Water Resources Management (2005) 19: 199–210
DOI: 10.1007/s11269-005-3473-z
C
Springer 2005
Impact Assessment of Different Management
Scenarios on Water Quality of Porsuk River
and Dam System – Turkey
AYSE MUHAMMETOGLU1,∗ , HABIB MUHAMMETOGLU2 , SEDAT OKTAS3 ,
LEVENT OZGOKCEN4 and SELCUK SOYUPAK5
1 Environmental
Pollution and Control Program, Akdeniz University, 07059 Antalya, Turkey;
of Environmental Engineering, Akdeniz University, 07059 Antalya, Turkey; 3 State
Hydraulic Works of Turkey, Third Division, Eskisehir, Turkey; 4 Su-Yapı, Engineering & Consulting
Company, 06550 Ankara, Turkey; 5 Department of Civil Engineering, Atılım University, Kızılcaşar
Köyü, İncek, 06836 Ankara, Turkey
(∗ author for correspondence, e-mail: [email protected]; Fax: +90-242-2274785;
Tel: +90-242-2274780)
2 Department
(Received: 22 December 2003; in final form: 12 July 2004)
Abstract. Porsuk Dam Reservoir (PDR), which is located on Porsuk River, is the main drinking
water resource of Eskisehir City-Turkey. Both the river and the reservoir are under the threat of
several domestic and industrial point sources and land-based diffuse pollution. The river water quality
is very poor with high concentrations of nitrogen and phosphorus compounds at the entrance to
Porsuk Reservoir. The reservoir shows symptoms of a hypertrophic lake. The expected responses
of the whole river and reservoir system under different pollution control scenarios were estimated
to develop plausible water quality management strategies. The adopted scenarios assumed different
levels of treatment for the major domestic point sources that include conventional treatment and tertiary
treatment. The contemporary Turkish Allowable Discharge Limits (ADLs) and the best available
technology choices were the investigated treatment options for the major industries. The expected
improvements of water quality characteristics under the management scenario options have been
estimated by means of mathematical models. The model choices were the QUAL2E for the river and
BATHTUB for the reservoir. Recommendations for different levels of treatment were derived in order
to improve the water quality both within the river and in the reservoir.
Key words: BATHTUB, QUAL2E, river basin management, reservoirs, scenario analysis, wastewater
treatment, water quality modeling
1. Introduction
Porsuk Dam Reservoir (PDR) is a reservoir that supplies water to Eskişehir (Turkey)
with an approximate present population of 500,000. The length, volume and surface
area of the reservoir is 16 km, 457 million m3 and 27.20 million m2 , respectively.
These quantities are applicable for maximum water surface elevation of 890.0 m
above sea level. Porsuk River is the main river that carries water to PDR and it
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A. MUHAMMETOGLU ET AL.
has a 1325 km2 total catchment area at the reservoir entrance. Two other streams,
namely, Kuduzlu and Sabuncupinar, also discharge to PDR. Figure 1 shows the
Porsuk River catchment area with the major point sources of pollution.
Both Porsuk River and PDR receive the discharges from several domestic and
industrial point sources and land-based diffuse pollution. Several water qualitymonitoring stations were established within the catchment of PDR to monitor the
water quality of Porsuk River and its tributaries, and also to monitor the major
point sources. In-lake stations were also established to monitor water quality of
PDR. Figure 2 shows schematically the locations of the main pollution sources and
monitoring stations for PDR and Porsuk River system upstream the reservoir.
The average nutrient concentrations and flow rates at the entrance of PDR were
calculated using the “Flux” model (Walker, 1999). The available hydrological and
water quality information covers the period between the years 1995 and 2000 and
listed in Table I. The present water quality of PDR can be classified as hypertrophic, utilizing OECD classification criteria (OECD, 1982). PDR experiences
seasonal high blooms of algae because of excessive nitrogen and phosphorous
loads. Epilimnion is supersaturated with dissolved oxygen (DO) while anoxic conditions prevail below epilimnion. Light penetration is limited to the top 1–1.5 m of
the reservoir. The average nutrient concentrations in PDR between the years 1995
and 2000 were calculated using the “Profile” model (Walker, 1999), and are given
in Table II. Only for Total-P, the information is limited to a single year (2000).
