What is river flooding?
River (fluvial) floods are one of the most common natural hazards, resulting in more disastrous and persistent impacts worldwide than coastal and pluvial floods (IPCC, 2012). Hydrologically, a river flood occurs when a river’s water level raises, exceeds the crest elevation of river bands and overflows onto the surrounding lands known as floodplains, normally caused by excess rain or snowmelt (Leandro et al., 2010). The severity of river floods is dependent on the duration and intensity (amount of rainfall in volume) of rainfall, soil water content due to previous rainfall and soil properties, and the terrain characteristics of the surrounding land (Zurich, 2019). The two main types of river flooding are (i) overbank flooding which occurs when the water rises to overflow the edges of a river and (ii) flash flooding, characterised by a high-velocity torrent of water and short duration that occurs because of high-intensity rainfall or a sudden breakage of a dam or levee (Yang & Liu, 2020).
Seasonal inundation of floodplains is essential for maintaining a complex river corridor that allows the aquatic ecosystem to thrive, besides creating and maintaining natural habitats, and enabling the transfer of nutrients that create fertile agricultural lands (Alfieri et al., 2017) Hence, floodplain occupancy and use is based on the economic advantages of ground, fertile soils, ease of access, and available water supplies despite flood risk. Globally, approximately 1 billion people live on floodplains (UNEP, 1998). . The Aqueduct Global Flood Analyzer estimates that on average, about 21 million around the world people are affected by river floods each year and can potentially increase to 54 million by the year 2030 due to climate change and socio-economic development. The 15 countries most exposed include India, Bangladesh, China, Vietnam, Pakistan, Indonesia, Egypt, Myanmar, Afghanistan, Nigeria, Brazil, Thailand, Democratic Republic of Congo, Iraq and Cambodia (WRI, 2015).
The impacts and intensity of river flood might be influenced by climate change. Typically, floods are affected by the various characteristics of precipitation, such as intensity, duration, amount, timing, and phase (snow or rain). Climate-driven changes in the above characteristics may consequently result in changes in the characteristics of floods. As noted in the IPCC special report on extremes, climate change has detectably influenced several of the water-related variables that contribute to floods (IPCC, 2012). Although climate change may not directly induce floods, it might compound many of the factors that do. However, it is difficult to find clear evidence because of the lack of data in most parts of the world. Except in Europe where extensive rainfall data exists that points to some evidence for a trend toward heavier rainfall.
In a study conducted by Alfieri et al. (2017), the frequency, magnitude and impact of river floods under scenarios corresponding to 1.5℃, 2℃ and 4℃ are modelled. The results derived indicate a positive correlation between atmospheric warming and future risk at a global scale. At a 4℃ rise in global temperature, the United States of America and countries in Asia and Europe will face a 500 per cent increase in river flood risk.
Conventional ground stations for monitoring hydrological parameters may be costly in the case of very large developing countries, do not record the occurrence of extreme events, and may not be cost-effective (Schumann et al., 2018; Zoka et al., 2018). Remote sensing represents an alternative or complementary source of observation data that compensates for the aforesaid limitations at a global scale, particularly in remote areas in such developing countries (Domeneghetti et al., 2019). Satellite data provide comprehensive, synoptic, multi-temporal and large spatial coverage in near real-time and at frequent intervals. This allows for before, during and after comparison of extreme events, monitoring small and large river basins. Flood detection, mapping and monitoring flood events and their impacts can also be done with satellites, and can contribute to planning and executing emergency responses (Ogashawara et al., 2013; Zoka et al., 2018).
Figure 1: Maps of flood inundation areas using Earth observation data.
Satellite-derived data are suited for mapping and monitoring (i) flood inundated areas, (ii) extent of flood damage, (ii) river configuration, silt deposits, shoal etc., (iv) watershed characteristics and (v) land cover/land use in command areas. More so, digital analysis of satellite data can be used to delineate the boundaries of flood-prone zones, detect changes in the sections of inundated floodplains as well as to identify ideal sites for the construction of structural flood control measures (Aggarwal et al., 2009). Furthermore, satellite-derived rainfall data can be used to infer flooding conditions or in a hydrology model to derive streamflow or runoff to monitor flooding conditions. EO data can also be used to validate and calibrate existing hydraulic models (NASA, 2015). Remote sensing techniques offer opportunities to glean previously unknown information, recognise flood patterns and trends, foster our understanding of flood dynamics and improve our ability to map and monitor river floods at local and global scales.
The sensors and data processing techniques that exist to derive information about floods are numerous. Instruments that record flood events may operate in the visible, thermal and microwave range of the electromagnetic spectrum. Satellite data are based on either passive optical systems or active radar or LiDAR systems. For river flooding monitoring, it is advantageous and effective to utilize active sensors, particularly radar, because they can penetrate rain and cloud cover which are issues that affect flood-hit locations. As river floods progress, active sensors can indicate the geographic extent of floods, which is an indicator of the intensity of the flood. On the other hand, the acquisition of images using optical sensors depends on daylight and weather conditions (Anusha & Bharathi, 2019).
