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Traditional flood inundation modelling methods are computationally expensive and not suitable for near-real time inundation prediction. In this study we explore a data-driven machine learning method to complement and, in some cases, replace existing methods. Given sufficient training data and model capacity, our design enables a single neural network instance to approximate the flow characteristics of any input region, opening the possibility of applying the model to regions without available training data. To demonstrate the method we apply it to a very large >8000 km2 region of the Fitzroy river basin in Western Australia with a spatial resolution of 30 m × 30 m, placing an emphasis on efficiency and scalability. In this work we identify and address a range of practical limitations, e.g., we develop a novel water height regression method and cost function to address extreme class imbalances and by carefully constructing the input data, we introduce some natural physical constraints. Furthermore, a compact neural network design and training method was developed to enable the training problem to fit within GPU memory constraints and a novel dataset was constructed from the output of a calibrated two-dimensional hydrodynamic model. A good correlation between the predicted and groundtruth water heights was observed. [ABSTRACT FROM AUTHOR]

Expanding urban impervious surface area (ISA) mapping is crucial to sustainable development, urban planning, and environmental studies. Multispectral ISA mapping is challenging because of the mixed-pixel problems with bare soil. This study presents a novel approach using spectral and temporal information to develop a Soil-Suppressed Impervious Surface Area Index (SISAI) using the Landsat Operational Land Imager (OLI) data set, which reduces the soil but enhances the ISA signature. This study mapped the top 12 populated megacities using SISAI and achieved an overall accuracy of 0.87 with an F1-score of 0.85. It also achieved a higher Spatial Dissimilarity Index between the ISA and bare soil. However, it is limited by bare gray soil and shadows of clouds and hills. SISAI encourages urban dynamics and inter-urban comparison studies owing to its automatic and unsupervised methodology. [ABSTRACT FROM AUTHOR]

The polders and islands in the salt-affected coastal zone of the Ganges Delta, home to approximately 15 million people in Bangladesh and 5 million in West Bengal, face challenges such as poverty, food insecurity, environmental vulnerability, and limited livelihood opportunities. Historically, agricultural production in these areas was mainly confined to the wet/Kharif season, with much of the land remaining fallow during the dry/Rabi season due to waterlogging, soil salinity, and perceived lack of irrigation water. Since 2015, researchers and NGOs from Australia, Bangladesh, and India collaborated on the project "Cropping system intensification in the salt-affected coastal zones of Bangladesh and West Bengal, India" to sustainably increase cropping intensity and productivity in this area. During 2015-2021, the project has made significant achievements and has demonstrated strategies that are likely to increase productivity, cropping intensity, and livelihoods of the people. However, to maintain these gains and address risks like climate change impacts, untimely rainfall, and market fluctuations, further research was deemed necessary. Thus, ACIAR funded Phase 2 of the project for 2021-2025 which aims to sustain and enhance the achievements. This special issue presents some research findings on projected climate change, agronomic experiments, irrigation and drainage management, and social and economic issues conducted under the project Phase 2 in Bangladesh and West Bengal, India. [ABSTRACT FROM AUTHOR]