Earth observation data has huge potential to enrich our understanding of the world around us. With increasingly advanced satellites continuously capturing high resolution data, it is possible to monitor the planet like never before. By analysing patterns in earth observation data, it is possible to better understand complex socio-ecological relationships. This data has huge potential to increase our understanding of and response to climate change and its impacts, especially in parts of the world where other forms of climate data are scarce. As a member of the Group on Earth Observations (GEO) and working with clients including ESA Acclimatise continues to explore how earth observation data can be used to inform climate adaptation and risk management.
As an illustration of the power of satellite data to inform us about the environment and human behaviour, NASA released a video that shows what the world looks like during Christmas, Ramadan, and celebrations around the world. Unsurprisingly the researchers found that more intense light is emitted around Christmas and New Year’s as people gather in clusters and turn on their festive decorations. However, some of the more subtle changes revealed other interesting things about human behaviour during festivities…
Photo:
source: Jesse Allen / NASA Earth Observatory: Green patches in Texas indicate
that Dallas and Houston celebrated hard in 2014.
The European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) has released a new brochure providing an overview of its work supporting Monrovia’s Integrated Development Project (MIDP). MIDP has applied EO data to support its efforts to identify pragmatic, spatially integrated and location specific integrations that contribute to service delivery, improved welfare, and the creation of jobs throughout the local area and in the rest of Liberia.
The brochure describes how the EO4SD CR cluster supported MIDP in combining high resolution EO data with projections relating to shoreline erosion and sea level rise in order to create a hot spot analysis of flooding in Monrovia. This flood risk model helps to identify the more effective actions to manage flood risk, develop adaptation plans, and consider where to natural flood management could be most effective. Additionally, by overlaying high-resolution satellite imagery, critical infrastructure and settlements can be identified, and their flood risk assessed. This enables decision makers to make informed decisions about the suitability of developing sites providing crucial infrastructure services, such as transport hubs and hospitals.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
This article was originally posted on the EO4SD website.
In 2019, the World Meteorological Congress and its 193
member countries agreed to establish the Global Basic Observing Network (GBON),
setting out an obligations and clear requirements for all members to acquire
and exchange the most essential surface-based observational data at a minimum
resolution and timeframe level. Achieving compliance with the GBON requirements
requires sustainable investments and strengthened capacity in many countries. Spearheaded
by the World Meteorological Organisation (WMO), the Systematic Observations
Financing Facility (SOFF) is being established to meet these needs.
This new information brief explains the GBON gap analysis undertaken by the WMO for each of its 193 Members and how to calculate it. The gap analysis provides a quantitative estimate of the number of surface-based observing stations that will need to be installed, rehabilitated or upgraded, and exchange data in order to meet the GBON requirements.
Preliminary findings indicate that Small Island Development
States (SIDS) and Least Developed Countries (LDCs) are currently far from
meeting the GBON requirements, largely due to a lack of infrastructure and
capacity. In order to close this gap, about 2000 new and/or rehabilitated
stations need to become operational which, in turn, will lead to massive
increases in exchanged observations. SOFF provides a new way to help close the
GBON gap by ensuring upgrades to weather and climate forecasting systems for a
fraction of the cost compared to current investment plans.
This information brief is one of several produced by the World Meteorological Organization in collaboration with Acclimatise. They are based on the work of the SOFF working groups that brought together 30 international partners to jointly develop the SOFF concept and design.
You can learn more about SOFF and read the other briefs here.
Spearheaded by the World Meteorological Organisation (WMO),
the Systematic Observations Financing Facility (SOFF) provides a new way to
upgrade our global weather and climate forecasting systems for a fraction of
the cost compared to current investment plans. SOFF applies internationally
agreed metrics – the requirements of the Global Basic Observing Network (GBON)
– to guide investments and create local benefits while delivering on a global
public good.
This new information brief explains how the world can benefit from improved surface-based observations and shows how targeted investments can yield the highest level of improvement in weather prediction and climate analysis.
