Water is recognised as a key driver for social and economic development in the Zambezi basin. The basin is riparian to eight southern African countries and the transboundary nature of the basin’s water resources can be viewed as an agent of cooperation between the basin countries. It is possible, however, that the same water resource can lead to conflicts between water users. The southern African Water Vision for ‘equitable and sustainable utilisation of water for social, environmental justice and economic benefits for the present and future generations’ calls for an integrated and efficient management of water resources within the basin. Ensuring water and food security in the Zambezi basin is, however, faced with challenges due to high variability in climate and the available water resources. Water resources are under continuous threat from pollution, increased population growth, development and urbanisation as well as global climate change. These factors increase the demand for freshwater resources and have resulted in water being one of the major driving forces for development. The basin is also vulnerable due to lack of adequate financial resources and appropriate water resources infrastructure to enable viable, equitable and sustainable distribution of the water resources. This is in addition to the fact that the basin’s economic mainstay and social well-being are largely dependent on rainfed agriculture. There is also competition among the different water users and this has the potential to generate conflicts, which further hinder the development of water resources in the basin. This thesis has focused on the Zambezi River basin emphasising climate variability and climate change. It is now considered common knowledge that the global climate is changing and that many of the impacts will be felt through water resources. If these predictions are correct then the Zambezi basin is most likely to suffer under such impacts since its economic mainstay is largely determined by the availability of rainfall. It is the belief of this study that in order to ascertain the impacts of climate change, there should be a basis against which this change is evaluated. If we do not know the historical patterns of variability it may be difficult to predict changes in the future climate and in the hydrological resources and it will certainly be difficult to develop appropriate management strategies. Reliable quantitative estimates of water availability are a prerequisite for successful water resource plans. However, such initiatives have been hindered by paucity in data especially in a basin where gauging networks are inadequate and some of them have deteriorated. This is further compounded by shortages in resources, both human and financial, to ensure adequate monitoring. To address the data problems, this study largely relied on global data sets and the CRU TS2.1 rainfall grids were used for a large part of this study. The study starts by assessing the historical variability of rainfall and streamflow in the Zambezi basin and the results are used to inform the prediction of change in the future. Various methods of assessing historical trends were employed and regional drought indices were generated and evaluated against the historical rainfall trends. The study clearly demonstrates that the basin has a high degree of temporal and spatial variability in rainfall and streamflow at inter-annual and multi-decadal scales. The Standardised Precipitation Index, a rainfall based drought index, is used to assess historical drought events in the basin and it is shown that most of the droughts that have occurred were influenced by climatic and hydrological variability. It is concluded, through the evaluation of agricultural maize yields, that the basin’s food security is mostly constrained by the availability of rainfall. Comparing the viability of using a rainfall based index to a soil moisture based index as an agricultural drought indicator, this study concluded that a soil moisture based index is a better indicator since all of the water balance components are considered in the generation of the index. This index presents the actual amount of water available for the plant unlike purely rainfall based indices, that do not account for other components of the water budget that cause water losses. A number of challenges were, however, faced in assessing the variability and historical drought conditions, mainly due to the fact that most parts of the Zambezi basin are ungauged and available data are sparse, short and not continuous (with missing gaps). Hydrological modelling is frequently used to bridge the data gap and to facilitate the quantification of a basin’s hydrology for both gauged and ungauged catchments. The trend has been to use various methods of regionalisation to transfer information from gauged basins, or from basins with adequate physical basin data, to ungauged basins. All this is done to ensure that water resources are accounted for and that the future can be well planned. A number of approaches leading to the evaluation of the basin’s hydrological response to future climate change scenarios are taken. The Pitman rainfall-runoff model has enjoyed wide use as a water resources estimation tool in southern Africa. The model has been calibrated for the Zambezi basin but it should be acknowledged that any hydrological modelling process is characterised by many uncertainties arising from limitations in input data and inherent model structural uncertainty. The calibration process is thus carried out in a manner that embraces some of the uncertainties. Initial ranges of parameter values (maximum and minimum) that incorporate the possible parameter uncertainties are assigned in relation to physical basin properties. These parameter sets are used as input to the uncertainty version of the model to generate behavioural parameter space which is then further modified through manual calibration. The use of parameter ranges initially guided by the basin physical properties generates streamflows that adequately represent the historically observed amounts. This study concludes that the uncertainty framework and the Pitman model perform quite well in the Zambezi basin. Based on assumptions of an intensifying hydrological cycle, climate changes are frequently expected to result in negative impacts on water resources. However, it is important that basin scale assessments are undertaken so that appropriate future management strategies can be developed. To assess the likely changes in the Zambezi basin, the calibrated Pitman model was forced with downscaled and bias corrected GCM data. Three GCMs were used for this study, namely; ECHAM, GFDL and IPSL. The general observation made in this study is that the near future (2046-2065) conditions of the Zambezi basin are expected to remain within the ranges of historically observed variability. The differences between the predictions for the three GCMs are an indication of the uncertainties in the future and it has not been possible to make any firm conclusions about directions of change. It is therefore recommended that future water resources management strategies account for historical patterns of variability, but also for increased uncertainty. Any management strategies that are able to satisfactorily deal with the large variability that is evident from the historical data should be robust enough to account for the near future patterns of water availability predicted by this study. However, the uncertainties in these predictions suggest that improved monitoring systems are required to provide additional data against which future model outputs can be assessed.

