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Filters: Tags: Delaware River Basin (X) > Date Range: {"choice":"year"} (X) > Types: OGC WMS Service (X)

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This data release contains one dataset and one model archive in support of the journal article, "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies," by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset.
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This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459 modeled stream segements across the Delaware River Basin (DRB). Results are provided for two time periods: the historical drought-of-record from 1965-10-02 to 1969-12-30, and that same drought evaluated in climatic conditions that are consistent with a LENS2 enseble climate projection from 2057-10-02 to 2061-12-30. Results are provided for a total of three Random Forest models, corresponding to three input attribute sets (dynamic attributes, dynamic and static attributes, and dynamic attributes and a minimum set of static attributes)....
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The data in this data release are from an effort focused on understanding social vulnerability to water insecurity, resiliency demonstrated by institutions, and conflict or crisis around water resource management. This data release focuses on definitions and metrics of resilience in water management institutions. Water resource managers, at various scales, are tasked with making complex and time-sensitive decisions in the face of uncertainty, competing objectives, and difficult tradeoffs. To do this, they must incorporate data, tacit knowledge, cultural and organizational norms, and individual or institutional values in a way that maintains consistent and predictable operations under normal circumstances, while...
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Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds was generated as part of the project Quantifying Floodplain Ecological Processes and Ecosystem Services in the Delaware River Watershed funded through the William Penn Foundation's Delaware Watershed Research fund. This dataset contains geomorphometry for streams and floodplains in the Chesapeake and Delaware River watersheds. Geomorphometry is a quantitative representation of landscape surface form (e.g., channel width and depth) obtained from digital elevation models (DEMs). The dataset contains geomorphometry derived from running 3-m DEMs through the Floodplain and Channel Evaluation Tool (FACET) version 0.1.0. FACET generates...
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Jointly managed by multiple states and the federal government, there are many ongoing efforts to characterize and understand water quality in the Delaware River Basin (DRB). Many State, Federal and non-profit organizations have collected surface-water-quality samples across the DRB for decades and many of these data are available through the National Water Quality Monitoring Council's Water Quality Portal (WQP). For this data release, WQP data in the DRB were harmonized, meaning that they were processed to create a clean and readily usable dataset. The harmonization process included the synthesis of parameter names and fractions, the condensation of remarks and other data qualifiers, the resolution of duplicate...
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This data release provides water-quality trends for rivers and streams in the Delaware River Basin determined using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model and the Seasonal Kendall Trend (SKT) test. Sixteen water-quality parameters were assessed, including nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate), major ions (calcium, chloride, magnesium, potassium, sodium, and sulfate), salinity indicators (total dissolved solids and specific conductance), and sediment (total suspended solids and suspended sediment concentration). The child items include the input and output data used in the modeling and testing of water-quality...
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The U.S. Geological Survey (USGS), as part of the Next Generation Water Observing System (NGWOS) has collected discrete stream samples for analysis of suspended-sediment concentrations at eight real-time streamflow and water-quality monitoring (turbidity and suspended sediment) stations located in Pennsylvania, New Jersey, and New York in the Delaware River Basin. Data were collected from 2019-2022 at these stations for the application of predicting suspended-sediment concentrations using real-time continuous turbidity, suspended sediment from an uncalibrated sensor, and streamflow data. Regression equations were developed by relating discrete-sample suspended sediment and continuous turbidity, suspended sediment...
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This data release includes cross section survey data collected during site visits to USGS gaging stations located throughout the Willamette and Delaware River Basins and multispectral images of these locations acquired as close in time as possible to the date of each site visit. In addition, MATLAB source code developed for the Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) framework is also provided. The site visit data were obtained from the Aquarius Time Series database, part of the USGS National Water Information System (NWIS), using the Publish Application Programming Interface (API). More specifically, a custom MATLAB function was used to query the FieldVisitDataByLocationServiceRequest...
The USGS’s FORE-SCE model was used to produce a long-term landscape dataset for the Delaware River Basin (DRB). Using historical landscape reconstruction and scenario-based future projections, the data provided land-use and land-cover (LULC) data for the DRB from year 1680 through 2100, with future projections from 2020-2100 modeled for 7 different socioeconomic-based scenarios, and 3 climate realizations for each socioeconomic scenario (21 scenario combinations in total). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (20 land use and land cover classes), 3) broad spatial extent (covering the entirety of the Delaware River basin, corresponding to USGS...
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A data and code repository for studies conducted as part of the Delaware River Basin (DRB) Integrated Water Availability Assessments program. The community serves as a site where hydrologic, physical, chemical, biological, and landscape data can be stored and used for modeling and geospatial analysis to improved understanding of water availability in the DRB.
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This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.


    map background search result map search result map Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, Water Year 1978-2018 Long-term database of historical, current, and future land cover for the Delaware River Basin (1680 through 2100) USGS Delaware River Basin Integrated Water Science Basin: Data and Code Repository Data supporting a spatiotemporal trend analysis of specific conductivity, streamflow, and landscape attributes of selected sub-basins within the Delaware River watershed, 1980 to 2018 Surrogate regression models for computation of time series suspended-sediment, Delaware River Basin NGWOS, 2019 through 2022 Data to support Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies Metrics of Resilience in Water Management Institutions in the Upper Colorado and Delaware River Basins, United States 2022 Site visit cross section surveys and multispectral image data from gaging stations throughout the Willamette and Delaware River Basins from 2022 and code for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) Delaware River Basin Stream Salinity Machine Learning Model Simulations for Past and Future Drought Surrogate regression models for computation of time series suspended-sediment, Delaware River Basin NGWOS, 2019 through 2022 Data supporting a spatiotemporal trend analysis of specific conductivity, streamflow, and landscape attributes of selected sub-basins within the Delaware River watershed, 1980 to 2018 Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin USGS Delaware River Basin Integrated Water Science Basin: Data and Code Repository Delaware River Basin Stream Salinity Machine Learning Model Simulations for Past and Future Drought Long-term database of historical, current, and future land cover for the Delaware River Basin (1680 through 2100) Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, Water Year 1978-2018 Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds Metrics of Resilience in Water Management Institutions in the Upper Colorado and Delaware River Basins, United States 2022 Site visit cross section surveys and multispectral image data from gaging stations throughout the Willamette and Delaware River Basins from 2022 and code for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) Data to support Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies