study on discharge characteristics of pollutant load at gyoungahn river with gis and water quality...

오염총량관리에 따른

GIS와 수질모델링에 의한

경안천 유역의

오염부하량 배출특성 연구

Study on Discharge Characteristics of Pollutant Load

at Gyoungahn River with GIS and Water Quality Modelling for Total Maximum Loads Management

서울시립대학교 물 환 경 연 구 실 Lee, Kwan-Woo

E X

1. Introduction

2. Materials and Methods

3. Results

D I N

1. Introduction

Background 1

Industrialization, Urbanization increase pollution loading on water supply source As Management of Point Pollution Source enhanced Contribution rate of Non-point Pollution Source increased Impose of Total Maximum Loads Management (‘99) Ratio of Non-point Pollution Source in Discharge : about 42% (‘05)

3

1. Introduction

Background 1

4

Discharge Loading By Pollution Source

1. Introduction

Background 1

5

strengthen Effluent Standard, more Installation of Sewerage Point Source Loading decreased Ratio of Discharge Loading by Non-point Pollution Source increased current Pollution Management reaches the limit related Laws revised ; Government demands Prediction and Countermeasure to Developer

1. Introduction

Purpose 1

6

Estimating Pollutant Load based on Land Use Plan, Precipitation and Pollution Rate Hard to predict on Runoff Loading of actual Sub-watersheds by Non-point Pollution Source

Non-point Pollution Source run off at rainfall Runoff flow vary on each Season, therefore hard to predict, quantify for more efficient Total Maximum Loads Management require exact Pollution State and Geographic Information System using Digital Elevation Model, to analyze corresponding Water Basins

7

Purpose 1

Input DATA - GIS DEM, Soils, Land Use - Climate Precipitation etc. - Point Source Sewage Treatment Plant Wastewater Treatment Plant

SWAT Model

Output for each stream - Flow - Constituent Yields

Additional DATA - Hydraulic, Hydrologic Coeff. of Sub-watershed - Slope Length

1. Introduction

1. Introduction

Purpose 1

8

Research on Land Use Plan, Soil, Climate, Precipitation Input SWAT Model = estimating Pollution Loads by Non-point Pollution Source Research on Characteristic Data about Streams / River Input SWAT Model = Slope Length Patch, Hydraulic / Hydrologic Coefficient

Analyzing Discharge Characteristics of Pollutant Load Apply to Policy, considering Priority

1. Introduction

Water Quality Modelling 2

9

Using Water Quality Modelling for Total Maximum Loads Management - to decrease Conflict between local Governments

- to seek balanced Development between local Governments

Therefore, Modelling required reasonable, fair, scientific Process

1. Introduction 10

1) Procedure - Setting up Purpose - Understanding current State - Setting up Scope - Making Scenario - Analyzing Prediction Results 2) Problem and Limit - lack of fundamental Data or unsuitable - not enough considering local Characteristics - Uncertainty of Prediction

Water Quality Modelling 2

1. Introduction

SWAT 3

11

SWAT(Soil and Water Assessment Tool) is River basin, or Watershed Scale Model developed by USDA Agricultural Research Service) to predict the impact of land management practices on water, sediment and agricultural chemical yields SWAT’s benefits are Watersheds with no monitoring data can be modeled, The relative impact of alternative input data on water quality or other variables of interest can be quantified SWAT is continuous time Model

1. Introduction

SWAT 3

12

SWAT Input : - Climate Solar Radiation, Temperature, Wind Speed Precipitation, Relative Humidity

- Hydrology Surface Runoff, Evapotranspiration, Sub-surface Water, Groundwater

- Nutrients / Pesticides Nitrogen, Phosphorus, Pesticides, Sediment, Nutrients, DO, CBOD

- Land Cover / Plant - Main Channel Processes Channel Characteristics, Flow rate and velocity

Preparation

1. Introduction

SWAT 3

13

Schematic of SWAT Model

Input - GIS DEM, Soils, Land Use - Climate Temp., Relative Humidity, Precipitation etc. - Point Source Sewage Treatment Plant Wastewater Treatment Plant Effluent Water Quality Data

