This paper presents the approach used to assess the global landslide hazard in the derivation of the Global Infrastructure Resilience Index (GIRI) for the Biennial Global Infrastructure Resilience Report of the Coalition for Disaster Resilient Infrastructure (CDRI). The methodology involves integrating landslide susceptibility and earthquake characteristics or rainfall data to determine, on a global scale, the probability of earthquake- and precipitation-induced landslides. The latter is assessed for both 10 present and future climate conditions. The susceptibility map categorizes different terrains into five susceptibility classes, considering factors such as slope, vegetation (land use), lithology, and soil moisture, using global datasets. Rainfall information is gathered from the W5E5 dataset for the time span of 1979-2016 and the IPSL-CM6A-LR climate model from the ISIMIP3b dataset, covering the SSP126 and SSP585 scenarios for 2061-2100. To evaluate the potential for rainfall-triggered 15 landslides, 24-hour rainfall intensities are utilized to classify areas into five rainfall hazard classes. The potential for earthquake-induced landslides is assessed based on the peak ground acceleration (PGA) of the earthquake event (scenario) at a given location and the susceptibility index of the terrain at that location. The landslide susceptibility map(s) and rainfall data or earthquake PGA are combined to produce a hazard matrix. The result is a probabilistic hazard map that can be used for scenario-20 based assessment of global landslide risk to critical infrastructure, with a resolution of three arc seconds (approximately 90 metres at the equator) for the whole globe.

Drought indicators are commonly used tools to describe drought conditions based on key hydrometeorological
variables such as precipitation, temperature, evapotranspiration, soil moisture,
streamflow, and groundwater. Due to the variety of dynamics involved in the propagation of
drought within the hydrological cycle and the implications for impacted sectors, numerous
drought indicators are available in the literature to capture different aspects of the drought
phenomenon (WMO and GWP, 2016). Meteorological drought indices aim at capturing the main
driver of drought represented by precipitation deficits, agricultural drought indices usually focus
on short to medium-term effects of a drought propagating in the hydrological cycle, and
hydrological drought indices focus on medium- to long-term effects recorded on slow-responding
hydrological variables. Often drought research studies focus on a single drought indicator,
selected as the most relevant for the specific application. In this study, we integrated three
drought indicators in order to cover all the above mentioned aspects of drought propagation. In
particular, we adopted indices based on the meteorological forcing (precipitation) and water
balance outputs (soil moisture and streamflow) derived from the Continuum model (see previous
sections for further details).

The GIRI, or the Global Infrastructure Risk and Resilience Model and Index of CDRI, is a comprehensive system of indicators of risk and resilience that encompasses all countries and territories worldwide. Currently, GIRI addresses six natural hazards: earthquakes, tsunamis, landslides, floods, tropical cyclones, and droughts1. The last four include the alterations induced by climate change, thus offering hydrometeorological risk metrics related to various greenhouse gas emission scenarios in the future, in addition to stationary risk metrics for geological hazards. GIRI, presently, encompasses nine infrastructure sectors: power, highways and railways, transportation, water and wastewater, communications, oil and gas, education, health, and housing.

Background Report, INGENIAR: Risk Intelligence for the CDRI Flagship Report.

The model comprises three primary components:

hazard, this component defines sets of mutually exclusive and collectively exhaustive events, covering all potential manifestations of hazards in each territory. As above mentioned, for hydrometeorological hazards, climate change modifications are considered; 

exposure: the collection of elements and components of infrastructure systems, including their replacement values; 

vulnerability: This component relates hazard intensity to the cost of damage for individual elements within the infrastructure. 

Their appropriate combination using a catastrophe risk modeling process rooted in random sets theory, provides essential metrics like the Loss Exceedance Curve (LEC), the Probable Maximum Loss (PML) curve, and the Average Annual Loss (AAL). The AAL represents the sum of the product, for all the stochastic events considered in the loss model, of the expected losses

Background Report, INGENIAR: Risk Intelligence for the CDRI Flagship Report.

This document include supplementary material about The GIRI global flood hazard model paper.

Exposure models are an integral part of risk assessment and management. Exposure models help quantify and characterize the elements at risk in a given area by providing information about what is exposed to a particular hazard. This can include buildings and infrastructure (such as roads and bridges).
In the context of the GIRI project, two separate exposure models were produced and subsequently integrated for risk calculation.
One model deals specifically with the building stock, both for residential use and for all other activities (work, health, education): this is the Global Building Exposure Model, or simply BEM, which is the sole subject of this report.
The second model for calculating the economic value of infrastructure is the Infrastructure Exposure Model (IEM). Its original methodology was developed by Ingeniar, and later the data collection and all processing were done by UNEP/GRID-Geneva.
The BEM provides essential data for understanding the potential impact of hazards on the built environment. An exposure model that includes a global inventory of the building stock based on a purely bottom-up approach would require considerable human and economic effort and is beyond the scope of this project. In the absence of a bottom-up approach, a spatial disaggregation was used. This consists of a top-down or 'downscaling' approach, where information including socio-economic, building type and capital stock at national or sub-national level (statistical data) is transferred to a regular grid, using GIS data such as geographic population and Gross Domestic Product (GDP) distribution models as proxies.

In the recent years, global flood models have emerged as practical tools to transform our understanding of global
flood risk. However, the large computational efforts needed to produce them limit the existing applications to a few scenarios, partial coverage, or coarse resolution products. In this article, we present a methodological approach for producing 90m resolution global flood hazard maps for different flood magnitudes under present and future scenarios.

The approach relies on a cascade of calibrated meteorological-hydrological-hydraulic models and integrates global datasets of atmospheric variables for the present climate and from bias corrected projections of future climate from the ISIMIP3b initiative, enabling the creation of comprehensive and detailed flood hazard maps for different return periods. The significance of such mapping lies in its ability to address the challenges posed by local and global-scale flood events, as well as the impact of climate change on flood risk management. Results contribute to the Global Infrastructure Risk Model and Resilience Index with an advanced hazard product with key implications for improved financial loss assessment, aid in disaster risk reduction efforts, and for global impact assessments.