Optical sensors detect energy naturally reflected by the Earth’s surface in the visible and infrared spectral bands. Despite the limitations of optical satellites, optical imagery can also be used to visualise flooded areas, separately, without comparison with non-flooded areas. Using the three-bands colour composite, flood and standing water absorb infrared wavelengths and appear dark blue or black (Ban et al., 2017).
There are several methods for the extraction of water information from optical imagery depending on the number of bands used, which are classified into single-band and multi-band methods. The single-band method involves choosing one band that best distinguishes open water features from other land features. A threshold is then set to further discriminate water from land. This threshold is highly subjective and often tends to overestimate or underestimate open water areas. The multi-band method can be performed in two ways. The first method is by analysing the spectral signatures of all features in the image and determining the signature differences between water and other targets. The second method involves a band-ratio approach using two bands. The first band is taken from the visible spectrum and divided by another band, usually from the NIR wavelengths. This results in obliviating the other non-water features without completely removing them. The Normalised Differential Water Index (NDWI) was developed by McFeeters (1996) to suppress the signal from non-water features making use of the green and NIR bands. Xu (2006) argued that water features extracted by the NDWI contain built-up land noise because of the similar reflectance behaviour of water and built-up areas in the green and NIR bands. To remedy this problem, the Modified Normalised Differential Water Index (MNDWI) was formulated using the mid-infrared or short-wave infrared and the green spectral bands.
Other indices include the Water Index (WI) and the Red Short-wave Infrared (RSWI) (Memon et al., 2015). Some attempts have been made to address extreme hydrological occurrences using Land Surface Temperature (LST). Parinussa et al., (2016) formulate those differences in day and night LST, which can be used as a proxy of the initial state and the flood inundation state over land. Hence, anomalies in LST day and night difference can serve as an indicator of inundation.
The spatial resolution of the sensor is an important factor affecting the usage of satellite images for detecting surface water. Common coarse-resolution sensors (>200m) like NOAA/AVHRR and MODIS have been used to detect large scale extreme hydrological events. In particular, MODIS has been used because of its high revisit time and extensive spatial coverage. The Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership (Suomi NPP_VIIRS) is a wide-swath multispectral sensor and is often considered an upgrade of AVHRR and MODIS. More so, the Medium Resolution Imaging Spectrometer (MERIS) onboard the Envisat platform has also been used for flood detection on a large scale. However, the imagery from these satellites is highly generalised and more suitable for detecting and monitoring large scale flood events. Medium resolution satellites (5-200m), like the Landsat series, SPOT, ASTER, HJ-1A/B and Sentinel-2 are more useful for detecting dynamics of almost all sizes of surface water bodies. High-resolution satellites (<5m) such as IKONOS, RapidEye, Worldview, GF-1/2, Quickbird and ZY_3 can generate images that identify small water bodies that cannot be detected with coarse and medium resolution satellites. However, these satellites have small screen coverages, making them unsuitable for mapping large water bodies. Also, the presence of shadows in high-resolution images especially in urban and mountainous areas can impede flood detection and the availability and revisit frequency of most high-resolution images are limited (Huang et al., 2018).
Unlike optical data, Synthetic-aperture radar (SAR) systems operate in the microwave band, which is characterized by long waves and have the capability of obtaining information irrespective of weather conditions (i.e. penetration through cloud cover, some degree of vegetation, rainfall, fog and snow). SAR transmits signals and receives the backscatter characteristics of different surface features. The backscatter intensity (strength of the radar backscatter) is dependent on multiple factors, including surface roughness and dielectric properties. The smooth open water surface acts as a specular reflector when in contact with the SAR signals, which scatters the radar energy away from the sensor, resulting in minimal backscatter. Stagnant water pixels appear dark in radar images which contrast non-water areas. This makes it easy to detect and distinguish non-water features from water features (Anusha & Bharathi, 2019).
Also, technological advances in SAR have improved the mapping and monitoring of floods because of the production of very high-resolution SAR satellites e.g. TerraSAR-X, DLR (German Aerospace Center) and Radarsat-2 (Canadian Space Agency). Other satellites include COSMO-SkyMed of the Italian Space Agency (ASI), Sentinel-1 of the European Space Agency( ESA) and ALOS-PALSAR 2 of the Japan Aerospace and Exploration Agency (JAXA) (Schumann et al., 2018).
SAR sensors utilize both short wavelengths (X (2.5-4cm) and C (4-8cm)) and long SAR wavelength (L (15-30cm) P (50-60cm)) to obtain information about surface features. The backscatter mechanism for both short and long SAR wavelengths depends on surface characteristics under flooded and non-flooded conditions as Figure 1 illustrates. Grass and soil under non-flooded conditions exhibit volume and diffused backscattering for both short and long wavelengths, albeit there is more penetration into the soil by long wavelengths.
Figure 2: scattering mechanisms of grass, vegetation and built-up areas under flood and non-flooded conditions (Schumann et al., 2018).
