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This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. A table of basin attributes is also supplied. Attributes, observations, and weather forcing data for these basins were used to train and test the stream temperature prediction models of Rahmani et al. (2021b).<\p>
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: AL,
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AZ,
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This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
Abstract(from:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024922)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedbacks (model penalties...
Categories: Publication;
Types: Citation;
Tags: Fish,
Northeast CASC,
Rivers, Streams and Lakes,
Water, Coasts and Ice,
Wildlife and Plants,
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. 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...
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. The data are organized into these child items: 1. Model...
This dataset includes model parameters and metadata used to configure models.
Observations related to water and thermal budgets in the Delaware River Basin. Data from reservoirs in the basin include reservoir characteristics (e.g., bathymetry), daily water levels, daily depth-resolved water temperature observations, and daily inflows, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Data were compiled from a variety of sources to cover the modeling period (1980-2021), including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream water temperature database, ReaLSAT, and the New York Department of Environmental Conservation. The data are formatted as a single csv (comma separated values) or zipped csv. For modeling...
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...
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 ...
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes predictions from several models, including a model pre-trainer that is predictions from a distance-weighted-average lotic-lentic input network (DWALLIN) model, reservoir outlet temperature predictions from a process-based model, forecasts from a persistence stream water temperature model, and stream water temperature forecasts from two deep learning models, a long-short term memory network and recurrent convolutional graph network model.
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes code that prepares data for model training and forecasts maximum stream temperature using neural network models. Finally, the code evaluates the models using various accuracy and uncertainty metrics.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg/L Cl- isochlor, also known as the salt front, using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001-2020, and we compare the ML model results to results from the hydrodynamic...
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.,...
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 ...
Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Climate change and land use change have been shown to influence lake temperatures and water clarity in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles in 2,332 lakes during 1980-2019. The data are organized into these items: This research was funded by the Department of the Interior Northeast and North Central Climate Adaptation Science Centers, a Midwest Glacial Lakes Fish Habitat Partnership grant through F&WS Access to computing facilities was provided by USGS Advanced Research Computing, USGS Yeti Supercomputer (https://doi.org/10.5066/F7D798MJ)....
This data release item contains water temperature predictions for 455 river sites across the U.S. Predictions are from the models described by Rahmani et al. (2021b).
Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish and mussel species. This data release supports a variety of flow and water temperature modeling efforts and provides the inputs and outputs of both machine learning and process-based modeling methods across 456 river reaches and 2 reservoirs in the DRB. The data are organized into these items: This research was funded by the USGS. Waterbody Information - One shapefile of polylines for the 456 river segments in this study, a reservoir polygon metadata file, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs...
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...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Hydrology,
NY,
New York,
PA,
Pennsylvania,
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