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Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
This data set consists of ground control polygons used for model training and evaluation (ground_control_polygons.gpkg): This dataset consists of refined vegetation polygons digitized across the island of Lāna‘i representing the 15 land cover classes of interest. High-resolution aerial imagery and extensive field experience were used to iteratively collect and improve the polygons through expert review and interpretation. The polygons were divided into a 250m grid overlaying the island to balance sample size and spatial resolution while reducing spatial autocorrelation, resulting in 1,754 smaller polygons. These polygon data served as the primary dataset used to train, validate, and evaluate the classification models...
These data were compiled to forecast climate exposure for 29 major plant communities in the southwestern United States to changing climate under two future climate change scenarios. An objective of our study was that species within plant communities have unique climate suitability signatures and forecast changes in climatic suitability will not be uniform within the species respective communities or among species within the community. We developed these spatial models where climate exposure is represented as a composite score of the climate exposure of characteristic plants for each community. Baseline climate exposure rasters represent a baseline climate change and were developed for current climate conditions...
These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive...
We employed decision-tree mapping models in two formats to establish a time series (2001 - 2015) of sagebrush condition class in the western United States. The formats were predictive and descriptive, and each model produced distinct spatially explicit datasets. The predictive model mapped the probability of sagebrush recovery, tipping point (environmental degradation), or stable classes. The descriptive model mapped rules that were defined by environmental thresholds. The thresholds were defined by the interaction between the independent variables and the dependent variable. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables...
This dataset includes model inputs (specifically, weather, water clarity, and flags for predicted ice-cover) and 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).
This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin...
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).
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 data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
This model archive (Rahmani et al. 2023a) provides all data, code, and model outputs used in Rahmani et al. (2023b) to improve model representations toward improved prediction of stream temperature and groundwater/subsurface flow contributions to stream temperature. Briefly, we modeled stream temperature at sites across the continental United States using a hybrid differentiable model that combines neural network components with differentiable implementations of several structural priors, i.e., process-based equations. The differentiable framework permits estimation of parameters and comparison of structural priors as well as prediction of stream temperature. The data are organized into these child items: 1. Model...
Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some areas by high concentrations of trace elements. The concentration of trace elements in groundwater is largely driven by oxidation-reduction (redox) processes. Redox processes are a group of biotically driven reactions in which energy is derived from the exchange of electrons. In groundwater, this commonly occurs through decomposition of organic matter (carbon) by microbes, which consumes dissolved oxygen (DO). Under low DO conditions, iron (Fe), manganese, and arsenic can dissolve from coatings on aquifer sediments and be released into groundwater. Therefore,...
The Machine Learning Asset Aggregation of the PDE (MLAAPDE) is a waveform archive, feature labeled catalog, and Python module that together provide a routine way to gather high-quality input data to train machine learning models. While all the data provided are already publicly available, MLAAPDE packages it in a format that allows a user to prepare input for common machine learning frameworks with few lines of code. Most of the features that are part of the MLAAPDE dataset are selected from the Preliminary Determination of Epicenters (PDE), the official earthquake catalog of the USGS National Earthquake Information Center (NEIC). The PDE aims to provide a complete catalog of source characterization estimates...
This dataset includes model parameters and metadata used to configure models.
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...
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Acadia National Park,
ArcGIS Pro,
Arcpy,
Autoclassification,
Automation,
We outfitted nine condors in a flight pen with patagial tags, each with a unique ID, and a proprietary solar powered Global Positioning System-Global System for Mobile Communications (GPS-GSM) telemetry device weighing 50 g (Cellular Tracking Technologies, LLC, Rio Grande, NJ). The units collected tri-axial acceleration data at a rate of 20 Hz. Data were transmitted once daily over cellular networks and then downloaded to a server.
Categories: Data;
Tags: Accelerometer,
Classification,
Ecology,
Machine learning,
USGS Science Data Catalog (SDC),
Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML...
Categories: Data;
Tags: Ecology,
Information Sciences,
Maine,
USGS Science Data Catalog (SDC),
Vermont,
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