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At weirs and dams, the spillway flow is a high-velocity turbulent motion operating at very-large Reynolds numbers and physical measurements are challenging. In these high-speed free-surface flows, the upstream flow is not aerated and some air entrainment takes place downstream of the inception of free-surface aeration. The state-of-the-art knowledge in large dam spillway hydrodynamics currently reaches a major knowledge gap because of a lack of detailed quantitative field measurements in both the non-aerated and air-water flow regions. In this contribution, a review of prototype observations and measurements is presented based upon optical data sets conducted between 2010 and 2022 during floods at several large dam spillways in eastern Australia, with a focus on the key requirements for successful optical velocity measurements in high-velocity (prototype) spillway flows. The experience and expertise gained in these studies provide new insights into the operation of large dam spillways and their modelling. The intricacy of field measurement techniques is discussed using the experience at dam spillways during large floods, encompassing optical velocity measurements of spillway hydrodynamics at two structures. Overall, the paper presents current challenges on the modelling of large dam spillway flows. • Observations and measurements at large dam spillways are too few. • Prototype observations were successfully gained during 2010-2022 flood events. • A review of field experience and optical velocity measurements was conducted. • Successful optical flow velocity measurements of high-Reynolds-number spillway chutes were achieved. [ABSTRACT FROM AUTHOR]

Studying and quantifying behaviour is important to understand how animals interact with their environments. However, manually extracting and analysing behavioural data from the large volume of camera footage collected is often time consuming. Deep learning techniques have emerged as useful tools in automating the analysis of certain behaviours under controlled or laboratory conditions, but the complexities of using raw footage from the field has resulted in this technology remaining largely unexplored as a possible data analysis alternative for animals in situ. Here, we use deep learning techniques to automate the analysis of fish grazing behaviour from real-world field imagery. We collected video footage in sea grass meadows in Queensland, Australia, and trained models on a training data set of over 3000 annotations. We used a combination of dense optical flow to assess pixel movement in underwater footage, spatiotemporal filtering to increase accuracy, and deep learning algorithms to classify grazing behaviour of luderick, Girella tricuspidata. When tested on novel videos the model had not seen in training, the model correctly identified nearly all individual grazing events. Deep learning shows promise as a viable tool for determining animal behaviour from underwater videos, and with further development offers an alternative to current time-consuming manual methods of data extraction. • Deep learning and dense optical flow can automate the analysis of behaviour. • This technique has a 92% accuracy in detecting grazing behaviour events. • Using spatiotemporal filtering postprocessing significantly increases performance. • Promising results call for further study into using automation of video analysis. [ABSTRACT FROM AUTHOR]

Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting - that is to say, very-short range forecasting (0-6 h). The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space-time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather. The pysteps library supports standard input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic, and neighbourhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, United States, and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters. [ABSTRACT FROM AUTHOR]

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts. [ABSTRACT FROM AUTHOR]