A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed.

A robust hydrological impact assessment is indispensable for mitigation and adaptation planning. This study presents an integrated modelling methodology for evaluating climate change impacts on water availability, sediment yield and extreme events at the catchment scale. We propose the use of the spatial–temporal Neyman–Scott Rectangular Pulses (STNSRP) model—RainSim V3 and the rainfall conditioned daily weather generator—ICAAM‐WG, as well as the physically based spatially distributed hydrological model—SHETRAN. The change factor approach was applied for obtaining unbiased rainfall and temperature statistics. The ICAAM‐WG was developed based on the modified Climate Research Unit daily Weather Generator (CRU‐WG). The methodology is proposed to generate synthetic series of hourly precipitation, daily temperature and potential evapotranspiration, hourly runoff and hourly sediment discharge. We demonstrated a possible application in a 705‐km² Mediterranean climate basin in southern Portugal. The case study showed the evaluation of future climate change impacts on annual and monthly water balance components and sediment yield, annual and seasonal flow duration curves, empirical extreme value distributions and the theoretical fits. It did not consider the possible uncertainty due to the limit of computational resources.The methodology can be well justified as follows: (a) the use of synthetic hourly instead of daily precipitation enables SHETRAN to be more capable of reproducing reliable storm runoff processes and the consequent sediment transport processes; (b) the use of SHETRAN makes possible the impact assessment to be accessible for any model grid square within the study basin; (c) the use of a statistical–stochastic downscaling method facilitates the generation of the synthetic series with unlimited length. It makes possible robust hydrological impact assessments if uncertainties related to the global climate model, regional climate model, greenhouse gas emission scenario, downscaling method, hydrological model and observational data are considered.

Downscaling is usually necessary for robust hydrological impact assessments. This may be undertaken using a wide range of methods, including a combination of dynamical and statistical‐stochastic downscaling. This study uses the Spatial–Temporal Neyman‐Scott Rectangular Pulses model—RainSimV3, the precipitation‐conditioned daily weather generator—ICAAM‐WG, and the change factor approach for downscaling synthetic climate scenarios for robust hydrological impact assessment at middle‐sized basins. The ICAAM‐WG was developed based on the concept of the Climate Research Unit daily weather generator (CRU‐WG), motivated by the need for improved representation of heat waves by downscaling methods given the positive feedback between low soil moisture and high air temperature. We demonstrated the validity of the proposed methodology in the 705‐km² Mediterranean climate basin in southern Portugal. The results show that, for the control period 1980–2010, both RainSimV3 and ICAAM‐WG reproduced not only the mean climatology, but also extreme wet and low precipitation events, as well as the extremes of temperature and heat waves. We found that downscaling with ICAAM‐WG (SIM6), which uses second‐order autoregressive processes for the simulation of temperature during consecutive dry and wet days, outperformed ICAAM‐WG (SIM4), which used only first‐order autoregressive processes, leading to improved simulation of heat waves. ICAAM‐WG (SIM6) well reproduced observed heatwave extremes with return periods of up to 30 years; however, ICAAM‐WG (SIM4) overestimated these extremes substantially. This indicates the importance of incorporating second‐order autoregressive processes in the simulation of heatwave length. In the context of climate warming, the proposed methodology provides a tool to improve downscaled projections of future extremes with confidence intervals for not only wet events but also dry spells and heat waves.