Climate change affecting the Okavango Delta

Transcription

1 Research Collection Master Thesis Climate change affecting the Okavango Delta Author(s): Burg, Vanessa Publication Date: 27 Permanent Link: Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library

2 Climate change affecting the Okavango Delta Diploma Thesis, July 27 Author: Vanessa Burg Supervisor: Prof. Dr. Wolfgang Kinzelbach Dipl. Natw. Christian Milzow Institute of Environmental Engineering (IfU) Swiss Federal Institute of Technology Zurich (ETH Zurich)

3 Cover picture: The Okavango Delta in Botswana, Africa.

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5 ABSTRACT The vast Okavango Delta, Northern Botswana, with its outstanding wetland flora and fauna, is under potential threat from climate change and local development. This thesis presents the possible impacts of global climate change on both the hydrology and the vegetation of the Delta. The investigation was done over two future time spans (25 28 and 27 21) using the IPCC emission scenario A2, and five different climate models (CCC, CCSR, CSIRO, GFDL and HadCM3). In addition, a development scenario was determined to get an idea of the extent of the effects of global climate change in comparison to those of local human interventions. Modifications of the hydrological characteristics were estimated by modifying the past time series of runoff, rainfall and potential evapotranspiration and running a hydrological model of the Okavango Delta using those inputs. Simulations of climate change predict a drying up of the Delta; i.e. deeper groundwater levels and lower inundation frequencies. The impacts of global climate change are expected to be much more important than those of the regional developments. In any case, deforestation along the Okavango River leads to an augmentation of the inflow to the delta, mitigating the local effects of climate change. Vegetation will have to adapt to the new hydrologic conditions. In this thesis, the distribution of the vegetation cover was analysed on the basis of flooding frequency and mean depth to the groundwater level. It was found that almost each vegetation type had a distinct preferred groundwater depth of occurrence. The trend of the effects of future climatic change and development on the different vegetation types can be simulated with this single parameter. The area covered by vegetation types that require rather wet conditions is expected to decrease. As there are many not widely spread vegetation types, which cover wet areas of the Delta, the decrease of the groundwater level will lead to a depletion of biodiversity. I

6 ACKNOWLEDGEMENTS This diploma thesis was written at the Institute of Environmental Engineering (IfU), Swiss Federal Institute of Technology Zurich (ETH Zurich). The work deals with the possible impacts of climate change on water allocation and vegetation in the Okavango Delta. I would like to thank the following persons: Prof. Dr. Wolfgang Kinzelbach 1 for making this diploma thesis possible and for his precious inputs My supervisor Dipl. Geol. Christian Milzow 1 for his competent feedback on various questions, his support and encouragement Prof. Dr. Peter J. Edwards 2 for the inspiring discussions and sharing his detailed knowledge of the vegetation Dr. Lotta Andersson and Dr. Julie Wilk 3 for providing data and valuable explanations Dipl. Ing. Philipp Meier 1 for supporting me in solving programming problems Vanessa Burg July 27 1 Institute of Environmental Engineering, ETH Zurich, Switzerland 2 Institute of Integrative Biology, ETH Zurich, Switzerland 3 Research Department Hydrology, SMHI Norrköping, Sweden II

7 TABLE OF CONTENTS CHAPTER 1 INTRODUCTION 1 CHAPTER 2 STUDY AREA General Description Climate Water Surface water Groundwater recharge and hydrogeology Flora and fauna Social and economic aspects... 4 CHAPTER 3 HYDROLOGICAL MODELLING APPROACH Model set up Temporarily constant input parameters Temporarily varying input parameters Climate change Local developments... 8 CHAPTER 4 RESULTS: THE HYDROLOGICAL IMPACTS Groundwater depth Inundation probability CHAPTER 5 VEGETATION MODELING APPROACH Classification method Statistical analysis Simulation of the vegetation CHAPTER 6 RESULTS: THE IMPACTS ON THE VEGETATION The climate change models The mean of the climate change models The development scenario CHAPTER 7 DISCUSSION 41 CHAPTER 8 CONCLUSIONS AND SUGGESTIONS 44 REFERENCES 45 Appendix A Appendix B Appendix C Appendix D (Results of the hydrological model).....i (Impacts on the vegetation 1).. iv (Impacts on the vegetation 2)... xix (Matlab scripts). xxix III

8 LIST OF FIGURES Figure 1: Model area... 2 Figure 2: Topography of the model area... 2 Figure 3: Average monthly precipitation and potential evapotranspiration ( )... 3 Figure 4: Monthly discharge of the Okavango River at Mukwe ( )... 3 Figure 5: Flow at the discharge stations Mohembo and Mukwe ( )... 6 Figure 6: Predicted change of the potential evapotranspiration relative to the past time series... 7 Figure 7: Predicted change of the precipitation relative to the past time series... 8 Figure 8: Simulated and observed inflow... 8 Figure 9: Observed and modified inflow... 9 Figure 1: Spatial distribution of the mean groundwater depth over the time period Figure 11: Spatial distribution of the mean groundwater depth over the time period Figure 12: Spatial distribution of the mean groundwater depth over the time period Figure 13: Spatial distribution of the mean groundwater depth( development scenario and past) Figure 14: Difference between past and predicted (scenario 14) mean depth to groundwater Figure 15: Difference between past and predicted (25 28) mean depth to groundwater Figure 16: Difference between past and predicted (27 21) mean depth to groundwater Figure 17: Difference between past and predicted mean depth to groundwater (average of the four climate models: CCC, CCSR, GFDL and HadCM3) Figure 18: Spatial distribution of the flooding probability for the time period Figure 19: Spatial distribution of the flooding probability for the time period Figure 2: Spatial distribution of the flooding probability for the time period Figure 21: Spatial distribution of the flooding probability (development scenario)... 2 Figure 22: Difference between past and predicted inundation probability (development scenario)... 2 Figure 23: Difference between past and predicted (25 28) inundation probability Figure 24: Difference between past and predicted (27 21) inundation probability Figure 25: Difference between past and predicted inundation probability (average of the four climate models: CCC, CCSR, GFDL and HadCM3) Figure 26: Spatial distribution of the ecoregions Figure 27: Mean and standard deviation of the inundation frequency for the all ecoregions Figure 28: Mean and standard deviation of the mean depth to groundwater for the all ecoregions Figure 29: Proportion of the area covered by each ecoregion over the inundation frequency Figure 3: Proportion of the area covered by each ecoregion over the mean groundwater depth Figure 31: Proportion of the area covered by the different ecoregions over the mean groundwater depth Figure 32: Spatial expectation of the ecoregions (past conditions ) Figure 33: Spatial distribution of the most expected ecoregions and corresponding confidence... 3 Figure 34: Areas covered by the different ecoregions (observed in comparison to simulated)... 3 Figure 35: Spatial distribution of the most expected ecoregions and corresponding confidence (climate models: CCC, CCSR, GFDL and HadCM3; 25 28) Figure 36: Spatial distribution of the most expected ecoregions and corresponding confidence (Climate models: CCC, CCSR, GFDL and HadCM3; 27 21) Figure 37: Difference between past and future (25 28) most expected ecoregions Figure 38: Difference between past and future (27 21) most expected ecoregions Figure 39: Expected areas covered by the different ecoregions (25 28) Figure 4: Expected areas covered by the different ecoregions (27 21) Figure 41: Spatial distribution of the most expected ecoregions and corresponding confidence (mean of the four climate modells, 25 28) Figure 42: Spatial distribution of the most expected ecoregions and corresponding confidence (mean of the four climate modells, 27 21) IV

9 Figure 43: Difference between past and future most expected ecoregion (mean of the four climate models, and 27 21) Figure 44: Expected areas covered by the different ecoregions (mean of the four climate models, and 27 21) Figure 45: Spatial distribution of the most expected ecoregions and corresponding confidence (Scenario 14) Figure 46: Difference between past and future (Scenario 14) most expected ecoregions Figure 47: Expected areas covered by the different ecoregiosn (Scenario 14)... 4 LIST OF TABLES Table 1: Daily discharge statistics for the gauging stations Mohembo and Mukwe... 6 Table 2: Classification scheme of the vegetation V

10 GLOSSARY L: Length, T: Time ET pot [L/T] Potential evapotranspiration. In this work, the Hargreaves model is used. P [L/T] Precipitation Q [L 3 /T] Discharge CCC CCSR CSIRO IPCC GCM GFDL HadCM3 RCM Global climate model from the Canadian Climate Center (Canada) Global climate model from the Center for Climate System Research (Japan) Global climate model from the Commonwealth Scientific and Industrial Research Organisation (Australia) Intergovernmental Panel on Climate Change Global Climate Model Global climate model from the Geophysical Fluid Dynamics Laboratory (United States of America) Global climate model from the Hadley Centre Coupled Model (United Kingdom) Regional Climate Model VI

11 CHAPTER 1 INTRODUCTION In some areas of the world, water shortage forms an obstacle to development. This is also true for South West Africa. One of the most important water resources of this fast developing region is the Okavango River, which forms the basis of water supply for about one million people. The Okavango River rises in the highlands of Angola, flows through Namibia, and finally ends in Botswana in a vast swamp, known as the Okavango Delta. Water is very abundant in the upstream section of the river, but rare downstream. The delta offers unique habitats and contains a corresponding high biodiversity of both fauna and flora. It is protected by the Ramsar Convention on Wetlands and provides the basis of the regional tourist industry. Climate change There is growing evidence that ecosystem functions and biodiversity are influenced by climate change (McCarthy et al., 21) and that there is an anthropogenic influence on global climate associated with greenhouse gas emissions (IPCC, 21). The diverse global climate models (GCMs) predict entirely different future climatic changes in the Okavango Delta namely from drier to wetter than at the present time. One reason is the complex interplay of changes in the catchment and changes in the Delta itself (Andersson et al., 26). Social and economic development Water resources in Angola are fairly unexploited and future development could have severe repercussions on the water abundance in the downstream countries (Pinheiro et al., 23). Economic growth of the three riparian countries will definitely make the water of the Okavango River more and more attractive, whether it is for agricultural and industrial water supply or the production of electricity. However, the Okavango Basin will change significantly and competition for resources has the potential to cause conflict (Andersson et al., 26). It is important to determine what consequences these changes will have. The use of models has been proven to be a successful method in identifying those impacts (Dahinden et al., 2). Model selection, development, and use should proceed with the participation and consultation of as many experts as possible (Sharing Water Final Report, 25). Research objectives The aim of this thesis is estimating, visualising, and analysing the impact of future changes on the Okavango Delta. Scenarios of future climatic conditions and local development are determined and their impacts on the delta are evaluated using an existing hydrological model. Modification of the hydrological characteristics are estimated by modifying the past time series of discharge, precipitation, and potential evapotranspiration to reproduce possible future changes and running the hydrological model of the Okavango Delta using those inputs. These hydrological characteristics are then compared to those simulated with the observed past time series. In doing so one obtains a comparison of past with potential future conditions. Vegetation will have to adapt to these new hydrologic conditions. Actual flood frequency and mean depth to groundwater are compared to the vegetation distribution; on the basis of this correlation, a prediction of the future vegetation pattern can be made. The combination of possible effects as increased temperature, augmentation of the evapotranspirative loss, decreased rainfall, and reduction in river flows may result in a considerable drying up of the delta and a loss of wetland. 1

