There is considerable excitement about the potential for Earth Observation (EO) data to support climate resilience planning, design and implementation. As the temporal and spatial resolution of satellite data increases, and costs continue to fall, the potential of EO data to support climate resilience projects is beginning to be realised. Over the past twelve months, the European Space Agency’s Earth Observation for Sustainable Development Climate Resilience cluster (EO4SD CR) has been working with the Asian Development Bank providing EO data in support of climate resilience projects in the Philippines and China.
Inundation Monitoring Service in the Philippines
Analysing flood basins in the Philippines required a tailored approach. The Philippines ranks third among countries with the highest disaster risk and is among the top 10 countries with the most number of people affected by disasters. Storms, typhoons, and associated floods devastate and account 80% of all deaths, 90% of affected people, and 92% of economic impact, and disproportionately affect vulnerable groups of people (World Risk Report 2016 from Alliance Development Works and United Nations University).
According to the Philippines Dashboard of the Climate Change Knowledge Portal Climate change is likely to increase the intensity and frequency of typhoons and heavy rainfalls, exacerbating flooding in existing flood-prone areas, increasing landslides and mudslides, and extend flooding to new areas (). Flood risk management in the Philippines has been ineffective due to a lack of integrated flood risk management planning, suboptimal flood protection infrastructure, limited investment, and inadequate local flood risk management.
To support improved flood management in the region, the EO4SD CR developed a prototype Inundation Monitoring Service (IMS) for the Jaluar River Basin, Panay Island. The IMS provides satellite-based, highly automated, open water surface identification tools that detect both seasonal fluctuations of water bodies and long-term changes. The service maps provide the extent of flooded areas over time, which can help build a picture of the flood response of an area.
Flood risk assessment in Yanji City
Yanji City in the People’s Republic of China is highly exposed to flood risk, having experienced between ten and twenty-seven large flood events during the period between 1985 and 2011. The city is home to half a million people, 90% of whom live in urban areas. At the same time, Yanji suffers from inadequate infrastructure and lacks basic flood protection and water management systems, causing inconvenience and disruptions to daily life. Climate change increases the flood risk and threat to water supply, only making these problems worse.
In order to improve the city’s infrastructure and increase climate resilience of vulnerable people, the ADB and Chinese government embarked on the Jilin Yanji Low-Carbon Climate-Resilient Urban Development project Yanji City wanted to learn how to fine-tune the design of their activities, based on the EO data. Specifically, the project aimed to:
- Develop an intelligent, sustainable, low carbon, urban transport system
- Create a climate resilience plan for a ‘sponge city’
- Improve water support and wastewater management systems
- Increase institutional capacity for carbon and climate resilient urban infrastructure planning.
In order to accomplish this, a climate risk assessment was conducted that went beyond using downscaled GCM data, and was practically applicable and stakeholder oriented in order to highlight particular vulnerabilities and solutions.
To support the assessment the EO4SD CR used past EO images and precipitation and soil moisture data to map in great detail (up to 10m resolution) the extent and location of past flooding events. This identified areas repeatedly affected by flooding, and those only affected in large or unusual flooding events. Understanding how an area floods, and when, means that effective investments can be made in adopting the most appropriate flood management systems and infrastructure.
The satellite imagery was able to provide detailed information about a recent, historic flood event in Yanji City (see figure above). This information helped to identify areas, infrastructure and populations at risk from similar flood events.
EO data was also able to identify changes in land use and vegetation cover over time. This helps measure the impact of wetland rehabilitation on urban hydrology and river base flow. Satellite imagery can also be used to monitor levels of terrestrial water storage, providing baseline records of water level fluctuation and monthly updates. Future climate models projected an increase in rainfall for every month of the year, and some change in wind speed which could indicate changes in cyclone behaviour. The water supply is forecast to experience greater stress driven mainly by increases in demand, but climate change is also projected to diminish the availability of water. This feedback was delivered to the project at the end of May 2019, and is now being integrated in the delivery of their four project activities.
As demonstrated, EO data clearly has a wide range of applications, and as technology and access to data improves, the ability for EO data to play a key role in improving climate resilience and identifying effective capacity building development only increases. The ADB projects in both Yanji and the Philippines are ongoing, and the EO data provided by the EO4SD CR feeds into the planning and risk assessments phases of the projects. This will provide more robust information on which the ADB can base its future feasibility studies and investment design and implementation phases.
The engagement with the EO4SD CR will continue in both locations. Alongside the EO service provision the EO4SD CR have identified capacity building support to stakeholders to help build their awareness of EO services and how they might usefully be applied. With several capacity building options having been scoped, the stakeholders are better equipped in their decision making by having access to and a greater understanding of the impacts from EO Data. In the Philippines for example, capacity building responses included nature-based flood protection solutions with EO data identifying suitable locations, and the use of real-time EO data to monitor rivers, helping to strengthen early flood warning systems.