The present level of eutrophication within PDR inspired implementation and
improvement of water pollution control practices within the catchment area, since
PDR supplies water to Eskisehir Water Treatment Plant. The prime objective is to
improve the water quality within the reservoir to an acceptable level that can be
Table I. Average nutrient concentrations and flow rates to PDR (between years 1995 and
2000)
Source
Porsuk River
Kuduzlu and
Sabuncupinar
Flow rate
(106 m3 per year)
PO4 -P
(mg/m3 )
Total-P
(mg/m3 )
Inorganic-N
(mg/m3 )
Total-N
(mg/m3 )
164.625
44.7
420.8
86.3
546.2
112.0
5276.4
812.3
5659.6
828.9
Table II. Average nutrient concentrations (mg/m3 ) in PDR
Parameter
Period
Concentration
Inorganic-N
Total-N
PO4 -P
Total-P
Chlorophyll a
1995–2000
1995–2000
1995–1999
2000
1995–2000
1018.8
1309.5
124.0
85.0
50.4
201
Figure 1. The catchment area of Porsuk River and the major industries.
IMPACT ASSESSMENT OF DIFFERENT MANAGEMENT SCENARIOS ON WATER QUALITY
202
A. MUHAMMETOGLU ET AL.
Figure 2. Main point sources and monitoring stations on Porsuk River and within PDR.
KWWTP: Kutahya City Wastewater Treatment Plant, SH: Kutahya Municipal Slaughter House,
SF: Kutahya Sugar-Beet Factory, FI: Kutahya Nitrogen Fertilizer Industry, FS: Felent Stream,
KS: Kuduzlu Stream, SS: Sabuncupinar Stream.
stated as improving PDR water from “highly hypertrophic” state to “eutrophic”, or
preferably to “mesotrophic” state. This specific study is realized as an initial step
of water quality management plan for Porsuk River–PDR system to determine the
expected achievements by implementing technically possible management strategies and to estimate the response of the system under each management scenario
option.
2. Methods
There can be several economically feasible and technically possible solutions to
achieve different acceptable and desirable water quality goals. One of the classical
approaches to study the expected improvements of water quality under different
management options is scenario analysis. Several scenarios have been examined
within the context of this study. The expected changes within Porsuk River and its
branches under accepted water pollution control practices of any specific scenario
were determined, and the expected pollution loads to the reservoir were quantified.
The response of PDR under conditions of each scenario was estimated. The changes
within Porsuk River system and also the quantification of pollution loads to PDR
under different scenario conditions were studied using the river water quality model
(QUAL2E) (QUAL2E, 1995). The responses of PDR under these scenarios were
estimated, utilizing the software (BATHTUB) (Walker, 1981a, 1981b, 1999).
2.1.
SCENARIO ANALYSIS
In order to define alternative scenarios, categorization of sources and treatment
levels were needed. The important point sources upstream PDR were categorized
as: (i) Kutahya City Wastewater Treatment Plant (KWWTP), (ii) Kutahya Nitrogen
Fertilizer Industry (FI), (iii) The other industrial point sources, and (iv) the drainage
water from the irrigation areas. Different levels of treatment were categorized as:
IMPACT ASSESSMENT OF DIFFERENT MANAGEMENT SCENARIOS ON WATER QUALITY
203
Table III. Description of the management scenarios in the catchment area of PDR
Scenario
Scenario description
S1
S2i
S2ii
S3i
Present conditions (as of year 2000)
Conventional treatment for Kutahya Wastewater Treatment Plant (KWWTP)
Tertiary treatment for KWWTP
Conventional treatment for KWWTP + ADLs for Kutahya Nitrogen Fertilizer
Industry
Tertiary treatment for KWWTP + ADLs for Kutahya Nitrogen Fertilizer
Industry
Tertiary treatment for KWWTP + BAT for all the industries
Tertiary treatment for KWWTP + BAT for all the industries + 50% reduction
of nitrogen and phosphorus of drainage water
Diverting the effluent of KWWTP + BAT for all the industries + 50% reduction
of nitrogen and phosphorus of drainage water
S3ii
S4i
S4ii
S5
(i) present conditions (as of year 2000), (ii) conventional (secondary) treatment of
domestic wastewater, (iii) tertiary treatment of domestic wastewater, (iv) Allowable
Discharge Limits (ADLs) for the industries, and v) best available technology (BAT)
for the industries. The adopted scenarios are defined and summarized in Table III.
Similar approaches of scenario analyses were applied previously by Soyupak et al.
(1997) and Muhammetoglu et al. (2002) to different river and lake systems.
The expected removal efficiencies by the conventional wastewater treatment are
accepted as 90% for BOD, 25% for total nitrogen, and 25% for total phosphorus.
