No abstract

Although China suffers from frequent and disastrous floods, the spatiotemporal pattern of its population living in the floodplain (PopF) is still unknown. This strongly limits our understanding of flood risk and the effectiveness of mitigation efforts. Here we present the first quantification of Chinese PopF and its dynamics, based on newly-available population datasets for years 1990, 2000, 2010, and 2015 and on a flood map. We found that the PopF in 2015 was 453.3 million and accounted for 33.0% of the total population, with a population density 3.6 times higher than outside floodplains. From 1990 to 2015, the PopF increased by 1.3% annually, overwhelmingly faster than elsewhere (0.5%). A rising proportion (from 53.2% in 1990 to 55.6% in 2015) of the PopF resided in flood zones deeper than 2 m. Moreover, the PopF is expected to increase rapidly in the coming decades. We also found the effect of flood memory on controlling PopF growth and its decay over time. These findings imply an exacerbating flood risk in China, which is concerning in the light of climate change and rapid socioeconomic development.

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

Abstract. Urbanization and climate change are critical challenges in the 21st century. Flooding by extreme weather events and human activities can lead to catastrophic impacts in fast-urbanizing areas. However, high uncertainty in climate change and future urban growth limit the ability of cities to adapt to flood risk. This study presents a multi-scenario risk assessment method that couples a future land use simulation (FLUS) model and floodplain inundation model (LISFLOOD-FP) to simulate and evaluate the impacts of future urban growth scenarios with flooding under climate change (two representative concentration pathways (RCP2.6 and RCP8.5)). By taking the coastal city of Shanghai as an example, we then quantify the role of urban planning policies in future urban development to compare urban development under multiple policy scenarios (business as usual, growth as planned, growth as eco-constraints). Geospatial databases related to anthropogenic flood protection facilities, land subsidence and storm surge are developed and used as inputs to the LISFLOOD-FP model to estimate flood risk under various urbanization and climate change scenarios. The results show that urban growth under the three scenario models manifests significant differences in expansion trajectories, influenced by key factors such as infrastructure development and policy constraints. Comparing the urban inundation results for the RCP2.6 and RCP8.5 scenarios, the urban inundation area under the growth-as-eco-constraints scenario is less than that under the business-as-usual scenario but more than that under the growth-as-planned scenario. We also find that urbanization tends to expand more towards flood-prone areas under the restriction of ecological environment protection. The increasing flood risk information determined by model simulations helps us to understand the spatial distribution of future flood-prone urban areas and promote the re-formulation of urban planning in high-risk locations.

Abstract. Urbanization and climate change are the critical challenges in the 21st century. Flooding by extreme weather events and human activities can lead to catastrophic impacts in fast-urbanizing areas. However, high uncertainty in climate change and future urban growth limit the ability of cities to adapt to flood risk. This study presents a multi-scenario risk assessment method that couples the future land use simulation model (FLUS) and floodplain inundation model (LISFLOOD-FP) to simulate and evaluate the impacts of future urban growth scenarios with flooding under climate change (two representative concentration pathways (RCPs 2.6 and 8.5)). By taking Shanghai coastal city as an example, we then quantify the role of urban planning policies in future urban development to compare urban development under multiple policy scenarios (Business as usual, BU; Growth as planned, GP; Growth as eco-constraints, GE). Geospatial databases related to anthropogenic flood protection facilities, land subsidence, and storm surge are developed and used as inputs to the LISFLOOD-FP model to estimate flood risk under various urbanization and climate change scenarios. The results show that urban growth under the three scenario models manifests significant differences in expansion trajectories, influenced by key factors such as infrastructure development and policy constraints. Comparing the urban inundation results for the RCP2.6 and RCP8.5 scenarios, the urban inundation area under the GE scenario is less than that under the BU scenario, but more than that under the GP scenario. We also find that urban will tend to expand to areas vulnerable to floods under the restriction of ecological environment protection. The increasing flood risk information determined by the coupling model helps to understand the spatial distribution of future flood-prone urban areas and promote the re-formulation of urban planning in high-risk locations.

No abstract

Human proximity to water and its dynamics are important to understanding the socio-hydrological dilemma between using floodplains and avoiding flood risk. However, previous studies have not distinguished between the water proximity of built-up land in floodplains (BLF) and the water proximity of built-up land outside floodplains (BLOF). This article provides a new and spatiotemporal perspective of the BLF water proximities for understanding the changing flood risk in China. The results show that China’s BLFs had an average water proximity of 5.41 km in 2014. Most of the BLFs (62%, 25.88×103 km2) were located within 3 km of waterbodies. From 1990 to 2014, China’s BLFs increased rapidly by 81% from 23.06×103 km2 to 41.74×103 km2, of which a large portion (57%) was concentrated in water surroundings (≤3 km), shortening the distance between BLFs and waterbodies by 169 m. The BLF growth concentrated in water surroundings even in areas where BLFs have an overall increasing distance from waterbodies. Both the increases in the BLFs and their proximity to waterbodies can increase flood exposure and exacerbate flood risk. The scientific community and policymakers should pay attention not only to the volume of BLF growth, but also its spatial relationship with waterbodies.