to integrate non-normal environmental variables into the models. GAMs are non-parametric

extensions of GLMs which can be applied to data from exponential families of distribu-

tions. The structure of GLM is maintained but the linear predictor of GLM is replaced by a

non-parametric smoothing procedure (smoothing filter) (Guisan et al., 2002; Verrall, 1996).

Generalized linear models are build up from three components; a response variable y, a linear

predictors xi, and the link function g, which describes the functional relationship between the

linear predictors and the expected value of the response variable:


g(µ(x)) = β0 + β1x1 + β2x2 + ...+ βmxm (2.9)

The link function is able to describe the many distributions including the normal, binomial,

Poisson, geometric, negative binomial, exponential, and inverse normal distributions (Myers

et al., 2002).

Fuzzy logic

Fuzzy logic is a soft computing technique which uses the fuzzy set theory to include impre-

cise information in a rule-based system by defining adaptable membership functions (Zadeh,

1965). Fuzzy logic can be interpreted as an extension of boolean logic. In boolean logic the

membership of an element to a set is equal to one - the element is a member of the set - or

zero - the element is not a member of the set. In fuzzy logic, an element belongs to the set

with a certain membership value ranging from zero to one. In addition fuzzy logic makes use

of linguistic variables, therefore describing the value of a variable in words. Linguistic if-then

rules are used to describe the relation between the fuzzy input and output. These type of

models can be useful in the field of water quality assessment and structural characteristics

where variables like degree of meandering and substrate type are often difficult to quantify or

classify in a crisp input variable. Furthermore, measurements of physical-chemical variables

characterized by a high uncertainty and temporal variables can also be used a fuzzy input for

these models. However, few fuzzy logic models have been used to support ecosystem man-

agement because of two reasons: the exploration phase in the model development and the

difficulty of convincing managers to use these ’subjective’ models (Goethals, 2005).

Bayesian belief networks

Bayesian belief network models (BBN) are models with a network structure that focus on

the explicit representation of “cause- and-effect” relationships between variables. Bayesian

belief networks consist out of 3 elements (Cain, 2001): a set of nodes representing a discrete

or continuous system variable, a set of links representing causal relationships between nodes

and a set of probabilities, specifying the belief that a node will be in particular state given the

states of the nodes affecting it (parent nodes). The probability distribution in the network

structure makes it possible for the structures to deal with uncertainty and variability in

models. These models are particularly useful in the description of ecological systems, where

cause and effect is a key feature to system dynamics (Regan et al., 2002). The strength of

these model is the “cause-and-effect” relationship integrated in these models; stakeholders

and decision-makers can deliver their input, the decision and furthermore easily understand

the output, the effect of the decision.


2.3.2 Integrated ecological models

The water quality models described in section 2.2 are able to cope with predictions of the

physical-chemical water quality and some ecological life forms (e.g. bacteria and algae).

Water quality models are not able to describe all the energy and mass streams in the river

life cycle. As indicated earlier, describing all the physical, chemical and biological laws in

one integrated framework might prove to be difficult. Water quality models cannot describe

the ecological responses expressed in biological water quality. However, integrated ecological

modelling goes further by making a link between physical-chemical, hydro-morphological and

biological aspects of the river system.

Examples of the application of integrated ecological modelling are provided by Tomsic et al.

(2007); Mouton et al. (2007); Holguin & Goethals (2010); Pauwels et al. (2010). Tomsic et al.

(2007) used a habitat suitability index model coupled to a hydrodynamic model (MIKE11)

integrated in an ArcGIS model. A habitat suitability index was set up for both a water

quality sensitive fish and a macro-invertebrate specie (Plecoptera) in order to evaluate the

success of a dam removal for the Sandusky River Ohio. Mouton et al. (2007) presented an

integrated modelling approach by using a fuzzy logic-based eco-hydraulic modelling system.

This modelling system integrated a fish habitat module based on fuzzy logic and a 1 dimen-

sional hydraulic module in order to asses ecological effects of changes in the physical habitat

of the river. The fuzzy approach proved to be a promising method to link different aspects

of the physical structure (hydro-morphology) to the habitat suitability for bullhead (Cottus

gobio L.). Holguin & Goethals (2010) linked the outputs of the water quality model MIKE11

to a GLMs to predict the composition of the macro-invertebrate communities and to asses

the ecological impact of wastewater discharge in a river in Colombia. Pauwels et al. (2010) re-

lated different output variables in the rivers of Flanders, Belgium, of the water quality model

PEGASE (VMM, Flemish environmental agency) to the ecological water quality by using re-

gression trees. Holguin & Goethals (2010) and Pauwels et al. (2010) showed the potential of

integrating water quality and ecological assessment models to evaluate the potential impacts

of the foreseen water quality management plans. Integrated model can function as a powerful

tool in assessing ecological impact of not only wastewater discharge, but also dams and other

impacts.

Chapter 3

Methodology

3.1 Introduction and study area

The Drava river is a cross country river which flows for 750 km from the Italian Alps in

South Tirol to the Donau delta at the Croatian-Serbian border. The Drava river ecosystem

with a catchment area of 40490 km2 is within its category, one of the most preserved river

ecosystems in Europe. The study area of the Drava river ecosystem is located to the north of

the city Varazdin, a city in upper north-east of Croatia (Figure 3.1). The system consists out

of a succession of three lakes called Varazdin, Cakovec and Dubrava. For every lake, a part

of the Drava river is diverted to three succesive hydro-electric power plants (HPP) through

a tailrace canals, while the remaining water is released through the dams in the old Drava

river. The upper boundary of the system is the border of Croatia and Slovenia and the lower

boundary is the end of Dubrava lake. This stretch of 36 km river is considered as one of the

most valuable wetland ecosystems in the Balkan and even Europe. Growing energy demand

in Croatia, during the eighties, initiated the plans for the construction of the three HPP

along this river. Since the construction of the HPP and the dams, this river has functioned

as a multifunctional ecosystem providing different ecosystem services such as recreation (e.g.

fishing), tourism (river viewing), gravel extraction, biodiversity and fresh water provision for

agricultural purposes & hydroelectric production. The human pressure on this ecosystem

is gradually growing because of increased industrialization in the vicinity. Human impacts

include an increased discharge of wastewater and a higher competition between the quantities

of water used for electricity production and ecosystem preservation (Sever et al., 2000).

19

Chapter 3. Methodology 20

Figure 3.1: Location of the Drava river in the Varazdin County, Croatia

The system in this research is quite complex, because it consists of different types of water-

bodies (i.e. rivers, channels, canals and lakes) and holds different key services. The system

and the impacts on the system are illustrated in Figure 3.2. The industrial activity is iden-

tified as the main driver of the pressures on the system. This activity needs energy in order

to manufacture goods and services. The need for energy drives the competition between the

quantity of water available for electricity production and the amount of water released to

the river system (biological minimum flow = 8 m3/s). Furthermore, the industrial discharges

are transported by the sewer system to the Varkom municipal wastewater treatment plant.

Higher loads of industrial waste result in higher loads of pollutants which will be discharged

in the river. The system itself is a composite of different types of water bodies: lakes, rivers,

artificial canals and drainage channels, all of them with different structural (geomorphologic)

properties. This subdivision in different subsystems will be important in the system analysis

and modelling process (Kezelj et al., 2010; Booz, 2001; Grian & Kerea, 2004). The research

goals are explained in the framework of the modelling exercise in section 3.4.1.

Chapter 3. Methodology 21!

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Figure 3.2: Illustration and identification of the main impacts and key problems. The big arrows are

the main inputs to the system, CSO = Combined Sewer Overload, WWTP = Varkom

Wastewater Treatment Plant, HPP = Hydro-electric Power Plant, WW = Wastewater,

E = energy production, BM = Biological minimum flow (8 m3/s).

3.2 Data and information collection to develop the model

During the month september (2011), 60 locations were sampled in the study area. These sam-

pling points were spatially distributed over different waterbodies (i.e. lakes, rivers, channels

and canals) in this area. For every location a water sample was taken and the concentration of

several components were determined in the laboratory. The following components were mea-

sured by the laboratory of Varkom: Dissolved Oxygen (DO, mg O2/l), Temperature (T, ◦C),

pH (-), Chemical Oxygen Demand (COD, mg O2/l), the 5-day Biological Oxygen Demand

(BOD, BOD5, mg O2/l), Total Nitrogen (TN, mg N/l), Nitrate (NO3, mg NO3-N/l), Total

Phosphorus (TP, mg P/l), Phosphate (PO4, mg PO4-P/l), Ammonium (NH4, mg NH4-N/l),

Total Suspended Solids (TSS, mg/l).


Macro-invertebrates were sampled by using a hand net and the kick sample method. This

method was performed by walking backwards against the current, where possible, following a

W-shaped path with the hand net (mesh size 250-500 µm). During the sampling procedure,

the person has to kick the bottom layer with his feet and sample just above the river bottom

(or sludge layer). A stretch of 10 to 20 meter was covered by the hand net sampling, this during

3 to 5 minutes, respectively for small and large rivers. At every location, different habitats

(stony areas, deeper stretches, shallower parts) were sampled in order to have a representative

sample for the considered location. Furthermore, stones, branches, leaves of different sizes

were checked and picked out manually. Every sample was examined for the presence of

macro-invertebrates and these organisms were identified up until a specific taxonomical level

as described by De Pauw & Vanhooren (1983). Additional information was collected at every

sampling location by using a field protocol. This information was related to land-use, river

morphology, vegetation, weather conditions and other specific properties of the location.

Historical data was also considered for the data set. Two monitoring campaigns were pre-

formed at in the framework of the project WATROPEC in april and october of 2010, in total

comprehending 46 samples.

3.3 Data exploration and analysis

All the data were processed in the software Matlab (MathWorks, Inc.) and Microsoft Ex-

cel (Microsoft Corporation). The abundance data of every taxa were used to calculate the

Multimetric Macroinvertebrate Index of Flanders (MMIF), a biological index to asses water

quality. The MMIF is a multimetric approach used for the biological assessment of rivers

in Flanders, Belgium, which applies the Ecological Quality Ratio (EQR) approach (Gabriels

et al., 2010). The physical-chemical data and field protocol information were implemented

in a Excel spreadsheet. Derivative data was calculated out of the available data. Organic

nitrogen was calculated assuming that total nitrogen consists of ammonia, nitrate and or-

ganic nitrogen. In the same way, it was assumed that total phosphorus consists of organic

phosphorus and phosphate (Vanrolleghem et al., 2001). A new variable “Type” was defined

which holds information of the hydro-morphologic structure of the waterbody:

1. Hydro-morphological favorable (value 1): natural bank structure, mixed bottom sub-

strate, thin sludge layer, meandering, heterogeneous bank and bottom structure.

2. Hydro-morphological unfavorable (value 2): artificial bank structure, tick sludge layer,

straight waterway, homogeneous bank and bottom structure.

The physical-chemical and biological data were evaluated by comparing the results with data

acquired in 2010 (WATROPEC project). The biological data were evaluated in function of

the habitat variables (chemical properties, river morphology, hydraulics).


All the general statistics were calculated: minimum, maximum, mean, median, standard

deviation, 25% and 75% quartiles and the interquartile distance (IQR). The identification of

outliers was performed with three methods: box plots, Cleveland dot plots and mass balance.

Box plots (Box-and-Whisker plots) were set up for the different variables. The box-plots were

only set up for the physical-chemical variables and not for the hydraulic variables. The values

of the upper- and lower-whisker were identified and the points outside the range of these

whiskers were evaluated. Afterwards Cleveland dot plots were used in order to evaluate the

outliers in the data. Cleveland dot plots are plots where the row number of an observation

is plotted vs. the observation value. Cleveland dot plots provide more detailed information

than a box plot. Points that stick out on the right-hand or left-side are observed values

that are considerable larger, or smaller, than the majority of the observations, and require

further investigation (Zuur et al., 2010). A simple mass balance model was set up to check

the physical-chemical data. This model simulates the concentrations of the physical-chemical

variables (BOD, COD, (in-)organic nitrogen and phosphorus, TSS, DO) at every sampling

point in the river given a certain input (= what goes in must come out). Exclusion of a value

from the data set needs to be justified, therefore measured sampling points which do not

coincide with the mass balance models were identified and were tested against the following

questions:

• Were the conditions extreme during the sampling?

• Is there a possible pollutant source near the sampling location?

• Is the value within the range of the values of other sampling campaigns?

• Do the measurement data of the other variables at the sampling location support the

measured value of the parameters? For example, it is highly unlikely that the BOD is

equal to 1 mg O2/l, when the COD is equal to 200 mg O2/l.

• Is there an over- or underestimation of the flow?

• Is the biological data in accordance with the physical-chemical and hydro-morphological

properties?

The data points which do not coincide with the model and where the observed patterns could

not be explained were removed from the data set. The mass balance model was retained to

build the water quality model.

In the last part of the data analysis, two analysis were performed to asses the correlation and

the collinearity between the different predictor variables. A correlation matrix (spearman)

was presented together with a Principal Component Analysis (PCA) of the reduced data

set. This correlation matrix and PCA help to identify the collinearity between the predictor

variables and support the choice of the included variables in the integrated model.


3.4 Integrated ecological model building procedure

The following procedure was followed in order to set up an “Integrated Ecological Model”:

1. Clear definition of the problem and the goal of the modelling practice.

2. Framework definition of the considered problem and the model structure.

3. Selection of the model structure.

4. Calibration and validation.

3.4.1 Definition of the problem and goal

As depicted in the introduction of this chapter, the industrial activity is the main driver for

the increasing pressure on this ecosystem. The growing energy demand and emerging poultry,

detergent and milk industry in Varazdin county are leading to an increased pressure on the

Drava ecosystem. The last couple of years, the amount of discharged industrial wastewater

has increased, thus increasing the pressure on the municipal wastewater facility. The capacity

of the wastewater treatment plant is reaching its limits, which increases the risk of discharging

more untreated wastewater (Kezelj et al., 2010). A second stakeholder in the problem are

the hydro-electric infrastructures. Hydro-electric power plants (HPP), all over the country,

together ensure the delivery of energy up to 62,6 % of the total energy production in Croatia

(HEP - Transmission System Operator LLC, 2010). The multipurpose hydro-electric projects

are very interesting subject for debate of the “greenness” of this renewable energy source. The

HPP are very efficient in the conversion of kinetic energy to electricity, the operation costs

are very low which makes them very cost efficient. Among provision of electricity, HPP can

support other services: water supply for agriculture (food production), recreation and flood

regulation. HPPs are therefore a very interesting form of renewable energy, but only if their

operation is in balance with the influenced system. The provision of a minimum biological

flow (8 m3/s) to the Drava river should ensure a steady supply of water to the ecosystem in

order to keep the ecological functioning of the system in balance. But as indicated, there is

an increasing competition between water quantity for the old river path and for electricity

production.

