DOWNSCALING CLIMATE CHANGE INFORMATION USING AN ENSEMBLE OF REGIONAL CLIMATE MODELS FOR AGRICULTURAL PLANNING: A CASE STUDY OF TANA RIVER COUNTY, KENYA KETIEM, PATRICK KIBET A Thesis Submitted to the Graduate School in Partial Fulfillment of the Requirements for the Degree of Doctor of philosophy in Environmental Science of Egerton University EGERTON UNIVERSITY November, 2016 i
DECLARATION AND RECOMMENDATION Declaration This thesis is my original work and has not been presented in any other institution of higher learning for the award of an academic degree. Signature: ---------------------------------------- Date----------------------------------------- Patrick Kibet Ketiem ND12/0338/12 Recommendation This thesis has been submitted with our recommendation as University supervisors Signature: ---------------------------------------- Date----------------------------------------- Dr. Makenzi, P.M. (PhD) Department of Environmental Science, Egerton University, EGERTON, KENYA Signature: ---------------------------------------- Date----------------------------------------- Dr. Omondi, P.A. (PhD) IGAD Climate Prediction and Applications Centre (ICPAC) NAIROBI, KENYA Signature: ---------------------------------------- Dr. Maranga, E.K. (PhD) Department of Natural Resources, Egerton University EGERTON, KENYA Date----------------------------------------- ii
ABSTRACT The truncated ecosystems of the Tana River County are highly vulnerable to climate change and variability due to their low adaptive capacities and high dependence on climate sensitive resources. Inadequacy of long term climate information is a serious constraint for long-term planning for enhanced food security and minimization of the adverse impacts of climate change and variability. This study was motivated by the need to downscale climate information using modelling procedures based on Regional Climate Models (RCMs). The objectives of the study revolved around evaluations of the performance of Coordinated Regional Climate Downscaling Experiment (CORDEX) RCMs in simulating rainfall and temperature conditions and use of these data sets in projecting future climate change scenarios and their implications on agricultural productivity and related resources. Assessments and validation tests were run to authenticate the plausibility of CORDEX RCMs and the relevance of historical climate data in evaluations of the impact of climate change and variability on agricultural productivity. Agricultural data (crops and livestock) for more than 20 years collected from the Ministry of Agriculture, Livestock and Fisheries (MALF) departments in Tana River County were utilized in the study. The gross yield of five widely grown crops in the region comprising of maize, green grams, rice, cassava and mangoes was collated. Livestock population data for specific livestock species was used. Subjective sampling was applied for three focused group discussions conducted. Bi-variate correlations and simple linear regressions were used to investigate crop/livestock production and rainfall relationships. Combination of dynamical and statistical downscaling approaches were used in RCMs evaluation and projecting the future climate scenarios for Tana River County. RCMs simulated above 84% observed climatology in Tana River County making them valuable tools for agricultural production planning. The ensemble model had better agreement with ground data observations than individual models. Seasonal rainfall variability was of the order of 70% during short and long rains making rainfed agriculture unreliable. Crop yields showed low correlations with March-May (MAM) seasonal rainfall (r = 0.3) as compared with October- December (OND) season (r = 0.55). Seasonal rainfall explained 8% of the variation in maize yields and 40-56% in livestock numbers. The OND season is more reliable for agricultural production activities in the region. A warming trend in the region of 3.0 to 3.5 o C under RCP4.5/8.5 scenarios is projected by the middle of 21 st century. A warming climate in the region will negatively impact food production, water availability and livelihood systems in the region. vi
