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. Forecasting System Provides Flood Warnings To Vulnerable Residents Of Bangladesh

The summer of 2004, the forecasting system developed by NCAR and Georgia Tech scientists generated these 10-day forecasts showing that the Brahmaputra River in Bangladesh would likely exceed critical flood level (the horizontal dotted line) on two occasions in July. At the time, the forecasts were not fully integrated into Bangladeshi warning systems, and approximately 500 people in Bangladesh and India died in the floods. This summer, for the first time, the forecasts are being distributed directly to more than 100,000 people living in flood-prone areas along the Brahmaputra and Ganges rivers. Illustration by Thomas Hopson, UCAR.
by Staff Writers
Boulder CO (SPX) Aug 03, 2007
As catastrophic floods worsen in Bangladesh, a pilot forecasting program is being used to warn thousands of residents in selected flood-prone regions. The forecasting system was designed by scientists at the National Center for Atmospheric Research (NCAR) and Georgia Institute of Technology. Bangladesh is one of the world's most vulnerable regions to floods. Rising waters in recent days have left dozens of people dead and several million marooned or displaced, with the toll likely to mount.

The pilot program began this summer with the aim of delivering 1- to 10-day forecasts directly to more than 100,000 people living on floodplains of the Brahmaputra and Ganges rivers, and then expanding to reach additional residents in coming years. It predicted the current floods a few days in advance, alerting a network of volunteers in Bangladesh last week to notify residents at risk. The volunteers cannot yet confirm the extent to which these efforts helped people prepare for the floods.

"Our goal is that long-range flood forecasts, for the first time, will consistently reach many rural individuals in Bangladesh who are in jeopardy of losing their homes, businesses, and possibly their lives," says NCAR scientist Thomas Hopson, who helped develop the forecasting system.

The system uses a combination of weather forecast models, satellite observations, river gauges, and new hydrologic modeling techniques. It is part of a larger initiative, known as Climate Forecast Applications in Bangladesh (CFAB), to improve flood and precipitation warnings in the low-lying nation. Peter Webster of the Georgia Institute of Technology is the principal investigator of the overall initiative.

Hopson and Webster have provided forecasts to Bangladeshi agencies since 2003, but the forecasts often have not reached rural regions, where many residents lack radios and even electricity. This year, the Thailand-based nonprofit Asia Disaster Preparedness Center has established a network of governmental and nongovernmental organizations, as well as volunteers, to distribute the forecasts directly to people in five districts along the Brahmaputra and Ganges, including impoverished families living on islands known as river chars. The center's Ramasamy Selvaraju and A.R. Subbiah are overseeing the distribution efforts.

The project, which has received funding from the National Science Foundation (NSF), is supported by the U.S. Agency for International Development and the relief agency CARE.

Protecting lives and income

Almost every other year in recent decades, the Brahmaputra and Ganges rivers have flooded for periods ranging from a few days to a month or more, often with devastating results for local residents. Farmers and fishers can easily lose a year's worth of income in a single flood.

Residents of the largely impoverished districts in the forecast area have said that advance notice of floods could help them ward off some of the worst impacts of rising waters. If they had sufficient warning, they could harvest at least a portion of their ripening crops, move some livestock to safety, encircle fish ponds with nets to prevent fish from escaping, and stock food and other supplies.

"The goal here is to help very local, grassroots economies," Hopson explains. "The forecasts also alert relief agencies to prepare to bring in drinking water, cholera tablets, and other essentials in case of a major flood."

The European Centre for Medium-Range Weather Forecasts provides data and weather forecasts, which are fed into hydrological models of the Ganges and Brahmaputra river basins. The system also incorporates estimates of precipitation from two satellite-based systems developed at the NASA Goddard Space Flight Center and NOAA Climate Prediction Center, along with discharge measurements of rivers in Bangladesh.

The forecasting system emphasizes modeling and satellite data to compensate for a lack of river gauge data upstream of Bangladesh, as well as for a lack of radar data. It is updated daily with new model runs and measurements.

Longer-range forecasts

Hopson, Webster, and Georgia Institute of Technology scientists Carlos Hoyos and Hai-Ru Chang have worked to create forecasts that go out more than 10 days, thereby giving residents additional time to prepare for floods. Over the next year or two, increasing numbers of Bangladeshis will begin to receive 20-day forecasts, followed by one- to six-month seasonal forecasts.

The team will also study the feasibility of applying its forecasting technology and methods to other vulnerable countries, such as Cambodia and Vietnam.

"We feel that the prediction modules we have developed for Bangladesh are templates for flood forecasting in developing nations with limited infrastructure and resources," Webster says.

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Japan To Send Quake Warnings Nationwide
Tokyo (AFP) Aug 03, 2007
Japan will soon start sending earthquake warnings to the public, giving them vital seconds to prepare before one of the country's frequent tremors strikes, a top seismologist said Friday. "We will start to offer the earthquake alert system to citizens starting from October 1," Makoto Saito, who runs the system at the Japan Meteorological Agency, told AFP. The system, the result of years of research, made its debut earlier this year but so far has been confined to major businesses and public utilities.

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