As urban population is forecast to exceed 60% of the world’s population by 2050, urban growth can be expected. However, research on spatial projections of urban growth at a global scale are limited. We constructed a framework to project global urban growth based on the SLEUTH urban growth model and a database with a resolution of 30 arc-seconds containing urban growth probabilities from 2020 to 2050. Using the historical distribution of the global population from LandScan TM as a proxy for urban land cover, the SLEUTH model was calibrated for the period from 2000 to 2013. This model simulates urban growth using two layers of 50 arc-minutes grids encompassing global urban regions. While varying growth rates are observed in each urban area, the global urban cover is forecast to reach 1.7 × 10 6 km 2 by 2050, which is approximately 1.4 times that of the year 2012. A global urban growth database is essential for future environmental planning and assessments, as well as numerical investigations of future urban climates.

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Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method . The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas. The representative locations selected following the SRR method have sufficient flood data for developing emulation models. Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.

Flood inundation emulation models based on deep neural networks have been developed to overcome the computational burden of two-dimensional (2D) hydrodynamic models. Challenges remain for flat and complex floodplains where many anabranches form during flood events. In this study, we propose a new approach to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. A U-Net-based spatial reduction and reconstruction method (USRR) is used to find representative locations on the floodplain with complex flow paths. The water depths at these locations are simulated using one-dimensional convolutional neural network (1D-CNN) models, which are well-suited to handling multivariate timeseries inputs. The flood surface is then reconstructed using the USRR method and the simulated flood depths at the representative locations. The combined 1D-CNN and USRR method is compared with a previously developed approach based on the long short-term memory recurrent neural network (LSTM) models and a 2D linear interpolation-based SRR method. Compared to the LSTM model, the 1D-CNN model is not only more accurate, but also takes less time to develop. Although both surface reconstruction methods take <1 s to produce an inundation map for a specific point in time, the USRR method is more accurate than the SRR method, leading to an increase of 5.6% in the proportion of correctly detected inundation area. The combination of 1D-CNN and USRR can detect over 95% of the inundated area simulated using a 2D hydrodynamic model but is 98 times faster.