Abstract Studying urban heat islands (UHIs) in Southern Europe is crucial, as they amplify heat risks under climate change. UHIs and their temporal variability at seven urban–rural pair locations in Spain were analysed from 1970 to 2023. The UHI was defined as the air temperature difference between each urban site and its neighbouring rural sites, and trends were analysed using the non-parametric Mann–Kendall test with Sen’s slope estimator. Based on daily minimum air temperature data, results indicated a mean UHI intensity ranging from −0.15 °C in Alicante to 2.28 °C in A Coruña. The UHI annual trend was significant, increasing in Valladolid (0.023 °C/year) and Alicante (0.009 °C/year) and decreasing in Santander (-0.015 °C/year). Seasonal analysis showed statistically significant trends in Valladolid, particularly in spring and summer (0.029 °C/year). In Alicante, an increase of around 0.012 °C/year was observed in spring and summer, while Madrid showed a trend of 0.012 °C/year in winter. However, a warming effect at the rural site was identified in Barcelona (−0.028 ºC/year in autumn) and in Santander −0.025 °C/year in spring and summer), corresponding to negative UHI trends. The influence of synoptic patterns on UHI yielded values between 3 and 4 °C in A Coruña and Madrid for anticyclonic southeasterly, anticyclonic southerly, and southeasterly air flows. Lower intensities were found in Barcelona (2.5 °C) and were associated with hybrid anticyclonic westerly flows. UHI intensities below 2 °C were obtained at the other locations, with the lowest values being linked to hybrid cyclonic westerly and cyclonic north-westerly flows.

Abstract Hydrologic models often exhibit inaccuracies in representing key hydrological fluxes due to uncertainties arising from the necessary simplification of complex processes and input data. Soil databases, commonly used in hydrological models, vary in format, resolution, and parameter range, leading to diverse approaches for generating soil inputs in process-based models. This study employs both linear (FOSM) and non-linear (iES) methods to quantify parameter and prediction uncertainty. A comparative perspective on how these approaches reflect uncertainty when using different soil databases is provided. The study area is the Mohaka catchment with an area of 2,428 km 2 , situated within the Hawke’s Bay Region of New Zealand. Four different soil databases were used in this study (FSL, S-map, HWSD, and ISRIC) with different spatial resolutions and the number of soil units covering the catchment. Although similar model evaluation metrics were obtained for streamflow simulation using the different soil databases, flow prediction uncertainty varied significantly for average, low, and high flows. For example, low and high flow predictions showed particularly high uncertainties for the global, low-resolution ISRIC database. Conversely, the local soil database S-map produced the lowest uncertainty range for low and high flow conditions. These findings highlight that while different soil databases may yield similar performance statistics during calibration, selecting those that minimise variance in key predictions can improve the reliability of model predictions. The findings emphasise the importance of selecting an appropriate soil database to enhance model reliability for the purpose under consideration.