This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph convolution network - RGCN, and a temporal convolution graph model - Graph WaveNet). The associated manuscript explores how the architectural differences between the two models influence how they learn spatial and temporal relationships, and how those learned relationships influence a model's ability to accurately predict stream temperature as domains shift towards out-of-bounds conditions. This data release and model archive contains three zipped folders for 1) Data Preparation, 2) Modelling Code, and 3) Model Predictions. Instructions for running data preparation code and modelling code can be found in the README.md files in 01_Data_Prep and 02_Model_Code respectively.

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 dataset includes model parameters and metadata used to configure models.

This dataset includes model parameters and metadata used to configure models.

Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from atmospheric signals. Including observed reservoir releases as inputs to models can improve water temperature predictions below reservoirs, but many reservoirs are not well-observed. This data release contains predictions from stream temperature models described in Jia et al. 2022, which describes different deep learning and process-guided deep learning model architectures that were developed to handle scenarios of missing reservoir releases. The spatial extent of this modeling effort was restricted to two spatially disjointed regions in the Delaware River Basin. The first region included streams above the Delaware River at Lordville, NY, and included the West Branch of the Delaware River above and below the Cannonsville Reservoir and the East Branch of the Delaware River above and below the Pepacton Reservoir. Additionally, the Neversink River which flows into the Delaware River at Port Jervis, New York, was included and contains river reaches above and below the Neversink Reservoir. For each model, there are test period predictions from 2006-12-26 through 2020-06-22. Model input, training, and validation data can be found in Oliver et al. (2021).

The publication associated with this data release is Jia X., Chen S., Xie Y., Yang H., Appling A., Oliver S., Jiang Z. 2022. Modeling reservoir release in stream temperature prediction using pseudo-prospective learning and physical simulations, SIAM International Conference on Data Mining (SDM). DOI: https://doi.org/10.1137/1.9781611977172.11

Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from atmospheric signals. Including observed reservoir releases as inputs to models can improve water temperature predictions below reservoirs, but many reservoirs are not well-observed. This data release contains predictions from stream temperature models described in Jia et al. 2022, which describes different deep learning and process-guided deep learning model architectures that were developed to handle scenarios of missing reservoir releases. The spatial extent of this modeling effort was restricted to two spatially disjointed regions in the Delaware River Basin. The first region included streams above the Delaware River at Lordville, NY, and included the West Branch of the Delaware River above and below the Cannonsville Reservoir and the East Branch of the Delaware River above and below the Pepacton Reservoir. Additionally, the Neversink River which flows into the Delaware River at Port Jervis, New York, was included and contains river reaches above and below the Neversink Reservoir. For each model, there are test period predictions from 2006-12-26 through 2020-06-22. Model input, training, and validation data can be found in Oliver et al. (2021).

The publication associated with this data release is Jia X., Chen S., Xie Y., Yang H., Appling A., Oliver S., Jiang Z. 2022. Modeling reservoir release in stream temperature prediction using pseudo-prospective learning and physical simulations, SIAM International Conference on Data Mining (SDM). DOI: https://doi.org/10.1137/1.9781611977172.11

Several models were used to improve water temperature prediction in the Delaware River Basin.

PRMS-SNTemp was used to predict daily temperatures at 456 stream reaches in the Delaware River Basin. Daily stream temperature predictions for inflow and outflow reaches for Cannonsville and Pepacton reservoirs were pulled aside into a separate csv to be used as inputs to the General Lake Model (GLM). Reservoir outflow predictions and in-reservoir temperature predictions were generated with calibrated models built using GLM v3.1. We calculated a decay rate based on the modeled reservoir outflow temperatures and observed downstream river temperature to estimate the decay of the reservoir influence on stream temperature as a function of distance downstream of a reservoir. These decay rates from Pepacton and Cannonsville were used to weight the predictions from GLM and PRMS-SNTemp on the East and West Branch of the Delaware River, respectively, to represent the mix of stream and reservoir processes that affect temperature dynamics. These weighted values were then used to pre-train the deep learning models that were used to forecast temperature. More details on these methods can be found in Zwart and others 2021.

Code that generated these results can be found in Zwart and others 2021 (https://doi.org/10.5281/zenodo.5164910).