Digital Twin of Alps

Building the cornerstone of a Digital Twin Earth with a focus on the Alps.


The DTA demonstrator is an example of how Digital Twins will act as decision-making support platforms that will help us better understand complex physical processes.

The goal of the demonstrator is to collect concrete feedback from key stakeholders and users, in order to provide a roadmap for the implementation of a future Digital Twin Component of the ESA DTE. To do so, we have implemented a demonstration platform based on a potential use-case: the unique combination of state-of-the-art developments in hydrology and geohazard sciences to represent in a holistic way the major environment-related hazards and water resource impacts faced by populations living in the Alps.

In the DTA platform, the services and products generated are split into two main themes: Water Resource Management and Disaster Risk Management. To discover more about the products available, keep scrolling!

The datasets that are discoverable in the demonstrator come from Earth Observation datasets, numerical modelling (physically-based or AI-driven) or a combination of both. Although the integration of datasets from ground-based measurements would be possible, in order to cover phenomena difficultly observable from space (e.g. rockfall), these are not present in the demonstrator currently. Furthermore, it is important to note that not all possible themes covering risks or water resource management in the Alps are addressed within the scope of this project, as the goal is to demonstrate capabilities and drive future requirements for a DTC.

Get started with the demonstrator

Start your journey within the Digital Twin of Alps by signing up with the button above. To learn more about the demonstrator's features, and how to search, visualise, compare and download data from the services provided by the DTA, you can watch our tutorial video below. To discover the themes addressed and the services available, keep scrolling!

Water resource management in the Alps

Credit: Maxim Lamare

The Alpine region is often referred to as the "Water Tower of Europe" because of its contribution to the formation of numerous rivers that flow through the continent, such as the Rhine, Rhône, Danube, and Po, which all provide water to millions of people in downstream areas. In this sense, the Alps play an essential role for populations who use the resource for irrigation, industrial use, and drinking purposes. Futhermore, the Alps harbor important ecosystems that heavily depend on water availability.

Because hydrological processes over Alpine basins are very complex and different from those over large river basins, there is a strong need for a decision-support tool that focusses on services and value-added datasets accounting for this complexity. Indeed, many public actors that are involved in water resource management have already developed a number of tools and systems for monitoring resources based on hydrological modelling and ground observations. However, remote sensing data are typically not considered so far in an operational context except for the use of optical observation for snow cover mapping. A strong requirement from the communities is the need of operational products available frequently and with a short latency to improve existing decision-making systems. To support this, the DTA focusses on providing novel analysis-ready datasets that benefit from a combinations of input sources (EO and non-EO) and offers easy access to multi-scale representations of complex hydrological processes and their interactions.

In the DTA demonstrator, three main hydrology related themes are addressed: snow, river discharge / water heights, and soil moisture / evapotranspiration. Although, the DTA does not cover all the hydrology themes in the Alps, such as glacial meltwater, groundwater, or reservoir levels, these example datasets are able to showcase how a comprehensive understanding of the complex water systems in the Alps can be obtained through advanced datasets.

Disaster risk management in the Alps

Credit: Jean-Philippe Malet.

Populations living in the Alps face a range of natural risks due to the region's unique geographical and climatic characteristics. Amongst these risks, avalanches, rockfall,landslides and flooding are of particular threat to human lives and infrastructure. Landslides involve a wide range of processes, extents, displacement rates and rheologies worldwide and affect human lives and activities. As an example, about 3.5 million people in Europe are nowadays threatened by slope instabilities, and this figure may evolve in relation to global changes.

To safeguard the populations living in the Alpine region, public agencies have adopted a number of mitigation actions which include comprehensive risk assessment, early warning systems, planning regulations and protective measures. Therefore, authorities in charge of risk management have to develop and use methods and services combining observations (in-situ and remote instrumentation, citizen survey) and models for quantitative risk assessments, meaning there is a strong need for advanced technologies, such as a Digital Twin instance, to gather data that will drive the decision-making processes. The DTA demonstrator aims to provide an advanced decision support system for actors involved in the observation and mitigation of environmental risks and impacts in the Alps, by offering access to information derived from state-of-the-art processing chains providing a holistic representation of some of the major physical processes specific to the Alpine context.

