Issues arising from climate change and long-term natural climate variability have become the focus of much recent research. In this study, we specifically explore the impacts of long-term climate variability and climate changes upon flood frequencies. The analyses of the flood frequencies are carried out in a comparative manner in catchments located in semiarid-temperate and tropical landscapes in Australia, namely Perth, Newcastle and Darwin, using a process-based derived flood frequency approach. The derived flood frequency analyses are carried out using deterministic rainfall-runoff models that capture the intrinsic water balance variability in the study catchments, and driven by temporal rainfall event sequences that are generated by a stochastic rainfall model that incorporates temporal variabilities over a multiplicity of time scales, ranging from within-event, between-event to seasonal, multi-annual and multi-decadal time scales. Six climate scenarios are considered for Newcastle, that combine the ENSO (El Niño Southern Oscillation) and IPO (Inter-decadal Pacific Oscillation) modes of variability, and six different climate scenarios are considered for Perth and Darwin that combine these different ENSO modes and step changes in climate (upwards or downwards) that occurred in 1970 in both regions, which were identified through statistical analysis. The results of the analyses showed that La Niña years cause higher annual maximum floods compared to El Niño and Neutral years in all three catchments. The impact of ENSO on annual maximum floods in the Newcastle catchment is enhanced when the IPO is negative and for Perth, the impact of ENSO weakens in the post-1970 period, while it strengthens in Darwin in the same period. In addition, the results of sensitivity and scenario analyses with the derived flood frequency model explored the change of dominant runoff generation processes contributing to floods in each of the study catchments. These analyses highlighted a switch from subsurface stormflow to saturation excess runoff with a change of return period, which was much more pronounced in Perth and Darwin, and not so in Newcastle. In Perth and Darwin this switch was caused by the interactions between the out-of-phase seasonal variabilities of rainfall and potential evaporation, whereas the seasonality was much weaker in Newcastle. On the other hand, the combination of higher rainfall intensities and shallower soil depths led to saturation excess runoff being the dominant mechanism in Newcastle across the full range of return periods. Consequently, within-storm rainfall intensity patterns were important in Newcastle in all major flood producing events (all return periods), where they were only important in Perth and Darwin for floods of high return periods, which occur during wet months in wet years, when saturation excess runoff was the dominant mechanism. Additionally, due to the possibility of a change of process from subsurface stormflow to saturation excess when conditions suited this switch, the estimates of flood frequency are highly uncertain especially at high return periods (in Darwin and Perth) and much less in Newcastle (when no process change was involved).

South Africa is a semi arid country in which the average rainfall of 450mm/year is well below the world average of about 860mm/year. As a result, South Africa’s water resources are scarce in global terms and limited in extent. Current predictions are that demand will outstrip water availability in the next 15 years. A coordinated approach to improve both water quality and quantity is needed and in order to achieve that, it is crucial to strengthen capacities of local community involvement in identifying the problems that affect them and strategies to solve them. This research was undertaken to develop a deeper understanding of community learning processes in integrated water resources management (IWRM) practices. The study drew on situated and social learning theory which explains that knowledge and skills are learned and embedded in the contexts in which knowledge is obtained and applied in everyday situations. Multiple data collection techniques were used within a case study design and included document analysis, interviews, focus group discussions and field observations. Data analysis was done in three phases and involved uncovering patterns and trends in the data sets. In this context I discovered, through careful observation and interviews with members of the different communities of practice, that people are learning through social learning interactions with other community members as they engage in their daily water management and food production practices. Learning interactions take place through both informal and formal processes such as meetings, training workshops, conversations and interactions with outsiders. I also discovered that people learn from ‘external groups’ or training programmes which bring new knowledge and expertise, but this needs to be contextualised in the local communities of practice. The research has also shown that there are a number of challenges that appear to exist in these learning contexts. For instance it was found that participation and social learning processes and interactions are influenced by a range of causal mechanisms that are contextual. These insights into how communities learn, as well as the tensions and difficulties that are experienced in the learning processes are important for furthering learning and participation in community-based IWRM practices, projects and programmes.

