1. HYDROLOGICAL TIME SERIES GENERATION IN INDIAN CONTEXT
Dr. R N Sankhua
Director
National Water Academy,
Central Water Commission
India

2. 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

3. River Basins in India

5. 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

6. 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

7. 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

8. Meteorologic Model - contains rainfall & ET0 data
Using HEC-HMS
11/14/2018
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

9. 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.

10. Conventional Models of Synthetic Stream flow generation
AR (Auto Regression)
AR(1) -1st order
AR(2) -2nd order
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
                                                                                                                                  

11. 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.

12. Conventional Models Stream flow generation
Periodical component,(parameters show variation)
Trend component (increase or decrease of process parameters (mean & std deviation) with time)
Independent (random) components & dependant components

13. 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

14. THOMAS-FIERRING MODEL PARAMETERS
Back

15. River Flow Forecasting - Krishna
11/14/2018
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 15

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17. Non-stationary Time series
Linear trend
Nonlinear trend
Multiplicative seasonality
Heteroscedastic error terms (non constant variance)

18. 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

19. Multiplicative seasonality & Heteroscedsatic errors
Taking logs
Multiplicative seasonality often occurs when growth is exponential.
Take logs then a seasonal difference to remove trend

20. Soft techniques for Synthetic Streamflow generation
Neural Network
Using ANN technique
Using daily flow data
Training of network
Validating network
Predicting flow
Back Propagation Xi Xm yi yn
Error
Fuzzy logic
ANFIS

21. ANN model developed for predicting daily Ref-ETr
Input Layer
Hidden Layer
Output Layer
Wind speed in km/hr
Minimum Temperature in ° C
Maximum Temperature in ° C
Mean Relative Humidity in %
REF-ET in
mm/day
ANN model developed for predicting daily Ref-ETr
11/14/2018

22. Stage- Discharge (DIBRUGARH- Brahmaputra river)
11/14/2018
Nash :
RMSE: 0.816
1-4-1

23. Model Parameters (DIBRUGARH)
11/14/2018
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
10.194
10.63
1.823
0.480
7.462
6.313
2.345
1.014
1.242
1.960
0.165
0.008
25.36
9.102
4.44
Testing
7.015
7.075
3.267
0.688
6.655
5.256
2.880
0.661
1.448
1.956
0.481
0.072
20.55
18.88
8.7225
2.22
0.85
C = Correlation coefficient, AE = Absolute Error = (Target value - desired value), ARE = Absolute Relative Error = (Target value - desired value)/(Target value)

24. MODELS FOR REAL-TIME STAGE FORECAST
11/14/2018
Brahmaputra
Pancharatna
Pandu
Station
Input data
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)th day stages (Pancharatna)
(t+1)th day Stages (Pancharatna)
1 day
(from Pandu)
Dhubri
(t)th day stages (Dhubri)
(t+1)th day Stages (Dhubri)
1 day (from Pancharatna)

25. ARCHITECTURE OF MODELS FOR STAGE FORECAST
11/14/2018
Neural Network model for Pandu
Neural Network model for Pancharatna
Neural Network model for Dhubri

26. REAL-TIME STAGE FORECAST (PANDU)
11/14/2018 =
2002
= 0.011

27. Stage-Discharge (h & Q) Nine Gaussian MF - IP/OP
11/14/2018
Using Fuzzy logic
Data-1998 to 2002
Stage-Discharge (h & Q)
Nine Gaussian MF - IP/OP
Pancharatna
Pandu
11/14/2018

28. FUZZY LOGIC (MFS & VALIDATION)
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29. FUZZY LOGIC (RULE VIEWER)
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31. Regression eqn between gauged & ungauged locations
Digital Precipitation Model
calculated relationship
P= h
Conclusion:
Data driven modelling, coupled with physical insights about the system, will produce more reliable results for medium-and long-term predictions.

32. 11/14/2018
THANK YOU 32