## 1 Introduction  
Historically, the representation and interpretation of spatial and environmental data have been predominantly grounded in 2D visualization practices [1]. These methods, commonly involving static graphical displays such as charts, maps, or plots, formed the foundation for data communication and analysis. By leveraging visual elements like color, shape, and layout, these 2D visualizations enabled researchers and analysts to identify patterns, detect changes, and derive insights from complex datasets, especially when presented on flat surfaces like paper or screens [2, 3]. With the advancement of computational technologies and increasing research demands, hydrological studies transitioned from empirical and qualitative approaches to the application of physical and statistical models, enabling more rigorous and quantitative analysis of hydrological processes [4]. It is noteworthy that as computational methodologies gained prominence, the evolution of data visualization techniques was also significantly influenced by technological advancements [5]. An illuminating review of this transformation can be found in the work of [PERSON] et al. [6], which not only elucidates the process of creating such visualizations but also proposes avenues for enhancement. Despite the prevalence of graphical methods in the field, a paradigm shift occurred with the ascendancy of data-driven analyses [7]. Hydrological data is inherently complex, characterized by multifaceted attributes, including multivariate properties, temporal dependencies, diverse origins, and data from various sources or models and simulations [8]. Compounded with growing efficiency in data collection, the intricate nature of hydrological data has presented formidable challenges to researchers and decision-makers [8]. The voluminous and multivariate nature of these datasets has made the extraction of critical insights a daunting task, thus hindering the potential to apply the data effectively [9]. In the realm of environmental management and planning, hydrological data plays a critical role in supporting intricate decision-making processes, with a pronounced emphasis on the social dimensions of water resource management [10]. The social dimension refers to the interaction between the water environment and human society, including how public attitudes, behaviors, and online activity inform and respond to water-related challenges [11]. A comprehensive hydroinformatics framework necessitates the engagement of a diverse set of stakeholders, including water utility operators, urban planners, researchers, technology providers, policy makers, and citizens, each contributing unique perspectives and benefiting from enhanced decision making, transparency, and system resilience [12]. Given this critical focus on comprehension and stakeholder engagement, effective representation of data assumes paramount significance in this field. This study introduces an innovative framework utilizing extended reality (XR) to transform the visualization of hydrologic data in environmental planning and decision making. This framework leverages the Unity Engine and Mapbox Maps Software Development Kit (SDK) version 2.1.1 as the base and WebXR exporter and Virtual Reality Tool Kit (VRTK) SDK for dynamic interaction with real-time hydrological and meteorological data. This system, accessible via web browsers and VR devices, enables users to navigate a 3D representation of the continental United States, interactively engaging with detailed environmental data from federal agencies like the United States Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). By integrating these diverse, spatially referenced data streams into a unified XR interface, the system ensures the relevance, accessibility, and contextualization of hydrological information for a variety of user groups. The project represents a significant departure from conventional 2D visualization paradigms in hydrology, offering a more intuitive, immersive, and interactive experience.