Improved surface-based observations deliver economic,
environmental, and social benefits. Alongside the provision of timely warnings
about extreme weather events, investments in weather and climate services yield
a strong positive cost-benefit ratio that will positively impact a wide range
of sectors. However, these benefits will only be realised if systems are in
place to translate them into useful information through improved forecasting.
Numerical Weather Predictions (NWPs) use computer-encoded
versions of predictive equations of atmospheric behaviour to understand how
climate has changed in the past, and how it may be evolving in the future. While
global NWP is useful, there is significant scope for improvement in NWP
accuracy. In fact, full implementation of GBON will roughly double the amount
of surface-based data available to global NWP.
The monetary benefits of implementing GBON will fall mainly
to regions with a high share of global GDP. However, there are massive cost
savings to be had by all. Currently, the world’s global observation system is
estimated to cost US$ 10 billion a year whereas the estimated funding to
supports Small Island Development States (SIDS) and Least Developed Countries (LDCs)
in achieving GBON compliance for an initial five-year period corresponds to USD
400 million.
This information brief is one of several produced by the World Meteorological Organization in collaboration with Acclimatise. They are based on the work of the SOFF working groups that brought together 30 international partners to jointly develop the SOFF concept and design.
You can learn more about SOFF and read the other briefs here.
The European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) cluster has released a new brochure describing its work supporting AGRYHMET, West Africa’s leading drought monitoring centre. The EO4SD CR cluster used Earth Observation (EO) data to support AGRYHMET’s work improving food security in West Africa, in partnership with the World Bank’s Africa Risk Management team.
The brochure describes how the EO4SD CR cluster supported AGRYHMET’s project preparation by providing a range of EO datasets and services that inform drought assessments and wetlands monitoring. The data include climate indicators such as rainfall, soil moisture, and water availability.
By integrating this data in to AGRYHMET’s existing processes, EO data helped to enhance the assessments that AGRYHMET already do by increasing the accuracy and timeliness of drought monitoring and forecasts. In combination with socioeconomic data, AGRYHMET can build a better picture of food security, desertification control, and water control and management, helping to enhance climate resilience in the region by identifying focus areas for intervention.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
Water Frequency map of water body – seasonal changes in surface extent. Source: GeoVille
Today, the EO4SD Climate Resilience Cluster releases the Rainfall Explorer, a cutting-edge tool that enables users to readily obtain near real-time extreme rainfall statistics for past major flood events recorded anywhere in the world.
All of this increases the imperative to leverage new and existing data to derive deeper insights that may improve early-warning of near-term flood risks and inform development of robust, climate resilient strategies to deal with future flood risk. Our tool, which is already used by the World Bank and Multilateral Investment Guarantee Agency, seeks to be part of the solution.
Rainfall return level and return period data during late July 2016 over Maryland (USA) visualised using the Rainfall Explorer.
It is well understood that prolonged heavy rainfall is a key trigger of major flood events globally. However, catchment characteristics, land cover, topography, drainage, flood control, and other factors (including how these change in these over time) result that flood propensity for similar amounts of rainfall varies significantly from place-to-place. Further, a range of factors lead an area to be flood-prone at a given point in time, including the height the water table, ground saturation, and river discharge. And whilst the Rainfall Explorer presents rainfall statistics over a 5-day period, destructive flash floods may be triggered by intensive rainfall falling in a few hours.
Nevertheless, analysing accumulated rainfall in the lead-up major floods in an area can help to illuminate the relationship between rainfall and flood propensity. Using this tool, users may select past floods or areas of interest to obtain rainfall statistics, including the amount of rainfall (mm) recorded 5-days prior to a flood, the return period (years) of this rainfall, and the range of the rainfall return period for the selected area or flood.
