ABSTRACT:

This resource contains data and code related to the development of the TempEst-NEXT river temperature forecasting model. The provided data and automatic validation suite should be sufficient to fully reproduce the performance analysis contained in the associated manuscript. The code includes model implementations for TempEst-NEWT (watershed-specific model) and TempEst-NEXT (ungaged model) as well as some utility functions for data retrieval and analysis. Data files include the development dataset (DevData) and independent test dataset (TestData) with both estimated weather (...Buffers.csv) and forecasted weather (...HRRR3.csv) as weather inputs. The NEXT_validation notebook automatically analyzes NEXT performance (with some random variation, so results may not be identical to the published values but should be close).

NEXT_validation_20250917.pdf is a knitted version of the validation notebook, containing all key results, as of September 17, 2025 (model version used for manuscript submission).

To run the entire Notebook, requirements are:
• Python dependencies: tempest-next, matplotlib, seaborn, and scikit-learn. tempest-next is available (under that name) on the Python Package Index, so pip install tempest-next. This will also install, as a dependency, TempEst-NEWT, the calibrated version of the model.
• A directory (specify location in bp= in the first cell) containing DevDataBuffers.csv, TestDataBuffers.csv, DevDataHRRR3.csv, and TestDataHRRR3.csv. These should be provided with the notebook. It should also contain a results subdirectory.
• In the same directory as the notebook:
– An ecoregions directory, containing NA_CEC_Eco_Level1.shp (EPA Level I Ecoregions).
– A usa_states directory, containing usa_states/cb_2018_us_state_20m.shp (US Census state outlines shapefile).
– A val_figures directory
– A results directory (in the directory `bp`, which can be the same directory)

The above requirements, other than the Python dependencies, are included in this data release, so, with `bp=(current directory)`, it should be possible to run everything from this download.

These data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.

ABSTRACT:

This resource contains code and data related to the development of the TempEst 2 stream temperature remote sensing model (manuscript in review with Journal of Hydrology). The code includes the model implementation (model.R), some utility functions (valfn.R), data retrieval scripts for Google Earth Engine (eeretrieval.py and datapts.py), and a reproducible validation notebook (validation.Rmd), along with the knitted PDF of the latter (validation.pdf). The main data include stream temperature daily mean/max observations retrieved from the USGS NWIS as well as remotely-sensed and gridded observations retrieved using Google Earth Engine from NLDAS, ESA WorldCover, MODIS, ERA5, and EPA Ecoregions (using eeretrieval.py). These are contained in three files. AllData.csv includes all observations for mean temperature. ExtData.csv ("extended data") adds maximum temperature, at the expense of fewer total observations being included. Ecoregions.csv is not central to the analysis, but includes EPA Level I ecoregion classifications for convenience.

Model performance tests can be reproduced using validation.Rmd. To run validation.Rmd in full, there must be a Data directory with subdirectories Density and TSLen, a Figures directory, at least one of the main data files (AllData.csv, ExtData.csv) or equivalent, and Ecoregions.csv. A knitted version of the Notebook is included in this resource. The error map plots also use an EPA Level I Ecoregions (https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/cec_na/na_cec_eco_l1.zip) shapefile, which is assumed to be in an Ecoregions subdirectory of the *parent* directory. This dependency can be removed by replacing the `plot.eco` function with ordinary ggplot plotting.

The two rda (RData) files contain different versions of a pre-trained model. model.rda contains a regular, pre-trained model function that can be used directly to generate predictions. krigs.rda contains a list of the actual fitted kriging models, which can be used for investigating model components (see demo.pdf).

These data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.

ABSTRACT:

This resource contains analysis code and downloaded stream temperature data (from USGS NWIS) used to develop an analysis of stream seasonal thermal regimes in the United States. The R Notebook, `analysis.Rmd`, will reproduce all supporting analysis and generate figures when run with the data files downloaded into a `Data` subdirectory of the working directory. `analysis.Rmd` calls more involved functions from `functions.R`. The knitted version of the notebook is also included as `analysis.pdf`.

To run the Notebook, in the working directory there must be a `Data` directory containing the data files and a `Figures` directory with a `MovingWindowSeasons` subdirectory, where figures will be stored. For map generation, `functions.R` will also look for an EPA Level I Ecoregions (https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/cec_na/na_cec_eco_l1.zip) shapefile in an `Ecoregions` directory.

These data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.