- India’s first digital flood inventory has digitised flooding events from 1985 to 2016 using data from the India Meteorological Department.
- The digital inventory will aid future data collection efforts, scientific research on floods, disaster risk reduction, loss and damage estimates and pave the way to develop financial products to tackle disaster risks.
- Stakeholders working to build financial products and mechanisms such as disaster risk insurances including crop insurances could also benefit from the dataset.
Data on fatalities and damage associated with India’s flooding events from 1985 to 2016 have been meticulously mined from printed government records and combined with information from global databases to create India’s first digital flood inventory freely available in modern geospatial formats.
The India Flood Inventory (IFI) pools 89% of its data from the India Meteorological Department’s Disastrous Weather Events, and the remaining from Emergency Events Database (EM-DAT) and Dartmouth Flood Observatory (DFO), states the paper documenting the inventory’s inception.
“We have taken decades of data from the published document by IMD’s DWE, digitised it, fixed a lot of things, and augmented it with geospatial information in the form of GIS files. Each historical flooding event now has its information digitised, which IMD didn’t have till now,” said Manabendra Saharia from IIT-Delhi’s Hydrosense Lab, who led the development of the inventory.
The objective was to provide a ready-made database to the community that could be used for future work including disaster management personnel and scientists working on flood research and management. The inventory will also aid future data collection efforts.
Floods are one of the most devastating natural disasters. India is one of the most affected countries with the increasing frequency and intensity of floods, leading to loss of lives and livelihoods. The latest assessment report from IPCC’s Working Group 1, finds that climate change is intensifying the water cycle which will bring more rainfall and associated flooding in some regions. In-depth research and better early warning systems (EWS) are needed for India to adapt to such disasters. The most important component for carrying on with in depth research and EWS is data.
Data to help with attribution, modeling and forecast
“If a disaster management personnel looks at the dataset, they would have a searchable archive of every flooding event as recorded by IMD. While IMD will admit there are observational deficiencies in the collection, this is the best possible available historical information which helps personnel to assess how many people have died in historical flooding events, or how much paddy has been destroyed to plan for food shortages etc. and for which months,” said Saharia.
The scientists marked every event with a Unique Event Identifier making it easy to reference with other datasets through a common database scheme; dates were made compliant to ISO 8601, an internationally accepted calendar-and-clock format. Information on human fatalities, animal fatalities, damages etc. was extracted from the dataset and coordinates of the events were verified.
India Meteorological Department (IMD) scientist D.S. Pai, the co-author of the paper, shares that the inventoried data would help with attribution analysis, improve modeling and forecast. “Improved modeling and flood forecasting would help strengthen our flood early warning systems,” the Pune-based Pai, Head, Climate Research & Services, IMD, Ministry of Earth Sciences told Mongabay-India. “The data would also aid with hazard mapping and vulnerability analysis,” he said.
“Analysis of the data in the inventory highlights that the majority of floods in the country happen in the monsoon season, which is 79% of the yearly total, with a peak in July. While the number of flood fatalities during the same period is 83% of the yearly total, with a peak in August,” the paper states.
Large flood plains such as Uttar Pradesh, Assam, Maharashtra, Bihar, and West Bengal experience the highest number of floods and fatalities. While, the hill states such as Uttarakhand have experienced catastrophic events, with some of the highest per-capita death rates in the country.
The inventory reveals three major flood events: the Brahmaputra floods in 1988 and 2004 and the 1987 Bihar floods that affected the entire state.
Sushma Guleria, assistant professor at the National Institute of Disaster Management, who was not associated with the inventory work, explained that historical data of flooding events also come in handy to implement post-disaster needs assessment (PDNA) contributing to the collection and standardisation of loss and damage data in the aftermath of floods. “Having access to historical data would also help to track the intangible damages such as displacement, property damage, impacts on ecosystems and psychological effects of loss of life, that can be long-lasting and earmark adequate compensation for flood-affected families factoring in the tangible and intangible impacts,” Guleria told Mongabay-India.
Saharia adds that it could also benefit stakeholders working to build financial products and mechanism such as disaster risk insurances including crop insurances.
Loss and damage due to climate disasters and slow onset events like sea-level rise and climate finance to address loss and damage and decarbonisation efforts by developing countries were important issues for these countries at the recently concluded United Nations climate conference in Glasgow or COP26.
The United Nations weather agency World Meteorological Organisation (WMO) in its report State of the Climate in Asia 2020 said India lost USD87 billion in 2020 due to cyclones, floods and droughts. Floods and cyclones contribute to maximum human deaths in India from extreme weather events (EWEs) in the past 50 years.
At COP26 which saw India pledge to net zero commitments by 2070, the nation also stood its ground demanding climate finance to act on its climate commitments. The richest one percent of the world’s population is responsible for more than twice the combined share of carbon emissions of the poorest 50 percent.