River streamflow forecasting is traditionally based on real-time measurements of rainfall over catchments and discharge at the outlet and upstream. These data are processed in mathematical models of varying complexity and allow to obtain accurate predictions for short times. In order to extend the forecast horizon to a few days - to be able to issue early warning - it is necessary to take into account the weather forecasts. However, the latter display the property of sensitivity to initial conditions, and for appropriate risk management, forecasts should therefore be considered in probabilistic terms. Currently, ensemble predictions are made using a numerical weather prediction model with perturbed initial conditions and allow to assess uncertainty. <p><p>The research began by analyzing the meteorological predictions at the medium-range (up to 10-15 days) and their use in hydrological forecasting. Precipitation from the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. A semi-distributed hydrological model was used to transform these precipitation forecasts into ensemble streamflow predictions. The performance of these forecasts was analyzed in probabilistic terms. A simple decision model also allowed to compare the relative economic value of hydrological ensemble predictions and some deterministic alternatives. <p><p>Numerical weather prediction models are imperfect. The ensemble forecasts are therefore affected by errors implying the presence of biases and the unreliability of probabilities derived from the ensembles. By comparing the results of these predictions to the corresponding observed data, a statistical model for the correction of forecasts, known as post-processing, has been adapted and shown to improve the performance of probabilistic forecasts of precipitation. This approach is based on retrospective forecasts made by the ECMWF for the past twenty years, providing a sufficient statistical sample. <p><p>Besides the errors related to meteorological forcing, hydrological forecasts also display errors related to initial conditions and to modeling errors (errors in the structure of the hydrological model and in the parameter values). The last stage of the research was therefore to investigate, using simple models, the impact of these different sources of error on the quality of hydrological predictions and to explore the possibility of using hydrological reforecasts for post-processing, themselves based on retrospective precipitation forecasts. <p>/<p>La prévision des débits des rivières se fait traditionnellement sur la base de mesures en temps réel des précipitations sur les bassins-versant et des débits à l'exutoire et en amont. Ces données sont traitées dans des modèles mathématiques de complexité variée et permettent d'obtenir des prévisions précises pour des temps courts. Pour prolonger l'horizon de prévision à quelques jours – afin d'être en mesure d'émettre des alertes précoces – il est nécessaire de prendre en compte les prévisions météorologiques. Cependant celles-ci présentent par nature une dynamique sensible aux erreurs sur les conditions initiales et, par conséquent, pour une gestion appropriée des risques, il faut considérer les prévisions en termes probabilistes. Actuellement, les prévisions d'ensemble sont effectuées à l'aide d'un modèle numérique de prévision du temps avec des conditions initiales perturbées et permettent d'évaluer l'incertitude.<p><p>La recherche a commencé par l'analyse des prévisions météorologiques à moyen-terme (10-15 jours) et leur utilisation pour des prévisions hydrologiques. Les précipitations issues du système de prévisions d'ensemble du Centre Européen pour les Prévisions Météorologiques à Moyen-Terme ont été utilisées. Un modèle hydrologique semi-distribué a permis de traduire ces prévisions de précipitations en prévisions d'ensemble de débits. Les performances de ces prévisions ont été analysées en termes probabilistes. Un modèle de décision simple a également permis de comparer la valeur économique relative des prévisions hydrologiques d'ensemble et d'alternatives déterministes.<p><p>Les modèles numériques de prévision du temps sont imparfaits. Les prévisions d'ensemble sont donc entachées d'erreurs impliquant la présence de biais et un manque de fiabilité des probabilités déduites des ensembles. En comparant les résultats de ces prévisions aux données observées correspondantes, un modèle statistique pour la correction des prévisions, connue sous le nom de post-processing, a été adapté et a permis d'améliorer les performances des prévisions probabilistes des précipitations. Cette approche se base sur des prévisions rétrospectives effectuées par le Centre Européen sur les vingt dernières années, fournissant un échantillon statistique suffisant.<p><p>A côté des erreurs liées au forçage météorologique, les prévisions hydrologiques sont également entachées d'erreurs liées aux conditions initiales et aux erreurs de modélisation (structure du modèle hydrologique et valeur des paramètres). La dernière étape de la recherche a donc consisté à étudier, à l'aide de modèles simples, l'impact de ces différentes sources d'erreur sur la qualité des prévisions hydrologiques et à explorer la possibilité d'utiliser des prévisions hydrologiques rétrospectives pour le post-processing, elles-même basées sur les prévisions rétrospectives des précipitations. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished