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Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

https://doi.org/10.1029/2022wr033880
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States
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Global river discharge and water temperature under climate change
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A regional neural network ensemble for predicting mean daily river water temperature
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RR-Former: Rainfall-runoff modeling based on Transformer
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Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
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Process‐Guided Deep Learning Predictions of Lake Water Temperature
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Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales
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Multi‐Task Deep Learning of Daily Streamflow and Water Temperature
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Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks
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Relationship between stream temperature, thermal refugia and rainbow trout Oncorhynchus mykiss abundance in arid‐land streams in the northwestern United States
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Key ecological responses to nitrogen are altered by climate change
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Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
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Global surface air temperatures in CMIP6: historical performance and future changes
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Advances in optimizing recurrent networks
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
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Ecoregions of the Conterminous United States
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Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
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Proceedings of the 24th international conference on Machine learning
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Rising stream and river temperatures in the United States
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Streams in the urban heat island: spatial and temporal variability in temperature
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The Probable Error of a Mean
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P2S--Coupled simulation with the Precipitation-Runoff Modeling System (PRMS) and the Stream Temperature Network (SNTemp) Models
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Summary of hydrologic modeling for the Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER)
inferredcites →https://doi.org/10.3133/tm6b7openalex-snapshot
PRMS-IV, the precipitation-runoff modeling system, version 4
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Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System
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inferredcites →https://doi.org/10.5194/hess-19-209-2015openalex-snapshot
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inferredcites →https://doi.org/10.5194/hess-20-4323-2016openalex-snapshot
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inferredcites →https://doi.org/10.5194/hess-22-6005-2018openalex-snapshot
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inferredcites →https://doi.org/10.5194/hess-26-5163-2022openalex-snapshot
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inferredcites →https://doi.org/10.5281/zenodo.1286817openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
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A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction
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Stream Nitrate Dynamics Driven Primarily by Discharge and Watershed Physical and Soil Characteristics at Intensively Monitored Sites: Insights From Deep Learning
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Model Inputs and Data Requirements for Process‐Based Stream Temperature Modeling in Regulated Peri‐Alpine Rivers
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Deep Learning Advances Arctic River Water Temperature Predictions
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Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification
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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation
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