Flood modelling can provide useful information to support flood risk assessment and management. The accuracy of flood simulation results is highly dependent on the quality of input data. In particular, digital elevation models (DEMs) may directly influence the performance of flood predictions and improper representation of complex urban features including buildings and bridges may lead to incorrect prediction of flooding paths and extents, and consequently miscalculate flood risk. In this work, a geographic information system (GIS)-based correction method is proposed to make modifications in high-resolution DEMs by adding building complexes and removing unphysical representations of bridges for a more realistic description of flood paths in considering the flow connectivity in intensely urbanized areas and with the objective of obtaining more accurate flood simulation results. The proposed DEM correction method is applied to support large-scale urban flood modelling in Fuzhou City, China, using an established hydrodynamic flood model known as High-Performance Integrated hydrodynamic Modelling System (HiPIMS). Comparisons are made to the simulation results with and without the DEM improvements using the proposed correction method. The results demonstrate that correct representation of the artificial structures in the urban DEM can significantly improve the flood simulation results.

Due to the projected increased frequency of occurrence of extreme flood events, it is becoming increasingly important to pay attention to agricultural flood management. The middle and lower reaches of the Yangtze River Basin (MLYRB), as one of the most important agricultural areas in the world, frequently suffer from the ravages of long-duration extreme flood events. Comprehensive flood risk evaluation can provide important support for effective management strategies by focusing on the combination of flood hazard and the consequences of flooding in areas exposed to the inundation. Previous satellite-based flood disturbance detection methods intended for use in single-cropping agricultural systems cannot be applied to the MLYRB with multi-cropping practices and long-duration flood events. Additionally, comprehensive agricultural flood risk evaluations traditionally neglect the characteristics of the impact of flooding with strong spatial and temporal variability. Thus, in this research, an integrated disturbance index (IDI) was developed to detect the impact of flood disturbance on crop growth, aiming to acquire a map of crop damage condition for a multi-cropping agricultural system with long-duration flood events that is spatially explicit and has a sufficiently high spatial resolution. A coupled hydrological and 2D hydraulic model parallelized using the GPU approach was employed to simulate flood flows, aiming at deriving sufficient meaningful detail at the local scale in terms of flood inundation patterns and processes over the whole natural watershed. Additionally, a spatial map of the combined effects of flood hazard and the consequences of flooding was used to investigate the relationship between flood characteristics and associated loss extent with the random forest model. The comprehensive evaluation framework was applied for the 2010 flood event in the MLYRB. The evaluation results indicate that the detection results based on IDI are consistent with the governmental statistics, the most hard-hit areas in related reports, and the spatial characteristics of river floods. The coupled hydrological–hydraulic model offers a clear picture of the flood characteristics over the whole basin, while simultaneously ensuring a sufficiently high spatial resolution. Our findings show that flood duration is the most important predictor in predicting crop damage extent.

Increasing resilience to natural hazards and climate change is critical for achieving many Sustainable Development Goals (SDGs). In recent decades, China has experienced rapid economic development and became the second-largest economy in the world. This rapid economic expansion has led to large-scale changes in terrestrial (e.g., land use and land cover changes), aquatic (e.g., construction of reservoirs and artificial wetlands) and marine (e.g., land reclamation) environments across the country. Together with climate change, these changes may significantly influence flood risk and, in turn, compromise SDG achievements. The Luanhe River Basin (LRB) is one of the most afforested basins in North China and has undergone significant urbanisation and land use change since the 1950s. However, basin-wide flood risk assessment under different development scenarios has not been considered, although this is critically important to inform policy-making to manage the synergies and trade-offs between the SDGs and support long-term sustainable development. Using mainly open data, this paper introduces a new framework for systematically assessing flood risk under different social and economic development scenarios. A series of model simulations are performed to investigate the flood risk under different land use change scenarios projected to 2030 to reflect different development strategies. The results are systematically analysed and compared with the baseline simulation based on the current land use and climate conditions. Further investigations are also provided to consider the impact of climate change and the construction of dams and reservoirs. The results potentially provide important guidance to inform future development strategies to maximise the synergies and minimise the trade-offs between various SDGs in LRB.

Urban flooding has become one of the most common natural hazards threatening people’s lives and assets globally due to climate change and rapid urbanization. Hydraulic structures, e.g., sluicegates and pumping stations, can directly influence flooding processes and should be represented in flood modeling and risk assessment. This study aims to present a robust numerical model by incorporating a hydraulic structure simulation module to accurately predict the highly transient flood hydrodynamics interrupted by the operation of hydraulic structures to support object-level risk assessment. Source-term and flux-term coupling approaches are applied and implemented to represent different types of hydraulic structures in the model. For hydraulic structures such as a sluicegate, the flux-term coupling approach may lead to more accurate results, as indicated by the calculated values of NSE and RMSE for different test cases. The model is further applied to predict different design flood scenarios with rainfall inputs created using Intensity-Duration-Frequency relationships, Chicago Design Storm, and surveyed data. The simulation results are combined with established vehicle instability formulas and depth-damage curves to assess the flood impact on individual objects in an urbanized case study area in Zhejiang Province, China.