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.

The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models. The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm. EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed. In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques. Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.

MESHWR / See the attached abstract below

MESHWR / Department of Hydrology and Water Resources / This study dealt with the influence of climate variability on flood and drought cycles and implications on rainy season characteristics in Luvuvhu River Catchment (LRC) in Limpopo of South Africa. Extreme weather events resulting in hazards such as floods and droughts are becoming more frequent due to climate change. Extreme events affect rainy season characteristics and hence have an influence on water availability and agricultural production. Annual temperature was obtained from Water Research Commission for stations 0723485W, 0766628W and 0766898W from 1950-2013 were used to show/or confirm if there is climate variability in LRC. Daily rainfall data was obtained from SAWS for stations 0766596 9, 0766563 1, 0723485 6 and 0766715 5 were used to detect climate variability and determine the onset, duration and cessation of the rainy season. Streamflow data obtained from the Department of Water and Sanitation for stations A9H004, A9H012, and A9H001 for at least a period of 30 years for each station were used for climate variability detection and determination of flood and drought cycles. Influence of climate variability on floods and droughts and rainy season characteristic were determined in the area of study. Trends were evaluated for temperature, rainfall and streamflow data in the area of study using Mann Kendall (MK) and linear regression (LR) methods. MK and LR detected positive trends for temperature (maximum and minimum) and streamflow stations. MK and LR results of rainfall stations showed increasing trends for stations 0766596 9, and 0766563 1 whereas stations 0723485 6 and 0766715 5 showed decreasing trends. Standardized precipitation index (SPI) was used to determine floods and droughts cycles. SPI results have been classified either as moderately, severely and extremely dry or, moderately, very and extremely wet. This SPI analysis provides more details of dominance of distinctive dry or wet conditions for a rainy season at a particular station. Mean onset of rainfall varied from day 255 to 297, with 0766715 5 showing the earliest onset compared to the rest of the stations. Cessation of rainfall for most of the hydrological years was higher than the mean days of 88, 83 and 86 days in 0766596 9, 0766563 1 and 0723485 6 stations. Mean duration of rainfall varied from 102 to 128, with station 0766715 5 showing shortest duration of rainfall. The results of the study showed that the mean onset, duration and cessation were comparable for all stations except 0766715 5 which had lower values. The study also found that climate variability greatly affects onset, duration and cessation of rainfall during dry years. This led to late onset, early cessation and relatively short duration of the rainfall season. Communities within the catchment must be educated to practice activities such as conservation of indigenous plants, reduce carbon dioxide emissions.

MESHWR / Department of Hydrology and Water Resources / This study was aimed at investigating the long term seasonal and annual changes in rainfall duration and magnitude at Luvuvhu River Catchment (LRC). Rainfall in this catchment is highly variable and is characterised of extreme events which shift runoff process, affect the timing and magnitude of floods and drought, and alter groundwater recharge. This study was motivated by the year to year changes of rainfall which have some effects on the availability of water resources. Computed long term total seasonal, annual rainfall and total number of seasonal rainy days were used to identify trends for the period of 51 years (1965- 2015), using Mann Kendal (MK), linear regression (LR) and quantile regression methods. The MK, LR and quantile regression methods have indicated dominance of decreasing trends of the annual, seasonal rainfall and duration of seasonal rainfall although they were not statistically significant. However, statistical significant decreasing trends in duration of seasonal rainfall were identified by MK and LR at Matiwa, Palmaryville, Levubu, and Entabeni Bos stations only. Quantile regression identified the statistically significant decreasing trends on 0.2, 0.5 and 0.7 quantiles only in the Palmaryville, Levubu and Entabeni Bos, respectively. Stations with non-statistically significant decreasing trends of annual and seasonal rainfall had magnitude of change ranging from 0.12 to 12.31 and 0.54 to 6.72 mm, respectively. Stations with non-statistically increasing trends of annual and seasonal rainfall magnitude had positive magnitude of change ranging from 1.51 to 6.78 and 2.05 to 6.51 mm, respectively. The Study recommended further studies using other approaches to determine the duration of rainfall to improve, update and compare the results obtained in the current study. Continuous monitoring and installation of rain gauges are recommended on the lower reaches of the catchment for the findings to be of complete picture for the whole catchment and to also minimize the rainfall gaps in the stations. Water resources should be used in a sustainable way to avoid water crisis risk in the next generations. / NRF