SWAT Model Analyze Output

Calibration & Validation

1. Introduction

SWAT 3

14

Schematic representation of the hydrologic cycle

Water Balance Equation

1. Introduction

SWAT 3

15

1. Introduction

SWAT 3

16

In-Stream Processes modeled by SWAT

1. Introduction

SWAT 3

17

Partitioning of Nitrogen in SWAT

1. Introduction

SWAT 3

18

Partitioning of Nitrogen in SWAT

1. Introduction

SWAT 3

19

Partitioning of Phosphorous in SWAT

1. Introduction

SWAT 3

20

Partitioning of Phosphorous in SWAT


Study Watershed 1

21

Location of Gyoungahn River

Watershed Overview


Study Watershed 1

22

Basin Length (km) Area (㎢) Sub-Watershed Area (㎢)

Gyoungahn

River 22.50 / 49.30 575.32

Gyoungahn A 198.4

Gyoungahn B 248.9

Location of Monitoring Site


Study Watershed 1

23

Aspect Analysis Altitude Analysis


Study Watershed 1

24

Slope Analysis Soil Analysis

25 2. Materials and Methods

SWAT Input 2

Soil and Land Use over DEM

26

Streams within Basin and Watershed delineation


SWAT Input 2

27

DEM 30m 20 Sub-watersheds

DEM 10m 31 Sub-watersheds


SWAT Input 2

Watershed Delineation by DEM

2. Materials and Methods 28

Streams within Basin Land Use and etc.

SWAT Input 2

29

ArcView GIS Patch 3

SWAT model calculates Average Slope using DEM, simulates with Average Field Slope Length existing SWAT model is developed that use Field Slope Length as 0.05m in the topography of average slope ≥ 25% existing SWAT model is suitable for U.S. topography, in generally gradual Slope these condition is hard to apply to Korean topography, sharp Slope


30

Applying SWAT ArcView GIS Patch II correct Field Slope Length to 10m

ArcView GIS Patch 3


31

ArcView GIS Patch 3


32

ArcView GIS Patch 3


Apply ArcView GIS Extension Patch II

33

Main Channel 4


34

Main Channel 4


35

Main Channel 4

Ch_side_slope

Fd_side_slope

existing SWAT model is suitable for the river of wide channel and gradual side slope Korean River : relatively narrow channel and sharp side slope Therefore, classify by Sub-watershed, modify manually and simulate


Fd_width

Ch_width

Ch_depth

1

1

36 2. Materials and Methods

Main Channel 4

subbasin bt_width ch_depth ch_width Fd_width ch_side_slp Fd_side_slp

1 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

2 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

3 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

4 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

5 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

6 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

7 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

8 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

9 20.5567 1.1296 33.8516 38.9060 2.2955 0.5059

10 5.9326 0.8904 10.7466 11.6786 0.8948 0.1078

11 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

12 20.5567 1.1296 33.8516 38.9060 2.2955 0.5059

13 5.9326 0.8904 10.7466 11.6786 0.8948 0.1078

14 5.9326 0.8904 10.7466 11.6786 0.8948 0.1078

15 13.3922 0.8283 26.5473 29.0065 2.4203 0.2587

16 13.3922 0.8283 26.5473 29.0065 2.4203 0.2587

17 5.9326 0.8904 10.7466 11.6786 0.8948 0.1078

18 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

19 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

20 21.5139 1.6663 57.9379 70.5742 4.7688 0.7109

21 21.5139 1.6663 57.9379 70.5742 4.7688 0.7109

22 13.3922 0.8283 26.5473 29.0065 2.4203 0.2587

23 21.5139 1.6663 57.9379 70.5742 4.7688 0.7109

24 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

25 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

26 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

27 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

28 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

29 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

30 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

31 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

bt_width ch_depth ch_width Fd_width ch_side_slp Fd_side_slp

경안하류 21.5168 0.9095 121.2859 328.4589 17.6411 9.6317

경안중류 21.5139 1.6663 57.9379 70.5742 4.7688 0.7109

경안상류 5.3814 0.2787 18.7709 19.8549 2.3044 0.1091

곤지암하 20.5567 1.1296 33.8516 38.9060 2.2955 0.5059

곤지암중 13.3922 0.8283 26.5473 29.0065 2.4203 0.2587

곤지암상 5.9326 0.8904 10.7466 11.6786 0.8948 0.1078

37 3. Results

Output 1

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d F

LOW

(CM

S)

Comparison of Observed and Simulated Flow

Precipitation

Observed FLOW

Simulated FLOW

SWAT simulate ; Patch II X, Main Channel X

38

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d S

S(m

g/L

)

