Transcript Slide 1

STATUS OF WATER RESOURCES IN INDIA
 India occupies only 3.29 million Ha area, (2.4%) of
world's land area.
4% of water resources of the World.
supports over 16% of the world's population.
livestock population 500 million, 15% of world's total
WR documentation of India at
www.cwc.nic.in
www.india-water.com/ffs/index.htm
SWOT analysis of Water Resources
 Strength

India is gifted with large number of rivers

4000 BCM of water available


Long-term average annual rainfall is 1160 mm, which is the highest
anywhere in the world for a country of comparable size
Annual precipitation of about 4000 BCM, including snowfall. monsoon
rainfall 3000 BCM
Highest rainfall (11,690 mm) recorded at Mousinram near Cherrapunji in
Meghalaya in northeast
 Weakness
 Spatial and temporal distribution
 690 BCM is utilizable form
 Storage insufficient to meet the demand
 Monsoon failure or excess rainfall in one monsoon

Event-Based Models

RAINFALL
• Usually based on statistical analysis
• Sometimes, historical storm information used

WATERSHED CHARACTERISTICS
Relationship between rainfall and runoff identified (e.g.
Rational Method “C” factor, Runoff CN).
coefficients depend on soil infiltration rate, vegetation,
land use, soil type, imperviousness, etc
Continuous Simulation Models
• Use long term rainfall record (20-30 years) and simulate
flows for entire period of record
• Incorporate ET0 and infiltration estimates – simulate
water balance
• HEC-HMS, SWMM, SWAT, HYMOS, Arc-CN runoff for
predicting variability in flow based on event/long term
observed hydrologic data
Using HEC-HMS
Three components

Basin model - contains elements of basin, connectivity,
runoff parameters

Meteorologic Model - contains rainfall & ET0 data

Control Specifications - contains start/stop timing and
calculation intervals for the run
Using SWMM
SWMM Visual Objects
- distributed, dynamic rainfall-runoff simulation model used
for single event or long-term (continuous) simulation of runoff
quantity and quality from primarily urban areas.
Conventional Models of Synthetic Stream flow generation
AR (Auto Regression)
AR(1) -1st order
AR(2)
-2nd
order
X k  X k 1    , k  0,1,2,...
p
 X
i 0
i
k 1
  k , k  0,1,2,...
ARMA (Auto Regression Moving Average)
ARIMA (Auto Regression Integrated Moving Average) - EVIEWS
THOMAS-FIERRING MODEL
 All the models use the statistical properties of the inflow
 Used for monthly, seasonal & annual inflow prediction



All stationary time series can be modeled as AR
or MA or ARMA models
constant  and 2
If a time series is not stationary it is often
possible to make it stationary by using fairly
simple transformations
Forecasts can be either in-sample or out-of-sample forecasts.
Conventional Models Stream flow generation
16
14
X(t)
Trend
Periodic component
12
Stochastic component
10
X(t)
8
6
4
2
0
-2
-4
0
12
24
36
48
Time t
 Periodical component,(parameters show variation)
 Trend component (increase or decrease of process
deviation) with time)
parameters (mean & std
 Independent (random) components & dependant components
AR Models of Synthetic Stream flow generation


Produce sequences of streamflows at multi sites for
low forecast horizon
Synthetic streamflows must behave statistically
similar to historical values and be consistent with
seasonal volume forecasts
AR(1) : Yt  0  1Yt 1  t
THOMAS-FIERRING MODEL PARAMETERS
Q
 S j 1 
 , Qav, j 
b j  rj 
 S 
 j 
pj
N
1
2
  (Q p , j  Qav ) 
Sj  

(
N

1
)


Q p , j Q p , j 1  Q p , j  Q p , J 1  / N

rj 
2
2
2
2
Q

(
Q
)
/
N
Q

(
Q
)
 p, j  p, j
 p , j 1  p, j 1 / N
2


where, N  No of years , ( p  years, j  month)
S j , S j 1  Std deviation for j & j  1 months
rj  correlatio n coefficient between j & j  1 months
Q  Discharge volume
Q J an  Q av, J an  b D ,(Q
J
1
2
2

Q
)

t
S
(1

r
D ec
av D
i j
DJ )
t p  Random independen t variant of mean zero & Std Dev 1

River Flow Forecasting - Krishna
Learning a model from existing data (e.g.
observations of the period from 1987 to 2000)
The resultant model will be used to forecast
future behaviour
Non-stationary Time series

Linear trend

Nonlinear trend

Multiplicative seasonality

Heteroscedastic error terms (non constant variance)
Making them stationary
Linear trend
 Take non-seasonal difference. What is left over
will be stationary AR, MA or ARMA
Non-Linear trend
Exponential growth
 Take logs – this makes the trend linear
 Take non-seasonal difference
Multiplicative seasonality & Heteroscedsatic errors
Taking logs

Multiplicative seasonality often occurs
when growth is exponential.

