The HEC-RAS model format, both as stand alone models or in archive formats, are incompatable 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 seamlessly with national scale hydrofabric efforts and data models, and applications such as T-Route and RAS2FIM. See the package documentation for more details.
Installation
It is recommended that you wait to start using this tool until parsing accuracy, edge case handling, and accounting is fully ironed out, the final form of these tables is still in flux. Per package notes: “Still in alpha don’t use this.” If that 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" testTutorials
There are several tutorials available at the Article index including: * 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 articles.
Referneces
- Model debug: code snippets useful in learning how to interact with RAS data.
- FEMA_BLE by Ali Forghani
Explanations
Statement of need
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.
Discussion
I am both an archivist/model creator and a RRASSLER user
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.
Limitations
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 will ingest, there was no consideration for those and is not accounting or copying .tif files so the value of these models is greatly diminished.
Getting involved
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.
A few next steps
Hardening and extention
I code out of necessity, not out of love, and I’ve been told my more than a few that I write awful code. More than half of the community will also think this is written in the wrong language. Efforts to harden the workflow and algorithm, extend this workflow into Python, and general improvements would all be uses of time.
RRASTAC
Although not the most obvious use of STAC, or SpatioTemporal Asset Catalog, HEC-RAS models (most easily the footprints generated in the cataloging of the models) could be extended to the STAC framework as a STAC Item, and by that analogy each version of the catalog is a STAC collection. Formalizing this, most likely through an additional function such as STAC_catalog.R, which would generate the needed json’s and then appropriately serve those, would be a worthy task to undertake. The obvious extension of that, exposing the model cross sections with XYZ LINESTRING geometry as STAC items, is in theory what RRASSLER is accomplishing but the framework and analogous STAC deployment is less clear and could also be explored.
Dependencies
Built using RStudio and rocker-versioned2, docker file provided for reproducibility. Typically deployed alongside a RAS2FIM conda environment in Windows.
Credits and references
Credit to the packages used in the development, testing, and deployment of RRASSLER including but not exclusive of the following packages: AOI, arrow, cowplot, data.table, dplyr, ggplot2, glue, gmailr, holyhull, 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.
For questions
Jim Coll (FIM Developer), Fernando Salas (Director, OWP Geospatial Intelligence Division)