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River bathymetry is needed to accurately simulate river hydrodynamics. River bathymetric data are typically collected through boat-mounted single- or multi-beam echosounder surveys. Detailed bathymetric data from multibeam surveys may exceed the requirements of standard river hydraulic models (1D and 2D). Compared to data-intensive but expensive multibeam surveys, single-beam surveys are cost-effective. Single-beam surveys can sufficiently inform river simulations when coupled with specific preprocessing and interpolation techniques. This study contrasts two survey patterns, including the commonly used but under-studied zigzag surveys, against the traditional cross-sectional surveys. Linear and anisotropic Kriging interpolations, two widely used methods, are applied to construct bathymetry mesh from different survey configurations. Results from this study highlight efficient survey configurations for both cross-sectional and zigzag patterns, balancing accuracy and cost. Notably, zigzag surveys approach the efficacy of cross-sectional surveys when spaced below a certain threshold, but Kriging interpolation shows diminished performance with sparse zigzag surveys. The findings from this study bridge gaps in previous research by offering nuanced comparisons between survey configurations and interpolations. This study offers a comparative analysis to guide more effective planning and utilization of single-beam surveys, without advocating for specific survey patterns or interpolation techniques. 

Spatial interpolation techniques play an important role in hydrology, as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, the Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for data interpolation. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating data used in hydrologic applications (hydrologic data) and compare it with other widely used methods such as inverse distance weighting (IDW), natural neighbor, and ordinary kriging. The performance of LF interpolation with other methods is evaluated using quantitative measures, including root mean squared error (RMSE) and coefficient of determination (R2) for accuracy, visual assessment for surface quality, and computational cost for operational efficiency and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to inverse distance weighting (IDW) and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrologic data. Further work is required to improve its computational efficiency. 

Abstract Meeting the United Nation’ Sustainable Development Goals (SDGs) calls for an integrative scientific approach, combining expertise, data, models and tools across many disciplines towards addressing sustainability challenges at various spatial and temporal scales. This holistic approach, while necessary, exacerbates the big data and computational challenges already faced by researchers. Many challenges in sustainability research can be tackled by harnessing the power of advanced cyberinfrastructure (CI). The objective of this paper is to highlight the key components and technologies of CI necessary for meeting the data and computational needs of the SDG research community. An overview of the CI ecosystem in the United States is provided with a specific focus on the investments made by academic institutions, government agencies and industry at national, regional, and local levels. Despite these investments, this paper identifies barriers to the adoption of CI in sustainability research that include, but are not limited to access to support structures; recruitment, retention and nurturing of an agile workforce; and lack of local infrastructure. Relevant CI components such as data, software, computational resources, and human-centered advances are discussed to explore how to resolve the barriers. The paper highlights multiple challenges in pursuing SDGs based on the outcomes of several expert meetings. These include multi-scale integration of data and domain-specific models, availability and usability of data, uncertainty quantification, mismatch between spatiotemporal scales at which decisions are made and the information generated from scientific analysis, and scientific reproducibility. We discuss ongoing and future research for bridging CI and SDGs to address these challenges. 

ABSTRACT River morphology data are critical for understanding and studying river processes and for managing rivers for multiple socio‐economic uses. While such data have been extensively acquired, several issues hinder their use such as data accessibility, various data formats, lack of data models for storage, and lack of processing tools to assemble data in products readily usable for research, management, and education. A multi‐university research team has prototyped a web‐based river morphology information system (RIMORPHIS) for hosting and creating new information (e.g., terrain and material composition data) and data processing tools for the broader earth science communities. The RIMORPHIS design principles include: (i) broad access via a publicly and freely available platform‐independent system; (ii) flexibility in handling existing and future data types; (iii) user‐friendly and interactive interfaces; and (iv) interoperability and scalability to ensure platform sustainability. Developing such an ambitious community resource is only possible and impactful by continuously engaging stakeholders from the project inception. This paper highlights the research team's strategy and activities to engage with river morphology data producers and potential users from academia, research, and practice. The paper also details outcomes of stakeholder engagement and illustrates how these interactions are positively shaping RIMORPHIS development and its path to long‐term sustainability.