are useful in hydrological modeling (e.g., HEC-RAS) in association with the digital
elevation model (DEM) for flood monitoring (Knebl et al. 2005). Flood monitoring
technique based on in situ sensor often fails to detect the rainfall and runoff record in
the upstream which results in an underestimation of flood impact in downstream.
Currently, many sophisticated hydrological models are developed for more precise
flood monitoring. Three essential components, rainfall measuring systems, soil
moisture updating systems, and surface discharge measuring systems, are necessary
for these hydrological modeling-based flood forecasting and monitoring (Hossain
2006). There are some drawbacks of a ground-based flood monitoring system such
as low coverage and cost ineffectiveness. Due to high maintenance costs, the total
number of gauging stations is declining since the 1980s (Vorosmarty et al. 1999).
Furthermore, sensor networks are inadequate in tropical areas where flood vulnerability is high, and these regions have limited financial capacity to adapt surface
network systems (Hossain 2006). Additionally, ground monitoring–based flood
monitoring systems are unable to monitor transboundary flood as data from crossboundary gauges are not available downstream.
16.2.2 Remote Sensing–Based Flood Monitoring
Since flood monitoring in large areas through ground base river gauge is not costefficient and the gauge station does not cover most parts of the world, remote sensing
offers a viable alternative system. It is now possible to obtain earth observation data
rapidly over a vast territory due to the advancement in remote sensing technologies.
Both spaceborne and airborne remote sensing provide data in various spatial,
spectral, and temporal resolutions and are widely used in flood monitoring (Lin
et al. 2016). Spaceborne remote sensing has an advantage over airborne data because
it can provide data for large areas and at an interval which is crucial for proper flood
monitoring. The advantage of airborne remote sensing is the flexibility, meaning it
can be operated on demand. However, flood monitoring based on airborne data for
large areas might not be cost-effective. Although data from both optical and microwave spectrum had some limitations, these data have widely been used in flood
monitoring in recent decades.
16.2.2.1 Remote Sensing in Flood Forecasting
Remotely sensed data and images have been used in various flood forecasting
models. Forecastings on precipitation, weather monitoring, and hydrological monitoring are key to effective flood forecasting (Tao and Kouwen 1989; Biancamaria
et al. 2011; Borga et al. 2011). Due to the time lag between rainfall and surface
runoff, rainfall estimation becomes the key to flood forecasting (Hossain and Katiyar
2006). Both optical and microwave remote sensing has been used to predict the
rainfall based on cloud thickness, raindrop particle shape, and cloud top temperature.
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R. M. Shrestha and M. S. Rahman
elevation model (DEM) for flood monitoring (Knebl et al. 2005). Flood monitoring
technique based on in situ sensor often fails to detect the rainfall and runoff record in
the upstream which results in an underestimation of flood impact in downstream.
Currently, many sophisticated hydrological models are developed for more precise
flood monitoring. Three essential components, rainfall measuring systems, soil
moisture updating systems, and surface discharge measuring systems, are necessary
for these hydrological modeling-based flood forecasting and monitoring (Hossain
2006). There are some drawbacks of a ground-based flood monitoring system such
as low coverage and cost ineffectiveness. Due to high maintenance costs, the total
number of gauging stations is declining since the 1980s (Vorosmarty et al. 1999).
Furthermore, sensor networks are inadequate in tropical areas where flood vulnerability is high, and these regions have limited financial capacity to adapt surface
network systems (Hossain 2006). Additionally, ground monitoring–based flood
monitoring systems are unable to monitor transboundary flood as data from crossboundary gauges are not available downstream.
16.2.2 Remote Sensing–Based Flood Monitoring
Since flood monitoring in large areas through ground base river gauge is not costefficient and the gauge station does not cover most parts of the world, remote sensing
offers a viable alternative system. It is now possible to obtain earth observation data
rapidly over a vast territory due to the advancement in remote sensing technologies.
Both spaceborne and airborne remote sensing provide data in various spatial,
spectral, and temporal resolutions and are widely used in flood monitoring (Lin
et al. 2016). Spaceborne remote sensing has an advantage over airborne data because
it can provide data for large areas and at an interval which is crucial for proper flood
monitoring. The advantage of airborne remote sensing is the flexibility, meaning it
can be operated on demand. However, flood monitoring based on airborne data for
large areas might not be cost-effective. Although data from both optical and microwave spectrum had some limitations, these data have widely been used in flood
monitoring in recent decades.
16.2.2.1 Remote Sensing in Flood Forecasting
Remotely sensed data and images have been used in various flood forecasting
models. Forecastings on precipitation, weather monitoring, and hydrological monitoring are key to effective flood forecasting (Tao and Kouwen 1989; Biancamaria
et al. 2011; Borga et al. 2011). Due to the time lag between rainfall and surface
runoff, rainfall estimation becomes the key to flood forecasting (Hossain and Katiyar
2006). Both optical and microwave remote sensing has been used to predict the
rainfall based on cloud thickness, raindrop particle shape, and cloud top temperature.
324
R. M. Shrestha and M. S. Rahman