This study aims to improve the accuracy of small-scale landslide susceptibility maps (LSMs) by comparing two critical terrain factors—slope and relief energy. Slope is commonly used in LSMs, but its values are significantly sensitive to the spatial resolution of digital elevation models (DEMs). Although some studies have also addressed the effect of DEM resolution on relief parameters, direct comparisons between slope and relief energy remain limited. This research examines how these factors perform at different DEM resolutions and compare them to identify the most effective predictor for small-scale LSMs. Using the frequency ratio method, two LSM scenarios were evaluated: one using slope alongside geological units, and another using relief energy instead of slope, with various neighborhood distances. The study was conducted over a 29,785 km² area in the Pannonian part of Croatia. The findings indicate that relief energy is more stable across different DEM resolutions and enhances the accuracy of LSMs, particularly in large and geologically diverse regions. These results suggest that relief energy may serve as a more reliable factor for small-scale LSMs, offering practical implications for improving landslide risk prediction and land management strategies.

Climate change is anticipated to have predominantly negative impacts on human habitats in the coming decades, leading to significant environmental transformations, including an increase in landslide frequency. This paper introduces a novel approach to evaluating the susceptibility of geological units (GUs) to landslides, based on an analysis of the energy relief index. The method is applied to Pannonian Croatia, specifically the North Croatian Basin and the Hrvatsko Zagorje Basin, where landslides are the predominant mass movement process. Previous attempts to create landslide susceptibility maps for Croatia did not adequately analyze landslide inventories. Our approach utilizes three key datasets: landslide inventory, geological maps, and relief energy, to clearly identify which GUs are most frequently in an unstable equilibrium state. The findings highlight that the youngest isolated Miocene unit (M₇) has the highest landslide susceptibility. This method shows significant promise by enabling precise identification of landslide-prone GUs and offering the potential for integration into the production of small-scale landslide susceptibility maps, particularly for determining weighting factors for GUs within specific study areas. However, it is crucial to ensure that the landslide inventory accurately represents the entire landslide population in the study area.

Due to its global coverage, high temporal and enhanced spatial resolution (compared with the ERA5 product), and open availability, the ERA5-Land global atmospheric reanalysis dataset offers a promising framework for various applications in environmental modeling. In this study, the ERA5-Land post-processed daily precipitation product was compared against ground-based observations to evaluate its reliability in representing regional precipitation patterns and its capacity to reproduce landslide-triggering rainfall conditions. The investigation was conducted across five landslide locations in Pannonian Croatia over a 35-year period (1990–2024). It encompassed a comparative statistical analysis of daily and monthly precipitation, an evaluation of rainfall day and event frequency distributions, and a reconstruction of specific historical landslide-triggering events. The results confirm that ERA5-Land exhibits a systematic dual bias, overestimating low-intensity precipitation frequencies while underestimating high-intensity rainfall peaks. Concurrently, the pronounced drizzle effect directly increases the duration of rainfall events, altering landslide-triggering rainfall conditions. The findings align with previous research, confirming that ERA5-Land data can effectively capture broad, long-term regional climate trends and track antecedent rainfall indices. However, its integration into operational landslide early warning systems would benefit from statistical bias-correction or localized calibration to reduce systematic errors.

As uniaxial compressive strength (UCS) of rock material is influenced by petrographic characteristics, special emphasis is given to the visual assessment and determination of rock properties. Visual determination of properties such as lithology, fabric, defects and porosity were conducted on six petrographically different limestone varieties from Istria, Croatia. In addition to the macroscopic and microscopical determination of petrographic characteristics, measurements of ultrasound propagation velocity through samples were also conducted. According to the gained results of visual macro and micro assessment and ultrasound propagation, velocity estimation of uniaxial compressive strength was performed. Using predicted values of UCS, classification of samples according to different classifications was conducted where four out of six varieties of limestone were classified correctly. Based on the achieved results, the method has proven successful for rocks, which have uniaxial compressive strength over 100 MPa.