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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.
Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics.
Observed water temperatures from 1980-2018 were compiled for 877 lakes in Minnesota (USA). There were four lakes included in this data release that did not have temperature observations available at the time of compilation or these data existed elsewhere and were unknown to the compilation team. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological...
Human factors that influence water availability in the Basin were discovered by reviewing hundreds of published literature items and articles from the literature following an extensive keyword search. The different factors were drawn from reviewing the literature, and datasets to support the factor were researched across open data catalogs and the world wide web. Data related to the Human Factors project water availability sectors of agriculture, industrial, municipal, and those related to ecosystem services, tourism, or other uses can be found here.
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: USGS Science Data Catalog (SDC)
Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. 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 DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022),...
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in degrees C) is calculated for each lake and each model type, and matched values for predicted and observed temperatures are also included to support more specific error estimation methods (for example, calculating error in a particular month). Errors for the process-based model are compared to predictions as shared in Model Predictions data since these models were not calibrated. Errors for the process-guided deep learning models were calculated from validation folds and therefore differ from the comparisons to Model Predictions because those...
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 dataset provides shapefile outlines of the 881 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. 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).
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: MN,
Minnesota,
SD,
South Dakota,
US,
This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with...
Categories: Data,
Data Release - Revised;
Tags: US,
USGS Science Data Catalog (SDC),
United States,
deep learning,
environment,
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...
This data release contains records from research focused on understanding social vulnerability to water insecurity, resiliency demonstrated by institutions, and conflict or crisis around water resource management. This data release focuses on social vulnerability to water insecurity. The data were derived from a meta-analysis of studies in the empirical literature which measured factors of social vulnerability associated with conditions of water insecurity. In the water security context this data and associated study identify the indicators used to measure social vulnerability, the frequency at which indicators are used, and the uncertainty associated with measurements based on those indictors. Assessed studies...
Categories: Data;
Tags: USGS Science Data Catalog (SDC),
United States,
inlandWaters,
social science,
society
The data in this data release are from an effort focused on understanding social vulnerability to water insecurity, resiliency demonstrated by institutions, and conflict or crisis around water resource management. This data release focuses on definitions and metrics of resilience in water management institutions. Water resource managers, at various scales, are tasked with making complex and time-sensitive decisions in the face of uncertainty, competing objectives, and difficult tradeoffs. To do this, they must incorporate data, tacit knowledge, cultural and organizational norms, and individual or institutional values in a way that maintains consistent and predictable operations under normal circumstances, while...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Delaware River Basin,
Institutions,
Resilience,
USGS Science Data Catalog (SDC),
Upper Colorado River Basin,
Human factors that influence water availability in the Basin were discovered by reviewing hundreds of published literature items and articles from the literature following an extensive keyword search. The different factors were drawn from reviewing the literature, and datasets to support the factor were researched across open data catalogs and the world wide web. Data related to the Human Factors project water availability sectors of agriculture, industrial, municipal, and those related to ecosystem services, tourism, or other uses can be found here. Reproducible R scripts used to pull data or process data can be found within the section for the sector itself. Reproducible R scripts used to manage the literature...
Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future national model development and characterization of HABs, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites across the United States. It includes nutrients, major ions, sediment,...
Categories: Data;
Tags: Aquatic Biology,
District of Columbia,
Ecology,
Puerto Rico,
USGS Science Data Catalog (SDC),
This data release component contains a shapefile of monitoring site locations coincident with the outlets of the 118 river basins modeled by Rahmani et al. (2020).<\p>
This data release component contains model code and configurations for the LSTM and linear regression models used to predict stream temperature.
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that made real-time predictions in the Delaware River Basin during 2021. The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7) at five sites. This data release captures the entire forecasting period that is reported in Zwart et al. 2022, and is an extension of a previous data release that contains all data needed to build these models but only extends to July 16, 2021 (Oliver et al. 2021). Additionally, this release contains a tidy version of the model predictions with paired...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Hydrology,
NY,
New York,
US,
USGS Science Data Catalog (SDC),
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 data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs ungaged sites, and the value of an ensemble of attribute subsets in improving prediction accuracy. The data are organized into these child items: Site Information - Attributes and spatial information about the monitoring sites and basins in this study Observations - Water temperature observations for the sites used in this study Model...
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