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Abstract Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3–10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2–3-day forecast for the Amur and Yangtze rivers in East Asia, 5–10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6–8-day forecasts for the Danube River in Europe, 2–5-day forecasts for the Parana River in South America, and 2–7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide. 

Abstract Translational research (TR) represents a promising systematic process for going from scientific discoveries to practical applications. Through conversations with academics, practitioners, decision‐makers and users, there has emerged a broad level of water science community support for including TR in Water Resources Research (WRR) publications. Based on this, we now open a continuing special collection of TR papers in WRR. The aim is to facilitate a community within hydrology and water science that seeks to provide actionable knowledge for societal benefit across disciplines, scales and contexts, with a focus on water as a key societal resource or a risk (e.g., of floods, droughts, or as pollutant carrier). This Editorial discusses what the multi‐faceted nature of TR may include in the context of WRR, why it is important to encourage TR papers in WRR, and how the opening of a continuing special collection of translational water research papers initiates a process to include such articles in the journal. 

The understanding of chaotic systems is challenging not only for theoretical research but also for many important applications. Chaotic behavior is found in many nonlinear dynamical systems, such as those found in climate dynamics, weather, the stock market, and the space-time dynamics of virus spread. A reliable solution for these systems must handle their complex space-time dynamics and sensitive dependence on initial conditions. We develop a deep learning framework to push the time horizon at which reliable predictions can be made further into the future by better evaluating the consequences of local errors when modeling nonlinear systems. Our approach observes the future trajectories of initial errors at a time horizon to model the evolution of the loss to that point with two major components: 1) a recurrent architecture, Error Trajectory Tracing, that is designed to trace the trajectories of predictive errors through phase space, and 2) a training regime, Horizon Forcing, that pushes the model’s focus out to a predetermined time horizon. We validate our method on classic chaotic systems and real-world time series prediction tasks with chaotic characteristics, and show that our approach outperforms the current state-of-the-art methods. 

Some of the most persistent challenges facing society and the environment arise from an intricate coupling of natural and human systems (CNHS). These challenges resist traditional expert-driven problem-solving approaches and require a careful synthesis of both “explanation” and “understanding” to achieve equity and sustainability. Whereas, explanations tend to be the domain of scientific experts who seek generalizable solutions through theory building, modeling, and testing, understandings represent the wisdom of practitioners that enables real-world problem solving to proceed by accounting for contextual values, capacities, and constraints. Using a case study from Bangladesh as an illustrative case of CNHS, we take an explanatory approach in using the extended case study method to show why and how an expert-led response to remediation of arsenic-contaminated wells led to unintended outcomes, which could have been accounted for if a complexity science informed framework of the problem was in place. The complexity frame keeps one alert to emergent patterns that otherwise remain unanticipated, and thereby, form the basis of adaptive actions. For a path forward in addressing complex CNHS problems, we introduce a novel problem-solving approach that combines pragmatic explanations and interpretive understandings with attention to emergent patterns. We argue that this problem-solving approach – which we term principled pragmatism – can effectively synthesize and apply scientific knowledge and local practical knowledge to develop and implement adaptive, actionable, and sustainable interventions.