The first goal was to develop a framework for integrated ecological modelling that can be ap-

plied to this problem to illustrate the strength and (dis-)advantages of integrated approach.

The integrated ecological modelling framework presented in the following text was build up

from the philosophy used by Chapra (1997). This author compares the quote from “Tales of

the Dervishes” of Shah (1970) to the problem of water quality modelling. The main reason for

this quote was to make readers aware that he wants to visualize “the whole picture”. There-

fore, by presenting an integrated ecological modelling framework, the goal was persuaded


to include the major elements of the system (and its impacts). Furthermore, the research

tries to identify the major problems related to integrated ecological modelling, by means of a

modelling example. Following research questions were formulated:

1. What can be a framework for an integrated ecological model?

2. How can the different elements be build up?

3. How is the data handled for these models? Furthermore, how do we use these data for

calibration and validation of these models? (see also section 3.3)

4. What are the advantages and disadvantages of every model element?

5. What are the implications for the study site?

3.4.2 Framework definition

Figure 3.3 presents the framework for the integrated ecological model, which allows assessing

different impacts on the river ecosystem and constructing different scenarios for river man-

agement. The impacts on the system are found in the upper left corner: the sewer system

transports wastewater to the wastewater treatment plant and the river. The dam structure

regulates the proportion of water that flows through the Drava river, therefore it releases a bi-

ological minimum flow to guarantee water provision for the ecosystem. The physical-chemical

water quality, the water quantity and the ecological water quality (EWQ) was modelled by

following the framework presented in the lower figure. The output of the water quality model

(physical-chemical variables) serves as an input for the data driven model. Eco-hydraulics

were included by using the outputs of the hydraulics as an input for the data driven model.

Furthermore, the hydro-morphology of the waterbody, in terms of favorable and unfavorable,

was included in the integrated framework.


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Water quantity

Physical-chemical water quality EWQ

IMPACTS

Scenario

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WWTP

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Hydro-morphology

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Pollutant transport

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QUALITY

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Figure 3.3: Framework for integrated ecological model, applied to the described problem. EWQ=

Ecological Water Quality, WWTP= Wastewater Treatment Plant

3.4.3 Model structure

Hydraulics: modelling of reservoirs in series

The tanks in series approach requires an initial subdivision of the river into different stretches.

Each stretch were assumed to have uniform hydraulic and morphologic features; the section

shape and discharge rating curve were assumed to be the same. The information of the

WATROPEC project and the new information was combined in a database which compre-

hended several hydraulic and morphologic properties for every sampling point. Figure 3.4

summarizes the approach for the estimation of flows and widths for different sampling points.

For the drainage channels, the methodology in case 1 was followed, where measurements of

water height, flow and width were used to estimate the flow. The methodology in case 2 was

followed for the lake, river and canals (lake, river, canal width not known).


The information concerning average flow and water height provided by the Croatian Electric-

ity Company (Hrvatska Elektroprivreda, Sever et al. (2000)) and Grian & Kerea (2004) were

used to estimate the average velocities and widths on several locations. The measured veloc-

ity was used to estimate the width. Since some of the measurements of velocity were at the

border of the waterbody (for instance the lake), the width estimation was biased. Therefore,

the estimated width was compared with the estimated width in the GIS platform ARKOD

available for free consulting by the Croatian Agency for payments in agriculture, fisheries

and rural development (Ministarstvo poljoprivrede, ribarstva i ruralnog razvoja, 2009). The

initial segmentation was based on the segmentation as proposed in the WATROPEC project.

The segments of the river were assumed to have a rectangular cross-section. The length of

every tank was verified with ARKOD and a finer segmentation was proposed for the Drava

river. The representation of the stretches is illustrated in Figure 3.5.

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Figure 3.4: Methodology for constructing the database for the hydraulic and morphologic properties

for every sampling point. Av. v = Average velocity, Av. h = Average water height, B

= Width, Q = Flow

To model the hydraulics of the system, two methods can be used: the hydraulic and hydrologic

routing method. Both methods use the mass balance equation in order to rout water through

the system. In this project, the hydrologic routing methods was used, which combines the

continuity equation with a relation between storage (S), outflow (Q) and/or inflow (Qin).

These relations are empirical or analytical. An example of such relation is the stage-discharge

relation, which can be modelled by applying the Manning equation:


Q =1

nAcrossR

2/3h S

1/2f (3.1)

with: Q = flow rate (m3/s); n = manning roughness coefficient (-); Across = cross-sectional

area of the river (m2); Rh = Hydraulic radius (Across/P) (m); P = wet perimeter (m); Sf =

friction slope (-).

It was assumed that the conditions of uniform steady flow were valid. The friction slope (or

slope of the water) was assumed equal to the slope of the river bed (S0=Sf ). In this approach,

backwater effects were not considered. Equation 3.1 and equation 3.2 were implemented in

Matlab (MathWorks, Inc) in order to model the hydraulics of the system.

dV

dt= Qin −Q (3.2)

In order to help the explanation of the methodology, the results and the discussion, the

stretches defined in Figure 3.5 are shortly explained:

• Stretches 1 till 5 represent the southern drainage channel receiving treated (Varazdin

WWTP) and untreated wastewater.

• Stretches 6 till 9 represent the lake waterbody, with high water levels and significant

backwater effects due to the dam and the hydropower plant.

• Stretches 10 till 20 represent the old trajectory of the Drava river, with deeper and

shallower zones.

Pollutant transport: cascade of continuous stirred tank reactors

The concept of a cascade of continuous stirred tank reactors (CSTR) was used to model the

transport of pollutants through the river bed. In this approach, a water body is represented

as one or more fully mixed tanks (stretches, applying a “box model”, Shanahan et al. (2001)).

In order to model the pollutant routing, the mass balance, for a given finite time period was

set up for every desired pollutant:

dmi

dt=

d(CiV )

dt=

∑in=1

Qin,iCin,i −∑out=1

Qout,iC + −V ri (3.3)

Equation 3.3 was simplified by applying equations 3.4

d(CiV )

dt= V

dCidt

+ CidV

dtdV

dt=

∑in=1Qin,i −

∑out=1Qout,i

(3.4)


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Figure 3.5: Visualization of the different stretches. This figure can be compared with Figure 3.2


The water quality constituents and model state variables characterizing carbon (C), oxygen

(O), nitrogen (N), and phosphorus (P) cycling were selected as the basis for the water quality

model. Table 3.1 gives an overview of the considered processes and parameters. All processes

were modelled as first order kinetic. The boundary values of the parameters are discussed in

3.4.4.

Table 3.1: Processes and included parameters in the model. C= Calibration, E=Estimation

Process Parameter C/E Range

Min Max

Settling of organic phosphorus (m/d) vs,ORGP C 0 2

Settling of phosphate (m/d) vs,PO4 C 0 2

Hydrolysis of organic phosphorus kd,ORGP C 0.001 0.1

Settling of organic nitrogen(m/d) vs,ORGN C 0 2

Hydrolysis of organic nitrogen (1/d) koa C 0 5

Nitrification (1/d) kan C 0 10

Denitrification (1/d) kdn C 0 2

Sink flux for NO3 (1/d) kn,s C 0 5

Settling of organic matter (m/d) vs,ORGC C 0 2

Decay of organic matter (m/d) kd,ORGC C 0 5

Diffuse organic pollution Lr C 0 5

Reareation (1/d) ka C/E - -

DOsat E - -

Nitrogen Oxygen Demand (1/d) NBOD E - -

The settling processes (rates) were defined in function of the settling velocity vs,x (m/d) of

the considered constituent and the average height of the water column H (m) (Chapra, 1997):

ks,x =vs,xH

(3.5)

The saturated dissolved oxygen DOsat was calculated by applying equation 3.6 (Apha, 2005):

lnDOsat = −139.34411 +1.575701.105

Ta− 6.642308.107

T 2a

(3.6)

+1.243800.1010

T 3a

− 8.621949.1011

T 4a

where Ta is the absolute temperature (K).


The reareation coefficient (ka) was estimated by using three methods:

1. ka was calculated as a function of the water depth H (m) and the water velocity U

(m/s) as described by (Covar, 1976):

Figure 3.6: Reaeration rate (/d) versus water depth (m) and velocity (mps) (Chapra & Pelletier,

2003).

2. ka was calculated in function of the water depth H (m), the water velocity U (m/s) and

the slope S0 (-) as described by (Melching & Flores, 1999) (USGS):

Table 3.2: Equations to calculate the ka for pool-riffle and channel-control. Q = flow (m3/s), H =

water depth (m), B=top width (m) (Melching & Flores, 1999)

If-rule Pool-riffle Channel-control

Q < 0.556m3/s 517(US0)0.524Q−0.242 88(US0)

0.313H−0.353

Q > 0.556m3/s 596(US0)0.528Q−0.136 142(US0)

0.333H−0.66B−0.243

3. If none of above methods yielded good results, the ka was be calibrated in function of

the stream type (Peavy et al., 1985):


Table 3.3: Typical values of the reaeration coefficient ka for various streams

Stream type ka (1/d)

Sluggish river 0.23 - 0.35

Large river of low velocity 0.35 - 0.46

Large stream of normal velocity 0.46 - 0.49

Swift streams 0.69 - 1.15

Rapids and waterfalls > 1.15

ka for lakes are typically not available and are mostly formulated in function of the wind

velocity. In this framework it was decided to calibrate the parameter (between 0 and 2 1/d)

(Bowie et al., 1985).

The NBOD was determined by:

NBOD = r ∗ kan ∗NH4 (3.7)

with r equal to 4.57gO

gN(Chapra, 1997).

The considered model state variables, processes and parameters were implemented in Matlab:

dORGP

dt=

∑in

QinV

(ORGPin −ORGP ) − ksORGCORGP

dPO4

dt=

∑in

QinV

(PO4,in − PO4) − ksPO4PO4

dORGN

dt=

∑in

QinV

(ORGNin −ORGN) − koaORGN

dNH4

dt=

∑in

QinV

(NH4,in −NH4) + koaORGN − kanNH4

dNO3

dt=

∑in

QinV

(NO3,in −NO3) + kanNH4 − kdnNO3 − kn,s

dBOD

dt=

∑in

QinV

(BODin −BOD) − kdORGCBOD − ksORGCBOD + Lr

dDO

dt=

∑in

QinV

(DOin −DO) − kdBOD + ka(DOsat −DO) −NBOD


In order to summarize, the following processes were considered:

• Settling processes of organic phosphorus, organic nitrogen, phosphate and organic mat-

ter.

• Hydrolysis of organic nitrogen, nitrification and a flux of nitrates to a sink.

• Hydrolysis of organic phosphorus.

• Decay of organic matter.

• Denitrification, diffuse pollution and infiltration water (infiltrated water of the lake) in

the southern drainage channel (Figure 3.2)

• Reaeration

The following processes or variables were not considered:

• Interactions between sediment layer and water column.

• Algae and bacteria growth.

• Total suspended solids.

Ecological model: modelling ecological water quality with regression trees

All the elements discussed in the previous text were integrated in one final model structure.

The model consists of a module which links the physical-chemical, the hydraulic and hydro-

morphological variables in order to model the EWQ. The modelled response variable will was

the MMIF. Regression trees models (RT) were used in order to model the MMIF index. The

advantages of RT are summarized:

• RT are a non-parametric technique which can use information of variables on different

levels of the tree.

• RT are able to integrate interactions which can be missed in multiple regression tech-

niques.

• RT are able to model discrete response variables (MMIF) in function of continues pre-

dictor variables.

• RT deliver a visual result, which can be used by river managers.

• Model development is quick, which makes it possible to generate multiple trees in a

short time span.

The M5’ (Quinlan, 1992; Wang & Witten, 1997) method in the statistical toolbox of Matlab

was used for the tree construction.


3.4.4 Calibration & validation

Hydraulic calibration

The calibration of the hydraulic model was based on a manual calibration of two parameters:

the manning roughness n (-) of the river bed and the slope of the river S0 (-). Based on

the available information, initial conditions were proposed for the manning roughness and

the slope. The initial manning coefficient for every stretch was estimated by using a table of

the manning roughness coefficient which describes the roughness in function of the material

or structure of the river bed (Chow et al., 1988; Verhoest, 2010). The slope was initialized

by assuming a research area with a uniform slope. The slope was varied in a range of the

initial slope and boundary values. The slope and roughness was adjusted in function of the

simulations and measurements of the flow and water height of the considered stretch. The

calibration was objectified by taking the difference between the estimated and the modelled

uniform steady-state flow and water height.

Water quality: Monte Carlo calibration

The process parameters of the water quality model were calibrated by preforming a Monte

Carlo analysis. The parameters in the model were considered as a degree of freedom each

bound by a lower and upper boundary value. The boundaries for every parameter are pre-

sented in Table 3.1 and are the boundaries proposed by Garcıa et al. (submitted); Park & Lee

(2002); Chapra (1997). The distribution of these parameters was assumed to be uniform. The

parameters were equal for every stretch in function of the type of water body (the drainage

channel, the river and the lake). For the every run, therefore every set of parameters, the

performance of the model was determined by calculating the root mean square error (RMSE)

between the simulations and the measurements. The errors of the different variables were

assumed not to be equal, thus a weighted sum of least squares (= dividing RMSE of the vari-

able by the measurement variance of the variable) was used in order to evaluated parameters

based on two or more variables. In order to evaluate the calibration, the spearman correlation

coefficient r and the coefficient of determination R2 (1-SSE/SST) were used. The model was

calibrated separately for:

• The southern drainage channel with inputs of the combined sewer overload, the wastew-

ater treatment plant and inputs of untreated wastewater;

• The Drava river (succession river-lake-river) with inputs of the Varazdin tailrace canal

and the southern drainage channel.

