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M. F. Chow
of more than US$ 38.171 billion, the human death toll of 12,330, affecting approximately 197,275 villages with over 616,598 km
2 of area inundated by the floodwaters during the past 69 years (FFC 2017). Especially the 2010 Megaflood event
in the Indus River which was driven by exceptional monsoon storms has affected
almost all areas in Pakistan (NDMA 2010). Flooding has become a big challenge
for many Asian countries in recent decades due to climate change and uncontrolled
urban developments (Bormudoi et al. 2011; Sugiura et al. 2014a, b). Severe flood
events had occurred in the past in countries like Myanmar (2008), Philippines (2009),
Pakistan (2010), Thailand (2011), the Philippines (2013), and Malaysia (2014). Huge
economic losses due to the damage of infrastructures and even human life losses are
burdening the responsibility of the country’s government. Since it is almost impossible to prevent from flood hazards completely with sufficient structural measures
due to its high cost, implementation of flood forecasting and early warning system
becomes the main strategy for the authorities to reduce the vulnerability against
flood risk (Sugiura et al. 2014a, b; Chinh et al. 2014; Chow and Jamil 2017). The
Sendai Framework for Disaster Risk Reduction 2015–2030 was adopted during the
Third UN World Conference to achieve the substantial reduction of disaster risk and
losses in lives, livelihoods, and health and in the economic, physical, social, cultural,
and environmental assets of persons, businesses, communities and countries over
the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030
outlines four priorities for action to prevent new and reduce existing disaster risks: (i)
Understanding disaster risk; (ii) Strengthening disaster risk governance to manage
disaster risk; (iii) Investing in disaster reduction for resilience; and, (iv) Enhancing
disaster preparedness for effective response, and to “Build Back Better” in recovery,
rehabilitation, and reconstruction.
Forecasting the flood event requires input data such as topography map, rainfall,
streamflow, river cross section, land use, and soil type data. These data are required
for the setup of hydrological and hydraulic models and undergo calibration and
validation processes before implemented for forecasting the flood events. However,
the problems of flood forecasting system installation in poorly gauged river basins
are including (i) difficulty to get real time hydrological data in the upstream of a
transboundary river basin; (ii) insufficient of implementation and maintenance of
ground-based real-time hydrological observation stations, such as rain gauge and
river discharge gauging station with data transmission system; (iii) lack of the data
required for the creation of a flood forecasting model such as altitude, land use,
and river channel network, etc.; (iv) lack of budget for flood forecasting system
installation and; (v) insufficient framework to enhance the technical capabilities
(Fukami et al. 2006; Miyamoto et al. 2014; Kimura et al. 2014; Sugiura et al. 2014b).
Therefore, there is a great need for developing a flood forecasting system that able to
acquire the necessary information in an ungauged river basin for efficient and accurate
flood prediction. As such, Integrated Flood Analysis System (IFAS) is developed by
ICHARM (International Centre for Water Hazard and Risk Management) and Public
Works Research Institute (PWRI) of Japan as advanced technology to forecast the
flood event using satellite rainfall data and Geographic Information System (GIS).
The objective of this paper is to review the applications and challenges of IFAS for
M. F. Chow
of more than US$ 38.171 billion, the human death toll of 12,330, affecting approximately 197,275 villages with over 616,598 km
2 of area inundated by the floodwaters during the past 69 years (FFC 2017). Especially the 2010 Megaflood event
in the Indus River which was driven by exceptional monsoon storms has affected
almost all areas in Pakistan (NDMA 2010). Flooding has become a big challenge
for many Asian countries in recent decades due to climate change and uncontrolled
urban developments (Bormudoi et al. 2011; Sugiura et al. 2014a, b). Severe flood
events had occurred in the past in countries like Myanmar (2008), Philippines (2009),
Pakistan (2010), Thailand (2011), the Philippines (2013), and Malaysia (2014). Huge
economic losses due to the damage of infrastructures and even human life losses are
burdening the responsibility of the country’s government. Since it is almost impossible to prevent from flood hazards completely with sufficient structural measures
due to its high cost, implementation of flood forecasting and early warning system
becomes the main strategy for the authorities to reduce the vulnerability against
flood risk (Sugiura et al. 2014a, b; Chinh et al. 2014; Chow and Jamil 2017). The
Sendai Framework for Disaster Risk Reduction 2015–2030 was adopted during the
Third UN World Conference to achieve the substantial reduction of disaster risk and
losses in lives, livelihoods, and health and in the economic, physical, social, cultural,
and environmental assets of persons, businesses, communities and countries over
the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030
outlines four priorities for action to prevent new and reduce existing disaster risks: (i)
Understanding disaster risk; (ii) Strengthening disaster risk governance to manage
disaster risk; (iii) Investing in disaster reduction for resilience; and, (iv) Enhancing
disaster preparedness for effective response, and to “Build Back Better” in recovery,
rehabilitation, and reconstruction.
Forecasting the flood event requires input data such as topography map, rainfall,
streamflow, river cross section, land use, and soil type data. These data are required
for the setup of hydrological and hydraulic models and undergo calibration and
validation processes before implemented for forecasting the flood events. However,
the problems of flood forecasting system installation in poorly gauged river basins
are including (i) difficulty to get real time hydrological data in the upstream of a
transboundary river basin; (ii) insufficient of implementation and maintenance of
ground-based real-time hydrological observation stations, such as rain gauge and
river discharge gauging station with data transmission system; (iii) lack of the data
required for the creation of a flood forecasting model such as altitude, land use,
and river channel network, etc.; (iv) lack of budget for flood forecasting system
installation and; (v) insufficient framework to enhance the technical capabilities
(Fukami et al. 2006; Miyamoto et al. 2014; Kimura et al. 2014; Sugiura et al. 2014b).
Therefore, there is a great need for developing a flood forecasting system that able to
acquire the necessary information in an ungauged river basin for efficient and accurate
flood prediction. As such, Integrated Flood Analysis System (IFAS) is developed by
ICHARM (International Centre for Water Hazard and Risk Management) and Public
Works Research Institute (PWRI) of Japan as advanced technology to forecast the
flood event using satellite rainfall data and Geographic Information System (GIS).
The objective of this paper is to review the applications and challenges of IFAS for