Because the main objectives of the DTA project are to build a roadmap for a future Digital Twin Component, the aim of the demonstration platform is to show-case a potential usecase for a Digital Twin of Earth instance. Therefore, the demonstrator does not cover all the natural disasters faced by populations in the Alps, but focuses on examples of shallow and deep-seated landslides, with the implementation of two automatic services, as well as flooding, with the analysis of a past event and scenario-based simulations.

Demonstrator services

Water resource management services

Snow cover

Why monitor snow?

Snow is a crucial variable in mountain environments. In the Alps, the majority of the precipitation during winter and early spring falls as snow over 1.000 m altitude, and remains stored in the snow pack until the melting season, when it returns in the hydrological cycle. Snow therefore represents a water reserve that affects water availability in the spring and summer months. Having knowledge of this reservoir makes it possible to understand whether the current season will be drought or abundant in order to plan activities in advance and implement preventive actions where possible to optimise availability.

What is the snow processing chain?

The snow processing chain allows the generation of information on the evolution of the snowpack. In particular, the processing chain generates information maps of: snow depth, snow water equivalent, snow covered area and snow melting. In addition to this, the service also provides input meteorological variables.

How does the service work?

The snow processing chain first involves the collection and processing of input data to be used in a physical model. These data are related to the main meteorological variables, (e.g. precipitation, temperature, radiation) which allow the determination of snowfall events and snowpack melting conditions in order to reconstruct the snowpack evolution. The process runs using the meteorological inputs, i.e., ERA5 reanalysis meteorological data (provided by ECMWF) and the data from the Weather Research & Forecasting Model (WRF) to fill in the gaps; these data are in fact practically always available (barring rare malfunctions of the platforms supplying them) and therefore represent a solid basis for carrying out the simulations. The chain also produces a satellite Snow Covered Area (SCA) product created through special algorithms that combine various information from the main indices obtainable from optical satellite products (e.g. NDSI, NDVI, NDWI) and geometric information in machine learning algorithms.

The input data, together with maps to represent the geometry of the area, are fed into the GEOtop distributed physical model that reconstructs the evolution of the snowpack by also ingesting ground-based information regarding the snow depth measured and the snow cover area produced with satellite data when available. This assimilation improves the performance of the model by correcting any misinterpreted situations.

What products are available in the demonstrator?

The following products are included in the demonstration platform:

  • Daily snow water equivalent maps for the Alps area from 2017 to 2022
  • Daily snow water equivalent maps for the Sarca river basin over the period 2002-2016
  • Bi-monthly snow water equivalent anomaly maps in the Sarca area for the period 2017-2022
  • Daily average temperature and total precipitation maps over the Sarca area for the period 2017-2022
  • Daily snow depth maps for the Alps area from 2017 to 2022
  • Weekly SCA maps for the Alps area from 2017 to 2022
  • 3-day forecast snow melt maps for the Alps area from 2017 to 2022.

All the products listed above are generated with a spatial resolution of 250 x 250 m.

Example of the Snow Depth product over Mont Blanc region (23.02.2023).

Who is the service useful for?

The service is useful first and foremost for all entities that use water in their daily activities (e.g. hydropower sector, municipalities) and need to plan their activities according to the water availability they will have during the season. In this sense, the snow water equivalent is an excellent indicator because it allows the estimation of available volumes. Other users that can benefit from this information are research centres that can obtain information to use in their models to simulate other physical phenomena and the tourism sector that needs to know where snow is located.

Products from external providers

The High-Resolution Snow and Ice Monitoring service (HR-S&I) of the Copernicus Land Monitoring Service (CLMS) provides complementary information to the Snow Cover products and showcases the integration of existing cloud-based services within DTA's decision-making platform. This service includes maps that describe the following snow properties: snow cover, snow state conditions (wet/dry snow) and persistent snow area. The production of the data is ongoing since 2016.

Snow State Classification in central Alps, where white represents dry snow and pink wet snow. For further information on the land cover categories represented in the map refer to the following documentation.

Water discharges and water heights

Why monitor rivers?