If the results for the automatic calibration were not satisfying, then manual fine tuning was

performed. The Monte Carlo analysis was performed to indicate the variability of the variable

values in function of the chosen parameters. The model was calibrated with the data of the


third sampling campaign, the validation was done with the second sampling campaign. The

model could not be validated for the first monitoring campaign since not enough input data

was available.

Training and validation of the regression trees

The tree training and validation was focused on finding the optimal tree, which satisfied several

performance criteria and provided ecological relevant results (Goethals, 2005). As mentioned

in the introduction, the training and validation requires the interaction of the user and might

be prone to subjectivity; selection of the tree size, selection of the ecological relevant tree.

The validation was based upon three types of validation as proposed by Goethals (2005):

1. Theoretical validation with correctly classified instances (CCI), regression coefficient

(r), coefficient of determination (R2=1-SSE

SST) and root mean square error (RMSE).

2. Validation by testing the tree to ecological knowledge.

3. Validation by practical use of the model.

The first two criteria were used for the validation of the regression tree. Two different ap-

proaches were used to build up the regression tree.

In the first part, the data sets of every sampling campaign were used to train the tree and

independently validate the tree. For this purpose, the data of sampling campaign 3 was used

for the training and the data of sampling campaign 1 and 2 was used for the validation.

This action was repeated for total the data set (with outliers!), the data set without outliers

identified with the Cleveland dot plots and the data set without outliers identified by the

Cleveland dot plots and mass balance model, therefore creating in total three models, which

could be compared in performance. In order to quantify the performance of the calibration

and validation the CCI, RMSE, r and R2 were calculated. These values were compared with

the average values of the performance criteria tested with a model which generates a random

class of EWQ (e.g. bad, poor, moderate, good). These values were generated by randomly

picking a MMIF class for all data points, then calculating the performance indices between

the randomly picked classes and the measured classes in order to repeat this procedure a 1000

times to calculate the average values of the performance indices of the 1000 random models.

Furthermore, in the second part, a bootstrapping approach was implemented to compare

performance criteria of different trees. The bootstrap approach is an approach in which a

smaller subsample (child data set) of the available data was used to train and create a model.

Therefore, a child database was used for the tree construction (Sipek et al., 2010; Gibson

et al., 2004). The child data for each of these models was based on stratified runs of the data

sets. A stratified run is run which generates a random stratified data set based on the total


data set. A stratified data set is a smaller data set, generated from the total data set, which

has x instances of every MMIF class (excellent, good, moderate, poor and bad) represented in

the set. The child data set is equal to the stratified data set. The approach of bootstrapping

and choosing stratified data sets for 3 stratified runs is illustrated in Figure 3.7. In this figure,

three models were build by using three child data sets.

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Figure 3.7: Example of choosing a child data set originating from the total data set. Three stratified

data sets, with equal instances in every class, were used to build 3 models. In this

example, the excellent en good water quality class were merged. In total, 30 instances

were retained for every class (excellent + good, moderate, poor and bad). The data

points in the moderate, poor and bad water quality class are chosen at random. The

possible combinations of datapoints is high, since there are more than 30 data points

present in the data set for the moderate, poor and bad class.

This approach was repeated a 1000 times and 1000 models were build. Every model was

validated by calculating the performance criteria CCI, r, R2, RMSE. These performance

criteria were tabulated and evaluated. The best 10 samples, in function of the CCI, were

retained and were checked for ecological relevance.

Chapter 4

Results

4.1 Data exploration and analysis

Biological monitoring

The results for the biological monitoring campaign (september 2011, sampling campaign 3)

are presented in Figure 4.1. The moderate (yellow) and good (green) ecological water quality

(EWQ) was mostly found at sampling locations in the old trajectory of Drava river (e.g.

US4, US5,... and 11.4, 11.5). No excellent (blue) water quality was monitored. The lakes

(e.g. sampling points 7, SP B3.1, 14, 15), channels (A1 to 9, B1 to B5) and canals (e.g.

US3, 2, 12) did not reach a higher ecological quality than moderate. In contrast, the water

quality in the Drava river reached the good status. The water quality in the canals and

lakes - artificial waterbodies - ranged from poor (orange) to moderate. The quality in the

southern drainage channel (A1 to 9), also an artificial waterbody and receiving the treated

and untreated wastewater, was moderate, poor or bad (red). The negative influence of the

drainage channel (sampling point 9) is clearly illustrated between sampling point 8 (moderate

water quality) - just after the dam - and sampling point 10 (poor water quality), at the

intersection of the Drava river and the drainage channel. The EWQ in the northern drainage

channel, mainly moderate water quality, was better than the quality in the southern drainage

channel.

General statistics

The general statistics of the data set with 103 samples are found in Table 4.1. The variable

“type” - referring to the hydro-morphologic structure of the river - was not included, since it

is a categorical variable (1 - favorable or 2 - non-favorable, see section 3.3).

37

Chapter 4. Results 38

Figure 4.1: Map with sampling points and the corresponding biological water quality: Green is good

water quality, yellow is moderate water quality, orange is poor water quality and red is

bad water quality. No excellent (blue) water quality was monitored


Table 4.1: Overview of general statistics for several variables. Med: Median, Min: Minimum, Max:

Maximum, LQ: Lower Quartile, UQ: Upper Quartile, SD: Standard Deviation, IQR: In-

terquartile Range

Variable Units Mean Med Min Max LQ UQ SD 1.5*IQR

MMIF - 0.41 0.40 0.05 0.85 0.30 0.55 0.19 0.38

DO mg O2/l 5.57 5.08 0.52 12.70 3.72 8.03 2.53 6.47

DO % 52.02 47.48 4.86 118.69 34.77 75.05 23.68 60.42

COD mg O2/l 37.01 14.50 1.00 356.00 5.00 33.75 67.01 43.13

BOD mg O2/l 4.29 2.00 0.00 35.00 1.00 4.25 5.72 4.88

ORGN mg N/l 1.85 1.56 0.08 6.31 0.99 2.33 1.29 2.01

NH4 mg N/l 0.33 0.14 0.00 3.07 0.02 0.48 0.51 0.69

NOx mg N/l 0.56 0.53 0.04 1.81 0.34 0.70 0.33 0.54

PO4 mg P/l 0.11 0.07 0.00 2.27 0.02 0.10 0.25 0.12

ORGP mg P/l 0.10 0.07 0.00 0.85 0.04 0.12 0.12 0.11

TSS mg/l 13.33 9.50 1.00 44.00 4.00 21.00 10.99 25.50

D m 1.92 0.51 0.12 10.00 0.25 2.57 2.92 3.47

V m/s 0.35 0.32 0.00 1.03 0.03 0.59 0.29 0.84

The comparison of the mean to the median of a variable gives an indication of the non-normal

distribution and presence of outliers in the data. For this data set, the distributions of most

variables were skewed (mean was not equal to median).

Outlier removal

The results of the box plots for the different components are found in Figures A.1 to A.4 in

the appendix. The Cleveland dot plots for the different physical-chemical variables are found

in Figures A.5 to A.16 in the appendix.

The results of the analysis of the box plots deviated from the results found by analyzing

the Cleveland dot plots. The box plots analysis gave a higher amount of points (15 sampling

points) which should analyzed into detail or deleted compared with the amount reported using

the analysis of the Cleveland dot plots (two sampling points). The mass balance analysis

yielded the deletion of five points. The observed ecological water quality of two of those five

had a poor relation with the observed physical-chemical and hydro-morphological conditions.

The other three points, in the lake, were deleted because the chemical oxygen demand (COD)

and biological oxygen demand (BOD) levels were not correlated to the observed COD and


BOD upstream and downstream the lake. The higher concentrations were likely related to

extreme lake condition in which these locations were sampled. Other outlying points were

identified in the mass balance model, but they were retained because it was assumed they

could hold valuable information for the model. Of the 103 sampling points, in total seven point

were deleted, two with the Cleveland dot plot analysis and five by checking mass balances.

Collinearity analysis

The correlation matrix for the predictor variables is shown in Figure 4.2. The eigenvalues

and the cumulative variability for the principal components are plotted in Figure 4.3. The

first five components accounted for 72.3% of all variability in the data. The eigenvectors

of all variables for the five first principal components is found in Table 4.2. The principle

component analysis (PCA) for the two first principal components, accounting for 39% of the

variability, is plotted in Figure A.17 (appendix).

Total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD) were respec-

tively highly positively correlated with organic nitrogen (ORGN) correlation coefficient r =

0.94 , organic phosphorus (ORGP) r = 0.77 and BOD r = 0.85. The Multimetric Macroinver-

tebrate Index of Flanders (MMIF) was highly correlated with dissolved oxygen (DO) r=0.72.

The lowest correlations were encountered between type and the other variables. The vari-

ables (1) BOD & COD, (2) TN & ORGN and (3) TP, PO4 & ORGP showed a high degree

of collinearity. Furthermore, the collinearity between DO and Depth is moderate, indicating

that deeper rivers have higher oxygen concentrations. This correlation was related to the

conditions in the lakes and the deeper stretches of the Drava river, where wind effects played

a more prominent role in the oxygen reaeration, thus initiating higher concentrations of oxy-

gen in the upper layers. Modelling efforts were focused on ORGP, PO4, ORGN, NH4, NO3,

DO, BOD, Depth and velocity, therefore TN, TP, COD and TSS were not considered in the

model. Furthermore, TN and TP were highly correlated to respectively ORGN and ORGP

(PO4). Following predictor variables were retained for the regression tree: BOD, DO, type,

average velocity, average water height, ORGN, NH4, NO3 and ORGP.


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Figure 4.2: Correlation r matrix (spearman). D = average water height, V = average velocity,

Type=1 (hydro-morphological favorable) / 2 (hydro-morphological unfavorable)


!"

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67+))*8$9&*

Figure 4.3: Scee plot: represents the fraction of total variance in the data as explained or represented

by each principal component F

Table 4.2: Eigenvalues of the variables for the first five principal components (F) accounting for

72.3% of all variability.

Variable Principal Component

F1 F2 F3 F4 F5

DO -0.155 -0.018 0.536 -0.112 -0.197

BOD -0.202 0.330 0.174 -0.022 -0.512

COD -0.266 0.269 0.154 0.375 -0.251

TN -0.263 0.513 -0.185 0.030 0.266

ORGN -0.312 0.427 -0.210 0.141 0.100

NH4 0.162 0.315 -0.231 -0.263 0.133

NO3 -0.175 0.088 0.365 -0.020 0.569

TP 0.503 0.296 0.186 0.128 0.017

PO4 0.460 0.285 0.146 0.012 0.013

ORGP 0.384 0.195 0.190 0.305 0.018

Depth -0.140 -0.042 0.459 -0.075 0.399

Velocity 0.091 0.147 -0.170 -0.525 0.054

Type 0.032 -0.188 -0.249 0.604 0.224


4.2 Integrated ecological model building

4.2.1 Hydraulic model

An example of a manual calibration for the hydraulic model is visualized in Figure 4.4. The

figures for the manual calibration for all the stretches are found in Figure B.1 to B.16 in

the appendix. Table 4.3 gives an overview of the values of the manning roughness n and

slope S0. There were no results for the calibration stretch of 6 till 9, because these stretches

represent lake Cakovec, which were subjected to severe backwater effects due to the dam and

hydropower plant construction. For these stretches, it was decided to keep water heights,

volumes and flow fixed.

0 1 2 3 4 5 6

x 105

0

1

2

3

4

5

6

7

Time t (s)

Flo

w Q

(m

3 /s)

Flow (m3/s), water height (m) and velocity (m/s) in function of time (s) for stretch 5

Simulation Sample Campaign 1 Sample Campaign 2 Sample Campaign 3

0 2 4 6

x 105

0

0.5

1

1.5

2

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.5

1

1.5

Vel

ocity

(m

/s)

Time t (s)

Figure 4.4: Result of manual calibration for stretch 5 (channel)

As indicated by Chow (1981), the slope should be greater than 0.0001 m/m in order justify

the modelling approach with the kinematic and hydrologic approach (using the manning

formula). The slopes in section 10, 11, 13, 15, 17, 18 and 20 were smaller than the 0.0001

m/m limit. These stretches had a typical high water level (h > 1 m) with low flow velocities

(v < 0.1 m/s). Stretches 12, 14, 16 and 19 are examples of shallower stretch, with high flow

velocities (v > 0.5 ms/s) and low water heights (h < 0.5 m). The average slope of the terrain

was estimated to be 0.00012 m/m (GIS-platform ARKOD), which was higher than 0.0001

m/m. The manning roughness n was within the range of expected values, where the values

range between 0.030 and 0.040.


Table 4.3: Values of the slope S0 (-) of the river bed and the manning roughness n values (-)

Stretch Slope S0 (-) Manning roughness n (-)

1 0.00015 0.040

2 0.00025 0.035

3 0.00050 0.035

4 0.00050 0.035

5 0.0025 0.030

10 <0.0001 0.030

11 <0.0001 0.035

12 0.0032 0.040

13 <0.0001 0.032

14 0.004 0.040

15 <0.0001 0.040

16 0.00418 0.040

17 <0.0001 0.040

18 <0.0001 0.034

19 0.004 0.032

20 <0.0001 0.035

4.2.2 Water quality model

The mass balance models are presented in Figures B.17 till B.22 in the appendix. The values

of the calibrated parameters found with the Monte Carlo simulations for the lake Cakovec, the

southern drainage channel and the Drava river are summarized in Table 4.4. The results for

the performance of the calibrated model (sampling campaign 3 = SC3) and the independent

validation (sampling campaign 2 = SC2) for both the Drava river (lake-river) and drainage

channel are summarized in Table 4.5. The best, minimum, maximum simulation and the

measurement data for every variable are plotted in Figures B.23 to B.30 in the appendix.

Figures B.31 to B.38 in the appendix present the results for the validation with the data of

sampling campaign 2.


