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    The influence of rainfall and river incision on the movement rate of a slow-moving, soft-rock landslide in the Rangitikei, New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Physical Geography at Massey University, Manawatu Campus, Palmerston North, New Zealand
    (Massey University, 2018) Holdsworth, Charlotte Naomi
    The Rangitikei Slide, a slow-moving landslide near Taihape, New Zealand, was monitored to determine the movement patterns and identify the primary movement drivers. The sediment delivery of landslide material to the Rangitikei River was also estimated to inform the sediment yield from slow-moving landslides connected to a fluvial system. RTK-dGPS monitoring, photogrammetry, and pixel tracking of time-lapse imagery was used to categorise movement patterns, and pixel tracking at different temporal resolutions (weekly and hourly) in conjunction with environmental data identified the drivers and classified the influence on movement. The findings aimed to improve the understanding of these landslide types in New Zealand in order to propose more effective management strategies both locally and around the world. It was found that the landslide comprised several blocks exhibiting different movement rates, and that movement was influenced by a seasonal trend likely from groundwater fluctuations increasing pore pressures in the landslide mass. River erosion by the Rangitikei was identified as a key movement driver and has likely influenced movement since landslide initiation. This was supported by historic aerial imagery and photogrammetry, which showed that the landslide has preserved historic movement phases and these indicate fluvial influence. The estimation of sediment contributions found that ~19,000 t/year of sediment is entering the Rangitikei River from the toe, which is considered a conservative estimate. This contribution is substantial; the Rangitikei Slide is producing almost 3,000 times more sediment per kilometre than the non-landsliding areas of the Rangitikei Catchment. Based on these findings, several management options were proposed for the Rangitikei Slide, with recommendations included for managing slow-moving landslides around the world. It was also evident that further research is needed to better understand slow-moving landslides due to the significant hazard they represent in regard to their sediment contribution to the surrounding environment.
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    A stochastic infilling algorithm for spatial-temporal rainfall data : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Statistics at Massey University
    (Massey University, 2005) Munroe, David Russell
    The purpose of this thesis is to develop an infilling algorithm for 24-hour (daily) rainfall data. An infilling algorithm replaces missing data within the historical records with sensible estimates, where any appropriate method (prediction from a fitted model, interpolation between points, or random sampling) could be used to select and/or produce the required estimates. The algorithm developed uses simulation data generated using a stochastic point-process model which has been fitted to historical data. In this thesis, the spatial-temporal Neyman-Scott rectangular pulse model as presented in Cowpertwait et al. (2002) is fitted to data provided by Thames Water from 23 sites in the Thames Valley (UK). The model is shown to fit the data reasonably well; however it fails to fit the proportion of dry sites (which is not used in the fitting process). Nevertheless, simulated data is generated using the model and an infilling algorithm is derived. The algorithm is tested by replacing valid historical data with missing values, infilling these missing values, and then comparing relevant statistics for the two samples. Three algorithms are developed in this thesis, of which the final algorithm maintains the statistical characteristics of the historical data, including the proportion of dry sites, while infilling values that are similar to the known historical record.