In turn, these statistics can assist users to:
Identify patterns and trends in flood occurrence, flood severity and rainfall totals, and rainfall return levels
Identify rainfall thresholds likely to trigger a large flood in an area of interest
Identify the amount of rainfall associated with a past material flood event of interest
Understand the likelihood of rainfall associated with past flood events
Find the return period (years) for a given 5-day rainfall amount (mm)
Find the 5-day rainfall return level (mm) for a given return period (years)
Map the region affected by a past flood event, together with the 5-day rainfall amount and return period associated with the event
The Rainfall Explorer currently leverages the Dartmouth Flood Observatory archive of large flood events (1984 to 2020) and a 40-year timeseries of processed Copernicus ERA5 Reanalysis daily precipitation data, at 30km x 30km spatial resolution, available near-real time (with 5 days delay from present time). ERA5-Land Reanalysis daily precipitation data, at enhanced 9km x 9km spatial resolution, will be available in early 2021. All data are stored and computed in the cloud, meaning that users may access the Rainfall Explorer using only a web browser, wherever they are.
The Rainfall Explorer is an EO4SD tool led by Telespazio Vega UK and Sistema GmbH, with the support of Acclimatise and GMV. We thank the World Bank and MIGA for valuable feedback provided during the development of this tool.
The European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) has released a new brochure providing an overview of its work with the Asian Development Bank (ADB) where EO data was provided to improve the flood risk management related to its projects in the Philippines.
The Philippines is regularly inundated by disasters, including flooding events. However, flood risk management in the country has been largely ineffective. To help ADB secure more detailed data on floods in the Philippines, the Cluster developed an Inundation Monitoring Service (IMS) for Jalaur River Basin that detects seasonal fluctuations in water bodies and monitors long-term changes. The maps provided by the IMS show the extent of flooded areas over time, helping the ADB build a more detailed understanding of the flood response needed in a particular area. This allows for better infrastructure investment in flood protection, and disaster response and early warning.
In addition to the delivery of the IMS product, the Cluster provided a capacity building programme to support ADB staff in better understanding EO-derived data and services so that they can apply it to their own work. Capacity building activities include targeted support through practical training, and awareness raising and knowledge transfer through online courses and webinars.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
The Jalaur River Basin is located on the Eastern side of the Philippine island of Panay.
This article was originally posted to the EO4SD website.
The European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) has released a new brochure providing an overview of its work with the World Bank in providing Earth Observation (EO) services to the Climate Change Knowledge Portal (CCKP).
The CCKP is one of the most high-profile, publicly accessible, climate data platforms in the world. Developed to service the needs of expert and non-expert users, the CCKP provides global data on past climate and future climate change projections, as well as socio-economic data to support users in their climate-resilient decision-making. The brochure describes how the CCKP has been successful in serving as a hub for climate-related information, data, and tools to inform policy and practice, providing online access to comprehensive global, regional, and country data related to climate change and development.
The EO4SD CR cluster worked with the World Bank during its most recent upgrade of the CCKP, identifying EO data that could be seamlessly integrated into the existing CCKP architecture so that it could be accessed instantly by users. Alongside the EO service provision, the Cluster delivers capacity building support to foster the sustained uptake of EO-based data and services by IFIs and Client States to support climate change resilience. For the World Bank, the capacity building will provide government officials and the World Bank’s Task Team Leaders with information on how to access and test EO-derived data, helping users to make sense of EO data and understanding how it can be useful for them.
he Cluster provided Essential Climate Variable (ECV) data in both map and time-series formats which allowed for images and time-series data to be easily integrated and overlaid. This data included air surface temperature, sea surface temperature, and sea level anomalies, amongst others. The ECI data can be displayed in a map format and allows for the selection of several data points compared through time series data. This helps to show levels of variation across different geographies and times.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
Graphic representation of data provided to CCKP (surface air temperature aggregated over Mozambique) as displayed in the EO4SD climate platform. The surface air temperature is obtained from the ERA5 meteorological reanalysis provided by the Copernicus program.
For over 18 months, the European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) Cluster has been working with the International Finance Corporation (IFC) and the World Bank’s Multilateral Investment Guarantee Agency (MIGA) to integrate Earth Observation (EO) data into its climate risk screening tool providing evidence of climate risks to its investments. The results of the collaboration to date have now been released in a new brochure presenting the types of EO services that were developed and how they were applied.