The monitoring of river flow allows to highlight drought or wetter periods to characterise water availability in the study basin. The behaviour of water in a river is closely related to snow and groundwater behaviour. The study of river dynamics can therefore be useful in various scientific and industrial applications to better understand the state of the water resource within a study area. Many agencies then use river water in their activities (e.g. hydroelectricity, irrigation) and must adhere to regulations limiting their withdrawals or discharges according to the condition within the river.

What is the water discharge processing chain?

The water discharge processing chain aims to generate, in selected closure sections, the flow rate of the watercourse using meteorological and snowmelt information as input, as well as information on the evapotranspiration of the area.

How does the service work?

First, a geomorphological extraction of the area of interest is carried out to identify the hydrological network and its related catchments. Then, the input data are collected and processed to derive precipitation, mean temperature, evapotranspiration and snowmelt. These variables, together with the geometry component, are ingested into the semi-distributed NewAge model. This model allows the generation of outflows in the desired closure sections from the inflows generated by precipitation and snowmelt considering the effect of evapotranspiration. The model was calibrated in defined sections where observed data were available in order to train it to simulate the real situation within the basin as best as possible.

In some cases, it is necessary to consider human activities that influence the river flow (e.g. withdrawals, releases) and that interrupt the hydrological continuity of the river channel (e.g. reservoir). This part of the anthropogenic component is not currently contemplated in the model and the results are generated by considering the study catchment as natural. In the future, it is considered to implement components to be able to consider the anthropogenic part as well.

What products are available in the demonstrator?

The following products are included in the demonstration platform:

  • Soil moisture maps in mm including the water content in the soil and the water vapour with a spatial resolution of 250x250 m and an hourly time step
  • River discharges as a temporal series in m³/s in one section of the Entremont catchment (Switzerland) and several sections of the Sarca catchment (Italy).

River discharge at the Pont de Rossettan site (Switzerland) over the year 2022.

Who is the service useful for?

Knowledge of river dynamics is useful for all those entities that interact with the river to draw or discharge water. In the first case, it is necessary to know the water available in order to assess the quantity that can be withdrawn, also considering the regulations applied. In the second case, it is necessary to consider the water already present in the river so as not to provoke problematic situations downstream linked, for example, to the flooding of certain areas. Related to this last case, the use of this data also provides information on the river's behaviour at certain intense precipitation events and therefore data to extract hydrograms to be used in 2D models that generate flood maps for risk and safety management activities.

Soil moisture/drought monitoring

Why monitor drought?

Drought monitoring in Alpine areas is complex, as it requires knowledge of the water availability in the soil, which is a critical driver for vegetation growth (i.e., agricultural drought). To correctly monitor drought, knowledge of where the water is stored (e.g., as snow) and of the fluxes (precipitation and evaporation) is also necessary.

The increase in frequency of drought events, for example the 2022 drought and the lack of snow precipitation during the winter 2022-2023 in Italy, has shown the critical importance of monitoring water scarcity at high-spatial resolution.

What is the drought processing chain?

The drought processing chain, implemented for the DTA project, is a service devoted to monitor drought conditions over three Alpine areas: the Sarca basin (Italy), the Entremont district (Switzerland) and the Alpes-de-Haute-Provence region (France). To do so, we exploit the information contained in both innovative satellite- and model-based estimates of different hydrological variables, being firstly soil moisture and additionally evapotranspiration, Snow Water Equivalent (SWE) and precipitation. To characterize and monitor drought events, monthly maps of the standardized anomalies at 1 km spatial resolution are computed based on the reference period 2017-2021. Water scarcity conditions for all different hydrological variables are highlighted by negative values (i.e., < 0) of the standardized anomalies.

How does the service work?

The drought monitoring service allows the integration and cross-comparison of multiple variables of the hydrological cycle which is expected to provide useful information to identify the spatial extent and severity of drought phenomena. Information from satellite-based products of soil moisture, evapotranspiration, precipitation and SWE are compared with the output of a hydrological modelling chain, providing the soil moisture and evapotranspiration outputs. The service generates monthly standardized anomalies maps of the different products and it allows to check water scarcity conditions from different water cycle components.

