A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2017 / The main problem to be investigated is how short duration data records can be augmented using existing data from nearby catchments with data with long periods of record. The purpose of the investigation is to establish a method of improving hydrological data using data from a gauged catchment to improve data from an ungauged catchment. The investigation is undertaken using rainfall data for catchments in Lesotho. Marginal distributions describing the annual maximum rainfall for the catchments, and a joint distribution of pairs of catchments were established. The parameters of these distributions were estimated using the Bayesian – Markov Chain Monte Carlo approach, and using both the single-site (univariate) estimation and the two-site (bivariate) estimations. The results of the analyses show that for catchments with data with short periods of record, the precision of the estimated location and scale parameters improved when the estimates were carried out using the two-site (bivariate) method. Rainfall events predicted using bivariate analyses parameters were generally higher than the univariate analyses parameters. From the results, it can be concluded that the two-site approach can be used to improve the precision of the rainfall predictions for catchments with data with short periods of record. This method can be used in practice by hydrologists and design engineers to enhance available data for use in designs and assessments. / CK2018

"The main objective of this study is to examine the influence of global SSTAs [sea surface temperature anomalies] on rainfall over eastern Africa (Fig. 1) using Florida State University T21 Global Spectral Model (FSUT21GSM) during the southern hemisphere summer of 1982 (wet year) and 1983 (dry year) (Fig. 2)"--Leaf 3. / Typescript. / "Summer Semester, 1991." / "Submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: T. N. Krishnamurti, Professor Directing Thesis. / Includes bibliographical references.

Accuracy in estimation of precipitation can be achieved by utilizing the combination of spatial radar reflectivity data (Z) and the high resolution temporal rain gage based rainfall data (R). The study proposes the use of optimization models for optimizing the Z-R coefficients and exponents for different storm types and seasons. Precipitation data based on reflectivity, collected from National Climatic Data Center (NCDC) and rain gage data from Southwest Florida Water Management District (SWFWMD) over same temporal resolutions were analyzed using the Rain-Radar- Retrieval (R3) system developed as a part of the study. Optimization formulations are proposed to obtain optimal coefficients and exponents in the Z-R relationships for different seasons and objective selection of storm-type specific Z-R relationships. Different approaches in selection of rain gage stations and selection of events for optimization are proposed using gradient based solver and genetic algorithms. / Kandarp Pattani. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.\

The Asian monsoon-affected area is one of the most vulnerable regions in the world facing hydroclimate changes. Anthropogenic climate change, particularly the emissions of greenhouse gases (GHGs) and aerosols, exerts significant impacts on monsoon rainfall and circulation. Understanding the effects of external forcing on monsoon rainfall is essential for improving the predictability, constraining the uncertainty, and assessing the climate risks. In this dissertation, I use a combination of observations, outputs from multiple Coupled Model Intercomparison Project - Phase 5 (CMIP5) models, and idealized atmospheric general circulation model (AGCM) experiments to examine the Asian summer monsoon variability and change. The main focus is understanding the responses to GHGs and anthropogenic aerosols and their differences for both the historical period and future projections. The Asian monsoon is an interactive system influenced by multiple natural and anthropogenic factors. GHGs and aerosols induce significantly different changes in monsoon rainfall through both thermodynamical and dynamical processes. These changes can be further separated into the fast adjustments related to radiation and cloud processes and the slow response due to changes in sea surface temperature (SST). This thesis provides a detailed analysis of the multiple physical processes entangled in the total response, advancing our mechanistic understanding of the effects of external forcing on the Asian monsoon system and the associated uncertainties. In Chapter 2, I first analyze the monsoon-ENSO (El Nino - Southern Oscillation) relationship in observations and CMIP5 models to determine the role of natural variability. Separating the natural and forced components shows that natural variability plays a dominant role in the 20th century, however enhanced monsoon rainfall associated with global warming may contribute to a weakened ENSO-monsoon relation in the 21st century. In Chapter 3, I examine the physical mechanisms causing the changes of the Asian summer monsoon during the 20th and 21st century using observations and CMIP5 models, attributing the rainfall changes to the relative roles of thermodynamic and dynamic processes. CMIP5 models show a distinct drying of the Asian summer monsoon rainfall during the historical period but strong wetting for future projections, which can be explained by the strong aerosol-induced dynamical weakening during the 20th century and the thermodynamic enhancement due to GHGs in the 21st century. In Chapters 4 and 5, I further use multiple AGCMs to separate the total monsoon response into a fast adjustment component independent of the sea surface temperature (SST) responses, and a slow response component associated with SST feedbacks. For GHGs (Chapter 4), the fast and slow monsoon circulation changes largely oppose each other, leading to an overall weak response and large inter-model spread. For aerosols (Chapter 5), the strongly weakened monsoon circulation over land due to aerosols is largely driven by the fast adjustments related to aerosol-radiation and aerosol-cloud interactions. Finally in Chapter 6, I design idealized AGCM experiments with prescribed SSTs using the Community Atmosphere Model (CAM5) and the Geophysical Fluid Dynamic Laboratory Model (GFDL-AM3) to investigate the relative roles of uniform SST warming/cooling as well as global and regional SST patterns in shaping the differing monsoon responses. While GHGs-induced SST changes affect the monsoon largely via the uniform warming effect, for aerosols the SST spatial pattern plays the dominant role through changes in atmospheric circulation.