The EO4SD CR cluster worked with IFC to introduced new extreme rainfall indicators to improve its assessment of future flood impacts, seamlessly integrating EO-based extreme rainfall return levels into IFC’s existing climate risk tool. In addition, working with MIGA, the Cluster produced a rainfall explorer tool that provides reliable insights into potential climate risks to existing and future investments.
The brochure outlines the challenges faced by both IFC and MIGA when assessing climate risks to their investments. The IFC’s existing climate risk screening tool faced challenges when projecting the future risk of drought, flooding, and related impacts of extreme rainfall as they are not well-captured by climate modelling for many of the regions where the IFC operates and are often not presented at the required resolutions.
Precipitation from GPCP for 31st August 2019, presented on the EO4SD Climate Resilience Cluster’s EO platform.
MIGA, the political risk insurance arm of the World Bank, faces similar problems when assessing climate risk to its projects. “MIGA evaluates potential climate risks to its projects and looks to align our financial flows with long-term climate resilient development pathways. Simply put, our team looks to identify appropriate climate resilience responses to reduce the likelihood of financial or environmental underperformance of our projects” explains MIGA’s Manu Sharma. “In conducting our project assessments, we make use of climate change projections and have found it challenging to very quickly interpret the significance of shifts in various rainfall indicators, specifically as it relates to flood risk.”
For MIGA, the EO4SD CR cluster developed an EO-based data product called the ‘Rainfall Explorer”, an interactive cloud-based tool, the rainfall explorer enables users to:
Quickly find the 5-day cumulative rainfall return level and return period preceding past major flood events;
Find the 5-day rainfall return period and return level for any terrestrial location globally and any date between 1979 and near-real time; and
Visualise data via interactive maps and box plots covering a given flood footprint or user-defined area.
The Rainfall Explorer is powerful as it allows the user to assess the statistical significance of near-real time rainfall events. This means that the IFC and MIGA can understand the likely flood risk associated with a particular level of rainfall, compared to historical events in the same area.
“[The Rainfall Explorer] really provides several benefits to climate adaptation practitioners across the Multilateral Development Banks” said Manu Sharma, “it allows us to look up any event that has occurred anywhere on the planet and we can work out the duration of that event, as well as the significance of that event. The significance is based on the historical record for that specific location.”
Outputs from the Rainfall Explorer tool allow users to visualise rainfall events, the return periods of those events, and their statistical significance.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
This article was originally posted on the EO4SD website.
The European Space Agency’s (ESA) Earth Observation for Sustainable Development Climate Resilience (EO4SD CR) has released a new brochure providing an overview of its work supporting the African Risk Capacity’s (ARC). ARC has applied EO data to support its efforts to help African governments improve their capacities to plan for, prepare for, and respond to extreme weather events and natural disasters.
The brochure describes how the EO4SD CR cluster supported ARC’s Africa RiskView (ARV) tool, combining EO data and population vulnerability data to create an early-warning model that measures food insecurity as well as estimating response costs, helping decision-makers to make early and effective interventions. This included making a on-demand flood mapping service called Flood Mapper that uses EO data to enhance the pan-African Flood Extent Depiction (AFED) service.
The EO4SD CR cluster provides insight about the potential of Earth Observation (EO) data to support climate-resilient decision making at the regional and national scale. In collaboration with several International Financial Institutions, the EO4SD CR cluster has developed EO-based screening and risk management products that can be integrated into different platforms and project cycles.
Other summaries of EO4SD CR projects can be found here, as well as a webinar series outlining how different EO data products have been used and hands-on guided sessions on how to use the different data platforms.
This article was originally posted on the EO4SD CR website.
Image: Example of a flood plain near Lake Turkwel (Kenya). Flooded area (left) and the flooded area extracted from the permanent water extent (right). The flood extent mapping provides information on the total water covered area (binary water/ non-water map). By taking away the permanent water bodies from that water map, the areas that are temporarily flooded can be identified (hatched). This flood product also provides a direct comparison of the EO4SD Flood Map with the AFED Flood Map as well as statistical information on flooded landcover. Source: Geoville
