The land–atmosphere interactions and the coupling between climate and land surface hydrological processes are gaining interest in the recent past. The increased knowledge in hydro climatology and the global hydrological cycle, with terrestrial and atmospheric feedbacks, led to the utilization of the climate variables and atmospheric tele-connections in modeling the hydrological processes like rainfall and runoff. Numerous statistical and dynamical models employing different combinations of predictor variables and mathematical equations have been developed on this aspect. The relevance of predictor variables is usually measured through the observed linear correlation between the predictor and the predictand. However, many predictor climatic variables are found to have been switching the relationships over time, which demands a replacement of these variables. The unsatisfactory performance of both the statistical and dynamical models demands a more authentic method for assessing the dependency between the climatic variables and hydrologic processes by taking into account the nonlinear causal relationships and the instability due to these nonlinear interactions. The most obvious cause for limited predictability in even a perfect model with high resolution observations is the nonlinearity of the hydrological systems [Bloschl and Zehe, 2005]. This is mainly due to the chaotic nature of the weather and its sensitiveness to initial conditions [Lorenz, 1963], which restricts the predictability of day-to-day weather to only a few days or weeks. The present thesis deals with developing association rules to extract the causal relationships between the climatic variables and rainfall and to unearth the frequent predictor patterns that precede the extreme episodes of rainfall using a time series data mining algorithm. The inherent nonlinearity and uncertainty due to the chaotic nature of hydrologic processes (rainfall and runoff) is modeled through a nonlinear prediction method. Methodologies are developed to increase the predictability and reduce the predictive uncertainty of chaotic hydrologic series. A data mining algorithm making use of the concepts of minimal occurrences with constraints and time lags is used to discover association rules between extreme rainfall events and climatic indices. The algorithm considers only the extreme events as the target episodes (consequents) by separating these from the normal episodes, which are quite frequent and finds the time-lagged relationships with the climatic indices, which are treated as the antecedents. Association rules are generated for all the five homogenous regions of India (as defined by Indian Institute of Tropical Meteorology) and also for All India by making use of the data from 1960-1982. The analysis of the rules shows that strong relationships exist between the extreme rainfall events and the climatic indices chosen, i.e., Darwin Sea Level Pressure (DSLP), North Atlantic Oscillation (NAO), Nino 3.4 and Sea Surface Temperature (SST) values. Validation of the rules using data for the period 1983-2005, clearly shows that most of the rules are repeating and for some rules, even if they are not exactly the same, the combinations of the indices mentioned in these rules are the same during validation period with slight variations in the representative classes taken by the indices. The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. In the present study, an attempt is made to identify chaos using various techniques and the behaviour of daily rainfall series in different regions. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha river basin, Mahanadi river basin and All India for the period 1955 to 2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series considered. Correlation dimension method is repeated on the phase randomized and first derivative of the data series to check the existence of any pseudo low-dimensional chaos [Osborne and Provenzale, 1989]. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A limit in predictability in chaotic system arises mainly due to its sensitivity to the infinitesimal changes in its initial conditions and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a nonlinear ensemble prediction. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996 to 2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are made from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. The predictability of the chaotic daily rainfall series is improved by utilizing information from various climatic indices and adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha river basin, India for the period 1955 to 2000 is used for the study. A multivariate phase space is generated, considering a climate data set of 16 variables. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to 8 principal components (PCs). This multivariate series (rainfall along with 8 PCs) are found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is reduced or in other words, the predictability is improved by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be reduced by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. Even though, the sensitivity to initial conditions limit the predictability in chaotic systems, a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All the traditional chaotic prediction methods are based on local models since these methods model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor [Sivakumar et al., 2002a]. This study focuses on combining a local learning wavelet analysis (decomposition) model with a global feedforward neural network model and its implementation on phase space prediction of chaotic streamflow series. The daily streamflow series at Basantpur station in Mahanadi basin, India is found to exhibit a chaotic nature with dimension varying from 5-7. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimension and delay time. Compared with traditional local approximation approach, the total predictive uncertainty in the streamflow is reduced when modeled with wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor is clearly demonstrated.

MENVSC (Geography) / Department of Geography and Geo-Information Sciences / Rainfall is undoubtedly the most significant factor for life’s continuity. South Africa is prone to future climate uncertainties due to global climate change. The aim of this study is to investigate rainfall variability and change in South Africa on a present day (1976-2005), near-future (2006-2035) and far-future (2036-2065) climate. For the study, 3 RCMs (REMO2009, RCA4 and CCLM4-8-17), forming part of CORDEX-Africa project were nested within 5 different CIMP5_GCMs of low resolution. GPCC precipitation, NOAA GHCN_CAMS Land Temperature and other NCEP reanalysis products were useful in validating models in simulations of present-day climate. RCP4.5 and RCP8.5 emission scenarios from IPCC-AR5 were used for future climate projections. On the validation, each regional climate model displayed different signature on simulations, rainfall in particular because this is a variable that is affected most by sub-grid process. Simulations nested within MIROC5 simulated more precipitation than simulations forced with other GCMs, due to more large-scale moisture convergence into the nested domain. There were differences in projections of RCM nested within the same GCM, as well as with the same RCM nested within different GCMs, on the future. Models nested within MPI project wetter conditions over the eastern parts of Limpopo, while the other two projected drier conditions in the same area. REMO2009 forced on MPI uniquely projected drying of Western Cape throughout the seasons on both RCPs and futures. Simulations conducted with the RCP8.5 scenario forcing are generally found to be associated with either a larger increase in temperature, or an increase in area associated with higher temperature increases. CCLM4-8-17 forced on HadGEM2 projected below average temperatures over the northwest parts of the country under the RCP8.5 scenarios. MPI driving model projected a general reduction of evaporation values, with lowest over northeast, northwest parts and south coastal parts of South Africa, in contrary to adjacent oceans. In this study, we have sought to identify the sources of uncertainties amongst model simulations between either the RCMs or the driving GCMs. / NRF

Call number: LD2668 .T4 1956 S34 / Master of Science

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

Distributed rainfall-runoff models are gaining widespread acceptance; yet, a fundamental issue that must be addressed by all users of these models is definition of an acceptable level of watershed discretization (geometric model complexity). The level of geometric model complexity is a function of basin and climatic scales as well as the availability of input and verification data. Equilibrium discharge storage is employed to develop a quantitative methodology to define a level of geometric model complexity commensurate with a specified level of model performance. Equilibrium storage ratios are used to define the transition from overland to channel-dominated flow response. The methodology is tested on four subcatchments in the USDA-ARS Walnut Gulch Experimental Watershed in southeastern Arizona. The catchments cover a range of basins scales of over three orders of magnitude. This enabled a unique assessment of watershed response behavior as a function of basin scale. High quality, distributed, rainfall-runoff data were used to verify the model (KINEROSR). Excellent calibration and verification results provided confidence in subsequent model interpretations regarding watershed response behavior. An average elementary channel support area of roughly 15% of the total basin area is shown to provide a watershed discretization level that maintains model performance for basins ranging in size from 1.5 to 631 hectares. Detailed examination of infiltration, including the role and impacts of incorporating small-scale infiltration variability in a distribution sense, into KINEROSR, over a range of soils and climatic scales was also addressed. The impacts of infiltration and channel losses on runoff response increase with increasing watershed scale as the relative influence of storms is diminished in a semi-arid environment such as Walnut Gulch. In this semi-arid environment, characterized by ephemeral streams, watershed runoff response does not become more linear with increasing watershed scale but appears to become more nonlinear.

Ph.D. (Geography) / Please refer to full text to view abstract

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A Research Report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science. Johannesburg, October 2016. / Rainfall is a complex phenomenon, which has previously been explored by assessing rainfall patterns in time and space, typically using ground-based weather stations. Rainfall patterns in southern Africa tend to have a direct impact on vegetation growth and surface water availability, and an indirect impact on animal movement. This study investigated rainfall in Botswana by analysing changes in spatial and temporal patterns from 1998 to 2013, using satellite imagery. Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset (1998-2013) was used to document monthly rainfall magnitude and variability over the 15-year period. Additionally, a GIS spatial analysis approach, the Anselin Local Moran’s I tool, was used to determine changes (i.e. persistence) of rainfall conditions on a year by year basis during the study period. WorldClim precipitation data (1950-2000) were utilised as a longer term average reference dataset against which TRMM data could be compared. This study found that the rainy season consisted of relatively high rainfall magnitudes and variability, while the post rainy season consisted of relatively lower rainfall magnitudes and variability across Botswana. Higher magnitudes persisted into April, indicating the occurrence of late summer rainfall during this observation period. From a regional perspective, the Okavango Delta remained a region of relatively higher rainfall magnitude and variability compared to surrounding regions, regardless of the season. The rainy season was associated with a high frequency of rainfall events above the long term WorldClim average, and the post rainy season with a high frequency of rainfall below the long term WorldClim average. The spatial analysis indicated an annual persistence of high rainfall clusters in northern Botswana, and a persistence of low rainfall clusters in southern Botswana throughout the 15-year analysis. In addition, a progressive drying trend towards the end of the time series was observed. These findings suggest that Botswana has experienced both wetter conditions and drier conditions within the 15-year analysis period, than have been historically documented. The progressive drying trend towards the end of the time series may be indicative of a changing climate in Botswana. However, due to the length of this analysis period it cannot be proven conclusively that the detected wetter and drier conditions, than historically documented, are a signal of climate change. This rainfall analysis provides a comprehensive understanding of recent spatial and temporal rainfall patterns and changes in Botswana. More specifically, this rainfall study fits into a bigger research project focused on herbivore conservation in Botswana. Together, these studies will collectively enable protected areas authorities to better manage herbivore migration, improving conservation in Botswana over time. Ultimately, this study stands to make a positive contribution towards the development of existing conservation practices in Botswana. / LG2017

