Advances in meteorological, hydrological and engineering sciences are fast generating a range of new methodologies for forecasting weather and flood events, including ensemble prediction systems (EPS) and new hydrological or hydrodynamic models (Drobot and Parker, 2007). However, many of these advances prediction system have not yet been incorporated into operational forecast systems and consequently, operational forecasts have not been integrated into decision making processes in order to reduce disaster risks. In a real world, it has been noticed that not all people noticed warning or able to understand the meaning of probabilistic forecasts and consider themselves at risk (Parker et al., 2009; Molinari and Handmerand, 2011). On the consequence no appropriate actions were taken to reduce damage ( Kelman, 2001).
The intense low pressure system in both Alberta and North India causes flood and creates disaster in lives and livelihoods. This is little pity that a developed country like Canada couldn’t able to predict well in advance the flood situations. The Alberta Government (http://environment.alberta.ca/forecasting/advisories/130620c.pdf) predicted 100 to 150 mm rain 24 hrs in advance which turns to 325 mm in reality! This may put difficulties to emergency personal and planner to plan properly to response to flood.
The various landforms (valleys and mountains) of Alberta create different physical characteristics for its watersheds and drainage basins. Significant geographic characteristics that affect the hydrology of Alberta are the mountains in the west, prairie regions in the south, and low lands around the Mackenzie River in the north (Environment Canada, 2008). The Province of Alberta is divided into ten river basins (Alberta Environment, 2008). The flooded Elbow River watershed is located in southern Alberta, Canada and covers approximately 1238 km2. 65% of the watershed is located in the Kananaskis improvement district. The remaining area is divided among the municipal district of Rocky View (20%), the Tsuu T’ina Nation (10%) and the City of Calgary (5%), a fast growing City of over one million inhabitants (ERWP, 2010). The watershed is the source of the Glenmore reservoir which fulfills part of the drinking water supply to the City of Calgary – approximately supplying one in six Albertans. Due to the rapid population growth of the City of Calgary, this watershed has been subjected to considerable pressure for development in the last decade (City of Calgary, 2005). It is predicted in Wijesekara et al. (2012) that in the near future, climate warming, through its effect on glaciers, snow packs and evaporation, will combine with cyclic droughts and rapidly increasing human activity in the Western Prairie Provinces to cause a crisis in water availability in this area (Schindler and Donahue, 2006). Therefore, investigating the rapid changes in land-use in the Elbow River watershed and their impact on the land phase of the hydrological cycle is becoming a crucial issue. With the advance technology for medium range flood forecasts ( Webster, 2010, Fakhruddin, 2013) it could be easily predict the flood in advance to take appropriate action.
The major floods in Elbow River happed in 1915, 1923, 1929, 1932, 2005. In 2005, In 2005, the main factor influencing the flood magnitude was precipitation upstream of Calgary. Thus an accurate precipitation prediction system is essential for this river basin. The river basin is highly permeable, sand and gravel bottomlands and thus celerity is very high. A total 75,000 evacuated and 3 death reported in Alberta flood, June 2013. Similarly 138 people death in similar characteristics of in North India. The death toll is higher in India as the community has lack of awareness and response to the flood warning and the local government preparedness to response. As mentioned Fakhruddin (2012), early warning systems alone do not prevent hazards turning into disasters. Early action is essential in order to mitigate potential damage (World Disaster Report, 2009). Early warning and early action together can save thousands of lives and livelihoods; reduce vulnerability and strengthen resilience. Nevertheless without lead time to react an early warning is almost ineffective. (Parker et al., 2005). For taking a good decision, the capacity of generation of long lead flood forecasts with an acceptable degree is essential (Alan et.al., 2002). Therefore A breakdown in any one of these elements of early warning can cause warning messages to fail to reach and motivate their intended recipients. It’s clear that early warning is not helpful unless its reach to the people who need to act. To response to the early warning the information need to understand and internalized by the people. Thus an interpretation and translation of the science information is essential. People do not immediately respond to early warnings because people worldwide first “search” for additional information to “confirm” that they are really at risk. This searching happens despite the technology used to give warnings. Searching is a social phenomenon. It involves talking things over with others and seeking to hear the same warning multiple times from different sources. Warned people turn to friends, relatives, and strangers to determine if they agree that risk is present and if protective actions are warranted. This process, constructing new perceptions of risk out of existing perceptions of safety adds time before protective actions are taken- it is fundamental to all human beings worldwide, and it is difficult to change. Early public warnings work best when they are under mandate from a government that is trusted as they can facilitate the process and speed it along (World Bank and UN, 2010). Ignoring these basic human warning elements may continue to cost lives. A decision support system incorporating all these users need could enables peoples to visualize the possible scenarios with probabilities of risk to reduce their vulnerabilities.