Prediction of Climate Change Impacts in Tanzania using Mathematical Models: The Case of Dar es Salaam City By Guido Uhinga PhD Student (Climate Change) Ardhi University Local Climate Solutions for Africa 2013 (LOCS 2013) 30 October 1 November 2013 Kunduchi Beach Hotel, Dar es Salaam, Tanzania 1
Introduction and research issue Methods used Preliminary findings and recommendations on how to mainstreaming information to decision makers 2
Global climate projections indicate that climate change is already happening, and will strengthen even if global greenhouse gas emissions are reduced significantly in the short to medium term (IPCC, 2007). Although Africa (Tanzania inclusive) is considered as one of the continents most vulnerable to the impacts of climate change, its real impact, particularly at a local scale, is still poorly understood (IPCC, 2007). 3
This is because, prediction of climate change impacts in Africa is still based on Global Circulation Models GCMs (Hulme et al., 2000 ). Although, GCMs provide adequate simulations of atmospheric general circulation at continental scale, they do not capture the detail required for regional and national assessments (White et al., 2007). Consequently, the climate change impacts at local level cannot be accurately discerned and hence appropriate measures cannot be formulated and put in place. 4
The main objective of this research is to predict the impacts of climate induced hazard (floods) for Dar es Salaam using mathematical modeling techniques in order to facilitate formulation of appropriate intervention measures against the hazard. 5
1. To determine and evaluate appropriate climate scenarios (potential predictors) that can be used to predict climate change impacts for Dar es Salaam City. 2. To downscale regional climate change scenarios in order to provide high resolution data that can be used to assess climate change impacts for Dar es Salaam City. 3. To develop statistical model for predicting climate change impacts at local scale which will be used to formulate appropriate intervention measures against the climate induced hazard. 4. To assess, in a GIS environment, and predict the impacts of climate induced hazard (flood) 6
Summary of Research Methodology Projected Impacts Downscaled Regional climate change scenarios Impact Modeling (GIS) Evaluate best Scenarios Develop Math-models Statistical downscaling Best scenarios Past extreme events data Local scenarios (sample site data) 7
Correlation analysis was used to determine the relationship (correlation) between observed and model scenarios. Model data from RCP 8.5 scenario is highly correlated with local historical data than RCP 4.5 for both temperature and precipitation (see Figures 1 & 2). 8
r = 0.249 r= 0.080 r= 0.082 RCP 4.5 r= 0.8 r = 0.89 r= 0.97 RCP 8.5 Figure 1 9
Historical data RCP 8.5 scenario Figure 2 10
Statistical downscaling using linear regression method The difference between statistical downscaled temperature scenarios and historical data for the period between 2011 and 2022 is inconsequential (see Figure 3). 11
T o C T 0 C 34 34 33 33 32 31 30 32 31 Projected from RCM 29 28 30 29 Statistical downscaled 27 28 26 1 2 3 4 5 6 7 8 9 10 11 12 Month Projected from RCM Statistical downscaled 1 27 1 2 3 4 5 6 7 8 9 10 11 12 Month 0.5 0-0.5 1 2 3 4 5 6 7 8 9 10 11 12 Figure 3-1 The difference between RCM projections and statistical downscaling results is inconsequential especially from 2011 to 2022 12
Block Multiple linear Regression method 13
Inadequate prevention and mitigation measures contributed more (56%) to the people affected by the December, 2011 floods in Magomeni Suna. Response factors contributed only 8.6 % of the impacts to people affected by the December, 2011 floods in Magomeni Suna. 14
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The higher the CN value the higher the runoff potential 17
600.00 500.00 400.00 300.00 y = 89.198ln(x) + 217.3 R² = 0.9389 200.00 100.00 0.00 1.00000 10.00000 100.00000 18
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Decision makers need to know what is happening where, when with what impacts? (usually in monitory terms). It is impossible to predict when the next flood will happen; simulating flood scenarios provides possibility to known when a particular flood event may occur once again and where. Flood hazard assessment complimented with risk assessment provides information on socio-economic consequences (impacts in monitory terms) which is needed by the decision makers. 20
Unavailability of reliable data and information related to climate change and its impacts (e.g., poor documentation of data about extreme weather events, Inadequacy of high resolution data in terms of spatial and temporal Unavailability of high capacity computers (e.g., supercomputers for processing multi-resolution data sets related to climate change induced hazards. 21
Lack of National standard to estimate the possible monitory damage for flood scenarios. (e.g., In the Netherlands, stage damage curve method is used which is based on the assumption that for each land-cover type a relationship exist between the inundation depth and the degree of direct damage to units of that particular land-cover (Alkema 2007). 22
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