12 CHAPTER 2 STUDY AREA 2.1 GE ENERAL DESCRIPTION The Okavango Delta is located in northwestern Botswana and is one of the world s largest inland deltas. It was once part of Lake Makgadikgadi, an ancient lake that dried up about 1 years ago. The study area s geographical location is 517 E/7 7 N for the south western corner and 917 E/8 1 N for the north eastern corner ( UTM Zone 34K). The project area covers approximately 16 km 2, whereas only about 9 km 2 consist of active cells (Figure 1). The Okavango River enters the active sector of the model in the south east and forms a huge alluvial fan. 1 km FIGURE 1: MODEL AREA (BLUE: INACTIVE CELLS, YELLOW: POLITICAL FRONTIER) The topography shows very low gradients (around 1/5) so that microtopography and vegetation roughness are key parameters controlling overland flow. m ASL northing [km] easting [km] FIGURE 2: TOPOGRAPHY OF THE MODEL AREA 2

13 2.2 CLIMATE The Okavango Delta lies in a semi arid region. Rain generally starts in October or November and persists through March or April. The annual amount of precipitation is 512 mm and is restricted to the rainy season (November March). The Hargreaves potential evapotranspiration (ET pot ) is 1858 mm/a, with the highest value of 198 mm in November and the lowest value of 1 mm in June (Figure 3). [mm] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Precipitation Pot. Evapotranspiration FIGURE 3: AVERAGE MONTHLY PRECIPITATION AND POTENTIAL EVAPOTRANSPIRATION ( ) 2.3 WATER SURFACE WATER The water of the Okavango River originates in the highlands of Angola and runs southeastward for 1 6 km. The river has no outlet to the sea; instead, it empties into the sands of the Kalahari Desert. The discharge of the river s inflow to the delta at Mukwe is shown in Figure 4. Its mean is 32.5 m 3 /s for the time period Discharge [m 3 /s] FIGURE 4: MONTHLY DISCHARGE OF THE OKAVANGO RIVER AT MUKWE ( ) The Okavango Delta is controlled by an annual flood. Due to the very low topographic gradient, the peak flood generated from wet season rains in southern Angola only reaches the Okavango Delta in the dry season. The flow can be described using the diffusive wave approximation of the Saint Venant equations (Bauer et al., 26). The amplitude of such an inundation is variable; it is strongly influenced by flood discharge and local rainfall and to a lesser extent by earlier conditions and evapotranspiration (McCarthy, 1998). 3

14 2.3.2 GROUNDWATER RECHARGE AND HYDROGEOLOGY The groundwater aquifer is essentially recharged through the flood plains and all the inflowing water is eventually lost by evapotranspiration. Soluble salts are accumulating in the deep groundwater. There is no large scale lateral flow of groundwater, the water movements in the system are essentially vertical (McCarthy, 1998). The Kalahari sediments have an average grain size of 25 µm and a depth of about 1 m, which varies strongly due to rifting and faulting (Bauer et. al., 26). 2.4 FLORA AND FAUNA In the Okavango Delta, the number of identified species is 1 3 for plants, 71 for fish, 33 for amphibians, 64 for reptiles, 444 for birds and 122 for mammals (Ramberg, 26). This richness in species is due to various factors. The main factor is the hydrological gradient, which ranges from permanent swamps to seasonal floodplains and dry woodlands. Other important factors are the gradient in soil and water chemistry (from freshwater swamps to saline pan) and the dynamic water flow patterns that occur over different time scales (Ellery and Tacheba, 23). The density of species in the Delta is greater than for most of the Southern African biomes (such as savannah and desert), is similar to the grassland and succulent karoo biomes, but less than that of the fynbos (Ellery and Tacheba, 23). The Okavango Delta system forms the largest Wetland of International Importance protected as a Ramsar site. As stated in Article 3 of the convention, its conservation and wise use should be promoted (Ramsar Convention on Wetlands, 1971). 2.5 SOCIAL AND ECONOMIC ASPECTS The beauty and the abundant wildlife of the Okanvango Delta form the basis of the fast growing tourism industry in Botswana. The Delta is situated at the end of the Okavango River and as such, all upstream developments in Namibia and Angola can have considerable impacts on its condition and dynamics. Augmentation of water supply and irrigation, construction of dams for energy supply, and deforestation in the riparian countries are the developments, most likely to affect the Okavango Delta. 4

15 CHAPTER 3 HYDROLOGICAL MODELLING APPROACH 3.1 MODEL SET UP The hydrological model computes the water allocation of the Okavango Delta in space and time. It is a coupled surface water groundwater model and has been built based on the software MODFLOW 2 (Milzow et al., 26). It forms the tool to evaluate the impacts of climate change and development on the hydrology of the Delta. The model contains two layers of 4 x 4 cells, whereas only about 3x3 cells are active. The size of one cell is 1 x 1 km. The lower layer represents the Kalahari Sand aquifer and the upper one reproduces the overland stream flow. Channel flow is represented after the major channels individually while all other surface flow is treated as overland flow. The aquifer thickness is spatially variable. The temporarily varying input parameters (precipitation, evapotranspiration, and discharge of the Okavango River at its inflow to the delta) are given for the time period Time discretisation consists then of 36 monthly periods whereas each period is further divided into 1 time steps. 3.2 TEMPORARILY CONSTANT INPUT PARAMETERS Temporally constant input parameters do not have to be changed when predicting the effects of future changes on the hydrology of the Okavango Delta. They have been retained from the earlier work of Milzow et al. (26) and the most important ones are shortly introduced in this paragraph. Channel positions were manually picked from aerial photographs and can be assumed to be constant over the time span of interest. Channel flow can be described by Manning s equation with.3 for the dimensionless Manning s roughness coefficient. Channel beds have a hydraulic conductivity of m/s and a thickness of.5 m. The upper and lower layers are connected through a leakage factor of l/s which is independent of heads. Pumping tests showed a quite consistent hydraulic conductivity of 1 4 m/s. Topography and aquifer thickness are spatially variable. 3.3 TEMPORARILY VARYING INPUT PARAMETERS Only three input parameters are temporally variable and have to be modified in order to model the impacts of climate change: precipitation, potential evapotranspiration, and discharge of the Okavango River at its inflow to the active model area (Mohembo). The first two parameters are combined to a net water exchange with the atmosphere. The discharge data simulated by a Pitman hydrological model (Andersson et al., 26) are given for the Mukwe Station. Mukwe is a gauging station situated about 4 km upstream of the Mohembo station. Before using these data, the consistency of the two time series was checked. A comparison of the discharge stations at a daily scale is shown in Figure 5 for the common gauged time span ( ). 5

16 12 1 Mohembo Mukwe Daily Flow [m3/s] FIGURE 5: FLOW AT THE DISCHARGE STATIONS MOHEMBO AND MUKWE ( ) The main difference between the two stations can be observed during extreme high water, when the peak flow at Mukwe is higher. The assumption is that the discharge station Mohembo lies in a curve so at higher levels, water starts to bypass the gauging station. The mean and the standard deviation of the daily discharge of the two gauging stations are shown in Table 1. The mean discharge gauged at Mukwe is about 7% higher than that at Mohembo. The standard deviation of the daily discharge at Mukwe is about 13% higher than the one at Mohembo. These two facts confirm the theory that during flooding only a fraction of the discharge is gauged at Mohembo. TABLE 1: DAILY DISCHARGE STATISTICS FOR THE GAUGING STATIONS MOHEMBO AND MUKWE Mohembo Mukwe Mean [m 3 /s] Standard deviation [m 3 /s] Joint gaugings at Mukwe and Mohembo have been carried out by the Botswanan Department of Water Affairs and there is an agreement that the flows measured at Mukwe are accurate (OKACOM, 1998) while the Mohembo gauging station is known to underestimate the flow during periods of high flow (Neuman, 25). The cross correlation is the correlation between time series shifted against one another. It quantifies the similarity between the series and identifies time shifts between them (the lag that maximizes the coherence). The maximal cross correlation between the two stations (.9424) is found at lag 2d. A time shift of two days is not significant for this work and is negligible. In conclusion, there is no major difference between the gauging stations at Mukwe and Mohembo; the only statement possible to make is, that Mukwe delivers more accurate measurements. The gauging of the Mukwe station can be used also for Mohembo, without any adjustment CLIMATE CHANGE Climate change may have an immediate effect on the hydrological inputs to the Okavango Delta. The intergovernmental panel on climate change (IPCC) was created in 1988 to evaluate the risk of human induced climate change. The IPCC developed a total of 4 emission scenarios, which were designed to determine changes in concentrations of greenhouse gases. Possible global future conditions range from sustainable development to collapse of social, economic, and environmental systems. 6

17 The climate change modelling of this study is based on the scenario A2 of the IPCC (special report of the IPCC, 2) which describes a very heterogeneous world. It is one of those that received the most scientific peer review and its output data are widely available. Compared to other plausible scenarios, global population is expected to increase at a high rate. Economic development is primarily regionally oriented. Energy consumption and changes in land use are high. Resources are becoming scarce and technological change is fragmented and slower than in other scenarios. The A2 scenario is not an extreme one, but has relatively high growth rates and the global greenhouse gas emissions are expected to increase by a factor of four between 199 and 21 (IPCC, 21). It is important to keep in mind that there is a large uncertainty about future changes and that depending on the emission scenario followed, the extent of climate change will be different. A global climate model simulates the climate system by solving mathematical equations based upon the laws of physics. The results of five different global climate models have been used in this paper. One of those was developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), which is the national government body for scientific research in Australia. Another was created by the Hadley Centre in the United Kingdom and is called HadCM3 (Hadley Centre Coupled Model, version 3). The CCC model was developed by the Canadian Climate Centre and the GFDL by the Geophysical Fluid Dynamics Laboratory in the United States. The last model applied in the study was created by the Centre for Climate System Research (CCSR) in Japan. Two different time periods of thirty years have been considered: and All five global climate models predict increased potential evapotranspiration (for both time spans and for all months). The monthly changes relative to the past time series are shown in Figure 6. The lowest increase is simulated by the CSIRO and the GFDL models for the time span (yearly average: factor 1.12); the highest is predicted by the HadCM3 model for the period (factor 1.2). To summarise, all climate models predict a similar augmentation of the potential evapotranspiration by an average of 12 to 2% A A CSIRO 1.2 CSIRO 1.15 HadCM3 CCC 1.15 HadCM3 CCC 1.1 GFDL CCSR 1.1 GFDL CCSR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec FIGURE 6: PREDICTED CHANGE OF THE POTENTIAL EVAPOTRANSPIRATION RELATIVE TO THE PAST TIME SERIES The predictions of precipitation changes are fairly different depending on the climate models, but none breaks ranks (Figure 7). The CSIRO and GFDL models simulate higher average yearly precipitation but very different changes on a monthly scale ranging from factor.2 to 4.2 for the CSIRO model and from.8 to 3.9 for the GFDL model. The other three climate models forecast on average less precipitation than in the past. 7

