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This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section contains observations related to the amount and quality of water in the Delaware River Basin. Data from a subset of reservoirs in the basin include observed daily depth-resolved water temperature, water levels, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Observations were compiled from a variety of sources, including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream...
Daily maximum water 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 species. 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 DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022),...
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 contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites...
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Tags: DE,
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Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). In this data release, multiple modeling approaches were used to generate predictions of daily temperature profiles for thousands of lakes in the Midwest. Predictions were generated using two modeling frameworks: a machine learning model (specifically an entity-aware long short-term memory or EA-LSTM model; Kratzert et al., 2019) and a process-based model (specifically the General Lake Model or GLM; Hipsey et al., 2019). Both the EA-LSTM and GLM frameworks were used to generate...
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section includes model drivers such as gridded weather data (NOAA GEFS and GridMET), and the stream network distance matrix for the Delaware River Basin. Additionally, inputs and outputs from an uncalibrated process-based stream temperature model (PRMS-SNTemp) are included.
This section provides spatial data files that describe the river and reservoirs in the Delaware River Basin included in this release. One shapefile of polylines describes the 70 river reaches that define the modeling network, and another shapefile of polygons includes the two reservoirs (Pepacton, Cannonsville) for which data are included in this release.
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...
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 model parameters and metadata used to configure reservoir models.
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release includes data that characterize both rivers and a subset of reservoirs in the basin. This release provides an update to many of the files provided in a previous data release (Oliver et al., 2021). The data are stored in 3 child folders: 1) spatial information, 2) observations, and 3) model driver data. 1) Spatial Information...
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.
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.
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...
Harmful algal blooms (HABs) have recently been observed in rivers, including the Illinois River in the Midwest United States. The Illinois River Basin has a history of eutrophication issues, primarily caused by the excessive loading of nitrogen and phosphorus from urban and agricultural sources. Recent events have seen the emergence of cyanobacterial harmful algal blooms in the area. This data release provides early warning indicator (EWI) metrics derived from a continuous chlorophyll concentration dataset obtained from seven water quality monitoring sites along the Illinois River. These metrics include the first-order autoregressive process (Ar1) and the standard deviation (SD) of chlorophyll, which serve as leading...
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...
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