Comparison of Observed and Simulated SSPrecipitation

Observed SS

Simulated SS


3. Results

Output 1

39

0

100

200

300

400

5000

10

20

30

40

50

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-N(m

g/L

)

Comparison of Observed and Simulated T-N

Precipitation

Observed T-N

Simulated T-N


3. Results

Output 1

40

0

100

200

300

400

5000

0.2

0.4

0.6

0.8

1

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-P(m

g/L

)

Comparison of Observed and Simulated T-PPrecipitation

Observed T-P

Simulated T-P


3. Results

Output 1

41

0

100

200

300

400

5000

4

8

12

16

20

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d B

OD

(mg

/L)

Comparison of Observed and Simulated BOD

Precipitation

Observed BOD

Simulated BOD


3. Results

Output 1

42

Num. of Data : 47

Nash-Sutcliffe Efficiency (NSE) : 0.8195

Coefficient of Determination (R2) : 0.8731

Absolute Percent Bias (APB, %) : 48.6066

Sum of Square Error (SSE) : 32787.2067

Root Mean Square Error (RMSE) : 26.4121

Mean Absolute Error (MAE) : 11.9737

Index of Aggrement (d) : 0.9365

Num. of Data : 47

Nash-Sutcliffe Efficiency (NSE) : -2.5798







Validation of Output of SWAT by NSE (Flow, SS)


3. Results

Output 1

NSE : Nash-Sutcliffe Efficiency

43

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d F

LOW

(CM

S)

Comparison of Observed and Simulated Flow

Precipitation

Observed FLOW

Simulated FLOW

SWAT simulate ; Patch II O, Main Channel X

3. Results

Output 2

44

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d S

S(m

g/L

)


Observed SS

Simulated SS


3. Results

Output 2

45

0

100

200

300

400

5000

10

20

30

40

50

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-N(m

g/L

)

Comparison of Observed and Simulated T-N

Precipitation

Observed T-N

Simulated T-N


3. Results

Output 2

46

0

100

200

300

400

5000

0.2

0.4

0.6

0.8

1

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-P(m

g/L

)


Observed T-P

Simulated T-P


3. Results

Output 2

47

0

100

200

300

400

5000

4

8

12

16

20

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d B

OD

(mg

/L)

Comparison of Observed and Simulated BODPrecipitation

Observed BOD

Simulated BOD


3. Results

Output 2

48

Num. of Data : 47








Num. of Data : 47









3. Results

Output 2



49

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d F

LOW

(CM

S)

Comparison of Observed and Simulated FlowPrecipitation

Observed FLOW

Simulated FLOW

SWAT simulate ; Patch II O, Main Channel O

3. Results

Output 3

50

0

100

200

300

400

5000

100

200

300

400

500

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d S

S(m

g/L

)


Observed SS

Simulated SS


3. Results

Output 3

51

0

100

200

300

400

5000

10

20

30

40

50

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-N(m

g/L

)

Comparison of Observed and Simulated T-NPrecipitation

Observed T-N

Simulated T-N


3. Results

Output 3

52

0

100

200

300

400

5000

0.2

0.4

0.6

0.8

1

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d T

-P(m

g/L

)


Observed T-P

Simulated T-P


3. Results

Output 3

53

0

100

200

300

400

5000

5

10

15

20

25

2009-01-15 2009-08-03 2010-02-19 2010-09-07 2011-03-26 2011-10-12

Pre

cip

itat

ion

(m

m)

Ob

serv

ed

&Si

mu

late

d B

OD

(mg

/L)

Comparison of Observed and Simulated BODPrecipitation

Observed BOD

Simulated BOD


3. Results

Output 3

54

Num. of Data : 47








Num. of Data : 47









Output 3



3. Results

55

Calibration and Validation 4

① Mean Error (ME)

② Mean Absolute Deviation (MAD)

③ Mean Absolute Error (MAE)

④ Root Mean Square Error (RMSE)

⑤ Nash-Sutcliffe Efficiency

Poor Fair Good Very Good

NSE for

daily Simulation < 0.60

0.60 ~

0.70

0.70 ~

0.80 0.80 <

Criteria for evaluating model performance (Donigian and Love, 2003)

3. Results

56

Calibration and Validation 4

3. Results

study on discharge characteristics of pollutant load at gyoungahn river with gis and water quality...

Environment