Escorregamentos de taludes no Sudeste do Brasil são causados principalmente, pelo efeito da água proveniente das chuvas. Nos últimos anos, vem se incrementando o número de desastres naturais, ao passo são registradas mudanças climáticas, que podem exercer influência na ocorrência de chuvas extremas. Muitas encostas permanecem grande parte do ano com o solo em estado não saturado, porém variações nas condições ambientais podem ocasionar mudanças bruscas da sucção, reduzindo ou até mesmo eliminando-a e gerando pressões neutras positivas. A dissertação apresenta, um estudo sobre a influência das chuvas, consideradas extremas, no processo de infiltração e de este nos eventos de escorregamentos, considerados catastróficos pela sua dimensão, e que causaram prejuízos ambientais, econômicos e sociais no Brasil. Para o estudo foram selecionados dois eventos de escorregamentos translacionais rasos relativamente típicos, considerados catastróficos, um deles aconteceu na região da Serra de Cubatão em janeiro do ano 1985, e o outro na Região Serrana do Rio de Janeiro em janeiro de 2011. Estes dois eventos apresentaram características de precipitações e mecanismos de escorregamentos próprios, que permitem ilustrar os diferentes mecanismos atuantes em cada caso. / Landslide in southeastern Brazil, are mainly caused by the effect of water from rainfall in infiltration process, in recent years has been increasing the number of natural disasters, while climate change that may exercising influence on the occurrence of extreme rainfall are recorded . Many slopes remain a large part of the year with unsaturated soil condition; however, changes in environmental conditions can cause sudden changes of suction, reducing or even deleting it and generate positive pore pressures. This dissertation presents a study about the influence of rainfall, considered extreme in the infiltration process and this one in the events regarded by their size of catastrophic landslides, which caused environmental, economic and social losses in Brazil. For the study were selected two events of shallow translational landslides relatively typical, considered catastrophic, one of them occurred in the Serra de Cubatão region on January 1985 and the other in the mountainous region of Rio de Janeiro on January 2011. Both events exhibit characteristics of rainfall and sliding mechanisms themselves, allowing illustrate the different mechanisms active in each case.

Alternativamente ao preenchimento de falhas, buscou-se neste estudo apresentar uma metodologia para a geração de séries de precipitações mensais médias apenas com os dados observados disponíveis nas estações pluviométricas presentes na área de estudo e seu entorno. Neste caso, para cada época da série, somente os dados disponíveis foram utilizados para o cálculo da precipitação média, assim em cada época admitiu-se que uma combinação diferente de postos pluviométricos fosse utilizada para o seu cálculo. Para isso foi desenvolvida uma ferramenta computacional em ambiente SIG para automatizar todas as etapas do estudo. Os resultados das séries de precipitação mensal média para uma bacia hidrográfica calculados segundo a metodologia alternativa apresentada foram comparados a dois métodos de preenchimento de falhas comumente utilizados nos estudos hidrológicos. Posteriormente foram avaliados os impactos do quantitativo de falhas nas séries. Também foram avaliados os reflexos na aplicação das séries na modelagem chuva-vazão e por fim a metodologia foi aplicada a um caso concreto. Os resultados encontrados e os testes estatísticos apontam resultados satisfatórios e equivalentes nas condições testadas. Verificou-se uma degradação na correlação entre a série gerada a partir dos dados sem falhas e as séries geradas com os dados com as imposições das falhas com o aumento do número de falhas impostas. As séries de vazões médias mensais geradas utilizando-se o modelo mensal SMAP quando da aplicação das séries de precipitação calculadas pela metodologia alternativa também se mostraram equivalentes, segundo os testes estatísticos realizados, às séries geradas de dados de precipitação sem falhas ou com as falhas preenchidas. As diferenças encontradas entre as séries foram pequenas o que se refletiu nos Índices de Nash-Sutcliffe que foram próximos. / In this study, we proposed a methodology for the generation of average monthly rainfall series only with the observed data available in the rainfall stations present in the study area and its surroundings. In this case, for each season of the series only the available data were used for the calculation of the average precipitation, so in each season it was admitted that a different combination of pluviometric stations was used for its calculation. For this, a computational tool was developed with a GIS approach to automate all stages of the study. The results of the average monthly rainfall series for a river basin calculated according to the alternative methodology presented were compared to two methods of filling gaps commonly used in hydrological studies. Subsequently, the impacts of the number of failures in the series were evaluated. We also evaluated the reflexes in the application of the series in the rainfall-flow modeling and finally the methodology was applied to a case study. The results found and the statistical tests show satisfactory and equivalent results under the conditions tested. There was a degradation in the correlation between the series generated from the data without fail and the series generated with the data with the impositions of the failures with the increase of the gaps imposed. The series of monthly average flows generated using the SMAP monthly model when applying the rainfall series calculated by the alternative methodology were also shown to be equivalent, according to the statistical tests carried out, to the series generated of data of precipitation without gaps or with the gaps fulfilled. The differences found between the series were small, which was reflected in the Nash-Sutcliffe Indices that were close.