18 A CSIRO HadCM A CSIRO HadCM3 2. CCC 2. CCC GFDL CCSR GFDL CCSR.5.5. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec FIGURE 7: PREDICTED CHANGE OF THE PRECIPITATION RELATIVE TO THE PAST TIME SERIES The modified runoff data are taken from a study on the impacts of climate change and development on flow pattern in the Okavango River (Andersson et al., 26). In that study, the modelling was based on a modified version of the monthly Pitman model, set up for the Okavango basin upstream of the delta panhandle (Hughes et al., 26). The simulated inflow is extremely different depending on the used model (Figure 8). The first eyecatching fact is that the inflow predicted by the CSIRO model is much higher than the ones simulated by the other four models. For both time spans, the discharge simulated by the CSIRO model is almost four times higher than the observed one from the past; the GFDL model predicts about the same average inflow (25 28: factor 1; 27 21: factor 1.1). The HadCM3 model forecasts about half as much inflow as observed. The CCC model predicts about the same average inflow for the period but a decrease of about 3% for the time span The CCSR model simulates about 1% less discharge for the first and a minus of 5% for the later one Discharge [m 3 /s] CSIRO HadCM3 CCC GFDL CCSR Observed Discharge [m 3 /s] CSIRO HadCM3 CCC GFDL CCSR Observed FIGURE 8: SIMULATED AND OBSERVED INFLOW As the discharge forecasted by the CSIRO model is suspiciously high and differs totally from the results of the four other models, one can assume that the CSIRO model is not adapted to simulate this region of the world. It is beyond the scope of this thesis to evaluate the probability and the confidence of the other models LOCAL DEVELOPMENTS Developments in the region are likely to occur, which will result among others in abstraction of water from the river. The only modified input parameter is the discharge of the river. Upstream abstractions for human use are likely to have small effects on the Delta, while damming or deforestation have more pronounced effects (Wolski et al., 26). For this analysis, the modified inflow time series of one of the scenarios obtained from Andersson et al. (26) was applied. The so called scenario 14 was chosen because it includes the highest changes. Therefore, it is something like a worst case scenario due to local human activity. The scenario is set 8

19 up to generate the changes expected in 225, but there was no scenario describing a farther time. Scenario 14 is based on predictions of levels of population growth and demand for water, including irrigation and industry. It incorporates the construction of dams as well as the Windhoek pipeline. Furthermore, deforestation is forecasted 2 km from the rivers. The effects of damming, reducing peak flows, and increasing low flows are especially well visible (Figure 9). The scenario induces opposite effects (deforestation causes increases while abstractions cause decreases in discharges) which damp one another. Scenario 14 forecasts a mean inflow to the Delta of approximately 1% more than observed in the past. This is due to high deforestation Observed at Mukwe Scenario 14 Discharge [m 3 /s] FIGURE 9: OBSERVED AND MODIFIED INFLOW Scenario 14 helps to get an idea of the importance of human activity in the region in comparison to climate change. 9

20 CHAPTER 4 RESULTS: THE HYDROLOGICAL IMPACTS Changes in the hydrological inputs of the Okavango Delta result in changes of flooding and groundwater dynamics. The hydrological characteristics presented in this work to assess the impacts of change in inputs are the mean groundwater depth and the inundation frequency. These two parameters are particularly important for the ecology of the Delta and will be used in the next chapter to simulate the impacts of climate change on the vegetation. 4.1 GROUNDWATER DEPTH The groundwater depth is one of the important factors that determines the vegetation type and consequently, one may assume that changes of groundwater depth will significantly affect the growth of the local vegetation. Groundwater at an appropriate depth is essential to the health of the vegetation and is of greater ecological significance than absolute hydraulic heads. Results are presented in the form of maps showing the spatial distribution of the mean groundwater depth for each climate change model and for the development scenario (Figures 1 13). They can be compared to the past time period (Figure 1). In addition to those maps, the mean groundwater depth averaged over the entire model area can be found in Table A 1 (Appendix A). This mean is only intended for having a quantitative dimension to compare the different models [m] FIGURE 1: SPATIAL DISTRIBUTION OF THE MODELLED MEAN GROUNDWATER DEPTH OVER THE TIME PERIOD The form of the Okavango Delta is well distinguishable (Figure 1 dark blue, where the groundwater level is higher than zero). The groundwater depth increases rapidly with the distance to the delta. The deepest groundwater levels are situated in the West of the model area. 1

21 Figure 11 shows the predicted mean groundwater depth over the time period of the five different climate models. One can readily see that the simulation of the HadCM3 model forecasts the deepest groundwater levels and that the CSIRO model yields the shallower groundwater levels. The predictions of the other three models (CCC, CCSR and GFDL) are very similar and cannot surely be differentiated and compared only on the basis of this figure CCC [m] CCSR [m] CSIRO [m] GFDL [m] HadCM3 [m] FIGURE 11: SPATIAL DISTRIBUTION OF THE PREDICTED MEAN GROUNDWATER DEPTH OVER THE TIME PERIOD FOR THE FIVE CLIMATE MODELS 11

22 Figure 12 shows the predicted mean groundwater depth over the time period of the five different climate models. As for the earlier time period 25 28, the CSIRO climate model forecasts much shallower groundwater levels than the other models. The HadCM3 model expects the deepest groundwater followed by the CCC model, which clearly forecasts deeper groundwater levels than over the earlier time period (25 28). The predictions of the other two models (CCSR and GFDL) are alike CCC [m] CCSR [m] CSIRO [m] GFDL [m] HadCM3 [m] FIGURE 12: SPATIAL DISTRIBUTION OF THE PREDICTED MEAN GROUNDWATER DEPTH OVER THE TIME PERIOD FOR THE FIVE CLIMATE MODELS 12

23 Figure 13 shows a comparison of the mean depth to the groundwater level predicted by the development scenario 14 in comparison to the past time period There is no eye catching difference between the two maps. Scenario 14 [m] [m] FIGURE 13: SPATIAL DISTRIBUTION OF THE PREDICTED MEAN GROUNDWATER DEPTH FOR THE DEVELOPMENT SCENARIO 14 (LEFT) IN COMPARISON TO THE PAST ( , RIGHT) Difference between past and predicted conditions The difference between past and predicted conditions can be visualised by subtracting the future mean groundwater depths from the past ones ( ). Those differences are displayed in Figures 14 to 16. Red symbolises deeper future groundwater levels and blue shallower ones. The highest differences can be observed in the Delta itself. The difference between past conditions and those simulated by the development scenario 14 is presented in Figure 14. The development scenario 14 predicts slightly shallower groundwater levels than the past. Scenario 14 [m] FIGURE 14: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED (SCENARIO 14) MEAN DEPTH TO GROUNDWATER 13

24 Figure 15 shows the differences between past ( ) and future (25 28) groundwater levels simulated by all five global climate models. The CCC, CCSR, GFDL, and HadCM3 models all predict deeper groundwater levels, whereas the HadCM3 model expects the strongest decrease. The CSIRO model forecasts much shallower groundwater levels. As observed in section 3.3.1, the prediction of the CSIRO model is very uncertain. The biggest change can be observed in the Delta itself CCC [m] CCSR [m] CSIRO [m] GFDL [m] HadCM3 [m] FIGURE 15: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED (25 28) MEAN DEPTH TO GROUNDWATER FOR THE FIVE CLIMATE MODELS 14

25 Figure 16 shows the differences between past ( ) and future (27 21) conditions simulated by all five global climate models. For the earlier time period 25 28, all climate models predict deeper groundwater levels, expect the CSIRO model, which forecasts much shallower levels than in the past ( ). The HadCM3 model predicts the deepest groundwater levels. Again, the largest change can be observed in the Delta itself. Difference between past mean depth to groundwater ( ) and predicted mean depth to groundwater (27-21, CCC) CCC [m] Difference between past mean depth to groundwater ( ) and predicted mean depth to groundwater (27-21, CCSR) CCSR [m] Difference between past mean depth to groundwater ( ) and predicted mean depth to groundwater (27-21, CSIRO) CSIRO [m] Difference between past mean depth to groundwater ( ) and predicted mean depth to groundwater (27-21, GFDL) GFDL [m] Difference between past mean depth to groundwater ( ) and predicted HadCM3 [m] FIGURE 16: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED (27 21) MEAN DEPTH TO GROUNDWATER FOR THE FIVE CLIMATE MODELS 15

26 Mean of the climate models As the four realistic climate models CCC, CCSR, GFDL and HadCM3 all predict a drying up of the Okavango Delta, it is possible to take the average of their predicted groundwater depths to show the mean expected impacts of climate change. Figure 17 shows the average of the differences between past ( ) and future conditions for both time spans [m] [m] FIGURE 17: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED MEAN DEPTH TO GROUNDWATER (AVERAGE OF THE FOUR CLIMATE MODELS: CCC, CCSR, GFDL AND HADCM3) One can see that deeper groundwater levels are expected for the time period (27 21) than for the time period (25 28). 16

27 4.2 INUNDATION PROBABILITY The expansion and contraction of the flooded area are the primary drivers of the vegetation ecology of the Delta (Smith, 1976). Without the flood pulse, the seasonally flooded swamps and grasslands would disappear as well as their rich flora and fauna. Additionaly, flooding can seriously affect the vegetation; the trees are the hardest hit, as they cannot survive long lasting inundations. For assessing effects of climate change and upstream development on the flood dynamics, the results are presented in the form of maps showing the spatial distribution of the inundation frequency for each climate change model and for the development scenario (Figures 18 21). Only areas that were flooded at least once and that consequently have an inundation probability higher than zero are illustrated. Additionally, Table A 2 in Appendix A quantifies the results by specifying the size of the area that is flooded at least once, and the mean inundation probability over this area. These values are mainly intended for having a quantitative dimension to compare the different models at once glance. In Appendix A, one can also find the mean size and the standard deviation of the flooded areas as well as graphs showing the flooded area over time. Figure 18 shows the spatial distribution of the inundation frequency during the past time period ( ). Zones that have been inundated only once during those thirty years are also displayed even if they record a rather extreme event [-] FIGURE 18: SPATIAL DISTRIBUTION OF THE MODELLED FLOODING PROBABILITY FOR THE TIME PERIOD

28 Figure 19 shows the spatial distribution of the inundation probability predicted by the different climate models for the time period The difference between the models is obvious. The CSIRO model predicts a much higher flooding probability than the other models. The prediction of this model is suspiciously different. The CCC model forecasts the lowest inundation probability; it is followed by the CCSR model, the GFDL and then the HadCM3 model CCC [-] CCSR [-] CSIRO [-] GFDL [-] HadCM3 [-] FIGURE 19: SPATIAL DISTRIBUTION OF THE MODELLED FLOODING PROBABILITY FOR THE TIME PERIOD FOR THE FIVE CLIMATE MODELS 18

29 Figure 2 shows the inundation probability predicted by the different climate models for the time period As for the earlier time period 25 28, the CSIRO climate model forecasts much higher inundation frequencies than the other models. The HadCM3 model expects the lowest inundation probability, followed by the CCC model, which clearly forecasts a lower probability than during the earlier time period (25 28). The predictions of the other two models (CCSR and GFDL) are similar to each other and to there of the time span CCC [-] CCSR [-] CSIRO [-] GFDL [-] HadCM3 [-] FIGURE 2: SPATIAL DISTRIBUTION OF THE MODELLED FLOODING PROBABILITY FOR THE TIME PERIOD FOR THE FIVE CLIMATE MODELS 19

30 Figure 21 shows a comparison of the inundation probability predicted by the development scenario 14 with the probability determined in the simulation over the past time period ( ). There is no obvious difference between the two maps. Scenario 14 [-] [-] FIGURE 21: SPATIAL DISTRIBUTION OF THE MODELLED FLOODING PROBABILITY (DEVELOPMENT SCENARIO 14) The difference between past and predicted conditions can be visualised by subtracting the future inundation probabilities from the past ones ( ). These differences are displayed in Figures 22 to 24. Red symbolises lower inundation probabilities and blue higher ones. The highest difference can be 1 if the model cell was permanently inundated in the past and will be never flooded again in the future or 1 if, on the contrary, the cell was constantly dry in the past and will be always inundated in the future. Difference between past and predicted conditions The difference between past inundation probabilities and those simulated by the development scenario 14 is presented in Figure 22. The development scenario generally predicts flooding frequency slightly higher than in the past. Scenario 14 [-] FIGURE 22: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED INUNDATION PROBABILITY (SCENARIO 14) 2

31 Figure 23 shows the differences between past ( ) and future (25 28) inundation frequencies simulated by all five global climate models. The difference between the models is obvious. The CCSR, GFDL and HadCM3 models clearly predict lower inundation probabilities, whereas the HadCM3 model expects the highest decrease. The CCC model predicts lower inundation frequencies, but the differences actually are only negligible lower than zero (that means, there is almost no change in comparison to the past). Again, the CSIRO model predicts, with much higher inundation probabilities, completely different future conditions than the other models CCC [-] CCSR [-] CSIRO [-] GFDL [-] HadCM3 [-] FIGURE 23: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED (25 28) INUNDATION PROBABILITY FOR THE FIVE CLIMATE MODELS 21

32 Figure 24 shows the differences between past ( ) and future (27 21) conditions simulated by all five global climate models. Apart from the CSIRO model, all models predict principally lower inundation probabilities than during the time span To be more exact, the CCSR and the GFDL models predict basically a bit lower flooding frequencies than in the past; while the CCC and the HadCM3 model forecast significantly lower inundation frequencies than in the past CCC [-] CCSR [-] CSIRO [-] GFDL [-] HadCM3 [-] FIGURE 24: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED (27 21) INUNDATION PROBABILITY FOR THE FIVE CLIMATE MODELS 22

33 Mean of the climate models As the four realistic climate models CCC, CCSR, GFDL and HadCM3 predict a general drying up of the Okavango Delta, it is possible to take the average of their predicted inundation probabilities to show the mean expected impacts of climate change. Figure 25 shows the average of the differences between past ( ) and future conditions for both time spans [-] [-] FIGURE 25: DIFFERENCE BETWEEN PAST ( ) AND PREDICTED INUNDATION PROBABILITY (AVERAGE OF THE FOUR CLIMATE MODELS: CCC, CCSR, GFDL AND HADCM3) One can see that over the time period (27 21) lower inundation probabilities are expected than over the earlier one (25 28). 23

34 CHAPTER 5 VEGETATION MODELING APPROACH The Okavango Delta has a huge variety of aquatic, wetland and terrestrial habitats and is characterized by a vegetation pattern ranging from permanent swamps, over seasonal floodplains, to riverine woodlands and dry savannas that are never under water (Ramberg et al., 26). In order to simulate impacts of climate change and upstream developments on the vegetation cover, today s vegetation first has to be examined. The vegetation needs to be summarised and classified. Thereafter, the hydrological parameters that could determine the vegetation type have to been investigated. 5.1 CLASSIFICATION METHOD The definition of vegetation classes is complicated and the identification of various species in a patch in the field is difficult. It is important to notice that the classification introduces a certain error as the vegetation type is not always well identifiable. The high spatial and temporal variability of the Okavango Delta makes the task even more complex. In this thesis, two different classification schemes have been taken into consideration. The first classification was developed by McCarthy et al. (26) and distinguishes 12 ecoregions. The land cover classification scheme is based on earlier vegetation surveys (e.g. Ellery and Ellery 1997) and with the aim of using the map in hydrological, sedimentological and ecological studies. Table 2 shows the classification scheme. Examples of key species of the different ecoregions are shown in Appendix B. The abbreviations ( codes ) are based on the knowledge about water tolerance and requirement of the different ecoregions; they are ordered ascending from wet areas to dry areas. TABLE 2: CLASSIFICATION SCHEME OF THE VEGETATION Ecoregions / land cover classes code Water 1 Permanent swamp communities 5 Primary flood plain 8 Secondary flood plain 11 Grassland (occasionally flooded) 13 Riverine forest 15 Dry grassland / salt pan (occasionally flooded) 19 Dry woodland (dominated by Acacia spp.) 2 Dry woodland (dominated by Mopane) 25 Dry woodland (dominated by Combretum spp.) 21 Sparse dry grassland / salt crust (occasionally flooded) 23 Sparse dry grassland / salt crust 25 The categorisation was achieved by supplementing snapshots of Landsat TM images with GIS data, flooding data, and by indices obtained from the original Landsat TM scenes. The initial spatial resolution was 28.5 m but was scaled to the same precision as the hydrological model (1 m). This spatial generalisation at cell size causes a loss of accuracy. Figure 26 shows a map of the different ecoregions in the Okavango Delta (resolution of 1 m). 24

35 FIGURE 26: SPATIAL DISTRIBUTION OF THE ECOREGIONS The second classification was found on the internet site of the Sharing Water Project ( The Sharing Water Project was designed to promote the long term sustainable management of the Okavango River. The original data had a resolution of 3 m and were also scaled to the same precision as the hydrological model (1 m). The vegetation is divided into 7 classes but only 47 of them appear in the model area. In Appendix C, Table C 1 shows the classification scheme as well as the key species of the different classes. Figure C 1 shows the spatial distribution of these classes. No further background information explaining this classification method could be found. Furthermore, this classification of the vegetation is too accurate to be described only by hydrological parameters as inundation frequency and mean depth to groundwater. Nevertheless, the simulation of the vegetation distribution has been done for this classification too. The results are shown in Appendix C and provide a possible comparison to the classification discussed in this paper. Apart from this, the expected behaviour of some particular vegetation types of interest can be consulted. If you do so, it is important to take into consideration that the results are associated with a high inaccuracy but that trends should be correct. 25

36 5.2 STATISTICAL ANALYSIS It was assumed that the driving parameters determining the vegetation type in the Okavango Delta are inundation frequency and depth to groundwater. These two parameters have been simulated (sections 4.1 and 4.2) for past and future conditions. Their means and standard deviations over the area covered by the different ecoregions can be calculated for the past time span (Figures 27 and 28). Inundation frequency [ ] Ecoregion FIGURE 27: MEAN AND STANDARD DEVIATION OF THE INUNDATION FREQUENCY FOR THE ALL ECOREGIONS The difference between the mean inundation frequencies of the several ecoregions is obvious (Figure 27): It is the highest for the ecoregion 5 (permanent swamp) and the lowest for the ecoregion 25 (sparse dry grassland/ salt crust). The means of the ecoregions 1 (water) and 5 (permanent swamp) should be 1 but are only.73 and.82, respectively; this is due to inaccuracies of the model or of the classification. The standard deviation shows the spread of values and may serve as a measure of uncertainty. In this case, the standard deviations are sizeable in comparison to the means especially for ecoregions with an expected low flooding frequency. Mean Std. Dev. Mean Depth of Groundwater [m] Mean Std. Dev Ecoregion FIGURE 28: MEAN AND STANDARD DEVIATION OF THE MEAN DEPTH TO GROUNDWATER FOR THE ALL ECOREGIONS Figure 28 shows the mean and the standard deviation of the mean depths to groundwater for all ecoregions. The mean is the highest for ecoregion 21 (dry woodland dominated by Combretum) and the lowest for ecoregion 5 (permanent swamp). The means of ecoregions 1 (water) and 5 (permanent swamp) should be < but are 1.2 m and.3 m respectively. As noticed for the inundation probability, this is due to inaccuracies of either the model or the classification. The standard deviations are quite high in comparison to the means (particularly for ecoregions covering areas with rather shallow groundwater). 26

37 The high standard deviations are due to the fact that most of the ecoregions cover a wide range of areas. Several strategies have been tested to optimise this range for each ecoregion by examining both the inundation frequency and the groundwater depth. Matlab scripts can be found in Appendix D. None of these methods was able to reproduce the allocation of the ecoregions sufficiently well; about a third of the area, at most, could be assigned to the right ecoregion. The spatial distribution of the ecoregions is difficult to reproduce, because different ecoregions may plausibly occur, even if the hydrological conditions are exactly the same (Figures B 1 and B 2, Appendix B). As the size of the area of the different inundation probabilities and, respectively, of the mean depths of groundwater varies widely, it is more significant to plot the proportion of the area occupied by a certain ecoregion than the absolute size of the area. Figure 29 shows the proportion of the area covered by the different ecoregions over the inundation frequency. FIGURE 29: PROPORTION OF THE AREA COVERED BY EACH ECOREGION OVER THE INUNDATION FREQUENCY It is obvious that most of the ecoregions do not have a pronounced preferential inundation frequency. Ecoregion 5 (permanent swamp community) tends to dominate areas with a high inundation frequency. Ecoregion 11 (secondary floodplain) slightly prefers a middle inundation probability, but may occur over the whole range of inundation frequencies. Ecoregion 13 (grassland, occasionally flooded) does not show any preference. Ecoregion 2 (dry woodland dominated by Acacia) tends to prefer zones with a low inundation frequency, but may also be found in regions inundated often. The only specification of ecoregion 23 (sparse dry grassland/ salt crust, occasionally flooded) is an inundation probability <.6. All these observations are insufficient to predict the spatial distribution of the ecoregions. Figure 3 shows the proportion of the area covered by the different ecoregions over the mean depth to groundwater. 27

