Post on 13-Apr-2017
Md Moudud Hasanr0435449IUPWAREDate 25.01.2017
Report on Systems Approach to Water Management
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•Background •Objectives•Study area•Data collection and evaluation •Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•Models are often used to predict the behavior of a natural system▫(i) Detailed physically-based models (white box), ▫(ii) Conceptual models (grey -box) ▫(iii) Empirical models
•White-box model: ▫accurate and reliable,▫requires powerful computing system and much time
•Black-box models:▫relation between input and output▫ inaccurate results for extrapolation
•Grey-box model: ▫balance between the detailed physically-based model and empirical model.
Background
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•To build precipitation runoff grey-box model using the system approach
concept
•To compare Grey-box model’s performance with the performance of the
white box model (semi-distributed SWAT model).
Objectives
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
Study area• Spring creek watershed ,Center County,
Pennsylvania, USA
• 370 km2 area
• Average elevation: 370m
▫ elevation varies from 675 m to 280 m.
• Groundwater basin: 22 percent larger
• land use:
▫ 34% agriculture
▫ 23% developed
▫ 43% forest
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
Data• Observed data:▫ daily discharge ▫precipitation▫maximum daily temperature ▫minimum daily temperature
• SWAT model simulated ▫ daily evapotranspiration▫ daily discharge
• 12 years (01-01-2002 to 31-12-2013) • Collected from M.G. Mostofa Amin the author of (Amin et al. 2017)
• Daily discharge data: http://waterdata.usgs.gov/pa/nwis/rt• Weather data: http://ches.communitymodeling.org/ and http://climate.psu.edu/
Data manipulation
1/1/02 5/16/03 9/27/04 2/9/06 6/24/07 11/5/08 3/20/10 8/2/11 12/14/120.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00 0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
Runoff (m3/s) Precipitation (mm)
Time (day)
Disc
harg
e (m
3/s)
Prec
ipita
tion
(mm
)
Daily discharge Daily precipitation
Water balance
0.001000000000.002000000000.003000000000.004000000000.005000000000.006000000000.00
Cumulative Runoff Cumulative Pcipitation
Loss Cumulative Evapotranspiration
Time (day)
Cum
ulati
ve v
olum
e (m
3)
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•Model-1:
Grey-box model construction Precipitation
Input flow
Baseflow Overland flow Interflow
Runoff
Net Input
Loss
Rainfall
WBF WOF WIF
KBFKOF KIF
𝑃𝑛𝑒𝑡=𝐷𝑎𝑖𝑙𝑦 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑖𝑜𝑛∗ 𝐿𝑜𝑠𝑠 𝑓𝑎𝑐𝑡𝑜𝑟
𝑞𝑜𝑢𝑡 (𝑡 )=𝑒𝑥𝑝(− 1𝑘 )𝑞𝑜𝑢𝑡 (𝑡−1 )+(1−𝑒𝑥𝑝(− 1𝑘 ))𝑞𝑖𝑛 (𝑡))
•Model-2:
Grey-box model construction
Precipitation
Input flow
Baseflow Overland flow Interflow
Runoff
Net Input
Loss
Rainfall
WBF WOF WIF
KBFKOF KIF
𝐿=¿𝑃𝑛𝑒𝑡 _ 𝑇𝑎𝑣𝑔=𝐷𝑎𝑖𝑙𝑦 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑖𝑜𝑛−𝐿
•Model-3:
Grey-box model construction Precipitation
Temperature <
Tc snow
Input flow
Snow pack
Snow melt
Baseflow Overland flow Interflow
Runoff
Net Input
Loss
No
Rainfall
YesSnowfall
WBF WOF WIF
KBFKOF KIF
𝑀=¿
𝑆𝑝𝑎𝑐 𝑘𝑡=𝑆𝑝𝑎𝑐𝑘𝑡 −1−𝑀𝑎𝑡 −1+𝑃𝑠𝑛𝑜 𝑤𝑡
𝑀𝑎𝑡={ 𝑀 𝑡 𝑖𝑓 𝑆𝑝𝑎𝑐 𝑘𝑡>𝑀𝑡
𝑆𝑝𝑎𝑐𝑘𝑡 𝑖𝑓 𝑆𝑝𝑎𝑐 𝑘𝑡<𝑀𝑡
0 𝑖𝑓 𝑆𝑝𝑎𝑐𝑘𝑡 ≤0}
• Input calibrated based on water balance. •Excel solver was used to optimize the parameter: ▫objective function (Nash Sutcliff Efficiency (NSE)) or error.
