Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018. Based on the computed extent of our LULC classes and accuracy assessment, we calculated area-adjusted estimates and 95% confidence intervals. Our classification achieved an overall accuracy of 86.4%. Compared to 2010, we found an increase in spatial extent for bare areas (40,011 km²), forests (2576 km²), and urban and peri-urban areas (3560 km²), whereas rangelands (37,005 km²) and cultivated areas (9425 km²) decreased by 2018. It is not possible to determine whether the changes in LULC in Syria will be permanent or temporary. Natural conditions such as climate fluctuations had an impact on the uses of the natural environment and cultivated areas during the study period, especially in regions suffering from water stress. Although seasonal precipitation patterns and temperature affect LULC change, however, we could not identify a prevailing climate trend towards more drought-prone conditions. Our analysis focuses on (potential) direct and indirect implications of the Syrian conflict on LULC change, which most notably occurred between 2014 and 2018. Conflict-related main drivers were human activities and demographic changes, which are mainly attributable to large-scale population displacement, military operations, concomitant socio-economic status, and control of local resources. As the study provides quantitative and qualitative information on the dynamics of LULC changes in Syria, it may serve as a framework for further relevant conflict-related research and support planning, management practices, and sustainable development.

We present palm_csd version 25.10, the current default preprocessing tool for generating the static driver for the building-resolving large-eddy simulation model PALM. The static driver defines the spatial surface characteristics of the simulation domain. This paper provides a technical description of the updated palm_csd workflow, focusing on the processing of buildings, vegetation, pavement, water bodies, terrain height and land cover in compliance with the PALM Input Data Standard (PIDS). Major extensions introduced since the previous description include the processing of georeferenced raster and vector data with automated reprojection, user-defined domain rotation and nesting, enhanced handling of building parameters, optimized generation of resolved vegetation, estimation of leaf area index from vegetation height, and the derivation of static input for non-building-resolving simulations based on Local Climate Zone classifications. We demonstrate the application of palm_csd using publicly available geodata for the city of Berlin (Germany), covering both building-resolving and LCZ-based simulation setups. Common data inconsistencies and sources of uncertainty in urban geodata are discussed. palm_csd 25.10 provides a reproducible, flexible and continuously maintained framework for transforming heterogeneous geospatial datasets into PALM-compatible static drivers to support both detailed urban morphology and coarser-scale urban climate applications.