Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP WELCOME TO THE SECOND AIACC WORKSHOP, DAKAR AF_42 RESEARCH TEAM BOTSWANA

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Forecasting impact of climate change on runoff coefficients in Limpopo basin using Artificial Neural Network presenter: Prof. B.P. Parida University of Botswana, Gaborone

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Area : km 2 ~ 1/8 area of Botswana 4 Dams: 350 M Cum. Farm Land : ~ Food Security

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP The Limpopo Basin Multi-cell representation

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP YearRun off Coeff YearRun off Coeff YearRun off Coeff

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Why runoff coefficient (roc) ? Rainfall ~ Runoff complex roc = (total runoff) / (total rainfall) Assumed to marginalize the impact of land use changes decrease in rainfall ~ increase in roc decrease in roc ~ decrease in flow

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Source: Hydrological Sciences Journal

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Biological Neuron - specific type of cell - provides cognitive and other related activities. - Neuron collects signals from dendrites -Spikes of electrical activity sent out by a neuron through – long thin strands – axon which is split into thousands of branches -At the end of each branch – synapse, which converts the activity from axon into electrical effects that excite activity. ( changing effectiveness of synapse, influence from preceding neuron is influenced)

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Artificial Neuron – simulates the four basic components as well as functioning of the natural neuron. -Each neuron receives output from many other neurons through input path. -Each of the inputs to a neuron is multiplied by a weight. -Products are then summed up and fed through a transfer function to generate an output.

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Output of the best minimized performance function adopted for the study

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Regression between target and modelled runoff coeffs T = TARGET A = ACHIEVEMENT

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Target and simulated runoff coefficients plotted for the entire study period

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Input variables used: Annual Rainfall and Annual Evaporation Target /Output variable:Water balance computed runoff coefficients. Training Algorithms Used: Automated regularization with early stopping (as it outclassed others) Transfer functions used: Log-sigmoid for hidden layer and purelin for the output layer. The optimum number of neurons in the hidden layer: Fifteen Final Choice of Architecture: Was arrived using PCA, was also found to be the best with two components used and at significance level. For Forecasting/Prediction: Model Predictive Control

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Simulations up to year 2000 & Forecasts into the year 2016

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP A comparison between the forecasted runoff co-efficient obtained from ANN, EXCEL Tool Box & Extrapolation.

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Comparison of Trend in Runoff Coefficints.

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP PeriodAvg. % increase ROCper year : : 0.41(2.5%) : 0.47(14.6%) : 0.48(2.13%) : 0.50(4.2%)3.8

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP In conclusion: It is evident that the by the next two decades runoff is likely to decrease so a good water management strategy will be necessary as a possible adaptation measure.

Forecasting impact of climate change on runoff coefficient in Limpopo basin using ANN MARCH , 2004AF_42 DAKAR WORKSHOP Thank You for listening