•Fine tuning was done manually
Model calibration
Parameter name Unit Model-1 Model-2 Model-3
1 Loss factor, Lf - 0.55
2 Slope parameter of loss, S - 0.6 0.56
3 The critical temperature for snow fall, Tc snow °C 1
4 Base temperature, Tbase °C 0
5 Melting factor, C mm day-1 °C-1 0.6
6 Critical net precipitation, Pc net m3/s 360 360 360
7 Overland flow portion, WBF - 0.08 0.08 0.08
8 Overland flow recession constant, kOF days 1 1 1
9 Interflow Portion, WBF - 0.22 0.35 0.35
10 Interflow recession constant, kIF days 20 30 30
11 Base flow portion, WBF - 0.7 0.57 0.57
12 Base flow recession constant, kBF days 170 170 170 Total number of parameters 8 8 11
Model parameter
Water balance check
4/19/2001 1/14/2004 10/10/2006 7/6/2009 4/1/2012 12/27/20140
500000000
1000000000
1500000000
2000000000
2500000000
3000000000 Cumulative Observed flowModel-1 Cumulative InputModel-2 Cumulative InputModel-3 Cumulative Input
Time (day
Cum
ulati
ve V
olum
e (m
3)
Simulated outflow and observed outflow
11/1/2007 2/9/2008 5/19/2008 8/27/20080
10
20
30
40
50
60
Observed flowModel-1 Simulated FlowModel-2 Simulated FlowModel-3 Simulated Flow
Time (day)
Flow
(m3/
s)
Model-1 Model-2 Model-3
Coefficient of efficiency (EF) [-] 0.36 0.65 0.71
Model selection
Black box Model
• Transfer function was also used with 5 poles and 4 zeros
• Mean squared error of transfer function was 9.25
0 500 1000 1500 2000 2500 3000 3500 4000 4500Time
0
20
40
60
80
100
120
140
Flow
(m3/
s)
Measured and simulated model output
•Performance of black-box was less accurate than Grey-box model
•Performance of selected model (Model-3) was acceptable
•Performance can be increased by including a soil storage process
Findings
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•To evaluate the performance of grey-box model by both graphical and
statistical goodness-of-fit methods
•To compare with white-box model (SWAT model)
•To determine the application field of the model
Objectives
•Statistical goodness-of-fit:
▫Mean error (ME)
▫Mean squared error (MSE)
▫Model residual variance (S²EQ)
▫Coefficient of efficiency (EF)
•Graphically evaluate model performance
▫ WETSPRO: Sub-flow filtering and POT selection.
▫Model validation
Methodology
Grey-box model (Model-3) White box model (SWAT)
Mean error (ME) [m3/s] -0.19 -0.47
Mean squared error (MSE) [m3/s] 4.49 3.94
Model residual variance (S²EQ) [m3/s] 4.52 3.71
Coefficient of efficiency (EF) [-] 0.71 0.75
Statistical goodness-of-fit methods
Parameters QUICK FLOW periods SLOW FLOW periods
Max. ratio difference with subflow [-]:0.4 0.3
Independency period [day]:30 130
min peak height [m3/s]:1 1
Sub flow filtering
0 500 1000 1500 2000 2500 3000 3500 4000 45000
20
40
60
80
100
120
140Time seriesPOT values indep. quick flow periodsHydrograph separation quick flow
Number of time steps
Flow
(m3/
s)
Independent of base flow method
Validation Extremes High
0.1 1 10 1000
20
40
60
80
100
120
140observedWhite-box (SWAT)Grey-box
Return period [years]
Flow
(m3/
s)
Validation Extremes Low
0.1 1 10 1000
0.1
0.2
0.3
0.4
0.5
0.6 observedWhite-box (SWAT)Grey-box
Return period [years]
1 /fl
ow (m
3/s)
Validation Maxima
2 3 4 5 6 7 8 9 100
2
4
6
8
10
12White-box (SWAT)Grey-boxbisectormean deviation
BC( Observed maxima )
BC
( Sim
ulat
ed m
axim
a )
Validation Minima
1 1.2 1.4 1.6 1.8 2 2.20
0.5
1
1.5
2
2.5White-box (SWAT)Grey-boxbisectormean deviation
BC( Observed minima )
BC
( Sim
ulat
ed m
inim
a )
Cumulative Flow
0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000
5000
10000
15000
20000
25000
30000
35000observedWhite-box (SWAT)Grey-box
Time
Cum
ulat
ive
Flow
(m3/
s)
•According to overall goodness-of-fit, both models were acceptable.
•White-box can simulate peak flow better
•Grey-box model simulates low flow more efficiently
•Grey-box model can be used for low flow analysis
Findings
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•To determine the total uncertainty of the grey-box model using nearly independent low flow values
Objectives
Box-Cox transformation
0 2 4 6 8 10 12 14 16 18
-2-1.5
-1-0.5
00.5
11.5
2 Before Box-Cox transformation After Box-Cox transformation
Mod
el re
sidua
l (m
3/s)
=0.25𝐵𝐶 (𝑄 )=𝑄𝜆−1𝜆
Box-Cox (Minimum flow)
Mean error (ME) -0.16
Model residual variance (S²EQ) 0.08
Model residual standard deviation (SEQ) 0.28
Model uncertainty
After Box-Cox transformation
0 0.5 1 1.5 2 2.5 3 3.5 40
0.5
1
1.5
2
2.5
3
3.5
4BisectorMean DeviationStandard Deviation
BC(Observed discharge (m3/s)
BC (S
imul
ated
dis
char
ge (m
3/s)
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
•To simulate a “high” climate change scenario in grey-box model.
