# Towards a Digital Twin Prototype of Alpine Glaciers: Proposal for a Possible Theoretical Framework
## 1 Introduction
Present satellite Earth observation capabilities provide, in fact, global and synoptic observations at unprecedented temporal and geometric resolution that greatly improve the performance and reliability of the system. Additionally, information coming from satellite data could be supported, and integrated, by other technologies, such as aerial acquisitions from both airplanes and UAVs (Unmanned Aerial Vehicles), a network of ground sensors  
Figure 1: Digital Twin concept scheme. possibly managed through smart and IoT-based systems, and coupled with distributed information relying on citizen science approaches for information gathering. Thanks to this integration, which should necessarily undergo a significant pre-processing step aimed at validating and homogenizing data, Earth DT will provide an accurate representation of the past, present and (simulated) future phenomena occurring in the world. A special focus will certainly be on instances of the monitoring of the planet's health, looking for answers about interconnections between the Earth system and human behaviour. In this context, the European Space Agency (ESA), in preparation of the Digital Twin Earth implementation activities, is encouraging research and precursor studies. In September 2020, the ESA launched several Digital Twin Earth Precursor Activities [6] to explore some of the main scientific and technical challenges in building a Digital Twin of the Earth. Explored sectors were forest, hydrology, Antarctica, food systems, ocean and climate hot spots. Each of them addresses a different scientific, technical and operational challenge where artificial intelligence, ICT infrastructure, and stakeholder engagement play a crucial role. For some of these activities, involved researchers have already produced dynamic models and virtual representations of normal interactions that typically characterize the investigated sectors. This is the case in an advanced dynamic reconstruction of the Antarctica hydrology and its interaction with oceans and atmosphere that was obtained by combining past and present observations with simulations of the Earth system in and around Antarctica [7]. In October 2021, a specific procurement was addressed by the ESA to generate an Earth Digital Twin prototype specific to the Alpine Region [8]. The procurement was mainly focused on specific themes defined during the \"Alpine Regional Initiative\" (eod4 lps) [9] in 2018, and identified by the ESA as of importance in contributing to the EU Green Deal [10]. For the specific Disaster Risk Management Theme, among the stated different priorities, one was specifically dedicated to \"glacier dynamics and related hazards\". Glaciers are already known to be related to climate change effects, representing one of the habitats where global warming consequences are more evident. For this reason, a need arises to have a dedicated DT for the real-time monitoring of their dynamics and responses to climate induced effects. In this work, we propose a theoretical framework for a Digital Twin of the Italian Alpine glaciers, through the review of the information which we use as building blocks for its realization. The work proceeds focusing on (i) EO data, (ii) auxiliary data from ground surveys or network of sensors, (iii) services and products made available by institutional players, and (iv) modelling approaches for glaciers studies. Some considerations are also given to applications on glacier monitoring that can be enabled by the DT at a regional level. Three groups of approaches can be assumed as possible references for a DT: Glacier Evolution Modelling (GEM), 4D Multi Glacier and Virtual Reality. It is worth stressing that the main goal of this paper is not to present an already developed DT for Alpine glaciers, but to draft a theoretical framework, based on existing data and sources, useful to enable the development of a proper Glacier DT that can profit from the numerous glacier mapping and modelling experiences.