Search for: All records

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. 

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. 

Abstract Several studies have focused on the importance of river bathymetry (channel geometry) in hydrodynamic routing along individual reaches. However, its effect on other watershed processes such as infiltration and surface water (SW)‐groundwater (GW) interactions has not been explored across large river networks. Surface and sbsurface processes are interdependent, therefore, errors due to inaccurate representation of one watershed process can cascade across other hydraulic or hydrologic processes. This study hypothesizes that accurate bathymetric representation is not only essential for simulating channel hydrodynamics but also affects subsurface processes by impacting SW‐GW interactions. Moreover, quantifying the effect of bathymetry on surface and subsurface hydrological processes across a river network can facilitate an improved understanding of how bathymetric characteristics affect these processes across large spatial domains. The study tests this hypothesis by developing physically based distributed models capable of bidirectional coupling (SW‐GW) with four configurations with progressively reduced levels of bathymetric representation. A comparison of hydrologic and hydrodynamic outputs shows that changes in channel geometry across the four configurations has a considerable effect on infiltration, lateral seepage, and location of water table across the entire river network. For example, when using bathymetry with inaccurate channel conveyance capacity but accurate channel depth, peak lateral seepage rate exhibited 58% error. The results from this study provide insights into the level of bathymetric detail required for accurately simulating flooding‐related physical processes while also highlighting potential issues with ignoring bathymetry across lower order streams such as spurious backwater flow, inaccurate water table elevations, and incorrect inundation extents. 

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable. 

Abstract Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.