The rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations.

Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

Many urban cities in Southeast Asia are vulnerable to climate change. However, these cities are unable to take effective countermeasures to address vulnerabilities and adaptation due to insufficient data for flood analysis. Two important inputs required in flood analysis are high accuracy Digital Elevation Model (DEM), and long term rainfall record. This paper presents an innovative and cost-effective flood hazard assessment using remote sensing technology and Artificial Neural Network (ANN) to overcome such lack of data. Shuttle Radar Topography Mission (SRTM) and multispectral imagery of Sentinel-2 are used to derive a high-accuracy DEM using ANN. The improvement of SRTM’s DEM is significant with a 42.3% of reduction on Root Mean Square Error (RMSE) which allows the flood modelling to proceed with confidence. The Intensity Duration Frequency (IDF) curves that were constructed from precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) were used in this study. Design storms, calculated from these IDF curves with different return periods were then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Kendal Regency, Indonesia. Flood map scenarios were generated using improved SRTM and design storms of 10-, 50- and 100-year re-turn periods were constructed using the MIKE 21 hydrodynamic model. This novel approach is innovative and cost-effective for flood hazard assessment using remote sensing and ANN to overcome lack of data. The results are useful for policy makers to understand the flood issues and to proceed flood mitigation adaptation/measures in addressing the impacts of climate change.

Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.

The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).