Thesis (M.Eng.)--Memorial University of Newfoundland, 2002. / Bibliography: leaves 78-80. Also available online.

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Appendices: leaves 194-207. Includes bibliographical references (leaves 208-209).

Thesis (Ph. D.)--Texas State University-San Marcos, 2007. / Vita. Appendices: leaves 194-207. Includes bibliographical references (leaves 208-209).

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This study describes and analyses cattle management in Tlokweng Sub District. Two methods were used. The two are households' interviews and a system dynamics STELLA model called the Rain Land Cattle model, which was adapted from the 1990 Braat and Opschoor model. Ninety households, 61% of the 1991 households in the study area, were interviewed. All the households had arable fields and fifty nine percent had cattle. The Rain Land Cattle model uses 52 parameters to predict several cattle management factors, which include rainfall, stocking rate, total grazing area and livestock water availability. The model explored the use of parameters to relate water availability to grazing area and show the seasonality of the water source. Sixty two percent of the household had access to an ideal livestock water source. Cattle graze from the 5000 hectares of arable area for four months after harvesting. This seasonal grazing, optimises the uses of the grazing resource in the small sub - district. The model simulated a S and 20 percent permanent grazing land loss. Such a grazing land loss, increased the stocking rate, decreased the carrying capacity and cast doubt on sustainable cattle production. The model shows that the stocking rate is chronically greater than the carrying capacity. Most households acknowledged that there was land pressure due to the loss of grazing land. A drier climate scenario will lead to a loss of seasonal grazing, reduced livestock water, which will increase cattle emigration and cause cattle management problems. The model is exploratory; it needs to be validated. It is easily understood, adaptable to other communal areas, and identifies the most influential factors in cattle management. The livestock water parameters functioned reliably in the model. Based on the understanding of the cattle management derived from this study, more fenced grazing land is unlikely to improve the cattle management in the area.

In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is shown to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study.

The capability of radar measured rainfall to enhance the simulation of storm hydrographs was assessed. Six rainfall events which occurred in 1986 and 1987 over an 8.13 km$ sp2$ agricultural watershed in south-western Quebec were used in model simulations. Radar measured rainfall rates were calibrated using measurements from a single tipping-bucket raingauge located at the study site. / A deterministic, event-based model, HYMO, was used to simulate streamflow using radar and gauge measured rainfall. The model utilized two rainfall abstraction techniques, i.e. the SCS Curve Number method and the Green-Ampt infiltration equation. Simulated streamflow hydrographs were compared with observed storm flows. / For short duration, high intensity, simple rainfall events, there were minor improvements in hydrograph simulations when calibrated radar measured rainfalls were input to the model, compared to tipping-bucket raingauge measurements. Complex, low intensity storms were poorly simulated by the model using either rainfall data source. Neither rainfall abstraction method proved consistently superior.

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020 / Several studies indicated a growing trend in terms of frequency and severity of extreme events. Extreme rainfall could cause disasters that lead to loss of property and life. The aim of the study was to model the monthly rainfall variability in selected provinces of South Africa using extreme value distributions. This study investigated the best-fit probability distributions in the five provinces of South Africa. Five probability distributions: gamma, Gumbel, log-normal, Pareto and Weibull, were fitted and the best was selected from the five distributions for each province. Parameters of these distributions were estimated by the method of maximum likelihood estimators. Based on the Akaike information criteria (AIC) and Bayesian information criteria (BIC), the Weibull distribution was found to be the best-fit probability distribution for Eastern Cape, KwaZulu-Natal, Limpopo and Mpumalanga, while in Gauteng the best-fit probability distribution was found to be the gamma distribution. Monthly rainfall trends detected using the Mann–Kendall test revealed significant monotonic decreasing long-term trend for Eastern Cape, Gauteng and KwaZulu-Natal, and insignificant monotonic decreasing longterm trends for Limpopo and Mpumalanga. Non-stationary generalised extreme value distribution (GEVD) and non-stationary generalized Pareto distribution (GPD) were applied to model monthly rainfall data. The deviance statistic and likelihood ratio test (LRT) were used to select the most appropriate model. Model fitting supported stationary GEVD model for Eastern Cape, Gauteng and KwaZulu-Natal. On the other hand, model fitting supported non-stationary GEVD models for maximum monthly rainfall with nonlinear quadratic trend in the location parameter and a linear trend in the scale parameter for Limpopo, while in Mpumalanga the non-stationary GEVD model, which has a nonlinear quadratic trend in the scale parameter and no variation in the location parameter fitted well to the maximum monthly rainfall data. Results from the non-stationary GPD models showed that inclusion of the time covariate in our models was not significant for Eastern Cape, hence the bestfit model was the stationary GPD model. Furthermore, the non-stationary GPD model with a linear trend in the scale parameter provided the best-fit for KwaZulu-Natal and Mpumalanga, while in Gauteng and Limpopo the nonstationary GPD model with a nonlinear quadratic trend in the scale parameter fitted well to the monthly rainfall data. Lastly, GPD with time-varying thresholds was applied to model monthly rainfall excesses, where a penalised regression cubic smoothing spline was used as a time-varying threshold and the GPD model was fitted to cluster maxima. The estimate of the shape parameter showed that the Weibull family of distributions is appropriate in modelling the upper tail of the distribution for Limpopo and Mpumalanga, while for Eastern Cape, Gauteng and KwaZulu-Natal, the exponential family of distributions was found to be appropriate in modelling the upper tail of the distribution. The dissertation contributes positively to the body of knowledge in extreme value theory application to rainfall data and makes recommendations to the government agencies on the long-term rainfall variability and their negative impact on the economy.

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A computerized system for estimating rainfall values was developed for western Virginia using data from 15 weather stations in that area. This system, called VARAINS for VirginiA RAINfall System, is designed to run on a microcomputer. System programs were created using Borland Pascal, version 7. The system models were developed using study station coordinates, elevation, aspect, distance from West Virginia, distance to the Virginia coast, as well as transformations, as independent variables. Additionally, a distance-weighting variable was developed using the inverse distance to each of the 5 closest study stations. The system provides estimates for annual, seasonal, and monthly rainfall at single or multiple ungaged sites. Rainfall models were validated using comparable periods of record from 3 stations within the study zone not previously used to develop study models. VARAINS was demonstrated using 4 sites occurring on the Havens Wildlife Management Area of the Virginia Department of Game and Inland Fisheries. Potential system applications for wildlife management include incorporating system output files into a geographic information system, using Outputs as independent variables in regression analyses of habitat phenomena, and in ecosystem models of interest in endangered species research. Ideas for supplemental research on the model are explored and include testing VARAINS in other areas, evaluating the impact of using fewer years of data in model development, and combining these models with those developed in a recently completed temperature study. / Master of Science