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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 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.
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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).
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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...
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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...
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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...
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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...
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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,...
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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...
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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.
Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife,...
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.
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The use of streamflow simulations from the Vflo model and subsequent calculation of streamflow metrics to investigate flow-ecology relationships may be hindered by our inability to accurately model flow variability and extreme flows of the arid Great Plains. The Canadian River and other rivers in the Great Plains tend to have highly variable flows and harsh environmental conditions. The combination of these environmental conditions makes semi-arid and arid regions difficult to represent with a hydrologic model, especially extreme events. In some cases, overestimating flows may be acceptable to water managers (e.g., vulnerability of infrastructures), but could greatly affect estimates of fish species persistence....
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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 (see RGHW-PRMS_baseline_input.zip), 2) effects of bark-beetle induced tree mortality (see RGHW-PRMS_BB_input.zip), and 3) effects of wildfire (see RGHW-PRMS_fire_input.zip), on components of the hydrologic cycle by hydrologic response unit (HRU) and subsequent seasonal streamflow runoff from April through September for water years 1980 through 2017. PRMS input files (control, climate-by-hru, data, parameter, dynamic...
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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...
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The dataset provided here and described in this metadata document contains predicted wetness probability (PWP) values for vernal pools under a variety of weather and climate conditions at several seasonal time points, generated using inundation models as described in the processing steps section of this metadata document and in the annotated R script included in this data release ("annotated_R_script_for_pool_inundation_modeling.R"). PWP values represent the predicted likelihood of a pool holding water according to a specified inundation threshold, as defined in this metadata document. PWP values can theoretically range from 0 (pool is predicted to have no chance of inundation) to 1 (pool is predicted to have 100...
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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).
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This dataset represents results from this study attributed to the Hydrologic Unit Code (HUC) 12 watershed boundaries. Human impacts occurring throughout the Northeast and Midwest United States, including urbanization, agriculture, and dams, have multiple effects on the region’s streams which support economically valuable stream fishes. Changes in climate are expected to lead to additional impacts in stream habitats and fish assemblages in multiple ways, including changing stream water temperatures. To manage streams for current impacts and future changes, managers need region-wide information for decision-making and developing proactive management strategies. Our project met that need by integrating results...
map background search result map search result map Wisconsin Lake Temperature Metrics Decreasing Clarity Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes Fishtail huc12: Indices and supporting data characterizing the current (1961-2000) and future (2041-2080) risk to fish habitat degradation in the Northeast Climate Science Center region Fish Data Collection and Streamflows on the Canadian River 1995-2015 Point locations of daily flow rates in the Canadian River watershed derived from hydrologic modeling 1994-2013 Model input for Precipitation-Runoff Modeling System simulations in the Rio Grande Headwaters, Colorado, for water years 1980 through 2017 Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Inundation observations and inundation model predictions for vernal pools of the northeastern United States Inundation observations, climate data, and landscape attributes for vernal pools of the northeastern United States Inundation predictions for vernal pools of the northeastern United States at various seasonal time points under various weather and climate scenarios Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Model input for Precipitation-Runoff Modeling System simulations in the Rio Grande Headwaters, Colorado, for water years 1980 through 2017 Point locations of daily flow rates in the Canadian River watershed derived from hydrologic modeling 1994-2013 Fish Data Collection and Streamflows on the Canadian River 1995-2015 Wisconsin Lake Temperature Metrics Decreasing Clarity Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes Inundation observations and inundation model predictions for vernal pools of the northeastern United States Inundation observations, climate data, and landscape attributes for vernal pools of the northeastern United States Inundation predictions for vernal pools of the northeastern United States at various seasonal time points under various weather and climate scenarios Fishtail huc12: Indices and supporting data characterizing the current (1961-2000) and future (2041-2080) risk to fish habitat degradation in the Northeast Climate Science Center region