What products are available in the demonstrator?

The demonstrator embeds 1 km maps of standardized anomalies over three Alpine areas from different products:

Example of the Evapotranspiration Anomaly product derived from GLEAM over Entremont region in Switzerland in August 2021.

Who is the service for?

Given the relevance of drought monitoring in the current context of climate change, as experienced in Summer 2022, the integration and cross-comparison of multiple observations of the hydrological cycle is expected to provide an overall understanding of water scarcity.

This service is particularly useful for a variety of actors across multiple fields, including: scientists studying the impacts of drought who require state-of-the-art analysis-ready datasets, government agencies looking to assess water availability, manage water resources, and implement appropriate policies and regulations, farmers who need to assess the risk to crops, plan irrigation strategies, and make informed decisions about planting, harvesting, and managing livestock, or insurance companies that offer policies related to agriculture and water resources, who may use drought monitoring data to assess risk levels and determine insurance premiums.

Disaster risk management services


Why monitor floods?

Flooding is a local phenomenon in alpine areas. The flood wave routing and inundation propagation in the valleys are strongly influenced by the morphological characteristics of the terrain and by the high river bed slope that make the duration of the flooding events typically short. In this context, developing forecast maps of flooded areas is particularly difficult. Therefore, flood forecasting should be based on meteorological forecasts, if available and reliable, and on previously developed hazard maps referring to different possible scenarios.

What is the flood processing chain?

The flooding model chain implemented in DTA project is a service devoted to reconstructing a past observed flood event that occurred on October 2020 in the Sarca River basin (river reach between Dro site and Garda lake), and developping a database of flood maps identifying the limit of the areas potentially affected by flooding for different scenarios as a tool to support flood risk management. THe service exploits the output of a hydrological model applied to the Sarca river basin, calibrated to simulate the significant flood of October 2020. The flood hydrograph provided by the rainfall-runoff model is used as upstream boundary condition for a two-dimensional model applied to reproduce the event. As downstream boundary condition, the lake level is used and the morphological characteristics of the computational domain are described through a DTM (1 m spatial resolution). The simulations allow to calibrate the roughness parameter, using as a benchmark the limits of the flooded areas as shown in a video acquired by the local fire department during the flood, and to set the model configuration for developing what-if scenario analyses.

How does the service work?

The service allows the consult the maps previously developed and stored in the system that identify the maximum extension of the areas potentially affected by flooding in the Sarca basin (the computational domain covers Sarca river reach between Dro site and the Garda lake) for different flood waves. The service can support real-time flood forecasting in the area considering the discharge hydrograph forecasted by the hydrological model (based on the meteorological forecast) and selecting the investigated scenario as close as possible to the future prediction. In this way, the map is selected among the ones already produced.

What products are available in the demonstrator?

The demonstrator provides 5 m flood maps over the computational domain of the two-dimensional hydraulic model for 10 different scenarios, i.e. 10 different flood hydrographs simulated by the rainfall-runoff model. The map provided by the modelling chain addressed to reproduce the flooding event occurred on October 2020 is also included in the demonstrator.

Map of the modelled flood of the Sarca River between 3-4 October 2020.

Who is the service for?

The flooding occurred during October 2020 clearly demonstrated the relevance of the problem. Indeed, currently, flooding monitoring over the Alps is not carried out and the activities carried out in previous DTE projects (i.e., DTE Hydrology) do not consider the Alps.

In this context, the possibility to consult a previously developed database of flood maps corresponding to different conditions is a useful tool for institutions and entities in charge of flood risk management in real time and territorial planning.

Shallow landslides

Why observe shallow landslides?

Shallow landslides affect slopes over the entire Alpine massif, and cause significant risks and impacts for the population, properties, and infrastructures. They generally occur spontaneously without major signs of activity prior to failure, which makes this natural hazard particularly difficult to detect and to predict. But we know that shallow landslides are generally produced on specific slope conditions and are often triggered by heavy rainfall events and snow melt episodes.

What is the shallow landslide chain?

The shallow landslide forecasting chain, implemented for the DTA project, is an automated service to spatially forecast, within 3 days leading time, the slope failure susceptibility and the material propagation after failure.

