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

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

River Basins in India

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

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

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 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 Periodical component,(parameters show variation) Trend component (increase or decrease of process parameters (mean & std deviation) with time) 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

THOMAS-FIERRING MODEL PARAMETERS Back

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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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 Training of network Validating network Predicting flow Back Propagation Xi Xm yi yn Error Fuzzy logic ANFIS

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

Stage- Discharge (DIBRUGARH- Brahmaputra river) 11/14/2018 Nash : 0.9864 RMSE: 0.816 1-4-1

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)

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)

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

REAL-TIME STAGE FORECAST (PANDU) 11/14/2018 = 0.9659 2002 = 0.011

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

FUZZY LOGIC (MFS & VALIDATION) 11/14/2018 11/14/2018

FUZZY LOGIC (RULE VIEWER) 11/14/2018 11/14/2018

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Regression eqn between gauged & ungauged locations Digital Precipitation Model calculated relationship P=115.6+0.258h Conclusion: Data driven modelling, coupled with physical insights about the system, will produce more reliable results for medium-and long-term predictions.

11/14/2018 THANK YOU 32