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Model Inputs: Midwest Climate Change Vulnerability Assessment for the U.S. Fish and Wildlife Service
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
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...
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....
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