Remote Sensing For Floods

Floods are among the most devastating natural hazards in the world, claiming more lives and causing more property damage than any other natural phenomena. As a result, floods are one of the greatest challenges to weather prediction. A flood can be defined as any relatively high water flow that overtops the natural or artificial banks in any portion of a river or stream. When a bank is overtopped, the water spreads over the flood plain and generally becomes a hazard to society. When extreme meteorological events occur in areas characterized by a high degree of urbanization, the flooding can be extensive, resulting in a great amount of damage and loss of life. Heavy rain, snowmelt, or dam failures cause floods. The events deriving from slope dynamics (gravitational phenomena) and fluvial dynamics (floods) are commonly triggered by the same factor: heavy rainfall. Especially in mountainous areas, analyzing flood risk is often impossible without considering all of the other phenomena associated with slope dynamics (erosion, slides, sediment transport, etc.) whereas in plains damages are caused by flood phenomena mainly controlled by water flow.

Forms of Floods: River Floods form from winter and spring rains, coupled with snow melt, and torrential rains from decaying tropical storms and monsoons; Coastal Floods are generated by winds from intense off-shore storms and Tsunamis; Urban Floods, as urbanization increases runoff two to six times what would occur on natural terrain; Flash Floods can occur within minutes or hours of excessive rainfall or a dam or levee failure, or a sudden release of water.

Flood Preparedness Phase

Flood Prone/Risk zone identification

The flood information (data) and experience (intuition) developed during the earlier floods may help in future events. The primary method for enhancing our knowledge of a particular flood event is through flood disaster surveys, where results such as damage assessment, lessons learned and recommendations are documented in a report (see the Natural Disaster Survey Report on "The Great Flood of 1993," Scofield and Achutuni, 1994). Flood risk zone map is of two types: (1) A detailed mapping approach, that is required for the production of hazard assessment for updating (and sometimes creating) risk maps. The maps contribute to the hazard and vulnerability aspects of flooding. (2) A larger scale approach that explores the general flood situation within a river catchment or coastal belt, with the aim of identifying areas that have greatest risk. In this case, remote sensing may contribute to mapping of inundated areas, mainly at the regional level.

Flood Prevention Phase

Flood Monitoring

Though flood monitoring can be carried out through remote sensing from global scale to storm scale, it is mostly used in the storm scale using hydrodynamic models (Figure 2) by monitoring the intensity, movement, and propagation of the precipitation system to determine how much, when, and where the heavy precipitation is going to move during the next zero to three hours (called NOWCASTING). Meteorological satellites (both GOES and POES) detect various aspects of the hydrological cycle —precipitation (rate and accumulations), moisture transport, and surface/ soil wetness (Scofield and Achutuni, 1996). Satellite optical observations of floods have been hampered by the presence of clouds that resulted in the lack of near real-time data acquisitions. Synthetic Aperture Radar (SAR) can achieve regular observation of the earth's surface, even in the presence of thick cloud cover. NOAA AHVRR allows for a family of satellites upon which flood monitoring and mapping can almost always be done in near real time. High-resolution infrared (10.7 micron) and visible are the principal data sets used in this diagnosis. The wetness of the soil due to a heavy rainfall event or snowmelt is extremely useful information for flood (flash flood) guidance. SSM/I data from the DMSP are the data sets used in this analysis. IRS, SAR, SPOT, and to some extent high resolution

NOAA images can be used to determine flood extent and areal coverage. Various precipitable water (PW) products have been developed and are available operationally for assessing the state of the atmosphere with respect to the magnitude of the moisture and its transport. These products include satellite derived PW from GOES (Holt et al,, 1998) and SSM/I (Ferraro et al, 1996), and a composite that includes a combination of GOES + SSM/I + model data (Scofield et al., 1996, 1995).

Figure 2: Remote sensing capabilities in Hydrodynamic models of flood Flood Forecasting

Hydrologic models play a major role in assessing and forecasting flood risk. The hydrologic models require several types of data as input, such as land use, soil type, soil moisture, stream/river base flow, rainfall amount/intensity, snow pack characterization, digital elevation model (DEM) data, and static data (such as drainage basin size). Model predictions of potential flood extent can help emergency managers develop contingency plans well in advance of an actual event to help facilitate a more efficient and effective response. Flood forecast can be issued over the areas in which remote sensing is complementary to direct precipitation and stream flow measurements, and those areas that are not instrumentally monitored (or the instruments are not working or are in error). In this second category, remote sensing provides an essential tool.

