Hydrological hazards (“hydro-hazards”) are defined as extreme events associated with the occurrence, movement and distribution of water, specifically resulting in floods and droughts. As a result of global climate change these hazards are expected to change in the future, with areas of the globe becoming “hotspots” for the intensification of these extremes. This paper is the first global review of the state- of-the-art research on hotspots for floods and drought. The work follows a systematic literature review of published research, and analyses and categorizes the results of 122 published papers after a methodical screening process. The analysis highlighted the geographical areas where increasing hazards are anticipated (e.g., Europe for both floods and droughts), and those areas of the globe where no significant research has been published (e.g., Russia). The methods used to undertake the research are analyzed and new trends identified. Potential avenues for future research are highlighted, including the incorporation of uncertainty analyses into hydro-hazard assessments, the consideration of multi-hazards and their interconnections, and finally the consideration of dynamic vulnerability and exposure in conjunction with changing hydro-hazards to understanding future risk.

Accurate spatial distribution information of rainfall is essential to rainfall-induced hazard predictions and statistical interpolation methods may serve as a useful tool to produce a detailed distribution from coarse data sources. Although numerous comparison studies about different interpolation methods have been conducted on irregular rain-gauge networks, there is a need to perform such work on the increasingly available gridded rainfall data. Carried out in the Emilia-Romagna region (Italy) from 2008 to 2018, this study aims to examine accurate and appropriate interpolation methods to produce finer rainfall surface maps based on the 0.25° × 0.25° ERA5 gridded precipitation datasets. Five interpolation techniques, namely Thiessen polygons, Inverse Distance Weighting (IDW), Thin Plate Spline (TPS), Ordinary Kriging (OK) and ordinary Co-Kriging (CoK), have been selected and compared at different time scales (annual, monthly and annual maximum daily precipitation). To assess the accuracy, the leave-one-out-cross-validation test was used by using the indexes of Bias, correlation coefficient, the Nash-Sutcliffe Efficiency, Root Mean Square Error and the Kling-Gupta Efficiency (KGE). Additionally, visual inspections are employed to evaluate the plausibility of the interpolated maps. Results show: (a) All five interpolation methods have certain capabilities to improve spatial resolution, but they fail on accuracy at the daily scale. The OK generally outperforms the other four methods, while TPS shows better performance through the visual inspection at the monthly scale; (b) Unlike in the case of the interpolation using conventional point-based rain-gauge data, the multivariate method CoK is inferior to the univariate ones and the p-power of the IDW also differs and (c) Winter and spring results are better than those of summer and autumn. This study has provided useful guidance on choosing suitable rainfall interpolation methods for gridded datasets, which can be expanded in other regions and data sources to explore the generality of the conclusions.