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

Two decades after the construction of the first major dam, the Mekong basin and its six riparian countries have seen rapid economic growth and development of the river system. Hydropower dams, aggregate mines, flood-control dykes, and groundwater-irrigated agriculture have all provided short-term economic benefits throughout the basin. However, it is becoming evident that anthropic changes are significantly affecting the natural functioning of the river and its floodplains. We now ask if these changes are risking major adverse impacts for the 70 million people living in the Mekong Basin. Many livelihoods in the basin depend on ecosystem services that will be strongly impacted by alterations of the sediment transport processes that drive river and delta morpho-dynamics, which underpin a sustainable future for the Mekong basin and Delta. Drawing upon ongoing and recently published research, we provide an overview of key drivers of change (hydropower development, sand mining, dyking and water infrastructures, climate change, and accelerated subsidence from pumping) for the Mekong's sediment budget, and their likely individual and cumulative impacts on the river system. Our results quantify the degree to which the Mekong delta, which receives the impacts from the entire connected river basin, is increasingly vulnerable in the face of declining sediment loads, rising seas and subsiding land. Without concerted action, it is likely that nearly half of the Delta's land surface will be below sea level by 2100, with the remaining areas impacted by salinization and frequent flooding. The threat to the Delta can be understood only in the context of processes in the entire river basin. The Mekong River case can serve to raise awareness of how the connected functions of river systems in general depend on undisturbed sediment transport, thereby informing planning for other large river basins currently embarking on rapid economic development.

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Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-second and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, leak detection, peak demand estimation, data storage, and availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most * Corresponding author. Tel.:+39-02-2399-9040Email address: andrea.cominola@polimi.it (A. Cominola)Preprint submitted to Environmental Modelling & Software October 22, 2017 large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters.

Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used

Advances in modeling and control have always played an important role in supporting water resources systems planning and management. Changes in climate and society are now introducing additional challenges for controlling these systems, motivating the emergence of complex, integrated simulation models to explore key causal relationships and dependences related to uncontrolled sources of variability. In this brief, we contribute a massively parallel implementation of the evolutionary multiobjective direct policy search method for controlling large-scale water resources systems under uncertainty. The method combines direct policy search with nonlinear approximating networks and a hierarchical parallelization of the Borg multiobjective evolutionary algorithm. This computational framework successfully identifies control policies that address both the presence of multidimensional tradeoffs and severe uncertainties in the system dynamics and policy performance. We demonstrate the approach on a challenging real-world application, represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin in Northern Vietnam, under observed and synthetically generated hydrologic conditions. Results show that the reliability of the computational framework in finding near-optimal solutions increases with the number of islands in the adopted hierarchical parallelization scheme. This setting reduces the vulnerabilities of the designed solutions to the system's uncertainty and improves the discovery of robust control policies addressing key system performance tradeoffs