The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions–whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.

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Robust Decision Making (RDM) is an established framework for decision making under deep uncertainty. RDM relies on the idea of scenario neutrality, namely that decision robustness is not affected by how scenarios are generated if these are uniformly distributed and span a sufficiently large range of future states of the world. Several authors have shown that scenario neutrality may not hold, but they did so by adopting either new or computationally expensive modeling. We introduce the Belief-Informed Robust Decision Making (BIRDM) framework to assess how robustness might change under an arbitrary large number of non-uniform distributions at virtually no additional costs with respect to RDM. We apply BIRDM to a flood management problem and find that alternative distributions change the robustness and ranking of measures. BIRDM allows identifying what distributions lead to these changes and under what set of distributions a measure has a specific robustness and rank.@en

Reliable hazard analysis is crucial in the flood risk management of river basins. For the floodplains of large, developed rivers, flood hazard analysis often needs to account for the complex hydrology of multiple tributaries and the potential failure of dikes. Estimating this hazard using deterministic methods ignores two major aspects of large-scale risk analysis: the spatial–temporal variability of extreme events caused by tributaries, and the uncertainty of dike breach development. Innovative stochastic methods are here developed to account for these uncertainties and are applied to the Po River in Italy. The effects of using these stochastic methods are compared against deterministic equivalents, and the methods are combined to demonstrate applications for an overall stochastic hazard analysis. The results show these uncertainties can impact extreme event water levels by more than 2 m at certain channel locations, and also affect inundation and breaching patterns. The combined hazard analysis allows for probability distributions of flood hazard and dike failure to be developed, which can be used to assess future flood risk management measures.

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Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

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Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2–19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.@en

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals.@en

We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends, inter alia, on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems.

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