When flooded, the reflection becomes specular (assuming the water surface is smooth). In forested areas volume scattering dominates short wavelengths and signals only reach the canopy. Longer wavelengths experience both volume scattering (from the branches of the trees) and double-bounce (penetration through the canopy causes signals to reach the surface and bounce on tree trunks). However, when inundated, double-bounce backscatter becomes more dominant. In urban landscapes, under both flooded and non-flooded conditions, double-bounce backscatter mechanisms dominate due to the presence of many perpendicular structures. However, if sensed with short wavelengths, the backscatter is stronger when there is an inundation than under non-flooded conditions suggesting that short wavelengths can better detect flooded areas in urban areas. Longer wavelengths return the same backscatter under both flooded and non-flooded conditions (Schumann et al., 2018).
HH polarisation is the preferred polarization for flood detection and extent mapping because it is less sensitive to minor vertical differences on the water surface caused by waves. VH is also widely suggested for flood mapping, since it is more sensitive to changes on the land surface, while VV is often the least advised because it is rather susceptible to vertical structures(Rahman & Thakur, 2018).
In general, three combinations of satellite imagery are used for flood inundation detection, respectively applied to the periods before and during the flood: (i) optical imagery / optical imagery, (ii) optical imagery / SAR imagery and (iii) SAR imagery / SAR imagery. The second combination is used as a multi-temporal and multi-source data approach for flood progression and flood detection analysis. The objective is to use radar images to accurately identify large-scale water area during floods and utilise optical imagery for mapping permanent water areas. Comparing individual results differentiates between permanent water bodies and flooded areas (Tong et al., 2018).
Table 1: List of satellite sensors that are typically used for river flood mapping and monitoring.
|Sensor Type||Satellite||Agency||Resolution (m)||Frequency (days)||Years Active||Advantages||Access|
|SAR X-Band||COSMO-SkyMed||ASI||2007 - present||Constellation of four satellites providing high-repeat coverage of up to 1m resolution||Open Access|
|TerraSAR-X||DLR||StripMap Mode: 3m
ScanSAR Mode: 18.5m
Wide ScanSAR Mode: 40m
|11||2007 - present||Available in 3m resolution mode||Open Access|
|SAR C-Band||Sentinel 1||ESA||Interferometric Wide Swath (IW): 5m x 20m
Extra Wide Swath (EW) Mode: 25m x 100m
Wave (WV) Mode: 5m x 20m
Strip Map (SM) Mode: 5m x 5m
|12||2014 - present||Capability of capturing images day/night, irrespective of the weather conditions
Open data access constellation of two satellites
|RADARSAT 2||Canadian Space Agency||1 - 100m||24||2007 - present|
|SAR L-Band||ALOS-PALSAR 2||JAXA||Strip Map: 3m/6m/10m
Spotlight: 1m x 3m
|14||2014 - present||Long wavelength (ability to map flooding beneath vegetation)|
|Optical (Low Resolution)||MODIS Terra/Aqua||NASA||250||1||1999 - present||Sub-daily repeat||Open Access|
|MODIS Terra/Aqua||NASA||500||1||1999 - present||Sub-daily repeat||Open Access|
|MODIS Terra/Aqua||NASA||1000||1||1999 - present||Sub-daily repeat||Open Access|
|Envisat/MERIS||ESA||300||3||2002 - 2012|
|IRS‐2/AWiFS||ISRO||56||5||2011 - present|
|Optical (Medium Resolution)||Landsat||NASA/USGS||30||16||1972 - present||Long historic archive
Water indices (NDWI and MNDWI)
|Sentinel 2||ESA/Copernicus||10||2015 - present||Open data access constellation of two satellites||Open Access|
|Optical (High Resolution)||Geo-Eye 1||3|
|SPOT-5||CNES||5||2002 - 2015||High spatial resolution|
With the advent of different EO data products, the use of satellite images for flood modelling has proliferated. EO data can be used to improve model uncertainty, especially in complex topography, by providing spatially detailed and accurate model parameters (Bates, 2012). Digital Elevation Models (DEM) have been used to estimate flood-prone areas. 2D hydrologic models can be routinely parameterized with DEMs to represent considerable topographic complexity, even in urban areas where flows can potentially be represented at the scale of individual buildings (Shen et al., 2015). Slope and elevation measurements derived from DEMs are useful in modelling the flow, direction and potential extent of floods. Water storage data e.g. from GRACE satellite and soil moisture data e.g ASCAT derived from satellite imagery are used to derive flood indicators (Notti et al., 2018). In a study of the river Rhine, temporal data (time series of water level measurements) and spatial data (flood maps derived from satellite data) were combined to increase the accuracy of flood forecasts (Barneveld et al., 2008). Additionally, remotely sensed data facilitates validation and calibration of flood inundation models (Bates, 2012).
