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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2020

NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters

Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest surrounding Crested Butte, CO with remotely sensed data, including LiDAR. The flight box design encompassed the watersheds, surveying a total area of 334 km2 across 72 lines. The instrument used was an Optech Gemini, with a pulse density of 2-9 pulses m-2 across the study area (see final report document for detailed information (Goulden and Musinsky 2020). These LiDAR data are the primary data that were provided by NEON, and include both LiDAR source data and derived surface products. The source data are contained in 'crbu_laz.zip' and 'classified_point_cloud.zip. 'classified_point_cloud.zip' represents an additional processing level on 'crbu_laz.zip'. The derived products are 'dsm_mosaic_min_phase_me.tif', 'dtm_mosaic_min_phase_me.tif', and 'tch_mosaic_min_phase_me.tif'. These files are raster mosaics of a digital surface elevation model (DSM), digital terrain elevation model (DTM), and a canopy height model (CHM) at 1 m resolution with a height threshold of > 2 m, respectively, in geotiff (.tif) format. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. [UPDATE] On 06/26/2026 the files 'crbu_laz.zip' and 'classified_point_cloud.zip' were replaced with original output files from NEON to address zip file corruption and naming errors. No changes were made to the underlying data. (See Methods Step 2 for details).

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2024

Debunking common myths in coastal circulation modeling

Despite tremendous progress in algorithm development, computational efficiency and transition into operations over the past two decades, coastal modeling still lacks scientific rigor due to proliferation of many ‘gray’ areas related to various modeling choices made by modelers. Here, in this paper, we propose some guiding principles for the modeling community to improve performance, and we also debunk commonly held myths that make the coastal modeling lack rigor. Using our own experience in developing seamless cross-scale unstructured-grid based models for the past two decades, we describe in unprecedented detail the end-to-end modeling process (i.e., from digital elevation models (DEMs) to mesh generation to post analysis), and demonstrate that defensible modeling is within reach for any end user by following three guiding principles: (1) Bathymetry is a first order forcing in coastal domains and thus should be respected in all aspects of modeling; (2) Oceanographic processes are driven across multiple spatial scales and so models should enable appropriate resolution as needed; and (3) Model assessment should focus on physical processes. Through qualitative and quantitative model assessments, we demonstrate the fundamental role played by bathymetry/topography as embedded in DEMs in making the results defensible, which is unfortunately glossed over in many modeling studies. Focusing on process-based assessment simplifies the calibration process. A major conclusion of this work is that model developers and operators should maximize the scientific rigor for in silico oceanography by avoiding some common pitfalls that rely on error compensation at the expense of representation of physical system processes. We present some best practice procedures for defensive and trustworthy numerical modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large-scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar-based hourly rainfall data, a series of hydrologic-hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D-GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km 2 , and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high-danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high-danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high-performance computing hydrodynamic large-scale flood simulations.

54 ENVIRONMENTAL SCIENCES↗