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This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
This dataset provides one shapefile of polylines for the 456 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs.
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: DE,
Delaware,
MD,
Maryland,
NJ,
This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.
This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.
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...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: DE,
Delaware,
Hydrology,
MD,
Maryland,
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 component contains evaluation metrics used to assess the predictive performance of each stream temperature model. For further description, see the metric calculations in the supplement of Rahmani et al. (2020), equations S1-S7.
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.
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,
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 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).
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