Study on Discharge Characteristics of Pollutant Load at Gyoungahn River with GIS and Water Quality...
-
Upload
simrc -
Category
Environment
-
view
84 -
download
0
Transcript of 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
2. Materials and Methods
Study Watershed 1
21
Location of Gyoungahn River
Watershed Overview
2. Materials and Methods
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
2. Materials and Methods
Study Watershed 1
23
Aspect Analysis Altitude Analysis
2. Materials and Methods
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
2. Materials and Methods
SWAT Input 2
27
DEM 30m 20 Sub-watersheds
DEM 10m 31 Sub-watersheds
2. Materials and Methods
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
2. Materials and Methods
30
Applying SWAT ArcView GIS Patch II correct Field Slope Length to 10m
ArcView GIS Patch 3
2. Materials and Methods
31
ArcView GIS Patch 3
2. Materials and Methods
32
ArcView GIS Patch 3
2. Materials and Methods
Apply ArcView GIS Extension Patch II
33
Main Channel 4
2. Materials and Methods
34
Main Channel 4
2. Materials and Methods
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
2. Materials and Methods
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
SWAT simulate ; Patch II X, Main Channel X
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
SWAT simulate ; Patch II X, Main Channel X
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
SWAT simulate ; Patch II X, Main Channel X
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
SWAT simulate ; Patch II X, Main Channel X
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
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
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
)
Comparison of Observed and Simulated SSPrecipitation
Observed SS
Simulated SS
SWAT simulate ; Patch II O, Main Channel X
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
SWAT simulate ; Patch II O, Main Channel X
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
)
Comparison of Observed and Simulated T-PPrecipitation
Observed T-P
Simulated T-P
SWAT simulate ; Patch II O, Main Channel X
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
SWAT simulate ; Patch II O, Main Channel X
3. Results
Output 2
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
Output 2
Validation of Output of SWAT by NSE (Flow, SS)
NSE : Nash-Sutcliffe Efficiency
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
)
Comparison of Observed and Simulated SSPrecipitation
Observed SS
Simulated SS
SWAT simulate ; Patch II O, Main Channel O
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
SWAT simulate ; Patch II O, Main Channel O
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
)
Comparison of Observed and Simulated T-PPrecipitation
Observed T-P
Simulated T-P
SWAT simulate ; Patch II O, Main Channel O
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
SWAT simulate ; Patch II O, Main Channel O
3. Results
Output 3
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
Output 3
Validation of Output of SWAT by NSE (Flow, SS)
NSE : Nash-Sutcliffe Efficiency
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