This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held out. This model archive includes data from 10 sites in the lower Delaware River Basin that were used in the model experiments. Model training target data include dissolved oxygen concentrations downloaded from the National Water Information System (NWIS) (U.S. Geological Survey 2023). Model input data include daily meteorological driver variables derived from gridded surface data (gridMET; Abatzoglou 2013); river and catchment characteristics (Wieczorek et al. 2018); and estimates of daily stream metabolism rates (Appling et al. 2018). The contents of this model archive are organized into files or file directories that have been aggregated into zip files:

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',

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',

This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches for improving groundwater process representation: 1) a custom loss function that leverages the unique patterns of air and water temperature coupling resulting from different temperature drivers, 2) inclusion of additional groundwater-relevant catchment attributes, 3) incorporation of additional process model outputs, and 4) a composite model. The associated manuscript examines changes in the predictive accuracy, feature importance, and predictive ability in un-seen reaches resulting from each of the four approaches. This model archive includes four zipped folders for 1) Data Preparation, 2) Model Code, 3) Model Predictions, and 4) the catchment attributes that were compiled for reaches in the study area. Instructions for running data preparation and modeling code can be found in the README.md files in 01_Data_Prep and 02_Model_Code respectively. File dictionaries have also been included and serve as metadata documentation for the files and datasets within the four zipped folders.

This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches for improving groundwater process representation: 1) a custom loss function that leverages the unique patterns of air and water temperature coupling resulting from different temperature drivers, 2) inclusion of additional groundwater-relevant catchment attributes, 3) incorporation of additional process model outputs, and 4) a composite model. The associated manuscript examines changes in the predictive accuracy, feature importance, and predictive ability in un-seen reaches resulting from each of the four approaches. This model archive includes four zipped folders for 1) Data Preparation, 2) Model Code, 3) Model Predictions, and 4) the catchment attributes that were compiled for reaches in the study area. Instructions for running data preparation and modeling code can be found in the README.md files in 01_Data_Prep and 02_Model_Code respectively. File dictionaries have also been included and serve as metadata documentation for the files and datasets within the four zipped folders.

This data release contains the model inputs, outputs, and source code (written in R) for the boosted regression tree (BRT) and artificial neural network (ANN) models developed for four sites in Upper Klamath Lake which were used to simulate daily maximum pH and daily minimum dissolved oxygen (DO) from May 18th to October 4th in 2005-12 and 2015-19 at four sites, and to evaluate variable effects and their importance. Simulations were not developed for 2013 and 2014 due to a large amount of missing meteorological data. The sites included: 1) Williamson River (WMR), which was located in the northern portion of the lake near the mouth of the Williamson River and had a depth between 0.7 and 2.9 meters; 2) Rattlesnake Point (RPT), which was located near the southern portion of the lake and had a depth between 1.9 and 3.4 meters; 3) Mid-North (MDN), which was located in the northwest portion of the lake and a depth between 2.4 and 4.2 meters; 4) Mid-Trench (MDT) , which was located in the trench that runs along the western portion of the lake and had a depth between 13.2 and 15 meters.

This data release contains the model inputs, outputs, and source code (written in R) for the boosted regression tree (BRT) and artificial neural network (ANN) models developed for four sites in Upper Klamath Lake which were used to simulate daily maximum pH and daily minimum dissolved oxygen (DO) from May 18th to October 4th in 2005-12 and 2015-19 at four sites, and to evaluate variable effects and their importance. Simulations were not developed for 2013 and 2014 due to a large amount of missing meteorological data. The sites included: 1) Williamson River (WMR), which was located in the northern portion of the lake near the mouth of the Williamson River and had a depth between 0.7 and 2.9 meters; 2) Rattlesnake Point (RPT), which was located near the southern portion of the lake and had a depth between 1.9 and 3.4 meters; 3) Mid-North (MDN), which was located in the northwest portion of the lake and a depth between 2.4 and 4.2 meters; 4) Mid-Trench (MDT) , which was located in the trench that runs along the western portion of the lake and had a depth between 13.2 and 15 meters.

Dissolved oxygen in the water column is a primary input into the calculation of metabolism and respiration (refer Metabolism BASE Model dataset). Data loggers are deployed at multiple locations in the rivers of each of the Flow-MER Area-scale project to measure continuously at 10 minute intervals: * Light * Water temperature * Dissolve oxygen * Atmospheric pressure The data record aims to be continuous over the summer water management period, however there are breaks in the data record due to high water levels preventing data retrieval and equipment losses. There is generally at least one data logger deployed near locations where fish are also sampled. The CEWH’s Flow-MER program examines the contribution of Commonwealth environmental water to the environmental objectives of the Basin Plan 2012 (Basin Plan) and is assisting the CEWH to demonstrate environmental outcomes and adaptively manage the water holdings. Monitoring and evaluation is focused in seven Selected Areas: the Junction of the Warrego and Darling rivers, Gwydir river system, Lachlan river system, Murrumbidgee river system, Edward/Kolety-Wakool river system, Goulburn River and Lower Murray River. This Flow-MER data set includes and extends the long-term data collected at the same sites during the Long Term Intervention Monitoring (LTIM) project (2014-2019). References to the metabolism methods using these oxygen data: Grace MR, Giling DP, Hladyz S, Caron V, Thompson RM, Mac Nally R (2015) Fast processing of diel oxygen curves: estimating stream metabolism with BASE (BAyesian Single-station Estimation). Limnology & Oceanography: Methods, 13, 103-114 Song C, Dodds WK, Trentman MT, Rüegg J, Ballantyne F (2016) Methods of approximation influence aquatic ecosystem metabolism estimates. Limnology and Oceanography: Methods 14(9), 557–569. ###Acknowledgement The Commonwealth Environmental Water Holder and Flow-MER program acknowledge the First Nations peoples as the Traditional Owners and Custodians of the lands, waterways and skies of the Murray-Darling Basin. We respect their continuing connection to culture and Country, and we thank them for their knowledge and science and the values reflected in these data. ###Citation CEWH (2024) Metabolism oxygen logger data. Flow-MER Program. Commonwealth Environmental Water Holder, Australian Government Department of Climate Change, Energy, the Environment and Water. Sourced from https://data.gov.au/data/dataset/flow-mer-oxygen-logger on [date-sourced].