Failure susceptibility is the geomechanical capability of slopes to fail as shallow landslides; it locates the potential source areas.
Propagation susceptibility locates areas that can be reached by the runout of moving muddy material in case of shallow landslide triggering.

How does the service work?

The service works with a sequence of models. First, the Shallow Landslide Failure Forecast (SLFF) model aims to assess the probability of landslides triggering on slopes over the next three days. In other words, the model combines spatial static landslide source maps to dynamic triggering factors corresponding to water-related variables. Therefore, per pixe of the spatial static landslide source map and hydro-meteorological forecasts (from third-party providers), the Shallow Landslide Failure Forecast model assess automatically (and forecast up to about day + 3) the failure probability of slopes that are prone to fail.

Second, the Shallow Landslide Runout model aims to assess the maximum run-out areas of landslide sources detected by the previous failure forecast model. The runout assessment is based on Flow-R, a software for rapid and robust propagation modelling of gravity-driven geohazards, optimized for regional and watershed runout susceptibility mapping.

What products are available in the demonstrator?

The following products are available in the DTA demonstrator:

  • Topographic hillshade, derived from a 5m resolution DEM (swisstopo) used as inputs to the landslide processing chain.
  • Land-use classes (based on anthropogenic activities, geological maps and vegetation) used to assign hydrological and geomechanical parameters controlling soil stability.
  • Maximum rainfall (in mm) for a rainfall event with 24h duration and a return period of 100 years from the Hydrological Atlas of Switzerland.
  • Minimum rainfall (in mm) for a rainfall event with 24h duration and a return period of 100 years from the Hydrological Atlas of Switzerland.
  • Static shallow landslide source area susceptibility map. The map distinguishes 5 landslide susceptibility classes based on the infinite slope stability model using a stochastic combination of hydrological and geomechanical parameters derived from the landuse map.
  • Cumulative precipitation (in mm) for the next three days (D0 to D+3) based on meteorological forecasts. These precipitation forecasts are combined with precipitations measurements of the past 3 days (D-3 to D-1) into an antecedent rainfall index with predefined weights.
  • Forecasted shallow landslides source areas. Source areas from the static susceptibility map for shallow landslides are forecasted as potentially active, when the antecedent rainfall index exceeds predefined thresholds (15 mm for unstable and very unstable sources, 20 mm for all sources).
  • Forecasted shallow landslides runout areas. The runout area of shallow landslides starting from forecasted source areas is modelled with Flow-R with predefined parameters for two scenarios: mudflow-like propagation (reach angle: 19°, maximum velocity: 8 m/s) and normal short propagation (reach angle: 27°, maximum velocity: 2 m/s).

Map of the Shallow Landslide Source Susceptibility in Entremont district (Switzerland) in 2023.

Who is the service for?

The modelling service is implemented at high spatial and temporal resolution and tailored to complex topographies, integrates both rain and snowmelt inputs as forcing factors, and proposes a forecast of both source and propagation areas. Therefore, the service outputs can support the work of various stakeholders in mountainous areas, especially public offices dealing with natural risk management such as local risk management authorities and civil protection, as well as landuse planners through the early identification of potentially endangered population and infrastructures within the next three days.

Large, deep slope failures, mostly deep-seated landslides and rockslides

Why monitor deep-seated landslides?

Active deep-seated landslides affect specific and generally known slopes over the Alps, threatening populations through a potential catastrophic acceleration or rupture of the unstable mass, involving long runout of huge volumes. A monitoring system is vital for public safety, minimising adverse impacts on the infrastructure and the environment, and empowering communities to prepare for and mitigate potential economic losses. Continuous monitoring also contributes to the advancement of scientific knowledge, essential to understand how landslides evolve and adapt to changing conditions, including climate change.

What is the deep-seated landslide chain?

The deep-seated landslide processing chain is a service that provides displacement (East-West and North - South components) and velocity data. It relies on the GDM-OPT-SLIDE, providing 10 m-resolution data over the period 2015 - 2023 in the La Valette (France) and the La Barmasse (Switzerland).

How does the service work?

