Filters: partyWithName: Kenny Eng (X)
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1) Improve predictions of streamflow at ungaged basins, and 2) understanding the causes of streamflow changes due to human changes to the environment, and 3) establish biological-flow relations.
Ecological assessment data from the USGS National Water-Quality Assessment Program and the USEPA National River and Stream Assessment were reviewed and records were retained from sampling sites co-located with active USGS stream gages. A limited amount of ancillary data, including location, physical watershed features, and basic water chemistry data for each site were also retained.
Categories: Data;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Aquatic Biology,
Ecology,
National Water Quality Program,
USGS Science Data Catalog (SDC),
United States,
This metadata record describes 99 streamflow (referred to as flow) metrics calculated using the observed flow records at 1851 streamflow gauges across the conterminous United States from 1950 to 2018. Calculation of these metrics are often used as dependent variables in statistical models to make predictions of these flow metrics at ungaged locations. Specifically, this record describes (1) the U.S. Geological Survey streamgauge identification number, (2) the 1-, 7-, and 30-day consecutive minimum flow normalized by drainage area, DA (Q1/DA, Q7/DA, and Q30/DA [cfs/sq km]), (3) the 1st, 10th, 25th, 50th, 75th, 90th, and 99th nonexceedence flows normalized by DA (P01/DA, P10/DA, P25/DA, P50/DA, P75/DA, P90/DA, P99/DA...
Abstract Downstream flow in rivers is repeatedly delayed by hydrologic exchange with offâchannel storage zones where biogeochemical processing occurs. We present a dimensionless metric that quantifies river connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains. The degree of connectivity directly influences downstream water quality--too little connectivity limits the amount of river water exchanged and leads to biogeochemically inactive water storage, while too much connectivity limits the contact time with sediments for reactions to proceed. Using a metric of reaction significance based on river connectivity, we provide evidence that intermediate levels...
Categories: Publication;
Types: Citation
This metadata record describes the following eight attributes: (1) the U.S. Geological Survey streamflow gage identification number, (2) aggregated level 2 ecoregion, (3) hydrologic metric abbreviation, (4) direct-human modification classification of watershed, (5) estimated hydrologic metric values for 1950 to 2014, (6) estimated hydrologic metric values for 1980 to 2014, (7) observed hydrologic metric values for 1980 to 2014, and (8) the direction of streamflow alteration (i.e., inflated, diminished, or indeterminant). Twelve hyrdologic metrics were calculated: low- and high-flow magnitude (LF_MAG(cfs/square km) and HF_MAG(cfs/square km)), low- and high-flow variability (LF_VAR(dimensionless) and HF_VAR(dimensionless)),...
This metadata record describes the observed and estimated hydrologic metrics for the 1980 to 2019 period for U.S. Geological Survey streamgage locations across the Conterminous United States. The datasets are arranged in four files: (1) CONUS_Observed_Estimated_HMs_Annual_Monthly.csv, (2) CONUS_Bootstrap_Validations_for_Models.csv, (3) CONUS_Streamflow_Gages_for_Models.csv, and (4) Data_Dictionary_Flow_Metrics.csv. The CONUS_Observed_Estimated_HMs_Annual_Monthly.csv file contains the following six attributes: (1) the U.S. Geological Survey streamgage identification number, (2) calendar year, (3) observed hydrologic metric, (4) estimated hydrologic metric, (5) hydrologic metric abbreviation, and (6) aggregated level...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Conterminous United States,
Ecology,
Hydrology,
USGS Science Data Catalog (SDC),
Water Resources,
Abstract Lakes, reservoirs, and other ponded waters are ubiquitous features of the aquatic landscape, yet their cumulative role in nitrogen removal in large river basins is often unclear. Here we use predictive modeling, together with comprehensive river water quality, land use, and hydrography datasets, to examine and explain the influences of more than 18,000 ponded waters on nitrogen removal through river networks of the Northeastern United States. Thresholds in pond density where ponded waters become important features to regional nitrogen removal are identified and shown to vary according to a ponded waters’ relative size, network position, and degree of connectivity to the river network, which suggests worldwide...
Categories: Publication;
Types: Citation
Rivers are the veins of the landscape, providing environmental benefits that are disproportionately high relative to their aerial extent; shedding flood waters, hosting aquatic ecosystems, transporting solutes and energy-rich materials, and storing and transforming pollutants into less harmful forms. From uplands to the coasts, rivers facilitate key biogeochemical reactions that cumulatively influence water quality. Many of the reactions are optimized outside the main channel, in hyporheic zones, riparian zones, and floodplain areas, where riverine water is in close contact with geochemically and microbially-active sediments. However, little is known about the distribution, intermittency, and overall effectiveness...
This metadata record describes observed and predicted baseflow recession characteristics for 300 streamflow gauges in the western United States and 282 streamflow gauges in the eastern United States. Specifically, this record describes (1) the streamflow gauge locations (west or east) in the United States (Location), (2) the U.S. Geological Survey streamflow gauge identification numbers (USGS_Site_Identifier), (3) observed regions of similar aquifer hydraulic properties (7 regions coded by color: blue, green, red, purple, grey, pink, and orange) by k-means clustering method (Observed_Class(k-means)), (4) predicted regions of similar aquifer hydraulic properties by random forest classification models (Predicted_Class(k-means)),...
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