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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. 

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.