As a global climate phenomenon, the El Ni??o-Southern Oscillation (ENSO) involves the coupling of the ocean and the atmosphere. Most climate prediction studies have, by far, only investigated the teleconnections between global climatic anomalies and the ???surface??? predictors of ENSO. The prediction models resulting from these studies have generally suffered from inadequate, if not the lack of, skill across the so-called boreal ???spring barrier???. This is illustrated in the first part of this thesis where the applicability of the SOI phase for long-lead rainfall projections in Australia is discussed. With the increasing availability of subsurface ocean temperature data, the characteristics of the Pacific Ocean???s heat content and its role in ENSO are now better understood. The second part of this thesis investigated the predictability of ENSO using the thermocline as a predictor. While the persistence and SST-based ENSO hindcasts dropped in skill across the spring barrier, the thermocline-based hindcasts remained skillful even up to a lag of eighteen months. Continuing on the favorable results of ENSO prediction, the third part of this thesis extended the use of the thermocline in the prediction of Australia???s rainfall and streamflow. When compared to models that use ???surface??? predictors, the model that incorporated thermocline information resulted in more skillful projections of rainfall and streamflow especially at long lead-times. More importantly, significant increases in skill of autumn and winter projections demonstrate the ability of the subsurface ocean to retain some climatic memory across the predictability barrier. This resilience can be attributed to the high persistence of the ocean heat content during the first half of the year. Based on weighting, the model averaging exercise also affirmed the superiority of the ???subsurface??? model over the ???surface??? models in terms of streamflow projections. The encouraging findings of this study could have far-reaching implications not only to the science of ENSO prediction but also to the more pragmatic realm of hydrologic forecasting. What this study has demonstrated is an alternative predictor that is suitable for the long range forecasting of ENSO, rainfall and streamflow. With better hydrologic forecasting comes significant improvement in the management of reservoirs which eventually leads to an increase in the reliability and sufficiency of water supply provision.

[Truncated abstract] Generally hydrologic and ecologic models operate on arbitrary time and space scales, selected by the model developer or user based on the availability of field data. In reality rainfall is highly variable not only annually, seasonally and monthly but also the intensities within a rainfall event and infiltration properties on semi-arid hillslopes can also be highly variable as a result of discontinuous vegetation cover that form mosaics of areas with vegetation and areas of bare soil. This thesis is directed at improving our understanding of the impacts of the temporal representation of rainfall and spatial heterogeneity on model predictions of hydrologic thresholds and surface runoff coefficients on semi-arid landscapes at the point and hillslope scales. We firstly quantified within storm rainfall variability across a climate gradient in Western Australia by parameterizing the bounded random cascade rainfall model with one minute rainfall from 15 locations across Western Australia. This study revealed that rainfall activity generated in the tropics had more within storm variability and a larger proportion of the storm events received the majority of rain in the first half of the event. Rainfall generated from fontal activity in the south was less variable and more evenly distributed throughout the event. Parameters from the rainfall analysis were then used as inputs into a conceptual point scale surface runoff model to investigate the sensitivity of point scale surface runoff thresholds to the resolution of rainfall inputs. This study related maximum infiltration capacities to average storm intensities (k*) and showed where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k* = 0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k* > 2). For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g* <2). The sensitivity of surface runoff predictions and the influence of specific within storm properties were then analysed on the hillslope scale. '...' It was found that using the flow model we still get threshold behaviour in surface runoff. Where conditions produce slow surface runoff velocities, spatial heterogeneity and temporal heterogeneity influences hillslope surface runoff amounts. Where conditions create higher surface runoff velocities, the temporal structure of within storm intensities has a larger influence on runoff amounts than spatial heterogeneity. Our results show that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions. The results of this study can be considered a contribution to understanding the way within storm properties effect the processes on the hillslope under a range of overall storm, slope and infiltration conditions as well as an improved understanding of how different vegetation patterns function to trap runoff at different total vegetation covers and rainfall intensities.

This thesis presents the development and application of a downscaling framework for multi site simulation of daily rainfall. The rainfall simulation is achieved in two stages. First, rainfall occurrences at multiple sites are downscaled, which is followed by the generation of daily rainfall amounts at each site identified as wet. A continuous weather state based nonparametric downscaling model conditional on atmospheric predictors and a previous day average rainfall state is developed for simulation of multi site rainfall occurrences. A nonparametric kernel density approach is used for simulation of rainfall amounts at individual sites conditional on atmospheric variables and the previous day rainfall amount. The proposed model maintains spatial correlation of rainfall occurrences by simulating concurrently at all stations and of amounts by using random innovations that are spatially correlated yet serially independent. Temporal dependence is reproduced in the occurrence series by conditioning on previous day average wetness fraction and assuming the weather states to be Markovian, and in the amount series by conditioning on the previous day rainfall amount. The seasonal transition is maintained by simulating rainfall on a day-to-day basis using a moving window formulation. The developed downscaling framework is calibrated using the relevant atmospheric variables and rainfall records of 30 stations around Sydney, Australia. Results indicate a better representation of the spatio-temporal structure of the observed rainfall as compared to existing alternatives. Subsequently, the framework is applied to predict plausible changes in rainfall in warmer conditions using the same set of atmospheric variables for future climate obtained as a General Circulation Model simulation. While the case studies presented are restricted to a specific region, the downscaling model is designed to be useful in any generic catchment modelling and management activity and/or for investigating possible changes that might be experienced by hydrological, agricultural and ecological systems in future climates.

Thesis (M.S.)--University of North Carolina at Wilmington, 2003. / Includes bibliographical references (leaves : [56]-59).

Thesis (M.S.)--University of North Carolina at Wilmington, 2003. / Includes bibliographical references (leaves : [41]-45).

published_or_final_version / Civil Engineering / Master / Master of Philosophy