38 FIGURE 3: PROPORTION OF THE AREA COVERED BY EACH ECOREGION OVER THE MEAN DEPTH OF GROUNDWATER Most of the ecoregions show a distinct preference for a certain mean depth to groundwater. From that point, their occurrence decreases significantly for lower or shallower groundwater levels. The preferred depth of ecoregion 5 (permanent swamp community) is about 2 meters over ground surface. Ecoregion 13 (grassland, occasionally flooded) prefers regions with a mean depth to groundwater of 4 5 meters. For some regions, like ecoregion 13, one can almost observe a normal distribution. Finally one may say that the driving parameter determining the occurrence of the different ecoregion is the mean depth to groundwater and not the inundation frequency. Hence, it was decided to continue the investigation by considering only the mean depth to groundwater. 5.3 SIMULATION OF THE VEGETATION If one sums all the diagrams, showing the proportion of the area covered by each ecoregion over the mean depth of groundwater (Figure 3), one obtains the diagram shown below (Figure 31 ). 28 FIGURE 31: PROPORTION OF THE AREA COVERED BY THE DIFFERENT ECOREGIONS OVER MEAN DEPTH OF GROUNDWATER

39 Figure 31 shows that for most of groundwater depths, one cannot forecast a single ecoregion. One can only predict the proportion or rather the probability of occurrence of each ecoregion. On the basis of this distribution and of the map of the mean groundwater depth, one can get the spatial expectation of the different ecoregions (Figure 32). FIGURE 32: SPATIAL EXPECTATION OF THE ECOREGIONS (PAST CONDITIONS ) 29

40 If, for each cell of the model, one picks the ecoregion with the highest probability of occurrence, one gets a spatial distribution of the most expected ecoregions (Figure 33). It is very important to make a corresponding map of the confidence of these most expected ecoregions. FIGURE 33: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE The weakness of the spatial distribution of the most expected ecoregions is that ecoregions with a low occurrence or with a probability of occurrence smaller than other ecoregions are lost. Only four of the twelve ecoregions are represented by this method. If the spatial distribution has no importance, it is better to calculate the expected area covered by the different ecoregions. To do this, one can start with the diagram showing the proportion of the area covered by the different ecoregions over the mean groundwater depth (Figure 31), calculate the size of the model area described by each groundwater depth, and multiply each ecoregion proportion with the corresponding area. The calculation of the expected area permits to recognise and quantify changes easily. The size of the areas covered by the different ecoregions computed that way do not fully correspond to the observed ones. The reason is that the resolution of the diagram showing the proportion of the area covered by the different ecoregions over the mean groundwater depth (Figure 31) is finite; namely.5 m. The differences between the simulated and the observed size of the areas covered by the different ecoregions are shown in Figure Observed Past conditions ( ) 25 Area [km 2 ] Ecoregions FIGURE 34: AREAS COVERED BY THE DIFFERENT ECOREGIONS (OBSERVED IN COMPARISON TO SIMULATED) 3

41 The precise errors (in percent of the area and km 2 ) can be found in Appendix B. The mean error of the expected areas compared to the observations is about 19 km 2. The rounding errors mostly affect the ecoregions with a low occurrence as e.g. ecoregion 25 (sparse grassland/ salt crust). Despite small imprecision of the simulation, it does not form a barrier to predict the evolution of the ecoregion distribution. A coarser resolution (1 m; Appendix B) induces higher rounding errors in the calculation of the size of the area having this particular mean groundwater depth. A more precise classification of the groundwater depth (.1 m; Appendix B) makes variability and outliers having a higher impact. The actual ecoregion pattern would be simulated very close to reality, but small changes in groundwater depth could create changes in ecoregion assignment, which are not realistic. These fluctuations are not due to the preference of a particular groundwaterdepth but to other independent factors such as previous history (e.g. fire), soil type, slope etc. In addition, it is not expected that the ecoregions react very sensitively to changes in groundwater depth. An accuracy of 5 cm should be enough. 31

42 CHAPTER 6 RESULTS: THE IMPACTS ON THE VEGETATION Figure 31 (the diagram showing the proportion of the area covered by the different ecoregions over the mean groundwater depth) provides the basis to simulate the impacts of mean groundwater depth changes on the ecoregions. Changes in the spatial distribution of the ecoregions are presented with the map of the most expected ecoregions. The changes can be quantified by comparing the future and the past size of the areas covered by the different ecoregions. 6.1 THE CLIMATE CHANGE MODELS As the hydrological results of the CSIRO model are not realistic and the simulation of the ecoregion distribution is based on these results, it does not make sense to forecast the ecoregion pattern with this model. Still, the impacts on the vegetation simulated with the CSIRO model are shown in Appendix B.III, but are not further discussed in this work. The spatial expectations of the ecoregions for forecasted conditions (25 28 and 27 21) are shown in Appendix B.IV. In Figures 35 and 36 one can see the spatial distributions of the most expected ecoregions for the different climate models. For all models and both time spans only four of the twelve ecoregions are simulated as the others always have a lower probability of occurrence. The four ecoregions are: 5 (permanent swamp community), 13 (grassland occasionally flooded), 2 (dry woodland dominated by Acacia), and 23 (sparse dry grassland/ salt crust; occasionally flooded). 32

43 CCC CCSR GFDL HadCM3 FIGURE 35: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (CLIMATE MODEL: CCC, CCSR, GFDL AND HADCM3; 25 28) 33

44 CCC CCSR GFDL HadCM3 FIGURE 36: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (CLIMATE MODEL: CCC, CCSR, GFDL AND HADCM3; 27 21) 34

45 The difference between past and future conditions is more apparent if only cells with a change of ecoregion are displayed (Figures 37 and 38). For all models and both time spans if a change occurred it only had the size of one ecoregion step (as there are only four ecoregions: either from 5 to 13, from 13 to 2, from 2 to 23, or in the opposite direction). Even if the simulated mean groundwater depth is deeper than in the past, some cells are expected to be covered by a wetter ecoregion. This is due to rounding errors in the assignment of the ecoregions to the different groundwater depths. The most important changes are expected in the delta itself where the strongest decrease of the groundwater level was obtained. The point like changes of ecoregions around the delta are due to rounding error and inaccuracies of the model. These changes do not indicate a particular direction, but how many cells are expected to be covered by a wetter ecoregion than by a dryer one. CCC CCSR GFDL HadCM3 FIGURE 37: DIFFERENCE BETWEEN PAST AND FUTURE (25 28) MOST EXPECTED ECOREGIONS 35

46 CCC CCSR GFDL HadCM3 FIGURE 38: DIFFERENCE BETWEEN PAST AND FUTURE (27 21) MOST EXPECTED ECOREGIONS To quantify the changes, one can calculate the expected areas covered by different ecoregions. The results are shown in Figures 39 and 4. The exact values of the expected area and the changes relative to the past conditions are listed in Appendix B.II. 3 Past conditions ( ) CCC (25 28) CCSR (25 28) GFDL (25 28) HadCM3 (25 28) 25 Area [km2] Ecoregions FIGURE 39: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (25 28) For the time span 25 28, all climate models predict a decrease of the ecoregions 1 to 19 and an increase of the ecoregions 2 to 23. The area covered by the ecoregion 25 is expected to decrease a little, but since this particular ecoregion is delicate to simulate (section 5.2), this trend is not to be taken too strictly. Important is that so called wet ecoregions will occupy a smaller area and dry ecoregions a larger one as than in the past. Only four of the twelve ecoregions are forecasted to grow. One of those (ecoregion 21: dry woodland dominated by Combretum) is rather expected to stay stable than to gain habitat. Two of the growing ecoregions (ecoregion 2: dry woodland dominated by Acacia and ecoregion 23: Sparse dry grassland/ salt crust; occasionally 36

47 flooded) are already by far the ecoregions with the highest occurrence. Consequently, the simulation predicts a delta with a lower biodiversity than in the past. 3 Past conditions ( ) CCC (27 21) CCSR (27 21) GFDL (27 21) HadCM3 (27 21) 25 Area [km2] Ecoregions FIGURE 4: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (27 21) For the time span 27 21, one can observe the same trend as for the earlier time period. All climate models predict a decrease of the ecoregions 1 to 19 and an increase of the ecoregions 2 to 23. The area covered by the ecoregion 25 is expected to decrease. The changes are more pronounced than for the time span and therefore confirm the tendency. 6.2 THE MEAN OF THE CLIMATE CHANGE MODELS As the four climate models (CCC, CCSR, GFDL, and HadCM3) predict a drying up of the Okavango Delta and therefore a shift from so called wet ecoregions to dry ecoregions, it is possible to take the average of their predicted mean groundwater depth to simulate the average of the expected effects on the vegetation. Below (in Figures 41 and 42), one can see the spatial distributions of the most expected ecoregions. Again, only four of the twelve ecoregions are simulated as the others always have a lower probability of occurrence. FIGURE 41: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (MEAN OF THE FOUR CLIMATE MODELLS, 25 28) 37

48 FIGURE 42: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (MEAN OF THE FOUR CLIMATE MODELLS, 27 21) The difference between past and future conditions is more apparent if only the cells for which the ecoregion switches, are displayed (Figure 43). As for the different climate models one on one, if a change occurred it only did of one ecoregion step. FIGURE 43: DIFFERENCE BETWEEN PAST AND FUTURE MOST EXPECTED ECOREGION (MEAN OF THE FOUR CLIMATE MODELLS, LEFT AND RIGHT) The expected areas covered by the different ecoregions have been calculated for both time spans. The results are shown in Figure 44. The exact values of the expected areas and the changes relative to the past conditions are listed in Appendix B.II. 3 Past conditions ( ) (mean of the 4 climate models) (mean of the 4 climate models) 25 Area [km2] Ecoregions FIGURE 44: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (MEAN OF THE FOUR CLIMATE MODELS FOR THE TIME SPANS AND 27 21) The chronological trend is obvious. The area covered by wet ecoregions decreases over time and that of the dry ecoregions increases. 38

49 6.3 THE DEVELOPMENT SCENARIO The spatial expectations of the different ecoregions can be simulated. Figure 45 shows the spatial distributions of the most expected ecoregions. FIGURE 45: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (SCENARIO 14) The difference between past and future conditions is more apparent if only the cells that change of ecoregion are displayed (Figure 46). The change is less pronounced than the one due to climate change and evolves in the other direction (to a wetter ecoregion). FIGURE 46: DIFFERENCE BETWEEN PAST AND FUTURE (SCENARIO 14) MOST EXPECTED ECOREGION The expected areas covered by the different ecoregions have been calculated and are shown in Figure 47. The exact values of the expected areas and the changes relative to the past conditions are listed in Appendix B.II. Compared to the total area for which a switch of ecoregion is expected because of climate change the change due to human activity in the region is comparatively small (about 7% less than simulated for the mean of the climate models for and even 8% less for the time span 27 21). It is important to underline that the switch of ecoregion occurs from dry to wet, which is the direction opposite to the change simulated due to climate change. 39

50 3 Past conditions ( ) Scenario Area [km2] Ecoregions FIGURE 47: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (SCENARIO 14) 4

