This study investigates the long-term behavior of mean annual air temperature and Day-to-Day (DTD) temperature variability across nine climatological stations representing three contrasting environmental settings: continental urban environments, the Istrian Peninsula, and Adriatic islands. The objective was to evaluate the spatial coherence of long-term temperature change while examining the extent to which local environmental characteristics influence short-term temperature variability. Mean annual air temperature exhibits a statistically significant increase at all stations and exceptionally strong inter-station correlations within each environmental setting, indicating a dominant regional-scale climate signal. In contrast, DTD variability displays considerably weaker spatial coherence and generally lacks statistically significant long-term trends, reflecting a stronger influence of local climatic and environmental factors. Urban stations exhibit relatively stable DTD behavior despite pronounced warming, whereas smaller urban centers demonstrate greater variability. Within the Istrian Peninsula, marked contrasts are observed between coastal and inland locations, while the Adriatic islands display remarkably homogeneous mean annual air temperature trends but locally distinct DTD characteristics despite their common maritime setting. These findings indicate that mean annual air temperature primarily reflects regional-scale climatic forcing, whereas DTD variability provides insight into local climatic stability and short-term atmospheric processes. The study demonstrates that analyses based solely on mean annual air temperature may overlook important aspects of local climate variability and highlights the value of DTD variability as a complementary metric for understanding climate change across contrasting environmental settings.
This paper presents a numerical scheme based on the second-order Runge‒Kutta method, combined with finite-difference spatial discretisation, for simulating dam-break flows governed by the one-dimensional Saint-Venant equations. The proposed method introduces a flow decomposition coupled with an artificial viscosity term inspired by previous work, in order to improve the numerical stability and accuracy of the results. A comparative analysis is carried out between the classical scheme and the modified Runge‒Kutta second-order scheme using an analytical solution as a reference. The results show that the modified scheme is significantly better matched to the analytical solution. The method is then applied to several dam failure scenarios including complex conditions such as triangular and trapezoidal obstacles, sloping bottoms and dry bed/wet bed transitions. The numerical results are compared with experimental data and simulation results published in the specialist literature. In all cases, the modified scheme performs better confirming its robustness and accuracy for modelling the propagation of dam burst waves under complex hydraulic conditions.
Stormwater control measures (SCMs) are technical elements designed to prevent and mitigate the negative effects of uncontrolled stormwater runoff. Different types of SCM can provide various co-benefits to the surrounding urban environment, making it important to select the most appropriate measure. This study presents a multi-criteria decision analysis (MCDA) framework for evaluating SCMs based on their optimized design parameters. Six SCM types – bio-retention cells, rain gardens, green roofs, infiltration trenches, detention ponds, and storage tanks – were optimized for a single objective: catchment outflow reduction. The resulting designs were evaluated using MCDA, incorporating additional performance criteria including capital expenditure (CAPEX), operating expenses (OPEX), land take, retained water, detained water, and plant space. The compromise programming method was applied to rank SCM scenarios based on their proximity to an ideal solution. Results indicate that landscape-integrated SCMs, particularly detention ponds, offer the most balanced performance, combining low costs with high co-benefits. Building-integrated SCMs, such as green roofs and rainwater-harvesting storage tanks, scored moderately, while underground storage tanks – representing grey infrastructure – performed the worst due to high costs and lack of co-benefits. The proposed evaluation framework enables transparent, criteria-based comparison of SCMs and supports informed decision-making in urban stormwater planning.
Water pipeline failures in urban networks are a significant source of non-revenue water, service disruptions, and high maintenance costs. This study develops a machine learning model to predict pipeline failure probabilities and inform risk-based maintenance strategies. Trained on real-world assets and geospatial data from 2010 to 2025, the model incorporates standard pipe attributes – such as material, age, diameter, network type, and maintenance history – alongside spatially derived indicators of the surrounding infrastructure. Notably, it quantifies the predictive impact of adjacent infrastructure systems, including electricity grids, gas pipelines, district heating, sewage systems, and roads, utilizing spatial buffering and overlay techniques. Several of these cross-utility features, particularly road category, electricity voltage, and sewer type, showed meaningful predictive importance, reflecting their indirect but consistent influence on the risk of pipe failure. The ML model, built with the XGBoost algorithm and validated through stratified K-fold cross-validation, achieved high performance (ROC AUC: 0.9102, recall: 0.7750, accuracy: 0.8750). Despite lower precision due to class imbalance, the F1 score (0.2261) and LogLoss (0.2500) confirm its reliability. This study introduces a novel, spatially enriched approach to failure prediction, advancing urban infrastructure management through context-aware, data-driven insights.
Understanding the water balance-modifying effects of urban forests is important for mitigating the impacts of climate change and urbanization. We established a paired plot-based hydrological research area in the Botanical Garden of the University of Sopron as part of a Slovenian–Hungarian OTKA project. The research area is designed for a paired plot examination of water balance (interception, transpiration, groundwater dynamics, infiltration, and runoff) with a forested plot and a grass-covered (control) parcel. Since the plots are located in the university's botanical garden, the proximity allows for diverse and frequent data collection and continuous monitoring. Long-term hydrological measurements offer significant opportunities for studying the water balance in forested areas (as is required for assessing the regime). The monitoring results based on only two years as the observational period can illustrate peculiarities and indicate the significant variability that must be captured, along with the factors required when designing such monitoring. The study presents a monitoring framework that provides insights into the complex water dynamics under the black pine canopy and compares the soil moisture and groundwater dynamics between grass and forest plots.
