By Wei Liu, IIASA Risk and Resilience Program

What do Rajapur, Nepal; Chosica, Peru; and Tabasco, Mexico all have in common? Flooding:  these areas are all threatened by floods, and they also face similar knowledge gaps, especially in terms of local level spatial information on risk, and the resources and the capacities of communities to manage risk.

To address these gaps, I and my colleagues at IIASA, in collaboration with Kathmandu Living Labs (KLL) and Practical Action (PA) Nepal are building on our experiences in Nepal’s Lower Karnali River basin to support flood risk mapping in flood-prone areas in Peru and Mexico.

Recent developments in data collection and communication via personal devices and social media have greatly enhanced citizens’ abilities to contribute spatial data, called Crowdsourced Geographic Information (CGI) in the mapping community. OpenStreetMap is the most widely used platform for sharing this free geographic data globally, and the fast growing Humanitarian OpenStreetMap Team has developed CGI in some of the world’s most disaster-prone and data-scarce regions. For example, after the 2015 Nepal Earthquake, thousands of global volunteers mapped infrastructure across Nepal, greatly supporting earthquake rescue, recovery, and reconstruction efforts.

Today there is excellent potential to engage citizen mappers in all stages of the disaster risk management cycle, including risk prevention and reduction, preparedness and reconstruction. In this project, we have successfully launched a series of such mapping activities for the Lower Karnali River basin in Nepal starting in early 2016. In an effort to share the experience and lessons of this work with other Zurich Global Flood Resilience Alliance field sites, in March 2017 we initiated two new mapathons  in Kathmandu, with support from Soluciones Prácticas (PA Peru) and the Mexican Red Cross, to remotely map basic infrastructure such as buildings and roads, as well as visible water surface, around flood-prone communities in Chosica, Peru and Tobasco, Mexico.

@ Wei Liu | IIASA

March 17th, 2017, staff and volunteers conducting remote mapping at Kathmandu Living Labs @ Wei Liu | IIASA

Prior to our efforts very few buildings in these areas were identified on online map portals, including Google Maps, Bing Maps, and OSM. Through our mapathons, dozens of Nepalese volunteers mapped over 15,000 buildings and 100 km of roads. The top scorer, Bishal Bhandari, mapped over 1,700 buildings and 6 km of roads for Chosica alone.

Having the basic infrastructure mapped before a flood event can be extremely valuable for increasing flood preparedness of communities and for local authorities and NGOs.  During the period of the mapathons, the Lima region in Peru, including Chosica, was hit by a severe flood induced by coastal El Niño conditions. Having almost all buildings in Chosica mapped on the OSM platform now makes visible the high flood risk faced by people living in this densely populated area with both formal and informal settlements. These data may support conducting a quick damage assessment, as suggested by Miguel Arestegui, a collaborator from PA Peru during his visit to IIASA in April, 2017.

Recognizing the value of crowdsourced spatial risk information, we are working closely with partners, including OpenStreetMap Peru, to mobilize the creativity, technical know-how, and practical experience from the Nepal study to Latin America countries. Collecting such information using CGI comes with low cost but high potential for modeling and estimating the amount of people and economic assets potentially being affected under different future flood situations, for improving development and land-use plans to support disaster risk reduction, and for increasing preparedness and helping with allocating humanitarian support in a timely manner after disaster events.

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Having the basic infrastructure mapped before a flood event can be extremely valuable for increasing flood preparedness of communities and for local authorities and NGOs.  During the period of the mapathons, the Lima region in Peru, including Chosica, was hit by a severe flood induced by coastal El Niño conditions. Having almost all buildings in Chosica mapped on the OSM platform now makes visible the high flood risk faced by people living in this densely populated area with both formal and informal settlements. These data may support conducting a quick damage assessment, as suggested by Miguel Arestegui, a collaborator from PA Peru during his visit to IIASA in April, 2017.

Recognizing the value of crowdsourced spatial risk information, we are working closely with partners, including OpenStreetMap Peru, to mobilize the creativity, technical know-how, and practical experience from the Nepal study to Latin America countries. Collecting such information using CGI comes with low cost but high potential for modeling and estimating the amount of people and economic assets potentially being affected under different future flood situations, for improving development and land-use plans to support disaster risk reduction, and for increasing preparedness and helping with allocating humanitarian support in a timely manner after disaster events.

Flood-inundated houses and local railway in Chosica, Peru, 18/03/2017 @ Miluska Ordoñez | Soluciones Prácticas

The United Nation’s Sendai Framework for Disaster Risk Reduction states that knowledge in “all dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and the environment” needs to be leveraged to inform policies and practices across all stages of the disaster risk management cycle. CGI has a great potential to involve citizens from around the world to help fill this critical knowledge gap. These pilot mapathons conducted between Nepal and Latin America are promising examples of supporting community flood resilience through the mobilization of CGI via international partnerships within the Global South.

This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

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