Table 4.4: Calibrated parameters for the channel, the lake and the river. C = channel, L = lake, R

= Drava river

Parameter Units C L R Range

Min Max

vs,ORGP m/d 0.29 0.07 0.51 0 2

vs,PO4 m/d 1.17 0.20 0.04 0 2

kd,ORGP 1/d 0.04 0.02 0.02 0.001 0.1

vs,ORGN m/d 0.01 0.03 0.1 0 5

koa 1/d 0.38 0.02 0.81 0 5

kan 1/d 2.47 0.20 1.91 0 10

kdn 1/d 0.05 - - 0 2

kn,s 1/d - 0.15 3 0 5

vs,ORGC m/d 0.02 0.20 0.33 0 2

kd,ORGC m/d 2.90 0.01 0.95 0 5

Lr mg/d 3.66 0 0 0 5

ka 1/d 0.60 1.15 - -

Fast 0.90 - -

Slow 0.35 - -

The processes in the lake were generally occurring at a slower rates compared to the processes

in the channel and river. The process rates of the nitrogen components (hydrolysis koa,

nitrification kan and settling velocity of organic nitrogen vs,ORGN ) in the river and channel

were higher compared to the rates in the lake. The process rates for (in-)organic phosphorus

(hydrolysis kd,ORGP , settling velocities of organic and inorganic phosphorus vs,ORGP and

vs,PO4) were generally lower in the lake, except for the settling velocity of phosphorus, which

was higher than the settling velocity in the river. The decay rate of organic carbon (kd,ORGC)

in the channel was relatively high, indicating a high rate of decomposition of organic material.

The denitrification rate (kdn) in the channel was low. The nitrate flux to a sink (kn,s) in the

river was higher than the flux in the lake. The reaeration rate ka were calibrated since the

implemented formula’s did not yield the proper results. The ka was highest in the river and

lowest in the slow flowing section of the drainage channel.


The evaluation of the calibration of the water quality model applied for the channel is satisfy-

ing (Figure 4.4). The correlation (r) between the simulations and measurements were mostly

above 0.75, except for ORGN and BOD. Coefficients of determination (R2) above 0.7 are

considered as good (Jha et al., 2007). The lowest R2 was equal to -0.24 (ORGN). The lower

R2 was related to the measurement in sampling point 9 (Figure 4.5). The R2 for ORGP, PO4,

NH4 and DO was high, the R2 for NO3 and BOD was lower. The correlation between the

simulations and measurements of the second sampling campaign (validation) was generally

high (r > 0.70), except for NO3 (r = 0.38) and BOD (r = 0.46). The R2 for BOD was equal to

-0.31, indicating it was better to use the average value of the measurements of the validation

set instead of the simulated value. Furthermore, the R2 for NO3 was equal to 0.18, which was

low. The value of R2 of ORGN and DO was moderate (0.53) and the R2 for ORGP, PO4 and

NH4 was good. The phosphorus variables were modelled well. The accuracy for the nitrogen

variables and the dissolved oxygen were lower, while the accuracy for BOD was poor.

Table 4.5: Correlation (r) and coefficient of determination (R2) between modelled values and obser-

vations. C = Calibration (sampling campaign 3), V = Validation (sampling campaign

2).

Variable r R2

C V C V

Channel

ORGP 0.86 0.72 0.77 0.70

PO4 0.88 0.95 0.77 0.78

ORGN 0.43 0.83 -0.24 0.53

NH4 0.95 0.92 0.97 0.93

NO3 0.93 0.38 0.68 0.18

BOD 0.67 0.46 0.60 -0.31

DO 0.90 0.79 0.72 0.53

Drava

ORGP 0.62 0.71 0.68 0

PO4 0.72 -0.19 0.34 0.1

ORGN 0.55 0.24 0.54 -0.63

NH4 0.91 0.59 0.61 0.24

NO3 0.95 0.52 0.47 -9.0

BOD 0.44 0.74 0.87 0.86

DO 0.87 0.65 0.89 0.58


The performance criteria for the calibration of the model for the Drava river were generally

lower than those for the channel. The performance of the calibration was good. The lowest r

reported was equal to 0.44 (BOD). The correlation for NO3, NH4 and DO were high (above

0.85), while the correlations for ORGP, PO4 and ORGN were lower. The R2 for DO, BOD

were higher than 0.85, while the R2 for PO4 was lower (0.34). The performance criteria for

the validation procedure were generally low. The r values for the validation were generally

lower than for the calibration process. The r for PO4 was negative (-0.19). The correlations

for ORGP and BOD were relatively high, while the correlations for other variables were

lower. The R2 values yielded a good result for BOD and DO. The other variables had a lower

R2 and two variables had a negative R2 (ORGN and NO3). According to the calibration

and validation process, the DO and BOD values for the river were modelled well, while the

nitrogen and phosphorus variables were modelled poorly.

Figure 4.5: Calibrated water quality model for organic nitrogen in the channel. The actual simulation

is given in the black fluid line. The dotted line indicates the maximal and minimal

simulated value for different sets of parameters (Monte Carlo, 1000 runs). SC3 = Data

of sampling campaign 3.

The data of the DO concentrations in the channel were prone to spatial fluctuation, but they

were simulated well by the model (Figure 4.6). Initially the DO rised because of the reaeration

processes. After this initial increase, the DO dropped fast due to infiltration water with low

oxygen concentrations. Afterwards, there was a second increase of the DO concentrations,

from approximately 2 to 3 mg O2/l, just after the outlet of the wastewater treatment plant

(WWTP). This phenomenon was related with the high levels of DO concentrations in WWTP

discharge. The high DO concentrations in the treated wastewater was generated by the

aeration processes that took place during the biological wastewater treatment (i.e. activated

sludge). At the end of the channel, the DO concentrations were lower than 1 mg O2/l.


Figure 4.6: Calibrated water quality model for oxygen in the channel. The actual simulation is given

in the black fluid line. The dotted line indicates the maximal and minimal simulated

value for different sets of parameters (Monte Carlo, 1000 runs). SC3 = Data of sampling

campaign 3.

The concentrations of ORGN, NH4 and NO3 were more or less stable in the lake (Figure 4.7).

The ORGN concentrations rised to 4 mg N/l after the inflow of the water of the drainage

channel. After this intersection point, the concentrations decreased to approximately 1 mg N/l

at the end of the river. The NH4 concentrations initially rised to 0.7 mg N/l during the first

4 km of the old Drava trajectory and finally dropped to 0.4 mg/l. The NO3 concentrations

in the river stayed stable. The dissolved oxygen concentrations in the lake and river are

illustrated in Figure 4.8. The lake water in the top layer had high levels of oxygen (> 8

mg/l). This oxygen-rich water was released to the old trajectory of the Drava river. The

oxygen-poor water (< 1 mg/l) of the drainage channel mixed with the water of the Drava

river which resulted in a significant decrease in the DO. After this decrease, the observed

levels of DO slowly rised until the DO concentration at the end of the river was equal to

approximately 5 mg O2/l.


Figure 4.7: Calibrated water quality model for nitrogen in the Drava river. The actual simulation





Figure 4.8: Calibrated water quality model for oxygen in the Drava river. The actual simulation




4.2.3 Ecological model

The following text will present the results for two used approaches to develop a regression

tree model (RT). The first part will handle the regression tree, calibrated with the data of

third monitoring campaign and validated with the data of the first and second monitoring

campaign. In this approach, three data sets were used to model the EWQ; one data set

with all 103 data points (database 1), one data set without the outliers identified with the

Cleveland dot analysis (101 points, database 2) and one data set without the outliers identified

with the Cleveland dot and mass balance model analysis (96 points, database 3). The second

part will handle the approach of stratified runs used to develop a RT. The data set with 96

points was used to create 1000 child data sets, for which each data set had an equal number

of instances for the four water quality class. These different data sets were used to develop a

1000 models. The 10 best models were retained for further evaluation (Figure 3.7).


Tree building based on independent validation

The results for calibration and validation of the RT based on the use of an independent data

set is presented in Table 4.6. The RT developed to model the MMIF in function of physical-

chemical, hydro-morphological and hydraulic variables is presented in Figure 4.9. This tree

was trained and validated with the data without outliers (database 2).

Table 4.6: Results for correctly classified instances (CCI), root mean square error (RMSE), correla-

tion coefficient (r) and coefficient of determination (R2).

CCI (%) RMSE r R2

Database 1: All data

Training 50 0.73 0.58 0.66

Validation 40 1.64 0.07 -0.20

All 46 2.37 0.28 0.36

Database 2: Outliers deleted

Training 49 0.72 0.58 0.66

Validation 41 1.58 0.07 -0.16

All 45 2.29 0.30 0.37

Database 3: Outliers and mass balance errors deleted

Training 47 0.66 0.56 0.66

Validation 41 1.57 0.07 -0.15

All 44 2.22 0.28 0.36

The performance criteria for the three RT models indicated a moderate prediction capacity.

The performance criteria for the training and validation were almost equal, indicating that

the deletion of the outliers and mass balance errors did not influence the results substantially.

The maximum percentage correctly classified instances (CCI) of 46% was obtained for the

validation on the total data set (validation + calibration set). Furthermore, the r was generally

low (< 0.70), which was also true for the R2 (< 0.70). For database 1, the performance of

the training was moderate (r = 0.58, R2 = 0.66, CCI = 49%), while the performance of the

validation was low (r = 0.07, R2 = -0.16, CCI = 41%). These values were compared with the

average values of the performance criteria tested with a model which generates a random class

of EWQ (e.g. bad, poor, moderate, good): CCI = 23%, RMSE = 12.04, R2 = -2.4989 and r

= 0. These values were generated by randomly picking a MMIF class for all data points, then

calculating the performance indices between the randomly picked classes and the measured

classes in order to repeat this procedure a 1000 times to calculate the average values of the


performance indices of the 1000 random models. The CCI for the training of the three models

was higher than the average value of the random model. Furthermore, the r and R2 were

higher and the RMSE of the three models were lower. This indicates that it is better to use

this tree, than picking a model which randomly chooses a class. The comparison between

the random model and the RT model indicate the RT provides an added value to model the

MMIF.

Performance criteria are not the only validation criteria for regression trees, the model should

also be tested for ecological relevance. The tree for database 2 is presented in Figure 4.9. Four

of the nine selected predictor variables were present in the regression tree model; DO, average

water height (Depth), NH4 and Type. The first rule defines poor or bad ecological water

quality conditions for values of DO lower than 3.47 mg/l and average water heights higher

than 0.43 m (bad) or lower (poor) than 0.43 m. Poor, moderate and good water quality

are defined by values of DO >= 3.47 mg/l according to the type of hydro-morphological

conditions (favorable = 1 and non-favorable = 2), the NH4 and DO concentrations.

!"##$%&'#(#

0.14

0.46

Depth

0.31

NH4

44 !"##$%$&)#(*+,#

0.51 0.7

#

#

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#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

Class MMIF

Score

Evaluation of

quality

Colour

code

I 0.9-1.0 High Blue

II 0.7-0.9 Good Green

III 0.5-0.7 Moderate Yellow

IV 0.3-0.5 Poor Orange

V 0.0-0.3 Bad Red

DO

Type

DO

-#'%&.#(*+,# !"#'%&.#(*+,#

"#/#-#$%&'#(#

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0.32

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!"#.%0'#(*+,#

Figure 4.9: Regression tree for predicting the ecological water quality based on the MMIF index.

DO = Dissolved Oxygen, Depth = average water height, Type (1= hydro-morphological

favorable, 2= hydro-morphological unfavorable), NH4 = Ammonia. CCI = 45%, r=0.30,

R2=0.37, RMSE=2.29


The bootstrap approach and use of stratified data sets

The statistical results and the best 10 samples (based on CCI) for 1000 bootstrap samples are

found in Table 4.7. The validation analysis was based the whole filtered data set (96 samples,

database 3). The maximum and minimum CCI obtained was equal to 59% and 27%. The R2

had a maximum and minimum value of 0.44 and -1.22, which indicated that in some cases it

is better to take the average value for the MMIF as predicted value. The model presented

based on the independent validation has the same CCI as the average observed CCI for the

1000 bootstrap samples. The RMSE for the model was lower, which is also the case for the

correlation coefficient. The R2 of the model is better than the average observed R2.

The main goal in this approach was to obtain a higher predictive power of correctly classified

instances of the ecological water quality. Furthermore, it was not possible to choose the tree

with the best performance if this tree was not supported by ecological knowledge. Simulation

98 provided a high CCI and significant ecological relevance. The regression tree is presented

in Figure 4.10. The overall performance was good, since all indices were higher than the

average indices.

Table 4.7: Performance criteria for 10 best samples for 1000 bootstrap samples. CCI = correctly

classified instances, r = correlation coefficient, R2 = coefficient of determination, RMSE

= root mean square error.

Stratified Run CCI R2 r RMSE

98 0.59 0.44 0.71 1.94

338 0.59 -0.08 0.49 3.73

766 0.57 0.39 0.63 2.12

516 0.57 0.32 0.63 2.36

302 0.57 0.28 0.57 2.47

931 0.57 0.23 0.60 2.64

565 0.57 0.21 0.58 2.72

890 0.57 0.13 0.55 3.01

846 0.56 0.36 0.63 2.22

Average 0.45 -0.04 0.47 3.57

Minimum 0.27 -1.22 -0.01 1.92

Maximum 0.59 0.44 0.71 7.67

Variance 0.00 0.06 0.01 0.68


Five of the nine predictor variables (Type, DO, ORGN, average velocity and NH4) are present

in the model. The bad ecological class is defined by conditions of DO (< 3.51 mg/l). In

conditions of higher DO (>= 3.51 mg/l) and low NH4 (< 1.48 mg/l) concentrations, the

poor, moderate and good class is defined by the average velocity, the type, the NH4 and

ORGN concentrations. Average flow velocities above 0.16 m/s indicate a good water class in

case of low NH4 (< 0.08 mg/l) concentrations and hydro-morphological favorable conditions

(= 1). In the same situation, only in hydro-morphological unfavorable conditions, the poor

class is obtained. The moderate class is determined by low average velocities and low NH4

concentrations (< 0.08 mg/l).

0.05 Av. V

44

0.28 0.47

NH4

44

ORGN

44

Type

0.15

0.38

0.73 0.48

0.4 0.67

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

! !

Class MMIF

Score

Evaluation

of quality

Colour

code

I 0.9-1.0 High Blue

II 0.7-0.9 Good Green

III 0.5-0.7 Moderate Yellow

IV 0.3-0.5 Poor Orange

V 0.0-0.3 Bad Red

DO

NH4

"!#$%&!'()*!

