Integrated Approaches for Runoff Forecasting Ashu Jain Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur-UP, INDIA

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

Hydrologic Cycle (Source:

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

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

Indian Scenario

Kanpur Scenario Dainik Jagran: 2 May 2007

Indian Scenario We depend on rainfall for meeting most of our water requirements 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) 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 The uneven spatio-temporal distribution of water and uncertain nature of rainfall patterns call for innovative methods for water utilization and forecasting

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

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

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

A Runoff Hydrograph

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

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

Rainfall Runoff Models Conceptual or Deterministic Systems Theoretic or Black Box Type Regression Time Series ANNsIntegrated

Integrated R-R Models Innovative Integrated approaches Innovative Integrated approaches – Conceptual + ANN – Decomposition + Aggregation – Time Series + ANN …

Integrated Rainfall-Runoff Model-1

Conceptual + ANN Conceptual Model

Conceptual + ANN ANN/Black Box Model

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.

Conceptual + ANN

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

Integrated R-R Model-1 Conceptual: Base flow, infiltration, continuous soil moisture accounting, and the evapotranspiration processes are modelled using conceptual/ deterministic techniques 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 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. Training: ANN training is carried out using GA.

Integrated R-R Model-1 Results

Observed and Predicted Runoff in 1986 (Dry Year)

ANN Model Results (Summer)

Integrated Model-1 Results (Summer)

Integrated Rainfall-Runoff Model-2

Decomposition + Aggregation

Integrated Model-2 Details

Integrated Model-2 Results

Scatter Plot from Model-V

Results-Model-V: Drought Year 1988

Integrated Rainfall-Runoff Model-3

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

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

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

Time Series + ANN

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

Conclusions Integrated modelling approaches have the potential for producing higher accuracy in runoff forecasts. 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. 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.

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