For the tertiary treatment, the removal efficiencies are accepted as 90% for BOD,
90% for total nitrogen and 85% for total phosphorus (Metcalf and Eddy, 1991). The
ADLs for specific industries were given by the Turkish Water Pollution and Control
norms, published on 4 September 1988, Official Gazette No. 19919. Table IV gives
the ADLs for the nitrogen fertilizer industry as an example.
2.2.
RIVER MODELING
The USA-EPA modeling approach (QUAL2E) was adopted to predict the expected
levels of water quality parameters in Porsuk River under each management scenario
Table IV. Allowable Discharge Limits for 24 h composite samples for only nitrogen
fertilizer production
COD
Suspended solids
(mg/l)
(kg/t)
(mg/l)
1500
2
100
(kg/t)
Ammonium-N
Nitrate-N
(mg/l)
(kg/t)
(mg/l)
(kg/t)
50
4
50
4
pH = 6–9 both in 24 h composite and grab samples.
204
A. MUHAMMETOGLU ET AL.
Table V. Pollution loads from the main point sources to Porsuk River in the winter season
Source
Flow rate Temperature DO
BOD IN
TN
IP
TP
(◦ C)
(mg/l) (mg/l) (mg N/l) (mg N/l) (mg P/l) (mg P/l)
(m3 /s)
SH
KWWTP
SF & FS
FI
0.015
0.430
0.660
0.150
17
14
10
24
0.0
2.0
4.0
2.0
1800
105
18
42
144.0
33.0
2.8
210.0
225.0
41.0
3.1
220.0
8.1
6.0
0.3
1.2
13.5
9.0
0.4
1.4
SH: Kutahya Municipal Slaughter House; KWWTP: Kutahya City Wastewater Treatment Plant;
SF: Kutahya Sugar-Beet Factory; FS: Felent Stream; FI: Kutahya Nitrogen Fertilizer Industry.
option. The river water quality was modeled to represent two distinct meteorological
and hydrological periods: namely winter and summer conditions. The simulated
length of the river upstream of PDR was 160 km. This length was divided into
11 reaches considering the hydraulic characteristics and river morphology. The
length of the computational elements was chosen as 1 km. The simulated water
quality parameters were temperature, nitrogen and phosphorous compounds, DO,
biochemical oxygen demand (BOD), and chlorophyll a.
The model was calibrated, using data collected during the winter season (from
the beginning of November till the end of April) of the year 2000, and then verified
with other data sets collected during the summer season (from the beginning of
May till the end of October) of the same year. The average flow rates of Porsuk
River at PDR entrance in the year 2000 were measured as 7.67 m3 /s in winter and
4.35 m3 /s in summer. The pollution loads from the main point sources to Porsuk
River, shown in Figure 2, for the winter season are given in Table V. The values of the
model reaction coefficients were selected to be the same for all reaches. The default
values of the temperature correction coefficients of QUAL2E were adopted in this
study. The atmospheric re-aeration coefficient was estimated using the equations
of O’Connor and Dobbins (1958). All the calibrated coefficients were within the
ranges reported in the literature (Bowie et al., 1985). Some values of the calibrated
reaction coefficients are given in Table VI. Good agreement is achieved between
model predictions and field measurements. Figure 3 depicts model predictions
and field measurements of inorganic nitrogen and BOD concentrations for the
Table VI. Some values of the calibrated QUAL2E coefficients for Porsuk River
Description of coefficient
Value
Carbonaceous de-oxygenation rate
Rate constant for the hydrolysis of organic N to ammonia
Rate constant for the biological oxidation of NH3 to NO2
Rate constant for the biological oxidation of NO2 to NO3
Rate constant for the decay of organic-P to dissolved-P
Ratio of chlorophyll a to algal biomass (A)
1.0 per day
0.2 per day
0.25 per day
1.0 per day
0.35 per day
50 µg-Chla/mg A
IMPACT ASSESSMENT OF DIFFERENT MANAGEMENT SCENARIOS ON WATER QUALITY
205
Figure 3. Model predictions and measurements of Inorganic Nitrogen (IN) in the winter season
of the year 2000 (top figure for calibration) and in the summer season of the year 2000 (middle
figure for verification); and model predictions and measurements of BOD in the winter season
of the year 2000 for calibration (bottom figure).
calibration and verification periods for a part of Porsuk River just upstream of the
reservoir, as an example. The major pollution sources and monitoring stations in
this part of Porsuk River are shown in Figure 2. As it can be seen, the part of Porsuk
River upstream of the reservoir is heavily polluted mainly with the partially treated
effluents of KWWTP and Kutahya Nitrogen Fertilizer Industry.
2.3.
RESERVOIR MODELING
The limited available measured data sets forced the selection of a simple model for
scenario analysis. The software “BATHTUB” has been adapted for this purpose.
