Flood early warning systems are crucial for disaster risk reduction strategies, enabling communities to take timely action against threats. However, the effectiveness of these systems depends on accurate and timely hydrological data, particularly river discharge and water level measurements. Unfortunately, many regions face significant challenges in obtaining hydrological data, especially discharge data, due to outdated rating curves, high equipment costs, and logistical constraints. In contrast, water-level measurements offer reduced uncertainty and are often more accessible, providing an alternative to hydrological modeling in data-scarce regions. To address these limitations, we developed and validated the Discharge-to-Water Level Transformation (DWLT) method, which uses the monthly duration curves to transform discharge simulations from the GEOGLOWS ECMWF Global Hydrological Model into water level predictions using data from over 19,000 ground- and satellite-based river gauge stations. The results indicate that the water levels generated by DWLT are closely aligned with the observed water levels, especially when using satellite measurements, which offer a valuable alternative when ground-based data are scarce. Despite quality issues such as spikes and zero-level inconsistencies in ground-based data and temporal limitations such as short monitoring periods and infrequent measurements in satellite-based data, the methodology shows promising potential for large-scale and local hydrological applications. This work supports future flood forecasting and water resource management efforts, highlighting water level as an effective variable in hydrological modeling.

Chinmay Deval

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Effective management of water resources and mitigation of water-related disasters are essential for human well-being, ecosystem health, and socio-economic development. However, the challenge lies in translating scientific insights into actions and decisions, resulting in a disconnect between knowledge production and practical decision-making. SERVIR, a collaborative initiative by the U.S. Agency for International Development (USAID) and the U.S. National Aeronautics & Space Administration (NASA), addresses this gap by partnering with regional organizations worldwide to integrate Earth Observation (EO) data into practical applications. This paper evaluates SERVIR’s experience translating cutting-edge science into actionable information for water security/water resources management through a collaborative approach, co-developing tools within diverse cultural contexts, and emphasizing capacity building. By discussing case studies and engagement strategies from SERVIR’s extensive experience, we highlight its collaborative efforts with regional bodies, governmental agencies, and other partners to transform water resources research into practical insights, supporting decision-making at various levels. This paper underscores the importance of continuous capacity-building workshops, stakeholder engagement, and adapting to technological advancements, such as cloud computing, for sustained impact. It also addresses the need for effective translators to navigate the complex EO toolkit, ensuring the appropriate application of tools for specific water management decisions. By reflecting on SERVIR’s journey, this paper offers guidance for decision-makers, practitioners, and researchers, encouraging dialogue, innovation, and collective action to support reliable access to water for all and sustainable water management. The insights and opportunities derived from SERVIR’s experience provide a framework for future initiatives, advancing water security and water resource management strategies.