Available online 11 September 2015Abstract not availableXun Sun, Benjamin Renard, Mark Thyer, Seth Westra, Michel Lan

The interaction between storm surge and concurrent precipitation is poorly understood in many coastal regions. This paper investigates the potential compound effects from these two flooding drivers along the coast of China for the first time by using the most comprehensive records of storm surge and precipitation. Statistically significant dependence between flooding drivers exists at the majority of locations that are analysed, but the strength of the correlation varies spatially and temporally and depending on how extreme events are defined. In general, we find higher dependence at the south-eastern tide gauges (TGs) (latitude < 30^∘ N) compared to the northern TGs. Seasonal variations in the dependence are also evident. Overall there are more sites with significant dependence in the tropical cyclone (TC) season, especially in the summer. Accounting for past sea level rise further increases the dependence between flooding drivers, and future sea level rise will hence likely lead to an increase in the frequency of compound events. We also find notable differences in the meteorological patterns associated with events where both drivers are extreme versus events where only one driver is extreme. Events with both extreme drivers at south-eastern TG sites are caused by low-pressure systems with similar characteristics across locations, including high precipitable water content (PWC) and strong winds that generate high storm surge. Based on historical disaster damages records of Hong Kong, events with both extreme drivers account for the vast majority of damages and casualties, compared to univariate flooding events, where only one flooding driver occurred. Given the large coastal population and low capacity of drainage systems in many Chinese urban coastal areas, these findings highlight the necessity to incorporate compound flooding and its potential changes in a warming climate into risk assessments, urban planning, and the design of coastal infrastructure and flood defences.

The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic–western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar–Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46 % of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods.

Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.