"!&$+,!'()*!-.!&$+,!'()*!

-.!#$%&!'()*!

-.!/$&0!')1!"!/$&0!')1!

-.!/$/,!'()*!"!/$/,!'()*!

.!&!.!2!

-.!2$3%!'()*!"!!2$3%!'()*!

Figure 4.10: Regression tree for Drava study area. DO = Dissolved Oxygen , ORGN = Organic

Nitrogen, Type (1 = hydro-morphological favorable, 2 = hydro-morphological unfavor-

able), NH4 = Ammonia, Av. V = Average Velocity. CCI = 59%, r = 0.71, R2 = 0.44,

RMSE = 1.94

Chapter 5

Discussion

5.1 Model development

5.1.1 Data collection and analysis

The framework, which was presented in this thesis (Figure 3.3 in the methodology) required in-

formation and data in several domains (e.g. hydraulics, physical-chemical parameters, hydro-

morphology, ecological). Furthermore, the data needed a good and accurate analysis, in order

to identify possible outliers. It was required to keep as much as possible valuable information

for the water quality and regression tree models (RT).

The outlier analysis was performed with three methods: box plots, Cleveland dot plots and

mass balances. The first method, the box plots, yielded the loss of 15 data points, while

the second method, the Cleveland dot plots, yielded the loss of two points. As depicted

by Zuur et al. (2010), box plots are not always the best solution to delete outliers. For

example, concentrations of ammonia in the river can be very high close to the discharge point

of a wastewater treatment facility. The use of box plots to delete the outliers could identify

the high ammonia concentration as an outlier. Removal of this point can be identified as

a “wrongly” removed data point. In the framework of water quality modelling it might be

better not to delete the data point, because it can hold valuable information of the relation

between the physical-chemical status and the biological status. Cleveland dot plots can offer

the solution, but compared to box plots, the analysis is more subjective. The third method,

the mass balance method, yielded the loss of five points. Also this method is more subjective,

because there are no quantifying criteria used to delete these data points. In this framework,

the combination of the Cleveland dot plots and the mass balance model was used. In order

to use this approach, the author stresses that the modeller must have significant knowledge

about the details of the system and sampling conditions (how, where and when were they

taken).

55

Chapter 5. Discussion 56

As indicated by Vayssieres et al. (2000), RT are robust against outlier data. In this research

at the Drava river, this property was illustrated. Three RT were developed for predicting the

Multimetric Macroinvertebrate Index of Flanders (MMIF) and the ecological water quality

(EWQ) class based on three data set; one with all data (103 points), one without outliers

identified with the Cleveland dot plots (101 points) and one without outliers identified with

Cleveland dot plots and the mass balance model (96 points). The performance indices, the

tree structure and the rules of all three models were almost identical. If certain outliers in

data sets are not directly explained by a possible condition or impact, the removal of the data

point is subject for debate. If RT are used to model the MMIF, these data points can be

retained in the data set without changing the results considerably.

5.1.2 Model calibration and validation

All the physical-chemical variables of the channel were modelled well, expect for the variable

biological oxygen demand (BOD) which had low performance for the validation. The BOD

and dissolved oxygen (DO) of the Drava river were modelled accurate, while the nitrogen and

phosphorus variables were modelled less well. Especially for the validation, the nitrogen and

phosphorus concentrations were simulated poorly. This might be related to the lower amount

of data which was available for the validation.

The values of the calibrated parameters were fairly in the range of the expected values. The

process rates in the lake were generally lower, indicating a slower turn-over rate. As indicated

by Shanahan et al. (1998), the determination of the reaeration coefficient is generally prob-

lematic in small rivers (e.g. channel). The implemented formulas did not yield the expected

results and that is why the reaeration rates were calibrated in function of the waterbody and

stretch properties (Table 3.3). The determination of the constant value results in a value

which might not be transferable to other conditions. The reaeration coefficient in rivers is

typically a function of the temperature and simple hydraulic characteristics such as stream

depth and velocity (Bowie et al., 1985). The reaeration processes in the lakes are mainly

driven by wind effects (Bowie et al., 1985) and since the considered lakes is located in a open

environment, the wind effects will significantly influence the oxygen balance in the lake. Sedi-

mentation and settling processes of suspended solids and solutes are very important processes

in lakes. The calibrated parameters (settling velocity of organic matter and phosphorus) for

the lake did not support this theory, but they do support the statement of Bonacci & Oskorus

(2008), which states that there is no significant sedimentation (and settling) in the Croatian

reservoirs (Varazdin, Cakovec and Dubrava) during their existence.


The diffuse pollution due to agricultural activity along the channel was translated into a high

rate of diffuse organic pollution. The higher process rates in the channel might be related

to the discharge of wastewater from the wastewater treatment plant (WWTP), which could

contain high contents of bacteria. It was assumed that some denitrification was possible in

the channel, since the DO concentrations at the end of the channel were low. The calibrated

value for the denitrification parameter was very low, indicating that these denitrification pro-

cesses were negligible. Chapra & Pelletier (2003) used 0.6 mg O2/l as a boundary value under

which denitrification is activated. This supports the idea that denitrification was insignificant

because the measured and simulated DO concentrations were above this boundary value. The

process rates for the nitrogen components in the Drava river are high, indicating a high rate

of conversion and degradation of the different forms of nitrogen. The sink flux for NO3 in

the Drava river was rather high, indicating a large flux of free nitrate to a certain sink. A

possible explanation for this high flux might be related to the periphyton communities present

in the river. Periphyton is complex mixture of benthic algae, cyanobacteria, heterotrophic

microbes and detritus that grow attached to the surface of rocks and macrophytes. Fur-

thermore, they are important components in the energy cycling of aquatic ecosystems, since

they are consumed by invertebrates and fish (Finlay et al., 2002). High water temperatures,

reduced managed flows and/or excess nutrient production can induce excessive growth of pe-

riphyton (Giorgi, 2003). Blumenshine et al. (1997) illustrated that periphyton communities

can sequester large amounts of nitrogen and phosphorus from the water column. A future

extension of the integrated ecological model to simulate these periphyton communities could

increase model performance in respect to phosphorus and nitrogen cycling.

For the development of the RT two different approaches were applied. In the first approach,

the data set was split for tree training and tree validation according to the sampling campaign.

The third monitoring campaign was used to train the model, the first and second campaign

was used to validate the model (= independent validation). The second approach used the

data set with 96 points to create 1000 child data sets, for which each data set had an equal

number of instances for the four water quality class. These different data sets were used to

develop a 1000 models. The 10 best models were retained and checked for ecological relevance

(Figure 3.7). The validation of the RT was based on three criteria (Goethals, 2005). The first

validation is a validation with theoretical indices (e.g. correctly classified instances CCI, root

mean square error, correlation coefficient and coefficient of determination). The tree build

with the second approach performed better than the tree developed with the first approach.

In order to have a satisfactory model performance, the % CCI should be at least 70% (Gabriels

et al., 2007). The author presented this value for models which simulate present or absence

of macro-invertebrates. In this thesis, the presented models simulate the ecological water

quality class with four possible classes as output. The highest % CCI was equal to 59%.


The second validation criteria is based on the ecological relevance of the tree. Both of the

models provided insight and relevant information with respect to the ecological functioning

of the system. Furthermore, both trees included physical-chemical, hydraulic and hydro-

morphological variables. The second tree included more variables than the first tree. The

third validation is by practical use of the model by water managers. This third validation was

not used, but could be used in order to evaluate the procedure and the results. Taking into

account the first two validation criteria, the second tree is identified as the best tree (Figure

4.10).

5.1.3 Integrated ecological model

The main advantage of the integrated ecological model approach is the amount of information

which is combined. Information in different domains of river research is combined (e.g. ecol-

ogy, hydraulics, hydro-morphology, physical-chemical water quality). These river properties

are linked in one structure, a RT, which describes the relation between the variables and the

EWQ. The advantages of using RT in this framework were:

• The tree automatically selects the most informative variable on every tree level.

• The robustness against outliers is a huge advantages. Data points can hold valuable

information even when there is an outlier present in one of the data variables. A data

point with an outlier in one of the data variables can be retained since the model output

will not be influenced significantly. The risk of losing informative data for the integrated

model is limited.

• The tree provides a easy-to-comprehend and visual result which is useful in river water

management

• The trees are able to process quantitative data and categorical data.

Furthermore, the selected tree contained information about the physical-chemical, the hy-

draulic and the hydro-morphologic status of the river. All these variables will determine the

ecological status and function of the river. The integration of several information sources

in one framework also has its disadvantages. For one, the propagation of the error through

the model structure could influence the final output significantly. This propagation was not

considered in this study, but should be assessed in future studies. Furthermore, a proper

uncertainty analysis will provide an added value for the interpretation and use of integrated

ecological model.


5.2 Implications for study area

In general, the ecological water quality of the Drava river was higher than the quality in

the lakes, channels and canals. This subdivision between river and lake, channel & canals

was highly correlated to the hydro-morphological properties of the waterbodies. The analysis

of the biological monitoring and the RT indicated that the type (1 = hydro-morphological

favorable conditions, 2 = hydro-morphological non-favorable) was an important explanatory

variable in this system. Furthermore, as indicated by the principal component analysis and

correlation matrix, the variable type was not highly correlated to any other predictor vari-

able, which indicated the importance of this variable. The river has a natural bank struc-

ture, mixed substrates (cobblestones, gravel, sand), a thin sludge layer and a meandering

path which results in a heterogenous environment for the river organisms. Compared to the

riverine ecosystems, lakes do not support different substrate types and habitats (artificial, ho-

mogeneous bank structure) (Beisel et al., 2000). The same statement is true for the drainage

channels (thick sludge layer, semi-artificial bank structure and substrates, non-meandering

pattern) and tail- and headrace canals (homogeneous bank structure, non-meandering path,

semi-artificial bank structure and substrates) of the hydro-electric plants (HPP). The biodi-

versity is affected by these homogeneous conditions Moyle & Mount (2007). The EWQ is

thus correlated to these properties, since the EWQ is highly correlated to the biodiversity

and composition of macro-invertebrate and other river organism communities.

The dam operation might also have its influence on the EWQ of the river. The ecological

quality in the first two stretches of the river, covering a reach of 1 kilometer (see Figure 4.1,

sampling point 6 and 8) was moderate, while upstream (of Lake Cakovec) mostly good water

quality was monitored (sampling point US4, US5, US6 and US7). As indicated by Poff &

Zimmerman (2010); Dewson et al. (2007); Cortes et al. (2002); Kaeiro et al. (2011); Timm

et al. (2011), the macro-invertebrate community is usually strongly influenced by the water

level and flow fluctuations up- and downstream of dams. Furthermore, many authors have

identified a link between the distribution of macro-invertebrates and the hydraulic conditions

(Newson et al., 2012; Kemp et al., 2000; Statzner & Higler, 1986; Ward & Stanford, 1979;

Statzner et al., 1988; Statzner & Higler, 1986). Spence & Hynes (1971) suggested that the

downstream difference in macro-invertebrate composition are comparable to those occurring

after a mild organic enrichment. The final RT (Figure 4.10) indicated the importance of flow

velocity. Higher classes of water quality could be obtained when the flow velocity was higher.

Grian & Kerea (2004) have identified these problems of water flow abstraction in the Drava

river downstream Cakovec lake. The authors describe the effects of the decreased flows after

the construction of the HPP Cakovec in 1982. The construction of the HPP and dam initiated

a water shortage in the river wetland ecosystem (Figure 5.1). The groundwater level in the

surrounding wetland of the Drava river lowered considerable due to the lower inputs of flow

(Figure 5.2). The wetland vegetation came under stress because of dropping water levels.


Figure 5.1: Illustration of water shortage after the construction of Cakovec HPP (Grian & Kerea,

2004).

Figure 5.2: Illustration of decrease in water levels and the lowering of the water table after the

construction of Cakovec HPP (Grian & Kerea, 2004).


It was decided to construct natural sills (natural overflow construction in a river which are

embedded with rocks, gravel and cobble stones) in order to re-establish the original water

levels in the old riverbed. An example of a sill is illustrated in Figure 5.3. These sills were

constructed by excavating parts of the river over a stretch of 300 m and by using the excavated

sediment to build the sills. The ground water levels were recovered to their original depth

and the water deficiency problem was solved. The steady flow supply of 8 m3/s (biological

minimum) and the river restoration actions should ensure the preservation of the ecosystem

value.

Figure 5.3: Example of sills in the Drava river

The use of the MMIF for this research could be under discussion since the index is extrapolated

from its general geographical application site. As indicated by Goethals (2005) and Holguin

(2009), the monitoring and assessment based on macro-invertebrates has some disadvantages

related to geographic distribution, since the incidence and frequency of occurrence of some

species is different in rivers of other regions. This could pose its implications for the MMIF,

since it is an assessment index which is based on the monitoring of macro-invertebrates.

The results of the sampling campaign and the use MMIF were satisfying, since the observed

patterns in Figure 4.1 agree with the impact analysis in Figure 3.2. Until now, there is not

a general biological assessment index used widespread in Croatia and research is ongoing to

decide which criteria will be used for the monitoring of the river water quality in this country

(Kerovec & Mihaljevic, 2010).


The impact of the physical-chemical status on the biological water quality of the river system

is the last important element in the system analysis. The biological assessment map (Figure

4.1) in the results indicated an impact of the drainage channel on the biological functioning of

the Drava river ecosystem. Furthermore, the water quality in the southern drainage channel,

which receives the wastewater discharge, was in average a water quality class lower than in

the northern drainage channel. The analysis of the simulations of the water quality model

indicated a fair dilution of the treated and untreated wastewater with oxygen-poor infiltration

water. Mainly the concentrations of organic nitrogen and ammonia showed an increase in the

Drava river after the joint with the southern drainage channel. The importance of organic

nitrogen and ammonia were also illustrated in the selected tree, where higher concentrations

resulted in lower water quality. Furthermore, the infiltration water with low DO coming

from the lake resulted in low and moderate oxygen levels in respectively the channel and

the river. The higher pollution load in the southern drainage channel was linked to the

discharge of treated and untreated wastewater in the channel. The side streams which mouth

in the channel seemed to have a limited influence on the physical-chemical water quality. The

WWTP had a significant influence. The stakeholders and WWTP managers are aware of the

influence and, as depicted by Kezelj et al. (2010), the pressure on the municipal WWTP is

rising mainly by an increased input of industrial wastewater.

