Abstract
Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.
Highlights
Flood damage assessment and analysis is a key component of any strategy for flood risk mitigation and management [1,2,3], especially considering the potential consequences of climate change [4], increasing human activities and high-value assets in vulnerable areas [5]
The present study proposes a simple and cost-effective model for large scale flood economic damage quantification and mapping in data-scarce environments that aims to improve the completeness of existing flood risk maps
The approaches that appear the most suitable for the purpose of this study are those based on the use of Digital Elevation Models (DEMs) for simplicity of application and reliability of results [23,24,25,26,27,28]
Summary
Flood damage assessment and analysis is a key component of any strategy for flood risk mitigation and management [1,2,3], especially considering the potential consequences of climate change [4], increasing human activities and high-value assets in vulnerable areas [5]. The high-computational power and number of newly developed algorithms to analyse Big Data have increased significantly (e.g., machine learning techniques) Their aim is to provide cost-effective solutions for large scale flood hazard estimation that are critical to economies and organizations with limited resources. Geographic Information System (GIS) software has increased significantly and has become a consolidated technique for the analysis, visualisation and transparent communication of flood risk worldwide [21] Such advances have increased the range of possibilities for geo-scientists, updating and re-inventing the way highly resource- and data-intensive processes, such as risk mapping and management, are carried out [22]. Several fast-processing methods have been proposed for a preliminary delineation of flood-prone areas using EO information that is readily available These approaches are based on indicators of the geomorphologic, climatic, hydrologic, geologic and land-use characteristics of the basins. The approaches that appear the most suitable for the purpose of this study are those based on the use of Digital Elevation Models (DEMs) for simplicity of application and reliability of results [23,24,25,26,27,28]
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