The HEC-RAS model format, both as stand alone models or in archive formats, are incompatible for end users whose use case includes widespread accounting and deployment of that data as inputs into other workflows. This tool should be deployed to ingest HEC-RAS models into a “HECRAS_model_catalog”, a normalized and spatialized representation of those models with the requisite metadata and formatted structure needed to mesh more seamlessly with national scale hydrofabric efforts and data models, and applications such as T-Route and RAS2FIM. See the package documentation, and the repo itself, for more details.
As the badges above indicate, this package is Active and experimental, although the core logic of record creation has been stabilized. If that cutting edge nature doesn’t dissuade you, install the development version of RRASSLER from GitHub with:
# install.packages("devtools") # You only need this if this is your very first time opening RStudio
# install.packages("BiocManager") # You only need this if this is your very first time opening RStudio
# BiocManager::install("rhdf5") # You only need this if this is your very first time opening RStudio
# utils::remove.packages("RRASSLER") # You only need to run this if you have a previous install and need to wipe it
remotes::install_github("NOAA-OWP/RRASSLER") # If it asks for package updates: press 1
library(data.table)
RRASSLER::marco() # A "hello world" testNote: RRASSLER generates data as part of it’s processing. Although every effort has been made to ensure that the form of that data is not so inflexible that future bug-fixes and enhancements break the resulting structure, we can make no guarantee that changes made, particularly to parsing process and accuracy, edge case handling, and ultimately accounting; would not require “reprocessing” records, something that is best accomplished using the original data. As noted above, this package and several of it’s cohort are still actively being developed. Although the core logic of record creation has been stabilized for the time being, the “final” form of these tables is still in flux.
There are several tutorials available at the Article index including:
- Data Model: The data standard we are building to.
- Ingest steps: What RRASSLER is doing to data?
- Deploying RRASSLER: How to make your own “HEC-RAS model catalog”.
- Mapping RRASSLER: Adding (geographic) context to our data.
See the package documentation for function references and additional information.
There are few hydraulic models as prolific as HEC-RAS, and since it’s first named release in 1995 users have created these models using public and private data and countless hours of engineering scrutinization in order to generate the best possible purpose-built representation of the world. Like any model, some level of input massaging is necessary in order to get the data into the specified mathematical format a model requires. Like most domain specific software solutions, that massaging was rather, forceful, to the point of permanently altering the shape of those inputs into something that most geospatial data readers are unable to handle. This creates a great deal of friction both in terms of model accountability and interoperability, particularly when you take the standpoint as a model consumer. The R based HEC-RAS Wrestler (RRASSLER) is here to mediate that. By internally versioning and aligning data and pointers, the resulting structure provides a bottoms up approach amenable to walking continental scale applications back to the specific point, cross section, and HEC-RAS model they were sourced from.
You will unfortunately have to keep two copies of the data. RRASSLER isn’t creating anything you don’t already have in the archive in one form or another, and completely removes all metadata and formatting that your archive has so painstakingly created. Don’t change your workflow, consider RRASSLER a “post-processing” step to your archiving work, whose primary purpose is to make a selection of your models more amenable to operational deployments.
Aligning the different model surfaces is hard. Although every effort was made to account for standard edge cases and unit cohesion, you will, more often than not, find that a surface you use and a model do not align. That is not particularly surprising, but it is often disconcerting. 3DEP timestamps, resolutions, and even order of reprojection operations may alter the surfaces slightly, even if they are stated to have come from the same input database. Do your own sanity checks and try not to lose your mind, it’s probably easier to go out and measure it again. Finally, this was developed, tested, and deployed over primarily 1D data. Although 2D model may appear to ingest correctly, there was no consideration for those in either parsing or processing, and is not accounting or copying .tif files so the value of these models is greatly diminished.
If you have questions, concerns, bug reports, etc, please file an issue in this repository’s Issue Tracker. I know we are not the only ones attempting to align the world. General instructions on how to contribute can be found at CONTRIBUTING. More specifically, the following are known shortcomings and next steps.
Efforts to harden the workflow and algorithm, extend this workflow into your language of choice, and general improvements would all be uses of time.
Many of the same considerations, concerns, and hurdles experienced working with and manipulating 1D RAS data will be encountered as we move into 2D model accounting. While many of the technical advancements and standards make the accounting of 2D models a bit more theoretically straightforward; since most valuable and standardized 2D models come with an associated .tif file which is footprintable, and new model formats are provided as .HDF, which has cloud compatible characteristics and is being developed, the actual work of constructing the utility which would account for those models needs to be done.
This was written tested, and partially deployed using using rocker-versioned2. Typically deployed via package installation on an RStudio instance and alongside a RAS2FIM conda environment in Windows.
Credit to the packages used in the development, testing, and deployment of RRASSLER including but not exclusive of the following: AOI, arrow, cowplot, data.table, dplyr, ggplot2, glue, gmailr, httr, leafem, leaflet, leafpop, lubridate, lwgeom, mapview, nhdplusTools, sf, sfheaders, stringi, stringr, tidyr, unglue, units, utils, and rhdf5. We are appreciative of the FEMA region 6 group and the BLE data they make publicly available. Built copying patterns from RAS2FIM.
Jim Coll (FIM Developer), Fernando Salas (Director, OWP Geospatial Intelligence Division)