Chapter 6

Conclusions and future perspectives

This study has illustrated the high potential of integrated ecological models. The framework

for the integrated ecological model was able to integrate all water quality driving variables

(physical-chemical, hydraulic, hydro-morphological and biological variables) in one structure

in order to quantify the major impacts. This approach contributes significantly to the insight

and knowledge of the ecological functioning of river ecosystems. The integrated model is a

powerful and effictive tool, because it is able to asses the ecological impacts of wastewater

discharges and dam operations on the Drava river. This research creates opportunities and

perspectives for the involved parties and stakeholders to reconsider elements in the water

management of the system. As indicated in the introduction, the Drava river in Croatia is

a river ecosystem with an unique value. It is up to the involved instances to preserve this

valuable ecosystem.

The key elements in the integrated ecological model were the water quality and regression

tree model. The performance of the calibration of the water quality model was good, while

the performance for the validation was lower. Possible future extentensions of the model

structure should be focus on modelling of algae and bacteria in order to yield better results.

An interesting approach which could be used for the water quality model could be the one

adopted by Reichert et al. (2001) for the development in the river water quality model no. 1

(RWQM no1). The goal of this model was to integrate a sewer, wastewater treatment and

river water quality model in one integrated framework. An advantages of the model is that it

is based on chemical oxygen demand (COD) modelling which makes it possible to close the

mass balance for carbon. Furthermore, the RWQM no1 is able to simulate microbial biomass

in the river column (Shanahan et al., 1998; Somlyody et al., 1998).

63

Chapter 6. Conclusions and future perspectives 64

The regression tree developed for the ecological assessment model provided an added value

to model the ecological water class. The bootstrap approach proved to be useful to find a

tree with satisfying performance criteria and relevant ecological information. As indicated

by Gabriels et al. (2007), the % CCI should at least be 70% for simulating 2 classes. In this

research, a maximum % CCI of 59% was obtained for a model which simulates 4 classes. The

selected tree provided significant insight in the ecological functioning of the Drava river. The

use of regression trees in this framework was very useful. The tree automatically selected the

most informative variables. The robustness of the trees to outliers was a huge advantages.

Data points can hold valuable information even when there is a outlier present in one of the

data variables. A data point with an outlier in one of the data variables can be retained

since the model output will not be influenced significantly. Additionally, the tree provided an

easy-to-comprehend and visual result, which is useful in river water management.

Additional data should be collected in order to increase model performance. Future moni-

toring should focus on sampling locations in the old trajectory of the Drava river in order

to increase knowledge about the functioning of the river ecosystem. More samples should be

collected in the river upstream of Cakovec lake, since this serves as a reference condition for

the river downstream. Additionally, the technical global performance and the propagation of

the error in the integrated ecological model should be assessed in future studies. A proper

uncertainty analysis should be performed and the relevance in practice and applicability of

the model should be evaluated. When these elements are taken into account, the model will

be able to simulate different scenario’s. These scenario’s can support decission making in

river managment.

The dam construction and its operations might have a influence on the functioning of the

system. Water shortage in the system was an issue after the construction of the hydro-electric

plants and might, till this day, still be an issue. The current biological minimum flow of 8

m3/s (minimal flow which should be released to the Drava river) might not be sufficient

to preserve the natural value of the river. It is possible that the value of 8 m3/s should

be reconsidered. Future studies could asses whether this value is high enough to guarantee

the preservation of the Drava river. Furthermore, the impact of the discharge of treated

and untreated wastewater on this river should be closely followed in future studies. The

pressure on the wastewater treatment plant (WWTP) of the city of Varazdin is rising due

to increased industrial activity in the city of Varazdin. In perspective of growing pressures

and impacts, the efficiency and capacity of the WWTP plant could be reconsidered for future

optimizations. Furthermore, the discharge regulations for the industries in this study area

could be reconsidered in order to decrease pressure on the WWTP.

References

Ahmadi-Nedushan, B., St-Hilaire, A., Berube, M., Robichaud, E., Thiemonge, N. & Bobee,

B. (2006). A review of statistical methods for the evaluation of aquatic habitat suitability

for instream flow assessment. RIVER RESEARCH AND APPLICATIONS, 22(5):503–523.

ISSN 1535-1459.

Ambelu, A., Lock, K. & Goethals, P. (2010). Comparison of modelling techniques to predict

macroinvertebrate community composition in rivers of Ethiopia. ECOLOGICAL INFOR-

MATICS, 5(2):147–152. ISSN 1574-9541.

Apha (2005). Standard methods for the examination of water and wastewater, volume 2009.

American Public Health Association.

Barre de Saint-Venant, A. (1871). Theorie du Mouvement Non Permanent des Eaux, avec

Application aux Crues de Rivieres et ‘a l’Introduction des Marees dans leur Lit. Comptes

Rendus des seances de l’Academie des Sciences, Paris, France.

Beisel, J., Usseglio-Polatera, P. & Moreteau, J. (2000). The spatial heterogeneity of a river

bottom: a key factor determining macroinvertebrate communities. HYDROBIOLOGIA,

422:163–171. ISSN 0018-8158. International Conference on Assessing the Ecological In-

tegrity of Running Waters, UNIV AGR SCI, DEPT HYDROBIO, VIENNA, AUSTRIA,

NOV, 1998.

Benedetti, L., Meirlaen, J., Sforzi, F., Facchi, A., Gandolfi, C. & Vanrolleghem, P. A. (2007).

Dynamic integrated water quality modelling: A case study of the Lambro River, northern

Italy. WATER SA, 33(5):627–632. ISSN 0378-4738.

Benedetti, L. & Sforzi, F. (1999). Dynamic Integrated Modelling: A Case Study On The

Lambro Catchment. Ph.D. thesis, University of Ghent, UGent.

Blumenshine, S., Vadeboncoeur, Y., Lodge, D., Cottingham, K. & Knight, S. (1997). Benthic-

pelagic links: responses of benthos to water-column nutrient enrichment. JOURNAL OF

THE NORTH AMERICAN BENTHOLOGICAL SOCIETY, 16(3):466–479. ISSN 0887-

3593.

65

References 66

Bockelmann, B., Fenrich, E., Lin, B. & Falconer, R. (2004). Development of an ecohydraulics

model for stream and river restoration. ECOLOGICAL ENGINEERING, 22(4-5):227–235.

ISSN 0925-8574.

Boets, P., Lock, K., Messiaen, M. & Goethals, P. L. M. (2010). Combining data-driven

methods and lab studies to analyse the ecology of Dikerogammarus villosus. ECOLOGICAL

INFORMATICS, 5(2):133–139. ISSN 1574-9541.

Bonacci, O. & Oskorus, D. (2008). The influence of three croation hydroelectric power plants

operation on the river drava hydrological and sediment regime.

Booz, Allen, H. (2001). Water project formulation for varazdin. Technical report, United

States Agency for International Development’s Regional Infrastructure Progra.

Bowie, G. L., William B. Mills, D. B. P., Campbell, C. L., Pagenkopf, J. R., Rupp, G. L.,

Johnson, K. M., Chan, P. W. H. & Gherini, S. A. (1985). Rates, constants, and kinetics

formulations in surface water quality modeling. United States Environmental Protection

Agency, Office of Research and Development, Environmental Research Laboratory, [Athens,

Ga.] :, 2nd ed. edition.

Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and Regression

Trees. Wadsworth, Belmont, USA.

Brown, L. C. & Barnwell, T. O. (2003). QUAL2E: The enhanced stream water quality mod-

els QUAL2E and QUAL2E-UNCAS: Documentation and User Manual. Environmental

Research Laboratory, U.S. Environmental Protection Agency, Athens, Georgia.

Cain, J. (2001). Planning improvements in natural resources management : guidelines for us-

ing Bayesian networks to support the planning and management of development programmes

in the water sector and beyond. Wallingford, UK: Centre for Ecology and Hydrology.

Chapra, S. & Pelletier, G. (2003). QUAL2K: Modeling Framework for Simulating River

and Stream Water Quality: Documentation and User’s Manual. Civil and Environmental

Engineering Dept., Tufts University, Medford, MA.

Chapra, S. C. (1997). Surface water-quality modeling, volume 606. McGraw-Hill.

Chow, V. T. (1981). Open-channel Hydraulics, international student edition. McGraw Hill

Book Company, Inc, New York.

Chow, V. T., Maidment, D. R. & Mays, L. W. (1988). Applied Hydrology. McGraw Hill Book

Company, Inc, New York.

Chuco, T. D. (2004). Dynamic Integrated Modelling Of Basic Water Quality And Fate And

Effect Of Organic Contaminants In Rivers. Ph.D. thesis, University of Ghent, UGent.

References 67

Clough, J. S. (2009). Aquatox, Modeling Environmental Fate and Ecological Effects in Aquatic

Ecosystems Volume1: User’s Manual. U.S. Environmental Protection Agency, Office of

water, Office of science and technology, Washington DC 20460.

Cortes, R., Ferreira, M., Oliveira, S. & Oliveira, D. (2002). Macroinvertebrate community

structure in a regulated river segment with different flow conditions. RIVER RESEARCH

AND APPLICATIONS, 18(4):367–382. ISSN 1535-1459.

Covar, A. (1976). Selecting the proper reareation coefficient for use in water quality mod-

els. In Proceedings of the Conference on Environmental Modeling and Simulation. U.S.

Environmental Protection Agency.

De Pauw, N. & Vanhooren, G. (1983). Method for biological quality assessment of water-

courses in Belgium. HYDROBIOLOGIA, 100:153–168. ISSN 0018-8158.

Deksissa, T., Meirlaen, J., Ashton, P. & Vanrolleghem, P. (2004). Simplifying dynamic river

water quality modelling: A case study of inorganic nitrogen dynamics in the Crocodile

River (South Africa). WATER AIR AND SOIL POLLUTION, 155(1-4):303–320. ISSN

0049-6979.

Dewson, Z. S., James, A. B. W. & Death, R. G. (2007). A review of the consequences of

decreased flow for instream habitat and macroinvertebrates. JOURNAL OF THE NORTH

AMERICAN BENTHOLOGICAL SOCIETY, 26(3):401–415. ISSN 0887-3593.

DHI Water & Environment (2003). MIKE11 - a Modelling System for RIvers and Channels:

Short Introduction Tutorial. DHI Water & Environment.

European Commission (2000). Directive 2000/60/ec of the european parliament and of the

council of 23 october 2000. establishing a framework for community action in the field of

water policy. Official Journal of the European Communities L327, pp. 1–71.

Everaert, G., Boets, P., Lock, K., Dzeroski, S. & Goethals, P. L. (2011). Using classification

trees to analyze the impact of exotic species on the ecological assessment of polder lakes in

flanders, belgium. Ecological Modelling, 222(14):2202 – 2212. ISSN 0304-3800.

Everaert, G., Pauwels, I. & Goethals, P. (2010). Development of data-driven models for the

assessment of macroinvertebrates in rivers in flanders. In D. A. Swayne, W. Yang, A. A.

Voinov, A. Rizzoli & T. Filatova, editors, Modelling for environment’s sake : proceedings

of the fifth biennial conference of the International Environmental Modelling and Software

Society, volume 3, pp. 1948–1956. International Environmental Modelling and Software

Society (iEMSs). ISBN 9788890357411.

Extence, C., Balbi, D. & Chadd, R. (1999). River flow indexing using British benthic macroin-

vertebrates: A framework for setting hydroecological objectives. REGULATED RIVERS-

RESEARCH & MANAGEMENT, 15(6):543–574. ISSN 0886-9375.

References 68

Finlay, J., Khandwala, S. & Power, M. (2002). Spatial scales of carbon flow in a river food

web. ECOLOGY, 83(7):1845–1859. ISSN 0012-9658.

Gabriels, W., Goethals, P. L. M., Dedecker, A. P., Lek, S. & De Pauw, N. (2007). Analysis of

macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection

procedure with artificial neural networks. AQUATIC ECOLOGY, 41(3):427–441. ISSN

1386-2588.

Gabriels, W., Lock, K., De Pauw, N. & Goethals, P. L. M. (2010). Multimetric Macroinver-

tebrate Index Flanders (MMIF) for biological assessment of rivers and lakes in Flanders

(Belgium). LIMNOLOGICA, 40(3):199–207. ISSN 0075-9511.

Garcıa, T., Pelletier, G. J. & Dıaz, J. (submitted). Water quality simulation of the chicamocha

river, colombia: an application of the qual2kw model.

Gibson, L., Wilson, B., Cahill, D. & Hill, J. (2004). Spatial prediction of rufous bristlebird

habitat in a coastal heathland: a GIS-based approach. JOURNAL OF APPLIED ECOL-

OGY, 41(2):213–223. ISSN 0021-8901.

Giorgi, D. (2003). Periphyton ( attached algae and aquatic plants ) as indicators of watershed

condition. Assessment, 126(3):228–43.

Goethals, P. L. (2005). Data driven development of predictive ecological models for benthic

macroinvertebrates in rivers. Ph.D. thesis, University of Ghent, UGent.

Grian, L. & Kerea, Z. (2004). Restoration of the old drava riverbed at the cakovec hydropower

plant. In 3rd European Conference on River Restoration, Zagreb, Croatia.

Guisan, A., Edwards, T. & Hastie, T. (2002). Generalized linear and generalized additive

models in studies of species distributions: setting the scene. ECOLOGICAL MODELLING,

157(2-3):89–100. ISSN 0304-3800.

Henze, M., Gujer, W., Mino, T. & van Loosdrecht, M. (2000). Activeted Sludge Models ASM1,

ASM2, ASM2d and ASM3. IWA Task Group on Mathematical Modelling for Design and

Operation of Biological Wastewater Treatment.

HEP - Transmission System Operator LLC (2010). Annual report: Hrvatski elektroenergetski

sustav u Croation electricity system.

