The study used GIS and the SWAT water quality model to estimate pollutant loads from point and non-point sources in the Gyoungahn River watershed. The SWAT model was set up using spatial data on soils, land use, and climate inputs. Model parameters were modified to better represent the steep slopes and narrow channels common in Korean rivers. Model outputs for flow, sediments, nitrogen, phosphorus and BOD showed good agreement with observed data based on statistical evaluations. The study demonstrated the ability of a modified SWAT model to estimate pollutant loads for the purposes of total maximum daily load management in the watershed.

1. 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

2. E X
1. Introduction
2. Materials and Methods
3. Results
DI N

3. 1. Introduction
Background1
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

4. 1. Introduction
Background1
4
Discharge Loading By Pollution Source

5. 1. Introduction
Background1
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

6. 1. Introduction
Purpose1
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. 7
Purpose1
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

8. 1. Introduction
Purpose1
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

9. 1. Introduction
Water Quality Modelling2
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

10. 1. Introduction10
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 Modelling2

11. 1. Introduction
SWAT3
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

12. 1. Introduction
SWAT3
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

13. Preparation
1. Introduction
SWAT3
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

14. 1. Introduction
SWAT3
14
Schematic representation of the hydrologic cycle

15. Water Balance Equation
1. Introduction
SWAT3
15

16. 1. Introduction
SWAT3
16
In-Stream Processes modeled by SWAT

17. 1. Introduction
SWAT3
17
Partitioning of Nitrogen in SWAT

18. 1. Introduction
SWAT3
18
Partitioning of Nitrogen in SWAT

19. 1. Introduction
SWAT3
19
Partitioning of Phosphorous in SWAT

20. 1. Introduction
SWAT3
20
Partitioning of Phosphorous in SWAT

21. 2. Materials and Methods
Study Watershed1
21
Location of Gyoungahn River

22. Watershed Overview
2. Materials and Methods
Study Watershed1
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

23. 2. Materials and Methods
Study Watershed1
23
Aspect Analysis Altitude Analysis

24. 2. Materials and Methods
Study Watershed1
24
Slope Analysis Soil Analysis

25. 25
2. Materials and Methods
SWAT Input2
Soil and Land Use over DEM

26. 26
Streams within Basin and Watershed delineation
2. Materials and Methods
SWAT Input2

27. 27
DEM 30m

20 Sub-
watersheds
DEM 10m

31 Sub-
watersheds
2. Materials and Methods
SWAT Input2
Watershed Delineation by DEM

28. 2. Materials and Methods28
Streams within Basin Land Use and etc.
SWAT Input2

29. 29
ArcView GIS Patch3
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
2. Materials and Methods

30. 30
Applying SWAT ArcView GIS Patch II
 correct Field Slope Length to 10m
ArcView GIS Patch3
2. Materials and Methods

31. 31
ArcView GIS Patch3
2. Materials and Methods

32. 32
ArcView GIS Patch3
2. Materials and Methods
Apply ArcView GIS
Extension Patch II

33. 33
Main Channel4
2. Materials and Methods

34. 34
Main Channel4
2. Materials and Methods

35. 35
Main Channel4
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
2. Materials and Methods
Fd_width
Ch_width
Ch_depth
1
1

36. 36
2. Materials and Methods
Main Channel4
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. 37
3. Results
Output1
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
Precipitation(mm)
Observed&SimulatedFLOW(CMS)
Comparison of Observed and Simulated Flow
Precipitation
Observed FLOW
Simulated FLOW
SWAT simulate ; Patch II X,
Main Channel X

38. 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
Precipitation(mm)
Observed&SimulatedSS(mg/L)
Comparison of Observed and Simulated SS
Precipitation
Observed SS
Simulated SS
SWAT simulate ; Patch II X,
Main Channel X
3. Results
Output1

39. 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
Precipitation(mm)
Observed&SimulatedT-N(mg/L)
Comparison of Observed and Simulated T-N
Precipitation
Observed T-N
Simulated T-N
SWAT simulate ; Patch II X,
Main Channel X
3. Results
Output1

40. 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
Precipitation(mm)
Observed&SimulatedT-P(mg/L)
Comparison of Observed and Simulated T-P
Precipitation
Observed T-P
Simulated T-P
SWAT simulate ; Patch II X,
Main Channel X
3. Results
Output1

41. 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
Precipitation(mm)
Observed&SimulatedBOD(mg/L)
Comparison of Observed and Simulated BOD
Precipitation
Observed BOD
Simulated BOD
SWAT simulate ; Patch II X,
Main Channel X
3. Results
Output1

