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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 data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
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,
AR,
AZ,
Alabama,
Arizona,
This dataset provides model parameters used to estimate water temperature from a process-based model (Hipsey et al. 2019) using uncalibrated model configurations (PB0) and the trained model parameters for process-guided deep learning models (PGDL; Read et al. 2019). This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota(https://doi.org/10.5066/P9PPHJE2).
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)....
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...
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).
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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