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Accurate information on rainfall is required for developing modelling tools and for the design and investigation of existing and planned infrastructure. More specifically, a long time series of spatially and temporally consistent high-resolution rainfall datasets are necessary for many hydro-meteorological applications, such as understanding the variation of extreme rainfall across space and time, modelling impactful weather events such as floods, assessing hydrological risks under climate variability and change, and forecasting agricultural water needs in the short and medium terms. However, such datasets are generally not available due to the sparse and variable nature of gauging networks. Global and regional reanalyses are alternative sources of precipitation data with consistent spatial and temporal resolution and coverage. A regional reanalysis, nested in a global reanalysis, typically assimilates more local observations using a higher resolution model to improve the representation of local climate features and extreme events in the global reanalysis. BARRA-R (The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia) is one such example for Australia. Rainfall observations are not assimilated in this reanalysis, and precipitation is estimated by model physics and parameterisation. As such, a comprehensive examination of the dataset is essential to ascertain its utility across various hydrological applications. The objective of this thesis is to assess the reanalysis rainfall data for its use in catchment hydrology applications. This research has been investigated in two stages. First, it examines whether estimates of rainfalls produced via an atmospheric reanalysis is representative of historical rainfall behaviour at daily and sub-daily scales. Second, it explores ways in which the dataset can be used in hydrological modelling and engineering design. A range of reference datasets at different temporal and spatial scales and asses

This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large-scale changes, such as significant changes in land use. Rating curve uncertainty estimates are provided for most (75 %) of the catchments, and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.org/10.1594/PANGAEA.921850 (Fowler et al., 2020a)., The authors acknowledge the Bureau of Meteorology, Australia, for their support and permission to undertake this project on the Hydrologic Reference Stations. The authors acknowledge the excellent work of Janet Stein and co-authors (Stein et al., 2011), to whom this dataset owes more than half the listed catchment attributes. Keirnan Fowler acknowledges funding from the Australian Research Council (grant no. LP170100598) and the Bureau of Meteorology (grant no. TP705654) during the period of preparation of this paper. Keirnan Fowler appreciates the support of Gemma Coxon (University of Bristol), who encouraged his foray into large-sample hydrology. Nans Addor acknowledges support from the Swiss National Science Foundation (fellowship no. P400P2_180791). The authors acknowledge Jan Seibert and the two anonymous reviewers, whose feedback significantly improved the manuscript., Peer Reviewed, Postprint (published version)