206
A. MUHAMMETOGLU ET AL.
First, the applicability of “BATHTUB” to PDR has been evaluated. A preliminary
evaluation revealed that the physical, chemical and biological characteristics of
water quality of PDR, as investigated by the “Profile” program, lie within the limits
of the tested data of the US Army Corps of Engineers reservoirs (Walker, 1999).
2.3.1. Spatial Discretization
Calculation of area-weighted mean pollutant concentrations for the entire reservoir
was required for scenario analysis. A single spatially averaged reservoir model was
adopted to satisfy such a requirement. Such a spatial discretization has yielded
necessary precision for scenario analysis. Modeling the entire reservoir with one
segment provides predictions of area-weighted mean concentrations, which is adequate to support management decisions. Another justification of adopting a single
spatially averaged reservoir model is relatively small spatial variability of water
quality along the length of PDR.
2.3.2. Temporal Discretization
One year averaging period was adopted during this study, that is the appropriate
averaging period for water bodies with relatively long nutrient residence times.
PDR has such characteristics. This averaging period of 1 year yields a turnover
ratio of 2.6. The turnover ratio approximates the number of times that the nutrient
mass in the reservoir is displaced during the averaging period. Ideally, the turnover
ratio should exceed 2.0.
2.3.3. Formulations
Generally, well-tested and acceptable equations were chosen to conduct PDR scenario analysis for management. A second-order decay model is the most generally
applicable formulation for representing phosphorus and nitrogen sedimentation in
reservoirs according to recent research findings (Walker, 1999). That is why such a
model was selected and utilized during this study. The predictive equations for water quality parameters can be obtained from literature (Walker, 1999). The adopted
kinetics and the variables to estimate major water quality parameters are listed in
Table VII.
3. Results and Discussion
The predictions of QUAL2E for the nutrient levels in Porsuk River at the reservoir
entrance under the determined management scenarios are given in Table VIII. The
Table VII. The adopted main kinetic formulations and variables involved in PDR modelling
Phosphorus balance
Nitrogen balance
Chlorophyll a
Secchi depth
Second-order, decay
Second-order, decay
Phosphorus, nitrogen,
light and temperature
Chlorophyll a and
turbidity
IMPACT ASSESSMENT OF DIFFERENT MANAGEMENT SCENARIOS ON WATER QUALITY
207
Table VIII. QUAL2E predictions of the nutrient concentrations from Porsuk River to PDR
under different management scenario options, for the winter and summer seasons, and for
the yearly weighted average
Input nutrient concentrations
Scenario
Season
Inorganic-N
(mg/m3 )
Total-N
(mg/m3 )
Inorganic-P
(mg/m3 )
Total-P
(mg/m3 )
S1
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
Winter
Summer
Average
8440.0
6380.0
7694.5
8130.0
6250.0
7449.6
6260.0
4730.0
5706.3
5990.0
5530.0
5823.5
4110.0
4000.0
4070.2
1740.0
1770.0
1750.9
1440.0
1300.0
1389.3
1220.0
1240.0
1227.0
9040.0
6950.0
8283.6
8320.0
6380.0
7617.9
6450.0
4840.0
5867.3
6180.0
5650.0
5988.2
4310.0
4120.0
4241.2
1750.0
1820.0
1775.3
1440.0
1340.0
1403.8
1220.0
1270.0
1246.0
430.0
420.0
426.4
350.0
360.0
353.6
110.0
140.0
120.9
350.0
360.0
353.6
110.0
140.0
120.9
50.0
60.0
53.6
50.0
50.0
50.0
20.0
30.0
23.6
600
590
596.4
470.0
480.0
473.6
170.0
210.0
184.5
470.0
480.0
473.6
170.0
210.0
184.5
110.0
110.0
110.0
100.0
100.0
100.0
30.0
40.0
33.6
S2i
S2ii
S3i
S3ii
S4i
S4ii
S5
total yearly flow from Porsuk River to PDR was taken as 189.5 million m3 , as
measured in the year 2000. The yearly averaged predictions of nutrient levels were
entered directly to the “BATHTUB” model to predict the response of the reservoir.
