An uncertainty approach to assessment of climate change impacts on the Zambezi River Basin

Charles Fant,Kenneth Strzepek,Yohannes Gebretsadik,Alyssa Mccluskey

doi:10.1007/s10584-014-1314-x

Abstract

Many residents of the Zambezi River Valley are dependent on water-related resources. Greenhouse gas (GHG) emissions may cause a significant change to the climate in the Zambezi Basin in the future, but there is much uncertainty about the future climate state. This situation leaves policy makers at a state of urgency to prepare for these changes as well as reduce the impacts of the changes through GHG mitigation strategies. First and foremost, we must better understand the economic sectors most likely impacted and the magnitude of those impacts, given the inherent uncertainty. In this study, we present a suite of models that assess the effects of climate change on water resources for four countries in the Zambezi basin: Malawi, Mozambique, Zambia, and Zimbabwe. We use information from a large ensemble (6800) of climate scenarios for two GHG emission policies which represent a distribution of impacts on water-related sectors, considering emissions uncertainty, climate sensitivity uncertainty, and regional climate uncertainty. Two GHG mitigation scenarios are used to understand the effect of global emissions reduction on the River Basin system out to 2050. Under both climate polices, the majority of the basin will likely be drier, except for a portion in the north around Malawi and northern Zambia. Three Key Performance Indicators are used—flood occurrence, unmet irrigation demand, and hydropower generation—to understand the impact channels of climate change effects on the four countries. We find that floods are likely to be worse in Mozambique, irrigation demands are likely to be unmet in Mozambique and Zimbabwe, and hydropower generation is likely to be reduced in Zambia. We also find that the range of possible impacts is much larger under an unconstrained GHG emissions case than under a strict mitigation strategy, suggesting that GHG mitigation would reduce uncertainties about the future climate state, reducing the risks of extreme changes as compared to the unconstrained emissions case.

Highlights

Climate change may pose substantial risk to Africa’s water resources (IPCC 2007) while most Africa nations have contributed almost nothing to global emissions
Arora and Boer (2001) provides a comprehensive review of the types of approaches used to answer this type of question. Two of these methods are: (i) a climate sensitivity analysis on existing or planned infrastructure using a wide range of future climate possibilities (e.g., Kurukulasuriya et al 2006; Wilks 1992); or (ii) use of select Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project (CMIP) used in the Intergovernmental Panel for Climate Change (IPCC) fourth Assessment Reports applied directly to assess the impacts of climate change (e.g., Liu et al 2013; Arndt et al 2010, World Bank 2009; Immerzeel 2008)
Dessai and Hulme (2007) perform a similar analysis on the East of England and find that regional climate change is the largest source of uncertainty, this analysis is only driven by 2 GCMs

Summary

Introduction

Climate change may pose substantial risk to Africa’s water resources (IPCC 2007) while most Africa nations have contributed almost nothing to global emissions. We combine methods (i) and (ii) on the front end, essentially aimed to quantify the impacts on biophysical measures—surface water supply, flooding events, and hydropower generation—of the Zambezi Basin countries using information from a large pool (6800) of climate mean projections, similar to method (i), which are based on the majority (17) of the IPCC AR4 GCMs, similar to method (ii). We do this for two mitigation strategies—strict greenhouse gas mitigation and business as usual—which consider the inherent uncertainties in emissions, climate sensitivity, and regional changes in climate (Schlosser et al 2013). We focus the uncertainty analysis on the effect of the change in future mean climate state, and do not consider other uncertainties, of which there are many, such as the past or present environment, biophysical modeling techniques, power demands, management decisions, etc. Dessai and Hulme (2007) perform a similar analysis on the East of England and find that regional climate change is the largest source of uncertainty, this analysis is only driven by 2 GCMs

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Conclusion