Hoang, T. H., Lock, K., Mouton, A. & Goethals, P. L. M. (2010). Application of classification

trees and support vector machines to model the presence of macroinvertebrates in rivers in

Vietnam. ECOLOGICAL INFORMATICS, 5(2):140–146. ISSN 1574-9541.

Holguin, J. E. G. (2009). Modelling The Ecological Impact Of Wastewaters on The Cauca

River (Colombia). Ph.D. thesis, University of Ghent, UGent.

References 69

Holguin, J. E. G. & Goethals, P. (2010). Modelling the ecological impact of discharged urban

waters upon receiving aquatic ecosystems : a tropical lowland river case study : city cali

and the cauca river in colombia. In D. A. Swayne, W. Yang, A. A. Voinov, A. Rizzoli &

T. Filatova, editors, Modelling for environment’s sake : proceedings of the fifth biennial

conference of the International Environmental Modelling and Software Society, volume 2,

pp. 1454–1462. International Environmental Modelling and Software Society (iEMSs). ISBN

9788890357411.

Hynes, H. B. N. (1974). The biology of polluted waters.

Jha, M. K., Gassman, P. W. & Arnold, J. G. (2007). Water quality modeling for the Raccoon

River watershed using SWAT. TRANSACTIONS OF THE ASABE, 50(2):479–493. ISSN

0001-2351.

Kaeiro, K., Moels, T., Timm, H., Virro, T. & Jaervekuelg, R. (2011). THE EFFECT

OF DAMMING ON BIOLOGICAL QUALITY ACCORDING TO MACROINVERTE-

BRATES IN SOME ESTONIAN STREAMS, CENTRAL-BALTIC EUROPE: A PILOT

STUDY. RIVER RESEARCH AND APPLICATIONS, 27(7):895–907. ISSN 1535-1459.

Kampichler, C., Wieland, R., Calme, S., Weissenberger, H. & Arriaga-Weiss, S. (2010). Clas-

sification in conservation biology: A comparison of five machine-learning methods. ECO-

LOGICAL INFORMATICS, 5(6):441–450. ISSN 1574-9541.

Kannel, P. R., Lee, S., Lee, Y. S., Kanel, S. R. & Pelletier, G. J. (2007). Application of

automated QUAL2Kw for water quality modeling and management in the Bagmati River,

Nepal. ECOLOGICAL MODELLING, 202(3-4):503–517. ISSN 0304-3800.

Kemp, J., Harper, D. & Crosa, G. (2000). The habitat-scale ecohydraulics of rivers. ECO-

LOGICAL ENGINEERING, 16(1):17–29. ISSN 0925-8574. International-Hydrological-

Programme Symposium on Ecohydrology, UNIV LODZ, LODZ, POLAND, MAY, 1998.

Kerovec, M. & Mihaljevic, Z. (2010). Comparison of two biological methods for assesment of

river water quality based on macrozoobenthos.

Kezelj, T., Cesarec, M., Mukic, R., Sivric, M., Malec, Z. & Leljak, T. (2010). Utjecaj okoline

na rad uredaja za prociscavanje otpadnih voda aglomeracije varazdin (environmental im-

pact on the operation of equipment for wastewater treatment agglomerations varazdin). In

Aktualna problematika u vodoopskrbi i odvodnji (Current issues in water and sanitation).

Kilsby, C., Large, A., Newson, M., Orr, H. & Walsh, C. (2006). Incorporating climate change

in river typologies for the water framework directive.

Laws, E. A. (2000). Aquatic pollution: an introductory text. John Wiley & Sons.

References 70

Masch, F. D., Associates & Board, T. W. D. (1970). QUAL-1 simulation of water quality in-

stream and canals. Program Documentation and User’s manual. Texas Water development

Board Texas.

Melching, C. & Flores, H. (1999). Reaeration equations derived from US geological survey

database. JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 125(5):407–414.

ISSN 0733-9372.

Ministarstvo poljoprivrede, ribarstva i ruralnog razvoja (2009). Arkod @ONLINE. URL

http://preglednik.arkod.hr/ARKOD-Web/.

Mouton, A. M., Schneider, M., Depestele, J., Goethals, P. L. M. & De Pauw, N. (2007).

Fish habitat modelling as a tool for river management. ECOLOGICAL ENGINEERING,

29(3):305–315. ISSN 0925-8574.

Moyle, P. B. & Mount, J. F. (2007). Homogenous rivers, homogenous faunas. PROCEED-

INGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF

AMERICA, 104(14):5711–5712. ISSN 0027-8424.

Myers, R. H., Montgomery, D. C. & Vining, G. G. (2002). Generalized linear mod-

els:generalized linear models: With applications in engineering and the sciences. American

Statistician, 57(1):67–68.

Nash, J. E. (1955). The form of the instantaneous unit hydrograph.

Nelder, J. A. & Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal Of The

Royal Statistical Sociaty Series A-General, 135(3):370–&.

Newson, M., Sear, D. & Soulsby, C. (2012). Incorporating hydromorphology in strategic

approaches to managing flows for salmonids. Fisheries Management and Ecology, pp. no–

no. ISSN 1365-2400.

Newson, M. D. & Large, A. R. G. (2006). ‘Natural’ rivers, ‘hydromorphological quality’ and

river restoration: a challenging new agenda for applied fluvial geomorphology. EARTH

SURFACE PROCESSES AND LANDFORMS, 31(13):1606–1624. ISSN 0197-9337. Joint

International Geomorphology Conference, Glasgow, SCOTLAND, AUG, 2004.

O’Brien, C. (2007). Analysing ecological data by alain f. zuur, elena n. ieno, graham m. smith.

International Statistical Review, 75(3):426–427. ISSN 1751-5823.

Orr, H. G., Large, A. R. G., Newson, M. D. & Walsh, C. L. (2008). A predictive typology for

characterising hydromorphology. GEOMORPHOLOGY, 100(1-2, SI):32–40. ISSN 0169-

555X.

http://preglednik.arkod.hr/ARKOD-Web/

References 71

Park, S. & Lee, Y. (2002). A water quality modeling study of the Nakdong River, Korea.

ECOLOGICAL MODELLING, 152(1):65–75. ISSN 0304-3800.

Pauwels, I., Everaert, G. & Goethals, P. (2010). Integrated river assessment by coupling

water quality and ecological assessment models. In D. A. Swayne, W. Yang, A. A. Voinov,

A. Rizzoli & T. Filatova, editors, Modelling for environment’s sake : proceedings of the

fifth biennial conference of the International Environmental Modelling and Software Soci-

ety, volume 1, pp. 876–884. International Environmental Modelling and Software Society

(iEMSs). ISBN 9788890357411.

Peavy, H., Rowe, D. & Tchobanoglous, G. (1985). Environmental Engineering. McGraw Hill

Book Company, Inc, New York.

Poff, N. L. & Zimmerman, J. K. H. (2010). Ecological responses to altered flow regimes: a

literature review to inform the science and management of environmental flows. FRESH-

WATER BIOLOGY, 55(1):194–205. ISSN 0046-5070.

Quinlan, J. R. (1992). Learning with continuous Classes. In 5th Australian Joint Conference

on Artificial Intelligence, pp. 343–348.

Rauch, W., Henze, M., Koncsos, L., Reichert, P., Shanahan, P., Somlyody, L. & Vanrol-

leghem, P. (1998). River water quality modelling: I. State of the art. WATER SCIENCE

AND TECHNOLOGY, 38(11):237–244. ISSN 0273-1223. 19th Biennial Conference of the

International-Association-on-Water-Quality, VANCOUVER, CANADA, JUN 21-26, 1998.

Reda, L. (1996). Simulation and control of stormwater impacts on river water quality.

Ph.D. thesis, Department of Civil Engineering, Imperial College of Science, Technology

and Medicine, London.

Regan, H., Colyvan, M. & Burgman, M. (2002). A taxonomy and treatment of uncertainty for

ecology and conservation biology. ECOLOGICAL APPLICATIONS, 12(2):618–628. ISSN

1051-0761.

Reichert, P., Borchardt, D., Henze, M., Rauch, W., Shanahan, P., Somlyody, L. & Vanrol-

leghem, P. A. (2001). River Water Quality: Model No. 1 - IWA Scientific & Technical

Reports No. 12. IWA Task Group on River Water Quality Modelling.

Sever, Z., Frankovic, B., Pavlin, Z. & Stankovic, V. (2000). Hydroelectric power plants in

croatia.

Shanahan, P., Borchardt, D., Henze, M., Rauch, W., Reichert, P., Somlyody, L. & Vanrol-

leghem, P. (2001). River water quality model no. 1 (RWQM1): I. Modelling approach.

WATER SCIENCE AND TECHNOLOGY, 43(5):1–9. ISSN 0273-1223. 1st World Wa-

ter Congress of the International-Water-Association (IWA), PARIS, FRANCE, JUL 03-07,

2000.

References 72

Shanahan, P., Henze, M., Koncsos, L., Rauch, W., Reichert, P., Somlyody, L. & Vanrolleghem,

P. (1998). River water quality modelling: II. Problems of the art. WATER SCIENCE



Sipek, V., Matouskova, M. & Dvorak, M. (2010). Comparative analysis of selected hydro-

morphological assessment methods. ENVIRONMENTAL MONITORING AND ASSESS-

MENT, 169(1-4):309–319. ISSN 0167-6369.

Somlyody, L., Henze, M., Koncsos, L., Rauch, W., Reichert, P., Shanahan, P. & Vanrolleghem,

P. (1998). River water quality modelling: III. Future of the art. WATER SCIENCE



Spellman, F. R. (1996). Stream Ecology & Self-Purification. Technomic Pub.

Spence, J. & Hynes, H. B. N. (1971). Differences in benthos upstream and downstream of an

impoundment. J. Fish. Res. BD. Canada, 18:35–43.

Statzner, B., Gore, J. & Resh, V. (1988). Hydraulic stream ecology - Observed patterns and

potential applications. JOURNAL OF THE NORTH AMERICAN BENTHOLOGICAL

SOCIETY, 7(4):307–360. ISSN 0887-3593.

Statzner, B. & Higler, B. (1986). Stream hydraulics as a major determinant of benthic

invertebrate zonation patterns. FRESHWATER BIOLOGY, 16(1):127–139. ISSN 0046-

5070.

Streeter, H. & Phelps, E. B. (1925). A study of the pollution and natural purification of the

ohio river: Iii. factors concerned in the phenomena of oxidation and reaeration.

Timm, H., Kaeiro, K., Moels, T. & Virro, T. (2011). An index to assess hydromorphological

quality of Estonian surface waters based on macroinvertebrate taxonomic composition.

LIMNOLOGICA, 41(4):398–410. ISSN 0075-9511.

Tomsic, C., Granata, T., Murphy, R. & Livchak, C. (2007). Using a coupled eco-hydrodynamic

model to predict habitat for target species following dam removal. Ecological Engineering,

30(3):215–230.

United States. Army. Corps of Engineers (1997). Flood-Runoff Analysis. Amer Society of

Civil Engineers.

Vanrolleghem, P., Borchardt, D., Henze, M., Rauch, W., Reichert, P., Shanahan, P. &

Somlyody, L. (2001). River water quality model no. 1 (RWQM1): III. Biochemical submodel

selection. WATER SCIENCE AND TECHNOLOGY, 43(5):31–40. ISSN 0273-1223. 1st

References 73

World Water Congress of the International-Water-Association (IWA), PARIS, FRANCE,

JUL 03-07, 2000.

Vaughan, I. P., Diamond, M., Gurnell, A. M., Hall, K. A., Jenkins, A., Milner, N. J., Naylor,

L. A., Sear, D. A., Woodward, G. & Ormerod, S. J. (2009). Integrating ecology with hydro-

morphology: a priority for river science and management. AQUATIC CONSERVATION-

MARINE AND FRESHWATER ECOSYSTEMS, 19(1):113–125. ISSN 1052-7613.

Vayssieres, M., Plant, R. & Allen-Diaz, B. (2000). Classification trees: An alternative non-

parametric approach for predicting species distributions. JOURNAL OF VEGETATION

SCIENCE, 11(5):679–694. ISSN 1100-9233.

Verhoest, N. (2010). Hydraulica, course notes.

Verrall, R. (1996). Claims reserving and generalised additive models. INSURANCE MATH-

EMATICS & ECONOMICS, 19(1):31–43. ISSN 0167-6687.

Vogel, R. M. (2011). Hydromorphology. JOURNAL OF WATER RESOURCES PLANNING

AND MANAGEMENT-ASCE, 137(2):147–149. ISSN 0733-9496.

Wang, Y. & Witten, I. H. (1997). Induction of model trees for predicting continuous classes.

In Poster papers of the 9th European Conference on Machine Learning. Springer.

Ward, J. V. & Stanford, J. A. (1979). The ecology of regulated streams.

Zadeh, L. A. (1965). Fuzzy sets. information and control. pp. 338–353.

Zuur, A. F., Ieno, E. N. & Elphick, C. S. (2010). A protocol for data exploration to avoid

common statistical problems. METHODS IN ECOLOGY AND EVOLUTION, 1(1):3–14.

ISSN 2041-210X.