Quantitative Precipitation Estimates (QPE) and Forecasts (QPF) use satellite data as one source of information to facilitate flood forecasts. New algorithms are being developed that integrate GOES precipitation estimates, with the more physically based POES microwave estimates. An improvement in rainfall spatial distribution measurements is being achieved by integrating radar, rain gauges and remote sensing techniques to improve real time flood forecasting (Vicente and Scofield, 1998). For regional forecast, the essential input data are geomorphology, hydrological analysis, and historical investigation of past events and climatology. GOES and POES weather satellites can provide climatological information on precipitation especially for those areas not instrumentally monitored.

Forecast on the local scale requires topography, hydraulic data, riverbed roughness, sediment grain size, hydraulic calculations, land cover, and surface roughness. Remote sensing may contribute to mapping topography (generation of DEMs) and in defining surface roughness and land cover. In this case, remote sensing may contribute to updating cartography for land use and DEM. Complex terrain and land use in many areas result in a requirement for very high spatial resolution data over very large areas, which can only be practically obtained by remote sensing systems. There is also a need to develop and implement distributed hydrological models, in order to fully exploit remotely sensed data and forecast and simulate stream flow (Leconte and Pultz, 1990 and Jobin and Pultz, 1996). Data from satellites such as ERS, RADARSAT, SPOT and IRS can provide DEM data at resolutions of about 3 to 10 meters. Land use information can be determined through the use of AVHRR, Landsat, SPOT and IRS data. The rainfall component can be determined through the use of existing POES and GOES platforms. Although there are no operational data sources for estimating soil type, soil moisture, snow water equivalent and stream/river base flow, there has been considerable research on the extraction of these parameters from existing optical and microwave polar orbiting satellites.

Models can also assist in the mitigation of coastal flooding. Wave run-up simulations can help planners determine the degree of coastal inundation to be expected under different, user-specified storm conditions. These types of models require detailed near-shore bathymetry for accurate wave effect predictions. While airborne sensors provide the best resolution data at present, this data source can be potentially cost-prohibitive when trying to assess large areas of coastline. In addition to DEM data, satellite based SAR can also be used to derive near-shore bathymetry for input into wave run-up models on a more cost-effective basis.

Response Phase

Assessment of Flood Damage (immediately during Flood)

The response category can also be called "relief," and refers to actions taken during and immediately following a disaster. During floods, timely and detailed situation reports are required by the authorities to locate and identify the affected areas and to implement corresponding damage mitigation. It is essential that information be accurate and timely, in order to address emergency situations (for example, dealing with diversion of flood water, evacuation, rescue, resettlement, water pollution, health hazards, and handling the interruption of utilities etc.). For remote sensing, this often takes the form of damage assessment. This is the most delicate management category since it involves rescue operations and the safety of people and property.

The following lists information used and analyzed in real time: flood extent mapping and real time monitoring (satellite, airborne, and direct survey), damage to buildings (remote sensing and direct inspections), damage to infrastructure (remote sensing and direct inspection), meteorological NOWCASTS (important real-time input from remote sensing data to show intensity/estimates, movement, and expected duration of rainfall for the next 0 - 3 hours), and evaluation of secondary disasters, such as waste pollution, to be detected and assessed during the crisis (remote sensing and others). In this category, communication is also important to speedy delivery.

Relief(after the Flood)

In this stage, re-building destroyed or damaged facilities and adjustments of the existing infrastructure will occur. At the same time, insurance companies require up-to-date information to settle claims. The time factor is not as critical as in the last stage. Nevertheless, both medium and high-resolution remote sensing images, together with an operational geographic information system, can help to plan many tasks. The medium resolution data can establish the extent of the flood damages and can be used to establish new flood boundaries. They can also locate landslides and pollution due to discharge and sediments. High-resolution data are suitable for pinpointing locations and the degree of damages. They can also be used as reference maps to rebuild bridges, washed-out roads, homes and facilities.

