Filters: Tags: 012 (X)
51 results (56ms)|
Filters
Date Range
Extensions Types Contacts
Categories Tag Types
|
This dataset provides timeseries data on water quality and quantity, as collected or computed from outside sources. The format is many tables with one row per time series observation (1 tab-delimited file per site-variable combination, 1 zip file per site). This compilation of data is intended for use in estimating or interpreting metabolism. Sites were included if they met the initial criteria of having at least 100 dissolved oxygen observations and one of the accepted NWIS site types ('ST','ST-CA','ST-DCH','ST-TS', or 'SP'). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release...
Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. 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 Research Project, and digitized temperature records from the MN Department of Natural Resources. This dataset is part of a larger data release of lake temperature model inputs and outputs for these same lakes...
The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core) and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Three general types of data are contained within this database: 1) Modern Field Data (2008-present), 2) Master list of location information on all modern sites, and 3) Core data - location information. Data are available for modern sites (from 2008 to present) and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight,...
This dataset provides input data formatted for use in estimating metabolism. The format is tables of prepared time series inputs (1 tab-delimited file per site, in 1 zip file per site). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release includes: modeled estimates of gross primary productivity, ecosystem respiration, and the gas exchange coefficient; model input data and alternative input data; model fit and diagnostic information; site catchment boundaries and site point locations; and potential predictors of metabolism such as discharge and light availability.
This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
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 dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
This dataset provides shapefile outlines of the 2,332 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 included, which includes lake metadata and all features that were considered for the meta transfer model (not all meta features were used). This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR).
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: 007,
012,
IA,
IL,
Illinois,
It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale. This data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July,...
It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale. This data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July,...
This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Predictions are evaluated against observed temperatures.
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance...
Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum...
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 dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
This dataset includes model inputs (specifically, meteorological inputs to the predictive models and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. states of North Dakota, South Dakota, Minnesota, Wisconsin, and Michigan (https://doi.org/10.5066/P9PPHJE2).
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance...
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. 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 physical constraint for...
|
|