Appendix A

Data processing

0

0.5

1

1.5

2

2.5

1TP (mg P/l)

0

0.5

1

1.5

2

1PO

4 (mg P/l)

Box plot for Phosphor components (mg P/l)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1ORGP(mg P/l)

Figure A.1: Box plots for Phosphor component; Total Phosphor (TP, mg P/l), Organic Phosphor

(ORGP, mg P/l), Phosphate (PO4, mg P/l)

74

Appendix A. Data processing 75

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1NO

3 (mg N/l)

0

0.5

1

1.5

2

2.5

3

1NH

4 (mg N/l)

Box plot for Nitrogen components (mg N/l)

0

1

2

3

4

5

6

1ORGN (mg N/l)

1

2

3

4

5

6

7

8

1TN (mg N/l)

Figure A.2: Box plots for Nitrogen component; Organic Nitrogen (ORGN, mg N/l), Ammonia (NH4,

mg N/l) and Nitrate (NO3, mg N/l), Total Nitrogen (TN, mg N/l)


0

5

10

15

20

25

30

35

40

45

1TSS (mg/l)

0

10

20

30

40

50

1BOD

5 (mg O

2/l)

Box plot for TSS (mg/l), BOD and COD (mg O2/l) components

0

50

100

150

200

250

300

350

400

450

1COD (mg O

2/l)

Figure A.3: Box plots for Total Suspended Solids (TSS, mg/l), Chemical Oxygen Demand (DO, mg

O2/l) and Biological Oxygen Demand(BOD, BOD5 mg O2/l)


0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1MMIF−value (−)

0

2

4

6

8

10

12

1DO (mg O

2/l)

Box plot for MMIF−value and DO (mg O2/l and %)

0

20

40

60

80

100

120

1DO (%)

Figure A.4: Box plots for MMIF and Dissolved Oxygen Concentration (DO, mg O2/l and %)


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

20

40

60

80

100

120Cleveland dot plot for Organic Phosphor (mg P/l)

ORGP(mg P/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101 102103104105106

Figure A.5: Cleveland dot plot for Organic Phosphor (mg P/l)

0 0.5 1 1.5 2 2.50

20

40

60

80

100

120Cleveland dot plot for Phosphate (mg P/l)

PO4 (mg P/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101102103104105106

Figure A.6: Cleveland dot plot for Phosphate (mg P/l)


0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

120Cleveland dot plot for Total Phosphor (mg P/l)

TP (mg P/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101102103104105106

Figure A.7: Cleveland dot plot for Total Phosphor (mg P/l)

0 1 2 3 4 5 6 70

20

40

60

80

100

120Cleveland dot plot for Organic Nitrogen (mg N/l)

ORGN (mg N/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101102 103104105106

Figure A.8: Cleveland dot plot for Organic Nitrogen (mg N/l)


0 0.5 1 1.5 2 2.5 3 3.50

20

40

60

80

100

120Cleveland dot plot for Ammonia (mg N/l)

NH4 (mg N/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101102103104105106

Figure A.9: Cleveland dot plot for Ammonia(mg N/l)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

20

40

60

80

100

120Cleveland dot plot for Nitrate (mg N/l)

NO3 (mg N/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100101 102103104105106

Figure A.10: Cleveland dot plot for Nitrate (mg N/l)


0 1 2 3 4 5 6 7 80

20

40

60

80

100

120Cleveland dot plot for Total Nitrogen (mg N/l)

TN (mg N/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100101 102 103104105106

Figure A.11: Cleveland dot plot for Total Nitrogen (mg N/l)

0 10 20 30 40 50 600

20

40

60

80

100

120

Cleveland dot plot for Biological Oxygen Demand (mg O2/l)

BOD (mg O2/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101 102103104105106

Figure A.12: Cleveland dot plot for Biological Oxygen Demand (mg O2/l)


0 50 100 150 200 250 300 350 400 450 5000

20

40

60

80

100

120

Cleveland dot plot for Chemical Oxygen Demand (mg O2/l)

COD (mg O2/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99100101 102103104105106

Figure A.13: Cleveland dot plot for Chemical Oxygen Demand (mg O2/l)

0 2 4 6 8 10 12 140

20

40

60

80

100

120

DO (mg O2/l)

Cleveland dot plot for Dissolved Oxygen (mg O2/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100101102 103 104105106

Figure A.14: Cleveland dot plot for Dissolved Oxygen (mg O2/l)


0 20 40 60 80 100 1200

20

40

60

80

100

120Cleveland dot plot for Dissolved Oxygen(%)

DO (%)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100101102 103 104105106

Figure A.15: Cleveland dot plot for Dissolved Oxygen (%)

0 5 10 15 20 25 30 35 40 450

20

40

60

80

100

120Cleveland dot plot for Total Suspended Solids (mg/l)

TSS (mg/l)

Num

ber

of s

ampl

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100101 102103104 105106

Figure A.16: Cleveland dot plot for Total Suspended Solids (mg/l)


DO

BOD

NO3

PO4 NH4

Depth

Velocity OrgP

ORGN

Type

COD

TN

TP

‐1

‐0,75

‐0,5

‐0,25

0

0,25

0,5

0,75

1

‐1 ‐0,75 ‐0,5 ‐0,25 0 0,25 0,5 0,75 1

F2 (1

8.43

%)

F1 (20.54 %)

Principal Component Analysis for predictor variables

Figure A.17: Principle component analysis. Depth = average water height, Velocity = average ve-

locity, Type=1 (hydro-morphological favorable) / 2 (hydro-morphological unfavorable)

Appendix B

Model development

B.1 Hydraulic model

0 1 2 3 4 5 6

x 105

0

0.1

0.2

0.3

0.4

0.5

0.6

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.05

0.1

0.15

0.2

Vel

ocity

(m

/s)

Time t (s)

Figure B.1: Manual calibration of stretch 1

85

Appendix B. Model development 86

0 1 2 3 4 5 6

x 105

0

0.2

0.4

0.6

0.8

1

1.2

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

1

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

0.5

1

1.5

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.1

0.2

0.3

0.4

0.5

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

0.5

1

1.5

2

2.5

3

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

1

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.1

0.2

0.3

0.4

0.5

0.6

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

1

2

3

4

5

6

7

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.5

1

1.5

2

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.5

1

1.5

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

2

4

6

8

10

12

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

5

10

15

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.02

0.04

0.06

0.08

0.1

0.12

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

5

10

15

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.1

0.2

0.3

0.4

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

5

10

15

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.01

0.02

0.03

0.04

0.05

0.06

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

5

10

15

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.1

0.2

0.3

0.4

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

5

10

15

20

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.005

0.01

0.015

0.02

0.025

0.03

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

5

10

15

20

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.01

0.02

0.03

0.04

Vel

ocity

(m

/s)

Time t (s)


0 1 2 3 4 5 6

x 105

0

5

10

15

20

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

0.05

0.1

0.15

0.2

0.25

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.2

0.4

0.6

0.8

Vel

ocity

(m

/s)

Time t (s)



0 1 2 3 4 5 6

x 105

0

5

10

15

20

Time t (s)

Flo

w Q

(m

3 /s)



0 2 4 6

x 105

0

1

2

3

4

5

6

Wat

er h

eigh

t h (

m)

Time t (s)0 2 4 6

x 105

0

0.01

0.02

0.03

0.04

Vel

ocity

(m

/s)

Time t (s)



B.2 Water quality model: mass balance model

0 1 2 3 4 5 6 7 8 90

2

4

6

8

Org

anic

Nitr

ogen

(m

g N

/l)

SC 1 SC 2 SC 3 Sim

0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

Am

mon

ia (

mg

N/l)

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

2

Nitr

ate

(mg

N/l)

Longitudinal profile of the south drainage channel (km)

Figure B.17: Mass balance for the southern drainage channel for nitrogen components


0 1 2 3 4 5 6 7 8 90

0.05

0.1

0.15

0.2

0.25

0.3

Org

anic

Pho

spho

r (m

g P

/l)

SC 1 SC 2 SC 3 Sim

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

Pho

spha

te (

mg

P/l)


Figure B.18: Mass balance for the southern drainage channel for phosphor components


0 1 2 3 4 5 6 7 8 90

2

4

6

8

10

12D

isso

lved

Oxy

gen

(mg

O2/l)

SC 1 SC 2 SC 3 Sim

0 1 2 3 4 5 6 7 8 90

5

10

15

Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)


Figure B.19: Mass balance for the southern drainage channel for carbon and oxygen components


0 2 4 6 8 10 12 14 16 18 200

0.05

0.1

0.15

0.2

0.25O

rgan

ic P

hosp

hor

(mg

P/l)

SC 1 SC 2 SC 3 Sim

0 2 4 6 8 10 12 14 16 18 200

0.05

0.1

0.15

0.2

0.25

Pho

spha

te (

mg

P/l)

Longitudinal profile of the Drava river (km)

Figure B.20: Mass balance for the Drava river for phosphor components


0 2 4 6 8 10 12 14 16 18 200

2

4

6

8O

rgan

ic N

itrog

en (

mg

N/l)

SC 1 SC 2 SC 3 Sim

0 2 4 6 8 10 12 14 16 18 200

0.5

1

1.5

2

Am

mon

ia (

mg

N/l)

0 2 4 6 8 10 12 14 16 18 200

0.5

1

1.5

2

Nitr

ate

(mg

N/l)


Figure B.21: Mass balance for the Drava river for nitrogen components


0 2 4 6 8 10 12 14 16 18 200

2

4

6

8

10

12

14D

isso

lved

Oxy

gen

(mg

O2/l)

SC 1 SC 2 SC 3 Sim

0 2 4 6 8 10 12 14 16 18 200

10

20

30

40

Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)


Figure B.22: Mass balance for the Drava river for carbon and oxygen components


B.3 Water quality model: calibration

0 1 2 3 4 5 6 7 8 90

0.05

0.1

0.15

0.2

0.25

0.3

Phosphor concentration (mg P/l)

Org

anic

Pho

spho

r (m

g P

/l)

SC 3 Sim Min & Max

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

Pho

spha

te (

mg

P/l)


Figure B.23: Calibrated water quality model for phosphorus in the channel. The actual simulation



of sample campaign 3.


0 1 2 3 4 5 6 7 8 90

2

4

6

8Nitrogen concentration (mg N/l)

Org

anic

Nitr

ogen

(m

g N

/l)

SC3 Sim Min & Max

0 1 2 3 4 5 6 7 8 90

1

2

3

Am

mon

ia (

mg

N/l)

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

Nitr

ate

(mg

N/l)


Figure B.24: Calibrated water quality model for nitrogen in the channel. The actual simulation





0 1 2 3 4 5 6 7 8 90

2

4

6

8

10

12

Biological oxygen demand concentration (mg O2/l)


Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)

SC 3 Sim Min & Max

Figure B.25: Calibrated water quality model for carbon in the channel. The actual simulation is

given in the black fluid line. The dotted line indicates the maximal and minimal




0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

6

7

8

Dissolved Oxygen concentration (mg O2/l)


Dis

solv

ed O

xyge

n (m

g O

2/l)

SC 3 Sim Min & Max

Figure B.26: Calibrated water quality model for oxygen in the channel. The actual simulation is

given in the black fluid line. The dotted line indicates the maximal and minimal




0 2 4 6 8 10 12 14 16 180

0.05

0.1

0.15

Phosphor concentration (mg P/l)O

rgan

ic P

hosp

hor

(mg

P/l)

SC 3 Sim Min & Max

0 2 4 6 8 10 12 14 16 180

0.05

0.1

Pho

spha

te (

mg

P/l)


Figure B.27: Calibrated water quality model for phosphorus in the Drava river. The actual simula-

tion is given in the black fluid line. The dotted line indicates the maximal and minimal




0 2 4 6 8 10 12 14 16 180

2

4

6


Org

anic

Nitr

ogen

(m

g N

/l)

SC 3 Sim Min & Max

0 2 4 6 8 10 12 14 16 180

0.5

1

Am

mon

ia (

mg

N/l)

0 2 4 6 8 10 12 14 16 180

1

2

3

4

Nitr

ate

(mg

N/l)


Figure B.28: Calibrated water quality model for nitrogen in the Drava river. The actual simulation





0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18



Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)

SC 3 Sim Min & Max

Figure B.29: Calibrated water quality model for carbon in the Drava river. The actual simulation





0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

Oxygen concentration (mg O2/l)


Dis

solv

ed O

xyge

n(m

g O

2/l)

SC 3 Sim Min & Max

Figure B.30: Calibrated water quality model for oxygen in the Drava river. The actual simulation





B.4 Water quality model: validation

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8Phosphor concentration (mg P/l)

Org

anic

Pho

spho

r (m

g P

/l)

SC 1 SC 2 Sim

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

Pho

spha

te (

mg

P/l)

Longitudinal profile of the south (A) drainage channel (km)

Figure B.31: Validation of the water quality model for phosphorus in the channel with the data of

the second monitoring campaign (SC2). The actual simulation is given in the black

fluid line.


0 1 2 3 4 5 6 7 8 90

1

2

3

Nitrogen concentration (mg N/l)O

rgan

ic N

itrog

en (

mg

N/l)

SC 1 SC 2 Sim

0 1 2 3 4 5 6 7 8 90

1

2

3

Am

mon

ia (

mg

N/l)

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

Nitr

ate

(mg

N/l)


Figure B.32: Validation of the water quality model for nitrogen in the channel with the data of the

second monitoring campaign (SC2). The actual simulation is given in the black fluid

line.


0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

6

7

8

9

10

11



Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)

SC 1 SC 2 Sim

Figure B.33: Validation of the water quality model for carbon in the Drava river with the data of


fluid line.


0 1 2 3 4 5 6 7 80

1

2

3

4

5

6

7

8

9

10



Dis

solv

ed O

xyge

n(m

g P

/l)

SC 1 SC 2 Sim

Figure B.34: Validation of the water quality model for oxygen in the channel with the data of the

second monitoring campaign (SC2). The actual simulation is given in the black fluid

line.


0 2 4 6 8 10 12 14 16 180

0.05

0.1

0.15

Phosphor concentration (mg P/l)O

rgan

ic P

hosp

hor

(mg

P/l)

SC 1 SC 2 Sim

0 2 4 6 8 10 12 14 16 180

0.05

0.1

Pho

spha

te (

mg

P/l)


Figure B.35: Validation of the water quality model for phosphate in the Drava river with the data

of the second monitoring campaign (SC2). The actual simulation is given in the black

fluid line.


0 2 4 6 8 10 12 14 16 180

2

4

6


Org

anic

Nitr

ogen

(m

g N

/l)

SC 1 SC 2 Sim

0 2 4 6 8 10 12 14 16 180

0.5

1

Am

mon

ia (

mg

N/l)

0 2 4 6 8 10 12 14 16 180

1

2

3

4

Nitr

ate

(mg

N/l)


Figure B.36: Validation of the water quality model for nitrogen in the Drava river with the data of


fluid line.


0 2 4 6 8 10 12 14 16 180

5

10

15

20

25



Bio

logi

cal O

xyge

n D

eman

d (m

g O

2/l)

SC 1 SC 2 Sim

Figure B.37: Validation of the water quality model for carbon in the Drava river with the data of


fluid line.


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12



Dis

solv

ed O

xyge

n(m

g O

2/l)

SC 1 SC 2 Sim

Figure B.38: Validation of the water quality model for oxygen in the Drava river with the data of


fluid line.

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