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    Estimating hill country rainfall without full data sets for the Manawatu River catchment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Resource and Environmental Planning at Massey University, Turitea, New Zealand
    (Massey University, 2014) Sijbertsma, Jorn Arjan
    Nowadays, people anticipate floods using flood warning systems, and building stop banks and flood ways in place that use flood models generated with hydrological information in their design. Nevertheless, various regions in the world are still hit by floods with catastrophic effects to urban areas, because of a lack of local hydrological knowledge, especially of upstream areas in their catchments. This lack of hydrological knowledge is a result of difficult accessible highly elevated upstream areas, which makes monitoring of hydrological variables difficult or impossible. This thesis examines models for determining montane rainfall using spatial estimation methods and data sets. The distribution and quantity of montane rainfall were assessed by applying five appropriated spatial estimation methods, data of historical and current rain gauges, and a performance measurement. The methodology applied to gain more knowledge about montane rainfall was established with the results of a literature analysis of 40 articles about montane rainfall. This literature analysis revealed that ordinary kriging is the most frequently applied spatial estimation method for montane rainfall, with regression and regression kriging completing the top three of the most applied methods. Also, two other spatial estimation methods, empirical Bayesian kriging and geostatistical simulation, performed well with rainfall data. The same literature analysis disclosed that the root mean square error was predominantly used as a performance measure of spatial estimation methods. The literature analysis revealed a number of data gap-filling techniques, with the inverse distance weighting method and the coefficient of correlation weighting method as the two most suitable techniques. These techniques were applied to complete historical rainfall data sets and their performance was compared within this research. The result showed that the coefficient of correlation weighting method outperformed the inverse distance weighting method in 74% of all data gap estimations, and the coefficient of correlation weighting method was 22% more accurate (based on the overall performance) than the inverse distance weighting method. The most accurate data gap-filling technique, the coefficient of correlation weighting method, was used to complete the historical rain gauges data. The overall ranking of the spatial estimation methods revealed that Gaussian geostatistical simulation performed the best. Regression kriging was the second best spatial estimation method, but there was no significant difference with Gaussian geostatistical simulation. At the same time, the results showed that the best performance of the spatial estimations was accomplished without the maximum number of rain gauges. However, better visual representation of the distinct pattern of rainfall was generated with the historical rain gauges in the second and third experiment of the spatial estimations. Finally, this research discussed the factors that can impact the performance of the spatial estimations. Two of these factors were the removal of ?bad data? and the the strategic placing of rain gauges. The results of this research clearly defined that the removal of ?bad data? increased the accuracy of estimation, while a more even and strategic distribution of rain gauges was suggested to increase the accuracy of the spatial estimation of rainfall.
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    Point process models for diurnal variation rainfall data : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany (Auckland), New Zealand
    (Massey University, 2014) Ismail, Norazlina binti
    The theoretical basis of the point process rainfall models were developed for midlatitude rainfall that have different temporal characteristics from the tropical rainfall. The diurnal cycle, a prominent feature in the tropical rainfall, is not represented in the point process models. An extension of the point process models were developed to address the diurnal variation in rainfall. An observed indicator of the rainfall, X is added to the point process models. Two point process models, Poisson white noise (PWN) and Neyman-Scott white noise (NSWN) model were used as the main rainfall event, Y . The rainfall is modelled assuming two cases for the variable X, independent and dependent. Bernoulli trials with Markov dependence are used for the dependent assumption. To allow the model to display the diurnal variation and correlation between hours, the model was fitted to monthly rain- fall data by using the properties of two hour blocks for each month of the year. However, the main point process models were assumed the same for each of the 12 blocks, thus having only one set of point process parameters for the models for each month. There are 12 rainfall occurrence parameters and 12 Markov dependence parameters, one for each block. A total of six models were fitted to the hourly rainfall data from 1974 to 2008 taken from a rain site in Empangan Genting Klang, Malaysia. The PWN and NSWN models with X were first fitted with the assumption that the rainfall indicators are independent between the hours within the two hour block. Simulation studies showed the model does not fit the moments properties adequently. The models were then modified based on a dependence assumption between the hours within the two hour block. These models are known as the Markov X-PWN and Markov X-NSWN models. Both models improve the fit of the moment properties. However, having only one point process model to represent the rainfall events for Malaysia rainfall data was not sufficient. Since tropical rainfall consists of two types of rain, convective and stratiform, the PWN and Markov X-NSWN model were superposed to represent the two types of rainfall. A simple method by assuming non-homogenous PWN process for every two hour block did not fit well the daily diurnal variation. A comparison between the six models show that the superposed PWN and Markov X-NSWN model improved the fitting of mean, variance and autocorrelation. The superposed model was then simplified to an 8-block model to reduce the number of parameters. This modification to the point process models succeeded in describing the diurnal variation in the rainfall, but some of the models were not able to fit other properties that were not included in the parameter estimation process such as the extreme values.