SAR-derived flood elevations have been assimilated into hydraulic models to estimate discharge from space and improve the forecast accuracy of models. To advance the river flood risk analysis in urban areas, high-resolution space-borne SAR such as TERRASAR-X are used to detect and predict urban flood patterns (Bates, 2012). High-resolution LiDAR-derived DEM data have been applied to floodplain flow models and to analyse the effects of varying spatial DEM data models on model accuracy (Shen et al., 2015). The resolution of spatial data is also of importance. If the goal of the hydraulic model is to determine large scale flood extent, low-resolution terrain data may be utilized. By contrast, monitoring flow pathways (floodplain channels, drainage ditches, gaps in embankments, etc.) of floodplains in both rural and urban settings require finer resolution spatial information (Bates, 2012). It is important to note that such vast information availability poses a problem of determining how best to integrate EO data into existing models within a computationally possible context.
In 2015, 197 countries adopted the Sendai Framework for Disaster Risk Reduction that emphasizes the importance of addressing disasters through (i) understanding the risk, (ii) strengthen disaster risk governance, (iii) invest in disaster risk reduction and (iv) enhance preparedness for response and to foster recovery and rehabilitation (UNDRR, 2015). Traditionally, river floods have been controlled by structural measures such as river training, construction of dikes, levees and bypass channels and retention areas including reservoirs and dams (Merz et al., 2010). In recent times, it has become increasingly clear that engineer-centred flood control measures alone cannot accommodate the future frequencies and impacts of floods. Such flood control measures aim at reducing the probability of flooding, i.e. the flood hazard, and ignore the impacts and vulnerability of the population related to floods ( WMO & GWP, 2009; Christin & Kline, 2017). More so, changes upstream, such as additional river engineering to prevent local flooding, can exacerbate the flooding problems downstream (Brakenridge et al., 2003) and by changes in land-cover and land-use upstream (Zink and Villagran de Leon, 2010).
The recognition of the limitations of structural flood controls led to a paradigm shift to non-structural measures often referred to as Integrated Flood Management (IFM). The IFM strategy attempts to balance the beneficial uses of floods for the ecosystem while acknowledging the risks posed by extreme events to the sustainable development in flood-prone areas. The goal is to manage floods based on risk management principles and reduce the vulnerability of people and activities in the floodplains (WMO & GWP, 2009). Figure 2 provides a schematic of the stages of flood risk assessment and risk management. It includes assessing the possible occurrence of floods and a vulnerability assessment to estimate the consequences should a flood of a certain magnitude and frequency occur. Furthermore, a risk assessment is conducted to delineate and classify acceptable and unacceptable risks. Based on the risk assessment, disaster management and mitigation measures can be identified.
Figure 3: Framework for Flood Risk Assessment and Risk Management (UNDRR, 2002).
Flood forecasting systems are used to assess flood risk while early warning systems issue warnings when a flood is imminent or already occurring. The UN Office for Disaster Risk Reduction recognises the importance of Flood Early Warning Systems (FEWS) and advocates for an increase in their availability (Perera et al., 2019). FEWS is a multi-functional system that comprises four components: (i) assessments and knowledge of flood risks in the area, (ii) local hazard monitoring (forecasts) and warming service, (ii) flood risk dissemination and communication service, and (iv) community response capabilities (UNEP-DHI Partnership et al., 2017). It enhances the understanding of risks and appropriate flood responses, hence, improves community preparedness for extreme weather events. Remotely sensed data such as rainfall, weather, soil moisture, crop conditions serve as inputs in early warning systems for river flood prediction.
Furthermore, EO data is a powerful tool used to identify and delineate flood-prone areas. The temporal length and spatial coverage of space-based data aid the analysis of flood inundation extent and frequency from historical satellite data (ESA, 2019). Past flood maps based on optical and radar Earth observation data indicate the maximum flood extent of former events and flood hazard susceptibility. For a given river, an inventory of past flood maps is generated and results are classified into certainly flooded areas and potentially flood areas. The former indicates areas that have been flooded frequently while the latter refer to occasionally flooded areas (Tambuyzer et al., 2010). Resulting maps may be overlaid with land use and population data to estimate flood risk and vulnerabilities and to foster a better understanding of impact patterns to exposed infrastructure assets, population, properties and key economic sectors such as agriculture (ESA, 2019).
Satellite data can detect temporary water storage during floods indicating what land areas have been inundated. Analysis of on-going flood inundation extent can be derived, and flood progressions may be monitored. Additionally, satellite-based tools, along with hydraulic models, can indicate how an excess rainfall event will impact river flow and whether there is a potential for flooding downstream away from the heavy rain event.
During a river flood event, EO data can accurately locate worst-hit areas, which aid decision-making concerning where best to direct needed resources. Besides, EO data aid the identification of access routes that have been impacted as well as possible alternate access routes (Boccardo & Tonolo, 2015). An example of an emergency response service utilizing EO data is DRONESAR. DRONESAR is a software that enables commercially available drones with a range of specific functions for search and rescue. The DRONESAR team have incorporated satellite images from the Copernicus Emergency Management System (EMS), by overlaying the images on the pilot interface allowing them to choose more efficient search patterns based on actual data post disaster situations. The images overlaid on a web browser which allows decision makers to have a better situational awareness and use of resources when time is of the essence.
