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A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfall–runoff applications around the world. Recent large-sample hydrology studies in North America and Europe have shown the LSTM model to successfully match conceptual model performance at a daily time step over hundreds of catchments. Here we investigate how these models perform in producing monthly runoff predictions in the relatively dry and variable conditions of the Australian continent. The monthly time step matches historic data availability and is also important for future water resources planning; however, it provides significantly smaller training datasets than daily time series. In this study, a continental-scale comparison of monthly deep learning (LSTM) predictions to conceptual rainfall–runoff (WAPABA model) predictions is performed on almost 500 catchments across Australia with performance results aggregated over a variety of catchment sizes, flow conditions, and hydrological record lengths. The study period covers a wet phase followed by a prolonged drought, introducing challenges for making predictions outside of known conditions – challenges that will intensify as climate change progresses. The results show that LSTM models matched or exceeded WAPABA prediction performance for more than two-thirds of the study catchments, the largest performance gains of LSTM versus WAPABA occurred in large catchments, the LSTMs struggled less to generalise than the WAPABA models (e.g. making predictions under new conditions), and catchments with few training observations due to the monthly time step did not demonstrate a clear benefit with either WAPABA or LSTM. [ABSTRACT FROM AUTHOR]

The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydro-meteorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM). This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average. [ABSTRACT FROM AUTHOR]

Peatlands are a distinctive and important component of many upland regions that commonly contain distinctive flora and fauna which are different from those of adjacent forests and grasslands. Peatlands also represent a significant long-term store of organic carbon. While their environmental importance has long since been recognised, water transit times within peatlands are not well understood. This study uses tritium (³H) to estimate the mean transit times of water from peatlands and from adjacent gullies that contain eucalypt forests in the Victorian Alps (Australia). The ³H activities of the peatland water range from 2.7 to 3.3 tritium units (TUs), which overlap the measured (2.9 to 3.0 TU) and expected (2.8 to 3.2 TU) average ³H activities of rainfall in this region. Even accounting for seasonal recharge by rainfall with higher ³H activities, the mean transit times of the peatland waters are < 6.5 years and may be less than 2 years. Water from adjacent eucalypt forest streams has ³H activities of 1.6 to 2.1 TU, implying much longer mean transit times of 5 to 29 years. Cation = Cl and Si = Cl ratios are higher in the eucalypt forest streams than the peatland waters and both of these water stores have higher cation = Cl and Si = Cl ratios than rainfall. The major ion geochemistry reflects the degree of silicate weathering in these catchments that is controlled by both transit times and aquifer lithology. The short transit times imply that, unlike the eucalypt forests, the peatlands do not represent a long-lived store of water for the local river systems. Additionally, the peatlands are susceptible to drying out during drought, which renders them vulnerable to damage by the periodic bushfires that occur in this region. [ABSTRACT FROM AUTHOR]

Headwater streams contribute a significant proportion of the total flow to many river systems, especially during summer low-flow periods. However, despite their importance, the time taken for water to travel through headwater catchments and into the streams (the transit time) is poorly understood. Here, ³H activities of stream water are used to define transit times of water contributing to streams from the upper reaches of the Ovens River in south-east Australia at varying flow conditions. ³H activities of the stream water varied from 1.63 to 2.45 TU, which are below the average ³H activity of modern local rainfall (2.85 to 2.99 TU). The highest ³H activities were recorded following higher winter flows and the lowest ³H activities were recorded at summer low-flow conditions. Variations of major ion concentrations and ³H activities with streamflow imply that different stores of water from within the catchment (e.g. from the soil or regolith) are mobilised during rainfall events rather than there being simple dilution of an older groundwater component by event water. Mean transit times calculated using an exponential-piston flow model range from 4 to 30 years and are higher at summer low-flow conditions. Mean transit times calculated using other flow models (e.g. exponential flow or dispersion) are similar. There are broad correlations between ³H activities and the percentage of rainfall exported from each catchment and between ³H activities and Na and Cl concentrations that allow first-order estimates of mean transit times in adjacent catchments or at different times in these catchments to be made. Water from the upper Ovens River has similar mean transit times to the headwater streams implying there is no significant input of old water from the alluvial gravels. The observation that the water contributing to the headwater streams in the Ovens catchment has a mean transit time of years to decades implies that these streams are buffered against rainfall variations on timescales of a few years. However, impacts of any changes to land use in these catchments may take years to decades to manifest themselves in changes to streamflow or water quality. [ABSTRACT FROM AUTHOR]