Global scenario on Remote Sensing use

There have been many demonstrations of the operational use of these satellites for detailed monitoring and mapping of floods and post-flood damage assessment. Remote Sensing information derived from different sensors and platforms (satellite, airplane, and ground etc.) are used for monitoring floods in China. A special geographical information system, flood analysis damage information system was developed for estimation of real time flood damages (Chen Xiuwan). Besides mapping the flood and damage assessment, highresolution satellite data were operationally used for mapping post flood river configuration, flood control works, drainage-congested areas, bank erosion and developing flood hazard zone maps (Rao et al., 1998). A variety of satellite images of the 1993 flooding in the St. Louis area were evaluated and combined into timely data sets. The resulting maps were valuable for a variety of users to quickly locate both natural and man-made features, accurately and quantitatively determine the extent of the flooding, characterize flood effects and flood dynamics. (Petrie et al., 1993). Satellite optical observations of floods have been hampered by the presence of clouds that resulted in the lack of near realtime data acquisitions. Synthetic Aperture Radar (SAR) can achieve regular observation of the earth's surface, even in the presence of thick cloud cover. Therefore, applications such as those in hydrology, which require a regularly acquired image for monitoring purposes, are able to meet their data requirements. SAR data are not restricted to flood mapping but can also be useful to the estimation of a number of hydrological parameters (Pultz et al., 1996). SAR data were used for estimation of soil moisture, which was used as an input in the TR20 model for flood forecasting (Heike Bach, 2000). Floods in Northern Italy, Switzerland, France and England during October 2000 were studied using ERS-SAR data. Using information gathered by the European Space Agency's Earth Observation satellites, scientists are now able to study, map and predict the consequences of flooding with unprecedented accuracy. SAR images are also particularly good at identifying open water - which looks black in most images. When combined with optical and infra-red photography from other satellites, an extremely accurate and detailed digital map can be created. Quantitative Precipitation Estimates (QPE) and Forecasts (QPF) use satellite data as one source of information to facilitate flood and flash flood forecasts in order to provide early warnings of flood hazard to communities. New algorithms are being developed that integrate the less direct but higher resolution (space and time) images. An improvement in rainfall spatial distribution measurements is being achieved by integrating radar, rain gauges and remote sensing techniques to improve real time flood forecasting (Vicente and Scofield, 1998). Potential gains from using weather radar in flood forecasting have been studied. (U.S. National Report to International Union of Geodesy and Geophysics 19911994). A distributed rainfall-runoff model was applied to a 785 km basin equipped with two rain gauges and covered by radar. Data recorded during a past storm provided inputs for computing three flood hydrographs from rainfall recorded by rain gauges, radar estimates of rainfall, and combined rain gauge measurements and radar estimates. The hydrograph computed from the combined input was the closest to the observed hydrograph. There has been considerable work devoted to developing the approach needed to integrate these remotely sensed estimates and in situ data into hydrological models for flood forecasting. A large-scale flood risk assessment model was developed for the River Thames for insurance industry. The model is based upon airborne Synthetic Aperture Radar data and was built using commonly used Geographic Information Systems and image processing tools. From the Ortho-rectified Images a land cover map was produced (Hélène M. Galy, 2000).


Droughts and Floods are among the most devastating natural hazards in the world, claiming more lives and causing extensive damage to agriculture, vegetation, human and wild life and local economies. The remote sensing and GIS technology significantly contributes in the activities of all the three major phases of drought and flood management namely, 1. Preparedness Phase where activities such as prediction and risk zone identification are taken up long before the event occurs. 2. Prevention Phase where activities such as Early warning/ Forecasting, monitoring and preparation of contingency plans are taken up just before or during the event and 3. Response/Mitigation Phase where activities just after the event includes damage assessment and relief management. In this lecture, brief review of remote sensing and GIS methods and its utilization for drought and flood management are discussed.


The author wishes to thank Dr. R.R. Navalgund, Director, Dr. A. Bhattacharya, Deputy Director (RS &GIS) and Dr. L. Venkataratnam, Group Director (A&SG) of NRSA for nominating the author to present the lecture in the WMO Sponsored Training/Workshop on Remote sensing data interpretation for Application in Agricultural Meteorology.

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