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Identifying structural priors in a hybrid differentiable model for stream water temperature modeling at 415 U.S. basin outlets, 2010-2016
<p>This model archive (Rahmani et al. 2023a) provides all data, code, and model outputs used in Rahmani et al. (2023b) to improve model representations toward improved prediction of stream temperature and groundwater/subsurface flow contributions to stream temperature. Briefly, we modeled stream temperature at sites across the continental United States using a hybrid differentiable model that combines neural network components with differentiable implementations of several structural priors, i.e., process-based equations. The differentiable framework permits estimation of parameters and comparison of structural priors as well as prediction of stream temperature.</p> <p>The data are organized into these child items: <li><a href="https://www.sciencebase.gov/catalog/item/648f9bbdd34ef77fcb001ffc"> 1. Model code </a>- Python files and README for reproducing model training and evaluation </li> <li><a href="https://www.sciencebase.gov/catalog/item/648f9c49d34ef77fcb001fff"> 2. Inputs </a>- Basin attributes and shapefiles, forcing data, and stream temperature observations </li> <li><a href="https://www.sciencebase.gov/catalog/item/648f9caed34ef77fcb002001"> 3. Simulations </a>- Simulation descriptions, configurations, and outputs </li> <li><a href="https://www.sciencebase.gov/catalog/item/6495df90d34ef77fcb01e285"> 4. Figure code </a>- Jupyter notebook to recreate the figures in Rahmani et al. (2023b) </li> </p> <p>The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. <a href=https://doi.org/10.1029/2023WR034420>https://doi.org/10.1029/2023WR034420</a>.</p> <p>This data compilation was funded by the Integrated Water Prediction Program at the U.S. Geological Survey.</p>
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3. Simulations for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling at 415 U.S. basin outlets, 2010-2016
공공데이터포털

This section provides model simulation outputs from the models described by Rahmani et al. (2023b), as well as a subset of model outputs produced by Rahmani et al. (2021) that were used for comparison within Rahmani et al. (2023b).

The full model archive is organized into these four child items:

  • 1. Model code - Python files and README for reproducing model training and evaluation
  • 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • [THIS ITEM] 3. Simulations - Simulation descriptions, configurations, and outputs
  • 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    2. Inputs for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. Three file formats describing basin attributes, and three file formats describing forcing and observational data, are also included. These data were used to train and test the stream temperature prediction models of Rahmani et al. (2023b).

    The full model archive is organized into these four child items:

  • 1. Model code - Python files and README for reproducing model training and evaluation
  • [THIS ITEM] 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    2. Inputs for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. Three file formats describing basin attributes, and three file formats describing forcing and observational data, are also included. These data were used to train and test the stream temperature prediction models of Rahmani et al. (2023b).

    The full model archive is organized into these four child items:

  • 1. Model code - Python files and README for reproducing model training and evaluation
  • [THIS ITEM] 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    1. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This section provides model code described by Rahmani et al. (2023b). This code accepts basin attributes and forcings and predicts stream temperatures using a differentiable model with neural network and process-based equation components. Code files are contained within code.zip. A description of each code file is given in the 01_code.xml metadata file and also in code_file_dictionary.csv. Instructions on how to run the code are given in code_readme.md.

    The full model archive is organized into these four child items:

  • [THIS ITEM] 1. Model code - Python files and README for reproducing model training and evaluation
  • 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    1. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This section provides model code described by Rahmani et al. (2023b). This code accepts basin attributes and forcings and predicts stream temperatures using a differentiable model with neural network and process-based equation components. Code files are contained within code.zip. A description of each code file is given in the 01_code.xml metadata file and also in code_file_dictionary.csv. Instructions on how to run the code are given in code_readme.md.

    The full model archive is organized into these four child items:

  • [THIS ITEM] 1. Model code - Python files and README for reproducing model training and evaluation
  • 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This section provides code for reproducing the figures in Rahmani et al. (2023b).

    The full model archive is organized into these four child items:

  • 1. Model code - Python files and README for reproducing model training and evaluation
  • 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
    공공데이터포털

    This section provides code for reproducing the figures in Rahmani et al. (2023b).

    The full model archive is organized into these four child items:

  • 1. Model code - Python files and README for reproducing model training and evaluation
  • 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations
  • 3. Simulations - Simulation descriptions, configurations, and outputs
  • [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b)

    • The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. https://doi.org/10.1029/2023WR034420.

    Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)
    공공데이터포털
    This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:
    1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
    2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
    3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
    4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
    5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
    6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
    7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
    8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
    9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
    10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
    11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B',
    Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)
    공공데이터포털
    This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:
    1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
    2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
    3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
    4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
    5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
    6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
    7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
    8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
    9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
    10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
    11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B',
    Process-guided deep learning water temperature predictions: 6 Model evaluation (test data and RMSE)
    공공데이터포털
    This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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