The GDM-OPT-SLIDE service enables the on-demand processing of optical Sentinel-2 imagery through image correlation algorithms. The GDM-OPT-SLIDE service is accessible on the Geohazards Exploitation Platform (GEP).

What products are available in the demonstrator?

The following products are available in the DTA demonstrator:

  • Average horizontal velocity map (in m/day)
  • Average horizontal East - West displacement map (in m)
  • Average horizontal North - South displacement map (in m)
  • Time series East - West displacement maps (in m)
  • Time series North - South displacement maps (in m)
  • Quality indicator: Root Mean Square (RMS) time series of East - West displacement maps (in cm)
  • Quality indicator: Root Mean Square (RMS) time series of North - South displacement maps (in cm)

Who is the service for?

The service provides information to stakeholders (public authorities, geophysical/geotechnical engineering consultancy companies) in charge of landslide early-warning systems and risk management.

Average landslide velocity showcasing La Valette slope, France. Positive and negative values indicate upslope (blue pixels) and downslope velocities (red pixels), respectively. Blue pins are locations where in-situ measurements are available.

Terrain motion

Why monitor terrain motion?

Terrain motion monitoring is essential for the early detection of areas prone to landslides, enabling a prompt response in the event of an occurrence. It is also a crucial factor in infrastructure development to ensure the stability of buildings, bridges, dams, and pipelines. Additionally, terrain motion is an indicator of human impact on the environment, including deforestation, mining, and urban development. It supports water resources management, as it provides information on the changes in groundwater levels, lake levels, glacier and permafrost melting, among others.

What is the terrain motion processing chain?

The terrain motion processing chain aims to provide a time series of terrain displacement, movement velocity and ancillary information such as quality indicators. Currently, the service supports terrain motion monitoring over North Western Alps and provides data for the period February 2020 - April 2022.

How does the service work?

Terrain motion products are derived from SNAPPING (Surface motioN mAPPING), an on-demand service for Sentinel-1 IW SLC (Interferometric Wide Swath Single Look Complex) based on integrated SNAP and StaMPS chain, which are available through the Geohazards Exploitation Platform - GEP.

What products are available in the demonstrator?

The following products are available in the DTA demonstrator:

  • Ground motion time-series expressed in mm.
  • Coherence between two complex SAR images expressing a measure of phase correlation (or phase reliability).
  • Surface motion (velocity) along the line of sight (LoS) given in mm/year.
  • Uncertainty in the estimation of LoS surface motion in mm/year.

Who is the service for?

The service support the work of those stakeholders that are mainly involved in disaster risk management, civil engineering, and land use planning in mountainous environments, with focus in the Alps.

Example of a surface motion product along the line of sight in Mont Blanc (France). White pixels represent no movement, positive values (in blue) indicate that the observed terrain is uplifting or moving towards the satellite, and negative values (in red) indicate subsidence or motion away from the satellite.

Products from external providers

SARScape ground deformation products produced by the Geological Survey of Slovenia (GeoZS) and University of Ljubljana - Faculty of Civil and Geodetic Engineering UL FGG) are provided as complementary products to the Terrain Motion processing chain. The data was produced in ENVI SARScape software, and includes the following products:

  • Average Line of Sight Velocities based on the period 2017 to 2021
  • Average SARScape Slope Orientation velocities based on the period 2017 to 2021.
For more information contact Mateja Jemec at this website.

Example of the SARScape Line of Sight velocities product over Slovensky Javornik, Potoki, and Moste (Slovenia). White pixels represent no movement, positive values (in blue) indicate that the observed terrain is uplifting or moving towards the satellite, and negative values (in red) indicate subsidence or motion away from the satellite.

Glacier motion

Why monitor glacier motion?

Alpine glaciers have significantly receded due to increasing temperatures of around 2 ℃ (Gobiet et al., 2014). A study focused on ice dynamics in the largest glacier of the European Alps, the Great Aletsch Glacier, indicates that in the most positive scenario (RCP2.6), 60% of the current ice volume will be lost by 2100 and it will almost completely disappear in a high-emissions scenario (RCP8.5) (Fox-Kemper et al., 2021). Monitoring the evolution of Alpine glaciers is hence key to understand the impact of such changes and anticipate future evolution of the Alpine ecosystem.

