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This dataset is a continuous parameter grid (CPG) of normal (average) annual precipitation data for the years 1981 through 2010 in the Pacific Northwest. Source precipitation data was produced by the PRISM Climate Group at Oregon State University.
These datasets are continuous parameter grids (CPG) of first-of-month snow water equivalent data for March through August, years 2004 through 2016, in the Pacific Northwest. Normal (average) first-of-month values for the same months, averaged across all years, are also located here. Source snow water equivalent data was produced by the Snow Data Assimilation System (SNODAS) at the National Snow and Ice Data Center.
These datasets are continuous parameter grids (CPG) of permeability (and impermeability) of surface geology in the Pacific Northwest. Source data come from work by Chris Konrad, U.S. Geological Survey (USGS), and geologic map databases produced by USGS scientists.
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 includes stream temperatures from two data loggers installed at one site in the Little Blitzen River of SE Oregon as part of a redband trout (Oncorhynchus mykiss gairdnerii) study. The site was used as an undisturbed reference in comparison with similar temperature monitoring sites in the Willow-Whitehorse watershed that experienced a 2012 fire that burned nearly the entire watershed.
Categories: Data;
Types: Citation;
Tags: Drought,
Drought, Fire and Extreme Weather,
GNIS ID 1123136,
Harney County,
Little Blitzen River,
The U.S. Geological Survey Precipitation-Runoff Modeling System (PRMS) was used to assess the effects of changing climate and land disturbance on seasonal streamflow in the Rio Grande Headwaters (RGHW) region. Three applications of PRMS in the RGHW were used to simulate 1) baseline effects of climate, 2) effects of bark-beetle induced tree mortality, and 3) effects of wildfire, on components of the hydrologic cycle and subsequent seasonal streamflow runoff from April through September for water years 1980 through 2017. PRMS input files and select PRMS output variables for each simulation are contained in this data release to accompany the journal article.
Geographic patterns and time trends of water-quality, modeled streamflow, and ecological data were compared along the Canadian River and selected tributaries in northeastern New Mexico to Lake Eufaula in Oklahoma to determine effects of climate change on water quality, streamflows, fish populations and ecological flows in this watershed from 1939 to 2013. Project participants included staff from the Oklahoma Cooperative Fish and Wildlife Research Unit, Vieux and Associates, USGS New Jersey Water Science Center and the USGS Oklahoma Water Science Center. Principal project funding was by the South Central Climate Science Center, with in-kind matching from the project participant organizations.
The development of a hydrologic foundation, essential for advancing our understanding of flow-ecology relationships, was accomplished using the high-resolution physics-based distributed rainfall-runoff model Vflo. We compared the accuracy and bias associated with flow metrics that were generated using Vflo at both a daily and monthly time step in the Canadian River basin, USA. First, we calibrated and applied bias correction to the Vflo model to simulate streamflow at ungaged catchment locations. Next, flow metrics were calculated using both simulated and observed data from stream gage locations. We found discharge predictions using Vflo were more accurate than using drainage area ratios. General correspondence...
Categories: Data;
Tags: Rivers, Streams and Lakes,
South Central CASC,
Vflo,
Water, Coasts and Ice,
biota,
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).
This data release includes data-processing scripts, data products, and associated metadata for a study to model the hydrology of several hundred vernal pools (i.e., seasonal pools or ephemeral wetlands) across the northeastern United States. More information on this study is available from the project website. This data release consists of several components: (1) an input dataset and associated metadata document ("pool_inundation_observations_and_climate_and_landscape_data"); (2) an annotated R script which processes the input dataset, performs inundation modeling, and generates model predictions ("annotated_R_script_for_pool_inundation_modeling.R"); and (3) a model prediction dataset and associated metadata document...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Hydrology,
Maine,
Maryland,
Massachusetts,
New Hampshire,
The dataset provided here and described in this metadata document consists of several components: (1) pool-specific attributes including name and geographic location, (2) time-varying inundation observations collected between May 2004 and July 2016; (3) landscape attributes associated with pool locations including geologic, soil, and landcover characteristics; (4) short- and medium-term weather and climate variables for time periods (for example, 5-days and 6-months) immediately preceding the dates of inundation observations; and (5) long-term (30-year average) climate variables associated with pool locations.
Categories: Data;
Tags: Drought,
Drought, Fire and Extreme Weather,
National CASC,
Rivers, Streams and Lakes,
Water, Coasts and Ice
This dataset is a continuous parameter grid (CPG) of normal (average) annual maximum air temperature data for the years 1981 through 2010 in the Pacific Northwest. Source temperature data was produced by the PRISM Climate Group at Oregon State University.
These datasets are continuous parameter grids (CPG) of topography data in the Pacific Northwest. Datasets include stream slope, basin slope, elevation, contributing area, and topographic wetness index. Source data come from the U.S. Geological Survey National Elevation Dataset.
The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. These values and variables, known as Continuous Parameter Grids, or CPGs, were used as the predictor variables in the model. The CPGs referenced...
The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. These values and variables, known as Continuous Parameter Grids, or CPGs, were used as the predictor variables in the model. The CPGs referenced...
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