51 CHAPTER 7 DISCUSSION Climate change The impacts of climate change on the hydrology of the Delta have been investigated using five different climate models: CCC, CCSR, CSIRO, GFDL, and HadCM3. All forecast that temperature may increase between 3 and 5 C in the next 1 years. All climate models also predict an augmentation of the potential evapotranspiration by 1 to 2%. The predictions of the precipitation are fairly different depending on the climate models, but no one breaks ranks. Four of the five models predict changes in the inflow to the Delta of between half as much as in the past and absolutely equal. The CSIRO model in combination with the Pitman model for the Okavango Basin forecast an average discharge four times higher than observed. This rate seems very suspicious, but it was decided to simulate the impacts on the hydrology of the Delta with this model too. The results of the simulation with the CSIRO model are extremely different from the ones of the four other models. The CSIRO model yields unrealistically wetter conditions than during the past while the other four models predict a drying up of the delta. It can be assumed that the CSIRO model is not suited to simulate the Okavango region. Climate change will affect every region of the world differently and GCMs are not always adapted to provide information about the impacts of climate change for any specific location. Making predictions of future climate change in the Okavango region is particularly difficult, on the one hand as a result of the complex climate, and on the other, because of the lack of data on the past conditions in Angola. As the results of the four other climate models are quite similar, they can be trusted and their mean constitutes a credible and solid expectation of future conditions at least as far as the trend is consistent. A possibility to minimise the uncertainty of GCMs is the development of regional climate models (RCMs) to model each region independently. The emission scenario followed in this work is A2. The world of this scenario is characterised by a regional heterogeneous development in which economic growth and technological changes are slow and the population is constantly increasing. The A2 scenario is not an extreme but a relatively high growth scenario and the global greenhouse gas emissions are expected to increase by a factor of four between 199 and 21 (IPCC, 21). It is important to keep in mind that there is a large uncertainty about future changes and that, depending on the emission scenario followed, the extent of climate change will be different. A possibility to get a larger view of the plausible impacts of climate change on the Okavango Delta is to use at least one other emission scenario which emphasises environmental conservation or constitutes an extreme. In any case, this uncertainty has to be considered along with the errors of the climate models. According to the large range of uncertainty, one cannot exactly say what the impacts of climate change on the Okavango Delta will be; but one can get a good idea using different models and a realistic emission scenario. Even if the extent of the change cannot strictly be quantified, the trend of the change can safely be predicted. The global climate models have been run for two different time spans (25 28 and 27 21). The four approved climate models (CCC, CCSR, GFDL and HadCM3) predict an even higher drying up for the later time span than for the earlier; this confirms the trend of the impacts of climate change on the Delta. 41

52 Groundwater depth and inundation frequency The four climate models forecast a general decrease of the groundwater level. The strongest augmentation of the groundwater depth is forecasted in the delta itself, where the groundwater levels are the highest. This is due on one hand to the increase of the potential evapotranspiration which has higher impacts on shallow groundwater and on the other to the decrease of the inflow into the Delta. Consequently, it is important to have a proper data base of the effects of climate change on the discharge of the Okavango River. Because of a prolonged civil war, the length of historical hydrological measurements in the Angolan sector of the river catchment is limited. The problem is that the largest portion of the rainfall, which drives the system, occurs in this part of the catchment. Consequently calibration and validation of predicted inflow to the Delta are especially ambiguous. All five climate models expect the potential evapotranspiration to increase in a very similar range (between 12 and 15% for and between 16 and 2% for 27 21). Therefore, it can be assumed that the potential evapotranspiration simulated by the different models is reliable and will not be a big source of error. The inundation frequency is expected to decrease. In this thesis, it was found that the fundamental factor determining the vegetation distribution is the mean depth to the groundwater. However, one can assume that changes of the flooding pattern will also affect the vegetation. Simulation of the vegetation The effects on the vegetation were simulated from the fraction of the different vegetation types (socalled ecoregions) for a particular mean groundwater depth. It was shown that not one single vegetation type may be expected for a defined groundwater depth but a distribution. The inundation frequency did not show a good correlation to the vegetation. Nevertheless, one may assume that the flooding pattern affects the vegetation in some way even if it is not the frequency itself, i.e. the duration of the single inundations could have a major importance. As the groundwater depth is not decisive enough to predict with certitude one vegetation type for each cell of the model, the probability of incidence was calculated. If one is interested by a precise simulation of the vegetation pattern, more parameters have to be involved, such as concurrence between some different types of vegetation, succession, previous history (i.e. fire), soil type etc. If, for each cell of the model, one picks the vegetation type with the highest probability of occurrence, one gets the spatial distribution of the most expected ecoregions. It is necessary to take into consideration the corresponding confidence of the most expected vegetation types. The highest certainty of the forecast can be found either in the western corner of the model area or as insular plots also rather located at the border of the area (Figure 33). These are zones with a very deep groundwater level. Between groundwater depths of 32 and 38 meters, the ecoregion 23 (sparse dry grassland/ salt crust; occasionally flooded) will be present with a probability of more than 8% (Figure 31). Between 38 and 4 meters a fast transition to another predominant vegetation type: ecoregion 2 (dry woodland, dominated by Acacia) occurs. At that groundwater depth, it is the only vegetation type that exists. Another area which can be simulated with a high certainty is the upper part of the Delta, the Panhandle, which forms a permanent swamp (ecoregion 5). If the mean water level is above the earth s surface, a permanent swamp community is expected with a probability of at least 45%. The area for which the vegetation type can be predicted with the least certainty is the delta itself, where the mean groundwater depth is between and 5 meters. This is the sector where the most different vegetation types may appear with a similar probability. To 42

53 obtain a more precise simulation of the vegetation in that area, more parameters would have to be considered. The problem by picking up only one ecoregion, the one with the highest probability of occurrence, is that rare vegetation types with a low occurrence are omitted. Only four of the twelve ecoregions are represented by this method. As especially the rare types of vegetation can be of interest, if the spatial distribution has no importance, it is better to calculate the expected area covered by the different ecoregions. Vegetation changes As the vegetation type is defined by the groundwater depth, changes in the groundwater level will affect the distribution of the ecoregions. Even if one cannot predict the exact future ecoregion for each cell of the model, the trend of the change can be identified. The most important change of vegetation is expected in the delta itself, logically where the largest changes of the groundwater depth were calculated. A shift from vegetation types which require and tolerate water to vegetation types which are adapted to drought is forecasted. Because there are less ecoregions that prefer dryness to wetness, and because the drought tolerant ecoregions are already more present in the region, the drying up of the delta induces some loss of biodiversity. In fact, only three of the twelve ecoregions are expected to increase. Development scenario Climate change is not the only parameter that has the potential to influence the Okavango Delta. Human activity and development along the Okavango River can modify the properties of the inflow to the Delta, which subsequently could have ample repercussions on the character of the Delta. In this thesis, only one development scenario was simulated that includes higher water demand for agriculture and industry, dam and pipeline construction, as well as high deforestation. Surprisingly, because of the deforestation, the development scenario forecasts a higher inflow and consequently wetter conditions in the delta. This means that the local human activity would work against the impacts of climate change on the Okavango Delta. However, the impacts of this development scenario would be minor in comparison to those of climate change. I.e. regarding the area, for which a shift of ecoregion is expected because of climate change (mean of the models), the area change of ecoregions due to development alone would be about 7% smaller for the time span and even 8% for It is important to underline that the shift of ecoregion would occur from dry to wet, which is the opposite direction to the change simulated due to climate change. Usually, deforestation is a gradual process and hence there will be regrowth in parts of the Delta at the same time as trees are cut in other parts. Thus, the effects of deforestation predicted by the development scenario rather constitute a maximum possible impact. With a lower rate of deforestation, the impacts on the hydrology and ecology would be even more severe. The development scenario predicts a mean inflow to the Delta approximately 1% higher than observed in the past. The mean of the climate change scenarios forecasts a mean inflow to the Delta about 1% (25 28) and 2% (27 21) lower than in the past. This large difference between the simulated impacts on the Delta can therefore not be explained by the modification of the river discharge alone. The hydrological parameter inducing the highest impacts on the structure of the Okavango Delta is the increasing of the potential evapotranspiration. The inflow to the Delta plays a less significant role. 43

54 CHAPTER 8 CONCLUSIONS AND SUGGESTIONS The Okavango Delta system is under threat from development and climate change. The impacts of climate change are expected to be much more important. Simulations of climate change from GCMs predict a drying up of the Delta, i.e. deeper groundwater levels and lower inundation frequencies. Deforestation along the Okavango River leads to an augmentation of the inflow to the Delta mitigating the local effects of climate change. Uncertainty remains high in GCM modelling and the development of a RCM may reduce the uncertainty in climate change projections. In this thesis, the distribution of the vegetation cover was analysed on the basis of hydrological parameters (flooding frequency and mean depth to groundwater). It was found that almost every vegetation type had a distinct preferred groundwater depth of occurrence. The trend of the effects of future climatic change and human disturbances on the different vegetation types can be simulated with this parameter only. The area covered by vegetation types that require rather wet conditions is expected to decrease. As there are more, but less frequent vegetation types that cover wet areas of the Delta, the decrease of the groundwater level will lead to a depletion of the biodiversity. However, predicting the exact spatial distribution and dynamic of the natural vegetation types is a non trivial task which would require a much more complex model. Such a model needs to be implemented with the cooperation of biologists and ecologists. The initial spatial resolution of the vegetation cover map was arbitrarily upscaled to the same resolution as the hydrological model (1 m). It is questionable whether this scale is exact enough to represent the vegetation cover. It would make sense to downscale the hydrological model to study the impacts of scales on the accuracy of the results. As the Okavango Delta is the main water source of regional wildlife during the dry season and the main bird breeding site in inland Southern Africa, it would be meaningful to forecast the potential impacts of climatic change and development on the fauna. To conclude, the Okavango Delta is one of the last pristine wetlands of the world and its preservation is essential. Unfortunately, the only realistic possibility to attenuate the predicted drying up is climate protection. 44

