Landslide Susceptibility

Note: This layer is produced using a different data source and modeling methodology than the climate‑change riverine flooding layer (CDRI-GIRI), which means there are areas identified as hazardous by one model but not the other; using both layers together is good practice to capture all areas that may be potentially affected by riverine flooding across the two modeling frameworks. This layer represents an aggregated riverine flood hazard classification derived from seven original flood‑inundation depth rasters corresponding to return periods (RP) of 10, 20, 50, 75, 100, 200, and 500 years. Each RP layer was classified as either Moderate or High hazard using the matrix shown below, following a hazard‑ranking approach derived from the Nicaragua MET‑ALARN thresholds. This classification was applied independently to the seven RP layers. A combination rule was then applied, assigning to each pixel the highest hazard class observed across all seven RPs. Intensity / Frequency RP ≤ 10 10 H ≥ 1.0 m High High 0.5 m ≤ H High Moderate 0.1 m ≤ H Moderate Moderate The original rasters come from the Global River Flood Hazard (GLOFAS) Maps dataset, a gridded global product representing simulated inundation depths along the global river network. The underlying flow simulations are produced with the open‑source LISFLOOD hydrological model, while inundation extents are simulated using the LISFLOOD‑FP hydrodynamic model. Each cell in the source rasters represents water depth in meters, and the dataset was developed under the Copernicus Emergency Management Service (CEMS) to support disaster‑risk management and infrastructure‑resilience planning. The map has a resolution of 3 arc-sec. (~93 meters at the equator; east–west distance decreases with latitude). The layers were clipped to the geographic boundaries of the IDB borrowing countries. Links to the original layers: 1‑in‑10 yr riverine flooding hazard 1‑in‑20 yr riverine flooding hazard 1‑in‑50 yr riverine flooding hazard 1‑in‑75 yr riverine flooding hazard 1‑in‑100 yr riverine flooding hazard 1‑in‑200 yr riverine flooding hazard 1‑in‑500 yr riverine flooding hazard VECTORIZED VERSION AVAILABLE HERE: Riverine Flooding Hazard. Disclaimer on Coastal Coverage Limitations The spatial dataset does not provide complete coverage in coastal areas. The data were clipped using the World Countries Generalized polygon, which does not fully capture detailed coastal geometries. As a result, certain coastal zones fall outside the clipping boundary and are therefore not represented in the dataset. This limitation should be taken into account when interpreting results for coastal or near‑shore areas. This will be corrected soon.

Abstract Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems. Data visualization Landslide susceptibility is shown using a 5-color Color Ramp from blue to red. Values represent Very Low (blue) to Very High (red) susceptibility to a landslide. Further reference Read more about the susceptibility map on the Global Precipitation Measurement (GPM) mission website. Stanley, T., and D. B. Kirschbaum (2017), A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 1–20, doi:10.1007/s11069-017-2757-y.

The tsunami hazard map indicates the mean wave run-up height on the coast for a return period of 475 years. The map has coastal coverage with points in the countries with tsunami hazard. Run-up values below 0.1 m are considered as “Low”; run-up values between 0.1 m and 2 m (inclusive) are considered as “Moderate”; run-up values greater than 2 m are considered as “High”. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.

Layer provides distribution of landslide hazard, describing by an index of very low to very high hazard. Data produced under the ADRF Sub Saharan Africa Risk Profiles project. The project provides also risk data produced, exposure and hazard data used in the analyses.

Dates Available: April 2021 - Present Update Frequency: Twice a day Summary: Landslides are a widespread and often underestimated natural hazard, causing thousands of deaths and significant economic damage each year. They routinely block roads, destroy infrastructure, and disrupt communities worldwide. While intense or prolonged rainfall is the most common landslide trigger, earthquakes and human activities can also cause them. The global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides users with a comprehensive view of landslide hazard in near real-time. Using machine learning, LHASA combines rainfall data from the Global Precipitation Measurement (GPM) satellite with antecedent soil moisture from the Soil Moisture Active Passive (SMAP) satellite to identify areas currently at risk from landslides. The model shows where and when landslides are most probable, then calculates the exposed population and road length within each administrative district, helping users understand both the hazard and potential impacts on communities and infrastructure. To learn more please visit: https://landslides.nasa.gov/ Suggested Use: LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard. LHASA nowcast Hazard: "yesterday", "today", and "tomorrow": This map shows the relative probability of landslide occurrence yesterday, today, and tomorrow; respectively, based on rainfall estimates from GPM and forecasts from GEOS-FP. Satellite/Sensor: GPM, SMAP Resolution: 30 arcseconds (~1 kilometer) Credits: NASA GSFC Users are requested to cite the following paper: Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043]

The storm surge hazard map indicates the mean run-up height on the coast for seven return periods: 10, 25, 50, 100, 250, 500, 1000 years. The map has coastal coverage with points in the countries with storm surge hazard. The data were classified into three hazard categories—low, moderate, and high—based on storm surge run-up heights for a 250-year return period, a widely used standard for coastal hazard zoning and infrastructure risk assessment. Run-up values below 0.1 meters are considered as “Low”; run-up values between 0.1 meters and 2 meters (inclusive) are considered as “Moderate”; run-up values greater than 2 meters are considered as “High”. Storm surge hazard is modeled only along the coastline. The process involves generating a grid along the coastline and 5km inland. In fact, no backwater effects were modeled, this is only due to the continent’s contour map (shapefile) version used to create the grid mentioned on the coastline. The tropical cyclone events used for the elaboration of this map consider climate change based on models representative concentration pathways (RCP) scenarios. Climate change scenario: SSP5 RCP8.5 In this project Mediterranean tropical-like cyclones were not included in the hazard assessment process. We considered only tropical cyclones as storms that develop in areas of low pressure over tropical oceans, and that are associated with several hazards such as strong winds and storm surge. These two hazards were assessed at a global level but given that although tropical cyclones are formed along the North Atlantic Ocean but do not occur in Europe, neither strong wings nor storm surge were modeled for European countries. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.