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Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales.

Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. This study attacks the prediction in ungauged catchment problem by exploiting advances in flow index selection and regionalization in Bayesian inference and by developing new statistical tests of model performance in ungauged catchments. First, an extensive set of available flow indices is reduced using principal component (PC) analysis to a compact orthogonal set of “flow index PCs.” These flow index PCs are regionalized under minimal assumptions using random forests regression augmented with a residual error model and used to condition hydrological model parameters using a Bayesian scheme. Second, “adequacy” tests are proposed to evaluate a priori the hydrological and regionalization model performance in the space of flow index PCs. The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is shown that (1) a small number of PCs capture approximately 87% of variability in the flow indices and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series and are hence proposed as a prerequisite for flow prediction in ungauged catchments. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. Hence, a focus on improving hydrological model structure and input data (the effects of which are not disaggregated in this work) is recommended.

Major coastal storms, associated with strong winds, high waves and intensified currents, and occasionally with heavy rains and flash floods, are mostly known because of the serious damage they can cause along the shoreline and the threats they pose to navigation. However, there is a profound lack of knowledge on the deep-sea impacts of severe coastal storms. Concurrent measurements of key parameters along the coast and in the deep-sea are extremely rare. Here we present a unique data set showing how one of the most extreme coastal storms of the last decades lashing the Western Mediterranean Sea rapidly impacted the deep-sea ecosystem. The storm peaked the 26th of December 2008 leading to the remobilization of a shallow-water reservoir of marine organic carbon associated with fine particles and resulting in its redistribution across the deep basin. The storm also initiated the movement of large amounts of coarse shelf sediment, which abraded and buried benthic communities. Our findings demonstrate, first, that severe coastal storms are highly efficient in transporting organic carbon from shallow water to deep water, thus contributing to its sequestration and, second, that natural, intermittent atmospheric drivers sensitive to global climate change have the potential to tremendously impact the largest and least known ecosystem on Earth, the deep-sea ecosystem.