55 REFERENCES Andersson L., Wilk J., Todd M.C., Hughes D.A, Earle A., Kniveton D., Layberry R., Savenije H.H.G, 26. Impact of climate change and development scenarios on flow patterns in the Okavango River. Journal of hydrology, volume 331 Bauer P., Gumbricht T., Kinzelbach W., 26. A regional coupled surface water/groundwater model of the Okavango Delta, Botswana. Water resources research, volume 42 Dahinden U., Querol C., Jäger J., Nilsson M., 2. Exploring the use of computer models in participatory integrated assessmentexperiences and recommendations for further steps. Integr. Assess. 1, pp Ellery K., Ellery W.N., Plants of the Okavango Delta, A field guide (Durban, South Africa: Tsaro) Ellery, W.N., Tacheba, B. (23), Floristic Diversity of the Okanvango Delta, Botswana, In: Alonso, L.E. and Nordin, L. (eds), A Rapid Biological Assessment of the Aquatic Ecosystems of the Okavango Delta, Botswana: High Water Survey (pp ) RAP Bulletin of Biological Assessment No. 27. Conservation International, Washington Hughes D.A., Andersson L., Wilk J., 26. Regional calibration of the Pitman model for the Okavango River. Journal of hydrology, volume 331 IPCC, 21. Third assessment report. Intergovernmental Panel of Global Change, UNEP and WMO. IPCC special report, 2. Special Report of the Intergovernmental Panel on Climate Change. Nebojsa Nakicenovic and Rob Swart (Eds.) Cambridge University Press, UK. pp. 57 McCarthy J.J., Canziani O.F., Leary N.A., Dokken D.J., Kasey S.W. (Eds.), 21. Climate Change 21: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the IPCC Third Assessment Report, Cambridge University Press, Cambridge, UK. pp. 132 McCarthy J.J., Gumbricht T., McCarthy T.S., 26. Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing. International Journal of Remote sensing, 26, pp McCarthy T.S., Bloem A., Larkin P.A., Observations on the hydrology and geohydrology of the Okavango Delta. South African Journal of Geology; June 1998; no. 2; pp Milzow C., Lesego K., Kinzelbach W., Meier P., Bauer Gottwein P., 26. The role of remote sensing in hydrological modelling of the Okavango Delta, Botswana Neuman B. R., Scenario Development Within the Okavango Delta Management Plan Project An Internship Project, 25. OKACOM (Okavango River Basin Transboundary Diagnostic Analysis), 1998 Pinheiro I., Gabaake G., Heyns P., 23. Cooperation in the Okavango River Basin: the OKACOM perspective. In: Turton, A., Ashton, P., Cloete, E. (Eds.), Transboundary Rivers, Sovereignty and Development: Hydropolitical Drivers in the Okavango River Basin. African Water Issues Research Unit/Green Cross International/ University of Pretoria, Pretoria, pp

56 Ramberg L., Hancock P., Lindholm M., Meyer T., Ringrose S., Sliva J., Van As J., VanderPost C., 26. Species diversity of the Okavango Delta, Botswana. Aquatic Sciences, volume 68, pp Ramsar Convention on Wetlands, Convention on Wetlands of International Importance Especially as Waterfowl Habitat Sharing Water Project, 27. Available from Smith P., An outline of the vegetation of the Okavango drainage system. In: Symposium on the Okavango Delta and ist future utilization. Botswana Society, National Museum, Gaborone, Botswana, pp Wolski P., Savenije H.H.G., Murray Hudson M., Gumbricht T., 26. Modelling of the flooding in the Okavango Delta, Botswana, using a hybrid reservoir GIS model. Journal of hydrology, volume

57 APPENDIX A: RESULTS OF THE HYDROLOGICAL MODEL A.I) MEAN DEPTH TO GROUNDWATER TABLE A 1: MEAN DEPTH TO GROUNDWATER AVERAGED OVER THE ENTIRE MODEL AREA Observed ( ) 15.19m CCC m CCSR m CSIRO 11.92m GFDL m HadCM m CCC 16.14m CCSR 15.62m CSIRO 12.45m GFDL 15.32m HadCM m Scenario m A.II) INUNDATION TABLE A 2: SIZE OF THE AREA FLOODED AT LEAST ONCE AND INUNDATION PROBABILITY AVERAGED OVER A GIVEN ARA Area flooded at least once [km 2 ] (Inund. prob. >) Inundation probability averaged over the area flooded at least once [-] Inundation probability averaged over the area flooded at least once during [-] Observed ( ) CCC CCSR CSIRO GFDL HadCM CCC CCSR CSIRO GFDL HadCM Scenario TABLE A 3: MEAN AND STANDARD DEVIATION OF THE FLOODED AREA Mean [km2] Std.Deviation [km2] Observed ( ) 5' ' CCC 4' ' CCSR 3'968. 1' CSIRO 19' ' GFDL 4' ' HadCM3 2'64.7 1' CCC 2' ' CCSR 4' ' CSIRO 16' ' GFDL 4'98.7 2' HadCM3 2' '6.76 Scenario 14 6' '62.15 i

58 CCC Observed ( ) Flooded area [km 2 ] CCSR Observed ( ) CSIRO Observed ( ) Flooded area [km 2 ] Flooded area [km 2 ] GFDL Observed ( ) HadCM3 Observed ( ) Flooded area [km 2 ] Flooded area [km 2 ] FIGURE A 1: SIZE OF THE PREDICTED FUTURE (25 28) FLOODED AREA IN COMPARISON TO THE PAST ii

59 CCC Observed ( ) Flooded area [km 2 ] CCSR Observed ( ) CSIRO Observed ( ) Flooded area [km 2 ] Flooded area [km 2 ] GFDL Observed ( ) HadCM3 Observed ( ) Flooded area [km 2 ] Flooded area [km 2 ] FIGURE A 2: SIZE OF THE PREDICTED FUTURE (27 21) FLOODED AREA IN COMPARISON TO THE PAST 2 18 Scenario14 Observed ( ) Flooded area [km 2 ] FIGURE A 3: SIZE OF THE PREDICTED FUTURE (SCENARIO 14) FLOODED AREA IN COMPARISON TO THE PAST iii

60 APPENDIX B: IMPACTS ON THE VEGETATION 1 B.I) CLASSIFICATION METHOD AND STATISTICAL ANALYSIS OF THE PAST CONDITIONS TABLE B 1 VEGETATION CLASSIFICATION AND KEY SPECIES BELONGING TO EACH CLASS (J. MCCARTY ET AL. 26) Code Ecoregions Example of key species 1 Water Nymphaea spp. 5 Permanent swamp community Cyperus papyrus, Vossia cuspidata, Phragmites communis L., Typha capensis 8 Primary floodplain Miscanthus junceus, Phragmites communis L., Cyperus articulatus, Schoenoplectus corymbosus 11 Secondary floodplain Panicum repens, Sorgastrum friesii, Imperata cylindrica 13 Grassland (occasionally flooded) Different grasses, sparse thickets, Pechuelloeschea leubnitziae 15 Riverine forest Dry grassland/salt pan (occasionally flooded) Dry Woodland (dominated by Acacia) Dry Woodland (dominated by Mopane) Dry Woodland (dominated by Combretum) Sparse dry grassland / salt crust (occasionally flooded) 25 Sparse dry grassland / salt crust Ficus natalensis, F. sycomorus, F. verrucolosa, Diospyros mespiliformis, Phoenix reclinata, Syzygium cordatum, Garcinia livingstonei Different grasses, sparse thickets, Pechuelloeschea leubnitziae Acacia erioloba, A. nigrescens, Combretum spp., Lonchocarpus spp. Colophospermum mopane Combretum spp. Sporobolus spicatus, Pechuel loeschea leubnitziae, Cynodon dactylon Sporobolus spicatus, Pechuel loeschea leubnitziae, Cynodon dactylon iv

61 Ecoregion 1 Ecoregion 5 Ecoregion Number of cells =Area [km 2 ] Number of cells=area [km 2 ] Number of cells=area [km 2 ] Inundation probability [ ] Inundation probability [ ] Inundation probability [ ] Ecoregion 11 Ecoregion 13 Ecoregion Number of cells=area [km 2 ] Number of cells=area [km 2 ] Number of cells=area [km 2 ] Inundation probability [ ] Inundation probability [ ] Inundation probability [ ] Ecoregion 19 Ecoregion 2 Ecoregion Number of cells=area [km 2 ] Number of cells=area [km 2 ] Number of cells=area [km 2 ] Inundation probability [ ] Inundation probability [ ] Inundation probability [ ] Ecoregion 21 Ecoregion 23 Ecoregion Number of cells=area [km 2 ] Number of cells=area [km 2 ] Number of cells=area [km 2 ] Inundation probability [ ] Inundation probability [ ] Inundation probability [ ] FIGURE B 1: INUNDATION PROBABILITY OVER SIZE OF THE AREA COVERED BY EACH ECOREGION v

62 Ecoregion 1 Ecoregion 5 Ecoregion Number of cells=area [km 2 ] Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Ecoregion 11 Ecoregion 13 Ecoregion Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Ecoregion 19 Ecoregion 2 Ecoregion Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Ecoregion 21 Ecoregion 23 Ecoregion Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Number of cellscells=area [km 2 ] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] Mean Depth of Groundwater [m] FIGURE B 2: MEAN GROUNDWATER DEPTH OVER SIZE OF THE AREA COVERED BY EACH ECOREGION vi

63 TABLE B 2: AREAS COVERED BY THE DIFFERENT ECOREGIONS (OBSERVED IN COMPARISON TO SIMULATED) Area [km 2 ] Error Ecoregion Observed Past conditions ( ) [km 2 ] [%] of obs. area FIGURE B 3: PROPORTION OF THE AREA COVERED BY THE DIFFERENT ECOREGIONS OVER THE MEAN GROUNDWATER DEPTH (RESOLUTION: 1 METER) FIGURE B 4: PROPORTION PROPORTION OF THE AREA COVERED BY THE DIFFERENT ECOREGIONS OVER THE MEAN GROUNDWATER DEPTH (RESOLUTION.1 METER) vii

64 B.II) IMPACTS OF CLIMATE CHANGE ON THE VEGETATION TABLE B 3: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (25 28) Area [km 2 ] Difference with past conditions [km 2 ] Ecoregion CCC CCSR GFDL HadCM3 CCC CCSR GFDL HadCM '927 1'623 1'875 1' '93 8 1' ' '35 1'757 2'41 1' ' '684 3'253 3'721 2' ' '94 24'389 23'921 25' ' ' '525 5'661 5'528 5' '711 26'388 25'69 27' ' ' TABLE B 4: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (27 21) Area [km 2 ] Difference with past conditions [km 2 ] Ecoregion CCC CCSR GFDL HadCM3 CCC CCSR GFDL HadCM '314 1'643 1' ' ' '31 1'887 2' ' ' '457 3'476 3'9 1'86 1' ' '216 24'221 23'722 25'971 1' ' '798 5'615 5'471 5' '497 26'18 25'415 28'357 2'519 1' ' TABLE B 5: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (MEAN OF THE CLIMATE MODELS AND SCENARIO 14) Area [km 2 ] Difference with past conditions [km2] Ecoregion Scenario 14 (mean) (mean) (mean) (mean) Scenario '568 1'331 2' ' '699 1'466 2' '196 2'847 4' ' '465 24'895 22'954 1'14 1' '693 5'776 5' '476 27'6 24'593 1'498 2' viii

65 B.III) IMPACTS OF CLIMATE CHANGE ON THE VEGETATION: CSIRO MODEL FIGURE B 5: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (CSIRO, 25 28) FIGURE B 6: DIFFERENCE BETWEEN PAST AND FUTURE (CSIRO, 25 28) MOST EXPECTED ECOREGIONS FIGURE B 7: SPATIAL DISTRIBUTION OF THE MOST EXPECTED ECOREGIONS AND CORRESPONDING CONFIDENCE (CSIRO, 27 21) FIGURE B 8: DIFFERENCE BETWEEN PAST AND FUTURE (CSIRO, 27 21) MOST EXPECTED ECOREGIONS ix

