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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 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 ...
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 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 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.,...
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