Case Study 1: Specialized Expert System for Agro-Meteorological Early Warning (SESAME) in Myanmar

The Specialized Expert System for Agro-Meteorological Early Warning (SESAME) is an agricultural decision support system that provides agriculture risk information based on the forecasted rainfall conditions for agriculture planning at farm level for more than 30 townships in central dry zones of Myanmar. The dynamic nature of the hazard variable (rainfall) and exposure elements (crop stages) and its interactions are considered while deriving the risk information in SESAME. The water requirement for various stages of crops helps to understand rainfall being optimal or at risk. The SESAME has been experimented at sub-national level and the agricultural advisories based on the rainfall forecast at different time scales are communicated to the farmers through the extension workers and a mobile application called DMH Sesame.

Case Study 2: Climate Data Access and Analysis System (CDAAS)

Climate Data Access and Analysis System (CDAAS) is a web based portal hosted at Regional Integrated Multi-hazard Early Warning System to access and analyse different climate data sets. One of the key application of this system is to work with complex Global Climate models, gridded observation data sets and downscaled regional climate model products in a user-friendly web based environment. The access to portal requires user to register for receiving the log-in and password details.

The CDAAS system allows the user to select parameter (precipitation, maximum and minimum temperature), downscaled Global Climate Models (GFDL CM3, NCAR CCSM4, and other 10 models), experiment (historic, RCP4.5, RCP8.5), date range and region. In this system, user can choose the time (date range) dynamically based on their requirement, to generate need based climate information for their adaptation planning, as the requirement of climate information varies from sector to sector. At times, the users are having access to derived climate change information for particular time slices, e.g. 2050s, 2090s. In CDAAS, it is possible to change the temporal dimension dynamically based on the user needs and prepare necessary climate information as maps or graphs. The user can dynamically interact with the time series plots to understand the variability and behaviour of extremes over time, and the same could be used as thresholds for risk profiling.

Case Study 3: Climate Risk Information System for Public Health (CRISH) in Tamil Nadu

Climate Risk Information System for public Health (CRISH) is a decision support system, through which health advisories for Malaria and Dengue are issued based on critical weather parameters such as rainfall, temperature, humidity and heat index for three and ten day lead-time. The system draw correlation of dynamically changing weather patterns with epidemics and then use the thresholds for providing health risk for the forecasted weather condition. The system has been developed and tested for issuing health advisories at block level in Tamil Nadu State, India. The tool provides risk based information for Malaria and Dengue, especially the possibility of spreading of the vector based on the forecasted weather condition based on the thresholds identified as well as with the help of expert inputs from public health experts and meteorologists. The system is only available to the collaborators involved and not open to the public.

Case Study 4: Flood Warning and Forecasting System in selected river basins of Myanmar, Nepal, Philippines, and Sri Lanka

The flood forecasting system is able to generate 3 days (72 hours) water level and discharge forecast for selected locations in the river basins based on the rainfall forecast and real time observation from telemetry stations. The forecast generation considers the dynamicity of rainfall parameter and water level in the rivers. The system also does real time monitoring of the river situations and provide color-coded advisories for warning levels. Currently, the system is operational for selected river basins of Myanmar, Nepal, Philippines, and Sri Lanka. The system is hosted at Regional Integrated Multi-hazard Early Warning System, and the access is only for the respective hydrological/ meteorological agencies in the pilot countries. The end product has been communicated to the end users via SMS or email.

Case Study 5: Ocean State Forecast and Advisory System for Maldives, Seychelles and Sri Lanka

The Integrated Ocean Information and Forecast System forecast developed by Indian National Centre for Ocean Information Services generates three days in advance at 3-6 hour temporal resolution for the parameters wind, waves, currents, temperature for customized locations. These dynamic ocean parameter information are shared to the focal points in Maldives, Seychelles, and Sri Lanka to translate this information as advisories to communicate to fishermen, navigation, and other coastal application purposes.

For more info, visit this link http://www.incois.gov.in/portal/osf/osf_rimes/index.jsp#

Case Study 6: Marine Fishery Advisory in India

Utilizing the remotely sensed data available from various satellites, ESSO-Indian National Centre for Ocean Information Services (INCOIS), provides Potential Fishing Zone advisories to the fishermen on a daily basis with specific references to 586 fish landing centres along the Indian coast. The dynamically changing ocean parameters are acquired from satellite observations to generate the potential fishing zone and the information is communicated via text and web-GIS form.

For more info, visit this link http://www.incois.gov.in/MarineFisheries/PfzAdvisory

Case Study 7: The Disaster Monitoring and Response System (DMRS) at AHA Center

AHA Centre’s Disaster Monitoring and Analysis Team uses the customized Disaster Monitoring and Response System (DMRS) to quickly identify the development of slow-onset hazards such as typhoons and volcanic eruptions and to forecast possible impacts of the hazards to the community, as well as identify potential ports of entry for the deployment of emergency relief items.