Take logs then a seasonal difference to
remove trend
Soft techniques for Synthetic Streamflow generation
Neural Network
Using ANN technique
Using daily flow data
yi
Xi
Training of network
Validating network
Predicting flow
Xm
yn
Fuzzy logic
ANFIS
Error
Back Propagation
Input Layer
Hidden Layer
Output Layer
Mean
Relative
Humidity in
%
Maximum
Temperature
in ° C
REF-ET in
mm/day
Minimum
Temperature
in ° C
10
ANN model developed for predicting daily RefETr
ANN model
Daily reference crop evapotranspiration (mm/day)
Wind speed
in km/hr
FAO modified Penman method
8
6
4
4/29/2020
2
1
201
401
601
Stage- Discharge (DIBRUGARH- Brahmaputra river)
Nash : 0.9864
RMSE: 0.816
1-4-1
Observed v/s ANN predicted discharge (Dibrugarh)
Discharge(1000 m3/sec)
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Data points
Observed
ANN predicted
Model Parameters (DIBRUGARH)
Training
Target
Output
AE
ARE
Mean:
7.324
7.307
1.931
0.460
Std Dev:
6.460
6.069
1.649
0.501
Min:
0.928
1.958
0.026
0.004
Max:
25.025
25.15
7.149
3.326
C
0.92
Validation
Target
Output
AE
ARE
Mean:
10.194
10.63
1.823
0.480
Std Dev:
7.462
6.313
2.345
1.014
Min:
1.242
1.960
0.165
0.008
Max:
25.36
25.15
9.102
4.44
C
0.92
Testing
Target
Output
AE
ARE
Mean:
7.015
7.075
3.267
0.688
Std Dev:
6.655
5.256
2.880
0.661
Min:
1.448
1.956
0.481
0.072
Max:
20.55
18.88
8.7225
2.22
C
0.85
C = Correlation coefficient, AE = Absolute Error = (Target value - desired value),
ARE = Absolute Relative Error = (Target value - desired value)/(Target value)
MODELS FOR REAL-TIME STAGE FORECAST
Brahmaputra
Station
Pancharatna
Input data
Pandu
Desired output data
Travel time
Pandu
(t)th day stages (Pandu)
(t-1)th day stages (Pandu)
(t+1)th day Stages
(Pandu)
Pancharatna
(t+1)th day Stages
(t)th day stages (Pandu)
(t)th day stages (Pancharatna) (Pancharatna)
1 day
(from Pandu)
Dhubri
(t-1)th day stages (Pandu)
(t)th day stages
(Pancharatna)
(t)th day stages (Dhubri)
(t+1)th day Stages
(Dhubri)
1 day (from
Pancharatna)
ARCHITECTURE OF MODELS FOR STAGE FORECAST
Neural Network model for Pandu
Neural Network model for Pancharatna
Neural Network model for Dhubri
REAL-TIME STAGE FORECAST (PANDU)
2002
= 0.9659
= 0.011 Observed data Vrs Predicted data
(Pandu)
48.50
48.00
Water level (m)
47.50
47.00
46.50
46.00
45.50
45.00
44.50
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
No of data
Using Fuzzy logic
Data-1998 to 2002
Stage-Discharge (h & Q)
Nine Gaussian MF - IP/OP
Pancharatna
4/29/2020
Pandu
FUZZY LOGIC (MFS & VALIDATION)
4/29/2020
FUZZY LOGIC (RULE VIEWER)
4/29/2020
Com parison of w ater level-discharge curve (Dibrugarh)
105.00
ANN
104.50
Neuro-Fuzzy
Water level (m)
104.00
103.50
Observ
103.00
102.50
Fuzzy
102.00
101.50
101.00
100.50
0
2000
4000
6000
8000 10000 12000 14000 16000 18000 20000 22000 24000
Discharge(m 3/sec)
4/29/2020
Regression eqn between gauged & ungauged locations
Qung   (Qg ) *
Areaung * PPTung
 Area
g
* PPTg
Where, Q  Streamflow
PPT 
M ung
Mg
Area  u/s area of point of interest,
M  Mean Annual Precipitai on in the area
Conclusion:
Data driven modelling, coupled with
physical insights about the system, will
produce more reliable results for
medium-and long-term predictions.
Digital Precipitation Model
calculated relationship
P=115.6+0.258h