First, the predictive power of BATHTUB was checked by comparing the predictions
for the first scenario (S1) with the average values of field measurements carried out
in June 2000 (see Table IX for comparisons). The results presented in this table show
that “BATHTUB” predictions for total nitrogen (TN), total phosphorus (TP) and
Secchi depths can be considered satisfactorily precise. The chlorophyll a prediction
values are relatively lower than actual measurements. This may be due to the fact
that model predictions were for yearly averages, while chlorophyll a measurements
208
A. MUHAMMETOGLU ET AL.
Table IX. Comparison of BATHTUB model predictions and average values of field measurements of the main water quality parameters in PDR for the year 2000
Data type
Total-N (mg/m3 )
Total-P (mg/m3 )
Chl-a (mg/m3 )
Secchi depth (m)
Predictions
Measurements
1702.9
1805
95.1
85
34.2
61.2
1.0
≈1.0
Table X. The expected responses of PDR to different management options
“BATHTUB” model predictions
Scenario
Total-N
(mg/m3 )
Total-P
(mg/m3 )
Chl-a
(mg/m3 )
Secchi depth
(m)
HODv
(mg/(m3 day))
MODv
(mg/(m3 day))
S1
S2i
S2ii
S3i
S3ii
S4i
S4ii
S5
1702.9
1646.9
1422.4
1438.7
1182.0
725.2
635.0
593.0
95.1
85.9
50.2
85.9
50.2
35.7
34.5
24.2
34.2
32.4
23.0
31.2
22.1
14.5
12.9
9.1
1.0
1.1
1.5
1.1
1.5
2.2
2.4
3.0
100.2
97.6
82.2
95.8
80.5
65.2
61.6
51.8
109.3
106.4
89.6
104.5
87.8
71.1
67.1
56.5
were carried out in June, a period that always exhibits algal blooms. The response of
the reservoir to the considered management scenarios, as predicted by BATHTUB,
is presented in Table X. In this table, HODv and MODv refer to the volumetric
oxygen depletion rates of the hypolimnion and metalimnion layers, respectively.
The presented results show that only marginal improvements can be achieved
with the application of conventional treatment of the domestic wastewater discharges and complying with the Turkish ADLs of the industries, because of the
low levels of nutrient removal efficiencies that can be achieved by applying this
option. However, tertiary treatment of the domestic wastewater and application of
BAT to the industrial wastewater will cause significant improvements in the water
quality of PDR. The highly “hypertrophic” state of the reservoir will be eliminated;
the trophic status of PDR will be improved to “low eutrophic” state by applying
this management scenario. Still, a comprehensive management for the whole watershed is required to further reduce the nutrient concentrations (DSI, Su-Yapi,
2001).
PDR, with a high volume of 457 million m3 , has relatively short hydraulic detention time. Thus, applying “in-reservoir measures” is not expected to improve the
water quality of the reservoir in an efficient way. Further, restoration methods and
techniques aimed at a reduction or elimination of the internal loading in irreversibly
damaged reservoirs should be applied after the external loading has been reduced
IMPACT ASSESSMENT OF DIFFERENT MANAGEMENT SCENARIOS ON WATER QUALITY
209
to normal levels. Phosphorus precipitation, aeration, sediment treatment, and sediment removal are the methods to counteract internal loading and to transform the
ecosystem from a nutrient source to a nutrient sink (IWRB, 1994).
The most optimistic expectation for the treatment of domestic wastewater and
industrial discharges will be that they will comply with the existing standards. Expectations that involve further treatment levels would be economically unrealistic,
if technically not impossible. This is because of the lack of financial resources in
Turkey, nowadays especially after the recent economical crisis.
4. Conclusions
Water quality models can beneficially be utilized as predictive management tools.
Within the scope of this specific study, the modeling approach, utilizing QUAL2E
for river modeling and BATHTUB for reservoir modeling, has been applied to
predict the response of Porsuk River–PDR system to different management scenario
options. 1) It is now possible to foresee the expected response of the system under
different pollution control options. 2) Some of the management options (S2i, S2ii,
S3i and S3ii) will create only marginal improvements. 3) The scenarios (S4i and
S4ii) involving higher levels of treatment to KWWTP and to industrial effluents
will create considerable improvements in water quality of PDR. 3) Diverting the
effluent of KWWTP, meanwhile applying BAT for all the industries and application
of 50% reduction of nitrogen and phosphorus of drainage water from the catchment
area will improve PDR water quality from highly hypertrophic state to eutrophic–
mesotrophic state. The results of this modeling approach will hopefully be utilized
by a further feasibility study to investigate the economical implications of the
management options and selection of the most feasible option under present socioeconomical conditions before any engineering applications.
Acknowledgement
This study has been supported by the State Hydraulic Works of Turkey (DSI),
Eskisehir, Su-Yapi Engineering & Consulting Company, Ankara, Atılım University,
Ankara, and the Project Management Unit of Akdeniz University, Antalya, Turkey.
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