Ionut Cristi Nicu

Heritage is a living process—our legacy from the past, including social and ecological systems, within which we live today and pass on to future generations. Assessing climate change risks is essential for understanding how hazards, exposure, vulnerability, and responses interact to produce impacts for heritage. These interactions operate across diverse spatial and temporal scales. Current approaches to risk assessment evaluate the scales of climate data, heritage processes, and governance decisions implicitly, leading to misalignments that limit how effectively risks are identified, interpreted, and managed. Here we show that these misalignments arise when observational, measurement, and operational scales diverge. The observational scale defines the boundary and timeframe of a risk assessment. The measurement scale concerns the resolutions of data used, from global climate models to site-level monitoring. The operational scale represents those of underlying processes, from short-term flooding to multi-decadal maintenance and knowledge transmission. When these scales diverge, mismatches obscure how climate change risks are understood and managed. These mismatches reveal not only technical challenges but also deeper divides between knowledge systems, institutional actions, and governance structures. A scale-aware approach can help translate risk assessments into effective actions, aligning data, processes, and responsibilities.

With the ongoing climate changes, the Arctic is experiencing large changes, which have the potential to negatively influence natural environments, human settlements, and cultural heritage. The permanently frozen ground (permafrost) and the seasonally thawing and freezing active layer is sensitive to changes in temperature and precipitation patterns. With permafrost degradation and active layer thickening, Arctic cultural heritage and modern buildings can be increasingly affected by permafrost-related hazards. Mitigating these hazards requires implementation of tools at the local to regional scale. This paper presents a spatial dataset of permafrost-related hazards in Svalbard, integrating three geospatial indicators documenting (1) geomorphology-based hazard susceptibility, (2) Interferometric Synthetic Aperture Radar (InSAR) ground movement, and (3) coastal erosion hazard susceptibility based on the distance to the coastlines. The resulting hazard indicator is combined with vulnerability indicators to provide risk estimates for cultural heritage sites and modern buildings in and around Longyearbyen and Ny-Ålesund. The dataset includes the products from all steps, from the initial spatial geodata to the hazard/vulnerability indicators and risk estimates.

With a warming climate, changing weather patterns and increased erosion from various permafrost- and marine-related processes, the cultural heritage (CH) sites in Svalbard are increasingly vulnerable to destruction. Documentation of CH sites in the Arctic can be very difficult on account of the harsh environment, remoteness (and associated costs), limited access time windows and—in Svalbard—the need to obtain permits from local authorities. The main objectives of this study are: (1) to document an isolated and prioritized CH site in Svalbard—the hut at Kapp Pettersen, Svenskøya—using an uncrewed aerial vehicle and ground photography; (2) to identify the effects of coastal erosion and permafrost-related processes on the cabin; and (3) to gain a better understanding of the landscape surrounding the site. We found that the shoreline had retreated, on average, by 0.42 m/yr between 2010 and 2024. This is significantly more than has been documented at other CH sites in Svalbard. The limited number of photographs of the hut meant that a three-dimensional representation could not be made. However, a detailed auditory description of the hut was made during the visit. This study highlights the challenges and limitations of Arctic CH documentation.

This study presents the first large-scale bibliometric analysis of academic research applying Geographic Information Systems (GIS) to explore the relationship between climate change and immovable cultural heritage. Drawing on 82 English-language publications indexed in the Web of Science Core Collection (1994–2024), the study maps the field’s evolution over the past three decades. Results show a steady increase in publications and citations, reflecting the growing importance of GIS as a methodological bridge linking environmental sciences, geosciences, and heritage management. Research has primarily focused on climate risk assessment (CRA), particularly hazard, exposure, and vulnerability mapping, while studies addressing adaptation planning or governance remain limited. The prominence of environmental and geoscientific disciplines underscores strong technical foundations, yet collaboration across social and policy domains remains fragmented. The relatively modest use of AI-based modelling and remote sensing also points to untapped potential for technological innovation. Overall, the findings indicate a rapidly expanding but still consolidating research landscape and highlight the need for greater interdisciplinarity, interoperable data infrastructures, and integrative approaches to support evidence-based heritage management and climate adaptation.

Retrogressive thaw slumps (RTS) are thermokarst landforms resulting from the thawing of ice-rich permafrost in the pan-Arctic and high mountain regions, recognized as climate-related phenomena. Using 2022 ESRI Wayback satellite imagery, we manually digitized 900 RTS polygons on the Kanin Peninsula (NW Russia), categorizing them into 633 inactive and 267 active features based on morphology and vegetation patterns. The primary goal was not to create a complete RTS inventory but to develop a highly representative dataset for training machine learning models to automatically detect and classify RTS into active and inactive categories. This approach will facilitate regional-scale monitoring of permafrost degradation and enhance understanding of RTS impacts, including their effects on indigenous Nenets reindeer herders’ travel routes. By classifying RTS polygons into morphologically active and inactive features, this dataset aims to enable ongoing analysis of change patterns over time, offering critical insights in the current climate context.