1. The biography of Crawford Munro (1904-76) describes his early life in Toowoomba and Sydney, and his maturation as an engineer, working for Sydney Water, Sydney Technical College and in the production of Cruiser tanks in World War II. He was a large confident man with a big voice and an optimistic, humorous personality. As the Foundation Professor of Civil Engineering at the University of New South Wales Munro was liberal, fostered humanist studies and developed the School of Engineering with a unique emphasis on water engineering. He recruited excellent staff for research and postgraduate education who led the nation across all phases of hydrology and hydraulics. Munro developed a remarkable, rational solution for flood mitigation at Launceston, and actively promoted research, partly through the Australian Water Research Foundation and the Institution of Engineers, Australia. He was much involved with predicting flood runoff, developing benefit/cost relations for irrigation schemes, which led him into public controversy, and other hydrological projects. Munro’s attempts to raise social consciousness about water problems, his multi-disciplinary approach to the evaluation of water resources and his campaigns for the collection of stream and rainfall data helped provide a better basis for proper planning. In his later years he undertook the first Australian environmental impact study. The concluding chapter outlines a vision for the current management of Australia’s water. Munro posed necessary questions about measuring the supply of water and bringing the demand of water into synchrony with its supply, while providing water security in terms of its availability and quality. He raised the debate about the balance between sustaining environmental flows, utilizing water for agriculture and secondary industry, and maintaining the health of communities. Munro hoped equitable decision making would emerge from public engagement on these issues. 2. Twentieth century science biography in Australia is the province of a group of elite male scientists, whose interests cover wide disciplinary fields; it is focused on popular imagination: health, food and adventure (Antarctica) accounting for fifteen of the seventeen scientists. Empathy for the subject is a significant feature of the nineteen biographers, of whom five are scientists. This small genre is often supported by institutions in small print runs. A key role of biography is to place through science history a more epistemologically plausible version of events. Public discourses of science treated in the essay include conflict about the attribution of scientific discovery, the vocation of the scientist as a contributor to a wider social polity, the light biography sheds on sources of creativity and the evolution of the research and culture of institutions. The biographer attempts to generate a personal portrait of the scientist which conveys authority about the significance and origins of his or her scientific discoveries and their impact in the wider social context. Daniel Söderqvist’s affirmation of the existential approach which ‘emphasizes the freedom and responsibility of the human individual’ resonates with the candidate as expressing characteristics of the lives of many Australian scientists in their passion for intellectual discovery, their motivation to self-empowerment, and their readiness top step outside their social conditioning. This essay extends Söderqvist’s paradigm to the context of Australian science biography and indicates some constraints on its depiction which arise in the practice of writing science biography. Some epistemological issues are raised in the texts, especially when dealing with oral history and family mythology, and thematic, thematic within a chronological framework or chronological structures of the text are compared. The level of detail and context influence the sustainability of the reader’s interest. Case studies of the biographies written by the candidate (Ian Clunies Ross, Samuel Wadham, Allan Callaghan, Victor Trikojus, Raymond Hoffenberg and Crawford Munro) illustrate issues which arise in the writing of science biography. The dominant question is the relationship of the biographer to the subject, and this determines the voice the reader hears. The motivation of the biographer may arise in varying degrees of empathy felt for the subject. The high affinity the candidate had for Clunies Ross and Hoffenberg causes him to offer a defence against the charge of hagiography, and the selectivity and subjectivity of the biographer is evident in the arrangement and presentation of factual material. The motivation of the biographer is additionally directed to the communication of the subject’s research outputs to the wider Australian community, and in the case of Callaghan, Wadham and Clunies Ross there was a specific programmatic function of advancing the status of agricultural science. It is argued that the description of the public life of the subject needs to be complemented from the private life if the biographer is ‘to view the world through the eyes of the subject’.