•To determine the impact of climate change on flood frequency
Objectives
•Climate Perturbation Tool – Precipitation & Temperature
•Target year 2080
•High winter (wet winter)
•Grey-box model to simulate the outflow
•WETSPRO to extract POT values
•Extreme value analysis tool (ECQ)
•General Pareto Distribution (GDP)
Methodology
Simulation of grey-box model
4/19/2001 1/14/2004 10/10/2006 7/6/2009 4/1/2012 12/27/20140
20
40
60
80
100
120
140
160
180Future scenario simulation Current scenario simulation
Time (day)
Flow
(m
3/s)
•Generalized Pareto distribution: heavy tail
Extreme value analysis
Calibrated GPD Parameter Value
Parameter name
Current scenario Future scenario (2080)
Gamma 0.30 0.44
Beta 5.15 6.57
Threshold flow xt 16.77 14.84
Threshold rank t 46 61
Flood frequency analysis
0.1 1 10 100 10000.0020.0040.0060.0080.00
100.00120.00140.00160.00180.00 Empirical 2080
Theoritical 2080Emperical Current Theoritical Current
Return period [years]
Peak
flow
[m3/
s]
•Flood frequency will be higher in future
•Climate change scenario of Belgium was used for this study area.
•Climate change scenario of respected area should be used
Findings
•Background •Objectives•Study area•Data collection and evaluation•Model setup and calibration•Model performance evaluation•Model uncertainty quantification •Climate change scenario•Control on a reservoir
Contents
• to understand the functioning of feedback and feedforward control on a
reservoir
• to get familiar with the simulation package Matlab/Simulink.
Objective
No Feed Control
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
5
5.2
5.4
5.6
5.8
6
6.2
Wat
er le
vel (
m)
Reservoir surface area, 𝐴=∆𝑉∆h =
𝑄𝑡∆ h=
3×36006.1015−5=9804.81𝑚
2
Feed-forward control
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
4.995
5
5.005
5.01
5.015
5.02
5.025
Wat
er le
vel (
m)
Feedback control
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
4.99
4.995
5
5.005
5.01
5.015
5.02
5.025
5.03
5.035
5.04
Wat
er le
vel (
m)
Feed-Back Control
Feedback control : integral gain (Ki) =0
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
5
5.01
5.02
5.03
5.04
5.05
5.06
5.07
Wat
er le
vel (
m)
Effect of the proportional gain (Kp)
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
5
5.005
5.01
5.015
5.02
5.025
5.03
Wat
er le
vel (
m)
Ki=0, Kp=155
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
5
5.005
5.01
5.015
5.02
5.025
wat
er le
vel (
m)
Ki=0, Kp=255
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
-15
-10
-5
0
5
10
15
20
25
Wat
er le
vel (
m)
Ki=0,Kp=355
Combine Feedback and Feedforward control
0 500 1000 1500 2000 2500 3000 3500 4000time (sec)
4.992
4.994
4.996
4.998
5
5.002
5.004
5.006
5.008
Wat
er le
vel (
m)
Feedforward and Feedback
Reference level at 4m
0 500 1000 1500 2000 2500 3000 3500 4000Time (sec)
3.6
3.8
4
4.2
4.4
4.6
4.8
5
Wat
er le
vel (
m)
Reference level 4m
1250 sec
•The feedforward can control water level with fast response but error in
measurement may cause problem.
•The feedback control system can stabilize water level effectively but
delay response is issue.
•For complicated and important system both control system should be
used for better control.
Findings
• Amin, M.G. Mostofa et al. 2017. “Simulating Hydrological and Nonpoint Source Pollution Processes in a Karst Watershed: A Variable Source Area Hydrology Model Evaluation.” Agricultural Water Management 180: 212–23.
• Van Uytven, E., and P. Willems. 2016. “Climate Perturbation Tool: A Climate Change Tool for Generating Perturbed Time Series - Manual Version January 2016.” KU Leuven - Hydraulics Section (January).
• Willems, Patrick. 2004. “Parsimonious Model for Combined Sewer Overflow Pollution.” In 4th International Conference on Sewer Processes & Networks (4th SPN), Funchal, Madeira, Portugal, 22-24 November, 10p.
• Willems, Patrick. 2008. “Modelling Guidelines for Water Engineering - 4. Model Calibration and Validation.” Hydraulics Laboratory, Kasteelpark Arenberg 40, B-3001 Leuven: 1–33.
• Willems, Patrick. 2009. “A Time Series Tool to Support the Multi-Criteria Performance Evaluation of Rainfall-Runoff Models.” Environmental Modelling and Software 24(3): 311–21
References
mdmoudud.hasan@student.kuleuven.be