Integrated Approaches for Runoff Forecasting

1. Integrated Approachesfor Runoff Forecasting Ashu Jain Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur-UP, INDIA

2. Outline • Hydrologic Cycle • Global Water Facts • Indian Scenario & Possible Solutions • Rainfall-Runoff Modelling • Existing Approaches • Integrated Approaches (3) • Conclusions

3. Hydrologic Cycle (Source: http://saturn.geog.umb.edu/wdripps/Hydrology/Hydrology%20Fall%202004/precipitation.ppt)

4. Global Water Facts • Total water – 1386 Million Kilometer^3 • 97% in oceans & 1% on land is saline • => only 35 MKm3 on land is fresh • Of which 25 MKm3 is solid • Only 10 MKm3 is fresh liquid water • Availability is CONSTANT • Water Demands are INCREASING (2050!) • Optimal use of existing WR is needed

5. Indian Scenario Water availability in India is highly uneven with respect to both space and time

6. Indian Scenario

7. Indian Scenario

8. Kanpur Scenario Dainik Jagran: 2 May 2007

9. Indian Scenario • We depend on rainfall for meeting most of our water requirements • Most of the rainfall in majority of the country is concentrated in monsoon season (June-September) • The uneven spatio-temporal distribution of water and uncertain nature of rainfall patterns call for innovative methods for water utilization and forecasting

10. Possible Solutions Solutions of water problems in India lie in its root causes Space => Interlinking Time => Rainwater Harvesting

11. Possible Solutions Other solutions include • Optimal Management of Existing WR • Runoff Forecasting • Technological Advancements • Innovative Integrated Approaches

12. Runoff Concepts • Amount of water at any time measured in m3/sec at any location in a river is called runoff. • A graph showing runoff as a function of time is called a runoff hydrograph.

13. A Runoff Hydrograph

14. Runoff Concepts Runoff at any time depends on • Catchment characteristics • Storm characteristics • Climatic characteristics • Geo-morphological characteristics

15. Rainfall Runoff Modelling • Physical processes involved in hydrologic cycle • Extremely complex • Dynamic • Non-linear • Fragmented • Not clearly understood • Very difficult to model

16. Rainfall Runoff Models Conceptual or Deterministic Systems Theoretic or Black Box Type Regression Time Series ANNs Integrated

17. Integrated R-R Models • Innovative Integrated approaches • Conceptual + ANN • Decomposition + Aggregation • Time Series + ANN…

18. IntegratedRainfall-Runoff Model-1

19. Conceptual + ANNConceptual Model

20. Conceptual + ANNANN/Black Box Model

21. Conceptual + ANN An integrated/hybrid model capable of exploiting the advantages of conceptual and ANN techniques may be able to provide superior performance in runoff forecasting.

22. Conceptual + ANN

23. Data Employed: Kentucky River • Spatially aggregated daily rainfall (mm) • Average daily river flow (m3/s) • Total length of data – 26 years • First 13 years for training/calibration • Next 13 years for testing/validation

24. Integrated R-R Model-1 • Conceptual: Base flow, infiltration, continuous soil moisture accounting, and the evapotranspiration processes are modelled using conceptual/ deterministic techniques • ANN: Complex, dynamic, and non-linear nature of the process of transformation of effective rainfalls into runoff in a watershed are modelled using ANNs • Training: ANN training is carried out using GA.

25. Integrated R-R Model-1 Results

26. Integrated R-R Model-1 Results Observed and Predicted Runoff in 1986 (Dry Year)

27. ANN Model Results (Summer)

28. Integrated Model-1 Results (Summer)

29. Integrated Rainfall-Runoff Model-2

30. Decomposition + Aggregation

31. Integrated Model-2 Details

32. Integrated Model-2 Results

33. Scatter Plot from Model-V

34. Results-Model-V: Drought Year 1988

35. IntegratedRainfall-Runoff Model-3

36. Time Series + ANN • Basic Steps in Time Series Modelling • Detrending • Deseasonalization • Auto-correlation • ANN modelling involves presenting raw data as inputs • Time series steps can be carried out before presenting data to ANN as inputs.

37. Time Series + ANN • ANN1 – Raw Data • ANN2 – Detrended Data • ANN3 – Detrended and Deseasonalized Data

38. Time Series + ANN Data Employed • Monthly runoff from Colorado River @ Lees Ferry, USA for 62 years • Past four months lag • 50 Years for training • 12 years for testing

39. Time Series + ANN

40. Conclusions • Runoff forecasting is important for efficient management of existing water resources. • An individual modelling technique provides reasonable accuracy in runoff forecasting. • Neural network based solutions can be better than those obtained using conventional methods.

41. Conclusions • Integrated modelling approaches have the potential for producing higher accuracy in runoff forecasts. • Innovative integrated approaches dependent on the nature of problem are needed in order to develop hybrid forecast models capable of exploiting the strengths of the available individual techniques.

42. Thank You