Floods are one of the natural hazards that could be most affected by climate change, causing great economic damage and casualties in the world. On December 2019 in Reinosa (Cantabria, Spain), took place one of the worst floods in memory. Implementation of DIRECTIVE 2007/60/EC for the assessment and management of flood risks in Spain enabled the detection of this river basin with a potential significant flood risk via a preliminary flood risk assessment, and flood hazard and flood risk maps were developed. The main objective of this paper is to present a methodology to estimate climate change’s effects on flood hazard and flood risk, with Reinosa as the case study. This river basin is affected by the snow phenomenon, even more sensitive to climate change. Using different climate models, regarding a scenario of comparatively high greenhouse gas emissions (RCP8.5), with daily temperature and precipitation data from years 2007–2070, and comparing results in relative terms, flow rate and flood risk variation due to climate change are estimated. In the specific case of Reinosa, the MRI-CGCM3 model shows that climate change will cause a significant increase of potential affected inhabitants and economic damage due to flood risk. This evaluation enables us to define mitigation actions in terms of cost–benefit analysis and prioritize the ones that should be included in flood risk management plans.

Climate change undoubtedly will affect snow events as temperature and precipitation are expected to change in the future. Spanish mountains are especially affected by that situation, since snow storage is there focussed on very specific periods of the hydrological year and plays a very important role in the management of water resources. In this study, an analysis of the behaviour of the complex snow-related phenomena in the four main mountain regions of Spain in the next 50 years is conducted. The ASTER hydrological model is applied using temperature and precipitation data as basic input, estimated under a climate change scenario. Results show different changes in the maximum and average expected flows, depending on the very different magnitude and sign of changes in precipitation. An increase of flooding episodes may occur as a result of a complex relation between changes in precipitation and an increase in maximum snowmelt intensities that range from 2.1% in the Pyrenees to 7.4% in the Cantabrian Mountains. However, common patterns are shown in a shorter duration of the snow bulk reserves, expected to occur 45 days earlier for the Cantabrian Mountains, and about 30 days for the rest of the studied mountain regions. Changes observed also lead to a concerning decrease in the regulatory effect of the snow-related phenomena in the Spanish rivers, with a decrease in the average snow accumulation that ranges from about 28% for the Pyrenees and Sierra Nevada to 42% for the Central System and the Cantabrian Mountains. A decrease in average flow is expected, fluctuating from 2.4% in the Pyrenees to 7.3% in Cantabrian Mountains, only increasing in the Central System by 4.0%, making all necessary to develop new adaptation measures to climate change.

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Spanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, in this study, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually being the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates ranging widely from 0.7 × 103 kg/L/day to 2 × 103 kg/L/day without showing a spatial pattern. The densification rate for the set of manual samples was set to 1.2 kg/L/day, very similar to the set of automatic measurements (1.3 kg/L/day). The results increase knowledge on SDEN in the Pyrenees. The SDEN regression models that are given in this work may allow us, in the future, to estimate SDEN, and consequently Snow Water Equivalent (SWE), using an economical and extensive SD and meteorological network, although the high spatial variability that has been found must be regarded. Estimating a relationship between SDEN and several climate drivers enables us to take into account the impact of climate variability on SDEN.