This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY

Unmanned Aerial Vehicles (UAVs) are now filling in the gaps between spaceborne and ground-based observations and enhancing the spatial resolution and temporal coverage of data acquisition. In the realm of hydrological observations, UAVs play a key role in quantitatively characterizing the surface flow, allowing for remotely accessing the water body of interest. In this paper, we propose a technology that uses a sensing platform encompassing a drone and a camera to determine the water level. The images acquired by means of the sensing platform are then analyzed using the Canny method to detect the edges of water level and of Ground Control Points (GCPs) used as reference points. The water level is then retrieved from images and compared to a benchmark value obtained by a traditional device. The method is tested at four locations in an artificial lake in central Italy. Results are encouraging, as the overall mean error between estimated and true water level values is around 0.05 m. This technology is well suited to improve hydraulic modeling and thus provides reliable support to flood mitigation strategies.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

A flood warning system based on rainfall thresholds makes it possible to issue alarms via an off-line approach. This technique is useful for mitigating the effects of flooding in small-to-medium-sized basins characterized by an extremely rapid response to rainfall. Rainfall threshold values specify the amount of precipitation that occurs over a given period of time and are dependent on both the amount of soil moisture and the spatiotemporal distribution of the rainfall. The precipitation generates a critical discharge in a particular river cross section. Exceeding these values can produce a critical situation in river sites that make them susceptible to flooding. In this work, we present a comparison of methodologies for estimating rainfall thresholds. Critical precipitation amounts are evaluated using empirical data, hydrological simulations and probabilistic methods. The study focuses on three small-to-medium-sized basins located in central Italy. For each catchment, historical data are first used to theoretically evaluate the empirical rainfall thresholds. Next, we calibrate a semi-distributed hydrological model that is validated using rain gauge and weather radar data. Critical rainfall depths over 30 min and 1, 3, 6, 12 and 24 h durations are then evaluated using the hydrological simulation. In the probabilistic approach, rainfall threshold values result from a minimization of two different functions, one following the Bayesian decision theory and the other following the informative entropy concept. In order to implement both functions, it is necessary to evaluate the joint probability function. The joint probability function is built up as a bivariate distribution of rainfall depth for a given duration with the corresponding flow peak value. Finally, in order to assess the performance of each methodology, we construct contingency tables to highlight the system performance. © 2014 Springer Science+Business Media Dordrecht

Abstract. Flash flood events are floods characterised by a very rapid response of basins to storms, often resulting in loss of life and property damage. Due to the specific spacetime scale of this type of flood, the lead time available for triggering civil protection measures is typically short. Rainfall threshold values specify the amount of precipitation for a given duration that generates a critical discharge in a given river cross section. If the threshold values are exceeded, it can produce a critical situation in river sites exposed to alluvial risk. It is therefore possible to directly compare the observed or forecasted precipitation with critical reference values, without running online real-time forecasting systems. The focus of this study is the Mignone River basin, located in Central Italy. The critical rainfall threshold values are evaluated by minimising a utility function based on the informative entropy concept and by using a simulation approach based on radar data. The study concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed alarms.

An accurate definition of river geometry is essential to implement one-dimensional (1D) hydraulic models and, in particular, appropriate spacing between cross-sections is key for capturing a river's hydraulic behaviour. This work explores the potential of an entropy-based approach, as a complementary method to existing guidelines, to determine the optimal number of cross-sections to support 1D hydraulic modelling. To this end, given a redundant collection of existing cross-sections, a location subset is selected minimizing total correlation (as a measure of redundancy) and maximizing joint entropy (as a measure of information content). The problem is posed as a multiobjective optimization problem and solved using a genetic algorithm: the Non-dominated Sorting Genetic Algorithm (NSGA)-II. The proposed method is applied to a river reach of the Po River (Italy) and compared to standard guidelines for 1D hydraulic modelling. Cross-sections selected through the proposed methodology were found to provide an accurate description of the flood water profile, while optimizing computational efficiency.Key words cross-sections; heuristic entropy; hydraulic modelling; one-dimensional flow; network optimization; genetic algorithm; NGSA-II Une approche fondée sur l'entropie pour l'optimisation de l'espacement entre sections en travers pour la modélisation en riviére Résumé Une définition précise de la géométrie de la riviére est essentielle pour mettre en oeuvre des modéles hydrauliques unidimensionnels (1D). L'espacement approprié entre les sections en travers de la riviére est un élément clé pour reproduire son comportement hydraulique. Ce travail a pour objectif de fournir une méthode se basant sur l'entropie, complémentaire des guides existants et permettant de fournir des informations supplémentaires pour l'optimisation des modéles hydrauliques 1D et la gestion des jeux de données sur les sections en travers. Plus spécifiquement, sur la base d'une collecte redondante de sites de sections en travers existantes, un sous-ensemble de sites est sélectionné en minimisant la corrélation totale (comme mesure de redondance) et en maximisant l'entropie conjointe (comme mesure du contenu en information). Le probléme est posé sous la forme d'une optimisation multi-objectif, résolue en utilisant un algorithme génétique (NSGA-II). Nous appliquons la méthode proposée aux données de sections en travers sur le Po, en Italie, et nous la comparons aux méthodes existantes en utilisant un modéle hydraulique 1D. Les jeux de sections en travers sélectionnées par la méthodologie proposée fournissent une description précise du profil des eaux en crue, tout en optimisant l'efficacité de calcul.Mots clefs sections en travers ; entropie heuristique ; modélisation hydraulique ; flux unidimensionnel ; optimisation de réseau, algorithme génétique ; NGSA-II 126

Knowing how people perceive multiple risks is essential to the management and promotion of public health and safety. Here we present a dataset based on a survey (N = 4,154) of public risk perception in Italy and Sweden during the COVID-19 pandemic. Both countries were heavily affected by the first wave of infections in Spring 2020, but their governmental responses were very different. As such, the dataset offers unique opportunities to investigate the role of governmental responses in shaping public risk perception. In addition to epidemics, the survey considered indirect effects of COVID-19 (domestic violence, economic crises), as well as global (climate change) and local (wildfires, floods, droughts, earthquakes, terror attacks) threats. The survey examines perceived likelihoods and impacts, individual and authorities’ preparedness and knowledge, and socio-demographic indicators. Hence, the resulting dataset has the potential to enable a plethora of analyses on social, cultural and institutional factors influencing the way in which people perceive risk.

Raindrop-impact-induced erosion starts when detachment of soil particles from the surface results from an expenditure of raindrop energy. Hence, rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Although it is widely recognized that knowledge of rain kinetic energy plays a fundamental role in soil erosion studies, its direct evaluation is not straightforward. Commonly, this issue is overcome through indirect estimation using another widely measured hydrological variable, namely, rainfall intensity. However, it has been challenging to establish the best expression to relate kinetic energy to rainfall intensity. In this study, first, kinetic energy values were determined from measurements of an optical disdrometer. Measured kinetic energy values were then used to assess the applicability of the rainfall intensity relationship proposed for central Italy and those used in the major equations employed to estimate the mean annual soil loss, that is, the Universal Soil Loss Equation (USLE) and its two revised versions (RUSLE and RUSLE2). Then, a new theoretical relationship was developed and its performance was compared with equations found in the literature.