Analysis of Climate Change Impacts in Ngerengere Sub-Basin Using Data-driven Model
Doglas Benjamin *
Department of Water Resources and Irrigation Engineering, Water Institute (WI), P.O.Box 35059, Dar es Salaam, Tanzania.
*Author to whom correspondence should be addressed.
Abstract
The basic premises of this study is to analyze climate change impacts on flow rate in Ngerengere sub-basin using the data-driven model. Stream flows of sub-basin were simulated by skilled GCMs using data-driven model and Polynomial regression model. The model was setup using observed downstream flows and rainfall data. A total of 5 GCMs from CMIP5 database named as Nor ESM1-M, GFDL-ESM, Had GEM2-ES, IPSL-CM5A-LR and MIROC-ESM-CHEM were incorporated in the model. Since runoff is greatly sensitive to precipitation in comparison to other variables such as temperature, precipitation chosen as climate changing variable for projection.GCMs used in analysis and simulation of climate change impact at Ngerengere sub basin with highest skill score is 92% NorESM1-M and lowest skill score of 90% IPSL-CM5A which are above threshold value 80%. GCMs projected (2010 – 2049) at Sub basin decrease in average precipitation January to November while August is projected to suffer more average decrease in precipitation. unsimillar projection in average precipitation occour in February, March, September and December. General Circulation Models projection (2010 – 2049) of stream flow in Ngerengere sub-basin is highly dependent upon the projected changes in precipitation because the patterns drawn by the precipiation changes are similar with those of stream flows. The projected (2010 – 2049) average annual decrease in stream flow of Ngerengere sub-basin is estimated to be around 18% taken as the average of the outputs of all 5 GCMs.
Keywords: Climate change impact, Ngeregere sub-Basin, data driven model, skill score test, polynomial regression analysis