TABLE OF CONTENTS TITLE... Error! Bookmark not defined. DECLARATION AND RECOMMENDATION... ii COPYRIGHT... iii DEDICATION... iv ACKNOWLEDGEMENTS... v ABSTRACT... vi TABLE OF CONTENTS... vi LIST OF TABLES... xi LIST OF FIGURES... xii LIST OF PLATES... xv LIST OF APPENDICES... xvi LIST OF ABBREVIATIONS AND ACRONYMS... xvii CHAPTER ONE... 1 1.0 INTRODUCTION... 1 1.1 Background information... 1 1.2 Statement of the problem... 3 1.3 Objectives of the study... 4 1.3.1 Broad objective... 4 1.3.2 Specific objectives... 4 1.4 Research hypotheses... 4 1.5 Justification of the study... 4 1.6 Scope of the study... 6 1.7 Study limitations and assumptions... 7 1.7.1 Study limitations... 7 1.7.2 Study assumptions... 8 1.8 Definition and operationalization of terms... 9 CHAPTER TWO... 13 2.0 LITERATURE REVIEW... 13 2.1 Introduction... 13 vii
2.2 CORDEX RCMs and their application in climate downscaling... 13 2.3 Climate downscaling techniques in the agricultural sector... 17 2.4 Characterization of rainfall variability in the eastern Africa... 19 2.5 Agricultural production and changing change... 19 2.6 Climate change projections and agricultural production... 23 CHAPTER THREE... 25 3.0 METHODOLOGY... 25 3.1 Introduction... 25 3.2 The study area... 25 3.2.1 Physical location... 25 3.2.2 Demographic characteristics and livelihood sources... 27 3.2.3 River Tana basin... 28 3.2.4 Climate... 29 3.2.5 Vegetation and biodiversity... 30 3.2.6 Soils... 30 3.3 Study research design... 31 3.3.1 Case study... 33 3.4 Downscaling approaches... 34 3.4.1 Dynamical downscaling... 35 3.4.2 Statistical downscaling... 36 3.5 Future projections... 37 3.6 Data collection... 38 3.6.1 Climate data... 39 3.6.2 Crop and livestock data... 41 3.6.3 CORDEX RCMs data... 41 3.7 Data sampling... 42 3.8 Data reliability and validity... 42 3.9 Data management... 43 3.10 Data analyses... 43 viii
CHAPTER FOUR... 47 4.0RESULTS AND DISCUSSION... 47 4.1Introduction... 47 4.2The performance of CORDEX RCMs in simulating rainfall and temperature characteristics in Tana River County... 47 4.2.1 Introduction... 47 4.2.2 Performance of CORDEX RCMs in simulating eastern Africa rainfall... 48 4.2.3 Performance of CORDEX RCMs in simulating mean annual rainfall cycle over eastern Africa... 53 4.2.4 Performance of CORDEX RCMs in simulating large-scale global climate forcing signals... 54 4.2. 5 Performance of CORDEX RCMs in simulating Tana River County rainfall characteristics... 54 4.2.6 Performance of CORDEX RCMs in simulating temperature characteristics... 56 4.3Agricultural production and climate in Tana River County... 59 4.3.1 Rainfall and temperature homogeneity tests... 59 4.3.2 Characterization of climate and relationships to crop and livestock productions in Tana River County... 60 4.3.3 Crop production characteristics in Tana River County... 72 4.4Projected climate change scenarios in Tana River County... 82 4.4.1 Introduction... 82 4.4.2 Projected rainfall changes over eastern Africa and Tana River County... 82 4.4.3 Projected temperature changes over eastern Africa and Tana River County... 87 4.4.4 Impacts of the projected climate and implications on agricultural production and related land resources in Tana River County... 95 4.4.4.3 Impacts and implications on agricultural production and other related land resources in Tana River County... 98 CHAPTER FIVE... 100 5.0CONCLUSIONS AND RECOMMENDATIONS... 100 5.1Introduction... 100 ix
5.2 Conclusions... 100 5.3 Recommendations... 100 5.3.1 Recommendations to policy makers and planners... 101 5.3.2 Recommendations to the National and County governments... 101 5.3.3 Recommendations to Kenya Meteorological Services (KMS)... 102 5.3.4 Recommendations to farmers and pastoralists... 102 5.4 Recommendations for further research... 103 REFERENCES... 104 APPENDICES... 125 x