The Global Flood Detection System
The Global Flood Detection System (GFDS, http://www.gdacs.org/flooddetection) uses passive microwave remote sensing to monitor and observe floods worldwide daily. Unlike conventional in situ gauging stations located in remote areas, it acts as an effective substitute and provides systematic detection of riverine flooding by using microwave radiation to differentiate between dry pixels (non-flooded areas) and wet pixels (flooded areas). GDFS uses the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and deviations in surface water over large extents (10 × 10 km) can be observed as it affects the emitted microwave signal, which allows the system to flag flooded areas when surface water significantly surges. This system also provides precise time series of flood surface variations, near-real-time flood maps and animations in over 10,000 monitoring areas (Schumann et al., 2018). Because of its importance, GFDS is being integrated within operations of flood forecasting centres in some countries (Brazil, Namibia, and Haiti) and used by some international aid organizations.
The Global Flood Awareness System
GloFAS is a web-based global hydrological forecasting and monitoring system jointly owned by the European Commission and the European Center for Medium-Range Weather Forecasts (ECMWF). The system incorporates contemporary weather forecasts with a hydrological model and with its continental scale set-up provides downstream countries with information on upstream river conditions as well as continental and global overviews. GloFAS comprises two complementary systems; i) GloFAS 30-day - a daily hydrological forecast which provides a quick overview of upcoming flood events for the next 30 days, and ii) GloFAS Seasonal - a monthly hydrological forecast which provides river flow outlooks highlighting unusually high or low river flow up to 16 weeks ahead. GloFAS forecasts of possible river flood episodes and unusually high/low flows for all major rivers of the world are displayed through its web platform, the GloFAS web viewer.
The Dartmouth Flood Observatory (DFO)
The Dartmouth Flood Observatory (DFO, http://floodobservatory.colorado.edu) seeks the acquisition and preservation of digital map records of the ever-changing Earth’s characteristics especially related to floods, droughts, wetlands, shorelines, lakes and reservoirs. DFO has tracked, monitored and archived flood events on a global scale since 1985, offering long historic data of flood disasters. Data is acquired through remote sensing, analysed and made open source in different formats (GIS maps, spreadsheets, graphics). In developing flood maps, presently all sensors available and useful are utilized. DFO primarily utilizes MODIS, however ESA's Sentinel-1A and -1B, Landsat, Sentinel-2 are also used. Radarsat, Cosmo SkyMed, EO-1 products have also been provided for aggregation that assist flood response teams through situational awareness across large scale coverages. In addition to these provided services, DFO, in collaboration with NASA, has ensured the provision and distribution of near-real-time (NRT) flood maps whose role in disaster response decisions cannot be overemphasized (Schumann et al., 2018). The DFO “River Watch” represents a fully automated platform where river discharges are measured and have a period-of-record from 1998 to the present (http://floodobservatory.colorado.edu/CriticalAreas/DischargeAccess.html).
The Global Flood Monitoring System
The GFMS is a NASA-funded experimental system at the University of Maryland (UMD’s GFMS http://flood.umd.edu) that uses real-time TRMM Multi-satellite Precipitation Analysis (TMPA) and Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) precipitation information as input for real-time quasi-global (50°N - 50°S) hydrological calculations at 0.125 degrees for river networks and 1km resolution for streamflow, surface water storage and inundation variables. While the GFMS has some shortcomings (low resolution, inaccuracy of data), the timely delivery of system outputs and its lead-time capability makes it invaluable to flood relief services and flood disaster response organizations (Schumann et al., 2018).
Automated SAR Flood Mapping with ESA’s G-POD
ESA’s G-POD (https://gpod.eo.esa.int/) provides a free registration-based service where SAR data (flood images) can be analyzed by registered users to generate flood maps. The system uses an algorithm that delineates flooded areas and non-flood areas based on the backscatter intensity of the image (open surface water has lower backscatter values as opposed to other land characteristics) (Schumann et al., 2018). The system uses a thresholding method (all pixels of an intensity image whose backscattering coefficient (σ0) is smaller than a given threshold value are classified as flooded) which is computationally inexpensive and suitable for rapid mapping purposes (Pulvirenti et al., 2011).
Rapid Flood Mapping from NOAA’s VIIRS Sensor
The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is one of the five major EO instruments onboard NOAA’s Suomi-NPP and JPSS satellites, a continuation of NOAA’s AVHRR legacy sensors. With a very large swath width of 3060 km, it provides full daily coverage of the Earth both during the day and at night. Consequently, it can be utilized for large scale river floods. Optionally, the low-resolution flood map derived from VIIRS can be downscaled to a higher spatial resolution using a DEM.