What is the glacier motion processing chain?

The terrain motion processing chain for ice glaciers aims to provide information about the horizontal displacements, velocity maps and quality indicators (RMS) at a 10 m spatial resolution. The service relies on Sentinel-2 images and the GDM-OPT-ICE web service. The service covers an extensive Alpine massif across the North Western Alps between 2015 and 2023.

How does the service work?

Glacier motion products disseminated through the DTA demonstrator are based on image correlation algorithms applied to Sentinel-2 (optical) imagery, using the GDM-OPT-ICE web service accessible on the Geohazards Exploitation Platform (GEP).

What products are available in the demonstrator?

The following products are available in the DTA demonstrator:

  • Mean glacier velocity in the East-West horizontal component (m/day)
  • Mean glacier velocity in the North-South horizontal component (m/day)
  • Mean velocity magnitude over all time steps (m/day)
  • Time series of glacier surface motion (in m) in the East - West horizontal component
  • Time series of glacier surface motion (in m) in the North - South horizontal component
  • Time series of the glacier surface motion accuracy (in cm) for the East - West horizontal component
  • Time series of the glacier surface motion accuracy (in cm) for the North - South horizontal component

Who is the service for?

The service outputs provide an understanding of glacier dynamics. Glacier retreat leaves slopes uncovered and therefore, areas more susceptible to rockfalls and debris flows. Avalanches and glacier lake outburst floods are other potential hazards linked to glaciers, with increasing probability of occurrence due to higher temperatures. Therefore, this information is essential for environmental and disaster risk agencies involved in public safety. It is also relevant for infrastructure planners when designing structures near glacier areas, and for public agencies and specialists working in the water resources sector to plan accordingly based on potential water availability changes.

Example of glacier displacement time series in the East-West and North-South horizontal components extracted at a pixel level.

Project description

The Alps are the most densely populated mountain range in Europe. As a result, hydrological and gravitational hazards constitute a major threat to human activity, and water resources play a central role in socio-economic developments (agriculture, tourism, hydropower production...). Furthermore, the Alps are particularly sensitive to the impacts of climate change. Over the last century, temperatures have risen twice as fast as the northern-hemisphere average, whereas precipitation has increased non-linearly. Because of the increasing pressure on human settlements and infrastructure, there is a strong priority for policy-makers to implement climate change adaptation strategies from the local to the regional scale. To support and improve the decision-making process, numerical decision support systems provide valuable information derived from observations or models to better manage increasing threats and weaknesses.

The main objective of the Digital Twin of Alps project is to provide a roadmap for the implementation of future Digital Twin Earth (DTE) instances, with a focus on the Alpine context. A demonstrator will be developed to act as a decision support system representing the major environment-related risks and impacts faced by populations living in the Alps, as well as water resource management indicators. Unifying both approaches enables a better understanding of the major environmental and societal challenges faced by human societies in the context of a rapidly-changing climate.

The development of the roadmap for a future DTE will be conducted in close collaboration with partner scientists, industry, ICT experts, modellers and policy makers to propose:

  • A Scientific and Technical Agenda covering all the relevant R&D requirements for a DTE.
  • A roadmap for developing and implementing a Digital Twin Component (DTC) of the DTE.

Decision Support System

About us

The Digital Twin of Alps activity is part of ESA's Regional Initiative 3, which paves the way towards a new representation of the Earth System based on the integration of Earth Observation data, inter-connected numerical simulations, and Artificial Intelligence.

Project Period: September 2022 - August 2023
ESA Contract No. 4000139281/22/I-DT


Sentinel Hub GmbH, Austria
Maxim Lamare, Lucia Guardamino
Sinergise d.o.o., Slovenia
Grega Milcinski, Primoz Drobnic
EOST/A2S Université de Strasbourg, France
Jean Philippe Malet
Terranum Sàrl, Switzerland
Clément Michoud, Thierry Oppikofer
MobyGIS, Italy
Matteo Dall’Amico, Nicolò Franceschetti, Federico Di Paolo
Stefania Camici, Hamidreza Mosaffa, Sara Modanesi, Luca Brocca

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