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Modeled temperature data developed for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
1D transient numerical simulations with a modified version of the SUTRA model (preliminary code) that accounts for variably-saturated freeze-thaw dynamics (e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature dynamics using coupled fluid and heat transport physics. The model simulations were run with a modified version of SUTRA_ICE (unreleased) that accomadates a time-variable sinusiodal upper temperature boundary. This data release also includes the source code and Argus One GUI files used to build the models, though this proprietary software is not needed to run the models as described in the upper-level "readme" file.
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Modeled temperature data developed for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
공공데이터포털
1D transient numerical simulations with a modified version of the SUTRA model (preliminary code) that accounts for variably-saturated freeze-thaw dynamics (e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature dynamics using coupled fluid and heat transport physics. The model simulations were run with a modified version of SUTRA_ICE (unreleased) that accomadates a time-variable sinusiodal upper temperature boundary. This data release also includes the source code and Argus One GUI files used to build the models, though this proprietary software is not needed to run the models as described in the upper-level "readme" file.
Temperature data for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
공공데이터포털
A combination of long-term daily temperature records and depth to bedrock measurements were used to parameterize one-dimensional models of shallow aquifer vertical heat transport in Shenandoah National Park, VA, USA. Spatially discontinuous roving water surface and bank temperatures surveys were performed with a handheld thermal infrared camera in September and December 2015 along the main channel of a headwater stream supporting coldwater-dependent brook trout (Salvelinus fontinalis). We also installed vertical arrays of thermal data loggers to estimate bulk thermal diffusivity of the saturated alluvium at two stations in the upper trout section. The methods are fully documented in the associated journal article, Briggs, M.A., J.W. Lane, C.D. Snyder, E. White, Z.C. Johnson, D.L. Nelms, and N.P. Hitt, 2017, Shallow mountain bedrock limits seepage-based headwater climate refugia, Limnologica, https://dx.doi.org/10.1016/j.limno.2017.02.005. This Data Release includes temperature measurements collected as part of the study. The directory RAW_DATA contains the measured temperature time series at streambed, stream, and air locations as described in the local read.me file. The OUTPUT directory contains the processed temperature time series and VFLUX2 calculations of thermal diffusivity (Ke) from streambed data, and annual temp signal amplitude/phase lag from stream/air data are listed.
Temperature data for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
공공데이터포털
A combination of long-term daily temperature records and depth to bedrock measurements were used to parameterize one-dimensional models of shallow aquifer vertical heat transport in Shenandoah National Park, VA, USA. Spatially discontinuous roving water surface and bank temperatures surveys were performed with a handheld thermal infrared camera in September and December 2015 along the main channel of a headwater stream supporting coldwater-dependent brook trout (Salvelinus fontinalis). We also installed vertical arrays of thermal data loggers to estimate bulk thermal diffusivity of the saturated alluvium at two stations in the upper trout section. The methods are fully documented in the associated journal article, Briggs, M.A., J.W. Lane, C.D. Snyder, E. White, Z.C. Johnson, D.L. Nelms, and N.P. Hitt, 2017, Shallow mountain bedrock limits seepage-based headwater climate refugia, Limnologica, https://dx.doi.org/10.1016/j.limno.2017.02.005. This Data Release includes temperature measurements collected as part of the study. The directory RAW_DATA contains the measured temperature time series at streambed, stream, and air locations as described in the local read.me file. The OUTPUT directory contains the processed temperature time series and VFLUX2 calculations of thermal diffusivity (Ke) from streambed data, and annual temp signal amplitude/phase lag from stream/air data are listed.
Seismic data for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
공공데이터포털
A combination of long-term daily temperature records and depth to bedrock measurements were used to parametrize one-dimensional models of shallow aquifer vertical heat transport in Shenandoah National Park, VA, USA. Depth to bedrock can directly influence shallow aquifer flow and thermal sensitivity, but is typically ill-defined along the stream corridor in steep mountain catchments. We employed rapid, cost-effective passive seismic measurements to evaluate the variable thickness of the shallow colluvial and alluvial aquifer sediments along a headwater stream supporting coldwater-dependent brook trout (Salvelinus fontinalis) in Shenandoah National Park. The methods are fully documented in the associated journal article, Briggs, M.A., J.W. Lane, C.D. Snyder, E.A. White, Z.C. Johnson, D.L. Nelms, and N.P. Hitt, 2017, Shallow mountain bedrock limits seepage-based headwater climate refugia, Limnologica, https://dx.doi.org/10.1016/j.limno.2017.02.005. This Data Release includes seismic data collected as part of the study.
Seismic data for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release
공공데이터포털
A combination of long-term daily temperature records and depth to bedrock measurements were used to parametrize one-dimensional models of shallow aquifer vertical heat transport in Shenandoah National Park, VA, USA. Depth to bedrock can directly influence shallow aquifer flow and thermal sensitivity, but is typically ill-defined along the stream corridor in steep mountain catchments. We employed rapid, cost-effective passive seismic measurements to evaluate the variable thickness of the shallow colluvial and alluvial aquifer sediments along a headwater stream supporting coldwater-dependent brook trout (Salvelinus fontinalis) in Shenandoah National Park. The methods are fully documented in the associated journal article, Briggs, M.A., J.W. Lane, C.D. Snyder, E.A. White, Z.C. Johnson, D.L. Nelms, and N.P. Hitt, 2017, Shallow mountain bedrock limits seepage-based headwater climate refugia, Limnologica, https://dx.doi.org/10.1016/j.limno.2017.02.005. This Data Release includes seismic data collected as part of the study.
Air-water temperature data for the study of groundwater influence on stream thermal regimes in Shenandoah National Park, Virginia (Ver. 2.0, May 2018)
공공데이터포털
This database contains hourly water and air temperature data from 120 site locations within 17 watersheds in Shenandoah National Park, Virginia between June 23,2012 and October 25, 2016. The database includes three separate table files (i.e, entities) in csv format: 1) Water temperature data, 2) air temperature data, and 3) site location data. All temperature data were collected using HOBO Pro V2 thermographs (accuracy = 0.2 degrees Celsius, drift = <0.1 degrees Celsius per year per year). These raw data were summarized to mean daily air and water temperatures for the analysis used in Johnson et al. (cited above).
Air-water temperature data for the study of groundwater influence on stream thermal regimes in Shenandoah National Park, Virginia (Ver. 2.0, May 2018)
공공데이터포털
This database contains hourly water and air temperature data from 120 site locations within 17 watersheds in Shenandoah National Park, Virginia between June 23,2012 and October 25, 2016. The database includes three separate table files (i.e, entities) in csv format: 1) Water temperature data, 2) air temperature data, and 3) site location data. All temperature data were collected using HOBO Pro V2 thermographs (accuracy = 0.2 degrees Celsius, drift = <0.1 degrees Celsius per year per year). These raw data were summarized to mean daily air and water temperatures for the analysis used in Johnson et al. (cited above).
Model archive component 1, Geospatial Information, in: Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021
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This model archive component contains shapefiles of (1) coarse (NHGFv1.1 / NHM) stream network polylines for the Delaware River Basin and (2) fine (NHDPlusV2.1) stream network polylines for six watersheds within the Delaware River Basin.

The parent model archive (Fan et al. 2025a) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed.

The data are organized into these child items:

  • [THIS ITEM] 1. Geospatial Information - Stream reach and catchment shapefiles
  • 2. Model Inputs - Meteorological data, river network matrices, and stream temperature observations
  • 3. Model Code - Python files and README for reproducing model training and evaluation
  • 4. Coarse Model - Trained coarse stream temperature model to be downscaled
  • 5. Model Outputs - Model simulation outputs and evaluation metrics

    • The publication associated with this model archive is: Fan, Yingda, Runlong Yu, Janet R. Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, and Xiaowei Jia. 2025. "Multi-Scale Graph Learning for Anti-Sparse Downscaling." In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. Washington, DC, USA: AAAI Press.

    This data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. Work was also supported by the U.S. Geological Survey, Water Availability and Use Science Program.

    Model archive component 4, Coarse Model, in: Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021
    공공데이터포털

    This model archive component contains model weights, inputs, outputs, and performance metrics for the source coarse model for which downscaling was desired. Some methods in Fan et al. (2025b) explore methods for downscaling from this source coarse model, while others explore different uses of these coarse-resolution source data in conjunction with fine-resolution data (see model archive component 2, Model Inputs, for the fine-resolution data).

    The parent model archive (Fan et al. 2025a) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed.

    The data are organized into these child items:

  • 1. Geospatial Information - Stream reach and catchment shapefiles
  • 2. Model Inputs - Meteorological data, river network matrices, and stream temperature observations
  • 3. Model Code - Python files and README for reproducing model training and evaluation
  • [THIS ITEM] 4. Coarse Model - Trained coarse stream temperature model to be downscaled
  • 5. Model Outputs - Model simulation outputs and evaluation metrics

    • The publication associated with this model archive is: Fan, Yingda, Runlong Yu, Janet R. Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, and Xiaowei Jia. 2025. "Multi-Scale Graph Learning for Anti-Sparse Downscaling." In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. Washington, DC, USA: AAAI Press.

    This data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. Work was also supported by the U.S. Geological Survey, Water Availability and Use Science Program.

    Simulated snowpack and meteorology at multiple resolutions for three headwater study areas in Colorado, USA, water years 1980-2019
    공공데이터포털
    This data release includes SnowModel output for three headwater study areas in Colorado at seven spatial resolutions and from two forcing datasets over a 40-year period from water year 1980 to 2019. The resolutions include 30 m, 50 m, 100 m, 150 m, 250 m, 500 m, and 1,000 m. The model was run with a 3-hour temporal resolution from September 1, 1980 to August 31, 2019. Two meteorology forcing datasets were used, including National Land Data Assimilation System-2 at 1/8th degree (about 12 km) resolution data and the Weather Research and Forecasting model data at 4 km resolution. Output variables include snow-water equivalent depth (swed), runoff (roff), air temperature (tair), snow-covered area (sca), snow depth (snod), precipitation (prec), and liquid precipitation (rpre). Additionally, topography and vegetation datasets are included for each combination of unique domain and resolution, as well as the model parameterization file for a representative year. The data are organized by water year (WY) for each forcing type. For example, 'XXXX_wyYYYY.zip', where XXXX is either NLDAS2 or WRFCTL, and YYYY is the water year, with each water year including subdirectories for each of the three headwater study areas ('ER', 'FR', and 'SB' for East River, Fraser River, and Senator Beck, respectively). Each headwater study area subdirectory contains a subdirectory for each spatial resolution ('30', '50', '100', '150', '250', '500', '1000'), and each of those subdirectories contains NetCDF files for the seven variables modeled at that resolution. For example,'SA_RES_VAR_wyYYYY.nc', where SA is one of the three headwater study areas, RES is one of the seven spatial resolutions, VAR is one of the seven output variables, and YYYY is the water year.
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