Aqueduct Global Flood Products
The Aqueduct flood products is a collection of products that analyse past, present and future extreme flood events and their impacts. The Aqueduct Global Flood Analyzer is a web-based platform which measures river flood impacts by urban damage, affected GDP and affected population at the country, state, and river basin scale across the globe. The Aqueduct Floods Hazard Maps measures river and coastal floods risks under both current baseline conditions and future projections in 2030, 2050 and 2080. River flood risk is estimated with the Aqueduct Global Flood Risk Maps. This online platform provides current and future river flood risk estimates in urban damage, affected GDP and affected population by country, state and river basin. Finally, the Aqueduct Global Flood Risk Country Ranking ranks 163 countries by their current annual average population affected by river floods.
SERVIR-Mekong’s Surface Water Mapping Tool
This web-based mapping tool was developed by SERVIR-Mekong, Technical University Delft, the Asian Disaster Preparedness Center (ADPC), eartH2Obsrve, Google, Stockholm Environmental Institute and Spatial Informatics Group covering the lower Mekong geographical region. This region comprises Cambodia, Laos, Myanmar, Thailand and Vietnam. The tool is used to map spatio-temporal changes in surface water distribution and provides insight into the ecological structure and function of rivers, patterns of flooding, and flood risks. Initially developed to document the historical dynamics of seasonal flooding cycles on the Mekong River, other uses include flood risk assessment for disaster preparedness, identifying areas of permanent water etc.
Flood Simulator for African Basins
This was developed by SERVIR-Eastern and Southern Africa in collaboration with the Famine Early Warning Systems Network (FEWS NET) and is used to estimate the extent of river flooding in Kenya, Uganda, Namibia and Rwanda. the tools is available both as a desktop console tool and as an online tool. The application combines CREST (Coupled Routing and Excess Storage) hydrologic model with digital elevation maps and sends users an email alert with real-time and short-term forecast flood inundation maps of select stream-gauge locations.
Historical Flood Analysis Tools for the Lower Mekong Region
The historical flood analysis tool is designed to provide the information regarding flood prone areas (e.g. frequency of seasonal flooding cycles) in the Lower Mekong Region. This tool provides essential information for river flood disaster preparedness and flood disaster management, especially in the context of advanced relief resource provision. With this tool, variable risk for floods can be found by identifying areas prone to such disasters.
HYDrologic Remote Sensing Analysis for Floods Viewer (HYDRAFloodsViewer)
HYDRAFloods is an open source Python application for downloading, processing, and delivering flood maps derived from remote sensing data. This tool leverages data acquired by multiple satellite platforms to automate the creation of daily flood maps. The HYDRAFloods tool is the backend behind the web based HYDRAViewer Geospatial tool which provides daily (sometimes twice daily) flood maps.
High-Impact Weather Assessment Toolkit (HIWAT) Streamflow Prediction Tool
This web-based tool was developed for steamflow forecasting, flood mapping, and data sharing that is openly accessed by a variety of stakeholders in the Hindu-Kush-Himalaya (HKH) region. The project uses advanced modeling, mapping, and visualization to make results intuitive and accessible for decision support. The following water resources applications are included:(i) Streamflow Prediction Tool - A medium-range, 15-day stream flow forecasting and flood awareness web app for the entire SERVIR-Himalaya region based on global ECMWF forecasts. (ii) Flood Map Viewer – A tool supporting a higher resolution forecast that includes inundation maps (for watersheds of interest) derived from the 15-day forecasts. (iii) Earth Observation Data Explorer – An Earth observation data explorer web app that leverages and is patterned after and can extend SERVIR ClimateSERV Viewer functionality. (iv) Water Observations Data Integrator – A customized version of the CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.) HydroServer web app to support managing, hosting, and discovering observational or modelled time series data with accepted World Meteorological Organization (WMO) and Open Geospatial Consortium (OGC) standards.
ESA Hydrology Thematic Exploitation Platform (TEP)
This is a web-based platform used to access, process, upload, visualise, manipulate and compare hydrological data. It offers a number of hydrological service products for flood monitoring, hydrological modelling, water quality, water level, small water body mapping, arctic inland water monitoring and forecasting, FANFAR (water-related applications for West Africa), and Water Observation and Information System (WOIS). Although some service features are open access, it also includes a paid self-managed processing option for expert users excluding FANFAR which is entirely free.
- Seasonal changes account for large-scale changes in river extent in many areas of the Earth leading to complexities in correctly isolating unusual changes from seasonal events.
- The spectral signature of river flooding is complex. Floods affect tropical riverine forests, agricultural lands, desert, steppe landscapes as well as urban areas. The spectral variability of these landscapes under flooded and non-flooded conditions poses difficult challenges to flood detection, mapping and monitoring.
- The unavailability of open access VHR satellite imagery hinders the use of EO data for mapping and monitoring flooded conditions of small rivers because river reaches tend to be monitored at a much smaller scale than that typically acquired with wide-swath EO imagery. Imagery from coarse and medium resolution satellites is highly generalised and more suitable for detecting and monitoring large scale flood events.