Case Study 8: Land Slip Warning System in Hong Kong

Geotechnical Engineering Office (GEO) identified the landslip-rainfall relationship in the past. With the help of identified past landslip-rainfall relationship, and dynamic rainfall information, both real-time rainfall data and forecasts from the Hong Kong Observatory (HKO), GEO identifies instance when landslide danger is high and when it would be appropriate to issue the Landslip Warning jointly with the HKO. The issuing of the Landslip Warning also triggers an emergency system within government departments, which mobilizes staff and other resources to deal with landslide incidents. GEO and HKO observed in the past, if heavy rain develops suddenly and unexpectedly, landslides can occur before the Landslip Warning is issued. It has to be noted, in addition to static elements, the dynamicity of elements such as rainfall and soil condition influences the landslip. Therefore, the criteria for the issue and cancellation of the Landslip Warning are reviewed regularly.

For more info, visit this link http://hkss.cedd.gov.hk/hkss/eng/landslip_info.aspx and http://www.hko.gov.hk/wservice/warning/landslip.htm

Case Study 9: Research and study for the development of a flooding simulator in Thailand

NEC Corporation developed an integrated risk management system for Thailand, in which flood simulation system is one of the modules. The integrated risk management system consists of a shared platform that has functions such as data integration, visualization, and early warning, and disaster modules specialized for particular disasters such as flooding, landslides, and earthquakes. The disaster modules or functions can be selected individually as required, or several disaster modules can be combined in order to predict multiple disasters simultaneously. The flood simulation system performs a simulation based on dynamic meteorological data (observed rainfall and forecast rainfall), topographical data (elevation values, land use purposes), and watercourse data (river networks, water levels, sewer systems, etc.), making it possible to predict inundation areas, maximum flood levels, and other flood-related information.

For more info, visit this link http://www.nec.com/en/press/201605/global_20160523_01.html

Case Study 10: Sentinel Asia - Integrated Flood Analysis System (IFAS)

IFAS utilizes near real time satellite based weather products and other variables (topography, land use, soil, etc.,) to forecast the flood over a region. The dynamic variables used in the system are global rainfall information at daily time step from International Flood Network, Global cloud cover image at hourly time steps from Japan Meteorological Agency, River discharge and flood identification from Dartmouth Satellite water and river watch products.

Case Study 11: Predicted Flood Area for selected locations around the world

The Dartmouth Flood Observatory’s River Watch Version 3.4 processor and associated algorithms use microwave radiometry, from existing and previous satellites, to monitor daily flow area changes along the gauging sites. A global hydrologic model is used to calibrate the flow areas to discharge information. Further, flood areas are predicted based on dynamic microwave satellite information and shared in the webpage. Users can click on the stations they are interested to know the river flow and flooding area.

Case Study 12: Cyclone potential impact modelling for forecasted cyclone track

Joint Research Centre (JRC) of the European Commission and Global Disaster Alert and Coordination System jointly developed a system that calculates the areas around the forecasted track information provided by the National Oceanic and Atmospheric Administration (NOAA) and the Joint Typhoon Warning Center (JTWC), and assesses the population and critical infrastructure exposed around the track affected region by high winds. The model also provides the population at risk based on various vulnerability indicator. To account for flooding and other effects of extreme rainfall, GDACS reports on the extreme rainfall estimated from ensemble tropical rainfall potential product (eTRaP), a high resolution estimate of rainfall based on multiple passive microwave remote sensing images. GDACS considers both the accumulated rainfall (for standing floods) and the rain rates (for landslides and flash floods). The dynamic nature of wind speed along the forecasted track is key element for estimating the potential impacts in the model. The potential impacts of the cyclone tracks are dynamically updated whenever new forecast is available.

Case Study 13: Need for dynamic forecast based decisions during severe weather events

On 30 November 2017, cyclone Ockhi made a huge impact in southern districts of Tamil Nadu and Kerala State of India, hundreds of fishermen were missing and thousands of acres of banana and rubber plantation were damaged. One of the key issues raised after the event was about the early warning mechanism and warning communication. Cyclone Ockhi is a peculiar event, as this system intensified as Cyclonic Storm (CS) Ockhi within 3 hours of time from Deep Depression (DD) on 30 November 2017 closer to Kanyakumari (southern tip of India). Therefore, RSMC’s cyclone bulletin issued on 30 November 2017 morning 5.30 and 8.30 AM did not declare the system as Cyclone, but the bulletin issued at 12.30 PM on the same day declared the system as Cyclonic Storm. The quick manifestation is very challenging for preparedness and warning communication, and thereby not enough time to act on warning information. The dynamic nature of wind variables and its associated impacts has to be assessed properly in the future for better preparedness to minimize the losses.

For more info, visit this link http://www.thehindu.com/news/national/kerala/ockhi-raises-questions-overstates-disaster-preparedness/article21244097.ece

Case Study 14: Need to consider the dynamics for high frequency low impact hazards for planning and preparedness activities

During the SASCOF-11 meeting in Maldives, the participants discussed about the importance of low impact high frequency disasters and how the warning formulations could be done effectively by National Disaster Management Center for such hazards. It has to be noted if a low impact hazard occurring continuously for five days, the impacts are very different from the isolated event on a day. Such consecutive occurrence of low impact hazards to be treated differently while warning formulation as the impacts of the recurrent occurrence of hazards are much higher after 3 or 4 days.

For more info, visit this link http://www.rimes.int/SASCOF11/uploads/Report_on_SASCOF-11_Maldives.pdf