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Jessica A Hopple

Trends are identified changes over time in the characteristics of groundwater and (or) surface water. The characteristics analyzed can include descriptions of both quantity and quality. The calculated trends are dependent upon the available data and the methods used to identify trends. (Updated 12/23/2015)
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This USGS Data Release represents the data used to develop multiple linear regression models for estimating the loads of total nitrogen in small streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (less than 585 square kilometers) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. These data support the following publication: Kronholm, S.C., Capel, P.D., and Terziotti, Silvia, 2016, Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams: Environmental Modeling and Assessment, 10 p., http://dx.doi.org/10.1007/s10666-016-9525-3.
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This U.S. Geological Survey (USGS) data release contains the data used in the USGS Scientific Investigations Report 2018-5053 entitled "An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data." The four datasets, which contain only ASCII characters in a column-oriented format, are: (1) sel_qw_parm_full_time_series.csv: A comma-delimited file containing an irregular time series of 713 rows of discrete water-quality measurements that start on February 21, 1973 and end on September 14, 2016. (2) baye_network_initialize.cas: This tab-delimited file can be used to initialize...
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This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a "full" model run, and a second time using the top 20 predictors from the original run, which...
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Reactive nitrogen is transported from the atmosphere to the landscape as wet and dry deposition that contributes to annual nitrogen loads to the Chesapeake Bay. Estimates of atmospheric inorganic nitrogen deposition to the Chesapeake Bay watershed during 1950 to 2050 are presented, and are based on field measurements, model simulations, statistical relations, and surrogate constituents used for estimates. Wet atmospheric nitrogen deposition has generally been quantified from weekly precipitation sample collections, whereas dry atmospheric nitrogen deposition has been simulated by a model at an hourly time step.
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