42. 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
Coefficient of Determination (R2) : 0.0023
Absolute Percent Bias (APB, %) : 231.2596
Sum of Square Error (SSE) : 163199.7104
Root Mean Square Error (RMSE) : 58.9265
Mean Absolute Error (MAE) : 39.8455
Index of Aggrement (d) : 0.2514
Validation of Output of SWAT by NSE (Flow, SS)
SWAT simulate ; Patch II X,
Main Channel X
3. Results
Output1
NSE : Nash-Sutcliffe Efficiency

43. 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
Precipitation(mm)
Observed&SimulatedFLOW(CMS)
Comparison of Observed and Simulated Flow
Precipitation
Observed FLOW
Simulated FLOW
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2

44. 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
Precipitation(mm)
Observed&SimulatedSS(mg/L)
Comparison of Observed and Simulated SS
Precipitation
Observed SS
Simulated SS
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2

45. 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
Precipitation(mm)
Observed&SimulatedT-N(mg/L)
Comparison of Observed and Simulated T-N
Precipitation
Observed T-N
Simulated T-N
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2

46. 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
Precipitation(mm)
Observed&SimulatedT-P(mg/L)
Comparison of Observed and Simulated T-P
Precipitation
Observed T-P
Simulated T-P
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2

47. 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
Precipitation(mm)
Observed&SimulatedBOD(mg/L)
Comparison of Observed and Simulated BOD
Precipitation
Observed BOD
Simulated BOD
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2

48. 48
Num. of Data : 47
Nash-Sutcliffe Efficiency (NSE) : 0.8510
Coefficient of Determination (R2) : 0.8793
Absolute Percent Bias (APB, %) : 47.4026
Sum of Square Error (SSE) : 27062.4067
Root Mean Square Error (RMSE) : 23.9957
Mean Absolute Error (MAE) : 11.6771
Index of Aggrement (d) : 0.9514
Num. of Data : 47
Nash-Sutcliffe Efficiency (NSE) : -2.4622
Coefficient of Determination (R2) : 0.0167
Absolute Percent Bias (APB, %) : 207.1894
Sum of Square Error (SSE) : 157834.4200
Root Mean Square Error (RMSE) : 57.9498
Mean Absolute Error (MAE) : 35.6983
Index of Aggrement (d) : 0.2930
SWAT simulate ; Patch II O,
Main Channel X
3. Results
Output2
Validation of Output of SWAT by NSE (Flow, SS)
NSE : Nash-Sutcliffe Efficiency

49. 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
Precipitation(mm)
Observed&SimulatedFLOW(CMS)
Comparison of Observed and Simulated Flow
Precipitation
Observed FLOW
Simulated FLOW
SWAT simulate ; Patch II O,
Main Channel O
3. Results
Output3

50. 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
Precipitation(mm)
Observed&SimulatedSS(mg/L)
Comparison of Observed and Simulated SS
Precipitation
Observed SS
Simulated SS
SWAT simulate ; Patch II O,
Main Channel O
3. Results
Output3

51. 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
Precipitation(mm)
Observed&SimulatedT-N(mg/L)
Comparison of Observed and Simulated T-N
Precipitation
Observed T-N
Simulated T-N
SWAT simulate ; Patch II O,
Main Channel O
3. Results
Output3

52. 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
Precipitation(mm)
Observed&SimulatedT-P(mg/L)
Comparison of Observed and Simulated T-P
Precipitation
Observed T-P
Simulated T-P
SWAT simulate ; Patch II O,
Main Channel O
3. Results
Output3

53. 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
Precipitation(mm)
Observed&SimulatedBOD(mg/L)
Comparison of Observed and Simulated BOD
Precipitation
Observed BOD
Simulated BOD
SWAT simulate ; Patch II O,
Main Channel O
3. Results
Output3

54. 54
Num. of Data : 47
Nash-Sutcliffe Efficiency (NSE) : 0.8012
Coefficient of Determination (R2) : 0.8486
Absolute Percent Bias (APB, %) : 51.6699
Sum of Square Error (SSE) : 36109.8475
Root Mean Square Error (RMSE) : 27.7181
Mean Absolute Error (MAE) : 12.7284
Index of Aggrement (d) : 0.9299
Num. of Data : 47
Nash-Sutcliffe Efficiency (NSE) : -3.5058
Coefficient of Determination (R2) : 0.0070
Absolute Percent Bias (APB, %) : 232.1376
Sum of Square Error (SSE) : 205414.7885
Root Mean Square Error (RMSE) : 66.1100
Mean Absolute Error (MAE) : 39.9968
Index of Aggrement (d) : 0.2304
SWAT simulate ; Patch II O,
Main Channel O
Output3
Validation of Output of SWAT by NSE (Flow, SS)
NSE : Nash-Sutcliffe Efficiency
3. Results

55. 55
Calibration and Validation4
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. 56
Calibration and Validation4
3. Results