66 TABLE B 6: EXPECTED AREAS COVERED BY THE DIFFERENT ECOREGION (CSIRO) Area [km 2 ] Difference with past conditions [km 2 ] CSIRO (25 CSIRO (27 CSIRO (25 (27 Ecoregion 28) 21) 28) 21) '719 6'646 6'432 4' '693 3'252 2'453 2' '134 5'39 2'793 2' '291 7'69 3'117 3' '23 1' '13 17'253 7'258 6' '724 3'881 1'639 1' '996 18'66 7'981 6' x

67 B. IV) SPATIAL EXPECTATION OF THE DIFFERENT ECOREGIONS No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 9: SPATIAL EXPECTATION OF THE ECOREGIONS (25 28, CCC) xi

68 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 1: SPATIAL EXPECTATION OF THE ECOREGIONS (25 28, CCSR) xii

69 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% Figure B 11: SPATIAL EXPECTATION OF THE ECOREGIONS (25 28, GFDL) xiii

70 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 12: SPATIAL EXPECTATION OF THE ECOREGIONS (25 28, HADCM3) xiv

71 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 13: EXPECTATION OF THE ECOREGIONS (27 21, CCC) xv

72 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 14: EXPECTATION OF THE ECOREGIONS (27 21, CCSR) xvi

73 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 15: EXPECTATION OF THE ECOREGIONS (27 21, GFDL) xvii

74 No occurence No Data % 1% 2% 3% 4% 5% 6% 7% 8% 9% 1% FIGURE B 16: EXPECTATION OF THE ECOREGIONS (27 21, HADCM3) xviii

75 APPENDIX C: IMPACTS ON THE VEGETATION 2 (OTHER CLASSIFICATION OF THE VEGETATION) FIGURE C 1: SPATIAL DISTRIBUTION OF THE VEGETATION CLASSES ACCORDING TO xix

76 TABLE C 1: VEGETATION CLASSIFICATION AND KEY SPECIES BELONGING TO EACH CLASS ( ID Describtion Structure Species Area 1 Low Open Grassed Shrubland on Dune Valley with Burkea and Baikiae LOGS Burkea, Baikiae and Combretum with mixed grasses Dune Valley 2 Low Open Grassed Shrubland on Dune Valley with Mopane and Combretum LOGS C. mopane mixed with Combretum spp. and B. massaiensis and mixed grasses Dune Valley 3 Low Open Grassed/Treed Shrubland on Dune Valley with Terminalia and Baffia LOG/TS T. sericea, B. massaiensis, B. petersiana Dune Valley 4 Low Open Shrubland on Dune Crest with mixed species LOSS G. bicolor and Combretum spp with other shrubs and intermittent trees Dune Crest 5 Dense Treed Shrubland on Dune Side with Burkea Baikiae LDTS B.africana, Baikiae plurijuga, Combretum spp. and B. massaiensis Dune Side 6 Low Open Grassed Shrubland on Dune Valley with Terminalia and Baffia LOGS T. sericea, B. massaiensis, B. petersiana Dune Valley 7 8 Low Open Grassed Shrubland in Paleo Delta with Accacia Low Open Shrubbed Grasland with Sage Bush LOGS Acacia mellifera, G.bicolor, G. flava, T.sericea and mixed grasses Paleo Delta LOSG* Mixed grasses and sage bush Other Dry Land 9 Open Treed Shrubland on Dune Side with Burkea and Baikiae LOTS B. massaiensis, C. gratissimus, G. flava and Baikiae plurijuga Dune Side Low Open Grassed Shrubland on Dune Valley with Terminalia and Baffia Low Open Grassed Shrublandin Paleo Delta with Acacia Densely vegetated paleo-riparian ridges Low Open Grassed Shrubland in Paleo Delta with Combretum Low Dense Thickets on Former Lake Ridges with Acacia LOGS A. mellifera, T. sericea, Sagebush Dune Valley LOGS A. tortilis and A. erioloba with other shrubs (including Grewia spp.) and intermittent trees Paleo Delta TDGS Combretum spp. with other shrubs Washed dunes LOGS Combretum spp., G. bicolor, G. senegalensis Paleo Delta LDSS* A.tortilis, A.mellifera, A. erioloba Former Lake 17 Low Open Shrubbed/Treed Grassland with Acacia and Terminalia LOS/TG Acacia spp. with T. sericea and mixed grasses Meso delta Floodplain 22 Low Open Bare Woodland in Meso Delta Channels with Tall Mopane 23 Tall Open Shrubbed Woodlands in Meso Delta with Tall Mopane 24 Low Open Shrubbed Woodland in Meso Delta with Mopane 25 Low Bare Shrubland in Meso Delta with Shrub Mopane 27 Low Open Bare Grasland with Sage Bush 29 Cloud and Shadow 3 Open Mixed Mopane in Meso Delta Channels with Mopane LOB/SW TOSW C.mopane with T. sericea, X. americana, L. nelsii ans R. brevispinosum C.mopane with G. flavescens and C. capassa Meso delta Channels Meso delta Islands LOSW C.mopane mixed with Sagebush Meso delta LO/DBS C.mopane Shrub Meso delta Islands LOBG Cloud and Shadow L/TOSW Mixed grass species, Sagebush, bare soil Cloud and Shadow C.mopane with G. flavescens, T. sericea and B. petersiana Other Dry and Cloud and Shadow Meso delta Channels 32 Low Open Grassland with Baikiaea Trees and mixed Shrubs LOS/TG Mixed grasses and shrubs with Grewia spp., C. mopane, D. cinerea and O. pulchra with B. plurijuga Other Dryland xx

77 33 Low Open Grassland with Burkea Trees and mixed Shrubs LOS/TG Mixed grasses and shrubs with B. plurijuga Other Dryland 34 Low Open Grassland with Baikiae/Mopane Trees and mixed Shrubs LOS/TG Mixed grasses and shrubs with B. africana and C. mopane Other Dryland Low Open Grassed/Treed Shrubland with Terminalia and Acacia Low Open Shrubbed Grasland with mixed Shrubs Low Open Shrubbed Woodland of Terminalia Treeislands LOT/GS LOSG 38 Low Open Treed Shrubland with Accacia LOTS 39 Tall Open Treed Grassland with Acacia TOTG Low Open/Dense Shrubbed Grassland on Former Floodplain Grassland on Dry Floodplain and Island Interiors Tall Open Shrubbed Woodland Dry Riparian zones and Adjacent Acacia thickets T. prunioides, A. fleckii, A. leuderitzii and mixed grasses Grasses with Sagebush, C. alexandrii, C. gratissimus and G. flavescens and other shrubs Other Dry Land Other Dry Land TOSW T. prunioides with mixed shrub species Other Dryland LO/DSG LOGG TOSW A. erioloba, A.fleckii, A. leuderitzii, A. mellifera, Combretum spp., C. gratissimus Grass species with A. fleckii, G. flavescens, L. nelsii, C. alexandrii, D. cinerea T. sericea and Acacia shrubland and intermittant grasses E. rigidor, E. lappula, P. repens, S.spicutas, S. ioclados, C. virgata A. erioloba, L. capassa, C. termitaria, C. mopane with mixed grass species Other Dryland Other Dryland Former Floodplain Island/Active Floodplain Active Floodplain 5 Open Shrubbed Woodlands on islands T/LOSW C. imberbe and C. mopane shrub thickets shrubs e.g. A. erioloba, Z. mucronata, A. tortilis Island 51 Low Open Shrubbed Grasland with Acacia LOSG A. tortilis, T sericea and C. mopane with mixed grass speciese. g. (Panicum spp..,aristidia spp., Eragrostis spp.) Other Dry Land Low Open Grassed/Treed Shrubland with Terminalia and Acacia Low Open Shrubbed Woodland on Former Floodplain with Acacia Low Open Shrubbed Grassland on Former Floodplain Low Open Grasslands on Former Floodplain with Burned Peat Low Open Combretum Woodlands of Former Riparian Zone Tall Dense Grassland on Inundated Higher Floodplain Dense Grassland on Inundated Lower Floodplain Tall Open Shrubbed Woodland Island Riparian Zone and Adjacent Acacia thickets Tall channel fringing emergents and mats of Reeds and Sedges Channals and Recently Inundated Floodplains Seasonal Swamp and Floodplain edges with Reeds, Sedges and Grasses Permanent Backswamp areas with Reeds and Sedges LOG/TS LOSW LOSG A. tortilis, T sericea and C. mopane woodlands with mixed grass speciese. g. (Panicum spp..,aristidia spp., Eragrostis spp.) A. erioloba, A. tortilis, Grewia spp. C. termitaria T. sericea and Acacia shrubland and intermittant grasses Dryland Former Floodplain Former Floodplain LOGG E. rigidor, E. lappula, P. repens Former Floodplain LOT/S S/W TDGG L/TDGG TOT/S S/W TDGG Water TDGG Water Combretum spp and Lonchocarpus spp with mixed shrubs Former Riparian P.repens, P. obtusifolium, E. inamoena, Active Floodplain S. spacelata P.repens, P. obtusifolium, E. inamoena, Active Floodplain S. spacelata Ficus sycamorus, A. nigrescens, A. erioloba, Hyphaena petersiana Cyperus papyrus, Phragmites australis, Typha spp. Water and Flooded Aquatics and Grasses (non emergent) Miscanthus junceus, Schoenoplectus corymbosus, Cyprus articulatus with mixed grass species Cyperus papyrus, Phragmites australis, Miscanthus junceus Island Permanent Swamp Active Floodplain Active Floodplain Permanent Swamp xxi

78 xxii FIGURE C 2: INUNDATION FREQUENCY OVER PART OF AREA COVERED BY THE VEGETATION CLASSES (PART 1)

79 FIGURE C 3: INUNDATION FREQUENCY OVER PART OF AREA COVERED BY THE VEGETATION CLASSES (PART 2) FIGURE C 4: MEAN GROUNDWATER DEPTH OVER PART OF AREA COVERED BY THE VEGETATION CLASSES (PART 1) xxiii

80 xxiv FIGURE C 5: MEAN GROUNDWATER DEPTH OVER PART OF AREA COVERED BY THE VEGETATION CLASSES (PART 2)

81 FIGURE C 6: PROPORTION OF THE AREA COVERED BY THE DIFFERENT VEGETATION CLASSES OVER THE MEAN GROUNDWATER DEPTH (RESOLUTION: 1 METER) FIGURE C 7: SPATIAL EXPECTATION OF THE VEGETATION CLASSES (PAST CONDITIONS ; PART 1) xxv

82 xxvi FIGURE C 8: SPATIAL EXPECTATION OF THE VEGETATION CLASSES (PAST CONDITIONS ; PART 2)

83 FIGURE C 9: SPATIAL EXPECTATION OF THE VEGETATION CLASSES (PAST CONDITIONS ; PART 3) xxvii

84 xxviii FIGURE C 1: SPATIAL EXPECTATION OF THE VEGETATION CLASSES (PAST CONDITIONS ; PART 4)