- Although SAR data are more suitable for river flood, optical sensor data are more cost-effective, visually interpretable and have an extensive historical archive. In developing countries, revisit frequency for radar images is between 6-12 days. Hence, images often provide fragmented moments and not the full evolution of flood events.
- In cases where urban areas are affected by river floods, it can be difficult to determine flooded areas using SAR. First, urban landscapes consist of strong scatterers (concrete, steel and building concerns) which impedes the detection of floods. Also, radar shadow, smooth surfaces and steep incidence angles are often confused with floods because shadow areas yield no backscatter as do smooth water surfaces (Rubinato et al., 2019).
- Adequate utilization of the quantity of available EO data requires new and more powerful computational tools that are often not available due to financial and capacity constraints, especially, in the developing world. There is limited time and personnel available during disasters and emergencies to understand and process geospatial data into meaningful products. Other issues include cumbersome production and data distribution due to limited near-real-time data accessibility, bandwidth and sharing capacity.
- Many of the web-based service interfaces are not intuitive and require personnel with geospatial skill sets to run specific queries for complex datasets from multiple data sources which can be time-consuming for emergency responders during flood disaster response.
- Stakeholders often require at least daily status updates on affected regions and at spatial resolutions detailed enough to assess local infrastructure assets at risk. However, nontrivial issues, such as inadequate satellite revisit times, adverse weather conditions (e.g., cloud cover, fog, or heavy precipitation) and densely vegetated and built-up areas, typically hinder analysts to meet end-user expectations.
Aggarwal, S., Thakur, P., & Dadhwal, V. (2009). Remote sensing and GIS Applications in Flood Management. Journal of Hydrological Research and Development, Theme Flood Management, 24, 145–158.
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., de Roo, A., Salamon, P., Wyser, K., & Feyen, L. (2017). Global projections of river flood risk in a warmer world. Earth’s Future, 5(2), 171–182. https://doi.org/10.1002/2016EF000485
Anusha, N., & Bharathi, B. (2019). Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data. Egyptian Journal of Remote Sensing and Space Science, xxxx, 1–13. https://doi.org/10.1016/j.ejrs.2019.01.001
Ban, H. J., Kwon, Y. J., Shin, H., Ryu, H. S., & Hong, S. (2017). Flood monitoring using satellite-based RGB composite imagery and refractive index retrieval in visible and near-infrared bands. Remote Sensing, 9(4). https://doi.org/10.3390/rs9040313
Barneveld, H. J., Silander, J. T., Sane, M., & Malnes, E. (2008). Application of Satellite Forecasting and Mapping Data for Improved. 4th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability, 1–8.
Bates, P. D. (2012). Integrating remote sensing data with flood inundation models: How far have we got? Hydrological Processes, 26(16), 2515–2521. https://doi.org/10.1002/hyp.9374
Boccardo, P., & Tonolo, F. G. (2015). Remote Sensing Role in Emergency Mapping for Disaster Response. Engineering Geology for Society and Territory - Volume 5: Urban Geology, Sustainable Planning and Landscape Exploitation, 5, 17–13. https://doi.org/10.1007/978-3-319-09048-1
Brakenridge, G. R., Anderson, E., Nghiem, S. V., Caquard, S., & Shabaneh, T. (2003). Flood warnings, flood disaster assessments, and flood hazard reduction: the roles of orbital remote sensing. Vv.
Christin, Z., & Kline, M. (2017). Why We Continue to Develop Floodplains: Examining the Disincentives for Conservation in Federal Policy. Earth Economics.
Domeneghetti, A., Schumann, G. J. P., & Tarpanelli, A. (2019). Preface: Remote sensing for flood mapping and monitoring of flood dynamics. Remote Sensing, 11(8), 11–14. https://doi.org/10.3390/rs11080940
ESA. (2019). EARTH OBSERVATION FOR SUSTAINABLE DEVELOPMENT Disaster Risk Reduction - Service Portfolio. LARGE SCALE EXPLOITATION OF SATELLITE DATA IN SUPPORT OF INTERNATIONAL DEVELOPMENT.
Huang, C., Chen, Y., Zhang, S., & Wu, J. (2018). Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review. Reviews of Geophysics, 56(2), 333–360. https://doi.org/10.1029/2018RG000598
IPCC. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. In Special Report of the Intergovernmental Panel on Climate Change (Vol. 9781107025). https://doi.org/10.1017/CBO9781139177245.009
Leandro, J., Savic, D. A., Chen, A. S., & Djordjevic, S. (2010). An analysis of the combined consequences of pluvial and fluvial flooding. 1491–1498. https://doi.org/10.2166/wst.2010.486
McFeeters, S.K. (1996) The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425-1432. http://dx.doi.org/10.1080/01431169608948714
Memon, A. A., Muhammad, S., Rahman, S., & Haq, M. (2015). Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012. Egyptian Journal of Remote Sensing and Space Science, 18(1), 99–106. https://doi.org/10.1016/j.ejrs.2015.03.003
Merz, B., Hall, J., Disse, M., & Schumann, A. (2010). Fluvial flood risk management in a changing world. Natural Hazards and Earth System Science, 10(3), 509–527. https://doi.org/10.5194/nhess-10-509-2010
NASA. (2015). Remote Sensing Observations for Flood Monitoring and Socioeconomic Data for Flood Management To present NASA remote sensing observations and.
