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This data release contains the climate change model inputs and Soil and Water Assessment Tool (SWAT) model outputs from 360 HUC-8 watersheds in the Midwest United States (Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin), that were generated using the HAWQS (Hydrologic and Water Quality System) platform (https://hawqs.tamu.edu). The summarized data for a watershed-based climate change vulnerability assessment for U.S. Fish and Wildlife Service is also provided, along with the R code used to summarize the raw outputs. Watershed-based Midwest Climate Change Vulnerability Assessment Tool: https://rconnect.usgs.gov/CC_Vulnerabi
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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 dataset contains predictions of habitat suitability of reed canarygrass (Phalaris arundinacea) in Upper Mississippi River floodplain forest understories from Pool 3 to Pool 13. Predictions were created using three machine learning algorithms (Bayesian additive regression trees, boosted trees, and random forest). This dataset contains rasters that provide habitat suitability predictions for each 12m raster cell that had forested landcover in 2010. In addition to one raster for each of the three algorithms an ensemble (mean prediction of all three algorithms) prediction raster for each pool is provided. The presence/absence observations used to train the model are contained in a .csv file with each plot location....
This is a shiny application for an online dashboard that allows users to weight the importance of climate change indicators, along with metrics of adaptive capacity, to create a custom climate change vulnerability assessment at the hydrologic unit code-8 scale. This dashboard was requested by, and developed with input from U.S. Fish and Wildlife Service managers to aid in regional assessments of climate change vulnerability.
Script 1: This R script takes in the model outputs and climate inputs used for each model run performed in, and downloaded from, HAWQS to summarize 15 climate change exposure indicators. Script 2: This script takes the summarized climate change exposure indicators created using Script 1 and calculates the percent difference between the future and baseline period for each of the climate models. Script 3: This script was used to calculate metrics that represent the adaptive capacity of each watershed using existing datasets. See sources for each adaptive capacity dataset below. Script 4: This script takes the exposure indicators and the adaptive capacity indicators created in scripts 1-3 and applies weights that...
    map background search result map search result map Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019 Estimates of habitat suitability of reed canarygrass (Phalaris arundinacea) in Upper Mississippi River floodplain forest understories (ver. 2.0, February 2024) Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019 Estimates of habitat suitability of reed canarygrass (Phalaris arundinacea) in Upper Mississippi River floodplain forest understories (ver. 2.0, February 2024)