William D Watkins

Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs

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, All tags... NY, New Jersey, New York, PA, Pennsylvania, US, United States, deep learning, environment, forecast, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, streams, temperature, water, water resources, Fewer tags

Predicting water temperature in the Delaware River Basin: 4 Model inputs

This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.

Categories: Data; Tags: DE, Delaware, MD, Maryland, NJ, All tags... NY, New Jersey, New York, PA, Pennsylvania, US, United States, deep learning, environment, forecast, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, streams, temperature, water, water resources, Fewer tags

Climate change has been shown to influence lake temperatures 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 68 lakes in Minnesota and Wisconsin during 1980-2018. The data are organized into these items: Spatial data - One shapefile of polygons for all 68 lakes in this study (.shp, .shx, .dbf, and .prj files) Model configurations - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 68 lakes, indexed by "site_id") Model inputs - Data formatted as model inputs for predicting temperature a. Lake...

Tags: 007, 012, Ecology, Fish, Limnology, All tags... MN, Minnesota, Northeast CASC, Rivers, Streams and Lakes, US, USGS Science Data Catalog (SDC), United States, WI, Water Quality, Water Resources, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags

This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different models and spatial extents (Colorado River Basin region or conterminous United States). See the list below or metadata files in each sub-folder for more details. Daily streamflow percentile predictions for the Colorado River Basin region — Outputs from long short-term memory (LSTM) deep learning models corresponding to...

Tags: Conterminous United States, Hydrology, USGS Science Data Catalog (SDC), Upper Colorado River, climatologyMeteorologyAtmosphere, All tags... deep learning, drought prediction, droughts, hydrology, long short-term memory, machine learning, modeling, river systems, statistical analysis, streamflow, streamflow percentiles, streamflow predictions, Fewer tags

Data-Driven Drought Prediction Project Model Outputs: Daily Streamflow and Streamflow Percentile Predictions for the Colorado River Basin Region

This metadata record describes outputs from 12 configurations of long short-term memory (LSTM) models which were used to predict streamflow drought occurrence at 384 stream gage locations in the Colorado River Basin region. The models were trained on data from 01-Oct-1981 to 31-Mar-2005 and validated over the period of record spanning 01-Apr-2005 to 31-Mar- 2014. The models use explanatory variable inputs described in Wieczorek (2023) (doi.org/10.5066/P98IG8LO) to predict daily streamflow and streamflow percentiles as described in Simeone (2022) (doi.org/10.5066/P92FAASD). Separate models were trained to predict daily streamflow and streamflow percentiles. Two types of percentiles were modeled: (1) fixed-threshold...

Categories: Data; Tags: Colorado River Basin, climatologyMeteorologyAtmosphere, deep learning, drought prediction, droughts, All tags... hydrology, long short-term memory, machine learning, modeling, river systems, statistical analysis, streamflow, streamflow percentiles, streamflow predictions, Fewer tags