Notti, D., Giordan, D., Caló, F., Pepe, A., Zucca, F., & Galve, J. P. (2018). Potential and limitations of open satellite data for flood mapping. Remote Sensing, 10(11). https://doi.org/10.3390/rs10111673
Ogashawara, I., Curtarelli, M. P., & Ferreira, C. M. (2013). The Use of Optical Remote Sensing For Mapping Flooded Areas. Journal of Engineering Research and Applications Www.Ijera.Com, 3(October), 1956–1960. www.ijera.com
Parinussa, R. M., Lakshmi, V., Johnson, F. M., & Sharma, A. (2016). A new framework for monitoring flood inundation using readily available satellite data. Geophysical Research Letters, 43(6), 2599–2605. https://doi.org/10.1002/2016GL068192
Pulvirenti, L., Pierdicca, N., Chini, M., & Guerriero, L. (2011). An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Natural Hazards and Earth System Science, 11(2), 529–540. https://doi.org/10.5194/nhess-11-529-2011
Rahman, M. R., & Thakur, P. K. (2018). Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. Egyptian Journal of Remote Sensing and Space Science, 21, S37–S41. https://doi.org/10.1016/j.ejrs.2017.10.002
Rubinato, M., Nichols, A., Peng, Y., Zhang, J. min, Lashford, C., Cai, Y. peng, Lin, P. zhi, & Tait, S. (2019). Urban and river flooding: Comparison of flood risk management approaches in the UK and China and an assessment of future knowledge needs. Water Science and Engineering, 12(4), 274–283. https://doi.org/10.1016/j.wse.2019.12.004
Schumann, G. J. P., Brakenridge, G. R., Kettner, A. J., Kashif, R., & Niebuhr, E. (2018). Assisting flood disaster response with earth observation data and products: A critical assessment. Remote Sensing, 10(8), 1–19. https://doi.org/10.3390/rs10081230
Shen, D., Wang, J., Cheng, X., Rui, Y., & Ye, S. (2015). Integration of 2-D hydraulic model and high-resolution lidar-derived DEM for floodplain flow modeling. Hydrology and Earth System Sciences, 19(8), 3605–3616. https://doi.org/10.5194/hess-19-3605-2015
Tambuyzer, H., Dosselaere, N., & Deleu, J. (2010). Building an earth observation flood risk analysis portfolio responding to the flood directive. GI4DM 2010 Conference - Geomatics for Crisis Management, October 2010, 162–170. https://doi.org/10.5721/itjrs20104236
Tong, X., Luo, X., Liu, S., Xie, H., Chao, W., Liu, S., Liu, S., Makhinov, A. N., Makhinova, A. F., & Jiang, Y. (2018). An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 136(February), 144–153. https://doi.org/10.1016/j.isprsjprs.2017.11.006
UNEP-DHI Partnership, UNEP-DTU, & CTCN. (2017). Early warning systems for floods. Technology Compendium, 3. https://www.ctc-n.org/resources/climate-change-adaptation-technologies-water-practitioner-s-guide-adaptation-technologies
UNDRR. (2002). Key Elements of Flood Disaster Management. 23–46. www.un.org/esa/sustdev/.../flood_guidelines_sec02.pdf
UNDRR. (2015). Sendai Framework for Disaster Risk Reduction 2015 - 2030. 144(2), 169–173.
UNU-INWEH. (n.d.). Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects 08. http://inweh.unu.edu/publications/
WMO, & GWP. (2009). Flood Management in a Changing Climate: A Tool for Integrated Flood Management. August 2009, 28. https://doi.org/10.1093/acprof
World Resource Institute (2016). Retrieved from https://www.wri.org/blog/2015/03/world-s-15-countries-most-people-exposed-river-floods.
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Yang, T. H., & Liu, W. C. (2020). A general overview of the risk-reduction strategies for floods and droughts. Sustainability (Switzerland), 12(7), 1–20. https://doi.org/10.3390/su12072687
Zink, M. & Villagran de Leon, J. C. (2010). Flood hazard assessment with the help of satellite imagery. Retrieved from http://www.un-spider.org/book/5113/4c-challenge-communication-coordination-cooperation-capacity-development.
Zoka, M., Psomiadis, E., & Dercas, N. (2018). The Complementary Use of Optical and SAR Data in Monitoring Flood Events and Their Effects. Proceedings, 2(11), 644. https://doi.org/10.3390/proceedings2110644
Zurich (2019). Three common types of flood explained. Retrieved from https://www.zurich.com/en/knowledge/topics/flood-and-water-damage/three-common-types-of-flood#:~:text=A%20fluvial%2C%20or%20river%20flood,banks%2C%20shores%20and%20neighboring%20land.