La pollution atmosphérique en milieu urbain est un problème préoccupant car une fraction croissante de la population mondiale vit dans les villes. Les effets de la pollution se manifestent également sur la végétation urbaine et sur notre patrimoine architectural, de sorte que c'est la qualité de la vie de l'ensemble des habitants des métropoles de la planète qui est en jeu. Il est indispensable de connaître la composition des atmosphères urbaines et de comprendre les mécanismes qui régissent cette composition pour évaluer les conséquences de la pollution, définir les exigences de réduction des émissions et établir des scénarios des tendances futures.<p><p>L'objectif du présent travail est de déterminer la composition chimique, les propriétés et le comportement des particules et des dépôts humides en Région bruxelloise. On a distingué les aérosols atmosphériques, les brouillards, les rosées (ou givres) récoltés à la fois sur les végétaux et sur un collecteur inerte, les pluies et les dépôts totaux (formés des pluies et des dépôts secs accumulés dans l'entonnoir de collecte en l'absence de pluie). Ce vaste objectif a été réalisé grâce à la collecte de nombreux échantillons sur une échelle de temps suffisante en différents endroits de la capitale, et à l'analyse de ces échantillons par des techniques variées et complémentaires (techniques classiques d'analyse d'échantillons liquides telles que spectrométrie d'absorption et d'émission atomique, chromatographie liquide, colorimétrie, ainsi que microscopie électronique et fluorescence des rayons-X). Trois collecteurs (pour le brouillard, la pluie et la rosée) ont été entièrement conçus et réalisés au laboratoire dans le cadre de ce travail. Les éléments suivants sont analysés: NO3, SO4, NH4, Na, Mg, Al, Si, P, S, CI, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Pb.<p><p>Afin de comprendre les causes de la variabilité spatio-temporelle des concentrations, l'influence de paramètres tels que la saison, la direction du vent, et le lieu de prélèvement a été examinée. De plus, dans le cas des pluies et des brouillards, l'étude de l'évolution des concentrations au cours d'un même épisode a permis d'investiguer les processus physico-chimiques qui contrôlent le dépôt humide. Elle a permis d'acquérir une meilleure connaissance des mécanismes d'incorporation des aérosols dans la phase aqueuse et du phénomène de lessivage de l'atmosphère. Tout au long de ce travail, les interactions entre la phase particulaire (aérosols) et les phases liquides (brouillards, rosées, pluies) ont été examinées. Une relation entre les concentrations en éléments dissous et le volume d'eau de l'échantillon a été établie dans le cas des pluies, des rosées et des brouillards. Cette relation traduit un effet de dilution et démontre l'importance du mécanisme de condensation-évaporation des gouttes d'eau. L'importance du phénomène de nucléation des sulfates, nitrates et chlorures d'ammonium constitutifs de la fraction fine de l'aérosol soluble a été démontrée. Ces sels d'ammonium sont formés secondairement par des réactions de conversion gaz-particules. L'abondance des ions ammonium, et l'importance de leur action de neutralisation de l'acidité, constituent une particularité de l'atmosphère bruxelloise.<p>L'identification des sources de particules et d'éléments en relation avec leurs propriétés chimiques et granulométriques a été réalisée en utilisant divers outils statistiques (corrélations entre éléments, analyse factorielle) et géochimiques (rapports de concentration, facteurs d'enrichissement, granulométrie). Les apports d'origine marine, continentale, biologique et anthropique (trafic, incinération des déchets, processus de combustion) ont ainsi été clairement mis en évidence dans l'aérosol et le brouillard bruxellois.<p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished