Water-Related Disasters (WRD), such as cyclones, floods, and droughts, account for 90% of natural disasters. Since the year 2000, over 5300 WRD have been reported, with over 325,000 fatalities and an economic loss exceeding USD 1.7 trillion globally [1
]. Floods account for approximately 54% of all WRD [2
]. Since the beginning of 2020, in South Asia alone, floods impacted over 17.5 M people, caused over 1000 deaths, and an economic loss of billions of dollars [3
Predicting the flood inundation extent of intense and extreme weather events is of critical importance [4
]. The extent of flooding is defined by how the river channel overflows and spreads across the surrounding topography [5
]; this overflow is a complex function of intense precipitation and generation of runoff and its accumulation and flows in streams [6
]. Models are available with varying complexities to compute the extent of flooding for cross-sections (1D) [7
], cells (2D) [8
], and planes (3D) [9
]. However, calibrating and running these complex models to map flood inundation extents at the national level is a resource and time-intensive exercise [10
]. In Canada, for example, it is expected to take one decade and USD 350 M to update national flood inundation extent maps [11
]. In addition, the availability of flood inundation extent and flood risk maps in most developing countries are limited, and existing those are out-of-date and have poor temporal and spatial resolution [12
A global survey of Flood Early Warning Systems (FEWS) conducted by the United Nations University Institute for Water Environment and Health (UNU-INWEH) shows that the majority of flood forecasting centers in flood-prone countries lack the ability to run complex flood forecasting models to improve the spatial coverage of FEWS and generate flood inundation extents [13
Two recent developments in the Earth Observation (EO) domain can significantly reduce the cost to map flood extents and improve the accuracy of the flood mapping and monitoring systems. The first development is the open access to data from operational satellites such as Landsat and Sentinel. This has enabled the mapping of the various natural phenomenon at a relatively high spatial and temporal resolution [14
]. The United States Geological Survey made their entire Landsat archive available to the public in 2008. The Landsat archive contains more than three decades of earth observation images and provides a unique opportunity to monitor changes in surface water at high spatial and temporal resolutions. Second, the wider availability and adoption of cloud computing architectures to process EO data. Technologies and models like high-performance computing, data cube, and analysis-ready data allow accelerated access and processing of a large volume of EO data [15
The Google Earth Engine (GEE), a planetary-scale platform for earth science data and analysis developed by Google has enabled the development of global-scale products, tools, and services using temporal EO data such as Landsat [16
]. GEE has been used to conduct various global and regional scale studies, including regional land cover mapping [17
], surface water mapping [18
], accessing food security situations [19
], settlement and population mapping [20
], and other applications [21
In this study, we present an innovative Flood Mapping Algorithm (FMA), which harnesses the power of cloud computing (GEE) EO data (Landsat) to generate historical global flood inundation extents at 30 m resolution. Previous relevant initiatives include an online tool launched in 2012 by the International Water Management Institute that maps significant floods in South Asia from 1980 to 2011 at 500 m resolution [22
], the European Commission Joint Research Centre’s online tool launched in 2016 that provides free access to global surface water indices [23
], SERVIR-Mekong’s flood analysis tool for Myanmar [18
], GEE4Flood [24
], a Sentinel-1 and Landsat data-based rapid and robust flood monitoring tool [25
] and, automatic extraction of flood-prone areas using digital elevation model based geomorphic approaches [26
Our approach improves these available models and tools by enhancing the flood inundation extent’s resolution to 30 m and generating flood inundation extents at a global scale. FMA is developed as a hindsight tool to generate historical flood inundation extents covering areas where the data and information gaps are prominent and annual losses due to floods are high. The inundation and flood risk maps are out-of-date in most of the Global South [6
]. Developing these maps using conventional techniques is a costly exercise for developing countries [11
]. FMA addresses this data gap.
Machine learning approaches provide faster and accurate possibilities for flood detection [27
]. However, these models have high computational and data requirements. FMA generates valuable training data in the form of historical flood inundation maps for machine learning algorithms.
However, because of FMA’s dependency on a dense data cube, it cannot be used to map and monitor current floods until a sufficient number of satellite imagery is available for the inundated area.
The rest of the paper is organized as follows. Section 2
describes the study area, details of data sources, and the research methodology adopted for the study. Section 3
presents the results, and in Section 4
, the discussion is presented. Finally, the conclusions of the study are summarized in Section 5
In the last decade, the floods have caused an economic loss of nearly USD 500 B, equal to Singapore’s GDP. Around 1.47 B people are exposed to the risk of intense flooding, which is more than Europe’s total population. The majority of this population segment lives in low and low-middle-income countries, exponentially increasing the disaster-driven socio-economic risk [47
Developing FEWS and historical flood and risk maps are the two primary approaches to address the food-related challenges. However, developing and deploying such systems in Global South is an expensive and time-consuming exercise. The resource intensiveness of these solutions can be gauged from the fact that if Canada had to update its historical flood maps, it would cost USD 350 M and one decade to complete the exercise [48
FMA has been designed to address the data and information gaps and challenges in the Global South. This section identifies the possible application areas for FMA; this is not an exhaustive list, and more application areas exist.
4.1. Land Use
Using the FMA in countries with poor or no flood risk maps can help add a new level of robustness to land use planning in such places. Identifying those locations which are at risk of flooding can allow for optimization of resources and investment such as upgrading infrastructure, developing agriculture, etc., in the short term. In the medium to longer-term, land-use planning can benefit from knowledge on where detailed flood studies are required if growth is to be sustained in a given location.
4.2. Emergency Services
Metrics such as investment in public infrastructure, natural disaster-related insurance rates, flood-related human and economic losses, etc., can be estimated using FMA. The flood inundation extents are also available as an interoperable service that can be merged with other open datasets for decision making.
In developing countries and emerging economies, data collection of hydrological and other environmental variables is rarely a priority. This activity is often compounded by difficult terrain or cost barriers to technology and tools [49
]. EO data is a simple solution to this problem in the short term, allowing those most vulnerable but poorly gauged locations to be observed. From these observations, low-resolution flood risk models can be generated. This will enable governments, funding agencies, and disaster management authorities to hone-in on the highest potential risk locations and generate higher resolution models. In addition, this methodology allows users to justify investment in higher resolution models around high-risk settlements and assets. In time this strengthens the ability of these countries to better plan for and respond to disasters.
The use and value of remotely sensed risk information in the insurance industry is not new [51
]. There were significant losses/damage to property and agricultural concerns in several of the flood events above. In the case of Queensland, Australia, huge insurance payouts had to be made. In poorer nations such as Cambodia and Bangladesh, this safety blanket did not exist, and many livelihoods and homes were permanently lost due to flood events. FMA could be part of the conversation between governments/international donor organizations and the insurance industry generating agriculture insurance support for persons living and farming at the subsistence level. Creating this safety net and closing this gap in financial security has far-reaching implications for global development goals and promoting more secure economies and nations.
An algorithm for flood inundation extent mapping using GEE is proposed, utilizing EO data to map any flood event from 1985-present. FMA can be an effective supplement to current inundation and flood risk maps, especially in the Global South, where data and technological gaps are common. FMA can also be used in an exploratory capacity prior to flood mapping, as it is significantly lower in cost, only requiring access to the internet and using open-source EO data. There are some limitations where the terrain influences the accuracy of the outputs, but these are easily characterized and can be further calibrated and accounted for with more event inputs. The FMA can be used to create historical flood inundation maps and potential flood risk maps. Room exists to improve this product with the addition of other remotely sensed datasets.
Additional aspects we hope to study include integrating Sentinel-2 data into the FMA workflow to increase the model performance and combining the FMA output with the demographic and utility data available under open access to create a comprehensive tool to convey the impacts of flooding in an area.