<p>Streamflow prediction in ungauged basins (PUB) is a process generating streamflow time series at ungauged reaches in a river network. PUB is essential for facilitating various engineering tasks such as managing stormwater, water resources, and water-related environmental impacts. Machine Learning (ML) has emerged as a powerful tool for PUB using its generalization process to capture the streamflow generation processes from hydrological datasets (observations). ML’s generalization process is impacted by two major components: data splitting process of observations and the architecture design. To unveil the potential limitations of ML’s generalization process, this dissertation explores its robustness and associated uncertainty. More precisely, this dissertation has three objectives: (1) analyzing the potential uncertainty caused by the data splitting process for ML modeling, (2) investigating the improvement of ML models’ performance by incorporating hydrological processes within their architectures, and (3) identifying the potential biases in ML’s generalization process regarding the trend and periodicity of streamflow simulations.</p><p>The first objective of this dissertation is to assess the sensitivity and uncertainty caused by the regular data splitting process for ML modeling. The regular data splitting process in ML was initially designed for homogeneous and stationary datasets, but it may not be suitable for hydrological datasets in the context of PUB studies. Hydrological datasets usually consist of data collected from diverse watersheds with distinct streamflow generation regimes influenced by varying meteorological forcing and watershed characteristics. To address the potential inconsistency in the data splitting process, multiple data splitting scenarios are generated using the Monte Carlo method. The scenario with random data splitting results accounts for frequent covariate shift and tends to add uncertainty and biases to ML’s generalization process. The findings in this objective suggest the importance of avoiding the covariate shift during the data splitting process when developing ML models for PUB to enhance the robustness and reliability of ML’s performance.</p><p>The second objective of this dissertation is to investigate the improvement of ML models’ performance brought by Physics-Guided Architecture (PGA), which incorporates ML with the rainfall abstraction process. PGA is a theory-guided machine learning framework integrating conceptual tutors (CTs) with ML models. In this study, CTs correspond to rainfall abstractions estimated by Green-Ampt (GA) and SCS-CN models. Integrating the GA model’s CTs, which involves information on dynamic soil properties, into PGA models leads to better performance than a regular ML model. On the contrary, PGA models integrating the SCS-CN model's CTs yield no significant improvement of ML model’s performance. The results of this objective demonstrate that the ML’s generalization process can be improved by incorporating CTs involving dynamic soil properties.</p><p>The third objective of this dissertation is to explore the limitations of ML’s generalization process in capturing trend and periodicity for streamflow simulations. Trend and periodicity are essential components of streamflow time series, representing the long-term correlations and periodic patterns, respectively. When the ML models generate streamflow simulations, they tend to have relatively strong long-term periodic components, such as yearly and multiyear periodic patterns. In addition, compared to the observed streamflow data, the ML models display relatively weak short-term periodic components, such as daily and weekly periodic patterns. As a result, the ML’s generalization process may struggle to capture the short-term periodic patterns in the streamflow simulations. The biases in ML’s generalization process emphasize the demands for external knowledge to improve the representation of the short-term periodic components in simulating streamflow.</p>

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Indiana University-Purdue University Indianapolis (IUPUI) / The transport of excess nutrients, sediment, and other contaminants to surface waters has been shown to cause a number of environmental and human health concerns. An understanding of the export pathways that these contaminants follow to surrounding water bodies is crucial to the anticipation and management of peak concentration events. Several studies have demonstrated that the majority of annual contaminant loading in the Midwest occurs during periods of elevated discharge. However, many studies use a limited number of sampling points to determine concentration patterns, loadings, and fluxes which decreases accuracy. Through high-resolution storm sampling conducted in a 2945 km2 (1137 mi2) area of central Indiana’s Upper White River Watershed, this research has documented the complex concentration signals and fluxes associated with a suite of cations, nutrients, and contaminants and isolated their primary transport pathways. Additionally, by comparing the results of similar studies conducted on smaller areas within this watershed, differences in concentration patterns and fluxes, as they relate to drainage area, have also been documented. Similar to the results of previous studies, NO3- concentrations lacked a well-defined relationship relative to discharge and was attributed to primarily subsurface contribution. DOC was exported along a shallow, lateral subsurface pathway, TP and TSS via overland flow, and TKN through a combination of both. Near or in-channel scouring of sediment increased DOC, TKN, TP, and TSS concentrations during Storm 2. Atrazine export was attributed to a combination of overland and subsurface pathways. 2-MIB and geosmin derived from different sources and pathways despite being produced by similar organisms. 2-MIB concentration patterns were characterized by dilution of an in-stream source during Storm 1 and potential sediment export during Storm 2 while in-stream concentrations or a sediment source of geosmin was rapidly exhausted during Storm 1. Many of the concentration patterns were subject to an exaggerated averaging effect due to the mixing of several larger watersheds, especially during Storm 1. This research illustrates the need for high-frequency sampling to accurately quantify contaminant loads for total maximum daily load (TMDL) values, developing best management practices (BMPs), and confronting the challenges associated with modeling increasingly larger-scale watersheds.

<p>  </p> <p>The management of water resources in East Africa is inherently challenged by rainfall variability and the uneven spatial distribution of freshwater resources. In addition to these issues, meteorological and water data collection has been inconsistent over the past decades, and unclearly defined purposes or end goals for collected data have left many datasets ineffectively curated. In light of the data intensiveness of current modelling and planning methods, data scarcity and inaccessibility have become substantial impediments to informed decision-making. Among the outputs of this research are 1) a revised technique for evaluating bias correction performance on reanalysis data for use in regions where precipitation data is temporally discontinuous which can potentially be applied to other types of climate data as well, 2) a new methodology for quantifying qualitative information contained in legislation and official documents and websites for the assessment of relationships between documented meteorological and water data policies and resulting outcomes in terms of data availability and accessibility, and 3) a fresh look at data needs and the value data holds with respect to water resources decision-making and management in the region.</p>

<p> The hydrological cycle is affected by land use variability. Land use spatial and temporal variability has the power to alter watershed runoff, water resource quantity and quality, ecosystems, and environmental sustainability. In recent decades, agriculture lands, pastures, plantations, and urban areas have increased, resulting in significant increases in energy, water, and fertilizer usage, as well as significant biodiversity losses. </p>

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