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The datasets are to accompany a manuscript describing the prediction of submersed aquatic vegetation presence and its potential vulnerability and recovery potential. The data and accompanying analysis scripts allow users to run the final random forests predictive model and reproduce the figures reported in the manuscript. Files from several data sources (aqa_2010_lvl3_pct_oute_joined_VEG_BARCODE.csv, eco_states_near_SAV.csv, ltrm_vegsrs_thru2019_GEOMORPHIC_METRICS_final.csv, vegetation_data.csv, and water_full.csv) were combined into a single .csv file (analysis_data_for_SAV_RandomForest.csv) used as the input for the random forest model. When intersecting points with geomorphic metrics some sites were moved slightly...
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This data release documents the data used for the associated publication "Evaluating hydrologic region assignment techniques for ungaged watersheds in Alaska, USA" (Barnhart and others, 2022) The data sets within this release are stored in 14 files: (1) Streamflow observations and sites used. (2) Statistically estimated streamflow values computed for each site. (3) Streamflow statistics computed from observed and estimated streamflow values at each site, basin characteristics for each site, and hydrologic regions (clusters) for each site. (4) A dataset describing the optimal number of hydrologic regions into which the considered sites were grouped. (5) P-values from a multiple comparisons analysis testing for statistical...
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To identify the degree of hydrologic alteration of streams in the Mississippi Alluvial Plain (MAP), we used random forest (RF) regression methods (Breiman, 2001) to model the relation between six selected streamflow characteristics and explanatory variables (such as drainage area, precipitation, soils, and other watershed characteristics). RFs were chosen for this study because they have been proven to be more robust and accurate than traditional linear regression methods (Carlisle and others, 2010; Lawler and others, 2006; Prasad and others, 2006; Cutler and others, 2007). Estimated expected monthly mean streamflow from the RF models were compared to observed monthly mean streamflow at 68 sites located within the...
As more hydrocarbon production from hydraulic fracturing and other methods produce large volumes of water, innovative methods must be explored for treatment and reuse of these waters. However, understanding the general water chemistry of these fluids is essential to providing the best treatment options optimized for each producing area. Machine learning algorithms can often be applied to datasets to solve complex problems. In this study, we used the U.S. Geological Survey’s National Produced Waters Geochemical Database (USGS PWGD) in an exploratory exercise to determine if systematic variations exist between produced waters and geologic environment that could be used to accurately classify a water sample to a given...
Categories: Data; Tags: Alabama, Alaska, Alaska Region, Arizona, Arkansas, All tags...
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This data release contains predictions of stream biological condition as defined by the Chesapeake basin-wide index of biotic integrity for stream macroinvertebrates (Chessie BIBI) using Random Forest models with landscape data for small streams (≤ 200 km2 in upstream drainage) across the Chesapeake Bay Watershed (CBW). Predictions were made at eight time periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019) according to changes in landcover using the National Land Cover Database (NLCD). The Chessie BIBI data used were provided by the Interstate Commission on the Potomac River Basin. Uncertainty was calculated using model prediction intervals. For complete data descriptions and data interpretation see associated...
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This dataset represents the cumulative result of multi-season classification of land cover in the GCPO LCC geography to NatureServe Ecological Systems based on 2011 seasonal Landsat Satellite Imagery. The approach used a Random Forest algorithm and several dozen input data layers to classify land cover at a 30 m pixel resolution. The description below is taken directly from the report titled “Update of the Eastern GCPO Land Cover Database to 2011 Using a LS2SRC Approach”, by Dr. Qingmin Meng, Department of Geosciences, Mississippi State University.Random Forest classifier is based on the general decision tree approach, which has been a popular approach to multilevel and multistage decision making. Its basic idea...
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Many aspects of recurring plant developmental events – vegetation phenology – are measured by remote sensing. By consistently measuring the timing and magnitude of the growing season, it is possible to study the complex relationships among drivers of the seasonal cycle of vegetation, including legacy conditions. We studied the role of current and legacy climate, and contextual factors on the land surface phenology of the U.S. Northern Great Plains. Specifically, we used annual and seasonal climate variables (e.g., temperature, precipitation, growing degree days, and vapor pressure deficit) covering the current year and the past four years derived from the PRISM climate dataset. We also included soils, disturbance,...
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Modeling streamflow is an important approach for understanding landscape-scale drivers of flow and estimating flows where there are no streamgage records. In this study conducted by the U.S. Geological Survey in cooperation with Colorado State University, the objectives were to model streamflow metrics on small, ungaged streams in the Upper Colorado River Basin and identify streams that are potentially threatened with becoming intermittent under drier climate conditions. The Upper Colorado River Basin is a region that is critical for water resources and also projected to experience large future climate shifts toward a drying climate. A random forest modeling approach was used to model the relationship between streamflow...
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Yellow sweetclover (Melilotus officinalis; YSC), an invasive biennial legume, bloomed throughout the Northern Great Plains (NGP) following greater-than-average precipitation during 2018-2019. YSC can increase nitrogen (N) levels and potentially cause broad changes in the composition of native plant species communities. There is little knowledge of the drivers behind its spatiotemporal variability, including conditions causing significant widespread blooms across western South Dakota (SD). We aimed to develop a generalized prediction model to map the relative abundance of YSC in suitable habitats across rangelands of western SD for the recent sweet clover year 2019. The following research questions were asked: 1....
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This data release contains predictions of selected  fish community metrics and fish species occurrence using Random Forest models with landscape data for inland reaches across the Chesapeake Bay Watershed (CBW). Predictions were made at four time intervals (2001, 2006, 2011, and 2016) according to changes in landcover using the National Land Cover Database (NLCD). The fish sampling data used to compute these metrics were compiled from various fish sampling programs conducted by state and federal agencies, county governments, universities, and river basin commissions across the watershed. Community metrics describe composition, tolerances, habitat preferences, and functional traits of fish communities (and were derived...


    map background search result map search result map Modeled Streamflow Metrics on Small, Ungaged Stream Reaches in the Upper Colorado River Basin Ecological Systems Classification 2011 Update for the Eastern GCPO LCC Geography Basin Characteristics and Climate Data Used in Random Forest Models to Determine Hydrologic Alteration in the Mississippi Alluvial Plain Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters Fish community and species distribution predictions for streams and rivers of the Chesapeake Bay Watershed Basin Characteristics and Streamflow Statistics for Selected Gages, Alaska, USA (ver. 2.0, September, 2022) Model predictions of biological condition for small streams in the Chesapeake Bay Watershed, USA Model performance and output variables for phenological events across land cover types in the Northwestern Plains, 1989-2014 Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019 Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019 Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains Basin Characteristics and Climate Data Used in Random Forest Models to Determine Hydrologic Alteration in the Mississippi Alluvial Plain Fish community and species distribution predictions for streams and rivers of the Chesapeake Bay Watershed Model predictions of biological condition for small streams in the Chesapeake Bay Watershed, USA Modeled Streamflow Metrics on Small, Ungaged Stream Reaches in the Upper Colorado River Basin Ecological Systems Classification 2011 Update for the Eastern GCPO LCC Geography Model performance and output variables for phenological events across land cover types in the Northwestern Plains, 1989-2014 Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters Basin Characteristics and Streamflow Statistics for Selected Gages, Alaska, USA (ver. 2.0, September, 2022)