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Ground-based discrete snowpack measurements were collected during winter field campaigns starting in 2020. These data were collected as part of the U.S. Geological Survey (USGS) Next Generation Water Observing System (NGWOS) Upper Colorado River Basin project focusing on the relation between snow dynamics and water resources. This data release consists of three child items. Each child item contains snow depth, snow density, snow temperature, or snow water equivalent values measured discretely in the field. The data are provided in comma separated value (CSV) files.
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Colorado,
Colorado River,
Hydrology,
Integrated Water Science,
Rocky Mountains,
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.
This data release includes simulation output from SnowModel (Liston and Elder, 2006), a well-validated process-based snow modeling system, and supporting snow, meteorological, and streamflow observations from the water years 2011 through 2015 (October 1, 2010, through September 30, 2015) across a 3,600 square kilometer model domain in the north-central Colorado Rocky Mountains. For each water year, SnowModel simulations were completed for a (1) baseline simulation, (2) bark-beetle disturbance condition simulation, (3) 2016 - 2035 future climate condition simulation (S1), and (4) 2046 - 2065 future climate condition simulation (S2). Sexstone and others (2018) provide details and summarize findings from each of the...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Bark beetles,
Climate warming,
Climatology,
Colorado Rocky Mountains,
Eddy covariance,
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...
Categories: Data;
Tags: Data Visualization & Tools,
Del Norte,
Rio Grande,
Rivers, Streams and Lakes,
Science Tools For Managers,
This webinar is part of a series featuring South Central Climate Science Center researchers studying the Rio Grande, a critical water resource for people and wildlife. Learn more at southcentralclimate.org and view the other webinars in this series here.
A list of stream gages within the conterminous United States that will serve as the initial list of sites (version 1.0) used for streamflow benchmarking of hydrologic models. Sites within this list were chosen based on their presence in the GAGES-II dataset, their availability of modeled streamflow data from the most recent version of the National Hydrologic Model application of Precipitation-Runoff Modeling System v1.0, and their availability of modeled streamflow data from the most recent version of the NOAA National Water Model application of WRF-hydro version 2.1 retrospective dataset.
Categories: Data;
Tags: Climatology,
Hydrology,
Information Sciences,
USGS Science Data Catalog (SDC),
United States,
Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release...
Growth potential of redband trout (Oncorhynchus mykiss newberri) was simulated across 175 river segments in the Donner und Blitzen River basin for water years 1980 through 2021 using a bioenergetics model. A previously published framework for assessing climate vulnerability of redband trout was used to simulate the growth potential of redband trout in relation to constraints on body size, physiological responses linked to variable thermal regimes, and variation in physiological adaptive capacity. For body size, three starting sizes of redband trout, 10 g, 50 g, and 150 g, were used for each day of the simulations. For thermal regimes, daily stream temperatures were estimated from PRISM. To account for variation...
Discrete snow depth data were collected during multiple winter campaigns during 2020–22. These data were collected as part of the U.S. Geological Survey (USGS) Next Generation Water Observing System (NGWOS) Upper Colorado River Basin project focusing on the relation of snow dynamics and water resources. Snow depth was measured using either an avalanche probe and handheld global positioning system (GPS) unit or a snow depth probe with attached Juniper Systems Geode GPS receiver and a Mesa tablet. These data are released in a comma separated value file.
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_simulation.zip), 2) effects of bark-beetle induced tree mortality (see RGHW-PRMS_BB_simulation.zip), and 3) effects of wildfire (see RGHW-PRMS_fire_simulation.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. Select PRMS output variables for each simulation are...
Categories: Data;
Tags: Data Visualization & Tools,
Del Norte,
Rio Grande,
Rivers, Streams and Lakes,
Science Tools For Managers,
Seasonal streamflow forecast bias, changes in climate, snowpack, and land cover, and the effects of these changes on relations between basin‐wide snowpack, SNOw TELemetry (SNOTEL) station snowpack, and seasonal streamflow were evaluated in the headwaters of the Rio Grande, Colorado. Results indicate that shifts in the seasonality of precipitation and changing climatology are consistent with periods of overprediction and underprediction in streamflow forecasts. Multiple linear regression of SNOTEL data, postcedent precipitation, and land‐cover changes explained 2%–18% more variability in streamflow prediction than using SNOTEL station data alone. Simulated basin‐wide snowpack from a physically based model had significant...
Categories: Publication;
Types: Citation
Streamflow and stream temperature in the Donner und Blitzen River Basin for water years 1980 through 2021 were simulated using the Precipitation-Runoff Modeling System (PRMS) with the "stream_temp" module. The model domain was discretized into 175 stream segments and calibrated to observed streamflow and stream temperature at points distributed throughout the basin. Model input files, including a PRMS control file, parameter file, and meteorological forcing files, are included in the Blitzen_PRMS_input.zip file. Select output variables for each hydrologic response unit (HRU), each stream segment, and PRMS basin summary outputs are included in the Blitzen_PRMS_output.zip file. Shapefiles of the model domain, model...
Discrete snowpack data were collected during winter field campaigns from 2020 to 2022. These data were collected as part of the U.S. Geological Survey (USGS) Next Generation Water Observing System (NGWOS) Upper Colorado River Basin project focusing on the relation between snow dynamics and water resources. After a snow pit was dug, the pit face was analyzed for discrete snowpack measurements. Measurements taken were mass, temperature, and total depth. Using the mass values taken with a density cutter, the snow density and snow water equivalent were calculated. These data are released in a comma separated value file.
Discrete snow data were collected during multiple winter field campaigns from 2021 to 2022. This data was collected as part of the U.S. Geological Survey (USGS) Next Generation Water Observing System (NGWOS) project focusing on the relation between snow dynamics and the water cycle of a basin. A Snow Water Equivalent (SWE) Coring Tube was used to measure snow depth and mass of snow within the core. These values were used to calculate snow density and snow water equivalent of the core. These data were released in a comma separated value file.
SedReview is a discrete sediment data review tool which facilitates rapid review of sediment sample data and checks for compliance with many data collection and data storage “best practices” and policies. The tool was written in the R programming language but has a stand-alone Shiny application/executable that opens a graphical user interface (GUI) within an internet browser. Use of the stand-alone executable does not require experience with the R programming language. SedReview is built on the same platform as WQReview, the discrete water quality data review tool announced in WaQI Note 2016.01. SedReview was developed by Colin Penn (COWSC), Cory Williams (COWSC), and Molly Wood (OSD HNB). For more information,...
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation,...
Categories: Data;
Tags: Data Release,
Hydrology,
Mean squared error,
Model evaluation,
Streamflow prediction,
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